From a911b856172741802083ab1ce2f92f09e9ec279f Mon Sep 17 00:00:00 2001
From: Yuge Zhang <scottyugochang@gmail.com>
Date: Thu, 21 Apr 2022 00:49:27 +0800
Subject: [PATCH] Resolve conflicts for #4760 (#4762)

---
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 .../ModelEvaluators.rst => nas/evaluator.rst} |   29 +-
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 rename ts/webui/src/components/{public-child => experiment/overview/table}/DefaultMetric.tsx (83%)
 rename ts/webui/src/components/{ => experiment}/overview/table/SuccessTable.tsx (93%)
 rename ts/webui/src/components/{modals => experiment/trialdetail}/ChangeColumnComponent.tsx (96%)
 rename ts/webui/src/components/{ => experiment/trialdetail}/TrialsDetail.tsx (84%)
 rename ts/webui/src/components/{trial-detail => experiment/trialdetail/chart}/DefaultMetricPoint.tsx (96%)
 rename ts/webui/src/components/{trial-detail => experiment/trialdetail/chart}/Duration.tsx (97%)
 rename ts/webui/src/components/{trial-detail => experiment/trialdetail/chart}/Intermediate.tsx (99%)
 rename ts/webui/src/components/{trial-detail => experiment/trialdetail/chart}/Para.tsx (96%)
 rename ts/webui/src/components/{trial-detail => experiment/trialdetail/table}/TableList.tsx (94%)
 rename ts/webui/src/components/{modals => experiment/trialdetail/table/tableFunction}/Compare.tsx (95%)
 rename ts/webui/src/components/{modals => experiment/trialdetail/table/tableFunction}/CustomizedTrial.tsx (96%)
 rename ts/webui/src/components/{modals => experiment/trialdetail/table/tableFunction/killTrial}/Killjob.tsx (67%)
 rename ts/webui/src/components/{trial-detail => experiment/trialdetail/table/tableFunction}/search/GeneralSearch.tsx (95%)
 rename ts/webui/src/components/{trial-detail => experiment/trialdetail/table/tableFunction}/search/Search.tsx (98%)
 rename ts/webui/src/components/{trial-detail => experiment/trialdetail/table/tableFunction}/search/SearchParameterConditions.tsx (97%)
 rename ts/webui/src/components/{trial-detail => experiment/trialdetail/table/tableFunction}/search/searchFunction.ts (97%)
 rename ts/webui/src/components/{modals => experiment/trialdetail/table/tableFunction}/tensorboard/TensorboardDialog.tsx (100%)
 rename ts/webui/src/components/{modals => experiment/trialdetail/table/tableFunction}/tensorboard/TensorboardUI.tsx (96%)
 rename ts/webui/src/components/{managementExp/ExperimentManager.tsx => experimentManagement/ExperimentManagerIndex.tsx} (96%)
 rename ts/webui/src/components/{managementExp => experimentManagement}/FilterBtns.tsx (98%)
 rename ts/webui/src/components/{managementExp => experimentManagement}/Header.tsx (83%)
 rename ts/webui/src/components/{managementExp => experimentManagement}/TrialIdColumn.tsx (93%)
 rename ts/webui/src/components/{managementExp => experimentManagement}/expFunction.ts (90%)
 rename ts/webui/src/components/{managementExp => experimentManagement}/experimentConst.ts (100%)
 rename ts/webui/src/components/{NavConst.ts => fluent/ChildrenGap.ts} (60%)
 rename ts/webui/src/components/{buttons => fluent}/Icon.tsx (100%)
 rename ts/webui/src/components/{buttons => fluent}/ModalTheme.tsx (100%)
 delete mode 100644 ts/webui/src/components/modals/ChildrenGap.ts
 rename ts/webui/src/components/{NavCon.tsx => nav/Nav.tsx} (92%)
 rename ts/webui/src/components/{modals => nav/slideNav}/ExperimentSummaryPanel.tsx (93%)
 rename ts/webui/src/components/{ => nav}/slideNav/IconButtonTemplet.tsx (100%)
 rename ts/webui/src/components/{modals => nav/slideNav}/LogPanel.tsx (83%)
 rename ts/webui/src/components/{stateless-component => nav/slideNav}/NNItabs.tsx (91%)
 rename ts/webui/src/components/{ => nav}/slideNav/SlideNavBtns.tsx (90%)
 rename ts/webui/src/components/{ => nav}/slideNav/TrialConfigPanel.tsx (93%)
 delete mode 100644 ts/webui/src/components/public-child/IntermediateVal.tsx
 rename ts/webui/src/static/{json_util.ts => jsonutil.ts} (100%)
 rename ts/webui/src/{ => static/style}/App.scss (100%)
 rename ts/webui/src/static/style/{ => common}/common.scss (100%)
 rename ts/webui/src/static/style/{overview/probar.scss => common/experimentStatusColor.scss} (93%)
 rename ts/webui/src/static/style/{tableStatus.css => common/trialStatus.css} (84%)
 rename ts/webui/src/static/style/{ => experiment}/overview/basic.scss (100%)
 rename ts/webui/src/static/style/{ => experiment}/overview/command.scss (100%)
 rename ts/webui/src/static/style/{ => experiment}/overview/count.scss (100%)
 rename ts/webui/src/static/style/{ => experiment}/overview/overview.scss (100%)
 rename ts/webui/src/static/style/{ => experiment}/overview/overviewTitle.scss (100%)
 rename ts/webui/src/static/style/{ => experiment/overview}/succTable.scss (100%)
 rename ts/webui/src/static/style/{ => experiment}/overview/topTrial.scss (100%)
 rename ts/webui/src/static/style/{ => experiment/trialdetail}/compare.scss (100%)
 rename ts/webui/src/{components/modals => static/style/experiment/trialdetail}/customized.scss (100%)
 rename ts/webui/src/static/style/{ => experiment/trialdetail}/para.scss (100%)
 rename ts/webui/src/static/style/{ => experiment/trialdetail}/search.scss (99%)
 rename ts/webui/src/static/style/{ => experiment/trialdetail}/tensorboard.scss (100%)
 rename ts/webui/src/static/style/{ => experiment/trialdetail}/trialsDetail.scss (100%)
 rename ts/webui/src/static/style/{experiment => experimentManagement}/experiment.scss (100%)
 rename ts/webui/src/{ => static/style}/index.css (100%)
 rename ts/webui/src/static/style/{logDrawer.scss => logPanel.scss} (100%)
 rename ts/webui/src/static/style/{overview/panel.scss => nav/slideNavBtns.scss} (100%)

diff --git a/.gitignore b/.gitignore
index eb9945b84..8783101f0 100644
--- a/.gitignore
+++ b/.gitignore
@@ -13,6 +13,9 @@
 /test/ut/retiarii/_debug_graph_data.json
 /test/ut/retiarii/out.tmp
 
+# example generated files
+/nni_assets/**/data/
+
 # Logs
 logs
 *.log
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
deleted file mode 100644
index 6ce618686..000000000
--- a/CONTRIBUTING.md
+++ /dev/null
@@ -1,68 +0,0 @@
-# Contributing to NNI
-
-Welcome, and thank you for your interest in contributing to NNI!
-
-There are many ways in which you can contribute, beyond writing code. The goal of this document is to provide a high-level overview of how you can get involved.
-
-# Provide feedback or ask a question
-
-* [File an issue](https://github.com/microsoft/nni/issues/new/choose) on GitHub.
-* Ask a question with NNI tags on [Stack Overflow](https://stackoverflow.com/questions/tagged/nni?sort=Newest&edited=true).
-* Discuss on the NNI [Gitter](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) in NNI.
-
-Join IM discussion groups:
-|Gitter||WeChat|
-|----|----|----|
-|![image](https://user-images.githubusercontent.com/39592018/80665738-e0574a80-8acc-11ea-91bc-0836dc4cbf89.png)| OR |![image](https://github.com/scarlett2018/nniutil/raw/master/wechat.png)|
-
-
-# Look for an existing issue
-Before you create a new issue, please do a search in [open issues](https://github.com/microsoft/nni/issues) to see if the issue or feature request has already been filed.
-
-Be sure to scan through the [most popular](https://github.com/microsoft/nni/issues?q=is%3Aopen+is%3Aissue+label%3AFAQ+sort%3Areactions-%2B1-desc) feature requests.
-
-If you find your issue already exists, make relevant comments and add your [reaction](https://github.com/blog/2119-add-reactions-to-pull-requests-issues-and-comments). Use a reaction in place of a "+1" comment:
-
-* 👍 - upvote
-* 👎 - downvote
-
-If you cannot find an existing issue that describes your bug or feature, create a new issue using the guidelines below.
-
-# Writing good bug reports or feature requests
-File a single issue per problem and feature request. Do not enumerate multiple bugs or feature requests in the same issue.
-
-Provide as much information as you think might relevant to the context (thinking the issue is assigning to you, what kinds of info you will need to debug it!!!). To give you a general idea about what kinds of info are useful for developers to dig out the issue, we had provided issue template for you.
-
-Once you had submitted an issue, be sure to follow it for questions and discussions. 
-
-Once the bug is fixed or feature is addressed, be sure to close the issue.
-
-# Contributing fixes or examples
-
-This project welcomes contributions and suggestions. Most contributions require you to agree to a
-Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
-the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
-
-When you submit a pull request, a CLA bot will automatically determine whether you need to provide
-a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
-provided by the bot. You will only need to do this once across all repos using our CLA.
-
-# Code of Conduct
-
-This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
-For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
-contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
-
-# How to Contribute
-
-After getting familiar with contribution agreements, you are ready to create your first PR =), follow the NNI developer tutorials to get start:
-
-* We recommend new contributors to start with simple issues: ['good first issue'](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) or ['help-wanted'](https://github.com/microsoft/nni/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22).
-* [NNI developer environment installation tutorial](docs/en_US/Tutorial/SetupNniDeveloperEnvironment.rst)
-* [How to debug](docs/en_US/Tutorial/HowToDebug.rst)
-* If you have any questions on usage, review [FAQ](https://github.com/microsoft/nni/blob/master/docs/en_US/Tutorial/FAQ.rst) first, if there are no relevant issues and answers to your question, try contact NNI dev team and users in [Gitter](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) or [File an issue](https://github.com/microsoft/nni/issues/new/choose) on GitHub.
-* [Customize your own Tuner](docs/en_US/Tuner/CustomizeTuner.rst)
-* [Implement customized TrainingService](docs/en_US/TrainingService/HowToImplementTrainingService.rst)
-* [Implement a new NAS trainer on NNI](docs/en_US/NAS/Advanced.rst)
-* [Customize your own Advisor](docs/en_US/Tuner/CustomizeAdvisor.rst)
-
diff --git a/CONTRIBUTING_zh_CN.md b/CONTRIBUTING_zh_CN.md
deleted file mode 100644
index 1626a8524..000000000
--- a/CONTRIBUTING_zh_CN.md
+++ /dev/null
@@ -1,62 +0,0 @@
-# 贡献代码
-
-非常感谢您有兴趣对 NNI 做出贡献！
-
-除了编写代码外，您还可以通过多种方式参与， 本文档的目的是提供一个如何参与贡献的高层次概述。
-
-# 反馈或提问
-
-* 在 Github 上创建 [issue](https://github.com/microsoft/nni/issues/new/choose)。
-* 在 [Stack Overflow](https://stackoverflow.com/questions/tagged/nni?sort=Newest&edited=true) 上使用 nni 标签提问。
-* 在 [Gitter](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) 中参与讨论。
-
-加入聊天组：
-| Gitter                                                                                                         |   | 微信                                                                      |
-| -------------------------------------------------------------------------------------------------------------- | - | ----------------------------------------------------------------------- |
-| ![image](https://user-images.githubusercontent.com/39592018/80665738-e0574a80-8acc-11ea-91bc-0836dc4cbf89.png) | 或 | ![image](https://github.com/scarlett2018/nniutil/raw/master/wechat.png) |
-
-
-# 查找现有问题
-在创建新 issue 之前，请在 [open issues](https://github.com/microsoft/nni/issues) 中进行搜索，以查看问题或功能请求是否已经存在。
-
-确保已经浏览了 [最热门](https://github.com/microsoft/nni/issues?q=is%3Aopen+is%3Aissue+label%3AFAQ+sort%3Areactions-%2B1-desc) 的功能请求。
-
-如果您的问题已经存在，请在下方发表评论或添加[回应](https://github.com/blog/2119-add-reactions-to-pull-requests-issues-and-comments)。 通过回应来代替“+1”评论：
-
-* 👍 - 赞成
-* 👎 - 反对
-
-如果未能找到描述您 Bug 或功能的现有问题，请使用以下指南创建一个新问题。
-
-# 编写良好的错误报告或功能请求
-针对每个错误和功能请求提交一个问题， 不要在同一问题中列举多个 Bug 或功能请求。
-
-尽可能多地提供您认为与上下文相关的信息（思考问题如果分配给您，您需要什么样的信息来调试它）。 为了让您大致了解哪些信息对开发人员解决问题有帮助，我们为您提供了问题模板。
-
-提交问题后，请务必跟进问题并参与讨论。
-
-修正 Bug 或实现功能后，请务必关闭此问题。
-
-# 贡献修复或示例
-
-此项目欢迎任何贡献和建议。 大多数贡献需要您同意参与者许可协议（CLA），来声明您有权并授予我们使用您贡献的权利。 有关详细信息，请访问 https://cla.opensource.microsoft.com。
-
-当你提交拉取请求时，CLA 机器人会自动检查你是否需要提供 CLA，并修饰这个拉取请求（例如标签、注释等）。 只需要按照机器人提供的说明进行操作即可。 CLA 只需要同意一次，就能应用到所有的代码仓库上。
-
-# 行为准则
-
-该项目采用了 [ Microsoft 开源行为准则 ](https://opensource.microsoft.com/codeofconduct/)。 有关详细信息，请参阅[行为守则常见问题解答](https://opensource.microsoft.com/codeofconduct/faq/)或联系 opencode@microsoft.com 咨询问题或评论。
-
-# 参与贡献
-
-熟悉贡献协议后，即可按照 NNI 开发人员教程，创建第一个 PR =)：
-
-* 推荐新贡献者先从简单的问题开始：['good first issue'](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) 或 ['help-wanted'](https://github.com/microsoft/nni/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22)。
-* [NNI 开发环境安装教程](docs/zh_CN/Tutorial/SetupNniDeveloperEnvironment.rst)
-* [如何调试](docs/zh_CN/Tutorial/HowToDebug.rst)
-* 如果有使用上的问题，可先查看[常见问题解答](https://github.com/microsoft/nni/blob/master/docs/zh_CN/Tutorial/FAQ.rst)。如果没能解决问题，可通过 [Gitter](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) 联系 NNI 开发团队或在 GitHub 上 [报告问题](https://github.com/microsoft/nni/issues/new/choose)。
-* [自定义 Tuner](docs/zh_CN/Tuner/CustomizeTuner.rst)
-* [实现定制的训练平台](docs/zh_CN/TrainingService/HowToImplementTrainingService.rst)
-* [在 NNI 上实现新的 NAS Trainer](docs/zh_CN/NAS/Advanced.rst)
-* [自定义 Advisor](docs/zh_CN/Tuner/CustomizeAdvisor.rst)
-
diff --git a/README.md b/README.md
index b17838c96..3be82e3eb 100644
--- a/README.md
+++ b/README.md
@@ -1,332 +1,245 @@
-<p align="center">
-<img src="docs/img/nni_logo.png" width="300"/>
-</p>
+<div align="center">
+<img src="docs/img/nni_logo.png" width="600"/>
+</div>
+
+<br/>
 
 [![MIT licensed](https://img.shields.io/badge/license-MIT-brightgreen.svg)](LICENSE)
-[![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/full%20test%20-%20linux?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=62&branchName=master)
 [![Issues](https://img.shields.io/github/issues-raw/Microsoft/nni.svg)](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen)
 [![Bugs](https://img.shields.io/github/issues/Microsoft/nni/bug.svg)](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen+label%3Abug)
 [![Pull Requests](https://img.shields.io/github/issues-pr-raw/Microsoft/nni.svg)](https://github.com/Microsoft/nni/pulls?q=is%3Apr+is%3Aopen)
-[![Version](https://img.shields.io/github/release/Microsoft/nni.svg)](https://github.com/Microsoft/nni/releases) [![Join the chat at https://gitter.im/Microsoft/nni](https://badges.gitter.im/Microsoft/nni.svg)](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
+[![Version](https://img.shields.io/github/release/Microsoft/nni.svg)](https://github.com/Microsoft/nni/releases)
 [![Documentation Status](https://readthedocs.org/projects/nni/badge/?version=stable)](https://nni.readthedocs.io/en/stable/?badge=stable)
+[![](https://img.shields.io/github/contributors-anon/microsoft/nni)](https://github.com/microsoft/nni/graphs/contributors)
 
-[NNI Doc](https://nni.readthedocs.io/) | [简体中文](README_zh_CN.md)
-
-**NNI (Neural Network Intelligence)** is a lightweight but powerful toolkit to help users **automate** <a href="https://nni.readthedocs.io/en/stable/FeatureEngineering/Overview.html">Feature Engineering</a>, <a href="https://nni.readthedocs.io/en/stable/NAS/Overview.html">Neural Architecture Search</a>, <a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html">Hyperparameter Tuning</a> and <a href="https://nni.readthedocs.io/en/stable/Compression/Overview.html">Model Compression</a>.
 
-The tool manages automated machine learning (AutoML) experiments, **dispatches and runs** experiments' trial jobs generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in **different training environments** like <a href="https://nni.readthedocs.io/en/stable/TrainingService/LocalMode.html">Local Machine</a>, <a href="https://nni.readthedocs.io/en/stable/TrainingService/RemoteMachineMode.html">Remote Servers</a>, <a href="https://nni.readthedocs.io/en/stable/TrainingService/PaiMode.html">OpenPAI</a>, <a href="https://nni.readthedocs.io/en/stable/TrainingService/KubeflowMode.html">Kubeflow</a>, <a href="https://nni.readthedocs.io/en/stable/TrainingService/FrameworkControllerMode.html">FrameworkController on K8S (AKS etc.)</a>, <a href="https://nni.readthedocs.io/en/stable/TrainingService/DLTSMode.html">DLWorkspace (aka. DLTS)</a>, <a href="https://nni.readthedocs.io/en/stable/TrainingService/AMLMode.html">AML (Azure Machine Learning)</a>, <a href="https://nni.readthedocs.io/en/stable/TrainingService/AdaptDLMode.html">AdaptDL (aka. ADL)</a> , other cloud options and even <a href="https://nni.readthedocs.io/en/stable/TrainingService/HybridMode.html">Hybrid mode</a>.
 
-## **Who should consider using NNI**
+[<img src="docs/img/readme_banner.png" width="100%"/>](https://nni.readthedocs.io/)
 
-* Those who want to **try different AutoML algorithms** in their training code/model.
-* Those who want to run AutoML trial jobs **in different environments** to speed up search.
-* Researchers and data scientists who want to easily **implement and experiment new AutoML algorithms**, may it be: hyperparameter tuning algorithm, neural architect search algorithm or model compression algorithm.
-* ML Platform owners who want to **support AutoML in their platform**.
+NNI automates feature engineering, neural architecture search, hyperparameter tuning, and model compression for deep learning. Find the latest features, API, examples and tutorials in our **[official documentation](https://nni.readthedocs.io/) ([简体中文版点这里](https://nni.readthedocs.io/zh/stable))**.
 
-## **What's NEW!** &nbsp;<a href="#nni-released-reminder"><img width="48" src="docs/img/release_icon.png"></a>
+## What's NEW! &nbsp;<a href="#nni-released-reminder"><img width="48" src="docs/img/release_icon.png"></a>
 
-* **New release**: [v2.6 is available](https://github.com/microsoft/nni/releases/tag/v2.6) - _released on Jan-19-2022_
-* **New demo available**: [Youtube entry](https://www.youtube.com/channel/UCKcafm6861B2mnYhPbZHavw) | [Bilibili 入口](https://space.bilibili.com/1649051673) - _last updated on May-26-2021_
+* **New release**: [v2.7 is available](https://github.com/microsoft/nni/releases/tag/v2.7) - _released on Apr-18-2022_
+* **New demo available**: [Youtube entry](https://www.youtube.com/channel/UCKcafm6861B2mnYhPbZHavw) | [Bilibili 入口](https://space.bilibili.com/1649051673) - _last updated on Apr-18-2022_
 * **New webinar**: [Introducing Retiarii: A deep learning exploratory-training framework on NNI](https://note.microsoft.com/MSR-Webinar-Retiarii-Registration-Live.html) - _scheduled on June-24-2021_
 * **New community channel**: [Discussions](https://github.com/microsoft/nni/discussions)
-* **New emoticons release**: [nnSpider](./docs/source/Tutorial/NNSpider.md)
-<p align="center">
-  <a href="#nni-spider"><img width="100%" src="docs/img/emoicons/home.svg" /></a>
-</p>
-
-## **NNI capabilities in a glance**
-
-NNI provides CommandLine Tool as well as an user friendly WebUI to manage training experiments. With the extensible API, you can customize your own AutoML algorithms and training services. To make it easy for new users, NNI also provides a set of build-in state-of-the-art AutoML algorithms and out of box support for popular training platforms.
-
-Within the following table, we summarized the current NNI capabilities, we are gradually adding new capabilities and we'd love to have your contribution.
-
-<p align="center">
-  <a href="#nni-has-been-released"><img src="docs/img/overview.svg" /></a>
-</p>
-
-<table>
-  <tbody>
-    <tr align="center" valign="bottom">
-    <td>
-      </td>
-      <td>
-        <b>Frameworks & Libraries</b>
-        <img src="docs/img/bar.png"/>
-      </td>
-      <td>
-        <b>Algorithms</b>
-        <img src="docs/img/bar.png"/>
-      </td>
-      <td>
-        <b>Training Services</b>
-        <img src="docs/img/bar.png"/>
-      </td>
-    </tr>
-    </tr>
-    <tr valign="top">
-    <td align="center" valign="middle">
-    <b>Built-in</b>
-      </td>
-      <td>
-      <ul><li><b>Supported Frameworks</b></li>
-        <ul>
-          <li>PyTorch</li>
-          <li>Keras</li>
-          <li>TensorFlow</li>
-          <li>MXNet</li>
-          <li>Caffe2</li>
-          <a href="https://nni.readthedocs.io/en/stable/SupportedFramework_Library.html">More...</a><br/>
-        </ul>
-        </ul>
-      <ul>
-        <li><b>Supported Libraries</b></li>
-          <ul>
-           <li>Scikit-learn</li>
-           <li>XGBoost</li>
-           <li>LightGBM</li>
-           <a href="https://nni.readthedocs.io/en/stable/SupportedFramework_Library.html">More...</a><br/>
-          </ul>
-      </ul>
-        <ul>
-        <li><b>Examples</b></li>
-         <ul>
-           <li><a href="examples/trials/mnist-pytorch">MNIST-pytorch</li></a>
-           <li><a href="examples/trials/mnist-tfv1">MNIST-tensorflow</li></a>
-           <li><a href="examples/trials/mnist-keras">MNIST-keras</li></a>
-           <li><a href="https://nni.readthedocs.io/en/stable/TrialExample/GbdtExample.html">Auto-gbdt</a></li>
-           <li><a href="https://nni.readthedocs.io/en/stable/TrialExample/Cifar10Examples.html">Cifar10-pytorch</li></a>
-           <li><a href="https://nni.readthedocs.io/en/stable/TrialExample/SklearnExamples.html">Scikit-learn</a></li>
-           <li><a href="https://nni.readthedocs.io/en/stable/TrialExample/EfficientNet.html">EfficientNet</a></li>
-           <li><a href="https://nni.readthedocs.io/en/stable/TrialExample/OpEvoExamples.html">Kernel Tunning</li></a>
-              <a href="https://nni.readthedocs.io/en/stable/SupportedFramework_Library.html">More...</a><br/>
-          </ul>
-        </ul>
-      </td>
-      <td align="left" >
-        <a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html">Hyperparameter Tuning</a>
-        <ul>
-          <b>Exhaustive search</b>
-          <ul>
-            <li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#Random">Random Search</a></li>
-            <li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#GridSearch">Grid Search</a></li>
-            <li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#Batch">Batch</a></li>
-            </ul>
-          <b>Heuristic search</b>
-          <ul>
-            <li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#Evolution">Naïve Evolution</a></li>
-            <li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#Anneal">Anneal</a></li>
-            <li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#Hyperband">Hyperband</a></li>
-            <li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#PBTTuner">PBT</a></li>
-          </ul>
-          <b>Bayesian optimization</b>
-            <ul>
-              <li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#BOHB">BOHB</a></li>
-              <li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#TPE">TPE</a></li>
-            <li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#SMAC">SMAC</a></li>
-            <li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#MetisTuner">Metis Tuner</a></li>
-            <li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#GPTuner">GP Tuner</a></li>
-            <li><a href="https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html#DNGOTuner">DNGO Tuner</a></li>
-            </ul>
-        </ul>
-          <a href="https://nni.readthedocs.io/en/stable/NAS/Overview.html">Neural Architecture Search (Retiarii)</a>
-          <ul>
-            <li><a href="https://nni.readthedocs.io/en/stable/NAS/ENAS.html">ENAS</a></li>
-            <li><a href="https://nni.readthedocs.io/en/stable/NAS/DARTS.html">DARTS</a></li>
-            <li><a href="https://nni.readthedocs.io/en/stable/NAS/SPOS.html">SPOS</a></li>
-            <li><a href="https://nni.readthedocs.io/en/stable/NAS/Proxylessnas.html">ProxylessNAS</a></li>
-            <li><a href="https://nni.readthedocs.io/en/stable/NAS/FBNet.html">FBNet</a></li>
-            <li><a href="https://nni.readthedocs.io/en/stable/NAS/ExplorationStrategies.html">Reinforcement Learning</a></li>
-            <li><a href="https://nni.readthedocs.io/en/stable/NAS/ExplorationStrategies.html">Regularized Evolution</a></li>
-            <li><a href="https://nni.readthedocs.io/en/stable/NAS/Overview.html">More...</a></li>
-          </ul>
-          <a href="https://nni.readthedocs.io/en/stable/Compression/Overview.html">Model Compression</a>
-          <ul>
-            <b>Pruning</b>
-            <ul>
-              <li><a href="https://nni.readthedocs.io/en/stable/Compression/Pruner.html#agp-pruner">AGP Pruner</a></li>
-              <li><a href="https://nni.readthedocs.io/en/stable/Compression/Pruner.html#slim-pruner">Slim Pruner</a></li>
-              <li><a href="https://nni.readthedocs.io/en/stable/Compression/Pruner.html#fpgm-pruner">FPGM Pruner</a></li>
-              <li><a href="https://nni.readthedocs.io/en/stable/Compression/Pruner.html#netadapt-pruner">NetAdapt Pruner</a></li>
-              <li><a href="https://nni.readthedocs.io/en/stable/Compression/Pruner.html#simulatedannealing-pruner">SimulatedAnnealing Pruner</a></li>
-              <li><a href="https://nni.readthedocs.io/en/stable/Compression/Pruner.html#admm-pruner">ADMM Pruner</a></li>
-              <li><a href="https://nni.readthedocs.io/en/stable/Compression/Pruner.html#autocompress-pruner">AutoCompress Pruner</a></li>
-              <li><a href="https://nni.readthedocs.io/en/stable/Compression/Overview.html">More...</a></li>
-            </ul>
-            <b>Quantization</b>
-            <ul>
-              <li><a href="https://nni.readthedocs.io/en/stable/Compression/Quantizer.html#qat-quantizer">QAT Quantizer</a></li>
-              <li><a href="https://nni.readthedocs.io/en/stable/Compression/Quantizer.html#dorefa-quantizer">DoReFa Quantizer</a></li>
-              <li><a href="https://nni.readthedocs.io/en/stable/Compression/Quantizer.html#bnn-quantizer">BNN Quantizer</a></li>
-            </ul>
-          </ul>
-          <a href="https://nni.readthedocs.io/en/stable/FeatureEngineering/Overview.html">Feature Engineering (Beta)</a>
-          <ul>
-          <li><a href="https://nni.readthedocs.io/en/stable/FeatureEngineering/GradientFeatureSelector.html">GradientFeatureSelector</a></li>
-          <li><a href="https://nni.readthedocs.io/en/stable/FeatureEngineering/GBDTSelector.html">GBDTSelector</a></li>
-          </ul>
-          <a href="https://nni.readthedocs.io/en/stable/Assessor/BuiltinAssessor.html">Early Stop Algorithms</a>
-          <ul>
-          <li><a href="https://nni.readthedocs.io/en/stable/Assessor/BuiltinAssessor.html#MedianStop">Median Stop</a></li>
-          <li><a href="https://nni.readthedocs.io/en/stable/Assessor/BuiltinAssessor.html#Curvefitting">Curve Fitting</a></li>
-          </ul>
-      </td>
-      <td>
-      <ul>
-        <li><a href="https://nni.readthedocs.io/en/stable/TrainingService/LocalMode.html">Local Machine</a></li>
-        <li><a href="https://nni.readthedocs.io/en/stable/TrainingService/RemoteMachineMode.html">Remote Servers</a></li>
-        <li><a href="https://nni.readthedocs.io/en/stable/TrainingService/HybridMode.html">Hybrid mode</a></li>
-        <li><a href="https://nni.readthedocs.io/en/stable/TrainingService/AMLMode.html">AML(Azure Machine Learning)</a></li>
-        <li><b>Kubernetes based services</b></li>
-        <ul>
-          <li><a href="https://nni.readthedocs.io/en/stable/TrainingService/PaiMode.html">OpenPAI</a></li>
-          <li><a href="https://nni.readthedocs.io/en/stable/TrainingService/KubeflowMode.html">Kubeflow</a></li>
-          <li><a href="https://nni.readthedocs.io/en/stable/TrainingService/FrameworkControllerMode.html">FrameworkController on K8S (AKS etc.)</a></li>
-          <li><a href="https://nni.readthedocs.io/en/stable/TrainingService/DLTSMode.html">DLWorkspace (aka. DLTS)</a></li>
-          <li><a href="https://nni.readthedocs.io/en/stable/TrainingService/AdaptDLMode.html">AdaptDL (aka. ADL)</a></li>
-        </ul>
-      </ul>
-      </td>
-    </tr>
-      <tr align="center" valign="bottom">
-      </td>
-      </tr>
-      <tr valign="top">
-       <td valign="middle">
-    <b>References</b>
-      </td>
-     <td style="border-top:#FF0000 solid 0px;">
-      <ul>
-        <li><a href="https://nni.readthedocs.io/en/stable/autotune_ref.html#trial">Python API</a></li>
-        <li><a href="https://nni.readthedocs.io/en/stable/Tutorial/AnnotationSpec.html">NNI Annotation</a></li>
-         <li><a href="https://nni.readthedocs.io/en/stable/installation.html">Supported OS</a></li>
-      </ul>
-      </td>
-       <td style="border-top:#FF0000 solid 0px;">
-      <ul>
-        <li><a href="https://nni.readthedocs.io/en/stable/Tuner/CustomizeTuner.html">CustomizeTuner</a></li>
-        <li><a href="https://nni.readthedocs.io/en/stable/Assessor/CustomizeAssessor.html">CustomizeAssessor</a></li>
-        <li><a href="https://nni.readthedocs.io/en/stable/Tutorial/InstallCustomizedAlgos.html">Install Customized Algorithms as Builtin Tuners/Assessors/Advisors</a></li>
-        <li><a href="https://nni.readthedocs.io/en/stable/NAS/QuickStart.html#define-your-model-space">Define NAS Model Space</a></li>
-        <li><a href="https://nni.readthedocs.io/en/stable/NAS/ApiReference.html">NAS/Retiarii APIs</a></li>
-      </ul>
-      </td>
-        <td style="border-top:#FF0000 solid 0px;">
-      <ul>
-        <li><a href="https://nni.readthedocs.io/en/stable/TrainingService/Overview.html">Support TrainingService</li>
-        <li><a href="https://nni.readthedocs.io/en/stable/TrainingService/HowToImplementTrainingService.html">Implement TrainingService</a></li>
-      </ul>
-      </td>
-    </tr>
-  </tbody>
-</table>
-
-## **Installation**
+* **New emoticons release**: [nnSpider](https://nni.readthedocs.io/en/latest/sharings/nn_spider.html)
+<div align="center">
+<a href="#nni-spider"><img width="100%" src="docs/source/sharings/images/nn_spider/home.svg" /></a>
+</div>
 
-### **Install**
+## Installation
 
-NNI supports and is tested on Ubuntu >= 18.04, Windows 10 >= 21H2, and macOS >= 11.
-Simply run the following `pip install` in an environment that has `python 64-bit >= 3.7`.
+See the [NNI installation guide](https://nni.readthedocs.io/en/stable/installation.html) to install from pip, or build from source.
 
-Linux or macOS
+To install the current release:
 
-```bash
-python3 -m pip install --upgrade nni
 ```
-
-Windows
-
-```bash
-python -m pip install --upgrade nni
+$ pip install nni
 ```
 
-If you want to try latest code, please [install NNI](https://nni.readthedocs.io/en/stable/installation.html) from source code.
-
-For detail system requirements of NNI, please refer to [here](https://nni.readthedocs.io/en/stable/Tutorial/InstallationLinux.html#system-requirements) for Linux & macOS, and [here](https://nni.readthedocs.io/en/stable/Tutorial/InstallationWin.html#system-requirements) for Windows.
-
-Note:
-
-* If there is any privilege issue, add `--user` to install NNI in the user directory.
-* Currently NNI on Windows supports local, remote and pai mode. Anaconda or Miniconda is highly recommended to install [NNI on Windows](https://nni.readthedocs.io/en/stable/Tutorial/InstallationWin.html).
-* If there is any error like `Segmentation fault`, please refer to [FAQ](https://nni.readthedocs.io/en/stable/Tutorial/FAQ.html). For FAQ on Windows, please refer to [NNI on Windows](https://nni.readthedocs.io/en/stable/Tutorial/InstallationWin.html#faq).
-
-### **Verify installation**
+To update NNI to the latest version, add `--upgrade` flag to the above commands.
 
-* Download the examples via clone the source code.
+## NNI capabilities in a glance
 
-  ```bash
-  git clone -b v2.6 https://github.com/Microsoft/nni.git
-  ```
+<img src="docs/img/overview.svg" width="100%"/>
 
-* Run the MNIST example.
-
-  Linux or macOS
-
-  ```bash
-  nnictl create --config nni/examples/trials/mnist-pytorch/config.yml
-  ```
-
-  Windows
-
-  ```powershell
-  nnictl create --config nni\examples\trials\mnist-pytorch\config_windows.yml
-  ```
-
-* Wait for the message `INFO: Successfully started experiment!` in the command line. This message indicates that your experiment has been successfully started. You can explore the experiment using the `Web UI url`.
-
-```text
-INFO: Starting restful server...
-INFO: Successfully started Restful server!
-INFO: Setting local config...
-INFO: Successfully set local config!
-INFO: Starting experiment...
-INFO: Successfully started experiment!
------------------------------------------------------------------------
-The experiment id is egchD4qy
-The Web UI urls are: http://223.255.255.1:8080   http://127.0.0.1:8080
------------------------------------------------------------------------
-
-You can use these commands to get more information about the experiment
------------------------------------------------------------------------
-         commands                       description
-1. nnictl experiment show        show the information of experiments
-2. nnictl trial ls               list all of trial jobs
-3. nnictl top                    monitor the status of running experiments
-4. nnictl log stderr             show stderr log content
-5. nnictl log stdout             show stdout log content
-6. nnictl stop                   stop an experiment
-7. nnictl trial kill             kill a trial job by id
-8. nnictl --help                 get help information about nnictl
------------------------------------------------------------------------
-```
-
-* Open the `Web UI url` in your browser, you can view detailed information of the experiment and all the submitted trial jobs as shown below. [Here](https://nni.readthedocs.io/en/stable/Tutorial/WebUI.html) are more Web UI pages.
+<table>
+<tbody>
+<tr align="center" valign="bottom">
+<td></td>
+<td>
+<b>Hyperparameter Tuning</b>
+<img src="docs/img/bar.png" />
+</td>
+<td>
+<b>Neural Architecture Search</b>
+<img src="docs/img/bar.png" />
+</td>
+<td>
+<b>Model Compression</b>
+<img src="docs/img/bar.png" />
+</td>
+</tr>
+<tr valign="top">
+<td align="center" valign="middle">
+<b>Algorithms</b>
+</td>
+<td>
+<ul>
+<li><b>Exhaustive search</b></li>
+<ul>
+<li><a href="https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.gridsearch_tuner.GridSearchTuner">Grid Search</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.random_tuner.RandomTuner">Random</a></li>
+</ul>
+<li><b>Heuristic search</b></li>
+<ul>
+<li><a href="https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.hyperopt_tuner.HyperoptTuner">Anneal</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.evolution_tuner.EvolutionTuner">Evolution</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.hyperband_advisor.Hyperband">Hyperband</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.pbt_tuner.PBTTuner">PBT</a></li>
+</ul>
+<li><b>Bayesian optimization</b></li>
+<ul>
+<li><a href="https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.bohb_advisor.BOHB">BOHB</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.dngo_tuner.DNGOTuner">DNGO</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.gp_tuner.GPTuner">GP</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.metis_tuner.MetisTuner">Metis</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.smac_tuner.SMACTuner">SMAC</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/reference/hpo.html#nni.algorithms.hpo.tpe_tuner.TpeTuner">TPE</a></li>
+</ul>
+</ul>
+</td>
+<td>
+<ul>
+<li><b>Multi-trial</b></li>
+<ul>
+<li><a href="https://nni.readthedocs.io/en/latest/nas/exploration_strategy.html#grid-search-strategy">Grid Search</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/nas/exploration_strategy.html#policy-based-rl-strategy">Policy Based RL</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/nas/exploration_strategy.html#random-strategy">Random</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/nas/exploration_strategy.html#regularized-evolution-strategy">Regularized Evolution</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/nas/exploration_strategy.html#tpe-strategy">TPE</a></li>
+</ul>
+<li><b>One-shot</b></li>
+<ul>
+<li><a href="https://nni.readthedocs.io/en/latest/nas/exploration_strategy.html#darts-strategy">DARTS</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/nas/exploration_strategy.html#enas-strategy">ENAS</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/nas/exploration_strategy.html#fbnet-strategy">FBNet</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/nas/exploration_strategy.html#proxylessnas-strategy">ProxylessNAS</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/nas/exploration_strategy.html#spos-strategy">SPOS</a></li>
+</ul>
+</ul>
+</td>
+<td>
+<ul>
+<li><b>Pruning</b></li>
+<ul>
+<li><a href="https://nni.readthedocs.io/en/latest/compression/pruner.html#level-pruner">Level</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/compression/pruner.html#l1-norm-pruner">L1 Norm</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/compression/pruner.html#taylor-fo-weight-pruner">Taylor FO Weight</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/compression/pruner.html#movement-pruner">Movement</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/compression/pruner.html#agp-pruner">AGP</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/compression/pruner.html#auto-compress-pruner">Auto Compress</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/compression/index.html">More...</a></li>
+</ul>
+<li><b>Quantization</b></li>
+<ul>
+<li><a href="https://nni.readthedocs.io/en/latest/compression/quantizer.html#naive-quantizer">Naive</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/compression/quantizer.html#qat-quantizer">QAT</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/compression/quantizer.html#lsq-quantizer">LSQ</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/compression/quantizer.html#observer-quantizer">Observer</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/compression/quantizer.html#dorefa-quantizer">DoReFa</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/compression/quantizer.html#bnn-quantizer">BNN</a></li>
+</ul>
+</ul>
+</td>
+<tr align="center" valign="bottom">
+<td></td>
+<td>
+<b>Supported Frameworks</b>
+<img src="docs/img/bar.png" />
+</td>
+<td>
+<b>Training Services</b>
+<img src="docs/img/bar.png" />
+</td>
+<td>
+<b>Tutorials</b>
+<img src="docs/img/bar.png" />
+</td>
+</tr>
+<tr valign="top">
+<td align="center" valign="middle">
+<b>Supports</b>
+</td>
+<td>
+<ul>
+<li>PyTorch</li>
+<li>TensorFlow</li>
+<li>Scikit-learn</li>
+<li>XGBoost</li>
+<li>LightGBM</li>
+<li>MXNet</li>
+<li>Caffe2</li>
+<li>More...</li>
+</ul>
+</td>
+<td>
+<ul>
+<li><a href="https://nni.readthedocs.io/en/latest/experiment/local.html">Local machine</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/experiment/remote.html">Remote SSH servers</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/experiment/aml.html">Azure Machine Learning (AML)</a></li>
+<li><b>Kubernetes Based</b></li>
+<ul>
+<li><a href="https://nni.readthedocs.io/en/latest/experiment/openpai.html">OpenAPI</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/experiment/kubeflow.html">Kubeflow</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/experiment/frameworkcontroller.html">FrameworkController</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/experiment/adaptdl.html">AdaptDL</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/experiment/paidlc.html">PAI DLC</a></li>
+</ul>
+<li><a href="https://nni.readthedocs.io/en/latest/experiment/hybrid.html">Hybrid training services</a></li>
+</ul>
+</td>
+<td>
+<ul>
+<li><b>HPO</b></li>
+<ul>
+<li><a href="https://nni.readthedocs.io/en/latest/tutorials/hpo_quickstart_pytorch/main.html">PyTorch</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/tutorials/hpo_quickstart_tensorflow/main.html">TensorFlow</a></li>
+</ul>
+<li><b>NAS</b></li>
+<ul>
+<li><a href="https://nni.readthedocs.io/en/latest/tutorials/hello_nas.html">Hello NAS</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/tutorials/nasbench_as_dataset.html">NAS Benchmarks</a></li>
+</ul>
+<li><b>Compression</b></li>
+<ul>
+<li><a href="https://nni.readthedocs.io/en/latest/tutorials/pruning_quick_start_mnist.html">Pruning</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/tutorials/pruning_speed_up.html">Pruning Speedup</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/tutorials/quantization_quick_start_mnist.html">Quantization</a></li>
+<li><a href="https://nni.readthedocs.io/en/latest/tutorials/quantization_speed_up.html">Quantization Speedup</a></li>
+</ul>
+</ul>
+</td>
+</tbody>
+</table>
 
 <img src="docs/static/img/webui.gif" alt="webui" width="100%"/>
 
-## **Releases and Contributing**
-NNI has a monthly release cycle (major releases). Please let us know if you encounter a bug by [filling an issue](https://github.com/microsoft/nni/issues/new/choose).
-
-We appreciate all contributions. If you are planning to contribute any bug-fixes, please do so without further discussions.
+## Resources
 
-If you plan to contribute new features, new tuners, new training services, etc. please first open an issue or reuse an exisiting issue, and discuss the feature with us. We will discuss with you on the issue timely or set up conference calls if needed.
+* [NNI Documentation Homepage](https://nni.readthedocs.io/)
+* [NNI Installation Guide](https://nni.readthedocs.io/en/stable/installation.html)
+* [NNI Examples](https://nni.readthedocs.io/en/latest/examples.html)
+* [Python API Reference](https://nni.readthedocs.io/en/latest/reference/python_api.html)
+* [Releases (Change Log)](https://nni.readthedocs.io/en/latest/release.html)
+* [Related Research and Publications](https://nni.readthedocs.io/en/latest/notes/research_publications.html)
+* [Youtube Channel of NNI](https://www.youtube.com/channel/UCKcafm6861B2mnYhPbZHavw)
+* [Bilibili Space of NNI](https://space.bilibili.com/1649051673)
+* [Webinar of Introducing Retiarii: A deep learning exploratory-training framework on NNI](https://note.microsoft.com/MSR-Webinar-Retiarii-Registration-Live.html)
+* [Community Discussions](https://github.com/microsoft/nni/discussions)
 
-To learn more about making a contribution to NNI, please refer to our [How-to contribution page](https://nni.readthedocs.io/en/stable/contribution.html). 
+## Contribution guidelines
 
-We appreciate all contributions and thank all the contributors!
+If you want to contribute to NNI, be sure to review the [contribution guidelines](https://nni.readthedocs.io/en/stable/notes/contributing.html), which includes instructions of submitting feedbacks, best coding practices, and code of conduct.
 
-<a href="https://github.com/microsoft/nni/graphs/contributors"><img src="https://contrib.rocks/image?repo=microsoft/nni" /></a>
+We use [GitHub issues](https://github.com/microsoft/nni/issues) to track tracking requests and bugs.
+Please use [NNI Discussion](https://github.com/microsoft/nni/discussions) for general questions and new ideas.
+For questions of specific use cases, please go to [Stack Overflow](https://stackoverflow.com/questions/tagged/nni).
 
+Participating discussions via the following IM groups is also welcomed.
 
-## **Feedback**
-* [File an issue](https://github.com/microsoft/nni/issues/new/choose) on GitHub.
-* Open or participate in a [discussion](https://github.com/microsoft/nni/discussions). 
-* Discuss on the NNI [Gitter](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) in NNI.
-
-Join IM discussion groups:
 |Gitter||WeChat|
 |----|----|----|
 |![image](https://user-images.githubusercontent.com/39592018/80665738-e0574a80-8acc-11ea-91bc-0836dc4cbf89.png)| OR |![image](https://github.com/scarlett2018/nniutil/raw/master/wechat.png)|
 
+Over the past few years, NNI has received thousands of feedbacks on GitHub issues, and pull requests from hundreds of contributors.
+We appreciate all contributions from community to make NNI thrive.
+
+<img src="https://img.shields.io/github/contributors-anon/microsoft/nni"/>
+
+<a href="https://github.com/microsoft/nni/graphs/contributors"><img src="https://contrib.rocks/image?repo=microsoft/nni&max=240&columns=18" /></a>
 
 ## Test status
 
@@ -363,6 +276,6 @@ Targeting at openness and advancing state-of-art technology, [Microsoft Research
 
 We encourage researchers and students leverage these projects to accelerate the AI development and research.
 
-## **License**
+## License
 
-The entire codebase is under [MIT license](LICENSE)
+The entire codebase is under [MIT license](LICENSE).
diff --git a/README_zh_CN.md b/README_zh_CN.md
deleted file mode 100644
index 9e551c8ef..000000000
--- a/README_zh_CN.md
+++ /dev/null
@@ -1,364 +0,0 @@
-<p align="center">
-<img src="docs/img/nni_logo.png" width="300"/>
-</p>
-
-* * *
-
-[![MIT 许可证](https://img.shields.io/badge/license-MIT-brightgreen.svg)](LICENSE) [![生成状态](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/full%20test%20-%20linux?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=62&branchName=master) [![问题](https://img.shields.io/github/issues-raw/Microsoft/nni.svg)](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen) [![Bug](https://img.shields.io/github/issues/Microsoft/nni/bug.svg)](https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen+label%3Abug) [![拉取请求](https://img.shields.io/github/issues-pr-raw/Microsoft/nni.svg)](https://github.com/Microsoft/nni/pulls?q=is%3Apr+is%3Aopen) [![版本](https://img.shields.io/github/release/Microsoft/nni.svg)](https://github.com/Microsoft/nni/releases) [![进入 https://gitter.im/Microsoft/nni 聊天室提问](https://badges.gitter.im/Microsoft/nni.svg)](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) [![文档状态](https://readthedocs.org/projects/nni/badge/?version=stable)](https://nni.readthedocs.io/zh/stable/?badge=stable)
-
-[NNI 文档](https://nni.readthedocs.io/zh/stable/) | [English](README.md)
-
-**NNI (Neural Network Intelligence)** 是一个帮助用户**自动**进行[特征工程](docs/zh_CN/FeatureEngineering/Overview.rst)，[神经网络架构搜索](docs/zh_CN/NAS/Overview.rst)，[超参调优](docs/zh_CN/Tuner/BuiltinTuner.rst)以及[模型压缩](docs/zh_CN/Compression/Overview.rst)的轻量且强大的工具包。
-
-NNI 管理自动机器学习 (AutoML) 的 Experiment，**调度运行**由调优算法生成的 Trial 任务来找到最好的神经网络架构和/或超参，支持**各种训练环境**，如[本机](docs/zh_CN/TrainingService/LocalMode.rst)，[远程服务器](docs/zh_CN/TrainingService/RemoteMachineMode.rst)，[OpenPAI](docs/zh_CN/TrainingService/PaiMode.rst)，[Kubeflow](docs/zh_CN/TrainingService/KubeflowMode.rst)，[基于 K8S 的 FrameworkController（如，AKS 等）](docs/zh_CN/TrainingService/FrameworkControllerMode.rst)， [DLWorkspace (又称 DLTS)](docs/zh_CN/TrainingService/DLTSMode.rst), [AML (Azure Machine Learning)](docs/zh_CN/TrainingService/AMLMode.rst), [AdaptDL（又称 ADL）](docs/zh_CN/TrainingService/AdaptDLMode.rst) ，和其他的云平台甚至 [混合模式](docs/zh_CN/TrainingService/HybridMode.rst) 。 DLTS)</a>，[AML (Azure Machine Learning)](https://nni.readthedocs.io/zh/stable/TrainingService/AMLMode.html)[AdaptDL（又称 ADL）](https://nni.readthedocs.io/zh/stable/TrainingService/AdaptDLMode.html) ，和其他的云平台甚至[混合模式](https://nni.readthedocs.io/zh/stable/TrainingService/HybridMode.html) 。
-
-## **使用场景**
-
-* 想要在自己的代码、模型中试验**不同的自动机器学习算法**。
-* 想要在**不同的环境中**加速运行自动机器学习。
-* 想要更容易**实现或试验新的自动机器学习算法**的研究员或数据科学家，包括：超参调优算法，神经网络搜索算法以及模型压缩算法。
-* 在机器学习平台中**支持自动机器学习**。
-
-## **最新消息！** &nbsp;[<img width="48" src="docs/img/release_icon.png" />](#nni-released-reminder)
-
-* **最新版本**：[v2.6 已发布](https://github.com/microsoft/nni/releases/tag/v2.6) - *2022年1月19日*
-* **最新视频 demo**：[Youtube 入口](https://www.youtube.com/channel/UCKcafm6861B2mnYhPbZHavw) | [Bilibili 入口](https://space.bilibili.com/1649051673) - *上次更新：2021年5月26日*
-* **最新网络研讨会**: [介绍Retiarii：NNI 上的深度学习探索性训练框架](https://note.microsoft.com/MSR-Webinar-Retiarii-Registration-Live.html) - *2021年6月24日*
-* **最新互动渠道**: [Discussions](https://github.com/microsoft/nni/discussions)
-* **最新粉丝福利表情包上线**： [nnSpider](./docs/en_US/Tutorial/NNSpider.md)
-<p align="center">
-  <a href="#nni-spider"><img width="100%" src="docs/img/emoicons/home.svg" /></a>
-</p>
-
-## **NNI 功能一览**
-
-NNI 提供命令行工具以及友好的 WebUI 来管理训练的 Experiment。 通过可扩展的 API，可定制自动机器学习算法和训练平台。 为了方便新用户，NNI 内置了最新的自动机器学习算法，并为流行的训练平台提供了开箱即用的支持。
-
-下表中，包含了 NNI 的功能，同时在不断地增添新功能，也非常希望您能贡献其中。
-
-<p align="center">
-  <a href="#nni-has-been-released"><img src="docs/img/overview.svg" /></a>
-</p>
-
-<table>
-  <tbody>
-    <tr align="center" valign="bottom">
-    <td>
-      </td>
-      <td>
-        <b>支持的框架和库</b>
-        <img src="docs/img/bar.png"/>
-      </td>
-      <td>
-        <b>算法</b>
-        <img src="docs/img/bar.png"/>
-      </td>
-      <td>
-        <b>训练平台</b>
-        <img src="docs/img/bar.png"/>
-      </td>
-    </tr>
-    </tr>
-    <tr valign="top">
-    <td align="center" valign="middle">
-    <b>内置</b>
-      </td>
-      <td>
-      <ul><li><b>支持的框架</b></li>
-        <ul>
-          <li>PyTorch</li>
-          <li>Keras</li>
-          <li>TensorFlow</li>
-          <li>MXNet</li>
-          <li>Caffe2</li>
-          <a href="https://nni.readthedocs.io/zh/stable/SupportedFramework_Library.html">更多...</a><br/>
-        </ul>
-        </ul>
-      <ul>
-        <li><b>支持的库</b></li>
-          <ul>
-           <li>Scikit-learn</li>
-           <li>XGBoost</li>
-           <li>LightGBM</li>
-           <a href="https://nni.readthedocs.io/zh/stable/SupportedFramework_Library.html">更多...</a><br/>
-          </ul>
-      </ul>
-        <ul>
-        <li><b>示例</b></li>
-         <ul>
-           <li><a href="examples/trials/mnist-pytorch">MNIST-pytorch</li></a>
-           <li><a href="examples/trials/mnist-tfv1">MNIST-tensorflow</li></a>
-           <li><a href="examples/trials/mnist-keras">MNIST-keras</li></a>
-           <li><a href="https://nni.readthedocs.io/zh/stable/TrialExample/GbdtExample.html">Auto-gbdt</a></li>
-           <li><a href="https://nni.readthedocs.io/zh/stable/TrialExample/Cifar10Examples.html">Cifar10-pytorch</li></a>
-           <li><a href="https://nni.readthedocs.io/zh/stable/TrialExample/SklearnExamples.html">Scikit-learn</a></li>
-           <li><a href="https://nni.readthedocs.io/zh/stable/TrialExample/EfficientNet.html">EfficientNet</a></li>
-           <li><a href="https://nni.readthedocs.io/zh/stable/TrialExample/OpEvoExamples.html">GPU Kernel 调优</li></a>
-              <a href="https://nni.readthedocs.io/zh/stable/SupportedFramework_Library.html">更多...</a><br/>
-          </ul>
-        </ul>
-      </td>
-      <td align="left" >
-        <a href="https://nni.readthedocs.io/zh/stable/Tuner/BuiltinTuner.html">超参调优</a>
-        <ul>
-          <b>穷举搜索</b>
-          <ul>
-            <li><a href="https://nni.readthedocs.io/zh/stable/Tuner/BuiltinTuner.html#Random">Random Search（随机搜索）</a></li>
-            <li><a href="https://nni.readthedocs.io/zh/stable/Tuner/BuiltinTuner.html#GridSearch">Grid Search（遍历搜索）</a></li>
-            <li><a href="https://nni.readthedocs.io/zh/stable/Tuner/BuiltinTuner.html#Batch">Batch（批处理）</a></li>
-            </ul>
-          <b>启发式搜索</b>
-          <ul>
-            <li><a href="https://nni.readthedocs.io/zh/stable/Tuner/BuiltinTuner.html#Evolution">Naïve Evolution（朴素进化）</a></li>
-            <li><a href="https://nni.readthedocs.io/zh/stable/Tuner/BuiltinTuner.html#Anneal">Anneal（退火算法）</a></li>
-            <li><a href="https://nni.readthedocs.io/zh/stable/Tuner/BuiltinTuner.html#Hyperband">Hyperband</a></li>
-            <li><a href="https://nni.readthedocs.io/zh/stable/Tuner/BuiltinTuner.html#PBTTuner">PBT</a></li>
-          </ul>
-          <b>贝叶斯优化</b>
-            <ul>
-              <li><a href="https://nni.readthedocs.io/zh/stable/Tuner/BuiltinTuner.html#BOHB">BOHB</a></li>
-              <li><a href="https://nni.readthedocs.io/zh/stable/Tuner/BuiltinTuner.html#TPE">TPE</a></li>
-            <li><a href="https://nni.readthedocs.io/zh/stable/Tuner/BuiltinTuner.html#SMAC">SMAC</a></li>
-            <li><a href="https://nni.readthedocs.io/zh/stable/Tuner/BuiltinTuner.html#MetisTuner">Metis Tuner</a></li>
-            <li><a href="https://nni.readthedocs.io/zh/stable/Tuner/BuiltinTuner.html#GPTuner">GP Tuner</a></li>
-            <li><a href="https://nni.readthedocs.io/zh/stable/Tuner/BuiltinTuner.html#DNGOTuner">PPO Tuner</a></li>
-            </ul>
-        </ul>
-          <a href="https://nni.readthedocs.io/zh/stable/NAS/Overview.html">神经网络架构搜索</a>
-          <ul>
-            <li><a href="https://nni.readthedocs.io/zh/stable/NAS/ENAS.html">ENAS</a></li>
-            <li><a href="https://nni.readthedocs.io/zh/stable/NAS/DARTS.html">DARTS</a></li>
-            <li><a href="https://nni.readthedocs.io/zh/stable/NAS/SPOS.html">SPOS</a></li>
-            <li><a href="https://nni.readthedocs.io/zh/stable/NAS/Proxylessnas.html">ProxylessNAS</a></li>
-            <li><a href="https://nni.readthedocs.io/zh/stable/NAS/FBNet.html">FBNet</a></li>
-            <li><a href="https://nni.readthedocs.io/zh/stable/NAS/ExplorationStrategies.html">基于强化学习</a></li>
-            <li><a href="https://nni.readthedocs.io/zh/stable/NAS/ExplorationStrategies.html">正则进化</a></li>
-            <li><a href="https://nni.readthedocs.io/zh/stable/NAS/Overview.html">更多...</a></li>
-          </ul>
-          <a href="https://nni.readthedocs.io/zh/stable/Compression/Overview.html">模型压缩</a>
-          <ul>
-            <b>剪枝</b>
-            <ul>
-              <li><a href="https://nni.readthedocs.io/zh/stable/Compression/Pruner.html#agp-pruner">AGP Pruner</a></li>
-              <li><a href="https://nni.readthedocs.io/zh/stable/Compression/Pruner.html#slim-pruner">Slim Pruner</a></li>
-              <li><a href="https://nni.readthedocs.io/zh/stable/Compression/Pruner.html#fpgm-pruner">FPGM Pruner</a></li>
-              <li><a href="https://nni.readthedocs.io/zh/stable/Compression/Pruner.html#netadapt-pruner">NetAdapt Pruner</a></li>
-              <li><a href="https://nni.readthedocs.io/zh/stable/Compression/Pruner.html#simulatedannealing-pruner">SimulatedAnnealing Pruner</a></li>
-              <li><a href="https://nni.readthedocs.io/zh/stable/Compression/Pruner.html#admm-pruner">ADMM Pruner</a></li>
-              <li><a href="https://nni.readthedocs.io/zh/stable/Compression/Pruner.html#autocompress-pruner">AutoCompress Pruner</a></li>
-              <li><a href="https://nni.readthedocs.io/zh/stable/Compression/Overview.html">更多...</a></li>
-            </ul>
-            <b>量化</b>
-            <ul>
-              <li><a href="https://nni.readthedocs.io/zh/stable/Compression/Quantizer.html#qat-quantizer">QAT Quantizer</a></li>
-              <li><a href="https://nni.readthedocs.io/zh/stable/Compression/Quantizer.html#dorefa-quantizer">DoReFa Quantizer</a></li>
-              <li><a href="https://nni.readthedocs.io/zh/stable/Compression/Quantizer.html#bnn-quantizer">BNN Quantizer</a></li>
-            </ul>
-          </ul>
-          <a href="https://nni.readthedocs.io/zh/stable/FeatureEngineering/Overview.html">特征工程（测试版）</a>
-          <ul>
-          <li><a href="https://nni.readthedocs.io/zh/stable/FeatureEngineering/GradientFeatureSelector.html">GradientFeatureSelector</a></li>
-          <li><a href="https://nni.readthedocs.io/zh/stable/FeatureEngineering/GBDTSelector.html">GBDTSelector</a></li>
-          </ul>
-          <a href="https://nni.readthedocs.io/zh/stable/Assessor/BuiltinAssessor.html">提前终止算法</a>
-          <ul>
-          <li><a href="https://nni.readthedocs.io/zh/stable/Assessor/BuiltinAssessor.html#MedianStop">Median Stop（中位数终止）</a></li>
-          <li><a href="https://nni.readthedocs.io/zh/stable/Assessor/BuiltinAssessor.html#Curvefitting">Curve Fitting（曲线拟合）</a></li>
-          </ul>
-      </td>
-      <td>
-      <ul>
-        <li><a href="https://nni.readthedocs.io/zh/stable/TrainingService/LocalMode.html">本机</a></li>
-        <li><a href="https://nni.readthedocs.io/zh/stable/TrainingService/RemoteMachineMode.html">远程计算机</a></li>
-        <li><a href="https://nni.readthedocs.io/zh/stable/TrainingService/HybridMode.html">混合模式</a></li>
-        <li><a href="https://nni.readthedocs.io/zh/stable/TrainingService/AMLMode.html">AML(Azure Machine Learning)</a></li>
-        <li><b>基于 Kubernetes 的平台</b></li>
-        <ul>
-          <li><a href="https://nni.readthedocs.io/zh/stable/TrainingService/PaiMode.html">OpenPAI</a></li>
-          <li><a href="https://nni.readthedocs.io/zh/stable/TrainingService/KubeflowMode.html">Kubeflow</a></li>
-          <li><a href="https://nni.readthedocs.io/zh/stable/TrainingService/FrameworkControllerMode.html">基于 Kubernetes（AKS 等）的 FrameworkController</a></li>
-          <li><a href="https://nni.readthedocs.io/zh/stable/TrainingService/DLTSMode.html">DLWorkspace（又称  DLTS）</a></li>
-          <li><a href="https://nni.readthedocs.io/zh/stable/TrainingService/AdaptDLMode.html">AdaptDL（又称 ADL）</a></li>
-        </ul>
-      </ul>
-      </td>
-    </tr>
-      <tr align="center" valign="bottom">
-      </td>
-      </tr>
-      <tr valign="top">
-       <td valign="middle">
-    <b>参考</b>
-      </td>
-     <td style="border-top:#FF0000 solid 0px;">
-      <ul>
-        <li><a href="https://nni.readthedocs.io/zh/stable/autotune_ref.html#trial">Python API</a></li>
-        <li><a href="https://nni.readthedocs.io/zh/stable/Tutorial/AnnotationSpec.html">NNI Annotation</a></li>
-         <li><a href="https://nni.readthedocs.io/zh/stable/installation.html">支持的操作系统</a></li>
-      </ul>
-      </td>
-       <td style="border-top:#FF0000 solid 0px;">
-      <ul>
-        <li><a href="https://nni.readthedocs.io/zh/stable/Tuner/CustomizeTuner.html">自定义 Tuner</a></li>
-        <li><a href="https://nni.readthedocs.io/zh/stable/Assessor/CustomizeAssessor.html">自定义 Assessor</a></li>
-        <li><a href="https://nni.readthedocs.io/zh/stable/Tutorial/InstallCustomizedAlgos.html">安装自定义的 Tuner，Assessor，Advisor</a></li>
-        <li><a href="https://nni.readthedocs.io/zh/stable/NAS/QuickStart.html#define-your-model-space">定义模型空间</a></li>
-        <li><a href="https://nni.readthedocs.io/zh/stable/NAS/ApiReference.html">NAS/Retiarii接口</a></li>
-      </ul>
-      </td>
-        <td style="border-top:#FF0000 solid 0px;">
-      <ul>
-        <li><a href="https://nni.readthedocs.io/zh/stable/TrainingService/Overview.html">支持训练平台</li>
-        <li><a href="https://nni.readthedocs.io/zh/stable/TrainingService/HowToImplementTrainingService.html">实现训练平台</a></li>
-      </ul>
-      </td>
-    </tr>
-  </tbody>
-</table>
-
-## **安装**
-
-### **安装**
-
-NNI 支持并在 Ubuntu >= 16.04, macOS >= 10.14.1, 和 Windows 10 >= 1809 通过了测试。 在 `python 64-bit >= 3.6` 的环境中，只需要运行 `pip install` 即可完成安装。
-
-Linux 或 macOS
-
-```bash
-python3 -m pip install --upgrade nni
-```
-
-Windows
-
-```bash
-python -m pip install --upgrade nni
-```
-
-如果想试试最新代码，可参考从源代码[安装 NNI](https://nni.readthedocs.io/zh/latest/installation.html)。
-
-Linux 和 macOS 下 NNI 系统需求[参考这里](https://nni.readthedocs.io/zh/latest/Tutorial/InstallationLinux.html#system-requirements) ，Windows [参考这里](https://nni.readthedocs.io/zh/latest/Tutorial/InstallationWin.html#system-requirements)。
-
-注意：
-
-* 如果遇到任何权限问题，可添加 `--user` 在用户目录中安装 NNI。
-* 目前，Windows 上的 NNI 支持本机，远程和 OpenPAI 模式。 强烈推荐使用 Anaconda 或 Miniconda [在 Windows 上安装 NNI](https://nni.readthedocs.io/zh/stable/Tutorial/InstallationWin.html)。
-* 如果遇到如 `Segmentation fault` 等错误参考[常见问题](docs/zh_CN/Tutorial/FAQ.rst)。 Windows 上的 FAQ 参考[在 Windows 上使用 NNI](docs/zh_CN/Tutorial/InstallationWin.rst#faq)。 Windows 上的 FAQ 参考[在 Windows 上使用 NNI](https://nni.readthedocs.io/zh/stable/Tutorial/InstallationWin.html#faq)。
-
-### **验证安装**
-
-* 通过克隆源代码下载示例。
-    
-    ```bash
-    git clone -b v2.6 https://github.com/Microsoft/nni.git
-    ```
-
-* 运行 MNIST 示例。
-    
-    Linux 或 macOS
-    
-    ```bash
-    nnictl create --config nni/examples/trials/mnist-pytorch/config.yml
-    ```
-    
-    Windows
-    
-    ```powershell
-    nnictl create --config nni\examples\trials\mnist-pytorch\config_windows.yml
-    ```
-
-* 在命令行中等待输出 `INFO: Successfully started experiment!`。 此消息表明 Experiment 已成功启动。 通过命令行输出的 `Web UI url` 来访问 Experiment 的界面。 此消息表明 Experiment 已成功启动。 通过命令行输出的 `Web UI url` 来访问 Experiment 的界面。
-
-```text
-INFO: Starting restful server...
-INFO: Successfully started Restful server!
-INFO: Setting local config...
-INFO: Successfully set local config!
-INFO: Starting experiment...
-INFO: Successfully started experiment!
------------------------------------------------------------------------
-The experiment id is egchD4qy
-The Web UI urls are: http://223.255.255.1:8080   http://127.0.0.1:8080
------------------------------------------------------------------------
-
-You can use these commands to get more information about the experiment
------------------------------------------------------------------------
-         commands                       description
-
-1. nnictl experiment show        show the information of experiments
-2. nnictl trial ls               list all of trial jobs
-3. nnictl top                    monitor the status of running experiments
-4. nnictl log stderr             show stderr log content
-5. nnictl log stdout             show stdout log content
-6. nnictl stop                   stop an experiment
-7. nnictl trial kill             kill a trial job by id
-8. nnictl --help                 get help information about nnictl
------------------------------------------------------------------------
-```
-
-* 在浏览器中打开 `Web UI url`，可看到下图的 Experiment 详细信息，以及所有的 Trial 任务。 查看[这里](docs/zh_CN/Tutorial/WebUI.rst)的更多页面。 查看[这里](https://nni.readthedocs.io/zh/stable/Tutorial/WebUI.html)的更多页面。
-
-<img src="docs/static/img/webui.gif" alt="webui" width="100%" />
-
-## **发布和贡献**
-
-NNI 有一个月度发布周期（主要发布）。 如果您遇到问题可以通过 [创建 issue](https://github.com/microsoft/nni/issues/new/choose) 来报告。
-
-我们感谢所有的贡献。 我们感谢所有的贡献。 如果您计划提供任何 Bug 修复，请放手去做，不需要任何顾虑。
-
-如果您计划提供新的功能、新的 Tuner 和 新的训练平台等， 请先创建一个新的 issue 或重用现有 issue，并与我们讨论该功能。 我们会及时与您讨论这个问题，如有需要会安排电话会议。
-
-再次感谢所有的贡献者！
-
-再次感谢所有的贡献者！
-
-<a href="https://github.com/microsoft/nni/graphs/contributors"><img src="docs/img/contributors.png" /></a>
-
-## **反馈**
-
-* [在 GitHub 上提交问题](https://github.com/microsoft/nni/issues/new/choose)。
-* 在 [Gitter](https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) 中参与讨论。 
-* NNI 有一个月度发布周期（主要发布）。 如果您遇到问题可以通过 [创建 issue](https://github.com/microsoft/nni/issues/new/choose) 来报告。
-
-加入聊天组： 
-
-| Gitter                                                                                                         |   | 微信                                                                      |
-| -------------------------------------------------------------------------------------------------------------- | - | ----------------------------------------------------------------------- |
-| ![image](https://user-images.githubusercontent.com/39592018/80665738-e0574a80-8acc-11ea-91bc-0836dc4cbf89.png) | 或 | ![image](https://github.com/scarlett2018/nniutil/raw/master/wechat.png) |
-
-## 测试状态
-
-### 必需
-
-|      类型      |                                                                                                                                        状态                                                                                                                                        |
-|:------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
-|  Fast test   |                                [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/fast%20test?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=54&branchName=master)                                |
-|  Full linux  | [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/full%20test%20-%20linux?repoName=microsoft%2Fnni&branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=62&repoName=microsoft%2Fnni&branchName=master) |
-| Full windows |                         [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/full%20test%20-%20windows?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=63&branchName=master)                         |
-
-### 训练平台
-
-|            类型             |                                                                                                                                状态                                                                                                                                 |
-|:-------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
-|  Remote - linux to linux  |  [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20remote%20-%20linux%20to%20linux?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=64&branchName=master)  |
-| Remote - linux to windows | [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20remote%20-%20linux%20to%20windows?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=67&branchName=master) |
-| Remote - windows to linux | [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20remote%20-%20windows%20to%20linux?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=68&branchName=master) |
-|          OpenPAI          |        [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20openpai%20-%20linux?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=65&branchName=master)        |
-|    Frameworkcontroller    |        [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20frameworkcontroller?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=70&branchName=master)        |
-|         Kubeflow          |             [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20kubeflow?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=69&branchName=master)              |
-|          Hybrid           |              [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20hybrid?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=79&branchName=master)               |
-|          AzureML          |                [![Build Status](https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20aml?branchName=master)](https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=78&branchName=master)                |
-
-## 相关项目
-
-针对开放性和推进最先进的技术，[微软研究院（MSR)](https://www.microsoft.com/en-us/research/group/systems-and-networking-research-group-asia/) 还发布了其他几个开源项目。
-
-* [OpenPAI](https://github.com/Microsoft/pai)：作为开源平台，提供了完整的 AI 模型训练和资源管理能力，能轻松扩展，并支持各种规模的私有部署、云和混合环境。
-* [FrameworkController](https://github.com/Microsoft/frameworkcontroller)：开源的通用 Kubernetes Pod 控制器，通过单个控制器来编排 Kubernetes 上所有类型的应用。
-* [MMdnn](https://github.com/Microsoft/MMdnn)：一个完整、跨框架的解决方案，能够转换、可视化、诊断深度神经网络模型。 MMdnn 中的 "MM" 表示 model management（模型管理），而 "dnn" 是 deep neural network（深度神经网络）的缩写。 MMdnn 中的 "MM" 表示 model management（模型管理），而 "dnn" 是 deep neural network（深度神经网络）的缩写。
-* [SPTAG](https://github.com/Microsoft/SPTAG) : Space Partition Tree And Graph (SPTAG) 是用于大规模向量的最近邻搜索场景的开源库。
-
-我们鼓励研究人员和学生利用这些项目来加速 AI 开发和研究。
-
-## **许可协议**
-
-代码库遵循 [MIT 许可协议](LICENSE)
diff --git a/SECURITY_zh_CN.md b/SECURITY_zh_CN.md
deleted file mode 100644
index bfd7da66a..000000000
--- a/SECURITY_zh_CN.md
+++ /dev/null
@@ -1,41 +0,0 @@
-<!-- BEGIN MICROSOFT SECURITY.MD V0.0.5 BLOCK -->
-
-## 安全
-
-微软非常重视软件产品和服务的安全性，包括通过我们的 GitHub 组织管理的所有源代码库，其中涵盖 [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin)，和 [我们 GitHub 的组织](https://opensource.microsoft.com/)。
-
-如果你在任何微软拥有的资源库中发现了安全漏洞，并且符合 [微软对安全漏洞的定义](https://docs.microsoft.com/en-us/previous-versions/tn-archive/cc751383(v=technet.10))，请按照下文所述向我们报告。
-
-## 报告安全问题
-
-**请不要通过公开的 GitHub 问题报告安全漏洞。**
-
-相反，请向微软安全响应中心（MSRC）报告，链接是 [https://msrc.microsoft.com/create-report](https://msrc.microsoft.com/create-report)。
-
-如果您希望在不登录的情况下提交，请发送电子邮件至 [secure@microsoft.com](mailto:secure@microsoft.com)。  如果可能的话，请用我们的 PGP 密钥对您的信息进行加密；请从以下网站下载该密钥 [微软安全响应中心 PGP 密钥页面](https://www.microsoft.com/en-us/msrc/pgp-key-msrc)。
-
-你应该在24小时内收到回复。 如果由于某些原因你没有收到，请通过电子邮件跟进，以确保我们收到你的原始信息。 其他信息可以在以下网站找到 [microsoft.com/msrc](https://www.microsoft.com/msrc)。
-
-请包括以下所要求的信息（尽可能多地提供），以帮助我们更好地了解可能的问题的性质和范围。
-
-  * 问题类型（如缓冲区溢出、SQL 注入、跨站脚本等）
-  * 与问题表现有关的源文件的完整路径
-  * 受影响的源代码位置（标签/分支/提交或 URL）
-  * 重现该问题所需的任何特殊配置
-  * 重现该问题的分步骤说明
-  * 概念证明或漏洞代码（如果可能的话）
-  * 该问题的影响，包括攻击者如何利用该问题
-
-这些信息将帮助我们更快地对你的报告进行分流。
-
-如果您需要报告错误赏金，更完整的报告可有助于获得更高的赏金奖励。 请访问我们的[微软漏洞赏金计划](https://microsoft.com/msrc/bounty)页面，以了解有关我们活动计划的更多详细信息。
-
-## 首选语言
-
-我们希望所有的交流都是用英语进行的。
-
-## 政策
-
-微软遵循[协调漏洞披露](https://www.microsoft.com/en-us/msrc/cvd)的原则。
-
-<!-- END MICROSOFT SECURITY.MD BLOCK -->
diff --git a/dependencies/develop.txt b/dependencies/develop.txt
index 56a4dcadc..0e87993cb 100644
--- a/dependencies/develop.txt
+++ b/dependencies/develop.txt
@@ -2,15 +2,20 @@ coverage
 cython
 flake8
 ipython
-jupyterlab
+jupyter
+jupyterlab == 3.0.9
 nbsphinx
 pylint
+pyright
 pytest
 pytest-azurepipelines
 pytest-cov
 rstcheck
-sphinx
+sphinx >= 4.5
 sphinx-argparse-nni >= 0.4.0
+sphinx-copybutton
 sphinx-gallery
+sphinx-intl
+sphinx-tabs
 sphinxcontrib-bibtex
-git+https://github.com/bashtage/sphinx-material.git
+git+https://github.com/bashtage/sphinx-material@6e0ef82#egg=sphinx_material
diff --git a/dependencies/recommended.txt b/dependencies/recommended.txt
index 572dac348..2459808c5 100644
--- a/dependencies/recommended.txt
+++ b/dependencies/recommended.txt
@@ -7,8 +7,9 @@ torch == 1.10.0+cpu ; sys_platform != "darwin"
 torch == 1.10.0 ; sys_platform == "darwin"
 torchvision == 0.11.1+cpu ; sys_platform != "darwin"
 torchvision == 0.11.1 ; sys_platform == "darwin"
-pytorch-lightning >= 1.5.0
+pytorch-lightning >= 1.5.0, < 1.6.0
 torchmetrics
+lightgbm
 onnx
 peewee
 graphviz
diff --git a/dependencies/recommended_gpu.txt b/dependencies/recommended_gpu.txt
index 54f75b066..f9a5b6d8f 100644
--- a/dependencies/recommended_gpu.txt
+++ b/dependencies/recommended_gpu.txt
@@ -4,7 +4,8 @@
 tensorflow
 torch == 1.10.0+cu111
 torchvision == 0.11.1+cu111
-pytorch-lightning >= 1.5.0
+pytorch-lightning >= 1.5.0, < 1.6.0
+lightgbm
 onnx
 peewee
 graphviz
diff --git a/dependencies/recommended_legacy.txt b/dependencies/recommended_legacy.txt
index 5c5b39ee6..da4cacb06 100644
--- a/dependencies/recommended_legacy.txt
+++ b/dependencies/recommended_legacy.txt
@@ -7,6 +7,7 @@ torchvision == 0.8.2+cpu
 pytorch-lightning
 torchmetrics
 
+lightgbm
 onnx
 peewee
 graphviz
diff --git a/dependencies/required.txt b/dependencies/required.txt
index 2ce30c9cd..bb8d0cc8b 100644
--- a/dependencies/required.txt
+++ b/dependencies/required.txt
@@ -6,6 +6,7 @@ hyperopt == 0.1.2
 json_tricks >= 3.15.5
 numpy < 1.22 ; python_version < "3.8"
 numpy ; python_version >= "3.8"
+packaging
 pandas
 prettytable
 psutil
@@ -18,5 +19,5 @@ scikit-learn >= 0.24.1
 scipy < 1.8 ; python_version < "3.8"
 scipy ; python_version >= "3.8"
 typeguard
-typing_extensions ; python_version < "3.8"
+typing_extensions >= 4.0.0 ; python_version < "3.8"
 websockets >= 10.1
diff --git a/docs/.gitignore b/docs/.gitignore
index 20b5bd428..522de2ff5 100644
--- a/docs/.gitignore
+++ b/docs/.gitignore
@@ -2,3 +2,12 @@ build/
 
 # legacy build
 _build/
+
+# ignored copied rst in tutorials
+/source/tutorials/**/cp_*.rst
+
+# auto-generated reference table
+_modules/
+
+# Machine-style translation files
+*.mo
diff --git a/docs/Makefile b/docs/Makefile
index ba501f6f5..1d14ccfe7 100644
--- a/docs/Makefile
+++ b/docs/Makefile
@@ -11,6 +11,11 @@ BUILDDIR      = build
 help:
 	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
 
+# Build message catelogs for translation
+i18n:
+	@$(SPHINXBUILD) -M getpartialtext "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+	sphinx-intl update -p "$(BUILDDIR)/getpartialtext" -d "$(SOURCEDIR)/locales" -l zh
+
 .PHONY: help Makefile
 
 # Catch-all target: route all unknown targets to Sphinx using the new
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similarity index 100%
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diff --git a/docs/source/Tutorial/HowToLaunchFromPython.rst b/docs/_removed/HowToLaunchFromPython.rst
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diff --git a/docs/source/Tutorial/HowToUseDocker.rst b/docs/_removed/HowToUseDocker.rst
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diff --git a/docs/source/Tuner/DngoTuner.rst b/docs/_removed/Tuner/DngoTuner.rst
similarity index 100%
rename from docs/source/Tuner/DngoTuner.rst
rename to docs/_removed/Tuner/DngoTuner.rst
diff --git a/docs/source/Tuner/EvolutionTuner.rst b/docs/_removed/Tuner/EvolutionTuner.rst
similarity index 100%
rename from docs/source/Tuner/EvolutionTuner.rst
rename to docs/_removed/Tuner/EvolutionTuner.rst
diff --git a/docs/source/Tuner/GPTuner.rst b/docs/_removed/Tuner/GPTuner.rst
similarity index 100%
rename from docs/source/Tuner/GPTuner.rst
rename to docs/_removed/Tuner/GPTuner.rst
diff --git a/docs/source/Tuner/HyperbandAdvisor.rst b/docs/_removed/Tuner/HyperbandAdvisor.rst
similarity index 100%
rename from docs/source/Tuner/HyperbandAdvisor.rst
rename to docs/_removed/Tuner/HyperbandAdvisor.rst
diff --git a/docs/source/Tuner/MetisTuner.rst b/docs/_removed/Tuner/MetisTuner.rst
similarity index 100%
rename from docs/source/Tuner/MetisTuner.rst
rename to docs/_removed/Tuner/MetisTuner.rst
diff --git a/docs/source/Tuner/PBTTuner.rst b/docs/_removed/Tuner/PBTTuner.rst
similarity index 100%
rename from docs/source/Tuner/PBTTuner.rst
rename to docs/_removed/Tuner/PBTTuner.rst
diff --git a/docs/source/Tuner/SmacTuner.rst b/docs/_removed/Tuner/SmacTuner.rst
similarity index 100%
rename from docs/source/Tuner/SmacTuner.rst
rename to docs/_removed/Tuner/SmacTuner.rst
diff --git a/docs/source/Tutorial/NNSpider.md b/docs/_removed/Tutorial/NNSpider.md
similarity index 100%
rename from docs/source/Tutorial/NNSpider.md
rename to docs/_removed/Tutorial/NNSpider.md
diff --git a/docs/source/Tutorial/python_api_connect.ipynb b/docs/_removed/Tutorial/python_api_connect.ipynb
similarity index 100%
rename from docs/source/Tutorial/python_api_connect.ipynb
rename to docs/_removed/Tutorial/python_api_connect.ipynb
diff --git a/docs/source/Tutorial/python_api_start.ipynb b/docs/_removed/Tutorial/python_api_start.ipynb
similarity index 100%
rename from docs/source/Tutorial/python_api_start.ipynb
rename to docs/_removed/Tutorial/python_api_start.ipynb
diff --git a/docs/source/Tutorial/WebUI.rst b/docs/_removed/WebUI.rst
similarity index 100%
rename from docs/source/Tutorial/WebUI.rst
rename to docs/_removed/WebUI.rst
diff --git a/docs/source/Tutorial/FAQ.rst b/docs/_removed/faq.rst
similarity index 100%
rename from docs/source/Tutorial/FAQ.rst
rename to docs/_removed/faq.rst
diff --git a/docs/_removed/installation.rst b/docs/_removed/installation.rst
new file mode 100644
index 000000000..5688b3b4c
--- /dev/null
+++ b/docs/_removed/installation.rst
@@ -0,0 +1,12 @@
+############
+Installation
+############
+
+Currently we support installation on Linux, Mac and Windows. We also allow you to use docker.
+
+..  toctree::
+    :maxdepth: 2
+
+    Linux & Mac <Tutorial/InstallationLinux>
+    Windows <Tutorial/InstallationWin>
+    Use Docker <Tutorial/HowToUseDocker>
\ No newline at end of file
diff --git a/docs/_removed/reference/python_api.rst b/docs/_removed/reference/python_api.rst
new file mode 100644
index 000000000..2926461ba
--- /dev/null
+++ b/docs/_removed/reference/python_api.rst
@@ -0,0 +1,10 @@
+:orphan:
+
+Python API Reference
+====================
+
+.. autosummary::
+   :toctree: _modules
+   :recursive:
+
+   nni
diff --git a/docs/source/training_services.rst b/docs/_removed/training_services.rst
similarity index 72%
rename from docs/source/training_services.rst
rename to docs/_removed/training_services.rst
index ab8f79fbd..854b09f0b 100644
--- a/docs/source/training_services.rst
+++ b/docs/_removed/training_services.rst
@@ -3,13 +3,9 @@ Introduction to NNI Training Services
 
 ..  toctree::
     Overview <./TrainingService/Overview>
-    Local<./TrainingService/LocalMode>
     Remote<./TrainingService/RemoteMachineMode>
     OpenPAI<./TrainingService/PaiMode>
     Kubeflow<./TrainingService/KubeflowMode>
-    AdaptDL<./TrainingService/AdaptDLMode>
     FrameworkController<./TrainingService/FrameworkControllerMode>
-    DLTS<./TrainingService/DLTSMode>
     AML<./TrainingService/AMLMode>
-    PAI-DLC<./TrainingService/DLCMode>
     Hybrid<./TrainingService/HybridMode>
diff --git a/docs/extension/cardlinkitem.py b/docs/extension/cardlinkitem.py
index d3c000f72..80a8902be 100644
--- a/docs/extension/cardlinkitem.py
+++ b/docs/extension/cardlinkitem.py
@@ -7,6 +7,7 @@ import os
 from docutils.parsers.rst import Directive, directives
 from docutils.statemachine import StringList
 from docutils import nodes
+from sphinx.addnodes import pending_xref
 
 TAG_TEMPLATE = """<span class="card-link-tag">{tag}</span>"""
 
@@ -14,12 +15,12 @@ TAGS_TEMPLATE = """
     <p class="card-link-summary">{tags}</p>
 """
 
-CARD_TEMPLATE = """
+CARD_HEADER = """
 .. raw:: html
 
     <div class="card-link admonition">
 
-    <a href="{link}">
+    <a class="card-link-clickable" href="#">
 
     <div class="card-link-body">
 
@@ -46,6 +47,10 @@ CARD_TEMPLATE = """
     </div>
 
     </a>
+"""
+
+CARD_FOOTER = """
+.. raw:: html
 
     </div>
 """
@@ -95,15 +100,30 @@ class CustomCardItemDirective(Directive):
         else:
             tags_rst = ''
 
-        card_rst = CARD_TEMPLATE.format(header=header,
-                                        image=image,
-                                        image_background=image_background,
-                                        link=link,
-                                        description=description,
-                                        tags=tags_rst)
-        card_list = StringList(card_rst.split('\n'))
+        card_rst = CARD_HEADER.format(
+            header=header,
+            image=image,
+            image_background=image_background,
+            link=link,
+            description=description,
+            tags=tags_rst)
         card = nodes.paragraph()
-        self.state.nested_parse(card_list, self.content_offset, card)
+        self.state.nested_parse(StringList(card_rst.split('\n')), self.content_offset, card)
+
+        # This needs to corporate with javascript: propagate_card_link.
+        # because sphinx can't correctly handle image in a `pending_xref` after `keepformat`.
+        link_node = pending_xref('<a/>',
+                                 reftype='doc',
+                                 refdomain='std',
+                                 reftarget=link,
+                                 refexplicit=False,
+                                 refwarn=True)
+        link_node += nodes.paragraph(header)
+        link_node['classes'] = ['card-link-anchor']
+        card += link_node
+
+        self.state.nested_parse(StringList(CARD_FOOTER.split('\n')), self.content_offset, card)
+
         return [card]
 
 
diff --git a/docs/extension/codesnippetcard.py b/docs/extension/codesnippetcard.py
new file mode 100644
index 000000000..d1c067c38
--- /dev/null
+++ b/docs/extension/codesnippetcard.py
@@ -0,0 +1,114 @@
+"""
+Code snippet card, used in index page.
+"""
+
+from docutils.parsers.rst import Directive, directives
+from docutils.statemachine import StringList
+from docutils import nodes
+
+from sphinx.addnodes import pending_xref
+
+
+CARD_TEMPLATE_HEADER = """
+.. raw:: html
+
+    <div class="codesnippet-card admonition">
+
+    <div class="codesnippet-card-body">
+
+    <div class="codesnippet-card-title-container">
+
+    <div class="codesnippet-card-icon">
+
+.. image:: {icon}
+
+.. raw:: html
+
+    </div>
+
+    <h4>{title}</h4>
+    </div>
+
+"""
+
+CARD_TEMPLATE_FOOTER = """
+.. raw:: html
+
+    </div>
+"""
+
+CARD_TEMPLATE_LINK_CONTAINER_HEADER = """
+.. raw:: html
+
+    <div class="codesnippet-card-footer">
+"""
+
+CARD_TEMPLATE_LINK = """
+.. raw:: html
+
+    <div class="codesnippet-card-link">
+    {seemore}
+    <span class="material-icons right">arrow_forward</span>
+    </div>
+"""
+
+
+class CodeSnippetCardDirective(Directive):
+    option_spec = {
+        'icon': directives.unchanged,
+        'title': directives.unchanged,
+        'link': directives.unchanged,
+        'seemore': directives.unchanged,
+    }
+
+    has_content = True
+
+    def run(self):
+        anchor_node = nodes.paragraph()
+
+        try:
+            title = self.options['title']
+            link = directives.uri(self.options['link'])
+            icon = directives.uri(self.options['icon'])
+            seemore = self.options.get('seemore', 'For a full tutorial, please go here.')
+        except ValueError as e:
+            print(e)
+            raise
+
+        # header, title, icon...
+        card_rst = CARD_TEMPLATE_HEADER.format(title=title, icon=icon)
+        card_list = StringList(card_rst.split('\n'))
+        self.state.nested_parse(card_list, self.content_offset, anchor_node)
+
+        # code snippet
+        self.state.nested_parse(self.content, self.content_offset, anchor_node)
+
+        # close body
+        self.state.nested_parse(StringList(CARD_TEMPLATE_FOOTER.split('\n')), self.content_offset, anchor_node)
+
+        # start footer
+        self.state.nested_parse(StringList(CARD_TEMPLATE_LINK_CONTAINER_HEADER.split('\n')), self.content_offset, anchor_node)
+
+        # full tutorial link
+        link_node = pending_xref(CARD_TEMPLATE_LINK,
+                                 reftype='doc',
+                                 refdomain='std',
+                                 reftarget=link,
+                                 refexplicit=False,
+                                 refwarn=True,
+                                 refkeepformat=True)
+        # refkeepformat is handled in `patch_autodoc.py`
+        self.state.nested_parse(StringList(CARD_TEMPLATE_LINK.format(seemore=seemore).split('\n')), self.content_offset, link_node)
+        anchor_node += link_node
+
+        # close footer
+        self.state.nested_parse(StringList(CARD_TEMPLATE_FOOTER.split('\n')), self.content_offset, anchor_node)
+
+        # close whole
+        self.state.nested_parse(StringList(CARD_TEMPLATE_FOOTER.split('\n')), self.content_offset, anchor_node)
+
+        return [anchor_node]
+
+
+def setup(app):
+    app.add_directive('codesnippetcard', CodeSnippetCardDirective)
diff --git a/docs/extension/getpartialtext.py b/docs/extension/getpartialtext.py
new file mode 100644
index 000000000..5f9ee2adf
--- /dev/null
+++ b/docs/extension/getpartialtext.py
@@ -0,0 +1,30 @@
+"""
+Basically same as
+`sphinx gettext buidler <https://www.sphinx-doc.org/en/master/_modules/sphinx/builders/gettext.html>`_,
+but only get texts from files in a whitelist.
+"""
+
+import re
+
+from docutils import nodes
+from sphinx.application import Sphinx
+from sphinx.builders.gettext import MessageCatalogBuilder
+
+
+class PartialMessageCatalogBuilder(MessageCatalogBuilder):
+    name = 'getpartialtext'
+
+    def init(self):
+        super().init()
+
+        self.whitelist_docs = [re.compile(x) for x in self.config.gettext_documents]
+
+    def write_doc(self, docname: str, doctree: nodes.document) -> None:
+        for doc_re in self.whitelist_docs:
+            if doc_re.match(docname):
+                return super().write_doc(docname, doctree)
+
+
+def setup(app: Sphinx):
+    app.add_builder(PartialMessageCatalogBuilder)
+    app.add_config_value('gettext_documents', [], 'gettext')
diff --git a/docs/extension/patch_autodoc.py b/docs/extension/patch_autodoc.py
new file mode 100644
index 000000000..d99ef8306
--- /dev/null
+++ b/docs/extension/patch_autodoc.py
@@ -0,0 +1,178 @@
+"""Hack autodoc to get more fine-grained docstring rendering contol.
+
+autodoc and autosummary didn't expose many of their controls to sphinx users via config.
+To customize them, the "correct" approach seems to copy and paste all their code and rewrite some part.
+To avoid doing this, I monkey-patched some of the functions to keep the changes minimal.
+
+Note that some of them are related to sphinx internal APIs, which can be broken when sphinx got upgraded.
+Try to keep them updated, or pin to a particular sphinx version.
+"""
+
+import inspect
+import os
+from typing import List, Tuple, List
+
+import sphinx
+from docutils import nodes
+from docutils.nodes import Node
+
+
+class ClassNewBlacklistPatch:
+    """Force some classes to skip ``__new__`` when generating signature."""
+
+    original = None
+
+    def restore(self, *args, **kwargs):
+        assert self.original is not None
+        sphinx.ext.autodoc._CLASS_NEW_BLACKLIST = self.original
+
+    def patch(self, *args, **kwargs):
+        self.original = sphinx.ext.autodoc._CLASS_NEW_BLACKLIST
+
+        blacklist = []
+
+        import nni.retiarii.nn.pytorch
+        for name in dir(nni.retiarii.nn.pytorch):
+            obj = getattr(nni.retiarii.nn.pytorch, name)
+            if inspect.isclass(obj):
+                new_name = "{0.__module__}.{0.__qualname__}".format(obj.__new__)
+                if new_name not in blacklist:
+                    blacklist.append(new_name)
+
+        sphinx.ext.autodoc._CLASS_NEW_BLACKLIST = self.original + blacklist
+
+
+def disable_trace_patch(*args, **kwargs):
+    """Disable trace by setting an environment variable."""
+    os.environ['NNI_TRACE_FLAG'] = 'DISABLE'
+
+
+def trial_tool_import_patch(*args, **kwargs):
+    """Insert dummy trial tool variable to ensure trial_tool can be imported.
+    See nni/tools/trial_tool/constants.py
+    """
+    os.environ.update({
+        'NNI_OUTPUT_DIR': '/tmp',
+        'NNI_PLATFORM': 'unittest',
+        'NNI_SYS_DIR': '/tmp',
+        'NNI_TRIAL_JOB_ID': 'dummy',
+        'NNI_EXP_ID': 'dummy',
+        'MULTI_PHASE': 'dummy'
+    })
+
+
+class AutoSummaryPatch:
+    """Ignore certain files as they are completely un-importable. It patches:
+
+    - find_autosummary_in_files: Some modules cannot be imported at all due to dependency issues or some special design.
+      They need to skipped when running autosummary generate.
+    - Autosummary.get_table: The original autosummary creates an index for each module, and the module links in autosummary table
+      points to the corresponding generated module page (by using ``:py:module:xxx``). This doesn't work for us,
+      because we have used automodule else (other than autosummary) in our docs, and to avoid duplicate index,
+      we have to set ``:noindex:`` in autosummary template (see docs/templates/autosummary/module.rst).
+      This breaks most of the links, where they fail to link to generated module page by using index.
+      We here update the python domain role, to a general domain role (``:doc:``), and link to the page directly.
+    """
+
+    find_autosummary_original = None
+    get_table_original = None
+
+    def restore(self, *args, **kwargs):
+        assert self.find_autosummary_original is not None and self.get_table_original is not None
+        sphinx.ext.autosummary.generate.find_autosummary_in_files = self.find_autosummary_original
+        sphinx.ext.autosummary.Autosummary.get_table = self.get_table_original
+
+    def patch(self, app, config):
+        from sphinx.ext.autosummary import Autosummary
+        from sphinx.ext.autosummary.generate import AutosummaryEntry
+
+        self.find_autosummary_original = sphinx.ext.autosummary.generate.find_autosummary_in_files
+        self.get_table_original = Autosummary.get_table
+
+        def find_autosummary_in_files(filenames: List[str]) -> List[AutosummaryEntry]:
+            items: List[AutosummaryEntry] = self.find_autosummary_original(filenames)
+            items = [item for item in items if item.name not in config.autosummary_mock_imports]
+            return items
+
+        def get_table(autosummary, items: List[Tuple[str, str, str, str]]) -> List[Node]:
+            col_spec, autosummary_table = self.get_table_original(autosummary, items)
+            if 'toctree' in autosummary.options:
+                # probably within modules
+                table = autosummary_table[0]
+                tgroup = table[0]
+                tbody = tgroup[-1]
+                for row in tbody:
+                    entry = row[0]
+                    paragraph = entry[0]
+                    pending_xref = paragraph[0]
+
+                    # get the reference path and check whether it has been generated
+                    # if path to reference is changed, this should also be changed
+                    reftarget_path = 'reference/_modules/' + pending_xref['reftarget']
+
+                    if reftarget_path in autosummary.env.found_docs:
+                        # make :py:obj:`xxx` looks like a :doc:`xxx`
+                        pending_xref['refdomain'] = 'std'
+                        pending_xref['reftype'] = 'doc'
+                        pending_xref['refexplicit'] = False
+                        pending_xref['refwarn'] = True
+                        pending_xref['reftarget'] = '/' + reftarget_path
+                        # a special tag to enable `ResolveDocPatch`
+                        pending_xref['refkeepformat'] = True
+
+            return [col_spec, autosummary_table]
+
+        sphinx.ext.autosummary.generate.find_autosummary_in_files = find_autosummary_in_files
+        sphinx.ext.autosummary.Autosummary.get_table = get_table
+
+
+class ResolveDocPatch:
+    """Original :doc: role throws away all the format, and keep raw text only.
+    We wish to keep module names literal. This patch is to keep literal format in :doc: resolver."""
+
+    original = None
+
+    def restore(self, *args, **kwargs):
+        assert self.original is not None
+        sphinx.domains.std.StandardDomain._resolve_doc_xref = self.original
+
+    def patch(self, *args, **kwargs):
+        self.original = sphinx.domains.std.StandardDomain._resolve_doc_xref
+
+        def doc_xref_resolver(std_domain, env, fromdocname, builder, typ, target, node, contnode):
+            if not node.get('refkeepformat'):
+                # redirect to original implementation to make it safer
+                return self.original(std_domain, env, fromdocname, builder, typ, target, node, contnode)
+
+            # directly reference to document by source name; can be absolute or relative
+            from sphinx.domains.std import docname_join, make_refnode
+            refdoc = node.get('refdoc', fromdocname)
+            docname = docname_join(refdoc, node['reftarget'])
+            if docname not in env.all_docs:
+                return None
+            else:
+                innernode = node[0]  # no astext here, to keep literal intact
+                return make_refnode(builder, fromdocname, docname, None, innernode)
+
+        sphinx.domains.std.StandardDomain._resolve_doc_xref = doc_xref_resolver
+
+
+def setup(app):
+    # See life-cycle of sphinx app here:
+    # https://www.sphinx-doc.org/en/master/extdev/appapi.html#sphinx-core-events
+    patch = ClassNewBlacklistPatch()
+    app.connect('env-before-read-docs', patch.patch)
+    app.connect('env-merge-info', patch.restore)
+
+    patch = ResolveDocPatch()
+    app.connect('env-before-read-docs', patch.patch)
+    app.connect('env-merge-info', patch.restore)
+
+    app.connect('env-before-read-docs', disable_trace_patch)
+
+    # autosummary generate happens at builder-inited
+    app.connect('config-inited', trial_tool_import_patch)
+
+    autosummary_patch = AutoSummaryPatch()
+    app.connect('config-inited', autosummary_patch.patch)
+    app.connect('env-merge-info', autosummary_patch.restore)
diff --git a/docs/extension/patch_docutils.py b/docs/extension/patch_docutils.py
deleted file mode 100644
index ed2d62c85..000000000
--- a/docs/extension/patch_docutils.py
+++ /dev/null
@@ -1,39 +0,0 @@
-"""Additional docutils patch to suppress warnings in i18n documentation build."""
-
-from typing import Any
-
-import docutils
-from docutils.utils import Reporter
-
-
-class Patch:
-    """
-    This is actually done in sphinx, but sphinx didn't replace all `get_language` occurrences.
-    https://github.com/sphinx-doc/sphinx/blob/680417a10df7e5c35c0ff65979bd22906b9a5f1e/sphinx/util/docutils.py#L127
-
-    Related issue:
-    https://github.com/sphinx-doc/sphinx/issues/10179
-    """
-
-    original = None
-
-    def restore(self, *args, **kwargs):
-        assert self.original is not None
-        docutils.parsers.rst.languages.get_language = self.original
-
-    def patch(self, *args, **kwargs):
-        from docutils.parsers.rst.languages import get_language
-        self.original = get_language
-
-        def patched_get_language(language_code: str, reporter: Reporter = None) -> Any:
-            return get_language(language_code)
-
-        docutils.parsers.rst.languages.get_language = patched_get_language
-
-
-def setup(app):
-    # See life-cycle of sphinx app here:
-    # https://www.sphinx-doc.org/en/master/extdev/appapi.html#sphinx-core-events
-    patch = Patch()
-    app.connect('env-before-read-docs', patch.patch)
-    app.connect('env-merge-info', patch.restore)
diff --git a/docs/extension/toctree_check.py b/docs/extension/toctree_check.py
new file mode 100644
index 000000000..d6ff1328a
--- /dev/null
+++ b/docs/extension/toctree_check.py
@@ -0,0 +1,52 @@
+"""
+Make sure pages that contain toctree only has a toctree,
+because, if our theme is used, other contents will not be visible.
+"""
+
+import re
+
+from docutils import nodes
+from sphinx.application import Sphinx
+from sphinx.addnodes import toctree
+from sphinx.util.logging import getLogger
+
+logger = getLogger('toctree_check')
+
+def _strip_compound(node):
+    if isinstance(node, nodes.compound):
+        return _strip_compound(node[0])
+    return node
+
+
+def toctree_check(app: Sphinx, doctree: nodes.document, docname: str):
+    whitelist = app.config.toctree_check_whitelist
+
+    if docname in whitelist:
+        return
+
+    # Scan top-level nodes
+    has_toctree = False
+
+    other_types = []
+
+    for i in range(len(doctree[0])):
+        node = doctree[0][i]
+        if isinstance(_strip_compound(node), toctree):
+            has_toctree = True
+        elif isinstance(_strip_compound(node), nodes.title):
+            # Allow title
+            pass
+        else:
+            other_types.append(type(_strip_compound(node)))
+
+    if has_toctree and other_types:
+        # We don't allow a document with toctree to have other types of contents
+        logger.warning('Expect a toctree document to contain only a toctree, '
+                       'but found other types of contents: %s', str(set(other_types)),
+                       location=docname)
+
+
+def setup(app):
+    app.connect('doctree-resolved', toctree_check)
+
+    app.add_config_value('toctree_check_whitelist', [], True)
diff --git a/docs/extension/tutorial_links.py b/docs/extension/tutorial_links.py
new file mode 100644
index 000000000..9556b40f1
--- /dev/null
+++ b/docs/extension/tutorial_links.py
@@ -0,0 +1,45 @@
+"""Creating hard links for tutorials in each individual topics."""
+
+import os
+import re
+
+HEADER = """.. THIS FILE IS A COPY OF {} WITH MODIFICATIONS.
+.. TO MAKE ONE TUTORIAL APPEAR IN MULTIPLE PLACES.
+
+"""
+
+def flatten_filename(filename):
+    return filename.replace('/', '_').replace('.', '_')
+
+def copy_tutorials(app):
+    # TODO: use sphinx logger
+    print('[tutorial links] copy tutorials...')
+    for src, tar in app.config.tutorials_copy_list:
+        target_path = os.path.join(app.srcdir, tar)
+        content = open(os.path.join(app.srcdir, src)).read()
+
+        # Add a header
+        content = HEADER.format(src) + content
+
+        # Add a prefix to labels to avoid duplicates.
+        label_map = {}
+
+        # find all anchors:   https://www.sphinx-doc.org/en/master/usage/restructuredtext/roles.html
+        # but not hyperlinks: https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html#external-links
+        for prefix, label_name in list(re.findall(r'(\.\.\s*_)(.*?)\:\s*\n', content)):
+            label_map[label_name] = flatten_filename(tar) + '_' + label_name
+            # anchor
+            content = content.replace(prefix + label_name + ':', prefix + label_map[label_name] + ':')
+            # :ref:`xxx`
+            content = content.replace(f':ref:`{label_name}`', f':ref:`{label_map[label_name]}')
+            # :ref:`yyy <xxx>`
+            content = re.sub(r"(\:ref\:`.*?\<)" + label_name + r"(\>`)", r'\1' + label_map[label_name] + r'\2', content)
+
+        open(target_path, 'w').write(content)
+
+
+def setup(app):
+    # See life-cycle of sphinx app here:
+    # https://www.sphinx-doc.org/en/master/extdev/appapi.html#sphinx-core-events
+    app.connect('builder-inited', copy_tutorials)
+    app.add_config_value('tutorials_copy_list', [], True, [list])
diff --git a/docs/img/compression_flow.jpg b/docs/img/compression_flow.jpg
deleted file mode 100644
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new file mode 100644
index 000000000..cbd591809
--- /dev/null
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@@ -0,0 +1,10 @@
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diff --git a/docs/source/Assessor/BuiltinAssessor.rst b/docs/source/Assessor/BuiltinAssessor.rst
deleted file mode 100644
index 6b85253a7..000000000
--- a/docs/source/Assessor/BuiltinAssessor.rst
+++ /dev/null
@@ -1,101 +0,0 @@
-.. role:: raw-html(raw)
-   :format: html
-
-
-Built-in Assessors
-==================
-
-NNI provides state-of-the-art tuning algorithms within our builtin-assessors and makes them easy to use. Below is a brief overview of NNI's current builtin Assessors.
-
-Note: Click the **Assessor's name** to get each Assessor's installation requirements, suggested usage scenario, and a config example. A link to a detailed description of each algorithm is provided at the end of the suggested scenario for each Assessor.
-
-Currently, we support the following Assessors:
-
-.. list-table::
-   :header-rows: 1
-   :widths: auto
-
-   * - Assessor
-     - Brief Introduction of Algorithm
-   * - `Medianstop <#MedianStop>`__
-     - Medianstop is a simple early stopping rule. It stops a pending trial X at step S if the trial’s best objective value by step S is strictly worse than the median value of the running averages of all completed trials’ objectives reported up to step S. `Reference Paper <https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/46180.pdf>`__
-   * - `Curvefitting <#Curvefitting>`__
-     - Curve Fitting Assessor is an LPA (learning, predicting, assessing) algorithm. It stops a pending trial X at step S if the prediction of the final epoch's performance worse than the best final performance in the trial history. In this algorithm, we use 12 curves to fit the accuracy curve. `Reference Paper <http://aad.informatik.uni-freiburg.de/papers/15-IJCAI-Extrapolation_of_Learning_Curves.pdf>`__
-
-
-Usage of Builtin Assessors
---------------------------
-
-Usage of builtin assessors provided by the NNI SDK requires one to declare the  **builtinAssessorName** and **classArgs** in the ``config.yml`` file. In this part, we will introduce the details of usage and the suggested scenarios, classArg requirements, and an example for each assessor.
-
-Note: Please follow the provided format when writing your ``config.yml`` file.
-
-:raw-html:`<a name="MedianStop"></a>`
-
-Median Stop Assessor
-^^^^^^^^^^^^^^^^^^^^
-
-..
-
-   Builtin Assessor Name: **Medianstop**
-
-
-**Suggested scenario**
-
-It's applicable in a wide range of performance curves, thus, it can be used in various scenarios to speed up the tuning progress. `Detailed Description <./MedianstopAssessor.rst>`__
-
-**classArgs requirements:**
-
-
-* **optimize_mode** (*maximize or minimize, optional, default = maximize*\ ) - If 'maximize', assessor will **stop** the trial with smaller expectation. If 'minimize', assessor will **stop** the trial with larger expectation.
-* **start_step** (*int, optional, default = 0*\ ) - A trial is determined to be stopped or not only after receiving start_step number of reported intermediate results.
-
-**Usage example:**
-
-.. code-block:: yaml
-
-   # config.yml
-   assessor:
-       builtinAssessorName: Medianstop
-       classArgs:
-         optimize_mode: maximize
-         start_step: 5
-
-:raw-html:`<br>`
-
-:raw-html:`<a name="Curvefitting"></a>`
-
-Curve Fitting Assessor
-^^^^^^^^^^^^^^^^^^^^^^
-
-..
-
-   Builtin Assessor Name: **Curvefitting**
-
-
-**Suggested scenario**
-
-It's applicable in a wide range of performance curves, thus, it can be used in various scenarios to speed up the tuning progress. Even better, it's able to handle and assess curves with similar performance. `Detailed Description <./CurvefittingAssessor.rst>`__
-
-**Note**\ , according to the original paper, only incremental functions are supported. Therefore this assessor can only be used to maximize optimization metrics. For example, it can be used for accuracy, but not for loss.
-
-**classArgs requirements:**
-
-
-* **epoch_num** (*int,** required***\ ) - The total number of epochs. We need to know the number of epochs to determine which points we need to predict.
-* **start_step** (*int, optional, default = 6*\ ) - A trial is determined to be stopped or not only after receiving start_step number of reported intermediate results.
-* **threshold** (*float, optional, default = 0.95*\ ) - The threshold that we use to decide to early stop the worst performance curve. For example: if threshold = 0.95, and the best performance in the history is 0.9, then we will stop the trial who's predicted value is lower than 0.95 * 0.9 = 0.855.
-* **gap** (*int, optional, default = 1*\ ) - The gap interval between Assessor judgements. For example: if gap = 2, start_step = 6, then we will assess the result when we get 6, 8, 10, 12...intermediate results.
-
-**Usage example:**
-
-.. code-block:: yaml
-
-   # config.yml
-   assessor:
-       builtinAssessorName: Curvefitting
-       classArgs:
-         epoch_num: 20
-         start_step: 6
-         threshold: 0.95
-         gap: 1
diff --git a/docs/source/Assessor/CustomizeAssessor.rst b/docs/source/Assessor/CustomizeAssessor.rst
deleted file mode 100644
index 34cef8ebf..000000000
--- a/docs/source/Assessor/CustomizeAssessor.rst
+++ /dev/null
@@ -1,65 +0,0 @@
-Customize Assessor
-==================
-
-NNI supports to build an assessor by yourself for tuning demand.
-
-If you want to implement a customized Assessor, there are three things to do:
-
-
-#. Inherit the base Assessor class
-#. Implement assess_trial function
-#. Configure your customized Assessor in experiment YAML config file
-
-**1. Inherit the base Assessor class**
-
-.. code-block:: python
-
-   from nni.assessor import Assessor
-
-   class CustomizedAssessor(Assessor):
-       def __init__(self, ...):
-           ...
-
-**2. Implement assess trial function**
-
-.. code-block:: python
-
-   from nni.assessor import Assessor, AssessResult
-
-   class CustomizedAssessor(Assessor):
-       def __init__(self, ...):
-           ...
-
-       def assess_trial(self, trial_history):
-           """
-           Determines whether a trial should be killed. Must override.
-           trial_history: a list of intermediate result objects.
-           Returns AssessResult.Good or AssessResult.Bad.
-           """
-           # you code implement here.
-           ...
-
-**3. Configure your customized Assessor in experiment YAML config file**
-
-NNI needs to locate your customized Assessor class and instantiate the class, so you need to specify the location of the customized Assessor class and pass literal values as parameters to the __init__ constructor.
-
-.. code-block:: yaml
-
-   assessor:
-     codeDir: /home/abc/myassessor
-     classFileName: my_customized_assessor.py
-     className: CustomizedAssessor
-     # Any parameter need to pass to your Assessor class __init__ constructor
-     # can be specified in this optional classArgs field, for example
-     classArgs:
-       arg1: value1
-
-Please noted in **2**. The object ``trial_history`` are exact the object that Trial send to Assessor by using SDK ``report_intermediate_result`` function.
-
-The working directory of your assessor is ``<home>/nni-experiments/<experiment_id>/log``\ , which can be retrieved with environment variable ``NNI_LOG_DIRECTORY``\ ,
-
-More detail example you could see:
-
-* :githublink:`medianstop-assessor <nni/algorithms/hpo/medianstop_assessor.py>`
-* :githublink:`curvefitting-assessor <nni/algorithms/hpo/curvefitting_assessor/>`
-
diff --git a/docs/source/Assessor/MedianstopAssessor.rst b/docs/source/Assessor/MedianstopAssessor.rst
deleted file mode 100644
index 5a307bf0d..000000000
--- a/docs/source/Assessor/MedianstopAssessor.rst
+++ /dev/null
@@ -1,7 +0,0 @@
-Medianstop Assessor on NNI
-==========================
-
-Median Stop
------------
-
-Medianstop is a simple early stopping rule mentioned in this `paper <https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/46180.pdf>`__. It stops a pending trial X after step S if the trial’s best objective value by step S is strictly worse than the median value of the running averages of all completed trials’ objectives reported up to step S.
diff --git a/docs/source/CommunitySharings/automodel.rst b/docs/source/CommunitySharings/automodel.rst
deleted file mode 100644
index b4cc7e7fd..000000000
--- a/docs/source/CommunitySharings/automodel.rst
+++ /dev/null
@@ -1,13 +0,0 @@
-######################
-Automatic Model Tuning
-######################
-
-NNI can be applied on various model tuning tasks. Some state-of-the-art model search algorithms, such as EfficientNet, can be easily built on NNI. Popular models, e.g., recommendation models, can be tuned with NNI. The following are some use cases to illustrate how to leverage NNI in your model tuning tasks and how to build your own pipeline with NNI.
-
-..  toctree::
-    :maxdepth: 1
-
-    Tuning SVD automatically <RecommendersSvd>
-    EfficientNet on NNI <../TrialExample/EfficientNet>
-    Automatic Model Architecture Search for Reading Comprehension <../TrialExample/SquadEvolutionExamples>
-    Parallelizing Optimization for TPE <ParallelizingTpeSearch>
\ No newline at end of file
diff --git a/docs/source/CommunitySharings/automodel_zh.rst b/docs/source/CommunitySharings/automodel_zh.rst
deleted file mode 100644
index a717ef099..000000000
--- a/docs/source/CommunitySharings/automodel_zh.rst
+++ /dev/null
@@ -1,15 +0,0 @@
-.. 21be18c35dee2702eb1c7a805dcfd939
-
-######################
-自动模型调优
-######################
-
-NNI 可以应用于各种模型调优任务。 一些最先进的模型搜索算法，如EfficientNet，可以很容易地在NNI上构建。 流行的模型，例如，推荐模型，可以使用 NNI 进行调优。 下面是一些用例，展示了如何在您的模型调优任务中使用 NNI，以及如何使用 NNI 构建您自己的流水线。
-
-..  toctree::
-    :maxdepth: 1
-
-    SVD 自动调优 <RecommendersSvd>
-    NNI 中的 EfficientNet <../TrialExample/EfficientNet>
-    用于阅读理解的自动模型架构搜索 <../TrialExample/SquadEvolutionExamples>
-    TPE 的并行优化 <ParallelizingTpeSearch>
\ No newline at end of file
diff --git a/docs/source/CommunitySharings/autosys.rst b/docs/source/CommunitySharings/autosys.rst
deleted file mode 100644
index 81ec1f559..000000000
--- a/docs/source/CommunitySharings/autosys.rst
+++ /dev/null
@@ -1,12 +0,0 @@
-#######################
-Automatic System Tuning
-#######################
-
-The performance of systems, such as database, tensor operator implementaion, often need to be tuned to adapt to specific hardware configuration, targeted workload, etc. Manually tuning a system is complicated and often requires detailed understanding of hardware and workload. NNI can make such tasks much easier and help system owners find the best configuration to the system automatically. The detailed design philosophy of automatic system tuning can be found in this `paper <https://dl.acm.org/doi/10.1145/3352020.3352031>`__\ . The following are some typical cases that NNI can help.
-
-..  toctree::
-    :maxdepth: 1
-
-    Tuning SPTAG (Space Partition Tree And Graph) automatically <SptagAutoTune>
-    Tuning the performance of RocksDB <../TrialExample/RocksdbExamples>
-    Tuning Tensor Operators automatically <../TrialExample/OpEvoExamples>
\ No newline at end of file
diff --git a/docs/source/CommunitySharings/autosys_zh.rst b/docs/source/CommunitySharings/autosys_zh.rst
deleted file mode 100644
index cf3c0e89a..000000000
--- a/docs/source/CommunitySharings/autosys_zh.rst
+++ /dev/null
@@ -1,14 +0,0 @@
-.. e0791b39c8c362669300ce55b42e997b
-
-#######################
-自动系统调优
-#######################
-
-数据库、张量算子实现等系统的性能往往需要进行调优，以适应特定的硬件配置、目标工作负载等。 手动调优系统非常复杂，并且通常需要对硬件和工作负载有详细的了解。 NNI 可以使这些任务变得更容易，并帮助系统所有者自动找到系统的最佳配置。 自动系统调优的详细设计思想可以在 `这篇论文 <https://dl.acm.org/doi/10.1145/3352020.3352031>`__ 中找到。 以下是 NNI 可以发挥作用的一些典型案例。
-
-..  toctree::
-    :maxdepth: 1
-
-    自动调优 SPTAG（Space Partition Tree And Graph）<SptagAutoTune>
-    调优 RocksDB 的性能<../TrialExample/RocksdbExamples>
-    自动调优张量算子<../TrialExample/OpEvoExamples>
\ No newline at end of file
diff --git a/docs/source/CommunitySharings/community_sharings.rst b/docs/source/CommunitySharings/community_sharings.rst
deleted file mode 100644
index a5fc0a841..000000000
--- a/docs/source/CommunitySharings/community_sharings.rst
+++ /dev/null
@@ -1,39 +0,0 @@
-#######################
-Use Cases and Solutions
-#######################
-
-Different from the tutorials and examples in the rest of the document which show the usage of a feature, this part mainly introduces end-to-end scenarios and use cases to help users further understand how NNI can help them. NNI can be widely adopted in various scenarios. We also encourage community contributors to share their AutoML practices especially the NNI usage practices from their experience.
-
-Use Cases and Solutions
-=======================
-..  toctree::
-    :maxdepth: 2
-
-    Automatic Model Tuning (HPO/NAS) <automodel>
-    Automatic System Tuning (AutoSys) <autosys>
-    Model Compression <model_compression>
-    Feature Engineering <feature_engineering>
-    Performance measurement, comparison and analysis <perf_compare>
-    Use NNI on Google Colab <NNI_colab_support>
-
-External Repositories and References
-====================================
-With authors' permission, we listed a set of NNI usage examples and relevant articles.
-
-External Repositories
-===================== 
-   * `Hyperparameter Tuning for Matrix Factorization <https://github.com/microsoft/recommenders/blob/master/examples/04_model_select_and_optimize/nni_surprise_svd.ipynb>`__ with NNI
-   * `scikit-nni <https://github.com/ksachdeva/scikit-nni>`__ Hyper-parameter search for scikit-learn pipelines using NNI
-
-Relevant Articles
-=================
-  * `Cost-effective Hyper-parameter Tuning using AdaptDL with NNI - Feb 23, 2021 <https://medium.com/casl-project/cost-effective-hyper-parameter-tuning-using-adaptdl-with-nni-e55642888761>`__
-  * `(in Chinese) A summary of NNI new capabilities in NNI 2.0 - Jan 21, 2021 <https://www.msra.cn/zh-cn/news/features/nni-2>`__
-  * `(in Chinese) A summary of NNI new capabilities in 2019 - Dec 26, 2019 <https://mp.weixin.qq.com/s/7_KRT-rRojQbNuJzkjFMuA>`__
-  * `Find thy hyper-parameters for scikit-learn pipelines using Microsoft NNI - Nov 6, 2019 <https://towardsdatascience.com/find-thy-hyper-parameters-for-scikit-learn-pipelines-using-microsoft-nni-f1015b1224c1>`__ 
-  * `(in Chinese) AutoML tools (Advisor, NNI and Google Vizier) comparison - Aug 05, 2019 <http://gaocegege.com/Blog/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/katib-new#%E6%80%BB%E7%BB%93%E4%B8%8E%E5%88%86%E6%9E%90>`__  
-  * `Hyper Parameter Optimization Comparison <./HpoComparison.rst>`__ 
-  * `Neural Architecture Search Comparison <./NasComparison.rst>`__ 
-  * `Parallelizing a Sequential Algorithm TPE <./ParallelizingTpeSearch.rst>`__ 
-  * `Automatically tuning SVD with NNI <./RecommendersSvd.rst>`__ 
-  * `Automatically tuning SPTAG with NNI <./SptagAutoTune.rst>`__ 
diff --git a/docs/source/CommunitySharings/feature_engineering.rst b/docs/source/CommunitySharings/feature_engineering.rst
deleted file mode 100644
index c0beecf78..000000000
--- a/docs/source/CommunitySharings/feature_engineering.rst
+++ /dev/null
@@ -1,10 +0,0 @@
-###################
-Feature Engineering
-###################
-
-The following is an article about how NNI helps in auto feature engineering shared by a community contributor. More use cases and solutions will be added in the future.
-
-..  toctree::
-    :maxdepth: 1
-
-    NNI review article from Zhihu: - By Garvin Li <NNI_AutoFeatureEng>
\ No newline at end of file
diff --git a/docs/source/CommunitySharings/feature_engineering_zh.rst b/docs/source/CommunitySharings/feature_engineering_zh.rst
deleted file mode 100644
index 7108b5c06..000000000
--- a/docs/source/CommunitySharings/feature_engineering_zh.rst
+++ /dev/null
@@ -1,12 +0,0 @@
-.. 6b887244cf8fbace30971173f8c6fe8a
-
-###################
-特征工程
-###################
-
-以下是关于 NNI 如何助力特征工程的文章，由社区贡献者分享。 将来会添加更多用例和解决方案。
-
-..  toctree::
-    :maxdepth: 1
-
-    来自知乎的评论：作者 Garvin Li <NNI_AutoFeatureEng>
\ No newline at end of file
diff --git a/docs/source/CommunitySharings/model_compression.rst b/docs/source/CommunitySharings/model_compression.rst
deleted file mode 100644
index ee569b084..000000000
--- a/docs/source/CommunitySharings/model_compression.rst
+++ /dev/null
@@ -1,10 +0,0 @@
-#################
-Model Compression
-#################
-
-The following one shows how to apply knowledge distillation on NNI model compression. More use cases and solutions will be added in the future.
-
-..  toctree::
-    :maxdepth: 1
-
-    Knowledge distillation with NNI model compression <../TrialExample/KDExample>
\ No newline at end of file
diff --git a/docs/source/CommunitySharings/model_compression_zh.rst b/docs/source/CommunitySharings/model_compression_zh.rst
deleted file mode 100644
index 409ca8e84..000000000
--- a/docs/source/CommunitySharings/model_compression_zh.rst
+++ /dev/null
@@ -1,12 +0,0 @@
-.. 8a4c54c8127199ad4c95e20247e33fe2
-
-#################
-模型压缩
-#################
-
-以下介绍了如何将知识蒸馏应用于 NNI 模型压缩。 将来会添加更多用例和解决方案。
-
-..  toctree::
-    :maxdepth: 1
-
-    使用 NNI 模型压缩进行知识蒸馏<../TrialExample/KDExample>
\ No newline at end of file
diff --git a/docs/source/CommunitySharings/perf_compare.rst b/docs/source/CommunitySharings/perf_compare.rst
deleted file mode 100644
index 2b80ccdc6..000000000
--- a/docs/source/CommunitySharings/perf_compare.rst
+++ /dev/null
@@ -1,12 +0,0 @@
-################################################
-Performance Measurement, Comparison and Analysis
-################################################
-
-Performance comparison and analysis can help users decide a proper algorithm (e.g., tuner, NAS algorithm) for their scenario. The following are some measurement and comparison data for users' reference.
-
-..  toctree::
-    :maxdepth: 1
-
-    Neural Architecture Search Comparison <NasComparison>
-    Hyper-parameter Tuning Algorithm Comparsion <HpoComparison>
-    Model Compression Algorithm Comparsion <ModelCompressionComparison>
\ No newline at end of file
diff --git a/docs/source/CommunitySharings/perf_compare_zh.rst b/docs/source/CommunitySharings/perf_compare_zh.rst
deleted file mode 100644
index 72618dab8..000000000
--- a/docs/source/CommunitySharings/perf_compare_zh.rst
+++ /dev/null
@@ -1,14 +0,0 @@
-.. 7d625bd21018f53834e9db08a619c494
-
-################################################
-性能测量，比较和分析
-################################################
-
-性能比较和分析可以帮助用户在他们的场景中选择适合的算法（例如 Tuner，NAS 算法）。 以下是一些供用户参考的测量和比较数据。
-
-..  toctree::
-    :maxdepth: 1
-
-    神经网络结构搜索（NAS）的对比<NasComparison>
-    超参调优算法的对比<HpoComparison>
-    模型压缩算法的对比<ModelCompressionComparison>
\ No newline at end of file
diff --git a/docs/source/Compression/AutoCompression.rst b/docs/source/Compression/AutoCompression.rst
deleted file mode 100644
index 9705c0516..000000000
--- a/docs/source/Compression/AutoCompression.rst
+++ /dev/null
@@ -1,122 +0,0 @@
-Auto Compression with NNI Experiment
-====================================
-
-If you want to compress your model, but don't know what compression algorithm to choose, or don't know what sparsity is suitable for your model, or just want to try more possibilities, auto compression may help you.
-Users can choose different compression algorithms and define the algorithms' search space, then auto compression will launch an NNI experiment and try different compression algorithms with varying sparsity automatically. 
-Of course, in addition to the sparsity rate, users can also introduce other related parameters into the search space.
-If you don't know what is search space or how to write search space, `this <./Tutorial/SearchSpaceSpec.rst>`__ is for your reference.
-Auto compression using experience is similar to the NNI experiment in python.
-The main differences are as follows:
-
-* Use a generator to help generate search space object.
-* Need to provide the model to be compressed, and the model should have already been pre-trained.
-* No need to set ``trial_command``, additional need to set ``auto_compress_module`` as ``AutoCompressionExperiment`` input.
-
-.. note::
-    Auto compression only supports TPE Tuner, Random Search Tuner, Anneal Tuner, Evolution Tuner right now.
-
-Generate search space
----------------------
-
-Due to the extensive use of nested search space, we recommend a using generator to configure search space.
-The following is an example. Using ``add_config()`` add subconfig, then ``dumps()`` search space dict.
-
-.. code-block:: python
-
-    from nni.algorithms.compression.pytorch.auto_compress import AutoCompressionSearchSpaceGenerator
-
-    generator = AutoCompressionSearchSpaceGenerator()
-    generator.add_config('level', [
-        {
-            "sparsity": {
-                "_type": "uniform",
-                "_value": [0.01, 0.99]
-            },
-            'op_types': ['default']
-        }
-    ])
-    generator.add_config('qat', [
-    {
-        'quant_types': ['weight', 'output'],
-        'quant_bits': {
-            'weight': 8,
-            'output': 8
-        },
-        'op_types': ['Conv2d', 'Linear']
-    }])
-
-    search_space = generator.dumps()
-
-Now we support the following pruners and quantizers:
-
-.. code-block:: python
-
-    PRUNER_DICT = {
-        'level': LevelPruner,
-        'slim': SlimPruner,
-        'l1': L1FilterPruner,
-        'l2': L2FilterPruner,
-        'fpgm': FPGMPruner,
-        'taylorfo': TaylorFOWeightFilterPruner,
-        'apoz': ActivationAPoZRankFilterPruner,
-        'mean_activation': ActivationMeanRankFilterPruner
-    }
-
-    QUANTIZER_DICT = {
-        'naive': NaiveQuantizer,
-        'qat': QAT_Quantizer,
-        'dorefa': DoReFaQuantizer,
-        'bnn': BNNQuantizer
-    }
-
-Provide user model for compression
-----------------------------------
-
-Users need to inherit ``AbstractAutoCompressionModule`` and override the abstract class function.
-
-.. code-block:: python
-
-    from nni.algorithms.compression.pytorch.auto_compress import AbstractAutoCompressionModule
-
-    class AutoCompressionModule(AbstractAutoCompressionModule):
-        @classmethod
-        def model(cls) -> nn.Module:
-            ...
-            return _model
-
-        @classmethod
-        def evaluator(cls) -> Callable[[nn.Module], float]:
-            ...
-            return _evaluator
-
-Users need to implement at least ``model()`` and ``evaluator()``.
-If you use iterative pruner, you need to additional implement ``optimizer_factory()``, ``criterion()`` and ``sparsifying_trainer()``.
-If you want to finetune the model after compression, you need to implement ``optimizer_factory()``, ``criterion()``, ``post_compress_finetuning_trainer()`` and ``post_compress_finetuning_epochs()``.
-The ``optimizer_factory()`` should return a factory function, the input is an iterable variable, i.e. your ``model.parameters()``, and the output is an optimizer instance.
-The two kinds of ``trainer()`` should return a trainer with input ``model, optimizer, criterion, current_epoch``.
-The full abstract interface refers to :githublink:`interface.py <nni/algorithms/compression/pytorch/auto_compress/interface.py>`.
-An example of ``AutoCompressionModule`` implementation refers to :githublink:`auto_compress_module.py <examples/model_compress/auto_compress/torch/auto_compress_module.py>`.
-
-Launch NNI experiment
----------------------
-
-Similar to launch from python, the difference is no need to set ``trial_command`` and put the user-provided ``AutoCompressionModule`` as ``AutoCompressionExperiment`` input.
-
-.. code-block:: python
-
-    from pathlib import Path
-    from nni.algorithms.compression.pytorch.auto_compress import AutoCompressionExperiment
-
-    from auto_compress_module import AutoCompressionModule
-
-    experiment = AutoCompressionExperiment(AutoCompressionModule, 'local')
-    experiment.config.experiment_name = 'auto compression torch example'
-    experiment.config.trial_concurrency = 1
-    experiment.config.max_trial_number = 10
-    experiment.config.search_space = search_space
-    experiment.config.trial_code_directory = Path(__file__).parent
-    experiment.config.tuner.name = 'TPE'
-    experiment.config.tuner.class_args['optimize_mode'] = 'maximize'
-    experiment.config.training_service.use_active_gpu = True
-
-    experiment.run(8088)
diff --git a/docs/source/Compression/CompressionReference.rst b/docs/source/Compression/CompressionReference.rst
deleted file mode 100644
index df2e27872..000000000
--- a/docs/source/Compression/CompressionReference.rst
+++ /dev/null
@@ -1,160 +0,0 @@
-Model Compression API Reference
-===============================
-
-.. contents::
-
-Compressors
------------
-
-Compressor
-^^^^^^^^^^
-
-..  autoclass:: nni.compression.pytorch.compressor.Compressor
-    :members:
-
-..  autoclass:: nni.compression.pytorch.compressor.Pruner
-    :members:
-
-..  autoclass:: nni.compression.pytorch.compressor.Quantizer
-    :members:
-
-
-Module Wrapper
-^^^^^^^^^^^^^^
-
-..  autoclass:: nni.compression.pytorch.compressor.PrunerModuleWrapper
-    :members:
-
-
-..  autoclass:: nni.compression.pytorch.compressor.QuantizerModuleWrapper
-    :members:
-
-Weight Masker
-^^^^^^^^^^^^^
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.weight_masker.WeightMasker
-    :members:
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.structured_pruning_masker.StructuredWeightMasker
-    :members:
-
-
-Pruners
-^^^^^^^
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.sensitivity_pruner.SensitivityPruner
-    :members:
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.one_shot_pruner.OneshotPruner
-    :members:
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.one_shot_pruner.LevelPruner
-    :members:
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.one_shot_pruner.L1FilterPruner
-    :members:
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.one_shot_pruner.L2FilterPruner
-    :members:
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.one_shot_pruner.FPGMPruner
-    :members:
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.iterative_pruner.IterativePruner
-    :members:
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.iterative_pruner.SlimPruner
-    :members:
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.iterative_pruner.TaylorFOWeightFilterPruner
-    :members:
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.iterative_pruner.ActivationAPoZRankFilterPruner
-    :members:
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.iterative_pruner.ActivationMeanRankFilterPruner
-    :members:
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.iterative_pruner.AGPPruner
-    :members:
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.iterative_pruner.ADMMPruner
-    :members:
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.auto_compress_pruner.AutoCompressPruner
-    :members:
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.net_adapt_pruner.NetAdaptPruner
-    :members:
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.simulated_annealing_pruner.SimulatedAnnealingPruner
-    :members:
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.lottery_ticket.LotteryTicketPruner
-    :members:
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.transformer_pruner.TransformerHeadPruner
-    :members:
-
-Quantizers
-^^^^^^^^^^
-..  autoclass:: nni.algorithms.compression.pytorch.quantization.NaiveQuantizer
-    :members:
-
-..  autoclass:: nni.algorithms.compression.pytorch.quantization.QAT_Quantizer
-    :members:
-
-..  autoclass:: nni.algorithms.compression.pytorch.quantization.DoReFaQuantizer
-    :members:
-
-..  autoclass:: nni.algorithms.compression.pytorch.quantization.BNNQuantizer
-    :members:
-
-..  autoclass:: nni.algorithms.compression.pytorch.quantization.LsqQuantizer
-    :members:
-
-..  autoclass:: nni.algorithms.compression.pytorch.quantization.ObserverQuantizer
-    :members:
-
-Model Speedup
--------------
-
-Quantization Speedup
-^^^^^^^^^^^^^^^^^^^^
-
-..  autoclass:: nni.compression.pytorch.quantization_speedup.backend.BaseModelSpeedup
-    :members:
-
-..  autoclass:: nni.compression.pytorch.quantization_speedup.integrated_tensorrt.ModelSpeedupTensorRT
-    :members:
-
-..  autoclass:: nni.compression.pytorch.quantization_speedup.calibrator.Calibrator
-    :members:
-
-
-Compression Utilities
----------------------
-
-Sensitivity Utilities
-^^^^^^^^^^^^^^^^^^^^^
-
-..  autoclass:: nni.compression.pytorch.utils.sensitivity_analysis.SensitivityAnalysis
-    :members:
-
-Topology Utilities
-^^^^^^^^^^^^^^^^^^
-
-..  autoclass:: nni.compression.pytorch.utils.shape_dependency.ChannelDependency
-    :members:
-
-..  autoclass:: nni.compression.pytorch.utils.shape_dependency.GroupDependency
-    :members:
-
-..  autoclass:: nni.compression.pytorch.utils.mask_conflict.GroupMaskConflict
-    :members:
-
-..  autoclass:: nni.compression.pytorch.utils.mask_conflict.ChannelMaskConflict
-    :members:
-
-Model FLOPs/Parameters Counter
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-..  autofunction:: nni.compression.pytorch.utils.counter.count_flops_params
diff --git a/docs/source/Compression/CustomizeCompressor.rst b/docs/source/Compression/CustomizeCompressor.rst
deleted file mode 100644
index 55efd5e88..000000000
--- a/docs/source/Compression/CustomizeCompressor.rst
+++ /dev/null
@@ -1,179 +0,0 @@
-Customize New Compression Algorithm
-===================================
-
-.. contents::
-
-In order to simplify the process of writing new compression algorithms, we have designed simple and flexible programming interface, which covers pruning and quantization. Below, we first demonstrate how to customize a new pruning algorithm and then demonstrate how to customize a new quantization algorithm.
-
-**Important Note** To better understand how to customize new pruning/quantization algorithms, users should first understand the framework that supports various pruning algorithms in NNI. Reference `Framework overview of model compression <../Compression/Framework.rst>`__
-
-Customize a new pruning algorithm
----------------------------------
-
-Implementing a new pruning algorithm requires implementing a ``weight masker`` class which shoud be a subclass of ``WeightMasker``\ , and a ``pruner`` class, which should be a subclass ``Pruner``.
-
-An implementation of ``weight masker`` may look like this:
-
-.. code-block:: python
-
-   class MyMasker(WeightMasker):
-       def __init__(self, model, pruner):
-           super().__init__(model, pruner)
-           # You can do some initialization here, such as collecting some statistics data
-           # if it is necessary for your algorithms to calculate the masks.
-
-       def calc_mask(self, sparsity, wrapper, wrapper_idx=None):
-           # calculate the masks based on the wrapper.weight, and sparsity, 
-           # and anything else
-           # mask = ...
-           return {'weight_mask': mask}
-
-You can reference nni provided :githublink:`weight masker <nni/algorithms/compression/pytorch/pruning/structured_pruning_masker.py>` implementations to implement your own weight masker.
-
-A basic ``pruner`` looks likes this:
-
-.. code-block:: python
-
-   class MyPruner(Pruner):
-       def __init__(self, model, config_list, optimizer):
-           super().__init__(model, config_list, optimizer)
-           self.set_wrappers_attribute("if_calculated", False)
-           # construct a weight masker instance
-           self.masker = MyMasker(model, self)
-
-       def calc_mask(self, wrapper, wrapper_idx=None):
-           sparsity = wrapper.config['sparsity']
-           if wrapper.if_calculated:
-               # Already pruned, do not prune again as a one-shot pruner
-               return None
-           else:
-               # call your masker to actually calcuate the mask for this layer
-               masks = self.masker.calc_mask(sparsity=sparsity, wrapper=wrapper, wrapper_idx=wrapper_idx)
-               wrapper.if_calculated = True
-               return masks
-
-Reference nni provided :githublink:`pruner <nni/algorithms/compression/pytorch/pruning/one_shot_pruner.py>` implementations to implement your own pruner class.
-
-----
-
-Customize a new quantization algorithm
---------------------------------------
-
-To write a new quantization algorithm, you can write a class that inherits ``nni.compression.pytorch.Quantizer``. Then, override the member functions with the logic of your algorithm. The member function to override is ``quantize_weight``. ``quantize_weight`` directly returns the quantized weights rather than mask, because for quantization the quantized weights cannot be obtained by applying mask.
-
-.. code-block:: python
-
-   from nni.compression.pytorch import Quantizer
-
-   class YourQuantizer(Quantizer):
-       def __init__(self, model, config_list):
-           """
-           Suggest you to use the NNI defined spec for config
-           """
-           super().__init__(model, config_list)
-
-       def quantize_weight(self, weight, config, **kwargs):
-           """
-           quantize should overload this method to quantize weight tensors.
-           This method is effectively hooked to :meth:`forward` of the model.
-
-           Parameters
-           ----------
-           weight : Tensor
-               weight that needs to be quantized
-           config : dict
-               the configuration for weight quantization
-           """
-
-           # Put your code to generate `new_weight` here
-
-           return new_weight
-
-       def quantize_output(self, output, config, **kwargs):
-           """
-           quantize should overload this method to quantize output.
-           This method is effectively hooked to `:meth:`forward` of the model.
-
-           Parameters
-           ----------
-           output : Tensor
-               output that needs to be quantized
-           config : dict
-               the configuration for output quantization
-           """
-
-           # Put your code to generate `new_output` here
-
-           return new_output
-
-       def quantize_input(self, *inputs, config, **kwargs):
-           """
-           quantize should overload this method to quantize input.
-           This method is effectively hooked to :meth:`forward` of the model.
-
-           Parameters
-           ----------
-           inputs : Tensor
-               inputs that needs to be quantized
-           config : dict
-               the configuration for inputs quantization
-           """
-
-           # Put your code to generate `new_input` here
-
-           return new_input
-
-       def update_epoch(self, epoch_num):
-           pass
-
-       def step(self):
-           """
-           Can do some processing based on the model or weights binded
-           in the func bind_model
-           """
-           pass
-
-Customize backward function
-^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Sometimes it's necessary for a quantization operation to have a customized backward function, such as `Straight-Through Estimator <https://stackoverflow.com/questions/38361314/the-concept-of-straight-through-estimator-ste>`__\ , user can customize a backward function as follow:
-
-.. code-block:: python
-
-   from nni.compression.pytorch.compressor import Quantizer, QuantGrad, QuantType
-
-   class ClipGrad(QuantGrad):
-       @staticmethod
-       def quant_backward(tensor, grad_output, quant_type):
-           """
-           This method should be overrided by subclass to provide customized backward function,
-           default implementation is Straight-Through Estimator
-           Parameters
-           ----------
-           tensor : Tensor
-               input of quantization operation
-           grad_output : Tensor
-               gradient of the output of quantization operation
-           quant_type : QuantType
-               the type of quantization, it can be `QuantType.INPUT`, `QuantType.WEIGHT`, `QuantType.OUTPUT`,
-               you can define different behavior for different types.
-           Returns
-           -------
-           tensor
-               gradient of the input of quantization operation
-           """
-
-           # for quant_output function, set grad to zero if the absolute value of tensor is larger than 1
-           if quant_type == QuantType.OUTPUT:
-               grad_output[torch.abs(tensor) > 1] = 0
-           return grad_output
-
-
-   class YourQuantizer(Quantizer):
-       def __init__(self, model, config_list):
-           super().__init__(model, config_list)
-           # set your customized backward function to overwrite default backward function
-           self.quant_grad = ClipGrad
-
-If you do not customize ``QuantGrad``\ , the default backward is Straight-Through Estimator. 
-*Coming Soon* ...
diff --git a/docs/source/Compression/DependencyAware.rst b/docs/source/Compression/DependencyAware.rst
deleted file mode 100644
index 05daafe7f..000000000
--- a/docs/source/Compression/DependencyAware.rst
+++ /dev/null
@@ -1,77 +0,0 @@
-Dependency-aware Mode for Filter Pruning
-========================================
-
-Currently, we have several filter pruning algorithm for the convolutional layers: FPGM Pruner, L1Filter Pruner, L2Filter Pruner, Activation APoZ Rank Filter Pruner, Activation Mean Rank Filter Pruner, Taylor FO On Weight Pruner. In these filter pruning algorithms, the pruner will prune each convolutional layer separately. While pruning a convolution layer, the algorithm will quantify the importance of each filter based on some specific rules(such as l1-norm), and prune the less important filters.
-
-As `dependency analysis utils <./CompressionUtils.rst>`__ shows, if the output channels of two convolutional layers(conv1, conv2) are added together, then these two conv layers have channel dependency with each other(more details please see `Compression Utils <./CompressionUtils.rst>`__\ ). Take the following figure as an example.
-
-
-.. image:: ../../img/mask_conflict.jpg
-   :target: ../../img/mask_conflict.jpg
-   :alt: 
-
-
-If we prune the first 50% of output channels(filters) for conv1, and prune the last 50% of output channels for conv2. Although both layers have pruned 50% of the filters, the speedup module still needs to add zeros to align the output channels. In this case, we cannot harvest the speed benefit from the model pruning.
-
- To better gain the speed benefit of the model pruning, we add a dependency-aware mode for the Filter Pruner. In the dependency-aware mode, the pruner prunes the model not only based on the l1 norm of each filter, but also the topology of the whole network architecture.
-
-In the dependency-aware mode(\ ``dependency_aware`` is set ``True``\ ), the pruner will try to prune the same output channels for the layers that have the channel dependencies with each other, as shown in the following figure.
-
-
-.. image:: ../../img/dependency-aware.jpg
-   :target: ../../img/dependency-aware.jpg
-   :alt: 
-
-
-Take the dependency-aware mode of L1Filter Pruner as an example. Specifically, the pruner will calculate the L1 norm (for example) sum of all the layers in the dependency set for each channel. Obviously, the number of channels that can actually be pruned of this dependency set in the end is determined by the minimum sparsity of layers in this dependency set(denoted by ``min_sparsity``\ ). According to the L1 norm sum of each channel, the pruner will prune the same ``min_sparsity`` channels for all the layers. Next, the pruner will additionally prune ``sparsity`` - ``min_sparsity`` channels for each convolutional layer based on its own L1 norm of each channel. For example, suppose the output channels of ``conv1`` , ``conv2`` are added together and the configured sparsities of ``conv1`` and ``conv2`` are 0.3, 0.2 respectively. In this case, the ``dependency-aware pruner`` will 
-
-.. code-block:: bash
-
-   - First, prune the same 20% of channels for `conv1` and `conv2` according to L1 norm sum of `conv1` and `conv2`. 
-   - Second, the pruner will additionally prune 10% channels for `conv1` according to the L1 norm of each channel of `conv1`.
-
-
-In addition, for the convolutional layers that have more than one filter group, ``dependency-aware pruner`` will also try to prune the same number of the channels for each filter group. Overall, this pruner will prune the model according to the L1 norm of each filter and try to meet the topological constrains(channel dependency, etc) to improve the final speed gain after the speedup process. 
-
-In the dependency-aware mode, the pruner will provide a better speed gain from the model pruning.
-
-Usage
------
-
-In this section, we will show how to enable the dependency-aware mode for the filter pruner. Currently, only the one-shot pruners such as FPGM Pruner, L1Filter Pruner, L2Filter Pruner, Activation APoZ Rank Filter Pruner, Activation Mean Rank Filter Pruner, Taylor FO On Weight Pruner, support the dependency-aware mode.
-
-To enable the dependency-aware mode for ``L1FilterPruner``\ :
-
-.. code-block:: python
-
-   from nni.algorithms.compression.pytorch.pruning import L1FilterPruner
-   config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
-   # dummy_input is necessary for the dependency_aware mode
-   dummy_input = torch.ones(1, 3, 224, 224).cuda()
-   pruner = L1FilterPruner(model, config_list, dependency_aware=True, dummy_input=dummy_input)
-   # for L2FilterPruner
-   # pruner = L2FilterPruner(model, config_list, dependency_aware=True, dummy_input=dummy_input)
-   # for FPGMPruner
-   # pruner = FPGMPruner(model, config_list, dependency_aware=True, dummy_input=dummy_input)
-   # for ActivationAPoZRankFilterPruner
-   # pruner = ActivationAPoZRankFilterPruner(model, config_list, optimizer, trainer, criterion, sparsifying_training_batches=1, dependency_aware=True, dummy_input=dummy_input)
-   # for ActivationMeanRankFilterPruner
-   # pruner = ActivationMeanRankFilterPruner(model, config_list, optimizer, trainer, criterion, sparsifying_training_batches=1, dependency_aware=True, dummy_input=dummy_input)
-   # for TaylorFOWeightFilterPruner
-   # pruner = TaylorFOWeightFilterPruner(model, config_list, optimizer, trainer, criterion, sparsifying_training_batches=1, dependency_aware=True, dummy_input=dummy_input)
-
-   pruner.compress()
-
-Evaluation
-----------
-
-In order to compare the performance of the pruner with or without the dependency-aware mode, we use L1FilterPruner to prune the Mobilenet_v2 separately when the dependency-aware mode is turned on and off. To simplify the experiment, we use the uniform pruning which means we allocate the same sparsity for all convolutional layers in the model.
-We trained a Mobilenet_v2 model on the cifar10 dataset and prune the model based on this pretrained checkpoint. The following figure shows the accuracy and FLOPs of the model pruned by different pruners.
-
-
-.. image:: ../../img/mobilev2_l1_cifar.jpg
-   :target: ../../img/mobilev2_l1_cifar.jpg
-   :alt: 
-
-
-In the figure, the ``Dependency-aware`` represents the L1FilterPruner with dependency-aware mode enabled. ``L1 Filter`` is the normal ``L1FilterPruner`` without the dependency-aware mode, and the ``No-Dependency`` means  pruner only prunes the layers that has no channel dependency with other layers. As we can see in the figure, when the dependency-aware mode enabled, the pruner can bring higher accuracy under the same Flops.
diff --git a/docs/source/Compression/Framework.rst b/docs/source/Compression/Framework.rst
deleted file mode 100644
index 453163b5e..000000000
--- a/docs/source/Compression/Framework.rst
+++ /dev/null
@@ -1,209 +0,0 @@
-Framework overview of model compression
-=======================================
-
-.. contents::
-
-Below picture shows the components overview of model compression framework.
-
-
-.. image:: ../../img/compressor_framework.jpg
-   :target: ../../img/compressor_framework.jpg
-   :alt: 
-
-
-There are 3 major components/classes in NNI model compression framework: ``Compressor``\ , ``Pruner`` and ``Quantizer``. Let's look at them in detail one by one:
-
-Compressor
-----------
-
-Compressor is the base class for pruner and quntizer, it provides a unified interface for pruner and quantizer for end users, so that pruner and quantizer can be used in the same way. For example, to use a pruner:
-
-.. code-block:: python
-
-   from nni.algorithms.compression.pytorch.pruning import LevelPruner
-
-   # load a pretrained model or train a model before using a pruner
-
-   configure_list = [{
-       'sparsity': 0.7,
-       'op_types': ['Conv2d', 'Linear'],
-   }]
-
-   pruner = LevelPruner(model, configure_list)
-   model = pruner.compress()
-
-   # model is ready for pruning, now start finetune the model,
-   # the model will be pruned during training automatically
-
-To use a quantizer:
-
-.. code-block:: python
-
-   from nni.algorithms.compression.pytorch.pruning import DoReFaQuantizer
-
-   configure_list = [{
-       'quant_types': ['weight'],
-       'quant_bits': {
-           'weight': 8,
-       },
-       'op_types':['Conv2d', 'Linear']
-   }]
-   optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9, weight_decay=1e-4)
-   quantizer = DoReFaQuantizer(model, configure_list, optimizer)
-   quantizer.compress()
-
-View :githublink:`example code <examples/model_compress>` for more information.
-
-``Compressor`` class provides some utility methods for subclass and users:
-
-Set wrapper attribute
-^^^^^^^^^^^^^^^^^^^^^
-
-Sometimes ``calc_mask`` must save some state data, therefore users can use ``set_wrappers_attribute`` API to register attribute just like how buffers are registered in PyTorch modules. These buffers will be registered to ``module wrapper``. Users can access these buffers through ``module wrapper``.
-In above example, we use ``set_wrappers_attribute`` to set a buffer ``if_calculated`` which is used as flag indicating if the mask of a layer is already calculated.
-
-Collect data during forward
-^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Sometimes users want to collect some data during the modules' forward method, for example, the mean value of the activation. This can be done by adding a customized collector to module.
-
-.. code-block:: python
-
-   class MyMasker(WeightMasker):
-       def __init__(self, model, pruner):
-           super().__init__(model, pruner)
-           # Set attribute `collected_activation` for all wrappers to store
-           # activations for each layer
-           self.pruner.set_wrappers_attribute("collected_activation", [])
-           self.activation = torch.nn.functional.relu
-
-           def collector(wrapper, input_, output):
-               # The collected activation can be accessed via each wrapper's collected_activation
-               # attribute
-               wrapper.collected_activation.append(self.activation(output.detach().cpu()))
-
-           self.pruner.hook_id = self.pruner.add_activation_collector(collector)
-
-The collector function will be called each time the forward method runs.
-
-Users can also remove this collector like this:
-
-.. code-block:: python
-
-   # Save the collector identifier
-   collector_id = self.pruner.add_activation_collector(collector)
-
-   # When the collector is not used any more, it can be remove using
-   # the saved collector identifier
-   self.pruner.remove_activation_collector(collector_id)
-
-----
-
-Pruner
-------
-
-A pruner receives ``model`` , ``config_list`` as arguments. 
-Some pruners like ``TaylorFOWeightFilter Pruner`` prune the model per the ``config_list`` during training loop by adding a hook on ``optimizer.step()``.
-
-Pruner class is a subclass of Compressor, so it contains everything in the Compressor class and some additional components only for pruning, it contains:
-
-Weight masker
-^^^^^^^^^^^^^
-
-A ``weight masker`` is the implementation of pruning algorithms, it can prune a specified layer wrapped by ``module wrapper`` with specified sparsity.
-
-Pruning module wrapper
-^^^^^^^^^^^^^^^^^^^^^^
-
-A ``pruning module wrapper`` is a module containing:
-
-
-#. the origin module
-#. some buffers used by ``calc_mask``
-#. a new forward method that applies masks before running the original forward method.
-
-the reasons to use ``module wrapper``\ :
-
-
-#. some buffers are needed by ``calc_mask`` to calculate masks and these buffers should be registered in ``module wrapper`` so that the original modules are not contaminated.
-#. a new ``forward`` method is needed to apply masks to weight before calling the real ``forward`` method.
-
-Pruning hook
-^^^^^^^^^^^^
-
-A pruning hook is installed on a pruner when the pruner is constructed, it is used to call pruner's calc_mask method at ``optimizer.step()`` is invoked.
-
-----
-
-Quantizer
----------
-
-Quantizer class is also a subclass of ``Compressor``\ , it is used to compress models by reducing the number of bits required to represent weights or activations, which can reduce the computations and the inference time. It contains:
-
-Quantization module wrapper
-^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Each module/layer of the model to be quantized is wrapped by a quantization module wrapper, it provides a new ``forward`` method to quantize the original module's weight, input and output.
-
-Quantization hook
-^^^^^^^^^^^^^^^^^
-
-A quantization hook is installed on a quntizer when it is constructed, it is call at ``optimizer.step()``.
-
-Quantization methods
-^^^^^^^^^^^^^^^^^^^^
-
-``Quantizer`` class provides following methods for subclass to implement quantization algorithms:
-
-.. code-block:: python
-
-   class Quantizer(Compressor):
-       """
-       Base quantizer for pytorch quantizer
-       """
-       def quantize_weight(self, weight, wrapper, **kwargs):
-           """
-           quantize should overload this method to quantize weight.
-           This method is effectively hooked to :meth:`forward` of the model.
-           Parameters
-           ----------
-           weight : Tensor
-               weight that needs to be quantized
-           wrapper : QuantizerModuleWrapper
-               the wrapper for origin module
-           """
-           raise NotImplementedError('Quantizer must overload quantize_weight()')
-
-       def quantize_output(self, output, wrapper, **kwargs):
-           """
-           quantize should overload this method to quantize output.
-           This method is effectively hooked to :meth:`forward` of the model.
-           Parameters
-           ----------
-           output : Tensor
-               output that needs to be quantized
-           wrapper : QuantizerModuleWrapper
-               the wrapper for origin module
-           """
-           raise NotImplementedError('Quantizer must overload quantize_output()')
-
-       def quantize_input(self, *inputs, wrapper, **kwargs):
-           """
-           quantize should overload this method to quantize input.
-           This method is effectively hooked to :meth:`forward` of the model.
-           Parameters
-           ----------
-           inputs : Tensor
-               inputs that needs to be quantized
-           wrapper : QuantizerModuleWrapper
-               the wrapper for origin module
-           """
-           raise NotImplementedError('Quantizer must overload quantize_input()')
-
-----
-
-Multi-GPU support
------------------
-
-On multi-GPU training, buffers and parameters are copied to multiple GPU every time the ``forward`` method runs on multiple GPU. If buffers and parameters are updated in the ``forward`` method, an ``in-place`` update is needed to ensure the update is effective.
-Since ``calc_mask`` is called in the ``optimizer.step`` method, which happens after the ``forward`` method and happens only on one GPU, it supports multi-GPU naturally.
diff --git a/docs/source/Compression/ModelSpeedup.rst b/docs/source/Compression/ModelSpeedup.rst
deleted file mode 100644
index 86f099ac9..000000000
--- a/docs/source/Compression/ModelSpeedup.rst
+++ /dev/null
@@ -1,208 +0,0 @@
-Speed up Masked Model
-=====================
-
-*This feature is in Beta version.*
-
-Introduction
-------------
-
-Pruning algorithms usually use weight masks to simulate the real pruning. Masks can be used
-to check model performance of a specific pruning (or sparsity), but there is no real speedup.
-Since model speedup is the ultimate goal of model pruning, we try to provide a tool to users
-to convert a model to a smaller one based on user provided masks (the masks come from the
-pruning algorithms).
-
-There are two types of pruning. One is fine-grained pruning, it does not change the shape of weights, and input/output tensors. Sparse kernel is required to speed up a fine-grained pruned layer. The other is coarse-grained pruning (e.g., channels), shape of weights and input/output tensors usually change due to such pruning. To speed up this kind of pruning, there is no need to use sparse kernel, just replace the pruned layer with smaller one. Since the support of sparse kernels in community is limited, we only support the speedup of coarse-grained pruning and leave the support of fine-grained pruning in future.
-
-Design and Implementation
--------------------------
-
-To speed up a model, the pruned layers should be replaced, either replaced with smaller layer for coarse-grained mask, or replaced with sparse kernel for fine-grained mask. Coarse-grained mask usually changes the shape of weights or input/output tensors, thus, we should do shape inference to check are there other unpruned layers should be replaced as well due to shape change. Therefore, in our design, there are two main steps: first, do shape inference to find out all the modules that should be replaced; second, replace the modules. The first step requires topology (i.e., connections) of the model, we use ``jit.trace`` to obtain the model graph for PyTorch.
-
-For each module, we should prepare four functions, three for shape inference and one for module replacement. The three shape inference functions are: given weight shape infer input/output shape, given input shape infer weight/output shape, given output shape infer weight/input shape. The module replacement function returns a newly created module which is smaller.
-
-Usage
------
-
-.. code-block:: python
-
-   from nni.compression.pytorch import ModelSpeedup
-   # model: the model you want to speed up
-   # dummy_input: dummy input of the model, given to `jit.trace`
-   # masks_file: the mask file created by pruning algorithms
-   m_speedup = ModelSpeedup(model, dummy_input.to(device), masks_file)
-   m_speedup.speedup_model()
-   dummy_input = dummy_input.to(device)
-   start = time.time()
-   out = model(dummy_input)
-   print('elapsed time: ', time.time() - start)
-
-For complete examples please refer to :githublink:`the code <examples/model_compress/pruning/speedup/model_speedup.py>`
-
-NOTE: The current implementation supports PyTorch 1.3.1 or newer.
-
-Limitations
------------
-
-Since every module requires four functions for shape inference and module replacement, this is a large amount of work, we only implemented the ones that are required by the examples. If you want to speed up your own model which cannot supported by the current implementation, you are welcome to contribute.
-
-For PyTorch we can only replace modules, if functions in ``forward`` should be replaced, our current implementation does not work. One workaround is make the function a PyTorch module.
-
-Speedup Results of Examples
----------------------------
-
-The code of these experiments can be found :githublink:`here <examples/model_compress/pruning/speedup/model_speedup.py>`.
-
-slim pruner example
-^^^^^^^^^^^^^^^^^^^
-
-on one V100 GPU,
-input tensor: ``torch.randn(64, 3, 32, 32)``
-
-.. list-table::
-   :header-rows: 1
-   :widths: auto
-
-   * - Times
-     - Mask Latency
-     - Speedup Latency
-   * - 1
-     - 0.01197
-     - 0.005107
-   * - 2
-     - 0.02019
-     - 0.008769
-   * - 4
-     - 0.02733
-     - 0.014809
-   * - 8
-     - 0.04310
-     - 0.027441
-   * - 16
-     - 0.07731
-     - 0.05008
-   * - 32
-     - 0.14464
-     - 0.10027
-
-
-fpgm pruner example
-^^^^^^^^^^^^^^^^^^^
-
-on cpu,
-input tensor: ``torch.randn(64, 1, 28, 28)``\ ,
-too large variance
-
-.. list-table::
-   :header-rows: 1
-   :widths: auto
-
-   * - Times
-     - Mask Latency
-     - Speedup Latency
-   * - 1
-     - 0.01383
-     - 0.01839
-   * - 2
-     - 0.01167
-     - 0.003558
-   * - 4
-     - 0.01636
-     - 0.01088
-   * - 40
-     - 0.14412
-     - 0.08268
-   * - 40
-     - 1.29385
-     - 0.14408
-   * - 40
-     - 0.41035
-     - 0.46162
-   * - 400
-     - 6.29020
-     - 5.82143
-
-
-l1filter pruner example
-^^^^^^^^^^^^^^^^^^^^^^^
-
-on one V100 GPU,
-input tensor: ``torch.randn(64, 3, 32, 32)``
-
-.. list-table::
-   :header-rows: 1
-   :widths: auto
-
-   * - Times
-     - Mask Latency
-     - Speedup Latency
-   * - 1
-     - 0.01026
-     - 0.003677
-   * - 2
-     - 0.01657
-     - 0.008161
-   * - 4
-     - 0.02458
-     - 0.020018
-   * - 8
-     - 0.03498
-     - 0.025504
-   * - 16
-     - 0.06757
-     - 0.047523
-   * - 32
-     - 0.10487
-     - 0.086442
-
-
-APoZ pruner example
-^^^^^^^^^^^^^^^^^^^
-
-on one V100 GPU,
-input tensor: ``torch.randn(64, 3, 32, 32)``
-
-.. list-table::
-   :header-rows: 1
-   :widths: auto
-
-   * - Times
-     - Mask Latency
-     - Speedup Latency
-   * - 1
-     - 0.01389
-     - 0.004208
-   * - 2
-     - 0.01628
-     - 0.008310
-   * - 4
-     - 0.02521
-     - 0.014008
-   * - 8
-     - 0.03386
-     - 0.023923
-   * - 16
-     - 0.06042
-     - 0.046183
-   * - 32
-     - 0.12421
-     - 0.087113
-
-
-SimulatedAnnealing pruner example
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-In this experiment, we use SimulatedAnnealing pruner to prune the resnet18 on the cifar10 dataset.
-We measure the latencies and accuracies of the pruned model under different sparsity ratios, as shown in the following figure.
-The latency is measured on one V100 GPU and the input tensor is  ``torch.randn(128, 3, 32, 32)``.
-
-
-.. image:: ../../img/SA_latency_accuracy.png
-
-
-User configuration for ModelSpeedup
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-..  autoclass:: nni.compression.pytorch.ModelSpeedup
diff --git a/docs/source/Compression/Overview.rst b/docs/source/Compression/Overview.rst
deleted file mode 100644
index b33ec148e..000000000
--- a/docs/source/Compression/Overview.rst
+++ /dev/null
@@ -1,131 +0,0 @@
-Model Compression with NNI
-==========================
-
-.. contents::
-
-As larger neural networks with more layers and nodes are considered, reducing their storage and computational cost becomes critical, especially for some real-time applications. Model compression can be used to address this problem.
-
-NNI provides a model compression toolkit to help user compress and speed up their model with state-of-the-art compression algorithms and strategies. There are several core features supported by NNI model compression:
-
-
-* Support many popular pruning and quantization algorithms.
-* Automate model pruning and quantization process with state-of-the-art strategies and NNI's auto tuning power.
-* Speed up a compressed model to make it have lower inference latency and also make it become smaller.
-* Provide friendly and easy-to-use compression utilities for users to dive into the compression process and results.
-* Concise interface for users to customize their own compression algorithms.
-
-
-Compression Pipeline
---------------------
-
-.. image:: ../../img/compression_flow.jpg
-   :target: ../../img/compression_flow.jpg
-   :alt: 
-
-The overall compression pipeline in NNI. For compressing a pretrained model, pruning and quantization can be used alone or in combination. 
-
-.. note::
-  Since NNI compression algorithms are not meant to compress model while NNI speedup tool can truly compress model and reduce latency. To obtain a truly compact model, users should conduct `model speedup <./ModelSpeedup.rst>`__. The interface and APIs are unified for both PyTorch and TensorFlow, currently only PyTorch version has been supported, TensorFlow version will be supported in future.
-
-Supported Algorithms
---------------------
-
-The algorithms include pruning algorithms and quantization algorithms.
-
-Pruning Algorithms
-^^^^^^^^^^^^^^^^^^
-
-Pruning algorithms compress the original network by removing redundant weights or channels of layers, which can reduce model complexity and mitigate the over-fitting issue.
-
-.. list-table::
-   :header-rows: 1
-   :widths: auto
-
-   * - Name
-     - Brief Introduction of Algorithm
-   * - `Level Pruner <Pruner.rst#level-pruner>`__
-     - Pruning the specified ratio on each weight based on absolute values of weights
-   * - `AGP Pruner <../Compression/Pruner.rst#agp-pruner>`__
-     - Automated gradual pruning (To prune, or not to prune: exploring the efficacy of pruning for model compression) `Reference Paper <https://arxiv.org/abs/1710.01878>`__
-   * - `Lottery Ticket Pruner <../Compression/Pruner.rst#lottery-ticket-hypothesis>`__
-     - The pruning process used by "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks". It prunes a model iteratively. `Reference Paper <https://arxiv.org/abs/1803.03635>`__
-   * - `FPGM Pruner <../Compression/Pruner.rst#fpgm-pruner>`__
-     - Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration `Reference Paper <https://arxiv.org/pdf/1811.00250.pdf>`__
-   * - `L1Filter Pruner <../Compression/Pruner.rst#l1filter-pruner>`__
-     - Pruning filters with the smallest L1 norm of weights in convolution layers (Pruning Filters for Efficient Convnets) `Reference Paper <https://arxiv.org/abs/1608.08710>`__
-   * - `L2Filter Pruner <../Compression/Pruner.rst#l2filter-pruner>`__
-     - Pruning filters with the smallest L2 norm of weights in convolution layers
-   * - `ActivationAPoZRankFilterPruner <../Compression/Pruner.rst#activationapozrankfilter-pruner>`__
-     - Pruning filters based on the metric APoZ (average percentage of zeros) which measures the percentage of zeros in activations of (convolutional) layers. `Reference Paper <https://arxiv.org/abs/1607.03250>`__
-   * - `ActivationMeanRankFilterPruner <../Compression/Pruner.rst#activationmeanrankfilter-pruner>`__
-     - Pruning filters based on the metric that calculates the smallest mean value of output activations
-   * - `Slim Pruner <../Compression/Pruner.rst#slim-pruner>`__
-     - Pruning channels in convolution layers by pruning scaling factors in BN layers(Learning Efficient Convolutional Networks through Network Slimming) `Reference Paper <https://arxiv.org/abs/1708.06519>`__
-   * - `TaylorFO Pruner <../Compression/Pruner.rst#taylorfoweightfilter-pruner>`__
-     - Pruning filters based on the first order taylor expansion on weights(Importance Estimation for Neural Network Pruning) `Reference Paper <http://jankautz.com/publications/Importance4NNPruning_CVPR19.pdf>`__
-   * - `ADMM Pruner <../Compression/Pruner.rst#admm-pruner>`__
-     - Pruning based on ADMM optimization technique `Reference Paper <https://arxiv.org/abs/1804.03294>`__
-   * - `NetAdapt Pruner <../Compression/Pruner.rst#netadapt-pruner>`__
-     - Automatically simplify a pretrained network to meet the resource budget by iterative pruning  `Reference Paper <https://arxiv.org/abs/1804.03230>`__
-   * - `SimulatedAnnealing Pruner <../Compression/Pruner.rst#simulatedannealing-pruner>`__
-     - Automatic pruning with a guided heuristic search method, Simulated Annealing algorithm `Reference Paper <https://arxiv.org/abs/1907.03141>`__
-   * - `AutoCompress Pruner <../Compression/Pruner.rst#autocompress-pruner>`__
-     - Automatic pruning by iteratively call SimulatedAnnealing Pruner and ADMM Pruner `Reference Paper <https://arxiv.org/abs/1907.03141>`__
-   * - `AMC Pruner <../Compression/Pruner.rst#amc-pruner>`__
-     - AMC: AutoML for Model Compression and Acceleration on Mobile Devices `Reference Paper <https://arxiv.org/pdf/1802.03494.pdf>`__
-   * - `Transformer Head Pruner <../Compression/Pruner.rst#transformer-head-pruner>`__
-     - Pruning attention heads from transformer models either in one shot or iteratively.
-
-
-You can refer to this `benchmark <../CommunitySharings/ModelCompressionComparison.rst>`__ for the performance of these pruners on some benchmark problems.
-
-Quantization Algorithms
-^^^^^^^^^^^^^^^^^^^^^^^
-
-Quantization algorithms compress the original network by reducing the number of bits required to represent weights or activations, which can reduce the computations and the inference time.
-
-.. list-table::
-   :header-rows: 1
-   :widths: auto
-
-   * - Name
-     - Brief Introduction of Algorithm
-   * - `Naive Quantizer <../Compression/Quantizer.rst#naive-quantizer>`__
-     - Quantize weights to default 8 bits
-   * - `QAT Quantizer <../Compression/Quantizer.rst#qat-quantizer>`__
-     - Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference. `Reference Paper <http://openaccess.thecvf.com/content_cvpr_2018/papers/Jacob_Quantization_and_Training_CVPR_2018_paper.pdf>`__
-   * - `DoReFa Quantizer <../Compression/Quantizer.rst#dorefa-quantizer>`__
-     - DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients. `Reference Paper <https://arxiv.org/abs/1606.06160>`__
-   * - `BNN Quantizer <../Compression/Quantizer.rst#bnn-quantizer>`__
-     - Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1. `Reference Paper <https://arxiv.org/abs/1602.02830>`__
-   * - `LSQ Quantizer <../Compression/Quantizer.rst#lsq-quantizer>`__
-     - Learned step size quantization. `Reference Paper <https://arxiv.org/pdf/1902.08153.pdf>`__
-   * - `Observer Quantizer <../Compression/Quantizer.rst#observer-quantizer>`__
-     - Post training quantizaiton. Collect quantization information during calibration with observers.
-
-
-Model Speedup
--------------
-
-The final goal of model compression is to reduce inference latency and model size. However, existing model compression algorithms mainly use simulation to check the performance (e.g., accuracy) of compressed model, for example, using masks for pruning algorithms, and storing quantized values still in float32 for quantization algorithms. Given the output masks and quantization bits produced by those algorithms, NNI can really speed up the model. The detailed tutorial of Masked Model Speedup can be found `here <./ModelSpeedup.rst>`__, The detailed tutorial of Mixed Precision Quantization Model Speedup can be found `here <./QuantizationSpeedup.rst>`__.
-
-
-Compression Utilities
----------------------
-
-Compression utilities include some useful tools for users to understand and analyze the model they want to compress. For example, users could check sensitivity of each layer to pruning. Users could easily calculate the FLOPs and parameter size of a model. Please refer to `here <./CompressionUtils.rst>`__ for a complete list of compression utilities.
-
-Advanced Usage
---------------
-
-NNI model compression leaves simple interface for users to customize a new compression algorithm. The design philosophy of the interface is making users focus on the compression logic while hiding framework specific implementation details from users. Users can learn more about our compression framework and customize a new compression algorithm (pruning algorithm or quantization algorithm) based on our framework. Moreover, users could leverage NNI's auto tuning power to automatically compress a model. Please refer to `here <./advanced.rst>`__ for more details.
-
-
-Reference and Feedback
-----------------------
-
-* To `report a bug <https://github.com/microsoft/nni/issues/new?template=bug-report.rst>`__ for this feature in GitHub;
-* To `file a feature or improvement request <https://github.com/microsoft/nni/issues/new?template=enhancement.rst>`__ for this feature in GitHub;
-* To know more about `Feature Engineering with NNI <../FeatureEngineering/Overview.rst>`__\ ;
-* To know more about `NAS with NNI <../NAS/Overview.rst>`__\ ;
-* To know more about `Hyperparameter Tuning with NNI <../Tuner/BuiltinTuner.rst>`__\ ;
diff --git a/docs/source/Compression/Overview_zh.rst b/docs/source/Compression/Overview_zh.rst
deleted file mode 100644
index f635776e5..000000000
--- a/docs/source/Compression/Overview_zh.rst
+++ /dev/null
@@ -1,133 +0,0 @@
-.. 37577199d91c137b881450f825f38fa2
-
-使用 NNI 进行模型压缩
-==========================
-
-.. contents::
-
-目前的大型神经网络较之以往具有更多的层和节点，而如何降低其存储和计算成本是一个重要的话题，尤其是针对于那些需要实时响应的应用程序。模型压缩的相关方法可以用于解决这些问题。
-
-NNI 的模型压缩工具包，提供了最先进的模型压缩算法和策略，帮助压缩并加速模型。NNI 模型压缩支持的主要功能有：
-
-
-* 支持多种流行的剪枝和量化算法。
-* 通过 NNI 强大的自动调优功能，可使用最先进的策略来自动化模型的剪枝和量化过程。
-* 加速压缩的模型，使其在推理时有更低的延迟，同时文件也会变小。
-* 提供友好易用的压缩工具，帮助用户深入了解压缩过程和结果。
-* 提供简洁的接口，帮助用户实现自己的压缩算法。
-
-
-压缩流水线
-----------
-
-.. image:: ../../img/compression_flow.jpg
-   :target: ../../img/compression_flow.jpg
-   :alt: 
-
-NNI整体的模型压缩流水线图。对于压缩一个预训练的模型，剪枝和量化可以单独使用或结合使用。 
-
-.. note::
-  NNI 压缩算法并不意味着真正使模型变小或者减少延迟，NNI 的加速工具才可以真正压缩模型并减少延迟。要获得真正压缩后的模型，用户应该进行 `模型加速 <./ModelSpeedup.rst>`__。* 注意，PyTorch 和 TensorFlow 有统一的 API 接口，当前仅支持 PyTorch 版本，未来会提供 TensorFlow 的支持。
-
-支持的算法
-----------
-
-包括剪枝和量化算法。
-
-剪枝算法
-^^^^^^^^
-
-剪枝算法通过删除冗余权重或层通道来压缩原始网络，从而降低模型复杂性并解决过拟合问题。
-
-.. list-table::
-   :header-rows: 1
-   :widths: auto
-
-   * - 名称
-     - 算法简介
-   * - `Level Pruner <Pruner.rst#level-pruner>`__
-     - 根据权重的绝对值，来按比例修剪权重。
-   * - `AGP Pruner <../Compression/Pruner.rst#agp-pruner>`__
-     - 自动的逐步剪枝（To prune, or not to prune: exploring the efficacy of pruning for model compression）`参考论文 <https://arxiv.org/abs/1710.01878>`__
-   * - `Lottery Ticket Pruner <../Compression/Pruner.rst#lottery-ticket>`__
-     - "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks" 提出的剪枝过程。 它会反复修剪模型。 `参考论文 <https://arxiv.org/abs/1803.03635>`__
-   * - `FPGM Pruner <../Compression/Pruner.rst#fpgm-pruner>`__
-     - Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration `参考论文 <https://arxiv.org/pdf/1811.00250.pdf>`__
-   * - `L1Filter Pruner <../Compression/Pruner.rst#l1filter-pruner>`__
-     - 在卷积层中具有最小 L1 权重规范的剪枝滤波器。（Pruning Filters for Efficient Convnets） `参考论文 <https://arxiv.org/abs/1608.08710>`__
-   * - `L2Filter Pruner <../Compression/Pruner.rst#l2filter-pruner>`__
-     - 在卷积层中具有最小 L2 权重规范的剪枝滤波器。
-   * - `ActivationAPoZRankFilterPruner <../Compression/Pruner.rst#activationapozrankfilter-pruner>`__
-     - 基于指标 APoZ（平均百分比零）的剪枝滤波器，该指标测量（卷积）图层激活值中零的百分比。 `参考论文 <https://arxiv.org/abs/1607.03250>`__
-   * - `ActivationMeanRankFilterPruner <../Compression/Pruner.rst#activationmeanrankfilter-pruner>`__
-     - 基于计算输出激活最小平均值指标的剪枝滤波器。
-   * - `Slim Pruner <../Compression/Pruner.rst#slim-pruner>`__
-     - 通过修剪 BN 层中的缩放因子来修剪卷积层中的通道。 (Learning Efficient Convolutional Networks through Network Slimming) `参考论文 <https://arxiv.org/abs/1708.06519>`__
-   * - `TaylorFO Pruner <../Compression/Pruner.rst#taylorfoweightfilter-pruner>`__
-     - 基于一阶泰勒展开的权重对滤波器剪枝。 (Importance Estimation for Neural Network Pruning) `参考论文 <http://jankautz.com/publications/Importance4NNPruning_CVPR19.pdf>`__
-   * - `ADMM Pruner <../Compression/Pruner.rst#admm-pruner>`__
-     - 基于 ADMM 优化技术的剪枝。 `参考论文 <https://arxiv.org/abs/1804.03294>`__
-   * - `NetAdapt Pruner <../Compression/Pruner.rst#netadapt-pruner>`__
-     - 在满足计算资源预算的情况下，对预训练的网络迭代剪枝。 `参考论文 <https://arxiv.org/abs/1804.03230>`__
-   * - `SimulatedAnnealing Pruner <../Compression/Pruner.rst#simulatedannealing-pruner>`__
-     - 通过启发式的模拟退火算法进行自动剪枝。 `参考论文 <https://arxiv.org/abs/1907.03141>`__
-   * - `AutoCompress Pruner <../Compression/Pruner.rst#autocompress-pruner>`__
-     - 通过迭代调用 SimulatedAnnealing Pruner 和 ADMM Pruner 进行自动剪枝。 `参考论文 - <https://arxiv.org/abs/1907.03141>`__
-   * - `AMC Pruner <../Compression/Pruner.rst#amc-pruner>`__
-     - AMC: AutoML for Model Compression and Acceleration on Mobile Devices `参考论文 <https://arxiv.org/pdf/1802.03494.pdf>`__
-   * - `Transformer Head Pruner <../Compression/Pruner.rst#transformer-head-pruner>`__
-     - 针对transformer中的注意力头的剪枝.
-
-
-参考此 :githublink:`基准测试 <../CommunitySharings/ModelCompressionComparison.rst>` 来查看这些剪枝器在一些基准问题上的表现。
-
-量化算法
-^^^^^^^^
-
-量化算法通过减少表示权重或激活函数所需的精度位数来压缩原始网络，这可以减少计算和推理时间。
-
-.. list-table::
-   :header-rows: 1
-   :widths: auto
-
-   * - 名称
-     - 算法简介
-   * - `Naive Quantizer <../Compression/Quantizer.rst#naive-quantizer>`__
-     - 默认将权重量化为 8 位。
-   * - `QAT Quantizer <../Compression/Quantizer.rst#qat-quantizer>`__
-     - Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference. `参考论文 <http://openaccess.thecvf.com/content_cvpr_2018/papers/Jacob_Quantization_and_Training_CVPR_2018_paper.pdf>`__
-   * - `DoReFa Quantizer <../Compression/Quantizer.rst#dorefa-quantizer>`__
-     - DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients. `参考论文 <https://arxiv.org/abs/1606.06160>`__
-   * - `BNN Quantizer <../Compression/Quantizer.rst#bnn-quantizer>`__
-     - Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1. `参考论文 <https://arxiv.org/abs/1602.02830>`__
-   * - `LSQ Quantizer <../Compression/Quantizer.rst#lsq-quantizer>`__
-     - Learned step size quantization. `参考论文 <https://arxiv.org/pdf/1902.08153.pdf>`__
-   * - `Observer Quantizer <../Compression/Quantizer.rst#observer-quantizer>`__
-     - Post training quantizaiton. 使用 observer 在校准期间收集量化信息。
-
-
-模型加速
---------
-
-模型压缩的目的是减少推理延迟和模型大小。但现有的模型压缩算法主要通过模拟的方法来检查压缩模型性能（如精度）。例如，剪枝算法中使用掩码，而量化算法中量化值仍然是以 32 位浮点数来存储。只要给出这些算法产生的掩码和量化位，NNI 可真正的加速模型。基于掩码的模型加速详细教程可以在 `这里 <./ModelSpeedup.rst>`__ 找到。混合精度量化的详细教程可以在 `这里 <./QuantizationSpeedup.rst>`__ 找到。
-
-
-压缩工具
---------
-
-压缩工具包括了一些有用的工具，能帮助用户理解并分析要压缩的模型。例如，可检查每层对剪枝的敏感度。可很容易的计算模型的 FLOPs 和参数数量。`点击这里 <./CompressionUtils.rst>`__，查看压缩工具的完整列表。
-
-高级用法
---------
-
-NNI 模型压缩提供了简洁的接口，用于自定义新的压缩算法。接口的设计理念是，将框架相关的实现细节包装起来，让用户能聚焦于压缩逻辑。用户可以进一步了解我们的压缩框架，并根据我们的框架定制新的压缩算法（剪枝算法或量化算法）。此外，还可利用 NNI 的自动调参功能来自动的压缩模型。参考 `这里 <./advanced.rst>`__ 了解更多细节。
-
-
-参考和反馈
-----------
-
-* 在Github 中 `提交此功能的 Bug <https://github.com/microsoft/nni/issues/new?template=bug-report.rst>`__
-* 在Github 中 `提交新功能或请求改进 <https://github.com/microsoft/nni/issues/new?template=enhancement.rst>`__
-* 了解更多关于 NNI 中的 `特征工程 <../FeatureEngineering/Overview.rst>`__\ ;
-* 了解更多关于 NNI 中的 `NAS <../NAS/Overview.rst>`__\ ;
-* 了解更多关于 NNI 中的 `超参调优 <../Tuner/BuiltinTuner.rst>`__\ ;
diff --git a/docs/source/Compression/Pruner.rst b/docs/source/Compression/Pruner.rst
deleted file mode 100644
index 8af360f03..000000000
--- a/docs/source/Compression/Pruner.rst
+++ /dev/null
@@ -1,824 +0,0 @@
-Supported Pruning Algorithms on NNI
-===================================
-
-We provide several pruning algorithms that support fine-grained weight pruning and structural filter pruning. **Fine-grained Pruning** generally results in  unstructured models, which need specialized hardware or software to speed up the sparse network. **Filter Pruning** achieves acceleration by removing the entire filter. Some pruning algorithms use one-shot method that prune weights at once based on an importance metric (It is necessary to finetune the model to compensate for the loss of accuracy). Other pruning algorithms **iteratively** prune weights during optimization, which control the pruning schedule, including some automatic pruning algorithms.
-
-
-**One-shot Pruning**
-
-* `Level Pruner <#level-pruner>`__ ((fine-grained pruning))
-* `Slim Pruner <#slim-pruner>`__
-* `FPGM Pruner <#fpgm-pruner>`__
-* `L1Filter Pruner <#l1filter-pruner>`__
-* `L2Filter Pruner <#l2filter-pruner>`__
-* `Activation APoZ Rank Filter Pruner <#activationAPoZRankFilter-pruner>`__
-* `Activation Mean Rank Filter Pruner <#activationmeanrankfilter-pruner>`__
-* `Taylor FO On Weight Pruner <#taylorfoweightfilter-pruner>`__
-
-**Iteratively Pruning**
-
-* `AGP Pruner <#agp-pruner>`__
-* `NetAdapt Pruner <#netadapt-pruner>`__
-* `SimulatedAnnealing Pruner <#simulatedannealing-pruner>`__
-* `AutoCompress Pruner <#autocompress-pruner>`__
-* `AMC Pruner <#amc-pruner>`__
-* `Sensitivity Pruner <#sensitivity-pruner>`__
-* `ADMM Pruner <#admm-pruner>`__
-
-**Others**
-
-* `Lottery Ticket Hypothesis <#lottery-ticket-hypothesis>`__
-* `Transformer Head Pruner <#transformer-head-pruner>`__
-
-Level Pruner
-------------
-
-This is one basic one-shot pruner: you can set a target sparsity level (expressed as a fraction, 0.6 means we will prune 60% of the weight parameters). 
-
-We first sort the weights in the specified layer by their absolute values. And then mask to zero the smallest magnitude weights until the desired sparsity level is reached.
-
-Usage
-^^^^^
-
-PyTorch code
-
-.. code-block:: python
-
-   from nni.algorithms.compression.pytorch.pruning import LevelPruner
-   config_list = [{ 'sparsity': 0.8, 'op_types': ['default'] }]
-   pruner = LevelPruner(model, config_list)
-   pruner.compress()
-
-User configuration for Level Pruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.LevelPruner
-
-**TensorFlow**
-
-..  autoclass:: nni.algorithms.compression.tensorflow.pruning.LevelPruner
-
-
-Slim Pruner
------------
-This is an one-shot pruner, which adds sparsity regularization on the scaling factors of batch normalization (BN) layers during training to identify unimportant channels. The channels with small scaling factor values will be pruned. For more details, please refer to `'Learning Efficient Convolutional Networks through Network Slimming' <https://arxiv.org/pdf/1708.06519.pdf>`__\.
-
-Usage
-^^^^^
-
-PyTorch code
-
-.. code-block:: python
-
-   from nni.algorithms.compression.pytorch.pruning import SlimPruner
-   config_list = [{ 'sparsity': 0.8, 'op_types': ['BatchNorm2d'] }]
-   pruner = SlimPruner(model, config_list, optimizer, trainer, criterion)
-   pruner.compress()
-
-User configuration for Slim Pruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.SlimPruner
-
-Reproduced Experiment
-^^^^^^^^^^^^^^^^^^^^^
-
-We implemented one of the experiments in `Learning Efficient Convolutional Networks through Network Slimming <https://arxiv.org/pdf/1708.06519.pdf>`__\ , we pruned ``70%`` channels in the **VGGNet** for CIFAR-10 in the paper, in which ``88.5%`` parameters are pruned. Our experiments results are as follows:
-
-.. list-table::
-   :header-rows: 1
-   :widths: auto
-
-   * - Model
-     - Error(paper/ours)
-     - Parameters
-     - Pruned
-   * - VGGNet
-     - 6.34/6.69
-     - 20.04M
-     - 
-   * - Pruned-VGGNet
-     - 6.20/6.34
-     - 2.03M
-     - 88.5%
-
-
-The experiments code can be found at :githublink:`examples/model_compress/pruning/basic_pruners_torch.py <examples/model_compress/pruning/basic_pruners_torch.py>`
-
-.. code-block:: python
-
-   python basic_pruners_torch.py --pruner slim --model vgg19 --sparsity 0.7 --speed-up
-
-
-----
-
-FPGM Pruner
------------
-
-This is an one-shot pruner, which prunes filters with the smallest geometric median. FPGM chooses the filters with the most replaceable contribution.
-For more details, please refer to `Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration <https://arxiv.org/pdf/1811.00250.pdf>`__.
-
-We also provide a dependency-aware mode for this pruner to get better speedup from the pruning. Please reference `dependency-aware <./DependencyAware.rst>`__ for more details.
-
-Usage
-^^^^^
-
-PyTorch code
-
-.. code-block:: python
-
-   from nni.algorithms.compression.pytorch.pruning import FPGMPruner
-   config_list = [{
-       'sparsity': 0.5,
-       'op_types': ['Conv2d']
-   }]
-   pruner = FPGMPruner(model, config_list)
-   pruner.compress()
-
-User configuration for FPGM Pruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.FPGMPruner
-
-L1Filter Pruner
----------------
-
-This is an one-shot pruner, which prunes the filters in the **convolution layers**.
-
-..
-   The procedure of pruning m filters from the ith convolutional layer is as follows:
-
-   #. For each filter :math:`F_{i,j}`, calculate the sum of its absolute kernel weights :math:`s_j=\sum_{l=1}^{n_i}\sum|K_l|`.
-
-   #. Sort the filters by :math:`s_j`.
-
-   #. Prune :math:`m` filters with the smallest sum values and their corresponding feature maps. The
-      kernels in the next convolutional layer corresponding to the pruned feature maps are also removed.
-
-   #. A new kernel matrix is created for both the :math:`i`-th and :math:`i+1`-th layers, and the remaining kernel
-      weights are copied to the new model.
-
-For more details, please refer to `PRUNING FILTERS FOR EFFICIENT CONVNETS <https://arxiv.org/abs/1608.08710>`__\.
-
-
-
-In addition, we also provide a dependency-aware mode for the L1FilterPruner. For more details about the dependency-aware mode, please reference `dependency-aware mode <./DependencyAware.rst>`__.
-
-Usage
-^^^^^
-
-PyTorch code
-
-.. code-block:: python
-
-   from nni.algorithms.compression.pytorch.pruning import L1FilterPruner
-   config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
-   pruner = L1FilterPruner(model, config_list)
-   pruner.compress()
-
-User configuration for L1Filter Pruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.L1FilterPruner
-
-Reproduced Experiment
-^^^^^^^^^^^^^^^^^^^^^
-
-We implemented one of the experiments in `PRUNING FILTERS FOR EFFICIENT CONVNETS <https://arxiv.org/abs/1608.08710>`__ with **L1FilterPruner**\ , we pruned **VGG-16** for CIFAR-10 to **VGG-16-pruned-A** in the paper, in which ``64%`` parameters are pruned. Our experiments results are as follows:
-
-.. list-table::
-   :header-rows: 1
-   :widths: auto
-
-   * - Model
-     - Error(paper/ours)
-     - Parameters
-     - Pruned
-   * - VGG-16
-     - 6.75/6.49
-     - 1.5x10^7
-     - 
-   * - VGG-16-pruned-A
-     - 6.60/6.47
-     - 5.4x10^6
-     - 64.0%
-
-
-The experiments code can be found at :githublink:`examples/model_compress/pruning/basic_pruners_torch.py <examples/model_compress/pruning/basic_pruners_torch.py>`
-
-.. code-block:: python
-
-   python basic_pruners_torch.py --pruner l1filter --model vgg16 --speed-up
-
-----
-
-L2Filter Pruner
----------------
-
-This is a structured pruning algorithm that prunes the filters with the smallest L2 norm of the weights. It is implemented as a one-shot pruner.
-
-We also provide a dependency-aware mode for this pruner to get better speedup from the pruning. Please reference `dependency-aware <./DependencyAware.rst>`__ for more details.
-
-Usage
-^^^^^
-
-PyTorch code
-
-.. code-block:: python
-
-   from nni.algorithms.compression.pytorch.pruning import L2FilterPruner
-   config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
-   pruner = L2FilterPruner(model, config_list)
-   pruner.compress()
-
-User configuration for L2Filter Pruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.L2FilterPruner
-
-----
-
-ActivationAPoZRankFilter Pruner
--------------------------------
-
-ActivationAPoZRankFilter Pruner is a pruner which prunes the filters with the smallest importance criterion ``APoZ`` calculated from the output activations of convolution layers to achieve a preset level of network sparsity. The pruning criterion ``APoZ`` is explained in the paper `Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures <https://arxiv.org/abs/1607.03250>`__.
-
-The APoZ is defined as:
-
-:math:`APoZ_{c}^{(i)} = APoZ\left(O_{c}^{(i)}\right)=\frac{\sum_{k}^{N} \sum_{j}^{M} f\left(O_{c, j}^{(i)}(k)=0\right)}{N \times M}`
-
-
-We also provide a dependency-aware mode for this pruner to get better speedup from the pruning. Please reference `dependency-aware <./DependencyAware.rst>`__ for more details.
-
-Usage
-^^^^^
-
-PyTorch code
-
-.. code-block:: python
-
-   from nni.algorithms.compression.pytorch.pruning import ActivationAPoZRankFilterPruner
-   config_list = [{
-       'sparsity': 0.5,
-       'op_types': ['Conv2d']
-   }]
-   pruner = ActivationAPoZRankFilterPruner(model, config_list, optimizer, trainer, criterion, sparsifying_training_batches=1)
-   pruner.compress()
-
-Note: ActivationAPoZRankFilterPruner is used to prune convolutional layers within deep neural networks, therefore the ``op_types`` field supports only convolutional layers.
-
-You can view :githublink:`example <examples/model_compress/pruning/basic_pruners_torch.py>` for more information.
-
-User configuration for ActivationAPoZRankFilter Pruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.ActivationAPoZRankFilterPruner
-
-----
-
-ActivationMeanRankFilter Pruner
--------------------------------
-
-ActivationMeanRankFilterPruner is a pruner which prunes the filters with the smallest importance criterion ``mean activation`` calculated from the output activations of convolution layers to achieve a preset level of network sparsity. The pruning criterion ``mean activation`` is explained in section 2.2 of the paper `Pruning Convolutional Neural Networks for Resource Efficient Inference <https://arxiv.org/abs/1611.06440>`__. Other pruning criteria mentioned in this paper will be supported in future release.
-
-We also provide a dependency-aware mode for this pruner to get better speedup from the pruning. Please reference `dependency-aware <./DependencyAware.rst>`__ for more details.
-
-Usage
-^^^^^
-
-PyTorch code
-
-.. code-block:: python
-
-   from nni.algorithms.compression.pytorch.pruning import ActivationMeanRankFilterPruner
-   config_list = [{
-       'sparsity': 0.5,
-       'op_types': ['Conv2d']
-   }]
-   pruner = ActivationMeanRankFilterPruner(model, config_list, optimizer, trainer, criterion, sparsifying_training_batches=1)
-   pruner.compress()
-
-Note: ActivationMeanRankFilterPruner is used to prune convolutional layers within deep neural networks, therefore the ``op_types`` field supports only convolutional layers.
-
-You can view :githublink:`example <examples/model_compress/pruning/basic_pruners_torch.py>` for more information.
-
-User configuration for ActivationMeanRankFilterPruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.ActivationMeanRankFilterPruner
-
-----
-
-TaylorFOWeightFilter Pruner
----------------------------
-
-TaylorFOWeightFilter Pruner is a pruner which prunes convolutional layers based on estimated importance calculated from the first order taylor expansion on weights to achieve a preset level of network sparsity. The estimated importance of filters is defined as the paper `Importance Estimation for Neural Network Pruning <http://jankautz.com/publications/Importance4NNPruning_CVPR19.pdf>`__. Other pruning criteria mentioned in this paper will be supported in future release.
-
-..
-
-:math:`\widehat{\mathcal{I}}_{\mathcal{S}}^{(1)}(\mathbf{W}) \triangleq \sum_{s \in \mathcal{S}} \mathcal{I}_{s}^{(1)}(\mathbf{W})=\sum_{s \in \mathcal{S}}\left(g_{s} w_{s}\right)^{2}`
-
-
-We also provide a dependency-aware mode for this pruner to get better speedup from the pruning. Please reference `dependency-aware <./DependencyAware.rst>`__ for more details.
-
-What's more, we provide a global-sort mode for this pruner which is aligned with paper implementation. Please set parameter 'global_sort' to True when instantiate TaylorFOWeightFilterPruner.
-
-Usage
-^^^^^
-
-PyTorch code
-
-.. code-block:: python
-
-   from nni.algorithms.compression.pytorch.pruning import TaylorFOWeightFilterPruner
-   config_list = [{
-       'sparsity': 0.5,
-       'op_types': ['Conv2d']
-   }]
-   pruner = TaylorFOWeightFilterPruner(model, config_list, optimizer, trainer, criterion, sparsifying_training_batches=1)
-   pruner.compress()
-
-User configuration for TaylorFOWeightFilter Pruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.TaylorFOWeightFilterPruner
-
-----
-
-AGP Pruner
-----------
-
-This is an iterative pruner, which the sparsity is increased from an initial sparsity value si (usually 0) to a final sparsity value sf over a span of n pruning steps, starting at training step :math:`t_{0}` and with pruning frequency :math:`\Delta t`:
-
-:math:`s_{t}=s_{f}+\left(s_{i}-s_{f}\right)\left(1-\frac{t-t_{0}}{n \Delta t}\right)^{3} \text { for } t \in\left\{t_{0}, t_{0}+\Delta t, \ldots, t_{0} + n \Delta t\right\}`
-
-For more details please refer to `To prune, or not to prune: exploring the efficacy of pruning for model compression <https://arxiv.org/abs/1710.01878>`__\.
-
-
-Usage
-^^^^^
-
-You can prune all weights from 0% to 80% sparsity in 10 epoch with the code below.
-
-PyTorch code
-
-.. code-block:: python
-
-   from nni.algorithms.compression.pytorch.pruning import AGPPruner
-   config_list = [{
-       'sparsity': 0.8,
-       'op_types': ['default']
-   }]
-
-   # load a pretrained model or train a model before using a pruner
-   # model = MyModel()
-   # model.load_state_dict(torch.load('mycheckpoint.pth'))
-
-   # AGP pruner prunes model while fine tuning the model by adding a hook on
-   # optimizer.step(), so an optimizer is required to prune the model.
-   optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9, weight_decay=1e-4)
-
-   pruner = AGPPruner(model, config_list, optimizer, trainer, criterion, pruning_algorithm='level')
-   pruner.compress()
-
-AGP pruner uses ``LevelPruner`` algorithms to prune the weight by default, however you can set ``pruning_algorithm`` parameter to other values to use other pruning algorithms:
-
-
-* ``level``\ : LevelPruner
-* ``slim``\ : SlimPruner
-* ``l1``\ : L1FilterPruner
-* ``l2``\ : L2FilterPruner
-* ``fpgm``\ : FPGMPruner
-* ``taylorfo``\ : TaylorFOWeightFilterPruner
-* ``apoz``\ : ActivationAPoZRankFilterPruner
-* ``mean_activation``\ : ActivationMeanRankFilterPruner
-
-
-User configuration for AGP Pruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.AGPPruner
-
-----
-
-NetAdapt Pruner
----------------
-
-NetAdapt allows a user to automatically simplify a pretrained network to meet the resource budget. 
-Given the overall sparsity, NetAdapt will automatically generate the sparsities distribution among different layers by iterative pruning.
-
-For more details, please refer to `NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications <https://arxiv.org/abs/1804.03230>`__.
-
-
-Usage
-^^^^^
-
-PyTorch code
-
-.. code-block:: python
-
-   from nni.algorithms.compression.pytorch.pruning import NetAdaptPruner
-   config_list = [{
-       'sparsity': 0.5,
-       'op_types': ['Conv2d']
-   }]
-   pruner = NetAdaptPruner(model, config_list, short_term_fine_tuner=short_term_fine_tuner, evaluator=evaluator,base_algo='l1', experiment_data_dir='./')
-   pruner.compress()
-
-You can view :githublink:`example <examples/model_compress/pruning/auto_pruners_torch.py>` for more information.
-
-User configuration for NetAdapt Pruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.NetAdaptPruner
-
-SimulatedAnnealing Pruner
--------------------------
-
-We implement a guided heuristic search method, Simulated Annealing (SA) algorithm, with enhancement on guided search based on prior experience. 
-The enhanced SA technique is based on the observation that a DNN layer with more number of weights often has a higher degree of model compression with less impact on overall accuracy.
-
-
-* Randomly initialize a pruning rate distribution (sparsities).
-* While current_temperature < stop_temperature:
-
-  #. generate a perturbation to current distribution
-  #. Perform fast evaluation on the perturbated distribution
-  #. accept the perturbation according to the performance and probability, if not accepted, return to step 1
-  #. cool down, current_temperature <- current_temperature * cool_down_rate
-
-For more details, please refer to `AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates <https://arxiv.org/abs/1907.03141>`__.
-
-Usage
-^^^^^
-
-PyTorch code
-
-.. code-block:: python
-
-   from nni.algorithms.compression.pytorch.pruning import SimulatedAnnealingPruner
-   config_list = [{
-       'sparsity': 0.5,
-       'op_types': ['Conv2d']
-   }]
-   pruner = SimulatedAnnealingPruner(model, config_list, evaluator=evaluator, base_algo='l1', cool_down_rate=0.9, experiment_data_dir='./')
-   pruner.compress()
-
-You can view :githublink:`example <examples/model_compress/pruning/auto_pruners_torch.py>` for more information.
-
-User configuration for SimulatedAnnealing Pruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.SimulatedAnnealingPruner
-
-AutoCompress Pruner
--------------------
-
-For each round, AutoCompressPruner prune the model for the same sparsity to achive the overall sparsity:
-
-.. code-block:: bash
-
-       1. Generate sparsities distribution using SimulatedAnnealingPruner
-       2. Perform ADMM-based structured pruning to generate pruning result for the next round.
-          Here we use `speedup` to perform real pruning.
-
-
-For more details, please refer to `AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates <https://arxiv.org/abs/1907.03141>`__.
-
-Usage
-^^^^^
-
-PyTorch code
-
-.. code-block:: python
-
-   from nni.algorithms.compression.pytorch.pruning import AutoCompressPruner
-   config_list = [{
-           'sparsity': 0.5,
-           'op_types': ['Conv2d']
-       }]
-   pruner = AutoCompressPruner(
-               model, config_list, trainer=trainer, evaluator=evaluator,
-               dummy_input=dummy_input, num_iterations=3, optimize_mode='maximize', base_algo='l1',
-               cool_down_rate=0.9, admm_num_iterations=30, admm_training_epochs=5, experiment_data_dir='./')
-   pruner.compress()
-
-You can view :githublink:`example <examples/model_compress/pruning/auto_pruners_torch.py>` for more information.
-
-User configuration for AutoCompress Pruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.AutoCompressPruner
-
-AMC Pruner
-----------
-
-AMC pruner leverages reinforcement learning to provide the model compression policy.
-This learning-based compression policy outperforms conventional rule-based compression policy by having higher compression ratio,
-better preserving the accuracy and freeing human labor.
-
-
-For more details, please refer to `AMC: AutoML for Model Compression and Acceleration on Mobile Devices <https://arxiv.org/pdf/1802.03494.pdf>`__.
-
-Usage
-^^^^^
-
-PyTorch code
-
-.. code-block:: python
-
-   from nni.algorithms.compression.pytorch.pruning import AMCPruner
-   config_list = [{
-           'op_types': ['Conv2d', 'Linear']
-       }]
-   pruner = AMCPruner(model, config_list, evaluator, val_loader, flops_ratio=0.5)
-   pruner.compress()
-
-You can view :githublink:`example <examples/model_compress/pruning/amc/>` for more information.
-
-User configuration for AMC Pruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.AMCPruner
-
-Reproduced Experiment
-^^^^^^^^^^^^^^^^^^^^^
-
-We implemented one of the experiments in `AMC: AutoML for Model Compression and Acceleration on Mobile Devices <https://arxiv.org/pdf/1802.03494.pdf>`__\ , we pruned **MobileNet** to 50% FLOPS for ImageNet in the paper. Our experiments results are as follows:
-
-.. list-table::
-   :header-rows: 1
-   :widths: auto
-
-   * - Model
-     - Top 1 acc.(paper/ours)
-     - Top 5 acc. (paper/ours)
-     - FLOPS
-   * - MobileNet
-     - 70.5% / 69.9%
-     - 89.3% / 89.1%
-     - 50%
-
-
-The experiments code can be found at :githublink:`examples/model_compress/pruning/ <examples/model_compress/pruning/amc/>`
-
-ADMM Pruner
------------
-
-Alternating Direction Method of Multipliers (ADMM) is a mathematical optimization technique,
-by decomposing the original nonconvex problem into two subproblems that can be solved iteratively. In weight pruning problem, these two subproblems are solved via 1) gradient descent algorithm and 2) Euclidean projection respectively. 
-
-During the process of solving these two subproblems, the weights of the original model will be changed. An one-shot pruner will then be applied to prune the model according to the config list given.
-
-This solution framework applies both to non-structured and different variations of structured pruning schemes.
-
-For more details, please refer to `A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers <https://arxiv.org/abs/1804.03294>`__.
-
-Usage
-^^^^^
-
-PyTorch code
-
-.. code-block:: python
-
-   from nni.algorithms.compression.pytorch.pruning import ADMMPruner
-   config_list = [{
-               'sparsity': 0.8,
-               'op_types': ['Conv2d'],
-               'op_names': ['conv1']
-           }, {
-               'sparsity': 0.92,
-               'op_types': ['Conv2d'],
-               'op_names': ['conv2']
-           }]
-   pruner = ADMMPruner(model, config_list, trainer, num_iterations=30, epochs_per_iteration=5)
-   pruner.compress()
-
-You can view :githublink:`example <examples/model_compress/pruning/auto_pruners_torch.py>` for more information.
-
-User configuration for ADMM Pruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.ADMMPruner
-
-Lottery Ticket Hypothesis
--------------------------
-
-`The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks <https://arxiv.org/abs/1803.03635>`__\ , authors Jonathan Frankle and Michael Carbin,provides comprehensive measurement and analysis, and articulate the *lottery ticket hypothesis*\ : dense, randomly-initialized, feed-forward networks contain subnetworks (*winning tickets*\ ) that -- when trained in isolation -- reach test accuracy comparable to the original network in a similar number of iterations.
-
-In this paper, the authors use the following process to prune a model, called *iterative prunning*\ :
-
-..
-
-   #. Randomly initialize a neural network f(x;theta_0) (where theta\ *0 follows D*\ {theta}).
-   #. Train the network for j iterations, arriving at parameters theta_j.
-   #. Prune p% of the parameters in theta_j, creating a mask m.
-   #. Reset the remaining parameters to their values in theta_0, creating the winning ticket f(x;m*theta_0).
-   #. Repeat step 2, 3, and 4.
-
-
-If the configured final sparsity is P (e.g., 0.8) and there are n times iterative pruning, each iterative pruning prunes 1-(1-P)^(1/n) of the weights that survive the previous round.
-
-Usage
-^^^^^
-
-PyTorch code
-
-.. code-block:: python
-
-   from nni.algorithms.compression.pytorch.pruning import LotteryTicketPruner
-   config_list = [{
-       'prune_iterations': 5,
-       'sparsity': 0.8,
-       'op_types': ['default']
-   }]
-   pruner = LotteryTicketPruner(model, config_list, optimizer)
-   pruner.compress()
-   for _ in pruner.get_prune_iterations():
-       pruner.prune_iteration_start()
-       for epoch in range(epoch_num):
-           ...
-
-The above configuration means that there are 5 times of iterative pruning. As the 5 times iterative pruning are executed in the same run, LotteryTicketPruner needs ``model`` and ``optimizer`` (\ **Note that should add ``lr_scheduler`` if used**\ ) to reset their states every time a new prune iteration starts. Please use ``get_prune_iterations`` to get the pruning iterations, and invoke ``prune_iteration_start`` at the beginning of each iteration. ``epoch_num`` is better to be large enough for model convergence, because the hypothesis is that the performance (accuracy) got in latter rounds with high sparsity could be comparable with that got in the first round.
-
-
-User configuration for LotteryTicket Pruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.LotteryTicketPruner
-
-Reproduced Experiment
-^^^^^^^^^^^^^^^^^^^^^
-
-We try to reproduce the experiment result of the fully connected network on MNIST using the same configuration as in the paper. The code can be referred :githublink:`here <examples/model_compress/pruning/lottery_torch_mnist_fc.py>`. In this experiment, we prune 10 times, for each pruning we train the pruned model for 50 epochs.
-
-
-.. image:: ../../img/lottery_ticket_mnist_fc.png
-   :target: ../../img/lottery_ticket_mnist_fc.png
-   :alt: 
-
-
-The above figure shows the result of the fully connected network. ``round0-sparsity-0.0`` is the performance without pruning. Consistent with the paper, pruning around 80% also obtain similar performance compared to non-pruning, and converges a little faster. If pruning too much, e.g., larger than 94%, the accuracy becomes lower and convergence becomes a little slower. A little different from the paper, the trend of the data in the paper is relatively more clear.
-
-Sensitivity Pruner
-------------------
-
-For each round, SensitivityPruner prunes the model based on the sensitivity to the accuracy of each layer until meeting the final configured sparsity of the whole model:
-
-.. code-block:: bash
-
-       1. Analyze the sensitivity of each layer in the current state of the model.
-       2. Prune each layer according to the sensitivity.
-
-
-For more details, please refer to `Learning both Weights and Connections for Efficient Neural Networks  <https://arxiv.org/abs/1506.02626>`__.
-
-Usage
-^^^^^
-
-PyTorch code
-
-.. code-block:: python
-
-   from nni.algorithms.compression.pytorch.pruning import SensitivityPruner
-   config_list = [{
-           'sparsity': 0.5,
-           'op_types': ['Conv2d']
-       }]
-   pruner = SensitivityPruner(model, config_list, finetuner=fine_tuner, evaluator=evaluator)
-   # eval_args and finetune_args are the parameters passed to the evaluator and finetuner respectively
-   pruner.compress(eval_args=[model], finetune_args=[model])
-
-User configuration for Sensitivity Pruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.SensitivityPruner
-
-Transformer Head Pruner
------------------------
-
-Transformer Head Pruner is a tool designed for pruning attention heads from the models belonging to the `Transformer family <https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf>`__. The following image from `Efficient Transformers: A Survey <https://arxiv.org/pdf/2009.06732.pdf>`__ gives a good overview the general structure of the Transformer.
-
-.. image:: ../../img/transformer_structure.png
-   :target: ../../img/transformer_structure.png
-   :alt: 
-
-Typically, each attention layer in the Transformer models consists of four weights: three projection matrices for query, key, value, and an output projection matrix. The outputs of the former three matrices contains the projected results for all heads. Normally, the results are then reshaped so that each head performs that attention computation independently. The final results are concatenated back before fed into the output projection. Therefore, when an attention head is pruned, the same weights corresponding to that heads in the three projection matrices are pruned. Also, the weights in the output projection corresponding to the head's output are pruned. In our implementation, we calculate and apply masks to the four matrices together.
-
-Note: currently, the pruner can only handle models with projection weights written as separate ``Linear`` modules, i.e., it expects four ``Linear`` modules corresponding to query, key, value, and an output projections. Therefore, in the ``config_list``, you should either write ``['Linear']`` for the ``op_types`` field, or write names corresponding to ``Linear`` modules for the ``op_names`` field. For instance, the `Huggingface transformers <https://huggingface.co/transformers/index.html>`_ are supported, but ``torch.nn.Transformer`` is not.
-
-The pruner implements the following algorithm:
-
-.. code-block:: bash
-
-    Repeat for each pruning iteration (1 for one-shot pruning):
-       1. Calculate importance scores for each head in each specified layer using a specific criterion.
-       2. Sort heads locally or globally, and prune out some heads with lowest scores. The number of pruned heads is determined according to the sparsity specified in the config.
-       3. If the specified pruning iteration is larger than 1 (iterative pruning), finetune the model for a while before the next pruning iteration.
-
-Currently, the following head sorting criteria are supported:
-
-    * "l1_weight": rank heads by the L1-norm of weights of the query, key, and value projection matrices.
-    * "l2_weight": rank heads by the L2-norm of weights of the query, key, and value projection matrices.
-    * "l1_activation": rank heads by the L1-norm of their attention computation output.
-    * "l2_activation": rank heads by the L2-norm of their attention computation output.
-    * "taylorfo": rank heads by l1 norm of the output of attention computation * gradient for this output. Check more details in `this paper <https://arxiv.org/abs/1905.10650>`__ and `this one <https://arxiv.org/abs/1611.06440>`__.
-
-We support local sorting (i.e., sorting heads within a layer) and global sorting (sorting all heads together), and you can control by setting the ``global_sort`` parameter. Note that if ``global_sort=True`` is passed, all weights must have the same sparsity in the config list. However, this does not mean that each layer will be prune to the same sparsity as specified. This sparsity value will be interpreted as a global sparsity, and each layer is likely to have different sparsity after pruning by global sort. As a reminder, we found that if global sorting is used, it is usually helpful to use an iterative pruning scheme, interleaving pruning with intermediate finetuning, since global sorting often results in non-uniform sparsity distributions, which makes the model more susceptible to forgetting.
-
-In our implementation, we support two ways to group the four weights in the same layer together. You can either pass a nested list containing the names of these modules as the pruner's initialization parameters (usage below), or simply pass a dummy input instead and the pruner will run ``torch.jit.trace`` to group the weights (experimental feature). However, if you would like to assign different sparsity to each layer, you can only use the first option, i.e., passing names of the weights to the pruner (see usage below). Also, note that we require the weights belonging to the same layer to have the same sparsity.
-
-Usage
-^^^^^
-
-Suppose we want to prune a BERT with Huggingface implementation, which has the following architecture (obtained by calling ``print(model)``). Note that we only show the first layer of the repeated layers in the encoder's ``ModuleList layer``. 
-
-.. image:: ../../img/huggingface_bert_architecture.png
-   :target: ../../img/huggingface_bert_architecture.png
-   :alt: 
-
-**Usage Example: one-shot pruning, assigning sparsity 0.5 to the first six layers and sparsity 0.25 to the last six layers (PyTorch code)**. Note that
-
-* Here we specify ``op_names`` in the config list to assign different sparsity to different layers.
-* Meanwhile, we pass ``attention_name_groups`` to the pruner so that the pruner may group together the weights belonging to the same attention layer.
-* Since in this example we want to do one-shot pruning, the ``num_iterations`` parameter is set to 1, and the parameter ``epochs_per_iteration`` is ignored. If you would like to do iterative pruning instead, you can set the ``num_iterations`` parameter to the number of pruning iterations, and the ``epochs_per_iteration`` parameter to the number of finetuning epochs between two iterations.
-* The arguments ``trainer`` and ``optimizer`` are only used when we want to do iterative pruning, or the ranking criterion is ``taylorfo``. Here these two parameters are ignored by the pruner.
-* The argument ``forward_runner`` is only used when the ranking criterion is ``l1_activation`` or ``l2_activation``. Here this parameter is ignored by the pruner.
-
-.. code-block:: python
-
-   from nni.algorithms.compression.pytorch.pruning import TransformerHeadPruner
-   attention_name_groups = list(zip(["encoder.layer.{}.attention.self.query".format(i) for i in range(12)],
-                                    ["encoder.layer.{}.attention.self.key".format(i) for i in range(12)],
-                                    ["encoder.layer.{}.attention.self.value".format(i) for i in range(12)],
-                                    ["encoder.layer.{}.attention.output.dense".format(i) for i in range(12)]))
-
-   kwargs = {"ranking_criterion": "l1_weight",
-             "global_sort": False,
-             "num_iterations": 1,
-             "epochs_per_iteration": 1,    # this is ignored when num_iterations = 1
-             "head_hidden_dim": 64,
-             "attention_name_groups": attention_name_groups,
-             "trainer": trainer,
-             "optimizer": optimizer,
-             "forward_runner": forward_runner
-             }
-   config_list = [{
-	"sparsity": 0.5,
-        "op_types": ["Linear"],
-        "op_names": [x for layer in attention_name_groups[:6] for x in layer]      # first six layers
-   }, {
-	"sparsity": 0.25,
-	"op_types": ["Linear"],
-	"op_names": [x for layer in attention_name_groups[6:] for x in layer]      # last six layers
-   }]
-
-   pruner = TransformerHeadPruner(model, config_list, **kwargs)
-   pruner.compress()
-
-In addition to this usage guide, we provide a more detailed example of pruning BERT (Huggingface implementation) for transfer learning on the tasks from the `GLUE benchmark <https://gluebenchmark.com/>`_. Please find it in this :githublink:`page <examples/model_compress/pruning/transformers>`. To run the example, first make sure that you install the package ``transformers`` and ``datasets``. Then, you may start by running the following command:
-
-.. code-block:: bash
-
-	./run.sh gpu_id glue_task
-
-By default, the code will download a pretrained BERT language model, and then finetune for several epochs on the downstream GLUE task. Then, the ``TransformerHeadPruner`` will be used to prune out heads from each layer by a certain criterion (by default, the code lets the pruner uses magnitude ranking, and prunes out 50% of the heads in each layer in an one-shot manner). Finally, the pruned model will be finetuned in the downstream task for several epochs. You can check the details of pruning from the logs printed out by the example. You can also experiment with different pruning settings by changing the parameters in ``run.sh``, or directly changing the ``config_list`` in ``transformer_pruning.py``.
-   
-User configuration for Transformer Head Pruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-..  autoclass:: nni.algorithms.compression.pytorch.pruning.TransformerHeadPruner
diff --git a/docs/source/Compression/QuantizationSpeedup.rst b/docs/source/Compression/QuantizationSpeedup.rst
deleted file mode 100644
index d355382b3..000000000
--- a/docs/source/Compression/QuantizationSpeedup.rst
+++ /dev/null
@@ -1,142 +0,0 @@
-Speed up Mixed Precision Quantization Model (experimental)
-==========================================================
-
-
-Introduction
-------------
-
-Deep learning network has been computational intensive and memory intensive 
-which increases the difficulty of deploying deep neural network model. Quantization is a 
-fundamental technology which is widely used to reduce memory footprint and speed up inference 
-process. Many frameworks begin to support quantization, but few of them support mixed precision 
-quantization and get real speedup. Frameworks like `HAQ: Hardware-Aware Automated Quantization with Mixed Precision <https://arxiv.org/pdf/1811.08886.pdf>`__\, only support simulated mixed precision quantization which will 
-not speed up the inference process. To get real speedup of mixed precision quantization and 
-help people get the real feedback from hardware, we design a general framework with simple interface to allow NNI quantization algorithms to connect different 
-DL model optimization backends (e.g., TensorRT, NNFusion), which gives users an end-to-end experience that after quantizing their model 
-with quantization algorithms, the quantized model can be directly speeded up with the connected optimization backend. NNI connects 
-TensorRT at this stage, and will support more backends in the future.
-
-
-Design and Implementation
--------------------------
-
-To support speeding up mixed precision quantization, we divide framework into two part, frontend and backend.  
-Frontend could be popular training frameworks such as PyTorch, TensorFlow etc. Backend could be inference 
-framework for different hardwares, such as TensorRT. At present, we support PyTorch as frontend and 
-TensorRT as backend. To convert PyTorch model to TensorRT engine, we leverage onnx as intermediate graph 
-representation. In this way, we convert PyTorch model to onnx model, then TensorRT parse onnx 
-model to generate inference engine. 
-
-
-Quantization aware training combines NNI quantization algorithm 'QAT' and NNI quantization speedup tool.
-Users should set config to train quantized model using QAT algorithm(please refer to `NNI Quantization Algorithms <https://nni.readthedocs.io/en/stable/Compression/Quantizer.html>`__\  ).
-After quantization aware training, users can get new config with calibration parameters and model with quantized weight. By passing new config and model to quantization speedup tool, users can get real mixed precision speedup engine to do inference.
-
-
-After getting mixed precision engine, users can do inference with input data.
-
-
-Note
-
-
-* Recommend using "cpu"(host) as data device(for both inference data and calibration data) since data should be on host initially and it will be transposed to device before inference. If data type is not "cpu"(host), this tool will transpose it to "cpu" which may increases unnecessary overhead.
-* User can also do post-training quantization leveraging TensorRT directly(need to provide calibration dataset).
-* Not all op types are supported right now. At present, NNI supports Conv, Linear, Relu and MaxPool. More op types will be supported in the following release.
-
-
-Prerequisite
-------------
-CUDA version >= 11.0
-
-TensorRT version >= 7.2
-
-Note
-
-* If you haven't installed TensorRT before or use the old version, please refer to `TensorRT Installation Guide <https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html>`__\  
-
-Usage
------
-quantization aware training:
-
-.. code-block:: python
-
-    # arrange bit config for QAT algorithm
-    configure_list = [{
-            'quant_types': ['weight', 'output'],
-            'quant_bits': {'weight':8, 'output':8},
-            'op_names': ['conv1']
-        }, {
-            'quant_types': ['output'],
-            'quant_bits': {'output':8},
-            'op_names': ['relu1']
-        }
-    ]
-
-    quantizer = QAT_Quantizer(model, configure_list, optimizer)
-    quantizer.compress()
-    calibration_config = quantizer.export_model(model_path, calibration_path)
-
-    engine = ModelSpeedupTensorRT(model, input_shape, config=calibration_config, batchsize=batch_size)
-    # build tensorrt inference engine
-    engine.compress()
-    # data should be pytorch tensor
-    output, time = engine.inference(data)
-
-
-Note that NNI also supports post-training quantization directly, please refer to complete examples for detail.
-
-
-For complete examples please refer to :githublink:`the code <examples/model_compress/quantization/mixed_precision_speedup_mnist.py>`.
-
-
-For more parameters about the class 'TensorRTModelSpeedUp', you can refer to `Model Compression API Reference <https://nni.readthedocs.io/en/stable/Compression/CompressionReference.html#quantization-speedup>`__\.
-
-
-Mnist test
-^^^^^^^^^^^^^^^^^^^
-
-on one GTX2080 GPU,
-input tensor: ``torch.randn(128, 1, 28, 28)``
-
-.. list-table::
-   :header-rows: 1
-   :widths: auto
-
-   * - quantization strategy
-     - Latency
-     - accuracy
-   * - all in 32bit
-     - 0.001199961
-     - 96%
-   * - mixed precision(average bit 20.4)
-     - 0.000753688
-     - 96%
-   * - all in 8bit
-     - 0.000229869
-     - 93.7%
-
-
-Cifar10 resnet18 test(train one epoch)
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-
-on one GTX2080 GPU,
-input tensor: ``torch.randn(128, 3, 32, 32)``
-
-
-.. list-table::
-   :header-rows: 1
-   :widths: auto
-
-   * - quantization strategy
-     - Latency
-     - accuracy
-   * - all in 32bit
-     - 0.003286268
-     - 54.21%
-   * - mixed precision(average bit 11.55)
-     - 0.001358022
-     - 54.78%
-   * - all in 8bit
-     - 0.000859139
-     - 52.81%
\ No newline at end of file
diff --git a/docs/source/Compression/Quantizer.rst b/docs/source/Compression/Quantizer.rst
deleted file mode 100644
index 79607d2f7..000000000
--- a/docs/source/Compression/Quantizer.rst
+++ /dev/null
@@ -1,385 +0,0 @@
-Supported Quantization Algorithms on NNI
-========================================
-
-Index of supported quantization algorithms
-
-
-* `Naive Quantizer <#naive-quantizer>`__
-* `QAT Quantizer <#qat-quantizer>`__
-* `DoReFa Quantizer <#dorefa-quantizer>`__
-* `BNN Quantizer <#bnn-quantizer>`__
-* `LSQ Quantizer <#lsq-quantizer>`__
-* `Observer Quantizer <#observer-quantizer>`__
-
-Naive Quantizer
----------------
-
-We provide Naive Quantizer to quantizer weight to default 8 bits, you can use it to test quantize algorithm without any configure.
-
-Usage
-^^^^^
-
-pytorch
-
-.. code-block:: python
-
-   model = nni.algorithms.compression.pytorch.quantization.NaiveQuantizer(model).compress()
-
-----
-
-QAT Quantizer
--------------
-
-In `Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference <http://openaccess.thecvf.com/content_cvpr_2018/papers/Jacob_Quantization_and_Training_CVPR_2018_paper.pdf>`__\ , authors Benoit Jacob and Skirmantas Kligys provide an algorithm to quantize the model with training.
-
-..
-
-   We propose an approach that simulates quantization effects in the forward pass of training. Backpropagation still happens as usual, and all weights and biases are stored in floating point so that they can be easily nudged by small amounts. The forward propagation pass however simulates quantized inference as it will happen in the inference engine, by implementing in floating-point arithmetic the rounding behavior of the quantization scheme
-
-
-   * Weights are quantized before they are convolved with the input. If batch normalization (see [17]) is used for the layer, the batch normalization parameters are “folded into” the weights before quantization.
-   * Activations are quantized at points where they would be during inference, e.g. after the activation function is applied to a convolutional or fully connected layer’s output, or after a bypass connection adds or concatenates the outputs of several layers together such as in ResNets.
-
-
-Usage
-^^^^^
-
-You can quantize your model to 8 bits with the code below before your training code.
-
-PyTorch code
-
-.. code-block:: python
-
-   from nni.algorithms.compression.pytorch.quantization import QAT_Quantizer
-   model = Mnist()
-
-   config_list = [{
-       'quant_types': ['weight'],
-       'quant_bits': {
-           'weight': 8,
-       }, # you can just use `int` here because all `quan_types` share same bits length, see config for `ReLu6` below.
-       'op_types':['Conv2d', 'Linear']
-   }, {
-       'quant_types': ['output'],
-       'quant_bits': 8,
-       'quant_start_step': 7000,
-       'op_types':['ReLU6']
-   }]
-   quantizer = QAT_Quantizer(model, config_list)
-   quantizer.compress()
-
-You can view example for more information
-
-User configuration for QAT Quantizer
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-common configuration needed by compression algorithms can be found at `Specification of `config_list <./QuickStart.rst>`__.
-
-configuration needed by this algorithm :
-
-
-* **quant_start_step:** int
-
-disable quantization until model are run by certain number of steps, this allows the network to enter a more stable
-state where activation quantization ranges do not exclude a significant fraction of values, default value is 0
-
-Batch normalization folding
-^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Batch normalization folding is supported in QAT quantizer. It can be easily enabled by passing an argument `dummy_input` to
-the quantizer, like:
-
-.. code-block:: python
-
-    # assume your model takes an input of shape (1, 1, 28, 28)
-    # and dummy_input must be on the same device as the model
-    dummy_input = torch.randn(1, 1, 28, 28)
-
-    # pass the dummy_input to the quantizer
-    quantizer = QAT_Quantizer(model, config_list, dummy_input=dummy_input)
-
-
-The quantizer will automatically detect Conv-BN patterns and simulate batch normalization folding process in the training
-graph. Note that when the quantization aware training process is finished, the folded weight/bias would be restored after calling
-`quantizer.export_model`.
-
-Quantization dtype and scheme customization
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-Different backends on different devices use different quantization strategies (i.e. dtype (int or uint) and
-scheme (per-tensor or per-channel and symmetric or affine)). QAT quantizer supports customization of mainstream dtypes and schemes.
-There are two ways to set them. One way is setting them globally through a function named `set_quant_scheme_dtype` like:
-
-.. code-block:: python
-
-    from nni.compression.pytorch.quantization.settings import set_quant_scheme_dtype
-
-    # This will set all the quantization of 'input' in 'per_tensor_affine' and 'uint' manner
-    set_quant_scheme_dtype('input', 'per_tensor_affine', 'uint)
-    # This will set all the quantization of 'output' in 'per_tensor_symmetric' and 'int' manner
-    set_quant_scheme_dtype('output', 'per_tensor_symmetric', 'int')
-    # This will set all the quantization of 'weight' in 'per_channel_symmetric' and 'int' manner
-    set_quant_scheme_dtype('weight', 'per_channel_symmetric', 'int')
-
-
-The other way is more detailed. You can customize the dtype and scheme in each quantization config list like:
-
-.. code-block:: python
-
-    config_list = [{
-       'quant_types': ['weight'],
-       'quant_bits':  8,
-       'op_types':['Conv2d', 'Linear'],
-       'quant_dtype': 'int',
-       'quant_scheme': 'per_channel_symmetric'
-   }, {
-       'quant_types': ['output'],
-       'quant_bits': 8,
-       'quant_start_step': 7000,
-       'op_types':['ReLU6'],
-       'quant_dtype': 'uint',
-       'quant_scheme': 'per_tensor_affine'
-   }]
-
-Multi-GPU training
-^^^^^^^^^^^^^^^^^^^
-QAT quantizer natively supports multi-gpu training (DataParallel and DistributedDataParallel). Note that the quantizer
-instantiation should happen before you wrap your model with DataParallel or DistributedDataParallel. For example:
-
-.. code-block:: python
-
-    from torch.nn.parallel import DistributedDataParallel as DDP
-    from nni.algorithms.compression.pytorch.quantization import QAT_Quantizer
-
-    model = define_your_model()
-
-    model = QAT_Quantizer(model, **other_params)  # <--- QAT_Quantizer instantiation
-
-    model = DDP(model)
-
-    for i in range(epochs):
-        train(model)
-        eval(model)
-
-
-----
-
-LSQ Quantizer
--------------
-
-In `LEARNED STEP SIZE QUANTIZATION <https://arxiv.org/pdf/1902.08153.pdf>`__\ , authors Steven K. Esser and Jeffrey L. McKinstry provide an algorithm to train the scales with gradients.
-
-..
-
-   The authors introduce a novel means to estimate and scale the task loss gradient at each weight and activation layer’s quantizer step size, such that it can be learned in conjunction with other network parameters.
-
-
-Usage
-^^^^^
-You can add codes below before your training codes. Three things must be done:
-
-
-1. configure which layer to be quantized and which tensor (input/output/weight) of that layer to be quantized.
-2. construct the lsq quantizer
-3. call the `compress` API
-
-
-PyTorch code
-
-.. code-block:: python
-
-    from nni.algorithms.compression.pytorch.quantization import LsqQuantizer
-    model = Mnist()
-
-    configure_list = [{
-            'quant_types': ['weight', 'input'],
-            'quant_bits': {
-                'weight': 8,
-                'input': 8,
-            },
-            'op_names': ['conv1']
-        }, {
-            'quant_types': ['output'],
-            'quant_bits': {'output': 8,},
-            'op_names': ['relu1']
-    }]
-
-    quantizer = LsqQuantizer(model, configure_list, optimizer)
-    quantizer.compress()
-
-You can view example for more information. :githublink:`examples/model_compress/quantization/LSQ_torch_quantizer.py <examples/model_compress/quantization/LSQ_torch_quantizer.py>`
-
-User configuration for LSQ Quantizer
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-common configuration needed by compression algorithms can be found at `Specification of `config_list <./QuickStart.rst>`__.
-
-configuration needed by this algorithm :
-
-
-----
-
-DoReFa Quantizer
-----------------
-
-In `DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients <https://arxiv.org/abs/1606.06160>`__\ , authors Shuchang Zhou and Yuxin Wu provide an algorithm named DoReFa to quantize the weight, activation and gradients with training.
-
-Usage
-^^^^^
-
-To implement DoReFa Quantizer, you can add code below before your training code
-
-PyTorch code
-
-.. code-block:: python
-
-   from nni.algorithms.compression.pytorch.quantization import DoReFaQuantizer
-   config_list = [{ 
-       'quant_types': ['weight'],
-       'quant_bits': 8, 
-       'op_types': ['default'] 
-   }]
-   quantizer = DoReFaQuantizer(model, config_list)
-   quantizer.compress()
-
-You can view example for more information
-
-User configuration for DoReFa Quantizer
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-common configuration needed by compression algorithms can be found at `Specification of ``config_list`` <./QuickStart.rst>`__.
-
-configuration needed by this algorithm :
-
-----
-
-BNN Quantizer
--------------
-
-In `Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1 <https://arxiv.org/abs/1602.02830>`__\ , 
-
-..
-
-   We introduce a method to train Binarized Neural Networks (BNNs) - neural networks with binary weights and activations at run-time. At training-time the binary weights and activations are used for computing the parameters gradients. During the forward pass, BNNs drastically reduce memory size and accesses, and replace most arithmetic operations with bit-wise operations, which is expected to substantially improve power-efficiency.
-
-
-Usage
-^^^^^
-
-PyTorch code
-
-.. code-block:: python
-
-   from nni.algorithms.compression.pytorch.quantization import BNNQuantizer
-   model = VGG_Cifar10(num_classes=10)
-
-   configure_list = [{
-       'quant_bits': 1,
-       'quant_types': ['weight'],
-       'op_types': ['Conv2d', 'Linear'],
-       'op_names': ['features.0', 'features.3', 'features.7', 'features.10', 'features.14', 'features.17', 'classifier.0', 'classifier.3']
-   }, {
-       'quant_bits': 1,
-       'quant_types': ['output'],
-       'op_types': ['Hardtanh'],
-       'op_names': ['features.6', 'features.9', 'features.13', 'features.16', 'features.20', 'classifier.2', 'classifier.5']
-   }]
-
-   quantizer = BNNQuantizer(model, configure_list)
-   model = quantizer.compress()
-
-You can view example :githublink:`examples/model_compress/quantization/BNN_quantizer_cifar10.py <examples/model_compress/quantization/BNN_quantizer_cifar10.py>` for more information.
-
-User configuration for BNN Quantizer
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-common configuration needed by compression algorithms can be found at `Specification of ``config_list`` <./QuickStart.rst>`__.
-
-configuration needed by this algorithm :
-
-Experiment
-^^^^^^^^^^
-
-We implemented one of the experiments in `Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1 <https://arxiv.org/abs/1602.02830>`__\ , we quantized the **VGGNet** for CIFAR-10 in the paper. Our experiments results are as follows:
-
-.. list-table::
-   :header-rows: 1
-   :widths: auto
-
-   * - Model
-     - Accuracy
-   * - VGGNet
-     - 86.93%
-
-
-The experiments code can be found at :githublink:`examples/model_compress/quantization/BNN_quantizer_cifar10.py <examples/model_compress/quantization/BNN_quantizer_cifar10.py>` 
-
-
-Observer Quantizer
-------------------
-
-..
-
-   Observer quantizer is a framework of post-training quantization. It will insert observers into the place where the quantization will happen. During quantization calibration, each observer will record all the tensors it 'sees'. These tensors will be used to calculate the quantization statistics after calibration.
-
-Usage
-^^^^^
-
-1. configure which layer to be quantized and which tensor (input/output/weight) of that layer to be quantized.
-2. construct the observer quantizer.
-3. do quantization calibration.
-4. call the `compress` API to calculate the scale and zero point for each tensor and switch model to evaluation mode.
-
-PyTorch code
-
-.. code-block:: python
-
-   from nni.algorithms.compression.pytorch.quantization import ObserverQuantizer
-
-   def calibration(model, calib_loader):
-       model.eval()
-       with torch.no_grad():
-           for data, _ in calib_loader:
-               model(data)
-
-   model = Mnist()
-
-   configure_list = [{
-       'quant_bits': 8,
-       'quant_types': ['weight', 'input'],
-       'op_names': ['conv1', 'conv2],
-   }, {
-       'quant_bits': 8,
-       'quant_types': ['output'],
-       'op_names': ['relu1', 'relu2],
-   }]
-
-   quantizer = ObserverQuantizer(model, configure_list)
-   calibration(model, calib_loader)
-   model = quantizer.compress()
-
-You can view example :githublink:`examples/model_compress/quantization/observer_quantizer.py <examples/model_compress/quantization/observer_quantizer.py>` for more information.
-
-User configuration for Observer Quantizer
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-Common configuration needed by compression algorithms can be found at `Specification of `config_list <./QuickStart.rst>`__.
-
-
-.. note::
-    This quantizer is still under development for now. Some quantizer settings are hard-coded:
-
-    - weight observer: per_tensor_symmetric, qint8
-    - output observer: per_tensor_affine, quint8, reduce_range=True
-
-    Other settings (such as quant_type and op_names) can be configured.
-
-About the compress API
-^^^^^^^^^^^^^^^^^^^^^^
-Before the `compress` API is called, the model will only record tensors' statistics and no quantization process will be executed.
-After the `compress` API is called, the model will NOT record tensors' statistics any more. The quantization scale and zero point will
-be generated for each tensor and will be used to quantize each tensor during inference (we call it evaluation mode)
-
-About calibration
-^^^^^^^^^^^^^^^^^
-Usually we pick up about 100 training/evaluation examples for calibration. If you found the accuracy is a bit low, try
-to reduce the number of calibration examples.
-
diff --git a/docs/source/Compression/QuickStart.rst b/docs/source/Compression/QuickStart.rst
deleted file mode 100644
index ab688a200..000000000
--- a/docs/source/Compression/QuickStart.rst
+++ /dev/null
@@ -1,126 +0,0 @@
-Quick Start
-===========
-
-.. code-block::
-
-   ..  toctree::
-      :hidden:
-
-      Notebook Example <compression_pipeline_example>
-
-
-Model compression usually consists of three stages: 1) pre-training a model, 2) compress the model, 3) fine-tuning the model. NNI mainly focuses on the second stage and provides very simple APIs for compressing a model. Follow this guide for a quick look at how easy it is to use NNI to compress a model. 
-
-.. A `compression pipeline example <./compression_pipeline_example.rst>`__ with Jupyter notebook is supported and refer the code :githublink:`here <examples/notebooks/compression_pipeline_example.ipynb>`.
-
-Model Pruning
--------------
-
-Here we use `level pruner <../Compression/Pruner.rst#level-pruner>`__ as an example to show the usage of pruning in NNI.
-
-Step1. Write configuration
-^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Write a configuration to specify the layers that you want to prune. The following configuration means pruning all the ``default``\ ops to sparsity 0.5 while keeping other layers unpruned.
-
-.. code-block:: python
-
-   config_list = [{
-       'sparsity': 0.5,
-       'op_types': ['default'],
-   }]
-
-The specification of configuration can be found `here <./Tutorial.rst#specify-the-configuration>`__. Note that different pruners may have their own defined fields in configuration. Please refer to each pruner's `usage <./Pruner.rst>`__ for details, and adjust the configuration accordingly.
-
-Step2. Choose a pruner and compress the model
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-First instantiate the chosen pruner with your model and configuration as arguments, then invoke ``compress()`` to compress your model. Note that, some algorithms may check gradients for compressing, so we may also define a trainer, an optimizer, a criterion and pass them to the pruner.
-
-.. code-block:: python
-
-   from nni.algorithms.compression.pytorch.pruning import LevelPruner
-
-   pruner = LevelPruner(model, config_list)
-   model = pruner.compress()
-
-Some pruners (e.g., L1FilterPruner, FPGMPruner) prune once, some pruners (e.g., AGPPruner) prune your model iteratively, the masks are adjusted epoch by epoch during training.
-
-So if the pruners prune your model iteratively or they need training or inference to get gradients, you need pass finetuning logic to pruner.
-
-For example:
-
-.. code-block:: python
-
-   from nni.algorithms.compression.pytorch.pruning import AGPPruner
-
-   pruner = AGPPruner(model, config_list, optimizer, trainer, criterion, num_iterations=10, epochs_per_iteration=1, pruning_algorithm='level')
-   model = pruner.compress()
-
-Step3. Export compression result
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-After training, you can export model weights to a file, and the generated masks to a file as well. Exporting onnx model is also supported.
-
-.. code-block:: python
-
-   pruner.export_model(model_path='pruned_vgg19_cifar10.pth', mask_path='mask_vgg19_cifar10.pth')
-
-Plese refer to :githublink:`mnist example <examples/model_compress/pruning/naive_prune_torch.py>` for example code.
-
-More examples of pruning algorithms can be found in :githublink:`basic_pruners_torch <examples/model_compress/pruning/basic_pruners_torch.py>` and :githublink:`auto_pruners_torch <examples/model_compress/pruning/auto_pruners_torch.py>`.
-
-
-Model Quantization
-------------------
-
-Here we use `QAT  Quantizer <../Compression/Quantizer.rst#qat-quantizer>`__ as an example to show the usage of pruning in NNI.
-
-Step1. Write configuration
-^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. code-block:: python
-
-   config_list = [{
-       'quant_types': ['weight', 'input'],
-       'quant_bits': {
-           'weight': 8,
-           'input': 8,
-       }, # you can just use `int` here because all `quan_types` share same bits length, see config for `ReLu6` below.
-       'op_types':['Conv2d', 'Linear'],
-       'quant_dtype': 'int',
-       'quant_scheme': 'per_channel_symmetric'
-   }, {
-       'quant_types': ['output'],
-       'quant_bits': 8,
-       'quant_start_step': 7000,
-       'op_types':['ReLU6'],
-       'quant_dtype': 'uint',
-       'quant_scheme': 'per_tensor_affine'
-   }]
-
-The specification of configuration can be found `here <./Tutorial.rst#quantization-specific-keys>`__.
-
-Step2. Choose a quantizer and compress the model
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. code-block:: python
-
-   from nni.algorithms.compression.pytorch.quantization import QAT_Quantizer
-
-   quantizer = QAT_Quantizer(model, config_list)
-   quantizer.compress()
-
-
-Step3. Export compression result
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-After training and calibration, you can export model weight to a file, and the generated calibration parameters to a file as well. Exporting onnx model is also supported.
-
-.. code-block:: python
-
-   calibration_config = quantizer.export_model(model_path, calibration_path, onnx_path, input_shape, device)
-
-Plese refer to :githublink:`mnist example <examples/model_compress/quantization/QAT_torch_quantizer.py>` for example code.
-
-Congratulations! You've compressed your first model via NNI. To go a bit more in depth about model compression in NNI, check out the `Tutorial <./Tutorial.rst>`__.
\ No newline at end of file
diff --git a/docs/source/Compression/QuickStart_zh.rst b/docs/source/Compression/QuickStart_zh.rst
deleted file mode 100644
index 0489667ed..000000000
--- a/docs/source/Compression/QuickStart_zh.rst
+++ /dev/null
@@ -1,128 +0,0 @@
-.. 98b0285bbfe1a01c90b9ba6a9b0d6caa
-
-快速入门
-===========
-
-.. code-block::
-
-   ..  toctree::
-      :hidden:
-
-      Notebook Example <compression_pipeline_example>
-
-
-模型压缩通常包括三个阶段：1）预训练模型，2）压缩模型，3）微调模型。 NNI 主要关注于第二阶段，并为模型压缩提供易于使用的 API。遵循本指南，您将快速了解如何使用 NNI 来压缩模型。更深入地了解 NNI 中的模型压缩模块，请查看 `Tutorial <./Tutorial.rst>`__。
-
-.. 提供了一个在 Jupyter notebook 中进行完整的模型压缩流程的 `示例 <./compression_pipeline_example.rst>`__，参考 :githublink:`代码 <examples/notebooks/compression_pipeline_example.ipynb>`。
-
-模型剪枝
--------------
-
-这里通过 `level pruner <../Compression/Pruner.rst#level-pruner>`__ 举例说明 NNI 中模型剪枝的用法。
-
-Step1. 编写配置
-^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-编写配置来指定要剪枝的层。以下配置表示剪枝所有的 ``default`` 层，稀疏度设为 0.5，其它层保持不变。
-
-.. code-block:: python
-
-   config_list = [{
-       'sparsity': 0.5,
-       'op_types': ['default'],
-   }]
-
-配置说明在 `这里 <./Tutorial.rst#specify-the-configuration>`__。注意，不同的 Pruner 可能有自定义的配置字段。详情参考每个 Pruner 的 `具体用法 <./Pruner.rst>`__，来调整相应的配置。
-
-Step2. 选择 Pruner 来压缩模型
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-首先，使用模型来初始化 Pruner，并将配置作为参数传入，然后调用 ``compress()`` 来压缩模型。请注意，有些算法可能会检查训练过程中的梯度，因此我们可能会定义一组 trainer, optimizer, criterion 并传递给 Pruner。
-
-.. code-block:: python
-
-   from nni.algorithms.compression.pytorch.pruning import LevelPruner
-
-   pruner = LevelPruner(model, config_list)
-   model = pruner.compress()
-
-然后，使用正常的训练方法来训练模型 （如，SGD），剪枝在训练过程中是透明的。有些 Pruner（如 L1FilterPruner、FPGMPruner）在开始时修剪一次，下面的训练可以看作是微调。有些 Pruner（例如AGPPruner）会迭代的对模型剪枝，在训练过程中逐步修改掩码。
-
-如果使用 Pruner 进行迭代剪枝，或者剪枝过程中需要训练或者推理，则需要将 finetune 逻辑传到 Pruner 中。
-
-例如：
-
-.. code-block:: python
-
-   from nni.algorithms.compression.pytorch.pruning import AGPPruner
-
-   pruner = AGPPruner(model, config_list, optimizer, trainer, criterion, num_iterations=10, epochs_per_iteration=1, pruning_algorithm='level')
-   model = pruner.compress()
-
-Step3. 导出压缩结果
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-训练之后，可将模型权重导出到文件，同时将生成的掩码也导出到文件， 也支持导出 ONNX 模型。
-
-.. code-block:: python
-
-   pruner.export_model(model_path='pruned_vgg19_cifar10.pth', mask_path='mask_vgg19_cifar10.pth')
-
-参考 :githublink:`mnist 示例 <examples/model_compress/pruning/naive_prune_torch.py>` 获取代码。
-
-更多剪枝算法的示例在 :githublink:`basic_pruners_torch <examples/model_compress/pruning/basic_pruners_torch.py>` 和 :githublink:`auto_pruners_torch <examples/model_compress/pruning/auto_pruners_torch.py>`。
-
-
-模型量化
-------------------
-
-这里通过 `QAT  Quantizer <../Compression/Quantizer.rst#qat-quantizer>`__ 举例说明在 NNI 中量化的用法。
-
-Step1. 编写配置
-^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. code-block:: python
-
-   config_list = [{
-       'quant_types': ['weight', 'input'],
-       'quant_bits': {
-           'weight': 8,
-           'input': 8,
-       }, # 这里可以仅使用 `int`，因为所有 `quan_types` 使用了一样的位长，参考下方 `ReLu6` 配置。
-       'op_types':['Conv2d', 'Linear'],
-       'quant_dtype': 'int',
-       'quant_scheme': 'per_channel_symmetric'
-   }, {
-       'quant_types': ['output'],
-       'quant_bits': 8,
-       'quant_start_step': 7000,
-       'op_types':['ReLU6'],
-       'quant_dtype': 'uint',
-       'quant_scheme': 'per_tensor_affine'
-   }]
-
-配置说明在 `这里 <./Tutorial.rst#quantization-specific-keys>`__。
-
-Step2. 选择 Quantizer 来压缩模型
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. code-block:: python
-
-   from nni.algorithms.compression.pytorch.quantization import QAT_Quantizer
-
-   quantizer = QAT_Quantizer(model, config_list)
-   quantizer.compress()
-
-
-Step3. 导出压缩结果
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-在训练和校准之后，你可以将模型权重导出到一个文件，并将生成的校准参数也导出到一个文件。 也支持导出 ONNX 模型。
-
-.. code-block:: python
-
-   calibration_config = quantizer.export_model(model_path, calibration_path, onnx_path, input_shape, device)
-
-参考 :githublink:`mnist example <examples/model_compress/quantization/QAT_torch_quantizer.py>` 获取示例代码。
-
-恭喜！ 您已经通过 NNI 压缩了您的第一个模型。 更深入地了解 NNI 中的模型压缩，请查看 `Tutorial <./Tutorial.rst>`__。
\ No newline at end of file
diff --git a/docs/source/Compression/Tutorial.rst b/docs/source/Compression/Tutorial.rst
deleted file mode 100644
index 9c2a8982d..000000000
--- a/docs/source/Compression/Tutorial.rst
+++ /dev/null
@@ -1,257 +0,0 @@
-Tutorial
-========
-
-.. contents::
-
-In this tutorial, we will explain more detailed usage about the model compression in NNI. 
-
-Setup compression goal
-----------------------
-
-Specify the configuration
-^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Users can specify the configuration (i.e., ``config_list``\ ) for a compression algorithm. For example, when compressing a model, users may want to specify the sparsity ratio, to specify different ratios for different types of operations, to exclude certain types of operations, or to compress only a certain types of operations. For users to express these kinds of requirements, we define a configuration specification. It can be seen as a python ``list`` object, where each element is a ``dict`` object. 
-
-The ``dict``\ s in the ``list`` are applied one by one, that is, the configurations in latter ``dict`` will overwrite the configurations in former ones on the operations that are within the scope of both of them. 
-
-There are different keys in a ``dict``. Some of them are common keys supported by all the compression algorithms:
-
-* **op_types**\ : This is to specify what types of operations to be compressed. 'default' means following the algorithm's default setting. All suported module types are defined in :githublink:`default_layers.py <nni/compression/pytorch/default_layers.py>` for pytorch.
-* **op_names**\ : This is to specify by name what operations to be compressed. If this field is omitted, operations will not be filtered by it.
-* **exclude**\ : Default is False. If this field is True, it means the operations with specified types and names will be excluded from the compression.
-
-Some other keys are often specific to a certain algorithm, users can refer to `pruning algorithms <./Pruner.rst>`__ and `quantization algorithms <./Quantizer.rst>`__ for the keys allowed by each algorithm.
-
-To prune all ``Conv2d`` layers with the sparsity of 0.6, the configuration can be written as:
-
-.. code-block:: python
-
-   [{
-    'sparsity': 0.6,
-    'op_types': ['Conv2d']
-   }]
-
-To control the sparsity of specific layers, the configuration can be written as:
-
-.. code-block:: python
-
-   [{
-      'sparsity': 0.8,
-      'op_types': ['default']
-   }, 
-   {
-      'sparsity': 0.6,
-      'op_names': ['op_name1', 'op_name2']
-   }, 
-   {
-      'exclude': True,
-      'op_names': ['op_name3']
-   }]
-
-It means following the algorithm's default setting for compressed operations with sparsity 0.8, but for ``op_name1`` and ``op_name2`` use sparsity 0.6, and do not compress ``op_name3``.
-
-Quantization specific keys
-^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Besides the keys explained above, if you use quantization algorithms you need to specify more keys in ``config_list``\ , which are explained below.
-
-* **quant_types** : list of string. 
-
-Type of quantization you want to apply, currently support 'weight', 'input', 'output'. 'weight' means applying quantization operation
-to the weight parameter of modules. 'input' means applying quantization operation to the input of module forward method. 'output' means applying quantization operation to the output of module forward method, which is often called as 'activation' in some papers.
-
-
-* **quant_bits** : int or dict of {str : int}
-
-bits length of quantization, key is the quantization type, value is the quantization bits length, eg. 
-
-.. code-block:: python
-
-   {
-      quant_bits: {
-         'weight': 8,
-         'output': 4,
-         },
-   }
-
-when the value is int type, all quantization types share same bits length. eg. 
-
-.. code-block:: python
-
-   {
-      quant_bits: 8, # weight or output quantization are all 8 bits
-   }
-
-* **quant_dtype** : str or dict of {str : str}
-
-quantization dtype, used to determine the range of quantized value. Two choices can be used:
-
-- int: the range is singed
-- uint: the range is unsigned
-
-Two ways to set it. One is that the key is the quantization type, and the value is the quantization dtype, eg.
-
-.. code-block:: python
-
-   {
-      quant_dtype: {
-         'weight': 'int',
-         'output': 'uint,
-         },
-   }
-
-The other is that the value is str type, and all quantization types share the same dtype. eg.
-
-.. code-block:: python
-
-   {
-      'quant_dtype': 'int', # the dtype of weight and output quantization are all 'int'
-   }
-
-There are totally two kinds of `quant_dtype` you can set, they are 'int' and 'uint'.
-
-* **quant_scheme** : str or dict of {str : str}
-
-quantization scheme, used to determine the quantization manners. Four choices can used:
-
-- per_tensor_affine: per tensor, asymmetric quantization
-- per_tensor_symmetric: per tensor, symmetric quantization
-- per_channel_affine: per channel, asymmetric quantization
-- per_channel_symmetric: per channel, symmetric quantization
-
-Two ways to set it. One is that the key is the quantization type, value is the quantization scheme, eg.
-
-.. code-block:: python
-
-   {
-      quant_scheme: {
-         'weight': 'per_channel_symmetric',
-         'output': 'per_tensor_affine',
-         },
-   }
-
-The other is that the value is str type, all quantization types share the same quant_scheme. eg.
-
-.. code-block:: python
-
-   {
-      quant_scheme: 'per_channel_symmetric', # the quant_scheme of weight and output quantization are all 'per_channel_symmetric'
-   }
-
-There are totally four kinds of `quant_scheme` you can set, they are 'per_tensor_affine', 'per_tensor_symmetric', 'per_channel_affine' and 'per_channel_symmetric'.
-
-The following example shows a more complete ``config_list``\ , it uses ``op_names`` (or ``op_types``\ ) to specify the target layers along with the quantization bits for those layers.
-
-.. code-block:: python
-
-   config_list = [{
-      'quant_types': ['weight'],
-      'quant_bits': 8,
-      'op_names': ['conv1'],
-      'quant_dtype': 'int',
-      'quant_scheme': 'per_channel_symmetric'
-   },
-   {
-      'quant_types': ['weight'],
-      'quant_bits': 4,
-      'quant_start_step': 0,
-      'op_names': ['conv2'],
-      'quant_dtype': 'int',
-      'quant_scheme': 'per_tensor_symmetric'
-   },
-   {
-      'quant_types': ['weight'],
-      'quant_bits': 3,
-      'op_names': ['fc1'],
-      'quant_dtype': 'int',
-      'quant_scheme': 'per_tensor_symmetric'
-   },
-   {
-      'quant_types': ['weight'],
-      'quant_bits': 2,
-      'op_names': ['fc2'],
-      'quant_dtype': 'int',
-      'quant_scheme': 'per_channel_symmetric'
-   }]
-
-In this example, 'op_names' is the name of layer and four layers will be quantized to different quant_bits.
-
-
-Export compression result
--------------------------
-
-Export the pruned model
-^^^^^^^^^^^^^^^^^^^^^^^
-
-You can easily export the pruned model using the following API if you are pruning your model, ``state_dict`` of the sparse model weights will be stored in ``model.pth``\ , which can be loaded by ``torch.load('model.pth')``. Note that, the exported ``model.pth``\ has the same parameters as the original model except the masked weights are zero. ``mask_dict`` stores the binary value that produced by the pruning algorithm, which can be further used to speed up the model.
-
-.. code-block:: python
-
-   # export model weights and mask
-   pruner.export_model(model_path='model.pth', mask_path='mask.pth')
-
-   # apply mask to model
-   from nni.compression.pytorch import apply_compression_results
-
-   apply_compression_results(model, mask_file, device)
-
-
-export model in ``onnx`` format(\ ``input_shape`` need to be specified):
-
-.. code-block:: python
-
-   pruner.export_model(model_path='model.pth', mask_path='mask.pth', onnx_path='model.onnx', input_shape=[1, 1, 28, 28])
-
-
-Export the quantized model
-^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-You can export the quantized model directly by using ``torch.save`` api and the quantized model can be loaded by ``torch.load`` without any extra modification. The following example shows the normal procedure of saving, loading quantized model and get related parameters in QAT.
-
-.. code-block:: python
-   
-   # Save quantized model which is generated by using NNI QAT algorithm
-   torch.save(model.state_dict(), "quantized_model.pth")
-
-   # Simulate model loading procedure
-   # Have to init new model and compress it before loading
-   qmodel_load = Mnist()
-   optimizer = torch.optim.SGD(qmodel_load.parameters(), lr=0.01, momentum=0.5)
-   quantizer = QAT_Quantizer(qmodel_load, config_list, optimizer)
-   quantizer.compress()
-   
-   # Load quantized model
-   qmodel_load.load_state_dict(torch.load("quantized_model.pth"))
-
-   # Get scale, zero_point and weight of conv1 in loaded model
-   conv1 = qmodel_load.conv1
-   scale = conv1.module.scale
-   zero_point = conv1.module.zero_point
-   weight = conv1.module.weight
-
-
-Speed up the model
-------------------
-
-Masks do not provide real speedup of your model. The model should be speeded up based on the exported masks, thus, we provide an API to speed up your model as shown below. After invoking ``apply_compression_results`` on your model, your model becomes a smaller one with shorter inference latency.
-
-.. code-block:: python
-
-   from nni.compression.pytorch import apply_compression_results, ModelSpeedup
-
-   dummy_input = torch.randn(config['input_shape']).to(device)
-   m_speedup = ModelSpeedup(model, dummy_input, masks_file, device)
-   m_speedup.speedup_model()
-
-
-Please refer to `here <ModelSpeedup.rst>`__ for detailed description. The example code for model speedup can be found :githublink:`here <examples/model_compress/pruning/model_speedup.py>`
-
-
-Control the Fine-tuning process
--------------------------------
-
-Enhance the fine-tuning process
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Knowledge distillation effectively learns a small student model from a large teacher model. Users can enhance the fine-tuning process that utilize knowledge distillation to improve the performance of the compressed model. Example code can be found :githublink:`here <examples/model_compress/pruning/finetune_kd_torch.py>`
diff --git a/docs/source/Compression/advanced.rst b/docs/source/Compression/advanced.rst
deleted file mode 100644
index e0df64214..000000000
--- a/docs/source/Compression/advanced.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-Advanced Usage
-==============
-
-..  toctree::
-    :maxdepth: 2
-
-    Framework <./Framework>
-    Customize a new algorithm <./CustomizeCompressor>
-    Automatic Model Compression (Beta) <./AutoCompression>
diff --git a/docs/source/Compression/advanced_zh.rst b/docs/source/Compression/advanced_zh.rst
deleted file mode 100644
index c65ebbf35..000000000
--- a/docs/source/Compression/advanced_zh.rst
+++ /dev/null
@@ -1,11 +0,0 @@
-.. acd3f66ad7c2d82b950568efcba1f175
-
-高级用法
-==============
-
-..  toctree::
-    :maxdepth: 2
-
-    框架 <./Framework>
-    自定义压缩算法 <./CustomizeCompressor>
-    自动模型压缩 (Beta) <./AutoCompression>
diff --git a/docs/source/Compression/compression_pipeline_example.ipynb b/docs/source/Compression/compression_pipeline_example.ipynb
deleted file mode 100644
index bae093e0f..000000000
--- a/docs/source/Compression/compression_pipeline_example.ipynb
+++ /dev/null
@@ -1,1281 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 1. Prepare model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "import torch.nn.functional as F\n",
-    "\n",
-    "class NaiveModel(torch.nn.Module):\n",
-    "    def __init__(self):\n",
-    "        super().__init__()\n",
-    "        self.conv1 = torch.nn.Conv2d(1, 20, 5, 1)\n",
-    "        self.conv2 = torch.nn.Conv2d(20, 50, 5, 1)\n",
-    "        self.fc1 = torch.nn.Linear(4 * 4 * 50, 500)\n",
-    "        self.fc2 = torch.nn.Linear(500, 10)\n",
-    "        self.relu1 = torch.nn.ReLU6()\n",
-    "        self.relu2 = torch.nn.ReLU6()\n",
-    "        self.relu3 = torch.nn.ReLU6()\n",
-    "        self.max_pool1 = torch.nn.MaxPool2d(2, 2)\n",
-    "        self.max_pool2 = torch.nn.MaxPool2d(2, 2)\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        x = self.relu1(self.conv1(x))\n",
-    "        x = self.max_pool1(x)\n",
-    "        x = self.relu2(self.conv2(x))\n",
-    "        x = self.max_pool2(x)\n",
-    "        x = x.view(-1, x.size()[1:].numel())\n",
-    "        x = self.relu3(self.fc1(x))\n",
-    "        x = self.fc2(x)\n",
-    "        return F.log_softmax(x, dim=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# define model, optimizer, criterion, data_loader, trainer, evaluator.\n",
-    "\n",
-    "import torch.optim as optim\n",
-    "from torchvision import datasets, transforms\n",
-    "from torch.optim.lr_scheduler import StepLR\n",
-    "\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "\n",
-    "model = NaiveModel().to(device)\n",
-    "\n",
-    "optimizer = optim.Adadelta(model.parameters(), lr=1)\n",
-    "\n",
-    "criterion = torch.nn.NLLLoss()\n",
-    "\n",
-    "transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])\n",
-    "train_dataset = datasets.MNIST('./data', train=True, download=True, transform=transform)\n",
-    "test_dataset = datasets.MNIST('./data', train=False, transform=transform)\n",
-    "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64)\n",
-    "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=1000)\n",
-    "\n",
-    "def trainer(model, optimizer, criterion, epoch):\n",
-    "    model.train()\n",
-    "    for batch_idx, (data, target) in enumerate(train_loader):\n",
-    "        data, target = data.to(device), target.to(device)\n",
-    "        optimizer.zero_grad()\n",
-    "        output = model(data)\n",
-    "        loss = criterion(output, target)\n",
-    "        loss.backward()\n",
-    "        optimizer.step()\n",
-    "        if batch_idx % 100 == 0:\n",
-    "            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
-    "                epoch, batch_idx * len(data), len(train_loader.dataset),\n",
-    "                100. * batch_idx / len(train_loader), loss.item()))\n",
-    "\n",
-    "def evaluator(model):\n",
-    "    model.eval()\n",
-    "    test_loss = 0\n",
-    "    correct = 0\n",
-    "    with torch.no_grad():\n",
-    "        for data, target in test_loader:\n",
-    "            data, target = data.to(device), target.to(device)\n",
-    "            output = model(data)\n",
-    "            test_loss += F.nll_loss(output, target, reduction='sum').item()\n",
-    "            pred = output.argmax(dim=1, keepdim=True)\n",
-    "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
-    "\n",
-    "    test_loss /= len(test_loader.dataset)\n",
-    "    acc = 100 * correct / len(test_loader.dataset)\n",
-    "\n",
-    "    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
-    "        test_loss, correct, len(test_loader.dataset), acc))\n",
-    "\n",
-    "    return acc"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train Epoch: 0 [0/60000 (0%)]\tLoss: 2.313423\n",
-      "Train Epoch: 0 [6400/60000 (11%)]\tLoss: 0.091786\n",
-      "Train Epoch: 0 [12800/60000 (21%)]\tLoss: 0.087317\n",
-      "Train Epoch: 0 [19200/60000 (32%)]\tLoss: 0.036397\n",
-      "Train Epoch: 0 [25600/60000 (43%)]\tLoss: 0.008173\n",
-      "Train Epoch: 0 [32000/60000 (53%)]\tLoss: 0.047565\n",
-      "Train Epoch: 0 [38400/60000 (64%)]\tLoss: 0.122448\n",
-      "Train Epoch: 0 [44800/60000 (75%)]\tLoss: 0.036732\n",
-      "Train Epoch: 0 [51200/60000 (85%)]\tLoss: 0.150135\n",
-      "Train Epoch: 0 [57600/60000 (96%)]\tLoss: 0.109684\n",
-      "\n",
-      "Test set: Average loss: 0.0457, Accuracy: 9857/10000 (99%)\n",
-      "\n",
-      "Train Epoch: 1 [0/60000 (0%)]\tLoss: 0.020650\n",
-      "Train Epoch: 1 [6400/60000 (11%)]\tLoss: 0.091525\n",
-      "Train Epoch: 1 [12800/60000 (21%)]\tLoss: 0.019602\n",
-      "Train Epoch: 1 [19200/60000 (32%)]\tLoss: 0.027827\n",
-      "Train Epoch: 1 [25600/60000 (43%)]\tLoss: 0.019414\n",
-      "Train Epoch: 1 [32000/60000 (53%)]\tLoss: 0.007640\n",
-      "Train Epoch: 1 [38400/60000 (64%)]\tLoss: 0.051296\n",
-      "Train Epoch: 1 [44800/60000 (75%)]\tLoss: 0.012038\n",
-      "Train Epoch: 1 [51200/60000 (85%)]\tLoss: 0.121057\n",
-      "Train Epoch: 1 [57600/60000 (96%)]\tLoss: 0.015796\n",
-      "\n",
-      "Test set: Average loss: 0.0302, Accuracy: 9902/10000 (99%)\n",
-      "\n",
-      "Train Epoch: 2 [0/60000 (0%)]\tLoss: 0.009903\n",
-      "Train Epoch: 2 [6400/60000 (11%)]\tLoss: 0.062256\n",
-      "Train Epoch: 2 [12800/60000 (21%)]\tLoss: 0.013844\n",
-      "Train Epoch: 2 [19200/60000 (32%)]\tLoss: 0.014133\n",
-      "Train Epoch: 2 [25600/60000 (43%)]\tLoss: 0.001051\n",
-      "Train Epoch: 2 [32000/60000 (53%)]\tLoss: 0.006128\n",
-      "Train Epoch: 2 [38400/60000 (64%)]\tLoss: 0.032162\n",
-      "Train Epoch: 2 [44800/60000 (75%)]\tLoss: 0.007687\n",
-      "Train Epoch: 2 [51200/60000 (85%)]\tLoss: 0.092295\n",
-      "Train Epoch: 2 [57600/60000 (96%)]\tLoss: 0.006266\n",
-      "\n",
-      "Test set: Average loss: 0.0259, Accuracy: 9920/10000 (99%)\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# pre-train model for 3 epoches.\n",
-    "\n",
-    "scheduler = StepLR(optimizer, step_size=1, gamma=0.7)\n",
-    "\n",
-    "for epoch in range(0, 3):\n",
-    "    trainer(model, optimizer, criterion, epoch)\n",
-    "    evaluator(model)\n",
-    "    scheduler.step()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "op_name: \n",
-      "op_type: <class '__main__.NaiveModel'>\n",
-      "\n",
-      "op_name: conv1\n",
-      "op_type: <class 'torch.nn.modules.conv.Conv2d'>\n",
-      "\n",
-      "op_name: conv2\n",
-      "op_type: <class 'torch.nn.modules.conv.Conv2d'>\n",
-      "\n",
-      "op_name: fc1\n",
-      "op_type: <class 'torch.nn.modules.linear.Linear'>\n",
-      "\n",
-      "op_name: fc2\n",
-      "op_type: <class 'torch.nn.modules.linear.Linear'>\n",
-      "\n",
-      "op_name: relu1\n",
-      "op_type: <class 'torch.nn.modules.activation.ReLU6'>\n",
-      "\n",
-      "op_name: relu2\n",
-      "op_type: <class 'torch.nn.modules.activation.ReLU6'>\n",
-      "\n",
-      "op_name: relu3\n",
-      "op_type: <class 'torch.nn.modules.activation.ReLU6'>\n",
-      "\n",
-      "op_name: max_pool1\n",
-      "op_type: <class 'torch.nn.modules.pooling.MaxPool2d'>\n",
-      "\n",
-      "op_name: max_pool2\n",
-      "op_type: <class 'torch.nn.modules.pooling.MaxPool2d'>\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[None, None, None, None, None, None, None, None, None, None]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# show all op_name and op_type in the model.\n",
-    "\n",
-    "[print('op_name: {}\\nop_type: {}\\n'.format(name, type(module))) for name, module in model.named_modules()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([20, 1, 5, 5])\n"
-     ]
-    }
-   ],
-   "source": [
-    "# show the weight size of `conv1`.\n",
-    "\n",
-    "print(model.conv1.weight.data.size())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tensor([[[[ 1.5338e-01, -1.1766e-01, -2.6654e-01, -2.9445e-02, -1.4650e-01],\n",
-      "          [-1.8796e-01, -2.9882e-01,  6.9725e-02,  2.1561e-01,  6.5688e-02],\n",
-      "          [ 1.5274e-01, -9.8471e-03,  3.2303e-01,  1.3472e-03,  1.7235e-01],\n",
-      "          [ 1.1804e-01,  2.2535e-01, -8.3370e-02, -3.4553e-02, -1.2529e-01],\n",
-      "          [-6.6012e-02, -2.0272e-02, -1.8797e-01, -4.6882e-02, -8.3206e-02]]],\n",
-      "\n",
-      "\n",
-      "        [[[-1.2112e-01,  7.0756e-02,  5.0446e-02,  1.5156e-01, -2.7929e-02],\n",
-      "          [-1.9744e-01, -2.1336e-03,  7.2534e-02,  6.2336e-02,  1.6039e-01],\n",
-      "          [-6.7510e-02,  1.4636e-01,  7.1972e-02, -8.9118e-02, -4.0895e-02],\n",
-      "          [ 2.9499e-02,  2.0788e-01, -1.4989e-01,  1.1668e-01, -2.8503e-01],\n",
-      "          [ 8.1894e-02, -1.4489e-01, -4.2038e-02, -1.2794e-01, -5.0379e-02]]],\n",
-      "\n",
-      "\n",
-      "        [[[ 3.8332e-02, -1.4270e-01, -1.9585e-01,  2.2653e-01,  1.0104e-01],\n",
-      "          [-2.7956e-03, -1.4108e-01, -1.4694e-01, -1.3525e-01,  2.6959e-01],\n",
-      "          [ 1.9522e-01, -1.2281e-01, -1.9173e-01, -1.8910e-02,  3.1572e-03],\n",
-      "          [-1.0580e-01, -2.5239e-02, -5.8266e-02, -6.5815e-02,  6.6433e-02],\n",
-      "          [ 8.9601e-02,  7.1189e-02, -2.4255e-01,  1.5746e-01, -1.4708e-01]]],\n",
-      "\n",
-      "\n",
-      "        [[[-1.1963e-01, -1.7243e-01, -3.5174e-02,  1.4651e-01, -1.1675e-01],\n",
-      "          [-1.3518e-01,  1.2830e-02,  7.7188e-02,  2.1060e-01,  4.0924e-02],\n",
-      "          [-4.3364e-02, -1.9579e-01, -3.6559e-02, -6.9803e-02,  1.2380e-01],\n",
-      "          [ 7.7321e-02,  3.7590e-02,  8.2935e-02,  2.2878e-01,  2.7859e-03],\n",
-      "          [-1.3601e-01, -2.1167e-01, -2.3195e-01, -1.2524e-01,  1.0073e-01]]],\n",
-      "\n",
-      "\n",
-      "        [[[-2.7300e-01,  6.8470e-02,  2.8405e-02, -4.5879e-03, -1.3735e-01],\n",
-      "          [-8.9789e-02, -2.0209e-03,  5.0950e-03,  2.1633e-01,  2.5554e-01],\n",
-      "          [ 5.4389e-02,  1.2262e-01, -1.5514e-01, -1.0416e-01,  1.3606e-01],\n",
-      "          [-1.6794e-01, -2.8876e-02,  2.5900e-02, -2.4261e-02,  1.0923e-01],\n",
-      "          [ 5.2524e-03, -4.4625e-02, -2.1327e-01, -1.7211e-01, -4.4819e-04]]],\n",
-      "\n",
-      "\n",
-      "        [[[ 7.2378e-02,  1.5122e-01, -1.2964e-01,  4.9105e-02, -2.1639e-01],\n",
-      "          [ 3.6547e-02, -1.5518e-02,  3.2059e-02, -3.2820e-02,  6.1231e-02],\n",
-      "          [ 1.2514e-01,  8.0623e-02,  1.2686e-02, -1.0074e-01,  2.2836e-02],\n",
-      "          [-2.6842e-02,  2.5578e-02, -2.5877e-01, -1.7808e-01,  7.6966e-02],\n",
-      "          [-4.2424e-02,  4.7006e-02, -1.5486e-02, -4.2686e-02,  4.8482e-02]]],\n",
-      "\n",
-      "\n",
-      "        [[[ 1.3081e-01,  9.9530e-02, -1.4729e-01, -1.7665e-01, -1.9757e-01],\n",
-      "          [ 9.6603e-02,  2.2783e-02,  7.8402e-02, -2.8679e-02,  8.5252e-02],\n",
-      "          [-1.5310e-02,  1.1605e-01, -5.8300e-02,  2.4563e-02,  1.7488e-01],\n",
-      "          [ 6.5576e-02, -1.6325e-01, -1.1318e-01, -2.9251e-02,  6.2352e-02],\n",
-      "          [-1.9084e-03, -1.4005e-01, -1.2363e-01, -9.7985e-02, -2.0562e-01]]],\n",
-      "\n",
-      "\n",
-      "        [[[ 4.0772e-02, -8.2086e-02, -2.7555e-01, -3.2547e-01, -1.2226e-01],\n",
-      "          [-5.9877e-02,  9.8567e-02,  2.5186e-01, -1.0280e-01, -2.3416e-01],\n",
-      "          [ 8.5760e-02,  1.0896e-01,  1.4898e-01,  2.1579e-01,  8.5297e-02],\n",
-      "          [ 5.4720e-02, -1.7226e-01, -7.2518e-02,  6.7099e-03, -1.6011e-03],\n",
-      "          [-8.9944e-02,  1.7404e-01, -3.6985e-02,  1.8602e-01,  7.2353e-02]]],\n",
-      "\n",
-      "\n",
-      "        [[[ 1.6276e-02, -9.6439e-02, -9.6085e-02, -2.4267e-01, -1.8521e-01],\n",
-      "          [ 6.3310e-02,  1.7866e-01,  1.1694e-01, -1.4464e-01, -2.7711e-01],\n",
-      "          [-2.4514e-02,  2.2222e-01,  2.1053e-01, -1.4271e-01,  8.7045e-02],\n",
-      "          [-1.9207e-01, -5.4719e-02, -5.7775e-03, -1.0034e-05, -1.0923e-01],\n",
-      "          [-2.4006e-02,  2.3780e-02,  1.8988e-01,  2.4734e-01,  4.8097e-02]]],\n",
-      "\n",
-      "\n",
-      "        [[[ 1.1335e-01, -5.8451e-02,  5.2440e-02, -1.3223e-01, -2.5534e-02],\n",
-      "          [ 9.1323e-02, -6.0707e-02,  2.3524e-01,  2.4992e-01,  8.7842e-02],\n",
-      "          [ 2.9002e-02,  3.5379e-02, -5.9689e-02, -2.8363e-03,  1.8618e-01],\n",
-      "          [-2.9671e-01,  8.1830e-03,  1.1076e-01, -5.4118e-02, -6.1685e-02],\n",
-      "          [-1.7580e-01, -3.4534e-01, -3.9250e-01, -2.7569e-01, -2.6131e-01]]],\n",
-      "\n",
-      "\n",
-      "        [[[ 1.1586e-01, -7.5997e-02, -1.4614e-01,  4.8750e-02,  1.8097e-01],\n",
-      "          [-6.7027e-02, -1.4901e-01, -1.5614e-02, -1.0379e-02,  9.5526e-02],\n",
-      "          [-3.2333e-02, -1.5107e-01, -1.9498e-01,  1.0083e-01,  2.2328e-01],\n",
-      "          [-2.0692e-01, -6.3798e-02, -1.2524e-01,  1.9549e-01,  1.9682e-01],\n",
-      "          [-2.1494e-01,  1.0475e-01, -2.4858e-02, -9.7831e-02,  1.1551e-01]]],\n",
-      "\n",
-      "\n",
-      "        [[[ 6.3785e-02, -1.8044e-01, -1.0190e-01, -1.3588e-01,  8.5433e-02],\n",
-      "          [ 2.0675e-01,  3.3238e-02,  9.2437e-02,  1.1799e-01,  2.1111e-01],\n",
-      "          [-5.2138e-02,  1.5790e-01,  1.8151e-01,  8.0470e-02,  1.0131e-01],\n",
-      "          [-4.4786e-02,  1.1771e-01,  2.1706e-02, -1.2563e-01, -2.1142e-01],\n",
-      "          [-2.3589e-01, -2.1154e-01, -1.7890e-01, -2.7769e-01, -1.2512e-01]]],\n",
-      "\n",
-      "\n",
-      "        [[[ 1.9133e-01,  2.4711e-01,  1.0413e-01, -1.9187e-01, -3.0991e-01],\n",
-      "          [-1.2382e-01,  8.3641e-03, -5.6734e-02,  5.8376e-02,  2.2880e-02],\n",
-      "          [-3.1734e-01, -1.0637e-02, -5.5974e-02,  1.0676e-01, -1.1080e-02],\n",
-      "          [-2.2980e-01,  2.0486e-01,  1.0147e-01,  1.4484e-01,  5.2265e-02],\n",
-      "          [ 7.4410e-02,  2.2806e-02,  8.5137e-02, -2.1809e-01,  3.1704e-02]]],\n",
-      "\n",
-      "\n",
-      "        [[[-1.1006e-01, -2.5311e-01,  1.8925e-02,  1.0399e-02,  1.1951e-01],\n",
-      "          [-2.1116e-01,  1.8409e-01,  3.2172e-02,  1.5962e-01, -7.9457e-02],\n",
-      "          [ 1.1059e-01,  9.1966e-02,  1.0777e-01, -9.9132e-02, -4.4586e-02],\n",
-      "          [-8.7919e-02, -3.7283e-02,  9.1275e-02, -3.7412e-02,  3.8875e-02],\n",
-      "          [-4.3558e-02,  1.6196e-01, -4.7944e-03, -1.7560e-02, -1.2593e-01]]],\n",
-      "\n",
-      "\n",
-      "        [[[ 7.6976e-02, -3.8627e-02,  1.2610e-01,  1.1994e-01,  2.1706e-03],\n",
-      "          [ 7.4357e-02,  6.7929e-02,  3.1386e-02,  1.4606e-01,  2.1429e-01],\n",
-      "          [-2.6569e-01, -4.2631e-04, -3.6654e-02, -3.0967e-02, -9.4961e-02],\n",
-      "          [-2.0192e-01, -3.5423e-01, -2.5246e-01, -3.5092e-01, -2.4159e-01],\n",
-      "          [ 1.7636e-02,  1.3744e-01, -1.0306e-01,  8.8370e-02,  7.3258e-02]]],\n",
-      "\n",
-      "\n",
-      "        [[[ 2.0016e-01,  1.0956e-01, -5.9223e-02,  6.4871e-03, -2.4165e-01],\n",
-      "          [ 5.6283e-02,  1.7276e-01, -2.2316e-01, -1.6699e-01, -7.0742e-02],\n",
-      "          [ 2.6179e-01, -2.5102e-01, -2.0774e-01, -9.6413e-02,  3.4367e-02],\n",
-      "          [-9.1882e-02, -2.9195e-01, -8.7432e-02,  1.0144e-01, -2.0559e-02],\n",
-      "          [-2.5668e-01, -9.8016e-02,  1.1103e-01, -3.0233e-02,  1.1076e-01]]],\n",
-      "\n",
-      "\n",
-      "        [[[ 1.0027e-03, -5.7955e-02, -2.1339e-01, -1.6729e-01, -2.0870e-01],\n",
-      "          [ 4.2464e-02,  2.3177e-01, -6.1459e-02, -1.0905e-01,  1.7613e-02],\n",
-      "          [-1.2282e-01,  2.1762e-01, -1.3553e-02,  2.7476e-01,  1.6703e-01],\n",
-      "          [-5.6282e-02,  1.2731e-02,  1.0944e-01, -1.7347e-01,  4.4497e-02],\n",
-      "          [ 5.7346e-02, -5.4657e-02,  4.8718e-02, -2.6221e-02, -2.6933e-02]]],\n",
-      "\n",
-      "\n",
-      "        [[[ 6.7697e-02,  1.5692e-01,  2.7050e-01,  1.5936e-02,  1.7659e-01],\n",
-      "          [-2.8899e-02, -1.4866e-01,  3.1838e-02,  1.0903e-01,  1.2292e-01],\n",
-      "          [-1.3608e-01, -4.3198e-03, -9.8925e-02, -4.5599e-02,  1.3452e-01],\n",
-      "          [-5.1435e-02, -2.3815e-01, -2.4151e-01, -4.8556e-02,  1.3825e-01],\n",
-      "          [-1.2823e-01,  8.9324e-03, -1.5313e-01, -2.2933e-01, -3.4081e-02]]],\n",
-      "\n",
-      "\n",
-      "        [[[-1.8396e-01, -6.8774e-03, -1.6675e-01,  7.1980e-03,  1.9922e-02],\n",
-      "          [ 1.3416e-01, -1.1450e-01, -1.5277e-01, -6.5713e-02, -9.5435e-02],\n",
-      "          [ 1.5406e-01, -9.1235e-02, -1.0880e-01, -7.1603e-02, -9.5575e-02],\n",
-      "          [ 2.1772e-01,  8.4073e-02, -2.5264e-01, -2.1428e-01,  1.9537e-01],\n",
-      "          [ 1.3124e-01,  7.9532e-02, -2.4044e-01, -1.5717e-01,  1.6562e-01]]],\n",
-      "\n",
-      "\n",
-      "        [[[ 1.1849e-01, -5.0517e-03, -1.8900e-01,  1.8093e-02,  6.4660e-02],\n",
-      "          [-1.5309e-01, -2.0106e-01, -8.6551e-02,  5.2692e-03,  1.5448e-01],\n",
-      "          [-3.0727e-01,  4.9703e-02, -4.7637e-02,  2.9111e-01, -1.3173e-01],\n",
-      "          [-8.5167e-02, -1.3540e-01,  2.9235e-01,  3.7895e-03, -9.4651e-02],\n",
-      "          [-6.0694e-02,  9.6936e-02,  1.0533e-01, -6.1769e-02, -1.8086e-01]]]],\n",
-      "       device='cuda:0')\n"
-     ]
-    }
-   ],
-   "source": [
-    "# show the weight of `conv1`.\n",
-    "\n",
-    "print(model.conv1.weight.data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 2. Prepare config_list for pruning"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# we will prune 50% weights in `conv1`.\n",
-    "\n",
-    "config_list = [{\n",
-    "    'sparsity': 0.5,\n",
-    "    'op_types': ['Conv2d'],\n",
-    "    'op_names': ['conv1']\n",
-    "}]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 3. Choose a pruner and pruning"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# use l1filter pruner to prune the model\n",
-    "\n",
-    "from nni.algorithms.compression.pytorch.pruning import L1FilterPruner\n",
-    "\n",
-    "# Note that if you use a compressor that need you to pass a optimizer,\n",
-    "# you need a new optimizer instead of you have used above, because NNI might modify the optimizer.\n",
-    "# And of course this modified optimizer can not be used in finetuning.\n",
-    "pruner = L1FilterPruner(model, config_list)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "op_name: \n",
-      "op_type: <class '__main__.NaiveModel'>\n",
-      "\n",
-      "op_name: conv1\n",
-      "op_type: <class 'nni.compression.pytorch.compressor.PrunerModuleWrapper'>\n",
-      "\n",
-      "op_name: conv1.module\n",
-      "op_type: <class 'torch.nn.modules.conv.Conv2d'>\n",
-      "\n",
-      "op_name: conv2\n",
-      "op_type: <class 'torch.nn.modules.conv.Conv2d'>\n",
-      "\n",
-      "op_name: fc1\n",
-      "op_type: <class 'torch.nn.modules.linear.Linear'>\n",
-      "\n",
-      "op_name: fc2\n",
-      "op_type: <class 'torch.nn.modules.linear.Linear'>\n",
-      "\n",
-      "op_name: relu1\n",
-      "op_type: <class 'torch.nn.modules.activation.ReLU6'>\n",
-      "\n",
-      "op_name: relu2\n",
-      "op_type: <class 'torch.nn.modules.activation.ReLU6'>\n",
-      "\n",
-      "op_name: relu3\n",
-      "op_type: <class 'torch.nn.modules.activation.ReLU6'>\n",
-      "\n",
-      "op_name: max_pool1\n",
-      "op_type: <class 'torch.nn.modules.pooling.MaxPool2d'>\n",
-      "\n",
-      "op_name: max_pool2\n",
-      "op_type: <class 'torch.nn.modules.pooling.MaxPool2d'>\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[None, None, None, None, None, None, None, None, None, None, None]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# we can find the `conv1` has been wrapped, the origin `conv1` changes to `conv1.module`.\n",
-    "# the weight of conv1 will modify by `weight * mask` in `forward()`. The initial mask is a `ones_like(weight)` tensor.\n",
-    "\n",
-    "[print('op_name: {}\\nop_type: {}\\n'.format(name, type(module))) for name, module in model.named_modules()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "NaiveModel(\n",
-       "  (conv1): PrunerModuleWrapper(\n",
-       "    (module): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))\n",
-       "  )\n",
-       "  (conv2): Conv2d(20, 50, kernel_size=(5, 5), stride=(1, 1))\n",
-       "  (fc1): Linear(in_features=800, out_features=500, bias=True)\n",
-       "  (fc2): Linear(in_features=500, out_features=10, bias=True)\n",
-       "  (relu1): ReLU6()\n",
-       "  (relu2): ReLU6()\n",
-       "  (relu3): ReLU6()\n",
-       "  (max_pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
-       "  (max_pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
-       ")"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# compress the model, the mask will be updated.\n",
-    "\n",
-    "pruner.compress()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([20, 1, 5, 5])\n"
-     ]
-    }
-   ],
-   "source": [
-    "# show the mask size of `conv1`\n",
-    "\n",
-    "print(model.conv1.weight_mask.size())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tensor([[[[1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.]]],\n",
-      "\n",
-      "\n",
-      "        [[[0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.]]],\n",
-      "\n",
-      "\n",
-      "        [[[1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.]]],\n",
-      "\n",
-      "\n",
-      "        [[[0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.]]],\n",
-      "\n",
-      "\n",
-      "        [[[0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.]]],\n",
-      "\n",
-      "\n",
-      "        [[[0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.]]],\n",
-      "\n",
-      "\n",
-      "        [[[0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.]]],\n",
-      "\n",
-      "\n",
-      "        [[[1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.]]],\n",
-      "\n",
-      "\n",
-      "        [[[1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.]]],\n",
-      "\n",
-      "\n",
-      "        [[[1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.]]],\n",
-      "\n",
-      "\n",
-      "        [[[0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.]]],\n",
-      "\n",
-      "\n",
-      "        [[[1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.]]],\n",
-      "\n",
-      "\n",
-      "        [[[1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.]]],\n",
-      "\n",
-      "\n",
-      "        [[[0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.]]],\n",
-      "\n",
-      "\n",
-      "        [[[1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.]]],\n",
-      "\n",
-      "\n",
-      "        [[[1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.]]],\n",
-      "\n",
-      "\n",
-      "        [[[0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.]]],\n",
-      "\n",
-      "\n",
-      "        [[[0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.]]],\n",
-      "\n",
-      "\n",
-      "        [[[1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.],\n",
-      "          [1., 1., 1., 1., 1.]]],\n",
-      "\n",
-      "\n",
-      "        [[[0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.],\n",
-      "          [0., 0., 0., 0., 0.]]]], device='cuda:0')\n"
-     ]
-    }
-   ],
-   "source": [
-    "# show the mask of `conv1`\n",
-    "\n",
-    "print(model.conv1.weight_mask)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tensor([[[[ 1.5338e-01, -1.1766e-01, -2.6654e-01, -2.9445e-02, -1.4650e-01],\n",
-      "          [-1.8796e-01, -2.9882e-01,  6.9725e-02,  2.1561e-01,  6.5688e-02],\n",
-      "          [ 1.5274e-01, -9.8471e-03,  3.2303e-01,  1.3472e-03,  1.7235e-01],\n",
-      "          [ 1.1804e-01,  2.2535e-01, -8.3370e-02, -3.4553e-02, -1.2529e-01],\n",
-      "          [-6.6012e-02, -2.0272e-02, -1.8797e-01, -4.6882e-02, -8.3206e-02]]],\n",
-      "\n",
-      "\n",
-      "        [[[-0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00, -0.0000e+00],\n",
-      "          [-0.0000e+00, -0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
-      "          [-0.0000e+00,  0.0000e+00,  0.0000e+00, -0.0000e+00, -0.0000e+00],\n",
-      "          [ 0.0000e+00,  0.0000e+00, -0.0000e+00,  0.0000e+00, -0.0000e+00],\n",
-      "          [ 0.0000e+00, -0.0000e+00, -0.0000e+00, -0.0000e+00, -0.0000e+00]]],\n",
-      "\n",
-      "\n",
-      "        [[[ 3.8332e-02, -1.4270e-01, -1.9585e-01,  2.2653e-01,  1.0104e-01],\n",
-      "          [-2.7956e-03, -1.4108e-01, -1.4694e-01, -1.3525e-01,  2.6959e-01],\n",
-      "          [ 1.9522e-01, -1.2281e-01, -1.9173e-01, -1.8910e-02,  3.1572e-03],\n",
-      "          [-1.0580e-01, -2.5239e-02, -5.8266e-02, -6.5815e-02,  6.6433e-02],\n",
-      "          [ 8.9601e-02,  7.1189e-02, -2.4255e-01,  1.5746e-01, -1.4708e-01]]],\n",
-      "\n",
-      "\n",
-      "        [[[-0.0000e+00, -0.0000e+00, -0.0000e+00,  0.0000e+00, -0.0000e+00],\n",
-      "          [-0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
-      "          [-0.0000e+00, -0.0000e+00, -0.0000e+00, -0.0000e+00,  0.0000e+00],\n",
-      "          [ 0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
-      "          [-0.0000e+00, -0.0000e+00, -0.0000e+00, -0.0000e+00,  0.0000e+00]]],\n",
-      "\n",
-      "\n",
-      "        [[[-0.0000e+00,  0.0000e+00,  0.0000e+00, -0.0000e+00, -0.0000e+00],\n",
-      "          [-0.0000e+00, -0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
-      "          [ 0.0000e+00,  0.0000e+00, -0.0000e+00, -0.0000e+00,  0.0000e+00],\n",
-      "          [-0.0000e+00, -0.0000e+00,  0.0000e+00, -0.0000e+00,  0.0000e+00],\n",
-      "          [ 0.0000e+00, -0.0000e+00, -0.0000e+00, -0.0000e+00, -0.0000e+00]]],\n",
-      "\n",
-      "\n",
-      "        [[[ 0.0000e+00,  0.0000e+00, -0.0000e+00,  0.0000e+00, -0.0000e+00],\n",
-      "          [ 0.0000e+00, -0.0000e+00,  0.0000e+00, -0.0000e+00,  0.0000e+00],\n",
-      "          [ 0.0000e+00,  0.0000e+00,  0.0000e+00, -0.0000e+00,  0.0000e+00],\n",
-      "          [-0.0000e+00,  0.0000e+00, -0.0000e+00, -0.0000e+00,  0.0000e+00],\n",
-      "          [-0.0000e+00,  0.0000e+00, -0.0000e+00, -0.0000e+00,  0.0000e+00]]],\n",
-      "\n",
-      "\n",
-      "        [[[ 0.0000e+00,  0.0000e+00, -0.0000e+00, -0.0000e+00, -0.0000e+00],\n",
-      "          [ 0.0000e+00,  0.0000e+00,  0.0000e+00, -0.0000e+00,  0.0000e+00],\n",
-      "          [-0.0000e+00,  0.0000e+00, -0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
-      "          [ 0.0000e+00, -0.0000e+00, -0.0000e+00, -0.0000e+00,  0.0000e+00],\n",
-      "          [-0.0000e+00, -0.0000e+00, -0.0000e+00, -0.0000e+00, -0.0000e+00]]],\n",
-      "\n",
-      "\n",
-      "        [[[ 4.0772e-02, -8.2086e-02, -2.7555e-01, -3.2547e-01, -1.2226e-01],\n",
-      "          [-5.9877e-02,  9.8567e-02,  2.5186e-01, -1.0280e-01, -2.3416e-01],\n",
-      "          [ 8.5760e-02,  1.0896e-01,  1.4898e-01,  2.1579e-01,  8.5297e-02],\n",
-      "          [ 5.4720e-02, -1.7226e-01, -7.2518e-02,  6.7099e-03, -1.6011e-03],\n",
-      "          [-8.9944e-02,  1.7404e-01, -3.6985e-02,  1.8602e-01,  7.2353e-02]]],\n",
-      "\n",
-      "\n",
-      "        [[[ 1.6276e-02, -9.6439e-02, -9.6085e-02, -2.4267e-01, -1.8521e-01],\n",
-      "          [ 6.3310e-02,  1.7866e-01,  1.1694e-01, -1.4464e-01, -2.7711e-01],\n",
-      "          [-2.4514e-02,  2.2222e-01,  2.1053e-01, -1.4271e-01,  8.7045e-02],\n",
-      "          [-1.9207e-01, -5.4719e-02, -5.7775e-03, -1.0034e-05, -1.0923e-01],\n",
-      "          [-2.4006e-02,  2.3780e-02,  1.8988e-01,  2.4734e-01,  4.8097e-02]]],\n",
-      "\n",
-      "\n",
-      "        [[[ 1.1335e-01, -5.8451e-02,  5.2440e-02, -1.3223e-01, -2.5534e-02],\n",
-      "          [ 9.1323e-02, -6.0707e-02,  2.3524e-01,  2.4992e-01,  8.7842e-02],\n",
-      "          [ 2.9002e-02,  3.5379e-02, -5.9689e-02, -2.8363e-03,  1.8618e-01],\n",
-      "          [-2.9671e-01,  8.1830e-03,  1.1076e-01, -5.4118e-02, -6.1685e-02],\n",
-      "          [-1.7580e-01, -3.4534e-01, -3.9250e-01, -2.7569e-01, -2.6131e-01]]],\n",
-      "\n",
-      "\n",
-      "        [[[ 0.0000e+00, -0.0000e+00, -0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
-      "          [-0.0000e+00, -0.0000e+00, -0.0000e+00, -0.0000e+00,  0.0000e+00],\n",
-      "          [-0.0000e+00, -0.0000e+00, -0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
-      "          [-0.0000e+00, -0.0000e+00, -0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
-      "          [-0.0000e+00,  0.0000e+00, -0.0000e+00, -0.0000e+00,  0.0000e+00]]],\n",
-      "\n",
-      "\n",
-      "        [[[ 6.3785e-02, -1.8044e-01, -1.0190e-01, -1.3588e-01,  8.5433e-02],\n",
-      "          [ 2.0675e-01,  3.3238e-02,  9.2437e-02,  1.1799e-01,  2.1111e-01],\n",
-      "          [-5.2138e-02,  1.5790e-01,  1.8151e-01,  8.0470e-02,  1.0131e-01],\n",
-      "          [-4.4786e-02,  1.1771e-01,  2.1706e-02, -1.2563e-01, -2.1142e-01],\n",
-      "          [-2.3589e-01, -2.1154e-01, -1.7890e-01, -2.7769e-01, -1.2512e-01]]],\n",
-      "\n",
-      "\n",
-      "        [[[ 1.9133e-01,  2.4711e-01,  1.0413e-01, -1.9187e-01, -3.0991e-01],\n",
-      "          [-1.2382e-01,  8.3641e-03, -5.6734e-02,  5.8376e-02,  2.2880e-02],\n",
-      "          [-3.1734e-01, -1.0637e-02, -5.5974e-02,  1.0676e-01, -1.1080e-02],\n",
-      "          [-2.2980e-01,  2.0486e-01,  1.0147e-01,  1.4484e-01,  5.2265e-02],\n",
-      "          [ 7.4410e-02,  2.2806e-02,  8.5137e-02, -2.1809e-01,  3.1704e-02]]],\n",
-      "\n",
-      "\n",
-      "        [[[-0.0000e+00, -0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
-      "          [-0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00, -0.0000e+00],\n",
-      "          [ 0.0000e+00,  0.0000e+00,  0.0000e+00, -0.0000e+00, -0.0000e+00],\n",
-      "          [-0.0000e+00, -0.0000e+00,  0.0000e+00, -0.0000e+00,  0.0000e+00],\n",
-      "          [-0.0000e+00,  0.0000e+00, -0.0000e+00, -0.0000e+00, -0.0000e+00]]],\n",
-      "\n",
-      "\n",
-      "        [[[ 7.6976e-02, -3.8627e-02,  1.2610e-01,  1.1994e-01,  2.1706e-03],\n",
-      "          [ 7.4357e-02,  6.7929e-02,  3.1386e-02,  1.4606e-01,  2.1429e-01],\n",
-      "          [-2.6569e-01, -4.2631e-04, -3.6654e-02, -3.0967e-02, -9.4961e-02],\n",
-      "          [-2.0192e-01, -3.5423e-01, -2.5246e-01, -3.5092e-01, -2.4159e-01],\n",
-      "          [ 1.7636e-02,  1.3744e-01, -1.0306e-01,  8.8370e-02,  7.3258e-02]]],\n",
-      "\n",
-      "\n",
-      "        [[[ 2.0016e-01,  1.0956e-01, -5.9223e-02,  6.4871e-03, -2.4165e-01],\n",
-      "          [ 5.6283e-02,  1.7276e-01, -2.2316e-01, -1.6699e-01, -7.0742e-02],\n",
-      "          [ 2.6179e-01, -2.5102e-01, -2.0774e-01, -9.6413e-02,  3.4367e-02],\n",
-      "          [-9.1882e-02, -2.9195e-01, -8.7432e-02,  1.0144e-01, -2.0559e-02],\n",
-      "          [-2.5668e-01, -9.8016e-02,  1.1103e-01, -3.0233e-02,  1.1076e-01]]],\n",
-      "\n",
-      "\n",
-      "        [[[ 0.0000e+00, -0.0000e+00, -0.0000e+00, -0.0000e+00, -0.0000e+00],\n",
-      "          [ 0.0000e+00,  0.0000e+00, -0.0000e+00, -0.0000e+00,  0.0000e+00],\n",
-      "          [-0.0000e+00,  0.0000e+00, -0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
-      "          [-0.0000e+00,  0.0000e+00,  0.0000e+00, -0.0000e+00,  0.0000e+00],\n",
-      "          [ 0.0000e+00, -0.0000e+00,  0.0000e+00, -0.0000e+00, -0.0000e+00]]],\n",
-      "\n",
-      "\n",
-      "        [[[ 0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
-      "          [-0.0000e+00, -0.0000e+00,  0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
-      "          [-0.0000e+00, -0.0000e+00, -0.0000e+00, -0.0000e+00,  0.0000e+00],\n",
-      "          [-0.0000e+00, -0.0000e+00, -0.0000e+00, -0.0000e+00,  0.0000e+00],\n",
-      "          [-0.0000e+00,  0.0000e+00, -0.0000e+00, -0.0000e+00, -0.0000e+00]]],\n",
-      "\n",
-      "\n",
-      "        [[[-1.8396e-01, -6.8774e-03, -1.6675e-01,  7.1980e-03,  1.9922e-02],\n",
-      "          [ 1.3416e-01, -1.1450e-01, -1.5277e-01, -6.5713e-02, -9.5435e-02],\n",
-      "          [ 1.5406e-01, -9.1235e-02, -1.0880e-01, -7.1603e-02, -9.5575e-02],\n",
-      "          [ 2.1772e-01,  8.4073e-02, -2.5264e-01, -2.1428e-01,  1.9537e-01],\n",
-      "          [ 1.3124e-01,  7.9532e-02, -2.4044e-01, -1.5717e-01,  1.6562e-01]]],\n",
-      "\n",
-      "\n",
-      "        [[[ 0.0000e+00, -0.0000e+00, -0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
-      "          [-0.0000e+00, -0.0000e+00, -0.0000e+00,  0.0000e+00,  0.0000e+00],\n",
-      "          [-0.0000e+00,  0.0000e+00, -0.0000e+00,  0.0000e+00, -0.0000e+00],\n",
-      "          [-0.0000e+00, -0.0000e+00,  0.0000e+00,  0.0000e+00, -0.0000e+00],\n",
-      "          [-0.0000e+00,  0.0000e+00,  0.0000e+00, -0.0000e+00, -0.0000e+00]]]],\n",
-      "       device='cuda:0')\n"
-     ]
-    }
-   ],
-   "source": [
-    "# use a dummy input to apply the sparsify.\n",
-    "\n",
-    "model(torch.rand(1, 1, 28, 28).to(device))\n",
-    "\n",
-    "# the weights of `conv1` have been sparsified.\n",
-    "\n",
-    "print(model.conv1.module.weight.data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[2021-07-26 22:26:05] INFO (nni.compression.pytorch.compressor/MainThread) Model state_dict saved to pruned_naive_mnist_l1filter.pth\n",
-      "[2021-07-26 22:26:05] INFO (nni.compression.pytorch.compressor/MainThread) Mask dict saved to mask_naive_mnist_l1filter.pth\n"
-     ]
-    }
-   ],
-   "source": [
-    "# export the sparsified model state to './pruned_naive_mnist_l1filter.pth'.\n",
-    "# export the mask to './mask_naive_mnist_l1filter.pth'.\n",
-    "\n",
-    "pruner.export_model(model_path='pruned_naive_mnist_l1filter.pth', mask_path='mask_naive_mnist_l1filter.pth')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 4. Speed Up"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "NaiveModel(\n",
-      "  (conv1): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))\n",
-      "  (conv2): Conv2d(20, 50, kernel_size=(5, 5), stride=(1, 1))\n",
-      "  (fc1): Linear(in_features=800, out_features=500, bias=True)\n",
-      "  (fc2): Linear(in_features=500, out_features=10, bias=True)\n",
-      "  (relu1): ReLU6()\n",
-      "  (relu2): ReLU6()\n",
-      "  (relu3): ReLU6()\n",
-      "  (max_pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
-      "  (max_pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
-      ")\n"
-     ]
-    }
-   ],
-   "source": [
-    "# If you use a wrapped model, don't forget to unwrap it.\n",
-    "\n",
-    "pruner._unwrap_model()\n",
-    "\n",
-    "# the model has been unwrapped.\n",
-    "\n",
-    "print(model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-1-0f2a9eb92f42>:22: TracerWarning: Converting a tensor to a Python index might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
-      "  x = x.view(-1, x.size()[1:].numel())\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) start to speed up the model\n",
-      "[2021-07-26 22:26:18] INFO (FixMaskConflict/MainThread) {'conv1': 1, 'conv2': 1}\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[2021-07-26 22:26:18] INFO (FixMaskConflict/MainThread) dim0 sparsity: 0.500000\n",
-      "[2021-07-26 22:26:18] INFO (FixMaskConflict/MainThread) dim1 sparsity: 0.000000\n",
-      "[2021-07-26 22:26:18] INFO (FixMaskConflict/MainThread) Dectected conv prune dim\" 0\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) infer module masks...\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Update mask for conv1\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Update mask for relu1\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Update mask for max_pool1\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Update mask for conv2\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Update mask for relu2\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Update mask for max_pool2\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Update mask for .aten::view.9\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.jit_translate/MainThread) View Module output size: [-1, 800]\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Update mask for fc1\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Update mask for relu3\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Update mask for fc2\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Update mask for .aten::log_softmax.10\n",
-      "[2021-07-26 22:26:18] ERROR (nni.compression.pytorch.speedup.jit_translate/MainThread) aten::log_softmax is not Supported! Please report an issue at https://github.com/microsoft/nni. Thanks~\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Update indirect sparsity for .aten::log_softmax.10\n",
-      "[2021-07-26 22:26:18] WARNING (nni.compression.pytorch.speedup.compressor/MainThread) Note: .aten::log_softmax.10 does not have corresponding mask inference object\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Update indirect sparsity for fc2\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Update the indirect sparsity for the fc2\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Update indirect sparsity for relu3\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Update the indirect sparsity for the relu3\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Update indirect sparsity for fc1\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Update the indirect sparsity for the fc1\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Update indirect sparsity for .aten::view.9\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Update the indirect sparsity for the .aten::view.9\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Update indirect sparsity for max_pool2\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Update the indirect sparsity for the max_pool2\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Update indirect sparsity for relu2\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Update the indirect sparsity for the relu2\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Update indirect sparsity for conv2\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Update the indirect sparsity for the conv2\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Update indirect sparsity for max_pool1\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Update the indirect sparsity for the max_pool1\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Update indirect sparsity for relu1\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Update the indirect sparsity for the relu1\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Update indirect sparsity for conv1\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Update the indirect sparsity for the conv1\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) resolve the mask conflict\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) replace compressed modules...\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) replace module (name: conv1, op_type: Conv2d)\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) replace module (name: relu1, op_type: ReLU6)\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) replace module (name: max_pool1, op_type: MaxPool2d)\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) replace module (name: conv2, op_type: Conv2d)\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) replace module (name: relu2, op_type: ReLU6)\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) replace module (name: max_pool2, op_type: MaxPool2d)\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Warning: cannot replace (name: .aten::view.9, op_type: aten::view) which is func type\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) replace module (name: fc1, op_type: Linear)\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compress_modules/MainThread) replace linear with new in_features: 800, out_features: 500\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) replace module (name: relu3, op_type: ReLU6)\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) replace module (name: fc2, op_type: Linear)\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compress_modules/MainThread) replace linear with new in_features: 500, out_features: 10\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) Warning: cannot replace (name: .aten::log_softmax.10, op_type: aten::log_softmax) which is func type\n",
-      "[2021-07-26 22:26:18] INFO (nni.compression.pytorch.speedup.compressor/MainThread) speedup done\n"
-     ]
-    }
-   ],
-   "source": [
-    "from nni.compression.pytorch import ModelSpeedup\n",
-    "\n",
-    "m_speedup = ModelSpeedup(model, dummy_input=torch.rand(10, 1, 28, 28).to(device), masks_file='mask_naive_mnist_l1filter.pth')\n",
-    "m_speedup.speedup_model()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "NaiveModel(\n",
-      "  (conv1): Conv2d(1, 10, kernel_size=(5, 5), stride=(1, 1))\n",
-      "  (conv2): Conv2d(10, 50, kernel_size=(5, 5), stride=(1, 1))\n",
-      "  (fc1): Linear(in_features=800, out_features=500, bias=True)\n",
-      "  (fc2): Linear(in_features=500, out_features=10, bias=True)\n",
-      "  (relu1): ReLU6()\n",
-      "  (relu2): ReLU6()\n",
-      "  (relu3): ReLU6()\n",
-      "  (max_pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
-      "  (max_pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
-      ")\n"
-     ]
-    }
-   ],
-   "source": [
-    "# the `conv1` has been replace from `Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))` to `Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))`\n",
-    "# and the following layer `conv2` has also changed because the input channel of `conv2` should aware the output channel of `conv1`.\n",
-    "\n",
-    "print(model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train Epoch: 0 [0/60000 (0%)]\tLoss: 0.306930\n",
-      "Train Epoch: 0 [6400/60000 (11%)]\tLoss: 0.045807\n",
-      "Train Epoch: 0 [12800/60000 (21%)]\tLoss: 0.049293\n",
-      "Train Epoch: 0 [19200/60000 (32%)]\tLoss: 0.031464\n",
-      "Train Epoch: 0 [25600/60000 (43%)]\tLoss: 0.005392\n",
-      "Train Epoch: 0 [32000/60000 (53%)]\tLoss: 0.005652\n",
-      "Train Epoch: 0 [38400/60000 (64%)]\tLoss: 0.040619\n",
-      "Train Epoch: 0 [44800/60000 (75%)]\tLoss: 0.016515\n",
-      "Train Epoch: 0 [51200/60000 (85%)]\tLoss: 0.092886\n",
-      "Train Epoch: 0 [57600/60000 (96%)]\tLoss: 0.041380\n",
-      "\n",
-      "Test set: Average loss: 0.0257, Accuracy: 9917/10000 (99%)\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# finetune the model to recover the accuracy.\n",
-    "\n",
-    "optimizer = torch.optim.SGD(model.parameters(), lr=0.01)\n",
-    "\n",
-    "for epoch in range(0, 1):\n",
-    "    trainer(model, optimizer, criterion, epoch)\n",
-    "    evaluator(model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 5. Prepare config_list for quantization"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "config_list = [{\n",
-    "    'quant_types': ['weight', 'input'],\n",
-    "    'quant_bits': {'weight': 8, 'input': 8},\n",
-    "    'op_names': ['conv1', 'conv2']\n",
-    "}]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 6. Choose a quantizer and quantizing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "NaiveModel(\n",
-       "  (conv1): QuantizerModuleWrapper(\n",
-       "    (module): Conv2d(1, 10, kernel_size=(5, 5), stride=(1, 1))\n",
-       "  )\n",
-       "  (conv2): QuantizerModuleWrapper(\n",
-       "    (module): Conv2d(10, 50, kernel_size=(5, 5), stride=(1, 1))\n",
-       "  )\n",
-       "  (fc1): Linear(in_features=800, out_features=500, bias=True)\n",
-       "  (fc2): Linear(in_features=500, out_features=10, bias=True)\n",
-       "  (relu1): ReLU6()\n",
-       "  (relu2): ReLU6()\n",
-       "  (relu3): ReLU6()\n",
-       "  (max_pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
-       "  (max_pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
-       ")"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from nni.algorithms.compression.pytorch.quantization import QAT_Quantizer\n",
-    "\n",
-    "quantizer = QAT_Quantizer(model, config_list, optimizer)\n",
-    "quantizer.compress()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train Epoch: 0 [0/60000 (0%)]\tLoss: 0.004960\n",
-      "Train Epoch: 0 [6400/60000 (11%)]\tLoss: 0.036269\n",
-      "Train Epoch: 0 [12800/60000 (21%)]\tLoss: 0.018744\n",
-      "Train Epoch: 0 [19200/60000 (32%)]\tLoss: 0.021916\n",
-      "Train Epoch: 0 [25600/60000 (43%)]\tLoss: 0.003095\n",
-      "Train Epoch: 0 [32000/60000 (53%)]\tLoss: 0.003947\n",
-      "Train Epoch: 0 [38400/60000 (64%)]\tLoss: 0.032094\n",
-      "Train Epoch: 0 [44800/60000 (75%)]\tLoss: 0.017358\n",
-      "Train Epoch: 0 [51200/60000 (85%)]\tLoss: 0.083886\n",
-      "Train Epoch: 0 [57600/60000 (96%)]\tLoss: 0.040433\n",
-      "\n",
-      "Test set: Average loss: 0.0247, Accuracy: 9917/10000 (99%)\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# finetune the model for calibration.\n",
-    "\n",
-    "for epoch in range(0, 1):\n",
-    "    trainer(model, optimizer, criterion, epoch)\n",
-    "    evaluator(model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[2021-07-26 22:34:41] INFO (nni.compression.pytorch.compressor/MainThread) Model state_dict saved to quantized_naive_mnist_l1filter.pth\n",
-      "[2021-07-26 22:34:41] INFO (nni.compression.pytorch.compressor/MainThread) Mask dict saved to calibration_naive_mnist_l1filter.pth\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'conv1': {'weight_bit': 8,\n",
-       "  'tracked_min_input': -0.42417848110198975,\n",
-       "  'tracked_max_input': 2.8212687969207764},\n",
-       " 'conv2': {'weight_bit': 8,\n",
-       "  'tracked_min_input': 0.0,\n",
-       "  'tracked_max_input': 4.246923446655273}}"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# export the sparsified model state to './quantized_naive_mnist_l1filter.pth'.\n",
-    "# export the calibration config to './calibration_naive_mnist_l1filter.pth'.\n",
-    "\n",
-    "quantizer.export_model(model_path='quantized_naive_mnist_l1filter.pth', calibration_path='calibration_naive_mnist_l1filter.pth')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 7. Speed Up"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# speed up with tensorRT\n",
-    "\n",
-    "engine = ModelSpeedupTensorRT(model, (32, 1, 28, 28), config=calibration_config, batchsize=32)\n",
-    "engine.compress()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/source/Compression/pruning.rst b/docs/source/Compression/pruning.rst
deleted file mode 100644
index 023b0feeb..000000000
--- a/docs/source/Compression/pruning.rst
+++ /dev/null
@@ -1,25 +0,0 @@
-#################
-Pruning
-#################
-
-Pruning is a common technique to compress neural network models.
-The pruning methods explore the redundancy in the model weights(parameters) and try to remove/prune the redundant and uncritical weights.
-The redundant elements are pruned from the model, their values are zeroed and we make sure they don't take part in the back-propagation process.
-
-From pruning granularity perspective, fine-grained pruning or unstructured pruning refers to pruning each individual weights separately.
-Coarse-grained pruning or structured pruning is pruning entire group of weights, such as a convolutional filter.
-
-NNI provides multiple unstructured pruning and structured pruning algorithms.
-It supports Tensorflow and PyTorch with unified interface.
-For users to prune their models, they only need to add several lines in their code.
-For the structured filter pruning, NNI also provides a dependency-aware mode. In the dependency-aware mode, the
-filter pruner will get better speed gain after the speedup.
-
-For details, please refer to the following tutorials:
-
-..  toctree::
-    :maxdepth: 2
-
-    Pruners <Pruner>
-    Dependency Aware Mode <DependencyAware>
-    Model Speedup <ModelSpeedup>
diff --git a/docs/source/Compression/pruning_zh.rst b/docs/source/Compression/pruning_zh.rst
deleted file mode 100644
index 04d2eb7a3..000000000
--- a/docs/source/Compression/pruning_zh.rst
+++ /dev/null
@@ -1,27 +0,0 @@
-.. 0f2050a973cfb2207984b4e58c4baf28
-
-#################
-剪枝
-#################
-
-剪枝是一种常用的神经网络模型压缩技术。
-剪枝算法探索模型权重（参数）中的冗余，并尝试去除冗余和非关键权重，
-将它们的值归零，确保其不参与反向传播过程。
-
-从剪枝粒度的角度来看，细粒度剪枝或非结构化剪枝是指分别对每个权重进行剪枝。
-粗粒度剪枝或结构化剪枝是修剪整组权重，例如卷积滤波器。
-
-NNI 提供了多种非结构化和结构化剪枝算法。
-其使用了统一的接口来支持 TensorFlow 和 PyTorch。
-只需要添加几行代码即可压缩模型。
-对于结构化滤波器剪枝，NNI 还提供了依赖感知模式。 在依赖感知模式下，
-滤波器剪枝在加速后会获得更好的速度增益。
-
-详细信息，参考以下教程：
-
-..  toctree::
-    :maxdepth: 2
-
-    Pruners <Pruner>
-    依赖感知模式 <DependencyAware>
-    模型加速 <ModelSpeedup>
diff --git a/docs/source/Compression/quantization_zh.rst b/docs/source/Compression/quantization_zh.rst
deleted file mode 100644
index ef2a474de..000000000
--- a/docs/source/Compression/quantization_zh.rst
+++ /dev/null
@@ -1,20 +0,0 @@
-.. fe32a6de0be31a992afadba5cf6ffe23
-
-#################
-量化
-#################
-
-量化是指通过减少权重表示或激活所需的比特数来压缩模型，
-从而减少计算量和推理时间。 在深度神经网络的背景下，模型权重主要的数据
-格式是32位浮点数。 许多研究工作表明，在不显着降低精度的情况下，权重和激活
-可以使用8位整数表示， 更低的比特位数，例如4/2/1比特，
-是否能够表示权重也是目前非常活跃的研究方向。
-
-一个 Quantizer 是指一种 NNI 实现的量化算法，NNI 提供了多个 Quantizer，如下所示。你也可以
-使用 NNI 模型压缩的接口来创造你的 Quantizer。
-
-..  toctree::
-    :maxdepth: 2
-
-    Quantizers <Quantizer>
-    量化加速 <QuantizationSpeedup>
diff --git a/docs/source/Compression/v2_pruning.rst b/docs/source/Compression/v2_pruning.rst
deleted file mode 100644
index e422eae79..000000000
--- a/docs/source/Compression/v2_pruning.rst
+++ /dev/null
@@ -1,26 +0,0 @@
-Pruning V2
-==========
-
-Pruning V2 is a refactoring of the old version and provides more powerful functions.
-Compared with the old version, the iterative pruning process is detached from the pruner and the pruner is only responsible for pruning and generating the masks once.
-What's more, pruning V2 unifies the pruning process and provides a more free combination of pruning components.
-Task generator only cares about the pruning effect that should be achieved in each round, and uses a config list to express how to pruning in the next step.
-Pruner will reset with the model and config list given by task generator then generate the masks in current step.
-
-For a clearer structure vision, please refer to the figure below.
-
-.. image:: ../../img/pruning_process.png
-   :target: ../../img/pruning_process.png
-   :alt:
-
-In V2, a pruning process is usually driven by a pruning scheduler, it contains a specific pruner and a task generator.
-But users can also use pruner directly like in the pruning V1.
-
-For details, please refer to the following tutorials:
-
-..  toctree::
-    :maxdepth: 2
-
-    Pruning Algorithms <v2_pruning_algo>
-    Pruning Scheduler <v2_scheduler>
-    Pruning Config List <v2_pruning_config_list>
diff --git a/docs/source/Compression/v2_pruning_algo.rst b/docs/source/Compression/v2_pruning_algo.rst
deleted file mode 100644
index 23b711894..000000000
--- a/docs/source/Compression/v2_pruning_algo.rst
+++ /dev/null
@@ -1,587 +0,0 @@
-Supported Pruning Algorithms in NNI
-===================================
-
-NNI provides several pruning algorithms that reproducing from the papers. In pruning v2, NNI split the pruning algorithm into more detailed components.
-This means users can freely combine components from different algorithms,
-or easily use a component of their own implementation to replace a step in the original algorithm to implement their own pruning algorithm.
-
-Right now, pruning algorithms with how to generate masks in one step are implemented as pruners,
-and how to schedule sparsity in each iteration are implemented as iterative pruners.
-
-**Pruner**
-
-* `Level Pruner <#level-pruner>`__
-* `L1 Norm Pruner <#l1-norm-pruner>`__
-* `L2 Norm Pruner <#l2-norm-pruner>`__
-* `FPGM Pruner <#fpgm-pruner>`__
-* `Slim Pruner <#slim-pruner>`__
-* `Activation APoZ Rank Pruner <#activation-apoz-rank-pruner>`__
-* `Activation Mean Rank Pruner <#activation-mean-rank-pruner>`__
-* `Taylor FO Weight Pruner <#taylor-fo-weight-pruner>`__
-* `ADMM Pruner <#admm-pruner>`__
-* `Movement Pruner <#movement-pruner>`__
-
-**Iterative Pruner**
-
-* `Linear Pruner <#linear-pruner>`__
-* `AGP Pruner <#agp-pruner>`__
-* `Lottery Ticket Pruner <#lottery-ticket-pruner>`__
-* `Simulated Annealing Pruner <#simulated-annealing-pruner>`__
-* `Auto Compress Pruner <#auto-compress-pruner>`__
-* `AMC Pruner <#amc-pruner>`__
-
-Level Pruner
-------------
-
-This is a basic pruner, and in some papers called it magnitude pruning or fine-grained pruning.
-
-It will mask the weight in each specified layer with smaller absolute value by a ratio configured in the config list.
-
-Usage
-^^^^^^
-
-.. code-block:: python
-
-   from nni.algorithms.compression.v2.pytorch.pruning import LevelPruner
-   config_list = [{ 'sparsity': 0.8, 'op_types': ['default'] }]
-   pruner = LevelPruner(model, config_list)
-   masked_model, masks = pruner.compress()
-
-For detailed example please refer to :githublink:`examples/model_compress/pruning/v2/level_pruning_torch.py <examples/model_compress/pruning/v2/level_pruning_torch.py>`
-
-User configuration for Level Pruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-.. autoclass:: nni.algorithms.compression.v2.pytorch.pruning.LevelPruner
-
-L1 Norm Pruner
---------------
-
-L1 norm pruner computes the l1 norm of the layer weight on the first dimension,
-then prune the weight blocks on this dimension with smaller l1 norm values.
-i.e., compute the l1 norm of the filters in convolution layer as metric values,
-compute the l1 norm of the weight by rows in linear layer as metric values.
-
-For more details, please refer to `PRUNING FILTERS FOR EFFICIENT CONVNETS <https://arxiv.org/abs/1608.08710>`__\.
-
-In addition, L1 norm pruner also supports dependency-aware mode.
-
-Usage
-^^^^^^
-
-.. code-block:: python
-
-   from nni.algorithms.compression.v2.pytorch.pruning import L1NormPruner
-   config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
-   pruner = L1NormPruner(model, config_list)
-   masked_model, masks = pruner.compress()
-
-For detailed example please refer to :githublink:`examples/model_compress/pruning/v2/norm_pruning_torch.py <examples/model_compress/pruning/v2/norm_pruning_torch.py>`
-
-User configuration for L1 Norm Pruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-.. autoclass:: nni.algorithms.compression.v2.pytorch.pruning.L1NormPruner
-
-L2 Norm Pruner
---------------
-
-L2 norm pruner is a variant of L1 norm pruner. It uses l2 norm as metric to determine which weight elements should be pruned.
-
-L2 norm pruner also supports dependency-aware mode.
-
-Usage
-^^^^^^
-
-.. code-block:: python
-
-   from nni.algorithms.compression.v2.pytorch.pruning import L2NormPruner
-   config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
-   pruner = L2NormPruner(model, config_list)
-   masked_model, masks = pruner.compress()
-
-For detailed example please refer to :githublink:`examples/model_compress/pruning/v2/norm_pruning_torch.py <examples/model_compress/pruning/v2/norm_pruning_torch.py>`
-
-User configuration for L2 Norm Pruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-.. autoclass:: nni.algorithms.compression.v2.pytorch.pruning.L2NormPruner
-
-FPGM Pruner
------------
-
-FPGM pruner prunes the blocks of the weight on the first dimension with the smallest geometric median.
-FPGM chooses the weight blocks with the most replaceable contribution.
-
-For more details, please refer to `Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration <https://arxiv.org/abs/1811.00250>`__.
-
-FPGM pruner also supports dependency-aware mode.
-
-Usage
-^^^^^^
-
-.. code-block:: python
-
-   from nni.algorithms.compression.v2.pytorch.pruning import FPGMPruner
-   config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
-   pruner = FPGMPruner(model, config_list)
-   masked_model, masks = pruner.compress()
-
-For detailed example please refer to :githublink:`examples/model_compress/pruning/v2/fpgm_pruning_torch.py <examples/model_compress/pruning/v2/fpgm_pruning_torch.py>`
-
-User configuration for FPGM Pruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-.. autoclass:: nni.algorithms.compression.v2.pytorch.pruning.FPGMPruner
-
-Slim Pruner
------------
-
-Slim pruner adds sparsity regularization on the scaling factors of batch normalization (BN) layers during training to identify unimportant channels.
-The channels with small scaling factor values will be pruned.
-
-For more details, please refer to `Learning Efficient Convolutional Networks through Network Slimming <https://arxiv.org/abs/1708.06519>`__\.
-
-Usage
-^^^^^^
-
-.. code-block:: python
-
-   import nni
-   from nni.algorithms.compression.v2.pytorch.pruning import SlimPruner
-
-   # make sure you have used nni.trace to wrap the optimizer class before initialize
-   traced_optimizer = nni.trace(torch.optim.Adam)(model.parameters())
-
-   config_list = [{ 'sparsity': 0.8, 'op_types': ['BatchNorm2d'] }]
-   pruner = SlimPruner(model, config_list, trainer, traced_optimizer, criterion, training_epochs=1)
-   masked_model, masks = pruner.compress()
-
-For detailed example please refer to :githublink:`examples/model_compress/pruning/v2/slim_pruning_torch.py <examples/model_compress/pruning/v2/slim_pruning_torch.py>`
-
-User configuration for Slim Pruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-.. autoclass:: nni.algorithms.compression.v2.pytorch.pruning.SlimPruner
-
-Activation APoZ Rank Pruner
----------------------------
-
-Activation APoZ rank pruner is a pruner which prunes on the first weight dimension,
-with the smallest importance criterion ``APoZ`` calculated from the output activations of convolution layers to achieve a preset level of network sparsity.
-The pruning criterion ``APoZ`` is explained in the paper `Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures <https://arxiv.org/abs/1607.03250>`__.
-
-The APoZ is defined as:
-
-:math:`APoZ_{c}^{(i)} = APoZ\left(O_{c}^{(i)}\right)=\frac{\sum_{k}^{N} \sum_{j}^{M} f\left(O_{c, j}^{(i)}(k)=0\right)}{N \times M}`
-
-Activation APoZ rank pruner also supports dependency-aware mode.
-
-Usage
-^^^^^^
-
-.. code-block:: python
-
-   import nni
-   from nni.algorithms.compression.v2.pytorch.pruning import ActivationAPoZRankPruner
-
-   # make sure you have used nni.trace to wrap the optimizer class before initialize
-   traced_optimizer = nni.trace(torch.optim.Adam)(model.parameters())
-
-   config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
-   pruner = ActivationAPoZRankPruner(model, config_list, trainer, traced_optimizer, criterion, training_batches=20)
-   masked_model, masks = pruner.compress()
-
-For detailed example please refer to :githublink:`examples/model_compress/pruning/v2/activation_pruning_torch.py <examples/model_compress/pruning/v2/activation_pruning_torch.py>`
-
-User configuration for Activation APoZ Rank Pruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-.. autoclass:: nni.algorithms.compression.v2.pytorch.pruning.ActivationAPoZRankPruner
-
-Activation Mean Rank Pruner
----------------------------
-
-Activation mean rank pruner is a pruner which prunes on the first weight dimension,
-with the smallest importance criterion ``mean activation`` calculated from the output activations of convolution layers to achieve a preset level of network sparsity.
-The pruning criterion ``mean activation`` is explained in section 2.2 of the paper `Pruning Convolutional Neural Networks for Resource Efficient Inference <https://arxiv.org/abs/1611.06440>`__.
-
-Activation mean rank pruner also supports dependency-aware mode.
-
-Usage
-^^^^^^
-
-.. code-block:: python
-
-   import nni
-   from nni.algorithms.compression.v2.pytorch.pruning import ActivationMeanRankPruner
-
-   # make sure you have used nni.trace to wrap the optimizer class before initialize
-   traced_optimizer = nni.traces(torch.optim.Adam)(model.parameters())
-
-   config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
-   pruner = ActivationMeanRankPruner(model, config_list, trainer, traced_optimizer, criterion, training_batches=20)
-   masked_model, masks = pruner.compress()
-
-For detailed example please refer to :githublink:`examples/model_compress/pruning/v2/activation_pruning_torch.py <examples/model_compress/pruning/v2/activation_pruning_torch.py>`
-
-User configuration for Activation Mean Rank Pruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-.. autoclass:: nni.algorithms.compression.v2.pytorch.pruning.ActivationMeanRankPruner
-
-Taylor FO Weight Pruner
------------------------
-
-Taylor FO weight pruner is a pruner which prunes on the first weight dimension,
-based on estimated importance calculated from the first order taylor expansion on weights to achieve a preset level of network sparsity.
-The estimated importance is defined as the paper `Importance Estimation for Neural Network Pruning <http://jankautz.com/publications/Importance4NNPruning_CVPR19.pdf>`__.
-
-:math:`\widehat{\mathcal{I}}_{\mathcal{S}}^{(1)}(\mathbf{W}) \triangleq \sum_{s \in \mathcal{S}} \mathcal{I}_{s}^{(1)}(\mathbf{W})=\sum_{s \in \mathcal{S}}\left(g_{s} w_{s}\right)^{2}`
-
-Taylor FO weight pruner also supports dependency-aware mode.
-
-What's more, we provide a global-sort mode for this pruner which is aligned with paper implementation.
-
-Usage
-^^^^^^
-
-.. code-block:: python
-
-   import nni
-   from nni.algorithms.compression.v2.pytorch.pruning import TaylorFOWeightPruner
-
-   # make sure you have used nni.trace to wrap the optimizer class before initialize
-   traced_optimizer = nni.trace(torch.optim.Adam)(model.parameters())
-
-   config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
-   pruner = TaylorFOWeightPruner(model, config_list, trainer, traced_optimizer, criterion, training_batches=20)
-   masked_model, masks = pruner.compress()
-
-For detailed example please refer to :githublink:`examples/model_compress/pruning/v2/taylorfo_pruning_torch.py <examples/model_compress/pruning/v2/taylorfo_pruning_torch.py>`
-
-User configuration for Activation Mean Rank Pruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-.. autoclass:: nni.algorithms.compression.v2.pytorch.pruning.TaylorFOWeightPruner
-
-ADMM Pruner
------------
-
-Alternating Direction Method of Multipliers (ADMM) is a mathematical optimization technique,
-by decomposing the original nonconvex problem into two subproblems that can be solved iteratively.
-In weight pruning problem, these two subproblems are solved via 1) gradient descent algorithm and 2) Euclidean projection respectively. 
-
-During the process of solving these two subproblems, the weights of the original model will be changed.
-Then a fine-grained pruning will be applied to prune the model according to the config list given.
-
-This solution framework applies both to non-structured and different variations of structured pruning schemes.
-
-For more details, please refer to `A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers <https://arxiv.org/abs/1804.03294>`__.
-
-Usage
-^^^^^^
-
-.. code-block:: python
-
-   import nni
-   from nni.algorithms.compression.v2.pytorch.pruning import ADMMPruner
-
-   # make sure you have used nni.trace to wrap the optimizer class before initialize
-   traced_optimizer = nni.trace(torch.optim.Adam)(model.parameters())
-
-   config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
-   pruner = ADMMPruner(model, config_list, trainer, traced_optimizer, criterion, iterations=10, training_epochs=1)
-   masked_model, masks = pruner.compress()
-
-For detailed example please refer to :githublink:`examples/model_compress/pruning/v2/admm_pruning_torch.py <examples/model_compress/pruning/v2/admm_pruning_torch.py>`
-
-User configuration for ADMM Pruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-.. autoclass:: nni.algorithms.compression.v2.pytorch.pruning.ADMMPruner
-
-Movement Pruner
----------------
-
-Movement pruner is an implementation of movement pruning.
-This is a "fine-pruning" algorithm, which means the masks may change during each fine-tuning step.
-Each weight element will be scored by the opposite of the sum of the product of weight and its gradient during each step.
-This means the weight elements moving towards zero will accumulate negative scores, the weight elements moving away from zero will accumulate positive scores.
-The weight elements with low scores will be masked during inference.
-
-The following figure from the paper shows the weight pruning by movement pruning.
-
-.. image:: ../../img/movement_pruning.png
-   :target: ../../img/movement_pruning.png
-   :alt: 
-
-For more details, please refer to `Movement Pruning: Adaptive Sparsity by Fine-Tuning <https://arxiv.org/abs/2005.07683>`__.
-
-Usage
-^^^^^^
-
-.. code-block:: python
-
-   import nni
-   from nni.algorithms.compression.v2.pytorch.pruning import MovementPruner
-
-   # make sure you have used nni.trace to wrap the optimizer class before initialize
-   traced_optimizer = nni.trace(torch.optim.Adam)(model.parameters())
-
-   config_list = [{'op_types': ['Linear'], 'op_partial_names': ['bert.encoder'], 'sparsity': 0.9}]
-   pruner = MovementPruner(model, config_list, trainer, traced_optimizer, criterion, 10, 3000, 27000)
-   masked_model, masks = pruner.compress()
-
-User configuration for Movement Pruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-.. autoclass:: nni.algorithms.compression.v2.pytorch.pruning.MovementPruner
-
-Reproduced Experiment
-^^^^^^^^^^^^^^^^^^^^^
-
-.. list-table::
-   :header-rows: 1
-   :widths: auto
-
-   * - Model
-     - Dataset
-     - Remaining Weights
-     - MaP acc.(paper/ours)
-     - MvP acc.(paper/ours)
-   * - Bert base
-     - MNLI - Dev
-     - 10%
-     - 77.8% / 73.6%
-     - 79.3% / 78.8%
-
-Linear Pruner
--------------
-
-Linear pruner is an iterative pruner, it will increase sparsity evenly from scratch during each iteration.
-For example, the final sparsity is set as 0.5, and the iteration number is 5, then the sparsity used in each iteration are ``[0, 0.1, 0.2, 0.3, 0.4, 0.5]``.
-
-Usage
-^^^^^^
-
-.. code-block:: python
-
-   from nni.algorithms.compression.v2.pytorch.pruning import LinearPruner
-   config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
-   pruner = LinearPruner(model, config_list, pruning_algorithm='l1', total_iteration=10, finetuner=finetuner)
-   pruner.compress()
-   _, model, masks, _, _ = pruner.get_best_result()
-
-For detailed example please refer to :githublink:`examples/model_compress/pruning/v2/iterative_pruning_torch.py <examples/model_compress/pruning/v2/iterative_pruning_torch.py>`
-
-User configuration for Linear Pruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-.. autoclass:: nni.algorithms.compression.v2.pytorch.pruning.LinearPruner
-
-AGP Pruner
-----------
-
-This is an iterative pruner, which the sparsity is increased from an initial sparsity value :math:`s_{i}` (usually 0) to a final sparsity value :math:`s_{f}` over a span of :math:`n` pruning iterations,
-starting at training step :math:`t_{0}` and with pruning frequency :math:`\Delta t`:
-
-:math:`s_{t}=s_{f}+\left(s_{i}-s_{f}\right)\left(1-\frac{t-t_{0}}{n \Delta t}\right)^{3} \text { for } t \in\left\{t_{0}, t_{0}+\Delta t, \ldots, t_{0} + n \Delta t\right\}`
-
-For more details please refer to `To prune, or not to prune: exploring the efficacy of pruning for model compression <https://arxiv.org/abs/1710.01878>`__\.
-
-Usage
-^^^^^^
-
-.. code-block:: python
-
-   from nni.algorithms.compression.v2.pytorch.pruning import AGPPruner
-   config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
-   pruner = AGPPruner(model, config_list, pruning_algorithm='l1', total_iteration=10, finetuner=finetuner)
-   pruner.compress()
-   _, model, masks, _, _ = pruner.get_best_result()
-
-For detailed example please refer to :githublink:`examples/model_compress/pruning/v2/iterative_pruning_torch.py <examples/model_compress/pruning/v2/iterative_pruning_torch.py>`
-
-User configuration for AGP Pruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-.. autoclass:: nni.algorithms.compression.v2.pytorch.pruning.AGPPruner
-
-Lottery Ticket Pruner
----------------------
-
-`The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks <https://arxiv.org/abs/1803.03635>`__\ ,
-authors Jonathan Frankle and Michael Carbin,provides comprehensive measurement and analysis,
-and articulate the *lottery ticket hypothesis*\ : dense, randomly-initialized, feed-forward networks contain subnetworks (*winning tickets*\ ) that
--- when trained in isolation -- reach test accuracy comparable to the original network in a similar number of iterations.
-
-In this paper, the authors use the following process to prune a model, called *iterative prunning*\ :
-
-..
-
-   #. Randomly initialize a neural network f(x;theta_0) (where theta\ *0 follows D*\ {theta}).
-   #. Train the network for j iterations, arriving at parameters theta_j.
-   #. Prune p% of the parameters in theta_j, creating a mask m.
-   #. Reset the remaining parameters to their values in theta_0, creating the winning ticket f(x;m*theta_0).
-   #. Repeat step 2, 3, and 4.
-
-If the configured final sparsity is P (e.g., 0.8) and there are n times iterative pruning,
-each iterative pruning prunes 1-(1-P)^(1/n) of the weights that survive the previous round.
-
-Usage
-^^^^^^
-
-.. code-block:: python
-
-   from nni.algorithms.compression.v2.pytorch.pruning import LotteryTicketPruner
-   config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
-   pruner = LotteryTicketPruner(model, config_list, pruning_algorithm='l1', total_iteration=10, finetuner=finetuner, reset_weight=True)
-   pruner.compress()
-   _, model, masks, _, _ = pruner.get_best_result()
-
-For detailed example please refer to :githublink:`examples/model_compress/pruning/v2/iterative_pruning_torch.py <examples/model_compress/pruning/v2/iterative_pruning_torch.py>`
-
-User configuration for Lottery Ticket Pruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-.. autoclass:: nni.algorithms.compression.v2.pytorch.pruning.LotteryTicketPruner
-
-Simulated Annealing Pruner
---------------------------
-
-We implement a guided heuristic search method, Simulated Annealing (SA) algorithm. As mentioned in the paper, this method is enhanced on guided search based on prior experience.
-The enhanced SA technique is based on the observation that a DNN layer with more number of weights often has a higher degree of model compression with less impact on overall accuracy.
-
-* Randomly initialize a pruning rate distribution (sparsities).
-* While current_temperature < stop_temperature:
-
-  #. generate a perturbation to current distribution
-  #. Perform fast evaluation on the perturbated distribution
-  #. accept the perturbation according to the performance and probability, if not accepted, return to step 1
-  #. cool down, current_temperature <- current_temperature * cool_down_rate
-
-For more details, please refer to `AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates <https://arxiv.org/abs/1907.03141>`__.
-
-Usage
-^^^^^^
-
-.. code-block:: python
-
-   from nni.algorithms.compression.v2.pytorch.pruning import SimulatedAnnealingPruner
-   config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
-   pruner = SimulatedAnnealingPruner(model, config_list, pruning_algorithm='l1', evaluator=evaluator, cool_down_rate=0.9, finetuner=finetuner)
-   pruner.compress()
-   _, model, masks, _, _ = pruner.get_best_result()
-
-For detailed example please refer to :githublink:`examples/model_compress/pruning/v2/simulated_anealing_pruning_torch.py <examples/model_compress/pruning/v2/simulated_anealing_pruning_torch.py>`
-
-User configuration for Simulated Annealing Pruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-.. autoclass:: nni.algorithms.compression.v2.pytorch.pruning.SimulatedAnnealingPruner
-
-Auto Compress Pruner
---------------------
-
-For total iteration number :math:`N`, AutoCompressPruner prune the model that survive the previous iteration for a fixed sparsity ratio (e.g., :math:`1-{(1-0.8)}^{(1/N)}`) to achieve the overall sparsity (e.g., :math:`0.8`):
-
-.. code-block:: bash
-
-       1. Generate sparsities distribution using SimulatedAnnealingPruner
-       2. Perform ADMM-based pruning to generate pruning result for the next iteration.
-
-For more details, please refer to `AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates <https://arxiv.org/abs/1907.03141>`__.
-
-Usage
-^^^^^^
-
-.. code-block:: python
-
-   import nni
-   from nni.algorithms.compression.v2.pytorch.pruning import AutoCompressPruner
-
-   # make sure you have used nni.trace to wrap the optimizer class before initialize
-   traced_optimizer = nni.trace(torch.optim.Adam)(model.parameters())
-
-   config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
-   admm_params = {
-        'trainer': trainer,
-        'traced_optimizer': traced_optimizer,
-        'criterion': criterion,
-        'iterations': 10,
-        'training_epochs': 1
-    }
-    sa_params = {
-        'evaluator': evaluator
-    }
-    pruner = AutoCompressPruner(model, config_list, 10, admm_params, sa_params, finetuner=finetuner)
-    pruner.compress()
-    _, model, masks, _, _ = pruner.get_best_result()
-
-The full script can be found :githublink:`here <examples/model_compress/pruning/v2/auto_compress_pruner.py>`.
-
-User configuration for Auto Compress Pruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-.. autoclass:: nni.algorithms.compression.v2.pytorch.pruning.AutoCompressPruner
-
-AMC Pruner
-----------
-
-AMC pruner leverages reinforcement learning to provide the model compression policy.
-According to the author, this learning-based compression policy outperforms conventional rule-based compression policy by having a higher compression ratio,
-better preserving the accuracy and freeing human labor.
-
-For more details, please refer to `AMC: AutoML for Model Compression and Acceleration on Mobile Devices <https://arxiv.org/pdf/1802.03494.pdf>`__.
-
-Usage
-^^^^^
-
-PyTorch code
-
-.. code-block:: python
-
-   from nni.algorithms.compression.v2.pytorch.pruning import AMCPruner
-   config_list = [{'op_types': ['Conv2d'], 'total_sparsity': 0.5, 'max_sparsity_per_layer': 0.8}]
-   pruner = AMCPruner(400, model, config_list, dummy_input, evaluator, finetuner=finetuner)
-   pruner.compress()
-
-The full script can be found :githublink:`here <examples/model_compress/pruning/v2/amc_pruning_torch.py>`.
-
-User configuration for AMC Pruner
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-**PyTorch**
-
-..  autoclass:: nni.algorithms.compression.v2.pytorch.pruning.AMCPruner
diff --git a/docs/source/Compression/v2_pruning_zh.rst b/docs/source/Compression/v2_pruning_zh.rst
deleted file mode 100644
index c44548be2..000000000
--- a/docs/source/Compression/v2_pruning_zh.rst
+++ /dev/null
@@ -1,28 +0,0 @@
-.. 1ec93e31648291b0c881655304116b50
-
-剪枝（V2版本）
-===============
-
-剪枝（V2版本）是对旧版本的重构，提供了更强大的功能。
-与旧版本相比，迭代剪枝过程与剪枝器（pruner）分离，剪枝器只负责剪枝且生成掩码一次。
-更重要的是，V2版本统一了剪枝过程，并提供了更自由的剪枝组件组合。
-任务生成器（task generator）只关心在每一轮中应该达到的修剪效果，并使用配置列表（config list）来表示下一步如何修剪。
-剪枝器将使用任务生成器提供的模型和配置列表重置，然后在当前步骤中生成掩码。
-
-有关更清晰的架构，请参考下图。
-
-.. image:: ../../img/pruning_process.png
-   :target: ../../img/pruning_process.png
-   :alt:
-
-在V2版本中，修剪过程通常由剪枝调度器（pruning scheduler）驱动，它包含一个特定的剪枝器和一个任务生成器。
-但是用户也可以像V1版本中那样直接使用剪枝器。
-
-有关详细信息，请参阅以下教程：
-
-..  toctree::
-    :maxdepth: 1
-
-    剪枝算法 <v2_pruning_algo>
-    剪枝调度器接口 <v2_scheduler>
-    剪枝配置 <v2_pruning_config_list>
diff --git a/docs/source/NAS/ApiReference.rst b/docs/source/NAS/ApiReference.rst
deleted file mode 100644
index 93e4fde9a..000000000
--- a/docs/source/NAS/ApiReference.rst
+++ /dev/null
@@ -1,111 +0,0 @@
-Retiarii API Reference
-======================
-
-.. contents::
-
-Inline Mutation APIs
---------------------
-
-..  autoclass:: nni.retiarii.nn.pytorch.LayerChoice
-    :members:
-
-..  autoclass:: nni.retiarii.nn.pytorch.InputChoice
-    :members:
-
-..  autoclass:: nni.retiarii.nn.pytorch.ValueChoice
-    :members:
-
-..  autoclass:: nni.retiarii.nn.pytorch.ChosenInputs
-    :members:
-
-..  autoclass:: nni.retiarii.nn.pytorch.Repeat
-    :members:
-
-..  autoclass:: nni.retiarii.nn.pytorch.Cell
-    :members:
-
-Graph Mutation APIs
--------------------
-
-..  autoclass:: nni.retiarii.Mutator
-    :members:
-
-..  autoclass:: nni.retiarii.Model
-    :members:
-
-..  autoclass:: nni.retiarii.Graph
-    :members:
-
-..  autoclass:: nni.retiarii.Node
-    :members:
-
-..  autoclass:: nni.retiarii.Edge
-    :members:
-
-..  autoclass:: nni.retiarii.Operation
-    :members:
-
-Evaluators
-----------
-
-..  autoclass:: nni.retiarii.evaluator.FunctionalEvaluator
-    :members:
-
-..  autoclass:: nni.retiarii.evaluator.pytorch.lightning.LightningModule
-    :members:
-
-..  autoclass:: nni.retiarii.evaluator.pytorch.lightning.Classification
-    :members:
-
-..  autoclass:: nni.retiarii.evaluator.pytorch.lightning.Regression
-    :members:
-
-Exploration Strategies
-----------------------
-
-..  automodule:: nni.retiarii.strategy
-    :members:
-    :imported-members:
-
-Retiarii Experiments
---------------------
-
-..  autoclass:: nni.retiarii.experiment.pytorch.RetiariiExperiment
-    :members:
-
-..  autoclass:: nni.retiarii.experiment.pytorch.RetiariiExeConfig
-    :members:
-
-CGO Execution
--------------
-
-..  autofunction:: nni.retiarii.evaluator.pytorch.cgo.evaluator.MultiModelSupervisedLearningModule
-
-..  autofunction:: nni.retiarii.evaluator.pytorch.cgo.evaluator.Classification
-
-..  autofunction:: nni.retiarii.evaluator.pytorch.cgo.evaluator.Regression
-
-One-shot Implementation
------------------------
-
-..  automodule:: nni.retiarii.oneshot
-    :members:
-    :imported-members:
-
-..  automodule:: nni.retiarii.oneshot.pytorch
-    :members:
-    :imported-members:
-
-Utilities
----------
-
-..  autofunction:: nni.retiarii.basic_unit
-
-..  autofunction:: nni.retiarii.model_wrapper
-
-..  autofunction:: nni.retiarii.fixed_arch
-
-Citations
----------
-
-.. bibliography::
diff --git a/docs/source/NAS/Benchmarks.rst b/docs/source/NAS/Benchmarks.rst
deleted file mode 100644
index 2385933d6..000000000
--- a/docs/source/NAS/Benchmarks.rst
+++ /dev/null
@@ -1,181 +0,0 @@
-NAS Benchmarks
-==============
-
-.. code-block::
-
-   ..  toctree::
-      :hidden:
-
-      Example Usages <BenchmarksExample>
-
-Introduction
-------------
-
-To improve the reproducibility of NAS algorithms as well as reducing computing resource requirements, researchers proposed a series of NAS benchmarks such as `NAS-Bench-101 <https://arxiv.org/abs/1902.09635>`__\ , `NAS-Bench-201 <https://arxiv.org/abs/2001.00326>`__\ , `NDS <https://arxiv.org/abs/1905.13214>`__\ , etc. NNI provides a query interface for users to acquire these benchmarks. Within just a few lines of code, researcher are able to evaluate their NAS algorithms easily and fairly by utilizing these benchmarks.
-
-Prerequisites
--------------
-
-
-* Please prepare a folder to household all the benchmark databases. By default, it can be found at ``${HOME}/.cache/nni/nasbenchmark``. Or you can place it anywhere you like, and specify it in ``NASBENCHMARK_DIR`` via ``export NASBENCHMARK_DIR=/path/to/your/nasbenchmark`` before importing NNI.
-* Please install ``peewee`` via ``pip3 install peewee``\ , which NNI uses to connect to database.
-
-Data Preparation
-----------------
-
-Option 1 (Recommended)
-^^^^^^^^^^^^^^^^^^^^^^
-
-You can download the preprocessed benchmark files via ``python -m nni.nas.benchmarks.download <benchmark_name>``, where ``<benchmark_name>`` can be ``nasbench101``, ``nasbench201``, and etc. Add ``--help`` to the command for supported command line arguments.
-
-Option 2
-^^^^^^^^
-
-.. note:: If you have files that are processed before v2.5, it is recommended that you delete them and try option 1.
-
-#. 
-   Clone NNI to your machine and enter ``examples/nas/benchmarks`` directory.
-
-   .. code-block:: bash
-
-      git clone -b ${NNI_VERSION} https://github.com/microsoft/nni
-      cd nni/examples/nas/benchmarks
-
-   Replace ``${NNI_VERSION}`` with a released version name or branch name, e.g., ``v2.4``.
-
-#. 
-   Install dependencies via ``pip3 install -r xxx.requirements.txt``. ``xxx`` can be ``nasbench101``\ , ``nasbench201`` or ``nds``.
-
-#. Generate the database via ``./xxx.sh``. The directory that stores the benchmark file can be configured with ``NASBENCHMARK_DIR`` environment variable, which defaults to ``~/.nni/nasbenchmark``. Note that the NAS-Bench-201 dataset will be downloaded from a google drive.
-
-Please make sure there is at least 10GB free disk space and note that the conversion process can take up to hours to complete.
-
-Example Usages
---------------
-
-Please refer to `examples usages of Benchmarks API <./BenchmarksExample.rst>`__.
-
-NAS-Bench-101
--------------
-
-* `Paper link <https://arxiv.org/abs/1902.09635>`__ 
-* `Open-source <https://github.com/google-research/nasbench>`__
-
-NAS-Bench-101 contains 423,624 unique neural networks, combined with 4 variations in number of epochs (4, 12, 36, 108), each of which is trained 3 times. It is a cell-wise search space, which constructs and stacks a cell by enumerating DAGs with at most 7 operators, and no more than 9 connections. All operators can be chosen from ``CONV3X3_BN_RELU``\ , ``CONV1X1_BN_RELU`` and ``MAXPOOL3X3``\ , except the first operator (always ``INPUT``\ ) and last operator (always ``OUTPUT``\ ).
-
-Notably, NAS-Bench-101 eliminates invalid cells (e.g., there is no path from input to output, or there is redundant computation). Furthermore, isomorphic cells are de-duplicated, i.e., all the remaining cells are computationally unique.
-
-API Documentation
-^^^^^^^^^^^^^^^^^
-
-.. autofunction:: nni.nas.benchmarks.nasbench101.query_nb101_trial_stats
-
-.. autoattribute:: nni.nas.benchmarks.nasbench101.INPUT
-
-.. autoattribute:: nni.nas.benchmarks.nasbench101.OUTPUT
-
-.. autoattribute:: nni.nas.benchmarks.nasbench101.CONV3X3_BN_RELU
-
-.. autoattribute:: nni.nas.benchmarks.nasbench101.CONV1X1_BN_RELU
-
-.. autoattribute:: nni.nas.benchmarks.nasbench101.MAXPOOL3X3
-
-.. autoclass:: nni.nas.benchmarks.nasbench101.Nb101TrialConfig
-
-.. autoclass:: nni.nas.benchmarks.nasbench101.Nb101TrialStats
-
-.. autoclass:: nni.nas.benchmarks.nasbench101.Nb101IntermediateStats
-
-.. autofunction:: nni.nas.benchmarks.nasbench101.graph_util.nasbench_format_to_architecture_repr
-
-.. autofunction:: nni.nas.benchmarks.nasbench101.graph_util.infer_num_vertices
-
-.. autofunction:: nni.nas.benchmarks.nasbench101.graph_util.hash_module
-
-NAS-Bench-201
--------------
-
-* `Paper link <https://arxiv.org/abs/2001.00326>`__ 
-* `Open-source API <https://github.com/D-X-Y/NAS-Bench-201>`__ 
-* `Implementations <https://github.com/D-X-Y/AutoDL-Projects>`__
-
-NAS-Bench-201 is a cell-wise search space that views nodes as tensors and edges as operators. The search space contains all possible densely-connected DAGs with 4 nodes, resulting in 15,625 candidates in total. Each operator (i.e., edge) is selected from a pre-defined operator set (\ ``NONE``\ , ``SKIP_CONNECT``\ , ``CONV_1X1``\ , ``CONV_3X3`` and ``AVG_POOL_3X3``\ ). Training appraoches vary in the dataset used (CIFAR-10, CIFAR-100, ImageNet) and number of epochs scheduled (12 and 200). Each combination of architecture and training approach is repeated 1 - 3 times with different random seeds.
-
-API Documentation
-^^^^^^^^^^^^^^^^^
-
-.. autofunction:: nni.nas.benchmarks.nasbench201.query_nb201_trial_stats
-
-.. autoattribute:: nni.nas.benchmarks.nasbench201.NONE
-
-.. autoattribute:: nni.nas.benchmarks.nasbench201.SKIP_CONNECT
-
-.. autoattribute:: nni.nas.benchmarks.nasbench201.CONV_1X1
-
-.. autoattribute:: nni.nas.benchmarks.nasbench201.CONV_3X3
-
-.. autoattribute:: nni.nas.benchmarks.nasbench201.AVG_POOL_3X3
-
-.. autoclass:: nni.nas.benchmarks.nasbench201.Nb201TrialConfig
-
-.. autoclass:: nni.nas.benchmarks.nasbench201.Nb201TrialStats
-
-.. autoclass:: nni.nas.benchmarks.nasbench201.Nb201IntermediateStats
-
-NDS
----
-
-* `Paper link <https://arxiv.org/abs/1905.13214>`__ 
-* `Open-source <https://github.com/facebookresearch/nds>`__
-
-*On Network Design Spaces for Visual Recognition* released trial statistics of over 100,000 configurations (models + hyper-parameters) sampled from multiple model families, including vanilla (feedforward network loosely inspired by VGG), ResNet and ResNeXt (residual basic block and residual bottleneck block) and NAS cells (following popular design from NASNet, Ameoba, PNAS, ENAS and DARTS). Most configurations are trained only once with a fixed seed, except a few that are trained twice or three times.
-
-Instead of storing results obtained with different configurations in separate files, we dump them into one single database to enable comparison in multiple dimensions. Specifically, we use ``model_family`` to distinguish model types, ``model_spec`` for all hyper-parameters needed to build this model, ``cell_spec`` for detailed information on operators and connections if it is a NAS cell, ``generator`` to denote the sampling policy through which this configuration is generated. Refer to API documentation for details.
-
-Available Operators
--------------------
-
-Here is a list of available operators used in NDS.
-
-.. autoattribute:: nni.nas.benchmarks.nds.constants.NONE
-
-.. autoattribute:: nni.nas.benchmarks.nds.constants.SKIP_CONNECT
-
-.. autoattribute:: nni.nas.benchmarks.nds.constants.AVG_POOL_3X3
-
-.. autoattribute:: nni.nas.benchmarks.nds.constants.MAX_POOL_3X3
-
-.. autoattribute:: nni.nas.benchmarks.nds.constants.MAX_POOL_5X5
-
-.. autoattribute:: nni.nas.benchmarks.nds.constants.MAX_POOL_7X7
-
-.. autoattribute:: nni.nas.benchmarks.nds.constants.CONV_1X1
-
-.. autoattribute:: nni.nas.benchmarks.nds.constants.CONV_3X3
-
-.. autoattribute:: nni.nas.benchmarks.nds.constants.CONV_3X1_1X3
-
-.. autoattribute:: nni.nas.benchmarks.nds.constants.CONV_7X1_1X7
-
-.. autoattribute:: nni.nas.benchmarks.nds.constants.DIL_CONV_3X3
-
-.. autoattribute:: nni.nas.benchmarks.nds.constants.DIL_CONV_5X5
-
-.. autoattribute:: nni.nas.benchmarks.nds.constants.SEP_CONV_3X3
-
-.. autoattribute:: nni.nas.benchmarks.nds.constants.SEP_CONV_5X5
-
-.. autoattribute:: nni.nas.benchmarks.nds.constants.SEP_CONV_7X7
-
-.. autoattribute:: nni.nas.benchmarks.nds.constants.DIL_SEP_CONV_3X3
-
-API Documentation
-^^^^^^^^^^^^^^^^^
-
-.. autofunction:: nni.nas.benchmarks.nds.query_nds_trial_stats
-
-.. autoclass:: nni.nas.benchmarks.nds.NdsTrialConfig
-
-.. autoclass:: nni.nas.benchmarks.nds.NdsTrialStats
-
-.. autoclass:: nni.nas.benchmarks.nds.NdsIntermediateStats
diff --git a/docs/source/NAS/BenchmarksExample.ipynb b/docs/source/NAS/BenchmarksExample.ipynb
deleted file mode 100644
index 4c0c8d6c2..000000000
--- a/docs/source/NAS/BenchmarksExample.ipynb
+++ /dev/null
@@ -1,396 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Example Usages of NAS Benchmarks"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import pprint\n",
-        "import time\n",
-        "\n",
-        "from nni.nas.benchmarks.nasbench101 import query_nb101_trial_stats\n",
-        "from nni.nas.benchmarks.nasbench201 import query_nb201_trial_stats\n",
-        "from nni.nas.benchmarks.nds import query_nds_trial_stats\n",
-        "\n",
-        "ti = time.time()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## NAS-Bench-101"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Use the following architecture as an example:\n",
-        "\n",
-        "![nas-101](../../img/nas-bench-101-example.png)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "arch = {\n",
-        "    'op1': 'conv3x3-bn-relu',\n",
-        "    'op2': 'maxpool3x3',\n",
-        "    'op3': 'conv3x3-bn-relu',\n",
-        "    'op4': 'conv3x3-bn-relu',\n",
-        "    'op5': 'conv1x1-bn-relu',\n",
-        "    'input1': [0],\n",
-        "    'input2': [1],\n",
-        "    'input3': [2],\n",
-        "    'input4': [0],\n",
-        "    'input5': [0, 3, 4],\n",
-        "    'input6': [2, 5]\n",
-        "}\n",
-        "for t in query_nb101_trial_stats(arch, 108, include_intermediates=True):\n",
-        "    pprint.pprint(t)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "An architecture of NAS-Bench-101 could be trained more than once. Each element of the returned generator is a dict which contains one of the training results of this trial config (architecture + hyper-parameters) including train/valid/test accuracy, training time, number of epochs, etc. The results of NAS-Bench-201 and NDS follow similar formats."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## NAS-Bench-201"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Use the following architecture as an example:\n",
-        "\n",
-        "![nas-201](../../img/nas-bench-201-example.png)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "arch = {\n",
-        "    '0_1': 'avg_pool_3x3',\n",
-        "    '0_2': 'conv_1x1',\n",
-        "    '1_2': 'skip_connect',\n",
-        "    '0_3': 'conv_1x1',\n",
-        "    '1_3': 'skip_connect',\n",
-        "    '2_3': 'skip_connect'\n",
-        "}\n",
-        "for t in query_nb201_trial_stats(arch, 200, 'cifar100'):\n",
-        "    pprint.pprint(t)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Intermediate results are also available."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "for t in query_nb201_trial_stats(arch, None, 'imagenet16-120', include_intermediates=True):\n",
-        "    print(t['config'])\n",
-        "    print('Intermediates:', len(t['intermediates']))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## NDS"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Use the following architecture as an example:<br>\n",
-        "![nds](../../img/nas-bench-nds-example.png)\n",
-        "\n",
-        "Here, `bot_muls`, `ds`, `num_gs`, `ss` and `ws` stand for \"bottleneck multipliers\", \"depths\", \"number of groups\", \"strides\" and \"widths\" respectively."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "model_spec = {\n",
-        "    'bot_muls': [0.0, 0.25, 0.25, 0.25],\n",
-        "    'ds': [1, 16, 1, 4],\n",
-        "    'num_gs': [1, 2, 1, 2],\n",
-        "    'ss': [1, 1, 2, 2],\n",
-        "    'ws': [16, 64, 128, 16]\n",
-        "}\n",
-        "# Use none as a wildcard\n",
-        "for t in query_nds_trial_stats('residual_bottleneck', None, None, model_spec, None, 'cifar10'):\n",
-        "    pprint.pprint(t)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "model_spec = {\n",
-        "    'bot_muls': [0.0, 0.25, 0.25, 0.25],\n",
-        "    'ds': [1, 16, 1, 4],\n",
-        "    'num_gs': [1, 2, 1, 2],\n",
-        "    'ss': [1, 1, 2, 2],\n",
-        "    'ws': [16, 64, 128, 16]\n",
-        "}\n",
-        "for t in query_nds_trial_stats('residual_bottleneck', None, None, model_spec, None, 'cifar10', include_intermediates=True):\n",
-        "    pprint.pprint(t['intermediates'][:10])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "model_spec = {'ds': [1, 12, 12, 12], 'ss': [1, 1, 2, 2], 'ws': [16, 24, 24, 40]}\n",
-        "for t in query_nds_trial_stats('residual_basic', 'resnet', 'random', model_spec, {}, 'cifar10'):\n",
-        "    pprint.pprint(t)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "# get the first one\n",
-        "pprint.pprint(next(query_nds_trial_stats('vanilla', None, None, None, None, None)))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "# count number\n",
-        "model_spec = {'num_nodes_normal': 5, 'num_nodes_reduce': 5, 'depth': 12, 'width': 32, 'aux': False, 'drop_prob': 0.0}\n",
-        "cell_spec = {\n",
-        "    'normal_0_op_x': 'avg_pool_3x3',\n",
-        "    'normal_0_input_x': 0,\n",
-        "    'normal_0_op_y': 'conv_7x1_1x7',\n",
-        "    'normal_0_input_y': 1,\n",
-        "    'normal_1_op_x': 'sep_conv_3x3',\n",
-        "    'normal_1_input_x': 2,\n",
-        "    'normal_1_op_y': 'sep_conv_5x5',\n",
-        "    'normal_1_input_y': 0,\n",
-        "    'normal_2_op_x': 'dil_sep_conv_3x3',\n",
-        "    'normal_2_input_x': 2,\n",
-        "    'normal_2_op_y': 'dil_sep_conv_3x3',\n",
-        "    'normal_2_input_y': 2,\n",
-        "    'normal_3_op_x': 'skip_connect',\n",
-        "    'normal_3_input_x': 4,\n",
-        "    'normal_3_op_y': 'dil_sep_conv_3x3',\n",
-        "    'normal_3_input_y': 4,\n",
-        "    'normal_4_op_x': 'conv_7x1_1x7',\n",
-        "    'normal_4_input_x': 2,\n",
-        "    'normal_4_op_y': 'sep_conv_3x3',\n",
-        "    'normal_4_input_y': 4,\n",
-        "    'normal_concat': [3, 5, 6],\n",
-        "    'reduce_0_op_x': 'avg_pool_3x3',\n",
-        "    'reduce_0_input_x': 0,\n",
-        "    'reduce_0_op_y': 'dil_sep_conv_3x3',\n",
-        "    'reduce_0_input_y': 1,\n",
-        "    'reduce_1_op_x': 'sep_conv_3x3',\n",
-        "    'reduce_1_input_x': 0,\n",
-        "    'reduce_1_op_y': 'sep_conv_3x3',\n",
-        "    'reduce_1_input_y': 0,\n",
-        "    'reduce_2_op_x': 'skip_connect',\n",
-        "    'reduce_2_input_x': 2,\n",
-        "    'reduce_2_op_y': 'sep_conv_7x7',\n",
-        "    'reduce_2_input_y': 0,\n",
-        "    'reduce_3_op_x': 'conv_7x1_1x7',\n",
-        "    'reduce_3_input_x': 4,\n",
-        "    'reduce_3_op_y': 'skip_connect',\n",
-        "    'reduce_3_input_y': 4,\n",
-        "    'reduce_4_op_x': 'conv_7x1_1x7',\n",
-        "    'reduce_4_input_x': 0,\n",
-        "    'reduce_4_op_y': 'conv_7x1_1x7',\n",
-        "    'reduce_4_input_y': 5,\n",
-        "    'reduce_concat': [3, 6]\n",
-        "}\n",
-        "\n",
-        "for t in query_nds_trial_stats('nas_cell', None, None, model_spec, cell_spec, 'cifar10'):\n",
-        "    assert t['config']['model_spec'] == model_spec\n",
-        "    assert t['config']['cell_spec'] == cell_spec\n",
-        "    pprint.pprint(t)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {
-        "tags": []
-      },
-      "outputs": [],
-      "source": [
-        "# count number\n",
-        "print('NDS (amoeba) count:', len(list(query_nds_trial_stats(None, 'amoeba', None, None, None, None, None))))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## NLP"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "pycharm": {
-          "metadata": false
-        }
-      },
-      "source": [
-        "Use the following two architectures as examples. \n",
-        "The arch in the paper is called \"receipe\" with nested variable, and now it is nunested in the benchmarks for NNI.\n",
-        "An arch has multiple Node, Node_input_n and Node_op, you can refer to doc for more details.\n",
-        "\n",
-        "arch1 : <img src=\"../../img/nas-bench-nlp-example1.jpeg\" width=400 height=300 /> \n",
-        "\n",
-        "\n",
-        "arch2 : <img src=\"../../img/nas-bench-nlp-example2.jpeg\" width=400 height=300 /> \n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {},
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "{'config': {'arch': {'h_new_0_input_0': 'node_3',\n                     'h_new_0_input_1': 'node_2',\n                     'h_new_0_input_2': 'node_1',\n                     'h_new_0_op': 'blend',\n                     'node_0_input_0': 'x',\n                     'node_0_input_1': 'h_prev_0',\n                     'node_0_op': 'linear',\n                     'node_1_input_0': 'node_0',\n                     'node_1_op': 'activation_tanh',\n                     'node_2_input_0': 'h_prev_0',\n                     'node_2_input_1': 'node_1',\n                     'node_2_input_2': 'x',\n                     'node_2_op': 'linear',\n                     'node_3_input_0': 'node_2',\n                     'node_3_op': 'activation_leaky_relu'},\n            'dataset': 'ptb',\n            'id': 20003},\n 'id': 16291,\n 'test_loss': 4.680262297102549,\n 'train_loss': 4.132040537087838,\n 'training_time': 177.05208373069763,\n 'val_loss': 4.707944253177966}\n"
-          ]
-        }
-      ],
-      "source": [
-        "import pprint\n",
-        "from nni.nas.benchmarks.nlp import query_nlp_trial_stats\n",
-        "\n",
-        "arch1 = {'h_new_0_input_0': 'node_3', 'h_new_0_input_1': 'node_2', 'h_new_0_input_2': 'node_1', 'h_new_0_op': 'blend', 'node_0_input_0': 'x', 'node_0_input_1': 'h_prev_0', 'node_0_op': 'linear','node_1_input_0': 'node_0', 'node_1_op': 'activation_tanh', 'node_2_input_0': 'h_prev_0', 'node_2_input_1': 'node_1', 'node_2_input_2': 'x', 'node_2_op': 'linear', 'node_3_input_0': 'node_2', 'node_3_op': 'activation_leaky_relu'}\n",
-        "for i in query_nlp_trial_stats(arch=arch1, dataset=\"ptb\"):\n",
-        "    pprint.pprint(i)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {},
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "[{'current_epoch': 46,\n  'id': 1796,\n  'test_loss': 6.233430054978619,\n  'train_loss': 6.4866799231542664,\n  'training_time': 146.5680329799652,\n  'val_loss': 6.326836978687959},\n {'current_epoch': 47,\n  'id': 1797,\n  'test_loss': 6.2402057403023825,\n  'train_loss': 6.485401405247535,\n  'training_time': 146.05511450767517,\n  'val_loss': 6.3239741605870865},\n {'current_epoch': 48,\n  'id': 1798,\n  'test_loss': 6.351145308363877,\n  'train_loss': 6.611281181173992,\n  'training_time': 145.8849437236786,\n  'val_loss': 6.436160816865809},\n {'current_epoch': 49,\n  'id': 1799,\n  'test_loss': 6.227155079159031,\n  'train_loss': 6.473414458249545,\n  'training_time': 145.51414465904236,\n  'val_loss': 6.313294354607077}]\n"
-          ]
-        }
-      ],
-      "source": [
-        "arch2 = {\"h_new_0_input_0\":\"node_0\",\"h_new_0_input_1\":\"node_1\",\"h_new_0_op\":\"elementwise_sum\",\"node_0_input_0\":\"x\",\"node_0_input_1\":\"h_prev_0\",\"node_0_op\":\"linear\",\"node_1_input_0\":\"node_0\",\"node_1_op\":\"activation_tanh\"}\n",
-        "for i in query_nlp_trial_stats(arch=arch2, dataset='wikitext-2', include_intermediates=True):\n",
-        "    pprint.pprint(i['intermediates'][45:49])"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {
-        "pycharm": {},
-        "tags": []
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Elapsed time:  5.60982608795166 seconds\n"
-          ]
-        }
-      ],
-      "source": [
-        "print('Elapsed time: ', time.time() - ti, 'seconds')"
-      ]
-    }
-  ],
-  "metadata": {
-    "file_extension": ".py",
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "name": "python",
-      "version": "3.8.5-final"
-    },
-    "mimetype": "text/x-python",
-    "name": "python",
-    "npconvert_exporter": "python",
-    "orig_nbformat": 2,
-    "pygments_lexer": "ipython3",
-    "version": 3
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/docs/source/NAS/DARTS.rst b/docs/source/NAS/DARTS.rst
deleted file mode 100644
index 20dfef7ba..000000000
--- a/docs/source/NAS/DARTS.rst
+++ /dev/null
@@ -1,66 +0,0 @@
-DARTS
-=====
-
-Introduction
-------------
-
-The paper `DARTS: Differentiable Architecture Search <https://arxiv.org/abs/1806.09055>`__ addresses the scalability challenge of architecture search by formulating the task in a differentiable manner. Their method is based on the continuous relaxation of the architecture representation, allowing efficient search of the architecture using gradient descent.
-
-Authors' code optimizes the network weights and architecture weights alternatively in mini-batches. They further explore the possibility that uses second order optimization (unroll) instead of first order, to improve the performance.
-
-Implementation on NNI is based on the `official implementation <https://github.com/quark0/darts>`__ and a `popular 3rd-party repo <https://github.com/khanrc/pt.darts>`__. DARTS on NNI is designed to be general for arbitrary search space. A CNN search space tailored for CIFAR10, same as the original paper, is implemented as a use case of DARTS.
-
-Reproduction Results
---------------------
-
-The above-mentioned example is meant to reproduce the results in the paper, we do experiments with first and second order optimization. Due to the time limit, we retrain *only the best architecture* derived from the search phase and we repeat the experiment *only once*. Our results is currently on par with the results reported in paper. We will add more results later when ready.
-
-.. list-table::
-   :header-rows: 1
-   :widths: auto
-
-   * - 
-     - In paper
-     - Reproduction
-   * - First order (CIFAR10)
-     - 3.00 +/- 0.14
-     - 2.78
-   * - Second order (CIFAR10)
-     - 2.76 +/- 0.09
-     - 2.80
-
-
-Examples
---------
-
-CNN Search Space
-^^^^^^^^^^^^^^^^
-
-:githublink:`Example code <examples/nas/oneshot/darts>`
-
-.. code-block:: bash
-
-   # In case NNI code is not cloned. If the code is cloned already, ignore this line and enter code folder.
-   git clone https://github.com/Microsoft/nni.git
-
-   # search the best architecture
-   cd examples/nas/oneshot/darts
-   python3 search.py
-
-   # train the best architecture
-   python3 retrain.py --arc-checkpoint ./checkpoints/epoch_49.json
-
-Reference
----------
-
-PyTorch
-^^^^^^^
-
-..  autoclass:: nni.retiarii.oneshot.pytorch.DartsTrainer
-    :noindex:
-
-Limitations
------------
-
-
-* DARTS doesn't support DataParallel and needs to be customized in order to support DistributedDataParallel.
diff --git a/docs/source/NAS/ENAS.rst b/docs/source/NAS/ENAS.rst
deleted file mode 100644
index 60415e545..000000000
--- a/docs/source/NAS/ENAS.rst
+++ /dev/null
@@ -1,43 +0,0 @@
-ENAS
-====
-
-Introduction
-------------
-
-The paper `Efficient Neural Architecture Search via Parameter Sharing <https://arxiv.org/abs/1802.03268>`__ uses parameter sharing between child models to accelerate the NAS process. In ENAS, a controller learns to discover neural network architectures by searching for an optimal subgraph within a large computational graph. The controller is trained with policy gradient to select a subgraph that maximizes the expected reward on the validation set. Meanwhile the model corresponding to the selected subgraph is trained to minimize a canonical cross entropy loss.
-
-Implementation on NNI is based on the `official implementation in Tensorflow <https://github.com/melodyguan/enas>`__\ , including a general-purpose Reinforcement-learning controller and a trainer that trains target network and this controller alternatively. Following paper, we have also implemented macro and micro search space on CIFAR10 to demonstrate how to use these trainers. Since code to train from scratch on NNI is not ready yet, reproduction results are currently unavailable.
-
-Examples
---------
-
-CIFAR10 Macro/Micro Search Space
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-:githublink:`Example code <examples/nas/oneshot/enas>`
-
-.. code-block:: bash
-
-   # In case NNI code is not cloned. If the code is cloned already, ignore this line and enter code folder.
-   git clone https://github.com/Microsoft/nni.git
-
-   # search the best architecture
-   cd examples/nas/oneshot/enas
-
-   # search in macro search space
-   python3 search.py --search-for macro
-
-   # search in micro search space
-   python3 search.py --search-for micro
-
-   # view more options for search
-   python3 search.py -h
-
-Reference
----------
-
-PyTorch
-^^^^^^^
-
-..  autoclass:: nni.retiarii.oneshot.pytorch.EnasTrainer
-    :noindex:
diff --git a/docs/source/NAS/ExplorationStrategies.rst b/docs/source/NAS/ExplorationStrategies.rst
deleted file mode 100644
index 68f0114ef..000000000
--- a/docs/source/NAS/ExplorationStrategies.rst
+++ /dev/null
@@ -1,74 +0,0 @@
-Exploration Strategies for Multi-trial NAS
-==========================================
-
-Usage of Exploration Strategy
------------------------------
-
-To use an exploration strategy, users simply instantiate an exploration strategy and pass the instantiated object to ``RetiariiExperiment``. Below is a simple example.
-
-.. code-block:: python
-
-  import nni.retiarii.strategy as strategy
-
-  exploration_strategy = strategy.Random(dedup=True)  # dedup=False if deduplication is not wanted
-
-Supported Exploration Strategies
---------------------------------
-
-NNI provides the following exploration strategies for multi-trial NAS.
-
-.. list-table::
-   :header-rows: 1
-   :widths: auto
-
-   * - Name
-     - Brief Introduction of Algorithm
-   * - `Random Strategy <./ApiReference.rst#nni.retiarii.strategy.Random>`__
-     - Randomly sampling new model(s) from user defined model space. (``nni.retiarii.strategy.Random``)
-   * - `Grid Search <./ApiReference.rst#nni.retiarii.strategy.GridSearch>`__
-     - Sampling new model(s) from user defined model space using grid search algorithm. (``nni.retiarii.strategy.GridSearch``)
-   * - `Regularized Evolution <./ApiReference.rst#nni.retiarii.strategy.RegularizedEvolution>`__
-     - Generating new model(s) from generated models using `regularized evolution algorithm <https://arxiv.org/abs/1802.01548>`__ . (``nni.retiarii.strategy.RegularizedEvolution``)
-   * - `TPE Strategy <./ApiReference.rst#nni.retiarii.strategy.TPEStrategy>`__
-     - Sampling new model(s) from user defined model space using `TPE algorithm <https://papers.nips.cc/paper/2011/file/86e8f7ab32cfd12577bc2619bc635690-Paper.pdf>`__ . (``nni.retiarii.strategy.TPEStrategy``)
-   * - `RL Strategy <./ApiReference.rst#nni.retiarii.strategy.PolicyBasedRL>`__
-     - It uses `PPO algorithm <https://arxiv.org/abs/1707.06347>`__ to sample new model(s) from user defined model space. (``nni.retiarii.strategy.PolicyBasedRL``)
-
-Customize Exploration Strategy
-------------------------------
-
-If users want to innovate a new exploration strategy, they can easily customize a new one following the interface provided by NNI. Specifically, users should inherit the base strategy class ``BaseStrategy``, then implement the member function ``run``. This member function takes ``base_model`` and ``applied_mutators`` as its input arguments. It can simply apply the user specified mutators in ``applied_mutators`` onto ``base_model`` to generate a new model. When a mutator is applied, it should be bound with a sampler (e.g., ``RandomSampler``). Every sampler implements the ``choice`` function which chooses value(s) from candidate values. The ``choice`` functions invoked in mutators are executed with the sampler.
-
-Below is a very simple random strategy, which makes the choices completely random.
-
-.. code-block:: python
-
-    from nni.retiarii import Sampler
-
-    class RandomSampler(Sampler):
-        def choice(self, candidates, mutator, model, index):
-            return random.choice(candidates)
-
-    class RandomStrategy(BaseStrategy):
-        def __init__(self):
-            self.random_sampler = RandomSampler()
-
-        def run(self, base_model, applied_mutators):
-            _logger.info('stargety start...')
-            while True:
-                avail_resource = query_available_resources()
-                if avail_resource > 0:
-                    model = base_model
-                    _logger.info('apply mutators...')
-                    _logger.info('mutators: %s', str(applied_mutators))
-                    for mutator in applied_mutators:
-                        mutator.bind_sampler(self.random_sampler)
-                        model = mutator.apply(model)
-                    # run models
-                    submit_models(model)
-                else:
-                    time.sleep(2)
-
-You can find that this strategy does not know the search space beforehand, it passively makes decisions every time ``choice`` is invoked from mutators. If a strategy wants to know the whole search space before making any decision (e.g., TPE, SMAC), it can use ``dry_run`` function provided by ``Mutator`` to obtain the space. An example strategy can be found :githublink:`here <nni/retiarii/strategy/tpe_strategy.py>`.
-
-After generating a new model, the strategy can use our provided APIs (e.g., ``submit_models``, ``is_stopped_exec``) to submit the model and get its reported results. More APIs can be found in `API References <./ApiReference.rst>`__.
diff --git a/docs/source/NAS/FBNet.rst b/docs/source/NAS/FBNet.rst
deleted file mode 100644
index 8e92b1cd4..000000000
--- a/docs/source/NAS/FBNet.rst
+++ /dev/null
@@ -1,153 +0,0 @@
-FBNet
-======
-
-.. note:: This one-shot NAS is still implemented under NNI NAS 1.0, and will `be migrated to Retiarii framework in v2.4 <https://github.com/microsoft/nni/issues/3814>`__.
-
-For the mobile application of facial landmark, based on the basic architecture of PFLD model, we have applied the FBNet (Block-wise DNAS) to design an concise model with the trade-off between latency and accuracy. References are listed as below:
-
-
-* `FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search <https://arxiv.org/abs/1812.03443>`__
-* `PFLD: A Practical Facial Landmark Detector <https://arxiv.org/abs/1902.10859>`__
-
-FBNet is a block-wise differentiable NAS method (Block-wise DNAS), where the best candidate building blocks can be chosen by using Gumbel Softmax random sampling and differentiable training. At each layer (or stage) to be searched, the diverse candidate blocks are side by side planned (just like the effectiveness of structural re-parameterization), leading to sufficient pre-training of the supernet. The pre-trained supernet is further sampled for finetuning of the subnet, to achieve better performance.
-
-.. image:: ../../img/fbnet.png
-   :target: ../../img/fbnet.png
-   :alt:
-
-
-PFLD is a lightweight facial landmark model for realtime application. The architecture of PLFD is firstly simplified for acceleration, by using the stem block of PeleeNet, average pooling with depthwise convolution and eSE module.
-
-To achieve better trade-off between latency and accuracy, the FBNet is further applied on the simplified PFLD for searching the best block at each specific layer. The search space is based on the FBNet space, and optimized for mobile deployment by using the average pooling with depthwise convolution and eSE module etc.
-
-
-Experiments
-------------
-
-To verify the effectiveness of FBNet applied on PFLD, we choose the open source dataset with 106 landmark points as the benchmark:
-
-* `Grand Challenge of 106-Point Facial Landmark Localization <https://arxiv.org/abs/1905.03469>`__
-
-The baseline model is denoted as MobileNet-V3 PFLD (`Reference baseline <https://github.com/Hsintao/pfld_106_face_landmarks>`__), and the searched model is denoted as Subnet. The experimental results are listed as below, where the latency is tested on Qualcomm 625 CPU (ARMv8):
-
-
-.. list-table::
-   :header-rows: 1
-   :widths: auto
-
-   * - Model
-     - Size
-     - Latency
-     - Validation NME
-   * - MobileNet-V3 PFLD
-     - 1.01MB
-     - 10ms
-     - 6.22%
-   * - Subnet
-     - 693KB
-     - 1.60ms
-     - 5.58%
-
-
-Example
---------
-
-`Example code <https://github.com/microsoft/nni/tree/master/examples/nas/oneshot/pfld>`__
-
-Please run the following scripts at the example directory.
-
-The Python dependencies used here are listed as below:
-
-.. code-block:: bash
-
-   numpy==1.18.5
-   opencv-python==4.5.1.48
-   torch==1.6.0
-   torchvision==0.7.0
-   onnx==1.8.1
-   onnx-simplifier==0.3.5
-   onnxruntime==1.7.0
-
-Data Preparation
------------------
-
-Firstly, you should download the dataset `106points dataset <https://drive.google.com/file/d/1I7QdnLxAlyG2Tq3L66QYzGhiBEoVfzKo/view?usp=sharing>`__ to the path ``./data/106points`` . The dataset includes the train-set and test-set:
-
-.. code-block:: bash
-
-   ./data/106points/train_data/imgs
-   ./data/106points/train_data/list.txt
-   ./data/106points/test_data/imgs
-   ./data/106points/test_data/list.txt
-
-
-Quik Start
------------
-
-1. Search
-^^^^^^^^^^
-
-Based on the architecture of simplified PFLD, the setting of multi-stage search space and hyper-parameters for searching should be firstly configured to construct the supernet, as an example:
-
-.. code-block:: bash
-
-   from lib.builder import search_space
-   from lib.ops import PRIMITIVES
-   from lib.supernet import PFLDInference, AuxiliaryNet
-   from nni.algorithms.nas.pytorch.fbnet import LookUpTable, NASConfig,
-
-   # configuration of hyper-parameters
-   # search_space defines the multi-stage search space
-   nas_config = NASConfig(
-          model_dir="./ckpt_save",
-          nas_lr=0.01,
-          mode="mul",
-          alpha=0.25,
-          beta=0.6,
-          search_space=search_space,
-      )
-   # lookup table to manage the information
-   lookup_table = LookUpTable(config=nas_config, primitives=PRIMITIVES)
-   # created supernet
-   pfld_backbone = PFLDInference(lookup_table)
-
-
-After creation of the supernet with the specification of search space and hyper-parameters, we can run below command to start searching and training of the supernet:
-
-.. code-block:: bash
-
-   python train.py --dev_id "0,1" --snapshot "./ckpt_save" --data_root "./data/106points"
-
-The validation accuracy will be shown during training, and the model with best accuracy will be saved as ``./ckpt_save/supernet/checkpoint_best.pth``.
-
-
-2. Finetune
-^^^^^^^^^^^^
-
-After pre-training of the supernet, we can run below command to sample the subnet and conduct the finetuning:
-
-.. code-block:: bash
-
-   python retrain.py --dev_id "0,1" --snapshot "./ckpt_save" --data_root "./data/106points" \
-                     --supernet "./ckpt_save/supernet/checkpoint_best.pth"
-
-The validation accuracy will be shown during training, and the model with best accuracy will be saved as ``./ckpt_save/subnet/checkpoint_best.pth``.
-
-
-3. Export
-^^^^^^^^^^
-
-After the finetuning of subnet, we can run below command to export the ONNX model:
-
-.. code-block:: bash
-
-   python export.py --supernet "./ckpt_save/supernet/checkpoint_best.pth" \
-                    --resume "./ckpt_save/subnet/checkpoint_best.pth"
-
-ONNX model is saved as ``./output/subnet.onnx``, which can be further converted to the mobile inference engine by using `MNN <https://github.com/alibaba/MNN>`__ .
-
-The checkpoints of pre-trained supernet and subnet are offered as below:
-
-* `Supernet <https://drive.google.com/file/d/1TCuWKq8u4_BQ84BWbHSCZ45N3JGB9kFJ/view?usp=sharing>`__
-* `Subnet <https://drive.google.com/file/d/160rkuwB7y7qlBZNM3W_T53cb6MQIYHIE/view?usp=sharing>`__
-* `ONNX model <https://drive.google.com/file/d/1s-v-aOiMv0cqBspPVF3vSGujTbn_T_Uo/view?usp=sharing>`__
\ No newline at end of file
diff --git a/docs/source/NAS/Hypermodules.rst b/docs/source/NAS/Hypermodules.rst
deleted file mode 100644
index e87bf3472..000000000
--- a/docs/source/NAS/Hypermodules.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-Hypermodules
-============
-
-Hypermodule is a (PyTorch) module which contains many architecture/hyperparameter candidates for this module. By using hypermodule in user defined model, NNI will help users automatically find the best architecture/hyperparameter of the hypermodules for this model. This follows the design philosophy of Retiarii that users write DNN model as a space.
-
-There has been proposed some hypermodules in NAS community, such as AutoActivation, AutoDropout. Some of them are implemented in the Retiarii framework.
-
-..  autoclass:: nni.retiarii.nn.pytorch.AutoActivation
-    :members:
\ No newline at end of file
diff --git a/docs/source/NAS/MutationPrimitives.rst b/docs/source/NAS/MutationPrimitives.rst
deleted file mode 100644
index 334631225..000000000
--- a/docs/source/NAS/MutationPrimitives.rst
+++ /dev/null
@@ -1,220 +0,0 @@
-Mutation Primitives
-===================
-
-.. TODO: this file will be merged with API reference in future.
-
-To make users easily express a model space within their PyTorch/TensorFlow model, NNI provides some inline mutation APIs as shown below.
-
-We show the most common use case here. For advanced usages, please see `reference <./ApiReference.rst>`__.
-
-.. note:: We can actively adding more mutation primitives. If you have any suggestions, feel free to `ask here <https://github.com/microsoft/nni/issues>`__.
-
-``nn.LayerChoice``
-""""""""""""""""""
-
-API reference: :class:`nni.retiarii.nn.pytorch.LayerChoice`
-
-It allows users to put several candidate operations (e.g., PyTorch modules), one of them is chosen in each explored model.
-
-..  code-block:: python
-
-    # import nni.retiarii.nn.pytorch as nn
-    # declared in `__init__` method
-    self.layer = nn.LayerChoice([
-      ops.PoolBN('max', channels, 3, stride, 1),
-      ops.SepConv(channels, channels, 3, stride, 1),
-      nn.Identity()
-    ])
-    # invoked in `forward` method
-    out = self.layer(x)
-
-``nn.InputChoice``
-""""""""""""""""""
-
-API reference: :class:`nni.retiarii.nn.pytorch.InputChoice`
-
-It is mainly for choosing (or trying) different connections. It takes several tensors and chooses ``n_chosen`` tensors from them.
-
-..  code-block:: python
-
-    # import nni.retiarii.nn.pytorch as nn
-    # declared in `__init__` method
-    self.input_switch = nn.InputChoice(n_chosen=1)
-    # invoked in `forward` method, choose one from the three
-    out = self.input_switch([tensor1, tensor2, tensor3])
-
-``nn.ValueChoice``
-""""""""""""""""""
-
-API reference: :class:`nni.retiarii.nn.pytorch.ValueChoice`
-
-It is for choosing one value from some candidate values. The most common use cases are:
-
-* Used as input arguments of :class:`nni.retiarii.basic_unit` (i.e., modules in ``nni.retiarii.nn.pytorch`` and user-defined modules decorated with ``@basic_unit``).
-* Used as input arguments of evaluator (*new in v2.7*).
-
-Examples are as follows:
-
-..  code-block:: python
-
-    # import nni.retiarii.nn.pytorch as nn
-    # used in `__init__` method
-    self.conv = nn.Conv2d(XX, XX, kernel_size=nn.ValueChoice([1, 3, 5]))
-    self.op = MyOp(nn.ValueChoice([0, 1]), nn.ValueChoice([-1, 1]))
-
-    # used in evaluator
-    def train_and_evaluate(model_cls, learning_rate):
-      ...
-
-    self.evaluator = FunctionalEvaluator(train_and_evaluate, learning_rate=nn.ValueChoice([1e-3, 1e-2, 1e-1]))
-
-Value choices supports arithmetic operators, which is particularly useful when searching for a network width multiplier:
-
-..  code-block:: python
-
-    # init
-    scale = nn.ValueChoice([1.0, 1.5, 2.0])
-    self.conv1 = nn.Conv2d(3, round(scale * 16))
-    self.conv2 = nn.Conv2d(round(scale * 16), round(scale * 64))
-    self.conv3 = nn.Conv2d(round(scale * 64), round(scale * 256))
-
-    # forward
-    return self.conv3(self.conv2(self.conv1(x)))
-
-Or when kernel size and padding are coupled so as to keep the output size constant:
-
-..  code-block:: python
-
-    # init
-    ks = nn.ValueChoice([3, 5, 7])
-    self.conv = nn.Conv2d(3, 16, kernel_size=ks, padding=(ks - 1) // 2)
-
-    # forward
-    return self.conv(x)
-
-Or when several layers are concatenated for a final layer.
-
-..  code-block:: python
-
-    # init
-    self.linear1 = nn.Linear(3, nn.ValueChoice([1, 2, 3], label='a'))
-    self.linear2 = nn.Linear(3, nn.ValueChoice([4, 5, 6], label='b'))
-    self.final = nn.Linear(nn.ValueChoice([1, 2, 3], label='a') + nn.ValueChoice([4, 5, 6], label='b'), 2)
-
-    # forward
-    return self.final(torch.cat([self.linear1(x), self.linear2(x)], 1))
-
-Some advanced operators are also provided, such as ``nn.ValueChoice.max`` and ``nn.ValueChoice.cond``. See reference of :class:`nni.retiarii.nn.pytorch.ValueChoice` for more details.
-
-.. tip::
-
-  All the APIs have an optional argument called ``label``, mutations with the same label will share the same choice. A typical example is,
-
-  .. code-block:: python
-
-      self.net = nn.Sequential(
-        nn.Linear(10, nn.ValueChoice([32, 64, 128], label='hidden_dim')),
-        nn.Linear(nn.ValueChoice([32, 64, 128], label='hidden_dim'), 3)
-      )
-
-.. warning::
-
-    It looks as if a specific candidate has been chosen (e.g., the way you can put ``ValueChoice`` as a parameter of ``nn.ValueChoice``), but in fact it's a syntax sugar as because the basic units and evaluators do all the underlying works. That means, you cannot assume that ``ValueChoice`` can be used in the same way as its candidates. For example, the following usage will NOT work:
-
-    .. code-block:: python
-
-      self.blocks = []
-      for i in range(nn.ValueChoice([1, 2, 3])):
-        self.blocks.append(Block())
-
-      # NOTE: instead you should probably write
-      # self.blocks = nn.Repeat(Block(), (1, 3))
-
-``nn.Repeat``
-"""""""""""""
-
-API reference: :class:`nni.retiarii.nn.pytorch.Repeat`
-
-Repeat a block by a variable number of times.
-
-.. code-block:: python
-
-  # import nni.retiarii.nn.pytorch as nn
-  # used in `__init__` method
-
-  # Block() will be deep copied and repeated 3 times
-  self.blocks = nn.Repeat(Block(), 3)
-
-  # Block() will be repeated 1, 2, or 3 times
-  self.blocks = nn.Repeat(Block(), (1, 3))
-
-  # Can be used together with layer choice
-  # With deep copy, the 3 layers will have the same label, thus share the choice
-  self.blocks = nn.Repeat(nn.LayerChoice([...]), (1, 3))
-
-  # To make the three layer choices independently
-  # Need a factory function that accepts index (0, 1, 2, ...) and returns the module of the `index`-th layer.
-  self.blocks = nn.Repeat(lambda index: nn.LayerChoice([...], label=f'layer{index}'), (1, 3))
-
-``nn.Cell``
-"""""""""""
-
-API reference: :class:`nni.retiarii.nn.pytorch.Cell`
-
-This cell structure is popularly used in `NAS literature <https://arxiv.org/abs/1611.01578>`__. High-level speaking, literatures often use the following glossaries.
-
-.. list-table::
-   :widths: 25 75
-
-   * - Cell
-     - A cell consists of several nodes.
-   * - Node
-     - A node is the **sum** of several operators.
-   * - Operator
-     - Each operator is independently chosen from a list of user-specified candidate operators.
-   * - Operator's input
-     - Each operator has one input, chosen from previous nodes as well as predecessors.
-   * - Predecessors
-     - Input of cell. A cell can have multiple predecessors. Predecessors are sent to *preprocessor* for preprocessing.
-   * - Cell's output
-     - Output of cell. Usually concatenation of several nodes (possibly all nodes) in the cell. Cell's output, along with predecessors, are sent to *postprocessor* for postprocessing.
-   * - Preprocessor
-     - Extra preprocessing to predecessors. Usually used in shape alignment (e.g., predecessors have different shapes). By default, do nothing.
-   * - Postprocessor
-     - Extra postprocessing for cell's output. Usually used to chain cells with multiple Predecessors
-       (e.g., the next cell wants to have the outputs of both this cell and previous cell as its input). By default, directly use this cell's output.
-
-Example usages:
-
-.. code-block:: python
-
-  # import nni.retiarii.nn.pytorch as nn
-  # used in `__init__` method
-
-  # Choose between conv2d and maxpool2d.
-  # The cell have 4 nodes, 1 op per node, and 2 predecessors.
-  cell = nn.Cell([nn.Conv2d(32, 32, 3), nn.MaxPool2d(3)], 4, 1, 2)
-  # forward
-  cell([input1, input2])
-
-  # Use `merge_op` to specify how to construct the output.
-  # The output will then have dynamic shape, depending on which input has been used in the cell.
-  cell = nn.Cell([nn.Conv2d(32, 32, 3), nn.MaxPool2d(3)], 4, 1, 2, merge_op='loose_end')
-
-  # The op candidates can be callable that accepts node index in cell, op index in node, and input index.
-  cell = nn.Cell([
-    lambda node_index, op_index, input_index: nn.Conv2d(32, 32, 3, stride=2 if input_index < 1 else 1),
-    ...
-  ], 4, 1, 2)
-
-  # predecessor example
-  class Preprocessor:
-    def __init__(self):
-      self.conv1 = nn.Conv2d(16, 32, 1)
-      self.conv2 = nn.Conv2d(64, 32, 1)
-
-    def forward(self, x):
-      return [self.conv1(x[0]), self.conv2(x[1])]
-
-  cell = nn.Cell([nn.Conv2d(32, 32, 3), nn.MaxPool2d(3)], 4, 1, 2, preprocessor=Preprocessor())
-  cell([torch.randn(1, 16, 48, 48), torch.randn(1, 64, 48, 48)])  # the two inputs will be sent to conv1 and conv2 respectively
diff --git a/docs/source/NAS/OneshotTrainer.rst b/docs/source/NAS/OneshotTrainer.rst
deleted file mode 100644
index e276a4812..000000000
--- a/docs/source/NAS/OneshotTrainer.rst
+++ /dev/null
@@ -1,43 +0,0 @@
-One-shot NAS
-============
-
-Before reading this tutorial, we highly recommend you to first go through the tutorial of how to `define a model space <./QuickStart.rst#define-your-model-space>`__.
-
-Model Search with One-shot Trainer
-----------------------------------
-
-With a defined model space, users can explore the space in two ways. One is using strategy and single-arch evaluator as demonstrated `here <./QuickStart.rst#explore-the-defined-model-space>`__. The other is using one-shot trainer, which consumes much less computational resource compared to the first one. In this tutorial we focus on this one-shot approach. The principle of one-shot approach is combining all the models in a model space into one big model (usually called super-model or super-graph). It takes charge of both search, training and testing, by training and evaluating this big model.
-
-We list the supported one-shot trainers here:
-
-* DARTS trainer
-* ENAS trainer
-* ProxylessNAS trainer
-* Single-path (random) trainer
-
-See `API reference <./ApiReference.rst>`__ for detailed usages. Here, we show an example to use DARTS trainer manually.
-
-.. code-block:: python
-
-  from nni.retiarii.oneshot.pytorch import DartsTrainer
-  trainer = DartsTrainer(
-      model=model,
-      loss=criterion,
-      metrics=lambda output, target: accuracy(output, target, topk=(1,)),
-      optimizer=optim,
-      num_epochs=args.epochs,
-      dataset=dataset_train,
-      batch_size=args.batch_size,
-      log_frequency=args.log_frequency,
-      unrolled=args.unrolled
-  )
-  trainer.fit()
-  final_architecture = trainer.export()
-
-After the searching is done, we can use the exported architecture to instantiate the full network for retraining. Here is an example:
-
-.. code-block:: python
-
-    from nni.retiarii import fixed_arch
-    with fixed_arch('/path/to/checkpoint.json'):
-        model = Model()
diff --git a/docs/source/NAS/Overview.rst b/docs/source/NAS/Overview.rst
deleted file mode 100644
index a2e48c3a4..000000000
--- a/docs/source/NAS/Overview.rst
+++ /dev/null
@@ -1,82 +0,0 @@
-Retiarii for Neural Architecture Search
-=======================================
-
-.. attention:: NNI's latest NAS supports are all based on Retiarii Framework, users who are still on `early version using NNI NAS v1.0 <https://nni.readthedocs.io/en/v2.2/nas.html>`__ shall migrate your work to Retiarii as soon as possible.
-
-.. contents::
-
-Motivation
-----------
-
-Automatic neural architecture search is playing an increasingly important role in finding better models. Recent research has proven the feasibility of automatic NAS and has led to models that beat many manually designed and tuned models. Representative works include `NASNet <https://arxiv.org/abs/1707.07012>`__\ , `ENAS <https://arxiv.org/abs/1802.03268>`__\ , `DARTS <https://arxiv.org/abs/1806.09055>`__\ , `Network Morphism <https://arxiv.org/abs/1806.10282>`__\ , and `Evolution <https://arxiv.org/abs/1703.01041>`__. In addition, new innovations continue to emerge.
-
-However, it is pretty hard to use existing NAS work to help develop common DNN models. Therefore, we designed `Retiarii <https://www.usenix.org/system/files/osdi20-zhang_quanlu.pdf>`__, a novel NAS/HPO framework, and implemented it in NNI. It helps users easily construct a model space (or search space, tuning space), and utilize existing NAS algorithms. The framework also facilitates NAS innovation and is used to design new NAS algorithms.
-
-Overview
---------
-
-There are three key characteristics of the Retiarii framework:
-
-* Simple APIs are provided for defining model search space within PyTorch/TensorFlow model.
-* SOTA NAS algorithms are built-in to be used for exploring model search space.
-* System-level optimizations are implemented for speeding up the exploration.
-
-There are two types of model space exploration approach: **Multi-trial NAS** and **One-shot NAS**. Mutli-trial NAS trains each sampled model in the model space independently, while One-shot NAS samples the model from a super model. After constructing the model space, users can use either exploration appraoch to explore the model space. 
-
-
-Multi-trial NAS
----------------
-
-Multi-trial NAS means each sampled model from model space is trained independently. A typical multi-trial NAS is `NASNet <https://arxiv.org/abs/1707.07012>`__. The algorithm to sample models from model space is called exploration strategy. NNI has supported the following exploration strategies for multi-trial NAS.
-
-.. list-table::
-   :header-rows: 1
-   :widths: auto
-
-   * - Exploration Strategy Name
-     - Brief Introduction of Algorithm
-   * - Random Strategy
-     - Randomly sampling new model(s) from user defined model space. (``nni.retiarii.strategy.Random``)
-   * - Grid Search
-     - Sampling new model(s) from user defined model space using grid search algorithm. (``nni.retiarii.strategy.GridSearch``)
-   * - Regularized Evolution
-     - Generating new model(s) from generated models using `regularized evolution algorithm <https://arxiv.org/abs/1802.01548>`__ . (``nni.retiarii.strategy.RegularizedEvolution``)
-   * - TPE Strategy
-     - Sampling new model(s) from user defined model space using `TPE algorithm <https://papers.nips.cc/paper/2011/file/86e8f7ab32cfd12577bc2619bc635690-Paper.pdf>`__ . (``nni.retiarii.strategy.TPEStrategy``)
-   * - RL Strategy
-     - It uses `PPO algorithm <https://arxiv.org/abs/1707.06347>`__ to sample new model(s) from user defined model space. (``nni.retiarii.strategy.PolicyBasedRL``)
-
-
-Please refer to `here <./multi_trial_nas.rst>`__ for detailed usage of multi-trial NAS.
-
-One-shot NAS
-------------
-
-One-shot NAS means building model space into a super-model, training the super-model with weight sharing, and then sampling models from the super-model to find the best one. `DARTS <https://arxiv.org/abs/1806.09055>`__ is a typical one-shot NAS.
-Below is the supported one-shot NAS algorithms. More one-shot NAS will be supported soon.
-
-.. list-table::
-   :header-rows: 1
-   :widths: auto
-
-   * - One-shot Algorithm Name
-     - Brief Introduction of Algorithm
-   * - `ENAS <ENAS.rst>`__
-     - `Efficient Neural Architecture Search via Parameter Sharing <https://arxiv.org/abs/1802.03268>`__. In ENAS, a controller learns to discover neural network architectures by searching for an optimal subgraph within a large computational graph. It uses parameter sharing between child models to achieve fast speed and excellent performance.
-   * - `DARTS <DARTS.rst>`__
-     - `DARTS: Differentiable Architecture Search <https://arxiv.org/abs/1806.09055>`__ introduces a novel algorithm for differentiable network architecture search on bilevel optimization.
-   * - `SPOS <SPOS.rst>`__
-     - `Single Path One-Shot Neural Architecture Search with Uniform Sampling <https://arxiv.org/abs/1904.00420>`__ constructs a simplified supernet trained with a uniform path sampling method and applies an evolutionary algorithm to efficiently search for the best-performing architectures.
-   * - `ProxylessNAS <Proxylessnas.rst>`__
-     - `ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware <https://arxiv.org/abs/1812.00332>`__. It removes proxy, directly learns the architectures for large-scale target tasks and target hardware platforms.
-
-Please refer to `here <one_shot_nas.rst>`__ for detailed usage of one-shot NAS algorithms.
-
-Reference and Feedback
-----------------------
-
-* `Quick Start <./QuickStart.rst>`__ ;
-* `Construct Your Model Space <./construct_space.rst>`__ ;
-* `Retiarii: A Deep Learning Exploratory-Training Framework <https://www.usenix.org/system/files/osdi20-zhang_quanlu.pdf>`__ ;
-* To `report a bug <https://github.com/microsoft/nni/issues/new?template=bug-report.rst>`__ for this feature in GitHub ;
-* To `file a feature or improvement request <https://github.com/microsoft/nni/issues/new?template=enhancement.rst>`__ for this feature in GitHub .
diff --git a/docs/source/NAS/Proxylessnas.rst b/docs/source/NAS/Proxylessnas.rst
deleted file mode 100644
index 3700d3192..000000000
--- a/docs/source/NAS/Proxylessnas.rst
+++ /dev/null
@@ -1,74 +0,0 @@
-ProxylessNAS on NNI
-===================
-
-Introduction
-------------
-
-The paper `ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware <https://arxiv.org/pdf/1812.00332.pdf>`__ removes proxy, it directly learns the architectures for large-scale target tasks and target hardware platforms. They address high memory consumption issue of differentiable NAS and reduce the computational cost to the same level of regular training while still allowing a large candidate set. Please refer to the paper for the details.
-
-Usage
------
-
-To use ProxylessNAS training/searching approach, users need to specify search space in their model using `NNI NAS interface <./MutationPrimitives.rst>`__\ , e.g., ``LayerChoice``\ , ``InputChoice``. After defining and instantiating the model, the following work can be leaved to ProxylessNasTrainer by instantiating the trainer and passing the model to it.
-
-.. code-block:: python
-
-   trainer = ProxylessTrainer(model,
-                              loss=LabelSmoothingLoss(),
-                              dataset=None,
-                              optimizer=optimizer,
-                              metrics=lambda output, target: accuracy(output, target, topk=(1, 5,)),
-                              num_epochs=120,
-                              log_frequency=10,
-                              grad_reg_loss_type=args.grad_reg_loss_type, 
-                              grad_reg_loss_params=grad_reg_loss_params, 
-                              applied_hardware=args.applied_hardware, dummy_input=(1, 3, 224, 224),
-                              ref_latency=args.reference_latency)
-   trainer.train()
-   trainer.export(args.arch_path)
-
-The complete example code can be found :githublink:`here <examples/nas/oneshot/proxylessnas>`.
-
-**Input arguments of ProxylessNasTrainer**
-
-
-* **model** (*PyTorch model, required*\ ) - The model that users want to tune/search. It has mutables to specify search space.
-* **metrics** (*PyTorch module, required*\ ) - The main term of the loss function for model train. Receives logits and ground truth label, return a loss tensor.
-* **optimizer** (*PyTorch Optimizer, required*\) - The optimizer used for optimizing the model.
-* **num_epochs** (*int, optional, default = 120*\ ) - The number of epochs to train/search.
-* **dataset** (*PyTorch dataset, required*\ ) - Dataset for training. Will be split for training weights and architecture weights.
-* **warmup_epochs** (*int, optional, default = 0*\ ) - The number of epochs to do during warmup.
-* **batch_size** (*int, optional, default = 64*\ ) - Batch size.
-* **workers** (*int, optional, default = 4*\ ) - Workers for data loading.
-* **device** (*device, optional, default = 'cpu'*\ ) - The devices that users provide to do the train/search. The trainer applies data parallel on the model for users.
-* **log_frequency** (*int, optional, default = None*\ ) - Step count per logging.
-* **arc_learning_rate** (*float, optional, default = 1e-3*\ ) - The learning rate of the architecture parameters optimizer.
-* **grad_reg_loss_type** (*'mul#log', 'add#linear', or None, optional, default = 'add#linear'*\ ) - Regularization type to add hardware related loss. The trainer will not apply loss regularization when grad_reg_loss_type is set as None.
-* **grad_reg_loss_params** (*dict, optional, default = None*\ ) - Regularization params. 'alpha' and 'beta' is required when ``grad_reg_loss_type`` is 'mul#log', 'lambda' is required when ``grad_reg_loss_type`` is 'add#linear'.
-* **applied_hardware** (*string, optional, default = None*\ ) - Applied hardware for to constraint the model's latency. Latency is predicted by Microsoft nn-Meter (https://github.com/microsoft/nn-Meter). 
-* **dummy_input** (*tuple, optional, default = (1, 3, 224, 224)*\ ) - The dummy input shape when applied to the target hardware.
-* **ref_latency** (*float, optional, default = 65.0*\ ) - Reference latency value in the applied hardware (ms).
-
-
-Implementation
---------------
-
-The implementation on NNI is based on the `offical implementation <https://github.com/mit-han-lab/ProxylessNAS>`__. The official implementation supports two training approaches: gradient descent and RL based. In our current implementation on NNI, gradient descent training approach is supported. The complete support of ProxylessNAS is ongoing.
-
-The official implementation supports different targeted hardware, including 'mobile', 'cpu', 'gpu8', 'flops'.  In NNI repo, the hardware latency prediction is supported by `Microsoft nn-Meter <https://github.com/microsoft/nn-Meter>`__. nn-Meter is an accurate inference latency predictor for DNN models on diverse edge devices. nn-Meter support four hardwares up to now, including *'cortexA76cpu_tflite21'*, *'adreno640gpu_tflite21'*, *'adreno630gpu_tflite21'*, and *'myriadvpu_openvino2019r2'*. Users can find more information about nn-Meter on its website. More hardware will be supported in the future. Users could find more details about applying ``nn-Meter`` `here <./HardwareAwareNAS.rst>`__ .
-
-Below we will describe implementation details. Like other one-shot NAS algorithms on NNI, ProxylessNAS is composed of two parts: *search space* and *training approach*. For users to flexibly define their own search space and use built-in ProxylessNAS training approach, we put the specified search space in :githublink:`example code <examples/nas/oneshot/proxylessnas>` using :githublink:`NNI NAS interface <nni/retiarii/oneshot/pytorch/proxyless>`.
-
-.. image:: ../../img/proxylessnas.png
-   :target: ../../img/proxylessnas.png
-   :alt: 
-
-
-ProxylessNAS training approach is composed of ProxylessLayerChoice and ProxylessNasTrainer. ProxylessLayerChoice instantiates MixedOp for each mutable (i.e., LayerChoice), and manage architecture weights in MixedOp. **For DataParallel**\ , architecture weights should be included in user model. Specifically, in ProxylessNAS implementation, we add MixedOp to the corresponding mutable (i.e., LayerChoice) as a member variable. The ProxylessLayerChoice class also exposes two member functions, i.e., ``resample``\ , ``finalize_grad``\ , for the trainer to control the training of architecture weights.
-
-ProxylessNasMutator also implements the forward logic of the mutables (i.e., LayerChoice).
-
-Reproduce Results
------------------
-
-To reproduce the result, we first run the search, we found that though it runs many epochs the chosen architecture converges at the first several epochs. This is probably induced by hyper-parameters or the implementation, we are working on it. 
\ No newline at end of file
diff --git a/docs/source/NAS/QuickStart.rst b/docs/source/NAS/QuickStart.rst
deleted file mode 100644
index 1a259ed32..000000000
--- a/docs/source/NAS/QuickStart.rst
+++ /dev/null
@@ -1,215 +0,0 @@
-Quick Start of Retiarii on NNI
-==============================
-
-
-.. contents::
-
-In this quick start, we use multi-trial NAS as an example to show how to construct and explore a model space. There are mainly three crucial components for a neural architecture search task, namely,
-
-* Model search space that defines a set of models to explore.
-* A proper strategy as the method to explore this model space.
-* A model evaluator that reports the performance of every model in the space.
-
-The tutorial for One-shot NAS can be found `here <./OneshotTrainer.rst>`__.
-
-Currently, PyTorch is the only supported framework by Retiarii, and we have only tested **PyTorch 1.7 to 1.10**. This documentation assumes PyTorch context but it should also apply to other frameworks, which is in our future plan.
-
-Define your Model Space
------------------------
-
-Model space is defined by users to express a set of models that users want to explore, which contains potentially good-performing models. In this framework, a model space is defined with two parts: a base model and possible mutations on the base model.
-
-Define Base Model
-^^^^^^^^^^^^^^^^^
-
-Defining a base model is almost the same as defining a PyTorch (or TensorFlow) model. Usually, you only need to replace the code ``import torch.nn as nn`` with ``import nni.retiarii.nn.pytorch as nn`` to use our wrapped PyTorch modules.
-
-Below is a very simple example of defining a base model.
-
-.. code-block:: python
-
-  import torch
-  import torch.nn.functional as F
-  import nni.retiarii.nn.pytorch as nn
-  from nni.retiarii import model_wrapper
-
-  @model_wrapper      # this decorator should be put on the out most
-  class Net(nn.Module):
-    def __init__(self):
-      super().__init__()
-      self.conv1 = nn.Conv2d(1, 32, 3, 1)
-      self.conv2 = nn.Conv2d(32, 64, 3, 1)
-      self.dropout1 = nn.Dropout(0.25)
-      self.dropout2 = nn.Dropout(0.5)
-      self.fc1 = nn.Linear(9216, 128)
-      self.fc2 = nn.Linear(128, 10)
-
-    def forward(self, x):
-      x = F.relu(self.conv1(x))
-      x = F.max_pool2d(self.conv2(x), 2)
-      x = torch.flatten(self.dropout1(x), 1)
-      x = self.fc2(self.dropout2(F.relu(self.fc1(x))))
-      output = F.log_softmax(x, dim=1)
-      return output
-
-.. tip:: Always keep in mind that you should use ``import nni.retiarii.nn.pytorch as nn`` and :meth:`nni.retiarii.model_wrapper`. Many mistakes are a result of forgetting one of those. Also, please use ``torch.nn`` for submodules of ``nn.init``, e.g., ``torch.nn.init`` instead of ``nn.init``. 
-
-Define Model Mutations
-^^^^^^^^^^^^^^^^^^^^^^
-
-A base model is only one concrete model not a model space. We provide `APIs and primitives <./MutationPrimitives.rst>`__ for users to express how the base model can be mutated. That is, to build a model space which includes many models.
-
-Based on the above base model, we can define a model space as below. 
-
-.. code-block:: diff
-
-  import torch
-  import torch.nn.functional as F
-  import nni.retiarii.nn.pytorch as nn
-  from nni.retiarii import model_wrapper
-
-  @model_wrapper
-  class Net(nn.Module):
-    def __init__(self):
-      super().__init__()
-      self.conv1 = nn.Conv2d(1, 32, 3, 1)
-  -   self.conv2 = nn.Conv2d(32, 64, 3, 1)
-  +   self.conv2 = nn.LayerChoice([
-  +       nn.Conv2d(32, 64, 3, 1),
-  +       DepthwiseSeparableConv(32, 64)
-  +   ])
-  -   self.dropout1 = nn.Dropout(0.25)
-  +   self.dropout1 = nn.Dropout(nn.ValueChoice([0.25, 0.5, 0.75]))
-      self.dropout2 = nn.Dropout(0.5)
-  -   self.fc1 = nn.Linear(9216, 128)
-  -   self.fc2 = nn.Linear(128, 10)
-  +   feature = nn.ValueChoice([64, 128, 256])
-  +   self.fc1 = nn.Linear(9216, feature)
-  +   self.fc2 = nn.Linear(feature, 10)
-
-    def forward(self, x):
-      x = F.relu(self.conv1(x))
-      x = F.max_pool2d(self.conv2(x), 2)
-      x = torch.flatten(self.dropout1(x), 1)
-      x = self.fc2(self.dropout2(F.relu(self.fc1(x))))
-      output = F.log_softmax(x, dim=1)
-      return output
-
-This example uses two mutation APIs, ``nn.LayerChoice`` and ``nn.ValueChoice``. ``nn.LayerChoice`` takes a list of candidate modules (two in this example), one will be chosen for each sampled model. It can be used like normal PyTorch module. ``nn.ValueChoice`` takes a list of candidate values, one will be chosen to take effect for each sampled model.
-
-More detailed API description and usage can be found `here <./construct_space.rst>`__ .
-
-.. note:: We are actively enriching the mutation APIs, to facilitate easy construction of model space. If the currently supported mutation APIs cannot express your model space, please refer to `this doc <./Mutators.rst>`__ for customizing mutators.
-
-Explore the Defined Model Space
--------------------------------
-
-There are basically two exploration approaches: (1) search by evaluating each sampled model independently, which is the search approach in multi-trial NAS and (2) one-shot weight-sharing based search, which is used in one-shot NAS. We demonstrate the first approach in this tutorial. Users can refer to `here <./OneshotTrainer.rst>`__ for the second approach.
-
-First, users need to pick a proper exploration strategy to explore the defined model space. Second, users need to pick or customize a model evaluator to evaluate the performance of each explored model.
-
-Pick an exploration strategy
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Retiarii supports many `exploration strategies <./ExplorationStrategies.rst>`__.
-
-Simply choosing (i.e., instantiate) an exploration strategy as below.
-
-.. code-block:: python
-
-  import nni.retiarii.strategy as strategy
-
-  search_strategy = strategy.Random(dedup=True)  # dedup=False if deduplication is not wanted
-
-Pick or customize a model evaluator
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-In the exploration process, the exploration strategy repeatedly generates new models. A model evaluator is for training and validating each generated model to obtain the model's performance. The performance is sent to the exploration strategy for the strategy to generate better models.
-
-Retiarii has provided `built-in model evaluators <./ModelEvaluators.rst>`__, but to start with, it is recommended to use ``FunctionalEvaluator``, that is, to wrap your own training and evaluation code with one single function. This function should receive one single model class and uses ``nni.report_final_result`` to report the final score of this model.
-
-An example here creates a simple evaluator that runs on MNIST dataset, trains for 2 epochs, and reports its validation accuracy.
-
-..  code-block:: python
-
-    def evaluate_model(model_cls):
-      # "model_cls" is a class, need to instantiate
-      model = model_cls()
-
-      optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
-      transf = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
-      train_loader = DataLoader(MNIST('data/mnist', download=True, transform=transf), batch_size=64, shuffle=True)
-      test_loader = DataLoader(MNIST('data/mnist', download=True, train=False, transform=transf), batch_size=64)
-
-      device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
-
-      for epoch in range(3):
-        # train the model for one epoch
-        train_epoch(model, device, train_loader, optimizer, epoch)
-        # test the model for one epoch
-        accuracy = test_epoch(model, device, test_loader)
-        # call report intermediate result. Result can be float or dict
-        nni.report_intermediate_result(accuracy)
-
-      # report final test result
-      nni.report_final_result(accuracy)
-
-    # Create the evaluator
-    evaluator = nni.retiarii.evaluator.FunctionalEvaluator(evaluate_model)
-
-The ``train_epoch`` and ``test_epoch`` here can be any customized function, where users can write their own training recipe. See :githublink:`examples/nas/multi-trial/mnist/search.py` for the full example.
-
-It is recommended that the ``evaluate_model`` here accepts no additional arguments other than ``model_cls``. However, in the `advanced tutorial <./ModelEvaluators.rst>`__, we will show how to use additional arguments in case you actually need those. In future, we will support mutation on the arguments of evaluators, which is commonly called "Hyper-parmeter tuning".
-
-Launch an Experiment
---------------------
-
-After all the above are prepared, it is time to start an experiment to do the model search. An example is shown below.
-
-.. code-block:: python
-
-  exp = RetiariiExperiment(base_model, evaluator, [], search_strategy)
-  exp_config = RetiariiExeConfig('local')
-  exp_config.experiment_name = 'mnist_search'
-  exp_config.trial_concurrency = 2
-  exp_config.max_trial_number = 20
-  exp_config.training_service.use_active_gpu = False
-  exp.run(exp_config, 8081)
-
-The complete code of this example can be found :githublink:`here <examples/nas/multi-trial/mnist/search.py>`. Users can also run Retiarii Experiment with `different training services <../training_services.rst>`__ besides ``local`` training service.
-
-Visualize the Experiment
-------------------------
-
-Users can visualize their experiment in the same way as visualizing a normal hyper-parameter tuning experiment. For example, open ``localhost:8081`` in your browser, 8081 is the port that you set in ``exp.run``. Please refer to `here <../Tutorial/WebUI.rst>`__ for details.
-
-We support visualizing models with 3rd-party visualization engines (like `Netron <https://netron.app/>`__). This can be used by clicking ``Visualization`` in detail panel for each trial. Note that current visualization is based on `onnx <https://onnx.ai/>`__ , thus visualization is not feasible if the model cannot be exported into onnx.
-
-Built-in evaluators (e.g., Classification) will automatically export the model into a file. For your own evaluator, you need to save your file into ``$NNI_OUTPUT_DIR/model.onnx`` to make this work. For instance,
-
-.. code-block:: python
-
-  def evaluate_model(model_cls):
-    model = model_cls()
-    # dump the model into an onnx
-    if 'NNI_OUTPUT_DIR' in os.environ:
-      torch.onnx.export(model, (dummy_input, ),
-                        Path(os.environ['NNI_OUTPUT_DIR']) / 'model.onnx')
-    # the rest are training and evaluation
-
-Export Top Models
------------------
-
-Users can export top models after the exploration is done using ``export_top_models``.
-
-.. code-block:: python
-
-  for model_code in exp.export_top_models(formatter='dict'):
-    print(model_code)
-
-The output is `json` object which records the mutation actions of the top model. If users want to output source code of the top model, they can use graph-based execution engine for the experiment, by simply adding the following two lines.
-
-.. code-block:: python
-
-  exp_config.execution_engine = 'base'
-  export_formatter = 'code'
diff --git a/docs/source/NAS/QuickStart_zh.rst b/docs/source/NAS/QuickStart_zh.rst
deleted file mode 100644
index 345a4b558..000000000
--- a/docs/source/NAS/QuickStart_zh.rst
+++ /dev/null
@@ -1,217 +0,0 @@
-.. d6ad1b913b292469c647ca68ac158840
-
-快速入门 Retiarii
-==============================
-
-
-.. contents::
-
-在快速入门教程中，我们以 multi-trial NAS 为例来展示如何构建和探索模型空间。 神经网络架构搜索任务主要有三个关键组件，即：
-
-* 模型搜索空间（Model search space），定义了要探索的模型集合。
-* 一个适当的策略（strategy），作为探索这个搜索空间的方法。
-* 一个模型评估器（model evaluator），报告一个给定模型的性能。
-
-One-shot NAS 教程在 `这里 <./OneshotTrainer.rst>`__。
-
-.. note:: 目前，PyTorch 是 Retiarii 唯一支持的框架，我们只用 **PyTorch 1.7 和 1.10** 进行了测试。 本文档基于 PyTorch 的背景，但它也应该适用于其他框架，这在我们未来的计划中。
-
-定义模型空间
------------------------
-
-模型空间是由用户定义的，用来表达用户想要探索、认为包含性能良好模型的一组模型。 模型空间是由用户定义的，用来表达用户想要探索、认为包含性能良好模型的一组模型。 在这个框架中，模型空间由两部分组成：基本模型和基本模型上可能的突变。
-
-定义基本模型
-^^^^^^^^^^^^^^^^^
-
-定义基本模型与定义 PyTorch（或 TensorFlow）模型几乎相同， 只有两个小区别。 对于 PyTorch 模块（例如 ``nn.Conv2d``, ``nn.ReLU``），将代码 ``import torch.nn as nn`` 替换为 ``import nni.retiarii.nn.pytorch as nn`` 。
-
-下面是定义基本模型的一个简单的示例，它与定义 PyTorch 模型几乎相同。
-
-.. code-block:: python
-
-  import torch
-  import torch.nn.functional as F
-  import nni.retiarii.nn.pytorch as nn
-  from nni.retiarii import model_wrapper
-
-  @model_wrapper      # this decorator should be put on the out most
-  class Net(nn.Module):
-    def __init__(self):
-      super().__init__()
-      self.conv1 = nn.Conv2d(1, 32, 3, 1)
-      self.conv2 = nn.Conv2d(32, 64, 3, 1)
-      self.dropout1 = nn.Dropout(0.25)
-      self.dropout2 = nn.Dropout(0.5)
-      self.fc1 = nn.Linear(9216, 128)
-      self.fc2 = nn.Linear(128, 10)
-
-    def forward(self, x):
-      x = F.relu(self.conv1(x))
-      x = F.max_pool2d(self.conv2(x), 2)
-      x = torch.flatten(self.dropout1(x), 1)
-      x = self.fc2(self.dropout2(F.relu(self.fc1(x))))
-      output = F.log_softmax(x, dim=1)
-      return output
-
-.. tip:: 记得使用 ``import nni.retiarii.nn.pytorch as nn`` 和 :meth:`nni.retiarii.model_wrapper`. 许多错误都源于忘记使用它们。同时，对于 ``nn`` 的子模块（例如 ``nn.init``）请使用 ``torch.nn``，比如，``torch.nn.init`` 而不是 ``nn.init``。
-
-定义模型突变
-^^^^^^^^^^^^^^^^^^^^^^
-
-基本模型只是一个具体模型，而不是模型空间。 我们为用户提供 `API 和原语 <./MutationPrimitives.rst>`__，用于把基本模型变形成包含多个模型的模型空间。
-
-基于上面定义的基本模型，我们可以这样定义一个模型空间：
-
-.. code-block:: diff
-
-  import torch
-  import torch.nn.functional as F
-  import nni.retiarii.nn.pytorch as nn
-  from nni.retiarii import model_wrapper
-
-  @model_wrapper
-  class Net(nn.Module):
-    def __init__(self):
-      super().__init__()
-      self.conv1 = nn.Conv2d(1, 32, 3, 1)
-  -   self.conv2 = nn.Conv2d(32, 64, 3, 1)
-  +   self.conv2 = nn.LayerChoice([
-  +       nn.Conv2d(32, 64, 3, 1),
-  +       DepthwiseSeparableConv(32, 64)
-  +   ])
-  -   self.dropout1 = nn.Dropout(0.25)
-  +   self.dropout1 = nn.Dropout(nn.ValueChoice([0.25, 0.5, 0.75]))
-      self.dropout2 = nn.Dropout(0.5)
-  -   self.fc1 = nn.Linear(9216, 128)
-  -   self.fc2 = nn.Linear(128, 10)
-  +   feature = nn.ValueChoice([64, 128, 256])
-  +   self.fc1 = nn.Linear(9216, feature)
-  +   self.fc2 = nn.Linear(feature, 10)
-
-    def forward(self, x):
-      x = F.relu(self.conv1(x))
-      x = F.max_pool2d(self.conv2(x), 2)
-      x = torch.flatten(self.dropout1(x), 1)
-      x = self.fc2(self.dropout2(F.relu(self.fc1(x))))
-      output = F.log_softmax(x, dim=1)
-      return output
-
-在这个例子中我们使用了两个突变 API， ``nn.LayerChoice`` 和 ``nn.ValueChoice``。 ``nn.LayerChoice`` 的输入参数是一个候选模块的列表（在这个例子中是两个），每个采样到的模型会选择其中的一个，然后它就可以像一般的 PyTorch 模块一样被使用。 ``nn.ValueChoice`` 输入一系列候选的值，然后对于每个采样到的模型，其中的一个值会生效。
-
-更多的 API 描述和用法可以请阅读 `这里 <./construct_space.rst>`__ 。
-
-.. note:: 我们正在积极的丰富突变 API，以简化模型空间的构建。如果我们提供的 API 不能满足您表达模型空间的需求，请阅读 `这个文档 <./Mutators.rst>`__ 以获得更多定制突变的资讯。
-
-探索定义的模型空间
--------------------------------
-
-简单来说，探索模型空间有两种方法：(1) 通过独立评估每个采样模型进行搜索；(2) 基于 One-Shot 的权重共享式搜索。 我们在本教程中演示了下面的第一种方法。 第二种方法可以参考 `这里 <./OneshotTrainer.rst>`__。
-
-首先，用户需要选择合适的探索策略来探索模型空间。然后，用户需要选择或自定义模型评估器来评估每个采样模型的性能。
-
-选择搜索策略
-^^^^^^^^^^^^^^^^^^^^^^^^
-
-Retiarii 支持许多 `探索策略（exploration strategies） <./ExplorationStrategies.rst>`__。
-
-简单地选择（即实例化）一个探索策略：
-
-.. code-block:: python
-
-  import nni.retiarii.strategy as strategy
-
-  search_strategy = strategy.Random(dedup=True)  # dedup=False 如果不希望有重复数据删除
-
-选择或编写模型评估器
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-在 NAS 过程中，探索策略反复生成新模型。 模型评估器用于训练和验证每个生成的模型。 生成的模型所获得的性能被收集起来，并送至探索策略以生成更好的模型。
-
-Retiarii 提供了诸多的 `内置模型评估器 <./ModelEvaluators.rst>`__，但是作为第一步，我们还是推荐使用 ``FunctionalEvaluator``，也就是说，将您自己的训练和测试代码用一个函数包起来。这个函数的输入参数是一个模型的类，然后使用 ``nni.report_final_result`` 来汇报模型的效果。
-
-这里的一个例子创建了一个简单的评估器，它在 MNIST 数据集上运行，训练 2 个 Epoch，并报告其在验证集上的准确率。
-
-..  code-block:: python
-
-    def evaluate_model(model_cls):
-      # "model_cls" 是一个类，需要初始化
-      model = model_cls()
-
-      optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
-      transf = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
-      train_loader = DataLoader(MNIST('data/mnist', download=True, transform=transf), batch_size=64, shuffle=True)
-      test_loader = DataLoader(MNIST('data/mnist', download=True, train=False, transform=transf), batch_size=64)
-
-      device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
-
-      for epoch in range(3):
-        # 训练模型，1 个 epoch
-        train_epoch(model, device, train_loader, optimizer, epoch)
-        # 测试模型，1 个 epoch
-        accuracy = test_epoch(model, device, test_loader)
-        # 汇报中间结果，可以是 float 或者 dict 类型
-        nni.report_intermediate_result(accuracy)
-
-      # 汇报最终结果
-      nni.report_final_result(accuracy)
-
-    # 创建模型评估器
-    evaluator = nni.retiarii.evaluator.FunctionalEvaluator(evaluate_model)
-
-在这里 ``train_epoch`` 和 ``test_epoch`` 可以是任意自定义的函数，用户可以写自己的训练流程。完整的样例可以参见 :githublink:`examples/nas/multi-trial/mnist/search.py`。
-
-我们建议 ``evaluate_model`` 不接受 ``model_cls`` 以外的其他参数。但是，我们在 `高级教程 <./ModelEvaluators.rst>`__ 中展示了其他参数的用法，如果您真的需要的话。另外，我们会在未来支持这些参数的突变（这通常会成为 "超参调优"）。
-
-发起 Experiment
---------------------
-
-一切准备就绪，就可以发起 Experiment 以进行模型搜索了。 样例如下：
-
-.. code-block:: python
-
-  exp = RetiariiExperiment(base_model, evaluator, [], search_strategy)
-  exp_config = RetiariiExeConfig('local')
-  exp_config.experiment_name = 'mnist_search'
-  exp_config.trial_concurrency = 2
-  exp_config.max_trial_number = 20
-  exp_config.training_service.use_active_gpu = False
-  exp.run(exp_config, 8081)
-
-一个简单 MNIST 示例的完整代码在 :githublink:`这里 <examples/nas/multi-trial/mnist/search.py>`。 除了本地训练平台，用户还可以在除了本地机器以外的 `不同的训练平台 <../training_services.rst>`__ 上运行 Retiarii 的实验。
-
-可视化 Experiment
-------------------------
-
-用户可以像可视化普通的超参数调优 Experiment 一样可视化他们的 Experiment。 例如，在浏览器里打开 ``localhost:8081``，8081 是在 ``exp.run`` 里设置的端口。 参考 `这里 <../Tutorial/WebUI.rst>`__ 了解更多细节。
-
-我们支持使用第三方工具（例如 `Netron <https://netron.app/>`__）可视化搜索过程中采样到的模型。您可以点击每个 trial 面板下的 ``Visualization``。注意，目前的可视化是基于导出成 `onnx <https://onnx.ai/>`__ 格式的模型实现的，所以如果模型无法导出成 onnx，那么可视化就无法进行。
-
-内置的模型评估器（比如 Classification）已经自动将模型导出成了一个文件。如果您自定义了模型，您需要将模型导出到 ``$NNI_OUTPUT_DIR/model.onnx``。例如，
-
-.. code-block:: python
-
-  def evaluate_model(model_cls):
-    model = model_cls()
-    # 把模型导出成 onnx
-    if 'NNI_OUTPUT_DIR' in os.environ:
-      torch.onnx.export(model, (dummy_input, ),
-                        Path(os.environ['NNI_OUTPUT_DIR']) / 'model.onnx')
-    # 剩下的就是训练和测试流程
-
-导出最佳模型
------------------
-
-探索完成后，用户可以使用 ``export_top_models`` 导出最佳模型。
-
-.. code-block:: python
-
-  for model_code in exp.export_top_models(formatter='dict'):
-    print(model_code)
-
-导出的 `json` 记录的是最佳模型的突变记录。如果用户想要最佳模型的代码，可以简单的使用基于图的执行引擎，增加如下两行代码即可：
-
-.. code-block:: python
-
-  exp_config.execution_engine = 'base'
-  export_formatter = 'code'
diff --git a/docs/source/NAS/SPOS.rst b/docs/source/NAS/SPOS.rst
deleted file mode 100644
index c3ba88531..000000000
--- a/docs/source/NAS/SPOS.rst
+++ /dev/null
@@ -1,102 +0,0 @@
-Single Path One-Shot (SPOS)
-===========================
-
-Introduction
-------------
-
-Proposed in `Single Path One-Shot Neural Architecture Search with Uniform Sampling <https://arxiv.org/abs/1904.00420>`__ is a one-shot NAS method that addresses the difficulties in training One-Shot NAS models by constructing a simplified supernet trained with an uniform path sampling method, so that all underlying architectures (and their weights) get trained fully and equally. An evolutionary algorithm is then applied to efficiently search for the best-performing architectures without any fine tuning.
-
-Implementation on NNI is based on `official repo <https://github.com/megvii-model/SinglePathOneShot>`__. We implement a trainer that trains the supernet and a evolution tuner that leverages the power of NNI framework that speeds up the evolutionary search phase.
-
-Examples
---------
-
-Here is a use case, which is the search space in paper. However, we applied latency limit instead of flops limit to perform the architecture search phase.
-
-:githublink:`Example code <examples/nas/oneshot/spos>`
-
-Requirements
-^^^^^^^^^^^^
-
-Prepare ImageNet in the standard format (follow the script `here <https://gist.github.com/BIGBALLON/8a71d225eff18d88e469e6ea9b39cef4>`__\ ). Linking it to ``data/imagenet`` will be more convenient.
-
-Download the checkpoint file from `here <https://1drv.ms/u/s!Am_mmG2-KsrnajesvSdfsq_cN48?e=aHVppN>`__ (maintained by `Megvii <https://github.com/megvii-model>`__\ ) if you don't want to retrain the supernet.
-Put ``checkpoint-150000.pth.tar`` under ``data`` directory.
-
-
-After preparation, it's expected to have the following code structure:
-
-.. code-block:: bash
-
-   spos
-   ├── architecture_final.json
-   ├── blocks.py
-   ├── data
-   │   ├── imagenet
-   │   │   ├── train
-   │   │   └── val
-   │   └── checkpoint-150000.pth.tar
-   ├── network.py
-   ├── readme.md
-   ├── supernet.py
-   ├── evaluation.py
-   ├── search.py
-   └── utils.py
-
-Step 1. Train Supernet
-^^^^^^^^^^^^^^^^^^^^^^
-
-.. code-block:: bash
-
-   python supernet.py
-
-Will export the checkpoint to ``checkpoints`` directory, for the next step.
-
-NOTE: The data loading used in the official repo is `slightly different from usual <https://github.com/megvii-model/SinglePathOneShot/issues/5>`__\ , as they use BGR tensor and keep the values between 0 and 255 intentionally to align with their own DL framework. The option ``--spos-preprocessing`` will simulate the behavior used originally and enable you to use the checkpoints pretrained.
-
-Step 2. Evolution Search
-^^^^^^^^^^^^^^^^^^^^^^^^
-
-Single Path One-Shot leverages evolution algorithm to search for the best architecture. In the paper, the search module, which is responsible for testing the sampled architecture, recalculates all the batch norm for a subset of training images, and evaluates the architecture on the full validation set.
-
-In this example, we have an incomplete implementation of the evolution search. The example only support training from scratch. Inheriting weights from pretrained supernet is not supported yet. To search with the regularized evolution strategy, run
-
-.. code-block:: bash
-
-   python search.py
-
-The final architecture exported from every epoch of evolution can be found in ``trials`` under the working directory of your tuner, which, by default, is ``$HOME/nni-experiments/your_experiment_id/trials``.
-
-Step 3. Train for Evaluation
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. code-block:: bash
-
-   python evaluation.py
-
-By default, it will use ``architecture_final.json``. This architecture is provided by the official repo (converted into NNI format). You can use any architecture (e.g., the architecture found in step 2) with ``--fixed-arc`` option.
-
-Reference
----------
-
-PyTorch
-^^^^^^^
-
-..  autoclass:: nni.retiarii.oneshot.pytorch.SinglePathTrainer
-    :noindex:
-
-Known Limitations
------------------
-
-
-* Block search only. Channel search is not supported yet.
-* In the search phase, training from the scratch is required. Inheriting weights from supernet is not supported yet.
-
-Current Reproduction Results
-----------------------------
-
-Reproduction is still undergoing. Due to the gap between official release and original paper, we compare our current results with official repo (our run) and paper.
-
-
-* Evolution phase is almost aligned with official repo. Our evolution algorithm shows a converging trend and reaches ~65% accuracy at the end of search. Nevertheless, this result is not on par with paper. For details, please refer to `this issue <https://github.com/megvii-model/SinglePathOneShot/issues/6>`__.
-* Retrain phase is not aligned. Our retraining code, which uses the architecture released by the authors, reaches 72.14% accuracy, still having a gap towards 73.61% by official release and 74.3% reported in original paper.
diff --git a/docs/source/NAS/WriteOneshot.rst b/docs/source/NAS/WriteOneshot.rst
deleted file mode 100644
index 19b546b3d..000000000
--- a/docs/source/NAS/WriteOneshot.rst
+++ /dev/null
@@ -1,56 +0,0 @@
-Customize a New One-shot Trainer
-================================
-
-One-shot trainers should inherit ``nni.retiarii.oneshot.BaseOneShotTrainer``, and need to implement ``fit()`` (used to conduct the fitting and searching process) and ``export()`` method (used to return the searched best architecture).
-
-Writing a one-shot trainer is very different to single-arch evaluator. First of all, there are no more restrictions on init method arguments, any Python arguments are acceptable. Secondly, the model fed into one-shot trainers might be a model with Retiarii-specific modules, such as LayerChoice and InputChoice. Such model cannot directly forward-propagate and trainers need to decide how to handle those modules.
-
-A typical example is DartsTrainer, where learnable-parameters are used to combine multiple choices in LayerChoice. Retiarii provides ease-to-use utility functions for module-replace purposes, namely ``replace_layer_choice``, ``replace_input_choice``. A simplified example is as follows: 
-
-.. code-block:: python
-
-    from nni.retiarii.oneshot import BaseOneShotTrainer
-    from nni.retiarii.oneshot.pytorch import replace_layer_choice, replace_input_choice
-
-
-    class DartsLayerChoice(nn.Module):
-        def __init__(self, layer_choice):
-            super(DartsLayerChoice, self).__init__()
-            self.name = layer_choice.label
-            self.op_choices = nn.ModuleDict(layer_choice.named_children())
-            self.alpha = nn.Parameter(torch.randn(len(self.op_choices)) * 1e-3)
-
-        def forward(self, *args, **kwargs):
-            op_results = torch.stack([op(*args, **kwargs) for op in self.op_choices.values()])
-            alpha_shape = [-1] + [1] * (len(op_results.size()) - 1)
-            return torch.sum(op_results * F.softmax(self.alpha, -1).view(*alpha_shape), 0)
-
-
-    class DartsTrainer(BaseOneShotTrainer):
-
-        def __init__(self, model, loss, metrics, optimizer):
-            self.model = model
-            self.loss = loss
-            self.metrics = metrics
-            self.num_epochs = 10
-
-            self.nas_modules = []
-            replace_layer_choice(self.model, DartsLayerChoice, self.nas_modules)
-
-            ... # init dataloaders and optimizers
-
-        def fit(self):
-            for i in range(self.num_epochs):
-                for (trn_X, trn_y), (val_X, val_y) in zip(self.train_loader, self.valid_loader):
-                    self.train_architecture(val_X, val_y)
-                    self.train_model_weight(trn_X, trn_y)
-
-        @torch.no_grad()
-        def export(self):
-            result = dict()
-            for name, module in self.nas_modules:
-                if name not in result:
-                    result[name] = select_best_of_module(module)
-            return result
-
-The full code of DartsTrainer is available to Retiarii source code. Please have a check at :githublink:`DartsTrainer <nni/retiarii/oneshot/pytorch/darts.py>`.
diff --git a/docs/source/NAS/construct_space.rst b/docs/source/NAS/construct_space.rst
deleted file mode 100644
index 362bb446e..000000000
--- a/docs/source/NAS/construct_space.rst
+++ /dev/null
@@ -1,12 +0,0 @@
-#####################
-Construct Model Space
-#####################
-
-NNI provides powerful APIs for users to easily express model space (or search space). First, users can use mutation primitives (e.g., ValueChoice, LayerChoice) to inline a space in their model. Second, NNI provides simple interface for users to customize new mutators for expressing more complicated model spaces. In most cases, the mutation primitives are enough to express users' model spaces.
-
-..  toctree::
-    :maxdepth: 1
-
-    Mutation Primitives <MutationPrimitives>
-    Customize Mutators <Mutators>
-    Hypermodule Lib <Hypermodules>
\ No newline at end of file
diff --git a/docs/source/NAS/construct_space_zh.rst b/docs/source/NAS/construct_space_zh.rst
deleted file mode 100644
index ab19c41fa..000000000
--- a/docs/source/NAS/construct_space_zh.rst
+++ /dev/null
@@ -1,14 +0,0 @@
-.. bb39a6ac0ae1f5554bc38604c77fb616
-
-#####################
-构建模型空间
-#####################
-
-NNI为用户提供了强大的API，以方便表达模型空间（或搜索空间）。 首先，用户可以使用 mutation 原语（如 ValueChoice、LayerChoice）在他们的模型中内联一个空间。 其次，NNI为用户提供了简单的接口，可以定制新的 mutators 来表达更复杂的模型空间。 在大多数情况下，mutation 原语足以表达用户的模型空间。
-
-..  toctree::
-    :maxdepth: 1
-
-    mutation 原语 <MutationPrimitives>
-    定制 mutator <Mutators>
-    Hypermodule Lib <Hypermodules>
\ No newline at end of file
diff --git a/docs/source/NAS/multi_trial_nas.rst b/docs/source/NAS/multi_trial_nas.rst
deleted file mode 100644
index e95f44669..000000000
--- a/docs/source/NAS/multi_trial_nas.rst
+++ /dev/null
@@ -1,12 +0,0 @@
-Multi-trial NAS
-===============
-
-In multi-trial NAS, users need model evaluator to evaluate the performance of each sampled model, and need an exploration strategy to sample models from a defined model space. Here, users could use NNI provided model evaluators or write their own model evalutor. They can simply choose a exploration strategy. Advanced users can also customize new exploration strategy. For a simple example about how to run a multi-trial NAS experiment, please refer to `Quick Start <./QuickStart.rst>`__.
-
-..  toctree::
-    :maxdepth: 2
-
-    Model Evaluators <ModelEvaluators>
-    Exploration Strategies <ExplorationStrategies>
-    Execution Engines <ExecutionEngines>
-    Serialization <Serialization>
diff --git a/docs/source/NAS/multi_trial_nas_zh.rst b/docs/source/NAS/multi_trial_nas_zh.rst
deleted file mode 100644
index f79b16ab8..000000000
--- a/docs/source/NAS/multi_trial_nas_zh.rst
+++ /dev/null
@@ -1,14 +0,0 @@
-.. 51734c9945d4eca0f9b5633929d8fadf
-
-Multi-trial NAS
-===============
-
-在 multi-trial NAS 中，用户需要模型评估器来评估每个采样模型的性能，并且需要一个探索策略来从定义的模型空间中采样模型。 在这里，用户可以使用 NNI 提供的模型评估器或编写自己的模型评估器。 他们可以简单地选择一种探索策略。 高级用户还可以自定义新的探索策略。 关于如何运行 multi-trial NAS 实验的简单例子，请参考 `快速入门 <./QuickStart.rst>`__。
-
-..  toctree::
-    :maxdepth: 1
-
-    模型评估器 <ModelEvaluators>
-    探索策略 <ExplorationStrategies>
-    执行引擎 <ExecutionEngines>
-    序列化 <Serialization>
diff --git a/docs/source/NAS/one_shot_nas.rst b/docs/source/NAS/one_shot_nas.rst
deleted file mode 100644
index 6d1082f7c..000000000
--- a/docs/source/NAS/one_shot_nas.rst
+++ /dev/null
@@ -1,16 +0,0 @@
-One-shot NAS
-============
-
-One-shot NAS algorithms leverage weight sharing among models in neural architecture search space to train a supernet, and use this supernet to guide the selection of better models. This type of algorihtms greatly reduces computational resource compared to independently training each model from scratch (which we call "Multi-trial NAS"). NNI has supported many popular One-shot NAS algorithms as following.
-
-
-..  toctree::
-    :maxdepth: 1
-
-    Run One-shot NAS <OneshotTrainer>
-    ENAS <ENAS>
-    DARTS <DARTS>
-    SPOS <SPOS>
-    ProxylessNAS <Proxylessnas>
-    FBNet <FBNet>
-    Customize One-shot NAS <WriteOneshot>
diff --git a/docs/source/NAS/one_shot_nas_zh.rst b/docs/source/NAS/one_shot_nas_zh.rst
deleted file mode 100644
index 223c84317..000000000
--- a/docs/source/NAS/one_shot_nas_zh.rst
+++ /dev/null
@@ -1,18 +0,0 @@
-.. c9ab8a1f91c587ad72d66b6c43e06528
-
-One-shot NAS
-============
-
-One-Shot NAS 算法利用了搜索空间中模型间的权重共享来训练超网络，并使用超网络来指导选择出更好的模型。 与从头训练每个模型（我们称之为 "Multi-trial NAS"）算法相比，此类算法大大减少了使用的计算资源。 NNI 支持下列流行的 One-Shot NAS 算法。
-
-
-..  toctree::
-    :maxdepth: 1
-
-    运行 One-shot NAS <OneshotTrainer>
-    ENAS <ENAS>
-    DARTS <DARTS>
-    SPOS <SPOS>
-    ProxylessNAS <Proxylessnas>
-    FBNet <FBNet>
-    自定义 One-shot NAS <WriteOneshot>
\ No newline at end of file
diff --git a/docs/source/Overview.rst b/docs/source/Overview.rst
deleted file mode 100644
index a4fe5d5dd..000000000
--- a/docs/source/Overview.rst
+++ /dev/null
@@ -1,123 +0,0 @@
-Overview
-========
-
-NNI (Neural Network Intelligence) is a toolkit to help users design and tune machine learning models (e.g., hyperparameters), neural network architectures, or complex system's parameters, in an efficient and automatic way. NNI has several appealing properties: ease-of-use, scalability, flexibility, and efficiency.
-
-
-* **Ease-of-use**\ : NNI can be easily installed through python pip. Only several lines need to be added to your code in order to use NNI's power. You can use both the commandline tool and WebUI to work with your experiments.
-* **Scalability**\ : Tuning hyperparameters or the neural architecture often demands a large number of computational resources, while NNI is designed to fully leverage different computation resources, such as remote machines, training platforms (e.g., OpenPAI, Kubernetes). Hundreds of trials could run in parallel by depending on the capacity of your configured training platforms.
-* **Flexibility**\ : Besides rich built-in algorithms, NNI allows users to customize various hyperparameter tuning algorithms, neural architecture search algorithms, early stopping algorithms, etc. Users can also extend NNI with more training platforms, such as virtual machines, kubernetes service on the cloud. Moreover, NNI can connect to external environments to tune special applications/models on them.
-* **Efficiency**\ : We are intensively working on more efficient model tuning on both the system and algorithm level. For example, we leverage early feedback to speedup the tuning procedure.
-
-The figure below shows high-level architecture of NNI.
-
-
-.. raw:: html
-
-   <p align="center">
-   <img src="https://user-images.githubusercontent.com/16907603/92089316-94147200-ee00-11ea-9944-bf3c4544257f.png" alt="drawing" width="700"/>
-   </p>
-
-
-Key Concepts
-------------
-
-
-* 
-  *Experiment*\ : One task of, for example, finding out the best hyperparameters of a model, finding out the best neural network architecture, etc. It consists of trials and AutoML algorithms.
-
-* 
-  *Search Space*\ : The feasible region for tuning the model. For example, the value range of each hyperparameter.
-
-* 
-  *Configuration*\ : An instance from the search space, that is, each hyperparameter has a specific value.
-
-* 
-  *Trial*\ : An individual attempt at applying a new configuration (e.g., a set of hyperparameter values, a specific neural architecture, etc.). Trial code should be able to run with the provided configuration.
-
-* 
-  *Tuner*\ : An AutoML algorithm, which generates a new configuration for the next try. A new trial will run with this configuration.
-
-* 
-  *Assessor*\ : Analyze a trial's intermediate results (e.g., periodically evaluated accuracy on test dataset) to tell whether this trial can be early stopped or not.
-
-* 
-  *Training Platform*\ : Where trials are executed. Depending on your experiment's configuration, it could be your local machine, or remote servers, or large-scale training platform (e.g., OpenPAI, Kubernetes).
-
-Basically, an experiment runs as follows: Tuner receives search space and generates configurations. These configurations will be submitted to training platforms, such as the local machine, remote machines, or training clusters. Their performances are reported back to Tuner. Then, new configurations are generated and submitted.
-
-For each experiment, the user only needs to define a search space and update a few lines of code, and then leverage NNI built-in Tuner/Assessor and training platforms to search the best hyperparameters and/or neural architecture. There are basically 3 steps:
-
-..
-
-   Step 1: `Define search space <Tutorial/SearchSpaceSpec.rst>`__
-
-   Step 2: `Update model codes <TrialExample/Trials.rst>`__
-
-   Step 3: `Define Experiment <reference/experiment_config.rst>`__
-
-
-
-.. raw:: html
-
-   <p align="center">
-   <img src="https://user-images.githubusercontent.com/23273522/51816627-5d13db80-2302-11e9-8f3e-627e260203d5.jpg" alt="drawing"/>
-   </p>
-
-
-For more details about how to run an experiment, please refer to `Get Started <Tutorial/QuickStart.rst>`__.
-
-Core Features
--------------
-
-NNI provides a key capacity to run multiple instances in parallel to find the best combinations of parameters. This feature can be used in various domains, like finding the best hyperparameters for a deep learning model or finding the best configuration for database and other complex systems with real data.
-
-NNI also provides algorithm toolkits for machine learning and deep learning, especially neural architecture search (NAS) algorithms, model compression algorithms, and feature engineering algorithms.
-
-Hyperparameter Tuning
-^^^^^^^^^^^^^^^^^^^^^
-
-This is a core and basic feature of NNI, we provide many popular `automatic tuning algorithms <Tuner/BuiltinTuner.rst>`__ (i.e., tuner) and `early stop algorithms <Assessor/BuiltinAssessor.rst>`__ (i.e., assessor). You can follow `Quick Start <Tutorial/QuickStart.rst>`__ to tune your model (or system). Basically, there are the above three steps and then starting an NNI experiment.
-
-General NAS Framework
-^^^^^^^^^^^^^^^^^^^^^
-
-This NAS framework is for users to easily specify candidate neural architectures, for example, one can specify multiple candidate operations (e.g., separable conv, dilated conv) for a single layer, and specify possible skip connections. NNI will find the best candidate automatically. On the other hand, the NAS framework provides a simple interface for another type of user (e.g., NAS algorithm researchers) to implement new NAS algorithms. A detailed description of NAS and its usage can be found `here <NAS/Overview.rst>`__.
-
-NNI has support for many one-shot NAS algorithms such as ENAS and DARTS through NNI trial SDK. To use these algorithms you do not have to start an NNI experiment. Instead, import an algorithm in your trial code and simply run your trial code. If you want to tune the hyperparameters in the algorithms or want to run multiple instances, you can choose a tuner and start an NNI experiment.
-
-Other than one-shot NAS, NAS can also run in a classic mode where each candidate architecture runs as an independent trial job. In this mode, similar to hyperparameter tuning, users have to start an NNI experiment and choose a tuner for NAS.
-
-Model Compression
-^^^^^^^^^^^^^^^^^
-
-NNI provides an easy-to-use model compression framework to compress deep neural networks, the compressed networks typically have much smaller model size and much faster
-inference speed without losing performance significantlly. Model compression on NNI includes pruning algorithms and quantization algorithms. NNI provides many pruning and
-quantization algorithms through NNI trial SDK. Users can directly use them in their trial code and run the trial code without starting an NNI experiment. Users can also use NNI model compression framework to customize their own pruning and quantization algorithms.
-
-A detailed description of model compression and its usage can be found `here <Compression/Overview.rst>`__.
-
-Automatic Feature Engineering
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Automatic feature engineering is for users to find the best features for their tasks. A detailed description of automatic feature engineering and its usage can be found `here <FeatureEngineering/Overview.rst>`__. It is supported through NNI trial SDK, which means you do not have to create an NNI experiment. Instead, simply import a built-in auto-feature-engineering algorithm in your trial code and directly run your trial code. 
-
-The auto-feature-engineering algorithms usually have a bunch of hyperparameters themselves. If you want to automatically tune those hyperparameters, you can leverage hyperparameter tuning of NNI, that is, choose a tuning algorithm (i.e., tuner) and start an NNI experiment for it.
-
-Learn More
-----------
-
-
-* `Get started <Tutorial/QuickStart.rst>`__
-* `How to adapt your trial code on NNI? <TrialExample/Trials.rst>`__
-* `What are tuners supported by NNI? <Tuner/BuiltinTuner.rst>`__
-* `How to customize your own tuner? <Tuner/CustomizeTuner.rst>`__
-* `What are assessors supported by NNI? <Assessor/BuiltinAssessor.rst>`__
-* `How to customize your own assessor? <Assessor/CustomizeAssessor.rst>`__
-* `How to run an experiment on local? <TrainingService/LocalMode.rst>`__
-* `How to run an experiment on multiple machines? <TrainingService/RemoteMachineMode.rst>`__
-* `How to run an experiment on OpenPAI? <TrainingService/PaiMode.rst>`__
-* `Examples <TrialExample/MnistExamples.rst>`__
-* `Neural Architecture Search on NNI <NAS/Overview.rst>`__
-* `Model Compression on NNI <Compression/Overview.rst>`__
-* `Automatic feature engineering on NNI <FeatureEngineering/Overview.rst>`__
diff --git a/docs/source/Overview_zh.rst b/docs/source/Overview_zh.rst
deleted file mode 100644
index be61cc35c..000000000
--- a/docs/source/Overview_zh.rst
+++ /dev/null
@@ -1,125 +0,0 @@
-.. 6e45ee0ddd5d0315e5c946149d4f9c31
-
-概述
-========
-
-NNI (Neural Network Intelligence) 是一个工具包，可有效的帮助用户设计并调优机器学习模型的神经网络架构，复杂系统的参数（如超参）等。 NNI 的特性包括：易于使用，可扩展，灵活，高效。
-
-
-* **易于使用**：NNI 可通过 pip 安装。 只需要在代码中添加几行，就可以利用 NNI 来调优参数。 可使用命令行工具或 Web 界面来查看 Experiment。
-* **可扩展**：调优超参或网络结构通常需要大量的计算资源。NNI 在设计时就支持了多种不同的计算资源，如远程服务器组，训练平台（如：OpenPAI，Kubernetes），等等。 根据您配置的培训平台的能力，可以并行运行数百个 Trial 。
-* **灵活**：除了内置的算法，NNI 中还可以轻松集成自定义的超参调优算法，神经网络架构搜索算法，提前终止算法等等。 还可以将 NNI 连接到更多的训练平台上，如云中的虚拟机集群，Kubernetes 服务等等。 此外，NNI 还可以连接到外部环境中的特殊应用和模型上。
-* **高效**：NNI 在系统及算法级别上不断地进行优化。 例如：通过早期的反馈来加速调优过程。
-
-下图显示了 NNI 的体系结构。
-
-
-.. raw:: html
-
-   <p align="center">
-   <img src="https://user-images.githubusercontent.com/16907603/92089316-94147200-ee00-11ea-9944-bf3c4544257f.png" alt="drawing" width="700"/>
-   </p>
-
-
-主要概念
-------------
-
-
-* 
-  *Experiment（实验）*： 表示一次任务，例如，寻找模型的最佳超参组合，或最好的神经网络架构等。 它由 Trial 和自动机器学习算法所组成。
-
-* 
-  *搜索空间*：是模型调优的范围。 例如，超参的取值范围。
-
-* 
-  *Configuration（配置）*：配置是来自搜索空间的实例，每个超参都会有特定的值。
-
-* 
-  *Trial*：是一次独立的尝试，它会使用某组配置（例如，一组超参值，或者特定的神经网络架构）。 Trial 会基于提供的配置来运行。
-
-* 
-  *Tuner（调优器）*：一种自动机器学习算法，会为下一个 Trial 生成新的配置。 新的 Trial 会使用这组配置来运行。
-
-* 
-  *Assessor（评估器）*：分析 Trial 的中间结果（例如，定期评估数据集上的精度），来确定 Trial 是否应该被提前终止。
-
-* 
-  *训练平台*：是 Trial 的执行环境。 根据 Experiment 的配置，可以是本机，远程服务器组，或其它大规模训练平台（如，OpenPAI，Kubernetes）。
-
-Experiment 的运行过程为：Tuner 接收搜索空间并生成配置。 这些配置将被提交到训练平台，如本机，远程服务器组或训练集群。 执行的性能结果会被返回给 Tuner。 然后，再生成并提交新的配置。
-
-每次 Experiment 执行时，用户只需要定义搜索空间，改动几行代码，就能利用 NNI 内置的 Tuner/Assessor 和训练平台来搜索最好的超参组合以及神经网络结构。 基本上分为三步：
-
-..
-
-   步骤一：`定义搜索空间 <Tutorial/SearchSpaceSpec.rst>`__
-
-   步骤二：`改动模型代码 <TrialExample/Trials.rst>`__
-
-   步骤三：`定义实验配置 <Tutorial/ExperimentConfig.rst>`__
-
-
-
-.. raw:: html
-
-   <p align="center">
-   <img src="https://user-images.githubusercontent.com/23273522/51816627-5d13db80-2302-11e9-8f3e-627e260203d5.jpg" alt="drawing"/>
-   </p>
-
-
-可查看 `快速入门 <Tutorial/QuickStart.rst>`__  来调优你的模型或系统。
-
-核心功能
--------------
-
-NNI 提供了并行运行多个实例以查找最佳参数组合的能力。 此功能可用于各种领域，例如，为深度学习模型查找最佳超参数，或查找具有真实数据的数据库和其他复杂系统的最佳配置。
-
-NNI 还希望提供用于机器学习和深度学习的算法工具包，尤其是神经体系结构搜索（NAS）算法，模型压缩算法和特征工程算法。
-
-超参调优
-^^^^^^^^^^^^^^^^^^^^^
-
-这是 NNI 最核心、基本的功能，其中提供了许多流行的 `自动调优算法 <Tuner/BuiltinTuner.rst>`__ （即 Tuner) 以及 `提前终止算法 <Assessor/BuiltinAssessor.rst>`__ （即 Assessor）。 可查看 `快速入门 <Tutorial/QuickStart.rst>`__ 来调优你的模型或系统。 基本上通过以上三步，就能开始 NNI Experiment。
-
-通用 NAS 框架
-^^^^^^^^^^^^^^^^^^^^^
-
-此 NAS 框架可供用户轻松指定候选的神经体系结构，例如，可以为单个层指定多个候选操作（例如，可分离的 conv、扩张 conv），并指定可能的跳过连接。 NNI 将自动找到最佳候选。 另一方面，NAS 框架为其他类型的用户（如，NAS 算法研究人员）提供了简单的接口，以实现新的 NAS 算法。 NAS 详情及用法参考 `这里 <NAS/Overview.rst>`__。
-
-NNI 通过 Trial SDK 支持多种 one-shot（一次性） NAS 算法，如：ENAS、DARTS。 使用这些算法时，不需启动 NNI Experiment。 在 Trial 代码中加入算法，直接运行即可。 如果要调整算法中的超参数，或运行多个实例，可以使用 Tuner 并启动 NNI Experiment。
-
-除了 one-shot NAS 外，NAS 还能以 NNI 模式运行，其中每个候选的网络结构都作为独立 Trial 任务运行。 在此模式下，与超参调优类似，必须启动 NNI Experiment 并为 NAS 选择 Tuner。
-
-模型压缩
-^^^^^^^^^^^^^^^^^
-
-NNI 提供了一个易于使用的模型压缩框架来压缩深度神经网络，压缩后的网络通常具有更小的模型尺寸和更快的推理速度，
-模型性能也不会有明显的下降。 NNI 上的模型压缩包括剪枝和量化算法。 这些算法通过 NNI Trial SDK 提供
-。 可以直接在 Trial 代码中使用，并在不启动 NNI Experiment 的情况下运行 Trial 代码。 用户还可以使用 NNI 模型压缩框架集成自定义的剪枝和量化算法。
-
-模型压缩的详细说明和算法可在 `这里 <Compression/Overview.rst>`__ 找到。
-
-自动特征工程
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-自动特征工程，可以为下游任务找到最有效的特征。 自动特征工程及其用法的详细说明可在 `这里 <FeatureEngineering/Overview.rst>`__ 找到。 通过 NNI Trial SDK 支持，不必创建 NNI Experiment， 只需在 Trial 代码中加入内置的自动特征工程算法，然后直接运行 Trial 代码。 
-
-自动特征工程算法通常有一些超参。 如果要自动调整这些超参，可以利用 NNI 的超参数调优，即选择调优算法（即 Tuner）并启动 NNI Experiment。
-
-了解更多信息
---------------------
-
-
-* `入门 <Tutorial/QuickStart.rst>`__
-* `如何为 NNI 调整代码？ <TrialExample/Trials.rst>`__
-* `NNI 支持哪些 Tuner？ <Tuner/BuiltinTuner.rst>`__
-* `如何自定义 Tuner？ <Tuner/CustomizeTuner.rst>`__
-* `NNI 支持哪些 Assessor？ <Assessor/BuiltinAssessor.rst>`__
-* `如何自定义 Assessor？ <Assessor/CustomizeAssessor.rst>`__
-* `如何在本机上运行 Experiment？ <TrainingService/LocalMode.rst>`__
-* `如何在多机上运行 Experiment？ <TrainingService/RemoteMachineMode.rst>`__
-* `如何在 OpenPAI 上运行 Experiment？ <TrainingService/PaiMode.rst>`__
-* `示例 <TrialExample/MnistExamples.rst>`__
-* `NNI 上的神经网络架构搜索 <NAS/Overview.rst>`__
-* `NNI 上的自动模型压缩 <Compression/Overview.rst>`__
-* `NNI 上的自动特征工程 <FeatureEngineering/Overview.rst>`__
diff --git a/docs/source/TrainingService/DLTSMode.rst b/docs/source/TrainingService/DLTSMode.rst
deleted file mode 100644
index 87be8befb..000000000
--- a/docs/source/TrainingService/DLTSMode.rst
+++ /dev/null
@@ -1,67 +0,0 @@
-**Run an Experiment on DLTS**
-=================================
-
-NNI supports running an experiment on `DLTS <https://github.com/microsoft/DLWorkspace.git>`__\ , called dlts mode. Before starting to use NNI dlts mode, you should have an account to access DLTS dashboard.
-
-Setup Environment
------------------
-
-Step 1. Choose a cluster from DLTS dashboard, ask administrator for the cluster dashboard URL.
-
-
-.. image:: ../../img/dlts-step1.png
-   :target: ../../img/dlts-step1.png
-   :alt: Choose Cluster
-
-
-Step 2. Prepare a NNI config YAML like the following:
-
-.. code-block:: yaml
-
-   # Set this field to "dlts"
-   trainingServicePlatform: dlts
-   authorName: your_name
-   experimentName: auto_mnist
-   trialConcurrency: 2
-   maxExecDuration: 3h
-   maxTrialNum: 100
-   searchSpacePath: search_space.json
-   useAnnotation: false
-   tuner:
-     builtinTunerName: TPE
-     classArgs:
-       optimize_mode: maximize
-   trial:
-     command: python3 mnist.py
-     codeDir: .
-     gpuNum: 1
-     image: msranni/nni
-   # Configuration to access DLTS
-   dltsConfig:
-     dashboard: # Ask administrator for the cluster dashboard URL
-
-Remember to fill the cluster dashboard URL to the last line.
-
-Step 3. Open your working directory of the cluster, paste the NNI config as well as related code to a directory.
-
-
-.. image:: ../../img/dlts-step3.png
-   :target: ../../img/dlts-step3.png
-   :alt: Copy Config
-
-
-Step 4. Submit a NNI manager job to the specified cluster.
-
-
-.. image:: ../../img/dlts-step4.png
-   :target: ../../img/dlts-step4.png
-   :alt: Submit Job
-
-
-Step 5. Go to Endpoints tab of the newly created job, click the Port 40000 link to check trial's information.
-
-
-.. image:: ../../img/dlts-step5.png
-   :target: ../../img/dlts-step5.png
-   :alt: View NNI WebUI
-
diff --git a/docs/source/TrainingService/LocalMode.rst b/docs/source/TrainingService/LocalMode.rst
deleted file mode 100644
index 95a2f2dbf..000000000
--- a/docs/source/TrainingService/LocalMode.rst
+++ /dev/null
@@ -1,174 +0,0 @@
-**Tutorial: Create and Run an Experiment on local with NNI API**
-================================================================
-
-In this tutorial, we will use the example in [nni/examples/trials/mnist-pytorch] to explain how to create and run an experiment on local with NNI API.
-
-..
-
-   Before starts
-
-
-You have an implementation for MNIST classifer using convolutional layers, the Python code is similar to ``mnist.py``.
-
-..
-
-   Step 1 - Update model codes
-
-
-To enable NNI API, make the following changes:
-
-1.1 Declare NNI API: include ``import nni`` in your trial code to use NNI APIs.
-
-1.2 Get predefined parameters
-
-Use the following code snippet:
-
-.. code-block:: python
-
-   tuner_params = nni.get_next_parameter()
-
-to get hyper-parameters' values assigned by tuner. ``tuner_params`` is an object, for example:
-
-.. code-block:: json
-
-   {"batch_size": 32, "hidden_size": 128, "lr": 0.01, "momentum": 0.2029}
-
-..
-
-1.3 Report NNI results: Use the API: ``nni.report_intermediate_result(accuracy)`` to send ``accuracy`` to assessor. Use the API: ``nni.report_final_result(accuracy)`` to send `accuracy` to tuner.
-
-**NOTE**\ :
-
-.. code-block:: bash
-
-   accuracy - The `accuracy` could be any python object, but  if you use NNI built-in tuner/assessor, `accuracy` should be a numerical variable (e.g. float, int).
-   tuner    - The tuner will generate next parameters/architecture based on the explore history (final result of all trials).
-   assessor - The assessor will decide which trial should early stop based on the history performance of trial (intermediate result of one trial).
-
-..
-
-   Step 2 - Define SearchSpace
-
-
-The hyper-parameters used in ``Step 1.2 - Get predefined parameters`` is defined in a ``search_space.json`` file like below:
-
-.. code-block:: bash
-
-    {
-        "batch_size": {"_type":"choice", "_value": [16, 32, 64, 128]},
-        "hidden_size":{"_type":"choice","_value":[128, 256, 512, 1024]},
-        "lr":{"_type":"choice","_value":[0.0001, 0.001, 0.01, 0.1]},
-        "momentum":{"_type":"uniform","_value":[0, 1]}
-    }
-
-Refer to `define search space <../Tutorial/SearchSpaceSpec.rst>`__ to learn more about search space.
-
-..
-
-   Step 3 - Define Experiment
-
-   ..
-
-To run an experiment in NNI, you only needed:
-
-
-* Provide a runnable trial
-* Provide or choose a tuner
-* Provide a YAML experiment configure file
-* (optional) Provide or choose an assessor
-
-**Prepare trial**\ :
-
-..
-
-   You can download nni source code and a set of examples can be found in ``nni/examples``, run ``ls nni/examples/trials`` to see all the trial examples.
-
-
-Let's use a simple trial example, e.g. mnist, provided by NNI. After you cloned NNI source, NNI examples have been put in ~/nni/examples, run ``ls ~/nni/examples/trials`` to see all the trial examples. You can simply execute the following command to run the NNI mnist example:
-
-.. code-block:: bash
-
-     python ~/nni/examples/trials/mnist-pytorch/mnist.py
-
-
-This command will be filled in the YAML configure file below. Please refer to `here <../TrialExample/Trials.rst>`__ for how to write your own trial.
-
-**Prepare tuner**\ : NNI supports several popular automl algorithms, including Random Search, Tree of Parzen Estimators (TPE), Evolution algorithm etc. Users can write their own tuner (refer to `here <../Tuner/CustomizeTuner.rst>`__\ ), but for simplicity, here we choose a tuner provided by NNI as below:
-
-.. code-block:: bash
-
-     tuner:
-       name: TPE
-       classArgs:
-         optimize_mode: maximize
-
-
-*name* is used to specify a tuner in NNI, *classArgs* are the arguments pass to the tuner (the spec of builtin tuners can be found `here <../Tuner/BuiltinTuner.rst>`__\ ), *optimization_mode* is to indicate whether you want to maximize or minimize your trial's result.
-
-**Prepare configure file**\ : Since you have already known which trial code you are going to run and which tuner you are going to use, it is time to prepare the YAML configure file. NNI provides a demo configure file for each trial example, ``cat ~/nni/examples/trials/mnist-pytorch/config.yml`` to see it. Its content is basically shown below:
-
-.. code-block:: yaml
-
-   experimentName: local training service example
-
-   searchSpaceFile ~/nni/examples/trials/mnist-pytorch/search_space.json
-   trailCommand: python3 mnist.py
-   trialCodeDirectory: ~/nni/examples/trials/mnist-pytorch
-
-   trialGpuNumber: 0
-   trialConcurrency: 1
-   maxExperimentDuration: 3h
-   maxTrialNumber: 10
-
-   trainingService:
-     platform: local
-
-   tuner:
-     name: TPE
-     classArgs:
-       optimize_mode: maximize
-
-
-With all these steps done, we can run the experiment with the following command:
-
-.. code-block:: bash
-
-     nnictl create --config ~/nni/examples/trials/mnist-pytorch/config.yml
-
-
-You can refer to `here <../Tutorial/Nnictl.rst>`__ for more usage guide of *nnictl* command line tool.
-
-View experiment results
------------------------
-
-The experiment has been running now. Other than *nnictl*\ , NNI also provides WebUI for you to view experiment progress, to control your experiment, and some other appealing features.
-
-Using multiple local GPUs to speed up search
---------------------------------------------
-
-The following steps assume that you have 4 NVIDIA GPUs installed at local and PyTorch with CUDA support. The demo enables 4 concurrent trail jobs and each trail job uses 1 GPU.
-
-**Prepare configure file**\ : NNI provides a demo configuration file for the setting above, ``cat ~/nni/examples/trials/mnist-pytorch/config_detailed.yml`` to see it. The trailConcurrency and trialGpuNumber are different from the basic configure file:
-
-.. code-block:: bash
-
-   ...
-
-   trialGpuNumber: 1
-   trialConcurrency: 4
-
-   ...
-
-   trainingService:
-     platform: local
-     useActiveGpu: false  # set to "true" if you are using graphical OS like Windows 10 and Ubuntu desktop
-
-
-We can run the experiment with the following command:
-
-.. code-block:: bash
-
-     nnictl create --config ~/nni/examples/trials/mnist-pytorch/config_detailed.yml
-
-
-You can use *nnictl* command line tool or WebUI to trace the training progress. *nvidia_smi* command line tool can also help you to monitor the GPU usage during training.
diff --git a/docs/source/TrialExample/Trials_zh.rst b/docs/source/TrialExample/Trials_zh.rst
deleted file mode 100644
index f3340de3d..000000000
--- a/docs/source/TrialExample/Trials_zh.rst
+++ /dev/null
@@ -1,216 +0,0 @@
-.. 263c2dcfaee0c2fd06c19b5e90b96374
-
-.. role:: raw-html(raw)
-   :format: html
-
-
-实现 NNI 的 Trial（试验）代码
-=================================
-
-**Trial（试验）** 是将一组参数组合（例如，超参）在模型上独立的一次尝试。
-
-定义 NNI 的 Trial，需要首先定义参数组（例如，搜索空间），并更新模型代码。 有两种方法来定义一个 Trial：`NNI Python API <#nni-api>`__ 和 `NNI Python annotation <#nni-annotation>`__。 参考 `这里 <#more-examples>`__ 更多 Trial 示例。
-
-:raw-html:`<a name="nni-api"></a>`
-
-NNI Trial API
--------------
-
-第一步：准备搜索空间参数文件。
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-样例如下：
-
-.. code-block:: json
-
-   {
-       "dropout_rate":{"_type":"uniform","_value":[0.1,0.5]},
-       "conv_size":{"_type":"choice","_value":[2,3,5,7]},
-       "hidden_size":{"_type":"choice","_value":[124, 512, 1024]},
-       "learning_rate":{"_type":"uniform","_value":[0.0001, 0.1]}
-   }
-
-参考 `SearchSpaceSpec.rst <../Tutorial/SearchSpaceSpec.rst>`__ 进一步了解搜索空间。 Tuner 会根据搜索空间来生成配置，即从每个超参的范围中选一个值。
-
-第二步：更新模型代码
-^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-
-* Import NNI
-
-在 Trial 代码中加上 ``import nni`` 。
-
-* 从 Tuner 获得参数值
-
-.. code-block:: python
-
-   RECEIVED_PARAMS = nni.get_next_parameter()
-
-``RECEIVED_PARAMS`` 是一个对象，如：
-
-``{"conv_size": 2, "hidden_size": 124, "learning_rate": 0.0307, "dropout_rate": 0.2029}``
-
-
-* 定期返回指标数据（可选）
-
-.. code-block:: python
-
-   nni.report_intermediate_result(metrics)
-
-``指标`` 可以是任意的 Python 对象。 如果使用了 NNI 内置的 Tuner/Assessor，``指标`` 只可以是两种类型：1) 数值类型，如 float、int， 2) dict 对象，其中必须有键名为 ``default`` ，值为数值的项目。 ``指标`` 会发送给 `assessor <../Assessor/BuiltinAssessor.rst>`__。 通常，``指标`` 包含了定期评估的损失值或精度。
-
-
-* 返回配置的最终性能
-
-.. code-block:: python
-
-   nni.report_final_result(metrics)
-
-``指标`` 可以是任意的 Python 对象。 如果使用了内置的 Tuner/Assessor，``指标`` 格式和 ``report_intermediate_result`` 中一样，这个数值表示模型的性能，如精度、损失值等。 ``指标`` 会发送给 `tuner <../Tuner/BuiltinTuner.rst>`__。
-
-第三步：启动 NNI Experiment (实验)
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-启动 NNI 实验，提供搜索空间文件的路径，即第一步中定义的文件：
-
-.. code-block:: yaml
-
-   searchSpacePath: /path/to/your/search_space.json
-
-参考 `这里 <../Tutorial/ExperimentConfig.rst>`__ 进一步了解如何配置 Experiment。
-
-参考 `这里 <../sdk_reference.rst>`__ ，了解更多 NNI Trial API （例如：``nni.get_sequence_id()``）。
-
-:raw-html:`<a name="nni-annotation"></a>`
-
-NNI Annotation
----------------------
-
-另一种实现 Trial 的方法是使用 Python 注释来标记 NNI。 NNI Annotation 很简单，类似于注释。 不必对现有代码进行结构更改。 只需要添加一些 NNI Annotation，就能够：
-
-
-* 标记需要调整的参数变量
-* 指定要在其中调整的变量的范围
-* 标记哪个变量需要作为中间结果范围给 ``assessor``
-* 标记哪个变量需要作为最终结果（例如：模型精度） 返回给 ``tuner``
-
-同样以 MNIST 为例，只需要两步就能用 NNI Annotation 来实现 Trial 代码。
-
-第一步：在代码中加入 Annotation
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-下面是加入了 Annotation 的 TensorFlow 代码片段，高亮的 4 行 Annotation 用于：
-
-
-#. 调优 batch_size 和 dropout_rate
-#. 每执行 100 步返回 test_acc
-#. 最后返回 test_acc 作为最终结果。
-
-值得注意的是，新添加的代码都是注释，不会影响以前的执行逻辑。因此这些代码仍然能在没有安装 NNI 的环境中运行。
-
-.. code-block:: diff
-
-   with tf.Session() as sess:
-       sess.run(tf.global_variables_initializer())
-   +   """@nni.variable(nni.choice(50, 250, 500), name=batch_size)"""
-       batch_size = 128
-       for i in range(10000):
-           batch = mnist.train.next_batch(batch_size)
-   +       """@nni.variable(nni.choice(0.1, 0.5), name=dropout_rate)"""
-           dropout_rate = 0.5
-           mnist_network.train_step.run(feed_dict={mnist_network.images: batch[0],
-                                                   mnist_network.labels: batch[1],
-                                                   mnist_network.keep_prob: dropout_rate})
-           if i % 100 == 0:
-               test_acc = mnist_network.accuracy.eval(
-                   feed_dict={mnist_network.images: mnist.test.images,
-                               mnist_network.labels: mnist.test.labels,
-                               mnist_network.keep_prob: 1.0})
-   +           """@nni.report_intermediate_result(test_acc)"""
-
-       test_acc = mnist_network.accuracy.eval(
-           feed_dict={mnist_network.images: mnist.test.images,
-                       mnist_network.labels: mnist.test.labels,
-                       mnist_network.keep_prob: 1.0})
-   +   """@nni.report_final_result(test_acc)"""
-
-**注意**：
-
-
-* ``@nni.variable`` 会对它的下面一行进行修改，左边被赋值变量必须与 ``@nni.variable`` 的关键字 ``name`` 相同。
-* ``@nni.report_intermediate_result``\ /\ ``@nni.report_final_result`` 会将数据发送给 assessor/tuner。
-
-Annotation 的语法和用法等，参考 `Annotation <../Tutorial/AnnotationSpec.rst>`__。
-
-第二步：启用 Annotation
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-在 YAML 配置文件中设置 *useAnnotation* 为 true 来启用 Annotation：
-
-.. code-block:: bash
-
-   useAnnotation: true
-
-用于调试的独立模式
------------------------------
-
-NNI 支持独立模式，使 Trial 代码无需启动 NNI 实验即可运行。 这样能更容易的找出 Trial 代码中的 Bug。 NNI Annotation 天然支持独立模式，因为添加的 NNI 相关的行都是注释的形式。 NNI Trial API 在独立模式下的行为有所变化，某些 API 返回虚拟值，而某些 API 不报告值。 有关这些 API 的完整列表，请参阅下表。
-
-.. code-block:: python
-
-   # 注意：请为 Trial 代码中的超参分配默认值
-   nni.get_next_parameter # 返回 {}
-   nni.report_final_result # 已在 stdout 上打印日志，但不报告
-   nni.report_intermediate_result # 已在 stdout 上打印日志，但不报告
-   nni.get_experiment_id # 返回 "STANDALONE"
-   nni.get_trial_id # 返回 "STANDALONE"
-   nni.get_sequence_id # 返回 0
-
-可使用 :githublink:`mnist 示例 <examples/trials/mnist-pytorch>` 来尝试独立模式。 只需在代码目录下运行 ``python3 mnist.py``。 Trial 代码会使用默认超参成功运行。
-
-更多调试的信息，可参考 `How to Debug <../Tutorial/HowToDebug.rst>`__。
-
-Trial 存放在什么地方？
-----------------------------------------
-
-本机模式
-^^^^^^^^^^
-
-每个 Trial 都有单独的目录来输出自己的数据。 在每次 Trial 运行后，环境变量 ``NNI_OUTPUT_DIR`` 定义的目录都会被导出。 在这个目录中可以看到 Trial 的代码、数据和日志。 此外，Trial 的日志（包括 stdout）还会被重定向到此目录中的 ``trial.log`` 文件。
-
-如果使用了 Annotation 方法，转换后的 Trial 代码会存放在另一个临时目录中。 可以在 ``run.sh`` 文件中的 ``NNI_OUTPUT_DIR`` 变量找到此目录。 文件中的第二行（即：``cd``）会切换到代码所在的实际路径。 ``run.sh`` 文件示例：
-
-.. code-block:: bash
-
-   #!/bin/bash
-   cd /tmp/user_name/nni/annotation/tmpzj0h72x6 #This is the actual directory
-   export NNI_PLATFORM=local
-   export NNI_SYS_DIR=/home/user_name/nni-experiments/$experiment_id$/trials/$trial_id$
-   export NNI_TRIAL_JOB_ID=nrbb2
-   export NNI_OUTPUT_DIR=/home/user_name/nni-experiments/$eperiment_id$/trials/$trial_id$
-   export NNI_TRIAL_SEQ_ID=1
-   export MULTI_PHASE=false
-   export CUDA_VISIBLE_DEVICES=
-   eval python3 mnist.py 2>/home/user_name/nni-experiments/$experiment_id$/trials/$trial_id$/stderr
-   echo $? `date +%s%3N` >/home/user_name/nni-experiments/$experiment_id$/trials/$trial_id$/.nni/state
-
-其它模式
-^^^^^^^^^^^
-
-当 Trial 运行在 OpenPAI 这样的远程服务器上时，``NNI_OUTPUT_DIR`` 仅会指向 Trial 的输出目录，而 ``run.sh`` 不会在此目录中。 ``trial.log`` 文件会被复制回本机的 Trial 目录中。目录的默认位置在 ``~/nni-experiments/$experiment_id$/trials/$trial_id$/``。
-
-更多调试的信息，可参考 `How to Debug <../Tutorial/HowToDebug.rst>`__。
-
-:raw-html:`<a name="more-examples"></a>`
-
-更多 Trial 的示例
--------------------
-
-
-* `将日志写入 TensorBoard 的 Trial 输出目录 <../Tutorial/Tensorboard.rst>`__
-* `MNIST 示例 <MnistExamples.rst>`__
-* `为 CIFAR 10 分类找到最佳的 optimizer <Cifar10Examples.rst>`__
-* `如何在 NNI 调优 SciKit-learn 的参数 <SklearnExamples.rst>`__
-* `在阅读理解上使用自动模型架构搜索。 <SquadEvolutionExamples.rst>`__
-* `如何在 NNI 上调优 GBDT <GbdtExample.rst>`__
-* `在 NNI 上调优 RocksDB <RocksdbExamples.rst>`__
diff --git a/docs/source/Tuner/AnnealTuner.rst b/docs/source/Tuner/AnnealTuner.rst
deleted file mode 100644
index 5d24ecb7a..000000000
--- a/docs/source/Tuner/AnnealTuner.rst
+++ /dev/null
@@ -1,23 +0,0 @@
-Anneal Tuner
-============
-
-This simple annealing algorithm begins by sampling from the prior but tends over time to sample from points closer and closer to the best ones observed. This algorithm is a simple variation on random search that leverages smoothness in the response surface. The annealing rate is not adaptive.
-
-Usage
------
-
-classArgs Requirements
-^^^^^^^^^^^^^^^^^^^^^^
-
-* **optimize_mode** (*maximize or minimize, optional, default = maximize*) - If 'maximize', the tuner will try to maximize metrics. If 'minimize', the tuner will try to minimize metrics.
-
-Example Configuration
-^^^^^^^^^^^^^^^^^^^^^
-
-.. code-block:: yaml
-
-   # config.yml
-   tuner:
-     name: Anneal
-     classArgs:
-       optimize_mode: maximize
diff --git a/docs/source/Tuner/BatchTuner.rst b/docs/source/Tuner/BatchTuner.rst
deleted file mode 100644
index d44b602c0..000000000
--- a/docs/source/Tuner/BatchTuner.rst
+++ /dev/null
@@ -1,37 +0,0 @@
-Batch Tuner
-===========
-
-Batch tuner allows users to simply provide several configurations (i.e., choices of hyper-parameters) for their trial code. After finishing all the configurations, the experiment is done. Batch tuner only supports the type ``choice`` in the `search space spec <../Tutorial/SearchSpaceSpec.rst>`__.
-
-Suggested scenario: If the configurations you want to try have been decided, you can list them in the SearchSpace file (using ``choice``) and run them using the batch tuner.
-
-Usage
------
-
-Example Configuration
-^^^^^^^^^^^^^^^^^^^^^
-
-.. code-block:: yaml
-
-   # config.yml
-   tuner:
-     name: BatchTuner
-
-Note that the search space for BatchTuner should look like:
-
-.. code-block:: json
-
-   {
-       "combine_params":
-       {
-           "_type" : "choice",
-           "_value" : [{"optimizer": "Adam", "learning_rate": 0.00001},
-                       {"optimizer": "Adam", "learning_rate": 0.0001},
-                       {"optimizer": "Adam", "learning_rate": 0.001},
-                       {"optimizer": "SGD", "learning_rate": 0.01},
-                       {"optimizer": "SGD", "learning_rate": 0.005},
-                       {"optimizer": "SGD", "learning_rate": 0.0002}]
-       }
-   }
-
-The search space file should include the high-level key ``combine_params``. The type of params in the search space must be ``choice`` and the ``values`` must include all the combined params values.
diff --git a/docs/source/Tuner/GridsearchTuner.rst b/docs/source/Tuner/GridsearchTuner.rst
deleted file mode 100644
index 26675bbc6..000000000
--- a/docs/source/Tuner/GridsearchTuner.rst
+++ /dev/null
@@ -1,19 +0,0 @@
-Grid Search Tuner
-=================
-
-Grid Search performs an exhaustive search through a search space.
-
-For uniform and normal distributed parameters, grid search tuner samples them at progressively decreased intervals.
-
-Usage
------
-
-Grid search tuner has no argument.
-
-Example Configuration
-^^^^^^^^^^^^^^^^^^^^^
-
-.. code-block:: yaml
-
-   tuner:
-     name: GridSearch
diff --git a/docs/source/Tuner/NetworkmorphismTuner.rst b/docs/source/Tuner/NetworkmorphismTuner.rst
deleted file mode 100644
index f97e6e3ad..000000000
--- a/docs/source/Tuner/NetworkmorphismTuner.rst
+++ /dev/null
@@ -1,296 +0,0 @@
-Network Morphism Tuner
-======================
-
-`Autokeras <https://arxiv.org/abs/1806.10282>`__ is a popular autoML tool using Network Morphism. The basic idea of Autokeras is to use Bayesian Regression to estimate the metric of the Neural Network Architecture. Each time, it generates several child networks from father networks. Then it uses a naïve Bayesian regression to estimate its metric value from the history of trained results of network and metric value pairs. Next, it chooses the child which has the best, estimated performance and adds it to the training queue. Inspired by the work of Autokeras and referring to its `code <https://github.com/jhfjhfj1/autokeras>`__, we implemented our Network Morphism method on the NNI platform.
-
-If you want to know more about network morphism trial usage, please see the :githublink:`Readme.md <examples/trials/network_morphism/README.rst>`.
-
-Usage
------
-
-Installation
-^^^^^^^^^^^^
-
-NetworkMorphism requires :githublink:`PyTorch <examples/trials/network_morphism/requirements.txt>`.
-
-classArgs Requirements
-^^^^^^^^^^^^^^^^^^^^^^
-
-* **optimize_mode** (*maximize or minimize, optional, default = maximize*) - If 'maximize', the tuner will try to maximize metrics. If 'minimize', the tuner will try to minimize metrics.
-* **task** (*('cv'), optional, default = 'cv'*) - The domain of the experiment. For now, this tuner only supports the computer vision (CV) domain.
-* **input_width** (*int, optional, default = 32*) - input image width
-* **input_channel** (*int, optional, default = 3*) - input image channel
-* **n_output_node** (*int, optional, default = 10*) - number of classes
-
-
-
-Config File
-^^^^^^^^^^^
-
-To use Network Morphism, you should modify the following spec in your ``config.yml`` file:
-
-.. code-block:: yaml
-
-   tuner:
-     #choice: NetworkMorphism
-     name: NetworkMorphism
-     classArgs:
-       #choice: maximize, minimize
-       optimize_mode: maximize
-       #for now, this tuner only supports cv domain
-       task: cv
-       #modify to fit your input image width
-       input_width: 32
-       #modify to fit your input image channel
-       input_channel: 3
-       #modify to fit your number of classes
-       n_output_node: 10
-
-Example Configuration
-^^^^^^^^^^^^^^^^^^^^^
-
-.. code-block:: yaml
-
-   # config.yml
-   tuner:
-     name: NetworkMorphism
-       classArgs:
-         optimize_mode: maximize
-         task: cv
-         input_width: 32
-         input_channel: 3
-         n_output_node: 10
-
-In the training procedure, it generates a JSON file which represents a Network Graph. Users can call the "json_to_graph()" function to build a PyTorch or Keras model from this JSON file.
-
-.. code-block:: python
-
-   import nni
-   from nni.networkmorphism_tuner.graph import json_to_graph
-
-   def build_graph_from_json(ir_model_json):
-       """build a pytorch model from json representation
-       """
-       graph = json_to_graph(ir_model_json)
-       model = graph.produce_torch_model()
-       return model
-
-   # trial get next parameter from network morphism tuner
-   RCV_CONFIG = nni.get_next_parameter()
-   # call the function to build pytorch model or keras model
-   net = build_graph_from_json(RCV_CONFIG)
-
-   # training procedure
-   # ....
-
-   # report the final accuracy to NNI
-   nni.report_final_result(best_acc)
-
-If you want to save and load the **best model**, the following methods are recommended.
-
-.. code-block:: python
-
-   # 1. Use NNI API
-   ## You can get the best model ID from WebUI
-   ## or `nni-experiments/experiment_id/log/model_path/best_model.txt'
-
-   ## read the json string from model file and load it with NNI API
-   with open("best-model.json") as json_file:
-       json_of_model = json_file.read()
-   model = build_graph_from_json(json_of_model)
-
-   # 2. Use Framework API (Related to Framework)
-   ## 2.1 Keras API
-
-   ## Save the model with Keras API in the trial code
-   ## it's better to save model with id in nni local mode
-   model_id = nni.get_sequence_id()
-   ## serialize model to JSON
-   model_json = model.to_json()
-   with open("model-{}.json".format(model_id), "w") as json_file:
-       json_file.write(model_json)
-   ## serialize weights to HDF5
-   model.save_weights("model-{}.h5".format(model_id))
-
-   ## Load the model with Keras API if you want to reuse the model
-   ## load json and create model
-   model_id = "" # id of the model you want to reuse
-   with open('model-{}.json'.format(model_id), 'r') as json_file:
-       loaded_model_json = json_file.read()
-   loaded_model = model_from_json(loaded_model_json)
-   ## load weights into new model
-   loaded_model.load_weights("model-{}.h5".format(model_id))
-
-   ## 2.2 PyTorch API
-
-   ## Save the model with PyTorch API in the trial code
-   model_id = nni.get_sequence_id()
-   torch.save(model, "model-{}.pt".format(model_id))
-
-   ## Load the model with PyTorch API if you want to reuse the model
-   model_id = "" # id of the model you want to reuse
-   loaded_model = torch.load("model-{}.pt".format(model_id))
-
-File Structure
---------------
-
-The tuner has a lot of different files, functions, and classes. Here, we will give most of those files only a brief introduction:
-
-
-* 
-  ``networkmorphism_tuner.py`` is a tuner which uses network morphism techniques.
-
-* 
-  ``bayesian.py`` is a Bayesian method to estimate the metric of unseen model based on the models we have already searched.
-
-* ``graph.py``  is the meta graph data structure. The class Graph represents the neural architecture graph of a model.
-
-  * Graph extracts the neural architecture graph from a model.
-  * Each node in the graph is an intermediate tensor between layers.
-  * Each layer is an edge in the graph.
-  * Notably, multiple edges may refer to the same layer.
-
-* 
-  ``graph_transformer.py`` includes some graph transformers which widen, deepen, or add skip-connections to the graph.
-
-* 
-  ``layers.py``  includes all the layers we use in our model.
-
-* ``layer_transformer.py`` includes some layer transformers which widen, deepen, or add skip-connections to the layer.
-* ``nn.py`` includes the class which generates the initial network.
-* ``metric.py`` some metric classes including Accuracy and MSE.
-* ``utils.py`` is the example search network architectures for the ``cifar10`` dataset, using Keras.
-
-The Network Representation Json Example
----------------------------------------
-
-Here is an example of the intermediate representation JSON file we defined, which is passed from the tuner to the trial in the architecture search procedure. Users can call the "json_to_graph()" function in the trial code to build a PyTorch or Keras model from this JSON file.
-
-.. code-block:: json
-
-   {
-        "input_shape": [32, 32, 3],
-        "weighted": false,
-        "operation_history": [],
-        "layer_id_to_input_node_ids": {"0": [0],"1": [1],"2": [2],"3": [3],"4": [4],"5": [5],"6": [6],"7": [7],"8": [8],"9": [9],"10": [10],"11": [11],"12": [12],"13": [13],"14": [14],"15": [15],"16": [16]
-        },
-        "layer_id_to_output_node_ids": {"0": [1],"1": [2],"2": [3],"3": [4],"4": [5],"5": [6],"6": [7],"7": [8],"8": [9],"9": [10],"10": [11],"11": [12],"12": [13],"13": [14],"14": [15],"15": [16],"16": [17]
-        },
-        "adj_list": {
-            "0": [[1, 0]],
-            "1": [[2, 1]],
-            "2": [[3, 2]],
-            "3": [[4, 3]],
-            "4": [[5, 4]],
-            "5": [[6, 5]],
-            "6": [[7, 6]],
-            "7": [[8, 7]],
-            "8": [[9, 8]],
-            "9": [[10, 9]],
-            "10": [[11, 10]],
-            "11": [[12, 11]],
-            "12": [[13, 12]],
-            "13": [[14, 13]],
-            "14": [[15, 14]],
-            "15": [[16, 15]],
-            "16": [[17, 16]],
-            "17": []
-        },
-        "reverse_adj_list": {
-            "0": [],
-            "1": [[0, 0]],
-            "2": [[1, 1]],
-            "3": [[2, 2]],
-            "4": [[3, 3]],
-            "5": [[4, 4]],
-            "6": [[5, 5]],
-            "7": [[6, 6]],
-            "8": [[7, 7]],
-            "9": [[8, 8]],
-            "10": [[9, 9]],
-            "11": [[10, 10]],
-            "12": [[11, 11]],
-            "13": [[12, 12]],
-            "14": [[13, 13]],
-            "15": [[14, 14]],
-            "16": [[15, 15]],
-            "17": [[16, 16]]
-        },
-        "node_list": [
-            [0, [32, 32, 3]],
-            [1, [32, 32, 3]],
-            [2, [32, 32, 64]],
-            [3, [32, 32, 64]],
-            [4, [16, 16, 64]],
-            [5, [16, 16, 64]],
-            [6, [16, 16, 64]],
-            [7, [16, 16, 64]],
-            [8, [8, 8, 64]],
-            [9, [8, 8, 64]],
-            [10, [8, 8, 64]],
-            [11, [8, 8, 64]],
-            [12, [4, 4, 64]],
-            [13, [64]],
-            [14, [64]],
-            [15, [64]],
-            [16, [64]],
-            [17, [10]]
-        ],
-        "layer_list": [
-            [0, ["StubReLU", 0, 1]],
-            [1, ["StubConv2d", 1, 2, 3, 64, 3]],
-            [2, ["StubBatchNormalization2d", 2, 3, 64]],
-            [3, ["StubPooling2d", 3, 4, 2, 2, 0]],
-            [4, ["StubReLU", 4, 5]],
-            [5, ["StubConv2d", 5, 6, 64, 64, 3]],
-            [6, ["StubBatchNormalization2d", 6, 7, 64]],
-            [7, ["StubPooling2d", 7, 8, 2, 2, 0]],
-            [8, ["StubReLU", 8, 9]],
-            [9, ["StubConv2d", 9, 10, 64, 64, 3]],
-            [10, ["StubBatchNormalization2d", 10, 11, 64]],
-            [11, ["StubPooling2d", 11, 12, 2, 2, 0]],
-            [12, ["StubGlobalPooling2d", 12, 13]],
-            [13, ["StubDropout2d", 13, 14, 0.25]],
-            [14, ["StubDense", 14, 15, 64, 64]],
-            [15, ["StubReLU", 15, 16]],
-            [16, ["StubDense", 16, 17, 64, 10]]
-        ]
-    }
-
-You can consider the model to be a `directed acyclic graph <https://en.wikipedia.org/wiki/Directed_acyclic_graph>`__. The definition of each model is a JSON object where:
-
-
-* ``input_shape`` is a list of integers which do not include the batch axis.
-* ``weighted`` means whether the weights and biases in the neural network should be included in the graph.
-* ``operation_history`` is a list saving all the network morphism operations.
-* ``layer_id_to_input_node_ids`` is a dictionary mapping from layer identifiers to their input nodes identifiers.
-* ``layer_id_to_output_node_ids`` is a dictionary mapping from layer identifiers to their output nodes identifiers
-* ``adj_list`` is a two-dimensional list; the adjacency list of the graph. The first dimension is identified by tensor identifiers. In each edge list, the elements are two-element tuples of (tensor identifier, layer identifier).
-* ``reverse_adj_list`` is a reverse adjacent list in the same format as adj_list.
-* ``node_list`` is a list of integers. The indices of the list are the identifiers.
-* 
-  ``layer_list`` is a list of stub layers. The indices of the list are the identifiers.
-
-
-  * 
-    For ``StubConv (StubConv1d, StubConv2d, StubConv3d)``, the numbering follows the format: its node input id (or id list), node output id, input_channel, filters, kernel_size, stride, and padding.
-
-  * 
-    For ``StubDense``, the numbering follows the format: its node input id (or id list), node output id, input_units, and units.
-
-  * 
-    For ``StubBatchNormalization (StubBatchNormalization1d, StubBatchNormalization2d, StubBatchNormalization3d)``, the numbering follows the format: its node input id (or id list), node output id, and features numbers.
-
-  * 
-    For ``StubDropout(StubDropout1d, StubDropout2d, StubDropout3d)``, the numbering follows the format: its node input id (or id list), node output id, and dropout rate.
-
-  * 
-    For ``StubPooling (StubPooling1d, StubPooling2d, StubPooling3d)``, the numbering follows the format: its node input id (or id list), node output id, kernel_size, stride, and padding.
-
-  * 
-    For else layers, the numbering follows the format: its node input id (or id list) and node output id.
-
-TODO
-----
-
-Next step, we will change the API from s fixed network generator to a network generator with more available operators. We will use ONNX instead of JSON later as the intermediate representation spec in the future.
diff --git a/docs/source/Tuner/RandomTuner.rst b/docs/source/Tuner/RandomTuner.rst
deleted file mode 100644
index 1b1022a5a..000000000
--- a/docs/source/Tuner/RandomTuner.rst
+++ /dev/null
@@ -1,17 +0,0 @@
-Random Tuner
-============
-
-In `Random Search for Hyper-Parameter Optimization <http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf>`__ we show that Random Search might be surprisingly effective despite its simplicity.
-We suggest using Random Search as a baseline when no knowledge about the prior distribution of hyper-parameters is available.
-
-Usage
------
-
-Example Configuration
-
-.. code-block:: yaml
-
-   tuner:
-     name: Random
-     classArgs:
-       seed: 100  # optional
diff --git a/docs/source/Tuner/TpeTuner.rst b/docs/source/Tuner/TpeTuner.rst
deleted file mode 100644
index d255b0f69..000000000
--- a/docs/source/Tuner/TpeTuner.rst
+++ /dev/null
@@ -1,114 +0,0 @@
-TPE Tuner
-=========
-
-The Tree-structured Parzen Estimator (TPE) is a sequential model-based optimization (SMBO) approach.
-SMBO methods sequentially construct models to approximate the performance of hyperparameters based on historical measurements,
-and then subsequently choose new hyperparameters to test based on this model.
-
-The TPE approach models P(x|y) and P(y) where x represents hyperparameters and y the associated evaluation matric.
-P(x|y) is modeled by transforming the generative process of hyperparameters,
-replacing the distributions of the configuration prior with non-parametric densities.
-
-This optimization approach is described in detail in `Algorithms for Hyper-Parameter Optimization <https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf>`__.
-
-Parallel TPE optimization
-^^^^^^^^^^^^^^^^^^^^^^^^^
-
-TPE approaches were actually run asynchronously in order to make use of multiple compute nodes and to avoid wasting time waiting for trial evaluations to complete.
-The original algorithm design was optimized for sequential computation.
-If we were to use TPE with much concurrency, its performance will be bad.
-We have optimized this case using the Constant Liar algorithm.
-For these principles of optimization, please refer to our `research blog <../CommunitySharings/ParallelizingTpeSearch.rst>`__.
-
-Usage
------
-
- To use TPE, you should add the following spec in your experiment's YAML config file:
-
-.. code-block:: yaml
-
-    ## minimal config ##
-    tuner:
-      name: TPE
-      classArgs:
-        optimize_mode: minimize
-
-.. code-block:: yaml
-
-    ## advanced config ##
-    tuner:
-      name: TPE
-      classArgs:
-        optimize_mode: maximize
-        seed: 12345
-        tpe_args:
-          constant_liar_type: 'mean'
-          n_startup_jobs: 10
-          n_ei_candidates: 20
-          linear_forgetting: 100
-          prior_weight: 0
-          gamma: 0.5
-        
-classArgs
-^^^^^^^^^
-
-.. list-table::
-    :widths: 10 20 10 60
-    :header-rows: 1
-
-    * - Field
-      - Type
-      - Default
-      - Description
-    
-    * - ``optimize_mode``
-      - ``'minimize' | 'maximize'``
-      - ``'minimize'``
-      - Whether to minimize or maximize trial metrics.
-
-    * - ``seed``
-      - ``int | null``
-      - ``null``
-      - The random seed.
-
-    * - ``tpe_args.constant_liar_type``
-      - ``'best' | 'worst' | 'mean' | null``
-      - ``'best'``
-      - TPE algorithm itself does not support parallel tuning. This parameter specifies how to optimize for trial_concurrency > 1. How each liar works is explained in paper's section 6.1.
-
-        In general ``best`` suit for small trial number and ``worst`` suit for large trial number.
-
-    * - ``tpe_args.n_startup_jobs``
-      - ``int``
-      - ``20``
-      - The first N hyper-parameters are generated fully randomly for warming up.
-
-        If the search space is large, you can increase this value. Or if max_trial_number is small, you may want to decrease it.
-
-    * - ``tpe_args.n_ei_candidates``
-      - ``int``
-      - ``24``
-      - For each iteration TPE samples EI for N sets of parameters and choose the best one. (loosely speaking)
-
-    * - ``tpe_args.linear_forgetting``
-      - ``int``
-      - ``25``
-      - TPE will lower the weights of old trials. This controls how many iterations it takes for a trial to start decay.
-
-    * - ``tpe_args.prior_weight``
-      - ``float``
-      - ``1.0``
-      - TPE treats user provided search space as prior.
-        When generating new trials, it also incorporates the prior in trial history by transforming the search space to
-        one trial configuration (i.e., each parameter of this configuration chooses the mean of its candidate range).
-        Here, prior_weight determines the weight of this trial configuration in the history trial configurations.
-
-        With prior weight 1.0, the search space is treated as one good trial.
-        For example, "normal(0, 1)" effectly equals to a trial with x = 0 which has yielded good result.
-
-    * - ``tpe_args.gamma``
-      - ``float``
-      - ``0.25``
-      - Controls how many trials are considered "good".
-
-        The number is calculated as "min(gamma * sqrt(N), linear_forgetting)".
diff --git a/docs/source/Tutorial/Contributing.rst b/docs/source/Tutorial/Contributing.rst
deleted file mode 100644
index 6f2fd5021..000000000
--- a/docs/source/Tutorial/Contributing.rst
+++ /dev/null
@@ -1,74 +0,0 @@
-Contributing to Neural Network Intelligence (NNI)
-=================================================
-
-Great!! We are always on the lookout for more contributors to our code base.
-
-Firstly, if you are unsure or afraid of anything, just ask or submit the issue or pull request anyways. You won't be yelled at for giving your best effort. The worst that can happen is that you'll be politely asked to change something. We appreciate any sort of contributions and don't want a wall of rules to get in the way of that.
-
-However, for those individuals who want a bit more guidance on the best way to contribute to the project, read on. This document will cover all the points we're looking for in your contributions, raising your chances of quickly merging or addressing your contributions.
-
-Looking for a quickstart, get acquainted with our `Get Started <QuickStart.rst>`__ guide.
-
-There are a few simple guidelines that you need to follow before providing your hacks.
-
-Raising Issues
---------------
-
-When raising issues, please specify the following:
-
-
-* Setup details needs to be filled as specified in the issue template clearly for the reviewer to check.
-* A scenario where the issue occurred (with details on how to reproduce it).
-* Errors and log messages that are displayed by the software.
-* Any other details that might be useful.
-
-Submit Proposals for New Features
----------------------------------
-
-
-* 
-  There is always something more that is required, to make it easier to suit your use-cases. Feel free to join the discussion on new features or raise a PR with your proposed change.
-
-* 
-  Fork the repository under your own github handle. After cloning the repository. Add, commit, push and sqaush (if necessary) the changes with detailed commit messages to your fork. From where you can proceed to making a pull request.
-
-Contributing to Source Code and Bug Fixes
------------------------------------------
-
-Provide PRs with appropriate tags for bug fixes or enhancements to the source code. Do follow the correct naming conventions and code styles when you work on and do try to implement all code reviews along the way.
-
-If you are looking for How to develop and debug the NNI source code, you can refer to `How to set up NNI developer environment doc <./SetupNniDeveloperEnvironment.rst>`__ file in the ``docs`` folder.
-
-Similarly for `Quick Start <QuickStart.rst>`__. For everything else, refer to `NNI Home page <http://nni.readthedocs.io>`__.
-
-Solve Existing Issues
----------------------
-
-Head over to `issues <https://github.com/Microsoft/nni/issues>`__ to find issues where help is needed from contributors. You can find issues tagged with 'good-first-issue' or 'help-wanted' to contribute in.
-
-A person looking to contribute can take up an issue by claiming it as a comment/assign their Github ID to it. In case there is no PR or update in progress for a week on the said issue, then the issue reopens for anyone to take up again. We need to consider high priority issues/regressions where response time must be a day or so.
-
-Code Styles & Naming Conventions
---------------------------------
-
-* We follow `PEP8 <https://www.python.org/dev/peps/pep-0008/>`__ for Python code and naming conventions, do try to adhere to the same when making a pull request or making a change. One can also take the help of linters such as ``flake8`` or ``pylint``
-* We also follow `NumPy Docstring Style <https://www.sphinx-doc.org/en/master/usage/extensions/example_numpy.html#example-numpy>`__ for Python Docstring Conventions. During the `documentation building <Contributing.rst#documentation>`__\ , we use `sphinx.ext.napoleon <https://www.sphinx-doc.org/en/master/usage/extensions/napoleon.html>`__ to generate Python API documentation from Docstring.
-* For docstrings, please refer to `numpydoc docstring guide <https://numpydoc.readthedocs.io/en/latest/format.html>`__ and `pandas docstring guide <https://python-sprints.github.io/pandas/guide/pandas_docstring.html>`__
-
-  * For function docstring, **description**, **Parameters**, and **Returns** **Yields** are mandatory.
-  * For class docstring, **description**, **Attributes** are mandatory.
-  * For docstring to describe ``dict``, which is commonly used in our hyper-param format description, please refer to `Internal Guideline on Writing Standards <https://ribokit.github.io/docs/text/>`__
-
-Documentation
--------------
-
-Our documentation is built with :githublink:`sphinx <docs>`.
-
-* Before submitting the documentation change, please **build homepage locally**: ``cd docs/en_US && make html``, then you can see all the built documentation webpage under the folder ``docs/en_US/_build/html``. It's also highly recommended taking care of **every WARNING** during the build, which is very likely the signal of a **deadlink** and other annoying issues.
-
-* 
-  For links, please consider using **relative paths** first. However, if the documentation is written in reStructuredText format, and:
-
-
-  * It's an image link which needs to be formatted with embedded html grammar, please use global URL like ``https://user-images.githubusercontent.com/44491713/51381727-e3d0f780-1b4f-11e9-96ab-d26b9198ba65.png``, which can be automatically generated by dragging picture onto `Github Issue <https://github.com/Microsoft/nni/issues/new>`__ Box.
-  * It cannot be re-formatted by sphinx, such as source code, please use its global URL. For source code that links to our github repo, please use URLs rooted at ``https://github.com/Microsoft/nni/tree/master/`` (:githublink:`mnist.py <examples/trials/mnist-pytorch/mnist.py>` for example).
diff --git a/docs/source/Tutorial/ExperimentConfig.rst b/docs/source/Tutorial/ExperimentConfig.rst
deleted file mode 100644
index 3805135ac..000000000
--- a/docs/source/Tutorial/ExperimentConfig.rst
+++ /dev/null
@@ -1,1159 +0,0 @@
-Experiment Config Reference (legacy)
-====================================
-
-This is the previous version (V1) of experiment configuration specification. It is still supported for now, but we recommend users to use `the new version of experiment configuration (V2) <../reference/experiment_config.rst>`_.
-
-A config file is needed when creating an experiment. The path of the config file is provided to ``nnictl``.
-The config file is in YAML format.
-This document describes the rules to write the config file, and provides some examples and templates.
-
-
-* `Experiment Config Reference <#experiment-config-reference>`__
-
-  * `Template <#template>`__
-  * `Configuration Spec <#configuration-spec>`__
-
-    * `authorName <#authorname>`__
-    * `experimentName <#experimentname>`__
-    * `trialConcurrency <#trialconcurrency>`__
-    * `maxExecDuration <#maxexecduration>`__
-    * `versionCheck <#versioncheck>`__
-    * `debug <#debug>`__
-    * `maxTrialNum <#maxtrialnum>`__
-    * `maxTrialDuration <#maxtrialduration>`__
-    * `trainingServicePlatform <#trainingserviceplatform>`__
-    * `searchSpacePath <#searchspacepath>`__
-    * `useAnnotation <#useannotation>`__
-    * `multiThread <#multithread>`__
-    * `nniManagerIp <#nnimanagerip>`__
-    * `logDir <#logdir>`__
-    * `logLevel <#loglevel>`__
-    * `logCollection <#logcollection>`__
-    * `tuner <#tuner>`__
-
-      * `builtinTunerName <#builtintunername>`__
-      * `codeDir <#codedir>`__
-      * `classFileName <#classfilename>`__
-      * `className <#classname>`__
-      * `classArgs <#classargs>`__
-      * `gpuIndices <#gpuindices>`__
-      * `includeIntermediateResults <#includeintermediateresults>`__
-
-    * `assessor <#assessor>`__
-
-      * `builtinAssessorName <#builtinassessorname>`__
-      * `codeDir <#codedir-1>`__
-      * `classFileName <#classfilename-1>`__
-      * `className <#classname-1>`__
-      * `classArgs <#classargs-1>`__
-
-    * `advisor <#advisor>`__
-
-      * `builtinAdvisorName <#builtinadvisorname>`__
-      * `codeDir <#codedir-2>`__
-      * `classFileName <#classfilename-2>`__
-      * `className <#classname-2>`__
-      * `classArgs <#classargs-2>`__
-      * `gpuIndices <#gpuindices-1>`__
-
-    * `trial <#trial>`__
-    * `localConfig <#localconfig>`__
-
-      * `gpuIndices <#gpuindices-2>`__
-      * `maxTrialNumPerGpu <#maxtrialnumpergpu>`__
-      * `useActiveGpu <#useactivegpu>`__
-
-    * `machineList <#machinelist>`__
-
-      * `ip <#ip>`__
-      * `port <#port>`__
-      * `username <#username>`__
-      * `passwd <#passwd>`__
-      * `sshKeyPath <#sshkeypath>`__
-      * `passphrase <#passphrase>`__
-      * `gpuIndices <#gpuindices-3>`__
-      * `maxTrialNumPerGpu <#maxtrialnumpergpu-1>`__
-      * `useActiveGpu <#useactivegpu-1>`__
-      * `pythonPath <#pythonPath>`__
-
-    * `kubeflowConfig <#kubeflowconfig>`__
-
-      * `operator <#operator>`__
-      * `storage <#storage>`__
-      * `nfs <#nfs>`__
-      * `keyVault <#keyvault>`__
-      * `azureStorage <#azurestorage>`__
-      * `uploadRetryCount <#uploadretrycount>`__
-
-    * `paiConfig <#paiconfig>`__
-
-      * `userName <#username>`__
-      * `password <#password>`__
-      * `token <#token>`__
-      * `host <#host>`__
-      * `reuse <#reuse>`__
-
-  * `Examples <#examples>`__
-
-    * `Local mode <#local-mode>`__
-    * `Remote mode <#remote-mode>`__
-    * `PAI mode <#pai-mode>`__
-    * `Kubeflow mode <#kubeflow-mode>`__
-    * `Kubeflow with azure storage <#kubeflow-with-azure-storage>`__
-
-Template
---------
-
-
-* **Light weight (without Annotation and Assessor)**
-
-.. code-block:: yaml
-
-   authorName:
-   experimentName:
-   trialConcurrency:
-   maxExecDuration:
-   maxTrialNum:
-   #choice: local, remote, pai, kubeflow
-   trainingServicePlatform:
-   searchSpacePath:
-   #choice: true, false, default: false
-   useAnnotation:
-   #choice: true, false, default: false
-   multiThread:
-   tuner:
-     #choice: TPE, Random, Anneal, Evolution
-     builtinTunerName:
-     classArgs:
-       #choice: maximize, minimize
-       optimize_mode:
-     gpuIndices:
-   trial:
-     command:
-     codeDir:
-     gpuNum:
-   #machineList can be empty if the platform is local
-   machineList:
-     - ip:
-       port:
-       username:
-       passwd:
-
-
-* **Use Assessor**
-
-.. code-block:: yaml
-
-   authorName:
-   experimentName:
-   trialConcurrency:
-   maxExecDuration:
-   maxTrialNum:
-   #choice: local, remote, pai, kubeflow
-   trainingServicePlatform:
-   searchSpacePath:
-   #choice: true, false, default: false
-   useAnnotation:
-   #choice: true, false, default: false
-   multiThread:
-   tuner:
-     #choice: TPE, Random, Anneal, Evolution
-     builtinTunerName:
-     classArgs:
-       #choice: maximize, minimize
-       optimize_mode:
-     gpuIndices:
-   assessor:
-     #choice: Medianstop
-     builtinAssessorName:
-     classArgs:
-       #choice: maximize, minimize
-       optimize_mode:
-   trial:
-     command:
-     codeDir:
-     gpuNum:
-   #machineList can be empty if the platform is local
-   machineList:
-     - ip:
-       port:
-       username:
-       passwd:
-
-
-* **Use Annotation**
-
-.. code-block:: yaml
-
-   authorName:
-   experimentName:
-   trialConcurrency:
-   maxExecDuration:
-   maxTrialNum:
-   #choice: local, remote, pai, kubeflow
-   trainingServicePlatform:
-   #choice: true, false, default: false
-   useAnnotation:
-   #choice: true, false, default: false
-   multiThread:
-   tuner:
-     #choice: TPE, Random, Anneal, Evolution
-     builtinTunerName:
-     classArgs:
-       #choice: maximize, minimize
-       optimize_mode:
-     gpuIndices:
-   assessor:
-     #choice: Medianstop
-     builtinAssessorName:
-     classArgs:
-       #choice: maximize, minimize
-       optimize_mode:
-   trial:
-     command:
-     codeDir:
-     gpuNum:
-   #machineList can be empty if the platform is local
-   machineList:
-     - ip:
-       port:
-       username:
-       passwd:
-
-Configuration Spec
-------------------
-
-authorName
-^^^^^^^^^^
-
-Required. String.
-
-The name of the author who create the experiment.
-
-*TBD: add default value.*
-
-experimentName
-^^^^^^^^^^^^^^
-
-Required. String.
-
-The name of the experiment created.
-
-*TBD: add default value.*
-
-trialConcurrency
-^^^^^^^^^^^^^^^^
-
-Required. Integer between 1 and 99999.
-
-Specifies the max num of trial jobs run simultaneously.
-
-If trialGpuNum is bigger than the free gpu numbers, and the trial jobs running simultaneously can not reach **trialConcurrency** number, some trial jobs will be put into a queue to wait for gpu allocation.
-
-maxExecDuration
-^^^^^^^^^^^^^^^
-
-Optional. String. Default: 999d.
-
-**maxExecDuration** specifies the max duration time of an experiment. The unit of the time is {**s**\ , **m**\ , **h**\ , **d**\ }, which means {*seconds*\ , *minutes*\ , *hours*\ , *days*\ }.
-
-Note: The maxExecDuration spec set the time of an experiment, not a trial job. If the experiment reach the max duration time, the experiment will not stop, but could not submit new trial jobs any more.
-
-versionCheck
-^^^^^^^^^^^^
-
-Optional. Bool. Default: true.
-
-NNI will check the version of nniManager process and the version of trialKeeper in remote, pai and kubernetes platform. If you want to disable version check, you could set versionCheck be false.
-
-debug
-^^^^^
-
-Optional. Bool. Default: false.
-
-Debug mode will set versionCheck to false and set logLevel to be 'debug'.
-
-maxTrialNum
-^^^^^^^^^^^
-
-Optional. Integer between 1 and 99999. Default: 99999.
-
-Specifies the max number of trial jobs created by NNI, including succeeded and failed jobs.
-
-maxTrialDuration
-^^^^^^^^^^^^^^^^
-
-Optional. String. Default: 999d.
-
-**maxTrialDuration** specifies the max duration time of each trial job. The unit of the time is {**s**\ , **m**\ , **h**\ , **d**\ }, which means {*seconds*\ , *minutes*\ , *hours*\ , *days*\ }. If current trial job reach the max duration time, this trial job will stop.
-
-trainingServicePlatform
-^^^^^^^^^^^^^^^^^^^^^^^
-
-Required. String.
-
-Specifies the platform to run the experiment, including **local**\ , **remote**\ , **pai**\ , **kubeflow**\ , **frameworkcontroller**.
-
-
-* 
-  **local** run an experiment on local ubuntu machine.
-
-* 
-  **remote** submit trial jobs to remote ubuntu machines, and **machineList** field should be filed in order to set up SSH connection to remote machine.
-
-* 
-  **pai**  submit trial jobs to `OpenPAI <https://github.com/Microsoft/pai>`__ of Microsoft. For more details of pai configuration, please refer to `Guide to PAI Mode <../TrainingService/PaiMode.rst>`__
-
-* 
-  **kubeflow** submit trial jobs to `kubeflow <https://www.kubeflow.org/docs/about/kubeflow/>`__\ , NNI support kubeflow based on normal kubernetes and `azure kubernetes <https://azure.microsoft.com/en-us/services/kubernetes-service/>`__. For detail please refer to `Kubeflow Docs <../TrainingService/KubeflowMode.rst>`__
-
-* 
-  **adl** submit trial jobs to `AdaptDL <https://www.kubeflow.org/docs/about/kubeflow/>`__\ , NNI support AdaptDL on Kubernetes cluster. For detail please refer to `AdaptDL Docs <../TrainingService/AdaptDLMode.rst>`__
-
-* 
-  TODO: explain frameworkcontroller.
-
-searchSpacePath
-^^^^^^^^^^^^^^^
-
-Optional. Path to existing file.
-
-Specifies the path of search space file, which should be a valid path in the local linux machine.
-
-The only exception that **searchSpacePath** can be not fulfilled is when ``useAnnotation=True``.
-
-useAnnotation
-^^^^^^^^^^^^^
-
-Optional. Bool. Default: false.
-
-Use annotation to analysis trial code and generate search space.
-
-Note: if **useAnnotation** is true, the searchSpacePath field should be removed.
-
-multiThread
-^^^^^^^^^^^
-
-Optional. Bool. Default: false.
-
-Enable multi-thread mode for dispatcher. If multiThread is enabled, dispatcher will start a thread to process each command from NNI Manager.
-
-nniManagerIp
-^^^^^^^^^^^^
-
-Optional. String. Default: eth0 device IP.
-
-Set the IP address of the machine on which NNI manager process runs. This field is optional, and if it's not set, eth0 device IP will be used instead.
-
-Note: run ``ifconfig`` on NNI manager's machine to check if eth0 device exists. If not, **nniManagerIp** is recommended to set explicitly.
-
-logDir
-^^^^^^
-
-Optional. Path to a directory. Default: ``<user home directory>/nni-experiments``.
-
-Configures the directory to store logs and data of the experiment.
-
-logLevel
-^^^^^^^^
-
-Optional. String. Default: ``info``.
-
-Sets log level for the experiment. Available log levels are: ``trace``\ , ``debug``\ , ``info``\ , ``warning``\ , ``error``\ , ``fatal``.
-
-logCollection
-^^^^^^^^^^^^^
-
-Optional. ``http`` or ``none``. Default: ``none``.
-
-Set the way to collect log in remote, pai, kubeflow, frameworkcontroller platform. There are two ways to collect log, one way is from ``http``\ , trial keeper will post log content back from http request in this way, but this way may slow down the speed to process logs in trialKeeper. The other way is ``none``\ , trial keeper will not post log content back, and only post job metrics. If your log content is too big, you could consider setting this param be ``none``.
-
-tuner
-^^^^^
-
-Required.
-
-Specifies the tuner algorithm in the experiment, there are two kinds of ways to set tuner. One way is to use tuner provided by NNI sdk (built-in tuners), in which case you need to set **builtinTunerName** and **classArgs**. Another way is to use users' own tuner file, in which case **codeDirectory**\ , **classFileName**\ , **className** and **classArgs** are needed. *Users must choose exactly one way.*
-
-builtinTunerName
-^^^^^^^^^^^^^^^^
-
-Required if using built-in tuners. String.
-
-Specifies the name of system tuner, NNI sdk provides different tuners introduced `here <../Tuner/BuiltinTuner.rst>`__.
-
-codeDir
-^^^^^^^
-
-Required if using customized tuners. Path relative to the location of config file.
-
-Specifies the directory of tuner code.
-
-classFileName
-^^^^^^^^^^^^^
-
-Required if using customized tuners. File path relative to **codeDir**.
-
-Specifies the name of tuner file.
-
-className
-^^^^^^^^^
-
-Required if using customized tuners. String.
-
-Specifies the name of tuner class.
-
-classArgs
-^^^^^^^^^
-
-Optional. Key-value pairs. Default: empty.
-
-Specifies the arguments of tuner algorithm. Please refer to `this file <../Tuner/BuiltinTuner.rst>`__ for the configurable arguments of each built-in tuner.
-
-gpuIndices
-^^^^^^^^^^
-
-Optional. String. Default: empty.
-
-Specifies the GPUs that can be used by the tuner process. Single or multiple GPU indices can be specified. Multiple GPU indices are separated by comma ``,``. For example, ``1``\ , or ``0,1,3``. If the field is not set, no GPU will be visible to tuner (by setting ``CUDA_VISIBLE_DEVICES`` to be an empty string).
-
-includeIntermediateResults
-^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Optional. Bool. Default: false.
-
-If **includeIntermediateResults** is true, the last intermediate result of the trial that is early stopped by assessor is sent to tuner as final result.
-
-assessor
-^^^^^^^^
-
-Specifies the assessor algorithm to run an experiment. Similar to tuners, there are two kinds of ways to set assessor. One way is to use assessor provided by NNI sdk. Users need to set **builtinAssessorName** and **classArgs**. Another way is to use users' own assessor file, and users need to set **codeDirectory**\ , **classFileName**\ , **className** and **classArgs**. *Users must choose exactly one way.*
-
-By default, there is no assessor enabled.
-
-builtinAssessorName
-^^^^^^^^^^^^^^^^^^^
-
-Required if using built-in assessors. String.
-
-Specifies the name of built-in assessor, NNI sdk provides different assessors introduced `here <../Assessor/BuiltinAssessor.rst>`__.
-
-codeDir
-^^^^^^^
-
-Required if using customized assessors. Path relative to the location of config file.
-
-Specifies the directory of assessor code.
-
-classFileName
-^^^^^^^^^^^^^
-
-Required if using customized assessors. File path relative to **codeDir**.
-
-Specifies the name of assessor file.
-
-className
-^^^^^^^^^
-
-Required if using customized assessors. String.
-
-Specifies the name of assessor class.
-
-classArgs
-^^^^^^^^^
-
-Optional. Key-value pairs. Default: empty.
-
-Specifies the arguments of assessor algorithm.
-
-advisor
-^^^^^^^
-
-Optional.
-
-Specifies the advisor algorithm in the experiment. Similar to tuners and assessors, there are two kinds of ways to specify advisor. One way is to use advisor provided by NNI sdk, need to set **builtinAdvisorName** and **classArgs**. Another way is to use users' own advisor file, and need to set **codeDirectory**\ , **classFileName**\ , **className** and **classArgs**.
-
-When advisor is enabled, settings of tuners and advisors will be bypassed.
-
-builtinAdvisorName
-^^^^^^^^^^^^^^^^^^
-
-Specifies the name of a built-in advisor. NNI sdk provides `BOHB <../Tuner/BohbAdvisor.rst>`__ and `Hyperband <../Tuner/HyperbandAdvisor.rst>`__.
-
-codeDir
-^^^^^^^
-
-Required if using customized advisors. Path relative to the location of config file.
-
-Specifies the directory of advisor code.
-
-classFileName
-^^^^^^^^^^^^^
-
-Required if using customized advisors. File path relative to **codeDir**.
-
-Specifies the name of advisor file.
-
-className
-^^^^^^^^^
-
-Required if using customized advisors. String.
-
-Specifies the name of advisor class.
-
-classArgs
-^^^^^^^^^
-
-Optional. Key-value pairs. Default: empty.
-
-Specifies the arguments of advisor.
-
-gpuIndices
-^^^^^^^^^^
-
-Optional. String. Default: empty.
-
-Specifies the GPUs that can be used. Single or multiple GPU indices can be specified. Multiple GPU indices are separated by comma ``,``. For example, ``1``\ , or ``0,1,3``. If the field is not set, no GPU will be visible to tuner (by setting ``CUDA_VISIBLE_DEVICES`` to be an empty string).
-
-trial
-^^^^^
-
-Required. Key-value pairs.
-
-In local and remote mode, the following keys are required.
-
-
-* 
-  **command**\ : Required string. Specifies the command to run trial process.
-
-* 
-  **codeDir**\ : Required string. Specifies the directory of your own trial file. This directory will be automatically uploaded in remote mode.
-
-* 
-  **gpuNum**\ : Optional integer. Specifies the num of gpu to run the trial process. Default value is 0.
-
-In PAI mode, the following keys are required.
-
-
-* 
-  **command**\ : Required string. Specifies the command to run trial process.
-
-* 
-  **codeDir**\ : Required string. Specifies the directory of the own trial file. Files in the directory will be uploaded in PAI mode.
-
-* 
-  **gpuNum**\ : Required integer. Specifies the num of gpu to run the trial process. Default value is 0.
-
-* 
-  **cpuNum**\ : Required integer. Specifies the cpu number of cpu to be used in pai container.
-
-* 
-  **memoryMB**\ : Required integer. Set the memory size to be used in pai container, in megabytes.
-
-* 
-  **image**\ : Required string. Set the image to be used in pai.
-
-* 
-  **authFile**\ : Optional string. Used to provide Docker registry which needs authentication for image pull in PAI. `Reference <https://github.com/microsoft/pai/blob/2ea69b45faa018662bc164ed7733f6fdbb4c42b3/docs/faq.rst#q-how-to-use-private-docker-registry-job-image-when-submitting-an-openpai-job>`__.
-
-* 
-  **shmMB**\ : Optional integer. Shared memory size of container.
-
-* 
-  **portList**\ : List of key-values pairs with ``label``\ , ``beginAt``\ , ``portNumber``. See `job tutorial of PAI <https://github.com/microsoft/pai/blob/master/docs/job_tutorial.rst>`__ for details.
-
-.. cannot find `Reference <https://github.com/microsoft/pai/blob/2ea69b45faa018662bc164ed7733f6fdbb4c42b3/docs/faq.rst#q-how-to-use-private-docker-registry-job-image-when-submitting-an-openpai-job>`__  and `job tutorial of PAI <https://github.com/microsoft/pai/blob/master/docs/job_tutorial.rst>`__ 
-
-In Kubeflow mode, the following keys are required.
-
-
-* 
-  **codeDir**\ : The local directory where the code files are in.
-
-* 
-  **ps**\ : An optional configuration for kubeflow's tensorflow-operator, which includes
-
-
-  * 
-    **replicas**\ : The replica number of **ps** role.
-
-  * 
-    **command**\ : The run script in **ps**\ 's container.
-
-  * 
-    **gpuNum**\ : The gpu number to be used in **ps** container.
-
-  * 
-    **cpuNum**\ : The cpu number to be used in **ps** container.
-
-  * 
-    **memoryMB**\ : The memory size of the container.
-
-  * 
-    **image**\ : The image to be used in **ps**.
-
-* 
-  **worker**\ : An optional configuration for kubeflow's tensorflow-operator.
-
-
-  * 
-    **replicas**\ : The replica number of **worker** role.
-
-  * 
-    **command**\ : The run script in **worker**\ 's container.
-
-  * 
-    **gpuNum**\ : The gpu number to be used in **worker** container.
-
-  * 
-    **cpuNum**\ : The cpu number to be used in **worker** container.
-
-  * 
-    **memoryMB**\ : The memory size of the container.
-
-  * 
-    **image**\ : The image to be used in **worker**.
-
-localConfig
-^^^^^^^^^^^
-
-Optional in local mode. Key-value pairs.
-
-Only applicable if **trainingServicePlatform** is set to ``local``\ , otherwise there should not be **localConfig** section in configuration file.
-
-gpuIndices
-^^^^^^^^^^
-
-Optional. String. Default: none.
-
-Used to specify designated GPU devices for NNI, if it is set, only the specified GPU devices are used for NNI trial jobs. Single or multiple GPU indices can be specified. Multiple GPU indices should be separated with comma (\ ``,``\ ), such as ``1`` or  ``0,1,3``. By default, all GPUs available will be used.
-
-maxTrialNumPerGpu
-^^^^^^^^^^^^^^^^^
-
-Optional. Integer. Default: 1.
-
-Used to specify the max concurrency trial number on a GPU device.
-
-useActiveGpu
-^^^^^^^^^^^^
-
-Optional. Bool. Default: false.
-
-Used to specify whether to use a GPU if there is another process. By default, NNI will use the GPU only if there is no other active process in the GPU. If **useActiveGpu** is set to true, NNI will use the GPU regardless of another processes. This field is not applicable for NNI on Windows.
-
-machineList
-^^^^^^^^^^^
-
-Required in remote mode. A list of key-value pairs with the following keys.
-
-ip
-^^
-
-Required. IP address or host name that is accessible from the current machine.
-
-The IP address or host name of remote machine.
-
-port
-^^^^
-
-Optional. Integer. Valid port. Default: 22.
-
-The ssh port to be used to connect machine.
-
-username
-^^^^^^^^
-
-Required if authentication with username/password. String.
-
-The account of remote machine.
-
-passwd
-^^^^^^
-
-Required if authentication with username/password. String.
-
-Specifies the password of the account.
-
-sshKeyPath
-^^^^^^^^^^
-
-Required if authentication with ssh key. Path to private key file.
-
-If users use ssh key to login remote machine, **sshKeyPath** should be a valid path to a ssh key file.
-
-*Note: if users set passwd and sshKeyPath simultaneously, NNI will try passwd first.*
-
-passphrase
-^^^^^^^^^^
-
-Optional. String.
-
-Used to protect ssh key, which could be empty if users don't have passphrase.
-
-gpuIndices
-^^^^^^^^^^
-
-Optional. String. Default: none.
-
-Used to specify designated GPU devices for NNI, if it is set, only the specified GPU devices are used for NNI trial jobs. Single or multiple GPU indices can be specified. Multiple GPU indices should be separated with comma (\ ``,``\ ), such as ``1`` or  ``0,1,3``. By default, all GPUs available will be used.
-
-maxTrialNumPerGpu
-^^^^^^^^^^^^^^^^^
-
-Optional. Integer. Default: 1.
-
-Used to specify the max concurrency trial number on a GPU device.
-
-useActiveGpu
-^^^^^^^^^^^^
-
-Optional. Bool. Default: false.
-
-Used to specify whether to use a GPU if there is another process. By default, NNI will use the GPU only if there is no other active process in the GPU. If **useActiveGpu** is set to true, NNI will use the GPU regardless of another processes. This field is not applicable for NNI on Windows.
-
-pythonPath
-^^^^^^^^^^
-
-Optional. String.
-
-Users can configure the python path environment on remote machine by setting **pythonPath**.
-
-remoteConfig
-^^^^^^^^^^^^
-
-Optional field in remote mode. Users could set per machine information in ``machineList`` field, and set global configuration for remote mode in this field.
-
-reuse
-^^^^^
-
-Optional. Bool. default: ``false``. It's an experimental feature.
-
-If it's true, NNI will reuse remote jobs to run as many as possible trials. It can save time of creating new jobs. User needs to make sure each trial can run independent in same job, for example, avoid loading checkpoint from previous trials. 
-
-kubeflowConfig
-^^^^^^^^^^^^^^
-
-operator
-^^^^^^^^
-
-Required. String. Has to be ``tf-operator`` or ``pytorch-operator``.
-
-Specifies the kubeflow's operator to be used, NNI support ``tf-operator`` in current version.
-
-storage
-^^^^^^^
-
-Optional. String. Default. ``nfs``.
-
-Specifies the storage type of kubeflow, including ``nfs`` and ``azureStorage``.
-
-nfs
-^^^
-
-Required if using nfs. Key-value pairs.
-
-
-* 
-  **server** is the host of nfs server.
-
-* 
-  **path** is the mounted path of nfs.
-
-keyVault
-^^^^^^^^
-
-Required if using azure storage. Key-value pairs.
-
-Set **keyVault** to storage the private key of your azure storage account. Refer to `the doc <https://docs.microsoft.com/en-us/azure/key-vault/key-vault-manage-with-cli2>`__ .
-
-
-* 
-  **vaultName** is the value of ``--vault-name`` used in az command.
-
-* 
-  **name** is the value of ``--name`` used in az command.
-
-azureStorage
-^^^^^^^^^^^^
-
-Required if using azure storage. Key-value pairs.
-
-Set azure storage account to store code files.
-
-
-* 
-  **accountName** is the name of azure storage account.
-
-* 
-  **azureShare** is the share of the azure file storage.
-
-uploadRetryCount
-^^^^^^^^^^^^^^^^
-
-Required if using azure storage. Integer between 1 and 99999.
-
-If upload files to azure storage failed, NNI will retry the process of uploading, this field will specify the number of attempts to re-upload files.
-
-paiConfig
-^^^^^^^^^
-
-userName
-^^^^^^^^
-
-Required. String.
-
-The user name of your pai account.
-
-password
-^^^^^^^^
-
-Required if using password authentication. String.
-
-The password of the pai account.
-
-token
-^^^^^
-
-Required if using token authentication. String.
-
-Personal access token that can be retrieved from PAI portal.
-
-host
-^^^^
-
-Required. String.
-
-The hostname of IP address of PAI.
-
-reuse
-^^^^^
-
-Optional. Bool. default: ``false``. It's an experimental feature.
-
-If it's true, NNI will reuse OpenPAI jobs to run as many as possible trials. It can save time of creating new jobs. User needs to make sure each trial can run independent in same job, for example, avoid loading checkpoint from previous trials.
-
-sharedStorage
-^^^^^^^^^^^^^
-
-storageType
-^^^^^^^^^^^
-
-Required. String.
-
-The type of the storage, support ``NFS`` and ``AzureBlob``.
-
-localMountPoint
-^^^^^^^^^^^^^^^
-
-Required. String.
-
-The absolute or relative path that the storage has been or will be mounted in local. If the path does not exist, it will be created automatically. Recommended to use an absolute path. i.e. ``/tmp/nni-shared-storage``.
-
-remoteMountPoint
-^^^^^^^^^^^^^^^^
-
-Required. String.
-
-The absolute or relative path that the storage will be mounted in remote. If the path does not exist, it will be created automatically. Note that the directory must be empty if using AzureBlob. Recommended to use a relative path. i.e. ``./nni-shared-storage``.
-
-localMounted
-^^^^^^^^^^^^
-
-Required. String.
-
-One of ``usermount``, ``nnimount`` or ``nomount``. ``usermount`` means you have already mount this storage on localMountPoint. ``nnimount`` means nni will try to mount this storage on localMountPoint. ``nomount`` means storage will not mount in local machine, will support partial storages in the future.
-
-nfsServer
-^^^^^^^^^
-
-Optional. String.
-
-Required if using NFS storage. The NFS server host.
-
-exportedDirectory
-^^^^^^^^^^^^^^^^^
-
-Optional. String.
-
-Required if using NFS storage. The exported directory of NFS server.
-
-storageAccountName
-^^^^^^^^^^^^^^^^^^
-
-Optional. String.
-
-Required if using AzureBlob storage. The azure storage account name.
-
-storageAccountKey
-^^^^^^^^^^^^^^^^^
-
-Optional. String.
-
-Required if using AzureBlob storage. The azure storage account key.
-
-containerName
-^^^^^^^^^^^^^
-
-Optional. String.
-
-Required if using AzureBlob storage. The AzureBlob container name.
-
-Examples
---------
-
-Local mode
-^^^^^^^^^^
-
-If users want to run trial jobs in local machine, and use annotation to generate search space, could use the following config:
-
-.. code-block:: yaml
-
-     authorName: test
-     experimentName: test_experiment
-     trialConcurrency: 3
-     maxExecDuration: 1h
-     maxTrialNum: 10
-     #choice: local, remote, pai, kubeflow
-     trainingServicePlatform: local
-     #choice: true, false
-     useAnnotation: true
-     tuner:
-       #choice: TPE, Random, Anneal, Evolution
-       builtinTunerName: TPE
-       classArgs:
-         #choice: maximize, minimize
-         optimize_mode: maximize
-     trial:
-       command: python3 mnist.py
-       codeDir: /nni/mnist
-       gpuNum: 0
-
-You can add assessor configuration.
-
-.. code-block:: yaml
-
-     authorName: test
-     experimentName: test_experiment
-     trialConcurrency: 3
-     maxExecDuration: 1h
-     maxTrialNum: 10
-     #choice: local, remote, pai, kubeflow
-     trainingServicePlatform: local
-     searchSpacePath: /nni/search_space.json
-     #choice: true, false
-     useAnnotation: false
-     tuner:
-       #choice: TPE, Random, Anneal, Evolution
-       builtinTunerName: TPE
-       classArgs:
-         #choice: maximize, minimize
-         optimize_mode: maximize
-     assessor:
-       #choice: Medianstop
-       builtinAssessorName: Medianstop
-       classArgs:
-         #choice: maximize, minimize
-         optimize_mode: maximize
-     trial:
-       command: python3 mnist.py
-       codeDir: /nni/mnist
-       gpuNum: 0
-
-Or you could specify your own tuner and assessor file as following,
-
-.. code-block:: yaml
-
-     authorName: test
-     experimentName: test_experiment
-     trialConcurrency: 3
-     maxExecDuration: 1h
-     maxTrialNum: 10
-     #choice: local, remote, pai, kubeflow
-     trainingServicePlatform: local
-     searchSpacePath: /nni/search_space.json
-     #choice: true, false
-     useAnnotation: false
-     tuner:
-       codeDir: /nni/tuner
-       classFileName: mytuner.py
-       className: MyTuner
-       classArgs:
-         #choice: maximize, minimize
-         optimize_mode: maximize
-     assessor:
-       codeDir: /nni/assessor
-       classFileName: myassessor.py
-       className: MyAssessor
-       classArgs:
-         #choice: maximize, minimize
-         optimize_mode: maximize
-     trial:
-       command: python3 mnist.py
-       codeDir: /nni/mnist
-       gpuNum: 0
-
-Remote mode
-^^^^^^^^^^^
-
-If run trial jobs in remote machine, users could specify the remote machine information as following format:
-
-.. code-block:: yaml
-
-     authorName: test
-     experimentName: test_experiment
-     trialConcurrency: 3
-     maxExecDuration: 1h
-     maxTrialNum: 10
-     #choice: local, remote, pai, kubeflow
-     trainingServicePlatform: remote
-     searchSpacePath: /nni/search_space.json
-     #choice: true, false
-     useAnnotation: false
-     tuner:
-       #choice: TPE, Random, Anneal, Evolution
-       builtinTunerName: TPE
-       classArgs:
-         #choice: maximize, minimize
-         optimize_mode: maximize
-     trial:
-       command: python3 mnist.py
-       codeDir: /nni/mnist
-       gpuNum: 0
-     #machineList can be empty if the platform is local
-     machineList:
-       - ip: 10.10.10.10
-         port: 22
-         username: test
-         passwd: test
-       - ip: 10.10.10.11
-         port: 22
-         username: test
-         passwd: test
-       - ip: 10.10.10.12
-         port: 22
-         username: test
-         sshKeyPath: /nni/sshkey
-         passphrase: qwert
-         # Below is an example of specifying python environment.
-         pythonPath: ${replace_to_python_environment_path_in_your_remote_machine}
-
-PAI mode
-^^^^^^^^
-
-.. code-block:: yaml
-
-     authorName: test
-     experimentName: nni_test1
-     trialConcurrency: 1
-     maxExecDuration:500h
-     maxTrialNum: 1
-     #choice: local, remote, pai, kubeflow
-     trainingServicePlatform: pai
-     searchSpacePath: search_space.json
-     #choice: true, false
-     useAnnotation: false
-     tuner:
-       #choice: TPE, Random, Anneal, Evolution, BatchTuner
-       #SMAC (SMAC should be installed through nnictl)
-       builtinTunerName: TPE
-       classArgs:
-         #choice: maximize, minimize
-         optimize_mode: maximize
-     trial:
-       command: python3 main.py
-       codeDir: .
-       gpuNum: 4
-       cpuNum: 2
-       memoryMB: 10000
-       #The docker image to run NNI job on pai
-       image: msranni/nni:latest
-     paiConfig:
-       #The username to login pai
-       userName: test
-       #The password to login pai
-       passWord: test
-       #The host of restful server of pai
-       host: 10.10.10.10
-
-Kubeflow mode
-^^^^^^^^^^^^^
-
-  kubeflow with nfs storage.
-
-.. code-block:: yaml
-
-     authorName: default
-     experimentName: example_mni
-     trialConcurrency: 1
-     maxExecDuration: 1h
-     maxTrialNum: 1
-     #choice: local, remote, pai, kubeflow
-     trainingServicePlatform: kubeflow
-     searchSpacePath: search_space.json
-     #choice: true, false
-     useAnnotation: false
-     tuner:
-       #choice: TPE, Random, Anneal, Evolution
-       builtinTunerName: TPE
-       classArgs:
-         #choice: maximize, minimize
-         optimize_mode: maximize
-     trial:
-       codeDir: .
-       worker:
-         replicas: 1
-         command: python3 mnist.py
-         gpuNum: 0
-         cpuNum: 1
-         memoryMB: 8192
-         image: msranni/nni:latest
-     kubeflowConfig:
-       operator: tf-operator
-       nfs:
-         server: 10.10.10.10
-         path: /var/nfs/general
-
-Kubeflow with azure storage
-^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. code-block:: yaml
-
-     authorName: default
-     experimentName: example_mni
-     trialConcurrency: 1
-     maxExecDuration: 1h
-     maxTrialNum: 1
-     #choice: local, remote, pai, kubeflow
-     trainingServicePlatform: kubeflow
-     searchSpacePath: search_space.json
-     #choice: true, false
-     useAnnotation: false
-     #nniManagerIp: 10.10.10.10
-     tuner:
-       #choice: TPE, Random, Anneal, Evolution
-       builtinTunerName: TPE
-       classArgs:
-         #choice: maximize, minimize
-         optimize_mode: maximize
-     assessor:
-       builtinAssessorName: Medianstop
-       classArgs:
-         optimize_mode: maximize
-     trial:
-       codeDir: .
-       worker:
-         replicas: 1
-         command: python3 mnist.py
-         gpuNum: 0
-         cpuNum: 1
-         memoryMB: 4096
-         image: msranni/nni:latest
-     kubeflowConfig:
-       operator: tf-operator
-       keyVault:
-         vaultName: Contoso-Vault
-         name: AzureStorageAccountKey
-       azureStorage:
-         accountName: storage
-         azureShare: share01
diff --git a/docs/source/Tutorial/QuickStart.rst b/docs/source/Tutorial/QuickStart.rst
deleted file mode 100644
index 8fb2ac4df..000000000
--- a/docs/source/Tutorial/QuickStart.rst
+++ /dev/null
@@ -1,285 +0,0 @@
-QuickStart
-==========
-
-Installation
-------------
-
-Currently, NNI supports running on Linux, macOS and Windows. Ubuntu 16.04 or higher, macOS 10.14.1, and Windows 10.1809 are tested and supported. Simply run the following ``pip install`` in an environment that has ``python >= 3.6``.
-
-Linux and macOS
-^^^^^^^^^^^^^^^
-
-.. code-block:: bash
-
-   python3 -m pip install --upgrade nni
-
-Windows
-^^^^^^^
-
-.. code-block:: bash
-
-   python -m pip install --upgrade nni
-
-.. Note:: For Linux and macOS, ``--user`` can be added if you want to install NNI in your home directory, which does not require any special privileges.
-
-.. Note:: If there is an error like ``Segmentation fault``, please refer to the :doc:`FAQ <FAQ>`.
-
-.. Note:: For the system requirements of NNI, please refer to :doc:`Install NNI on Linux & Mac <InstallationLinux>` or :doc:`Windows <InstallationWin>`. If you want to use docker, refer to :doc:`HowToUseDocker <HowToUseDocker>`.
-
-
-"Hello World" example on MNIST
-------------------------------
-
-NNI is a toolkit to help users run automated machine learning experiments. It can automatically do the cyclic process of getting hyperparameters, running trials, testing results, and tuning hyperparameters. Here, we'll show how to use NNI to help you find the optimal hyperparameters on the MNIST dataset.
-
-Here is an example script to train a CNN on the MNIST dataset **without NNI**:
-
-.. code-block:: python
-
-    def main(args):
-        # load data
-        train_loader = torch.utils.data.DataLoader(datasets.MNIST(...), batch_size=args['batch_size'], shuffle=True)
-        test_loader = torch.tuils.data.DataLoader(datasets.MNIST(...), batch_size=1000, shuffle=True)
-        # build model
-        model = Net(hidden_size=args['hidden_size'])
-        optimizer = optim.SGD(model.parameters(), lr=args['lr'], momentum=args['momentum'])
-        # train
-        for epoch in range(10):
-            train(args, model, device, train_loader, optimizer, epoch)
-            test_acc = test(args, model, device, test_loader)
-            print(test_acc)
-        print('final accuracy:', test_acc)
-         
-    if __name__ == '__main__':
-        params = {
-            'batch_size': 32,
-            'hidden_size': 128,
-            'lr': 0.001,
-            'momentum': 0.5
-        }
-        main(params)
-
-The above code can only try one set of parameters at a time. If you want to tune the learning rate, you need to manually modify the hyperparameter and start the trial again and again.
-
-NNI is born to help users tune jobs, whose working process is presented below:
-
-.. code-block:: text
-
-   input: search space, trial code, config file
-   output: one optimal hyperparameter configuration
-
-   1: For t = 0, 1, 2, ..., maxTrialNum,
-   2:      hyperparameter = chose a set of parameter from search space
-   3:      final result = run_trial_and_evaluate(hyperparameter)
-   4:      report final result to NNI
-   5:      If reach the upper limit time,
-   6:          Stop the experiment
-   7: return hyperparameter value with best final result
-
-.. note::
-
-   If you want to use NNI to automatically train your model and find the optimal hyper-parameters, there are two approaches:
-
-   1. Write a config file and start the experiment from the command line.
-   2. Config and launch the experiment directly from a Python file
-
-   In the this part, we will focus on the first approach. For the second approach, please refer to `this tutorial <HowToLaunchFromPython.rst>`__\ .
-
-
-Step 1: Modify the ``Trial`` Code
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Modify your ``Trial`` file to get the hyperparameter set from NNI and report the final results to NNI.
-
-.. code-block:: diff
-
-    + import nni
-
-      def main(args):
-          # load data
-          train_loader = torch.utils.data.DataLoader(datasets.MNIST(...), batch_size=args['batch_size'], shuffle=True)
-          test_loader = torch.tuils.data.DataLoader(datasets.MNIST(...), batch_size=1000, shuffle=True)
-          # build model
-          model = Net(hidden_size=args['hidden_size'])
-          optimizer = optim.SGD(model.parameters(), lr=args['lr'], momentum=args['momentum'])
-          # train
-          for epoch in range(10):
-              train(args, model, device, train_loader, optimizer, epoch)
-              test_acc = test(args, model, device, test_loader)
-    -         print(test_acc)
-    +         nni.report_intermediate_result(test_acc)
-    -     print('final accuracy:', test_acc)
-    +     nni.report_final_result(test_acc)
-           
-      if __name__ == '__main__':
-    -     params = {'batch_size': 32, 'hidden_size': 128, 'lr': 0.001, 'momentum': 0.5}
-    +     params = nni.get_next_parameter()
-          main(params)
-
-*Example:* :githublink:`mnist.py <examples/trials/mnist-pytorch/mnist.py>`
-
-
-Step 2: Define the Search Space
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Define a ``Search Space`` in a YAML file, including the ``name`` and the ``distribution`` (discrete-valued or continuous-valued) of all the hyperparameters you want to search.
-
-.. code-block:: yaml
-
-   searchSpace:
-      batch_size:
-         _type: choice
-         _value: [16, 32, 64, 128]
-      hidden_size:
-         _type: choice
-         _value: [128, 256, 512, 1024]
-      lr:
-         _type: choice
-         _value: [0.0001, 0.001, 0.01, 0.1]
-      momentum:
-         _type: uniform
-         _value: [0, 1]
-
-*Example:* :githublink:`config_detailed.yml <examples/trials/mnist-pytorch/config_detailed.yml>`
-
-You can also write your search space in a JSON file and specify the file path in the configuration. For detailed tutorial on how to write the search space, please see `here <SearchSpaceSpec.rst>`__.
-
-
-Step 3: Config the Experiment
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-In addition to the search_space defined in the `step2 <step-2-define-the-search-space>`__, you need to config the experiment in the YAML file. It specifies the key information of the experiment, such as the trial files, tuning algorithm, max trial number, and max duration, etc.
-
-.. code-block:: yaml
-
-   experimentName: MNIST               # An optional name to distinguish the experiments
-   trialCommand: python3 mnist.py      # NOTE: change "python3" to "python" if you are using Windows
-   trialConcurrency: 2                 # Run 2 trials concurrently
-   maxTrialNumber: 10                  # Generate at most 10 trials
-   maxExperimentDuration: 1h           # Stop generating trials after 1 hour
-   tuner:                              # Configure the tuning algorithm
-      name: TPE
-      classArgs:                       # Algorithm specific arguments
-         optimize_mode: maximize
-   trainingService:                    # Configure the training platform
-      platform: local
-
-Experiment config reference could be found `here <../reference/experiment_config.rst>`__.
-
-.. _nniignore:
-
-.. Note:: If you are planning to use remote machines or clusters as your :doc:`training service <../TrainingService/Overview>`, to avoid too much pressure on network, NNI limits the number of files to 2000 and total size to 300MB. If your codeDir contains too many files, you can choose which files and subfolders should be excluded by adding a ``.nniignore`` file that works like a ``.gitignore`` file. For more details on how to write this file, see the `git documentation <https://git-scm.com/docs/gitignore#_pattern_format>`__.
-
-*Example:* :githublink:`config_detailed.yml <examples/trials/mnist-pytorch/config_detailed.yml>` and :githublink:`.nniignore <examples/trials/mnist-pytorch/.nniignore>`
-
-All the code above is already prepared and stored in :githublink:`examples/trials/mnist-pytorch/<examples/trials/mnist-pytorch>`.
-
-
-Step 4: Launch the Experiment
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Linux and macOS
-***************
-
-Run the **config_detailed.yml** file from your command line to start the experiment.
-
-.. code-block:: bash
-
-   nnictl create --config nni/examples/trials/mnist-pytorch/config_detailed.yml
-
-Windows
-*******
-
-Change ``python3`` to ``python`` of the ``trialCommand`` field in the **config_detailed.yml** file, and run the **config_detailed.yml** file from your command line to start the experiment.
-
-.. code-block:: bash
-
-   nnictl create --config nni\examples\trials\mnist-pytorch\config_detailed.yml
-
-.. Note:: ``nnictl`` is a command line tool that can be used to control experiments, such as start/stop/resume an experiment, start/stop NNIBoard, etc. Click :doc:`here <../reference/nnictl>` for more usage of ``nnictl``.
-
-Wait for the message ``INFO: Successfully started experiment!`` in the command line. This message indicates that your experiment has been successfully started. And this is what we expect to get:
-
-.. code-block:: text
-
-   INFO: Starting restful server...
-   INFO: Successfully started Restful server!
-   INFO: Setting local config...
-   INFO: Successfully set local config!
-   INFO: Starting experiment...
-   INFO: Successfully started experiment!
-   -----------------------------------------------------------------------
-   The experiment id is egchD4qy
-   The Web UI urls are: [Your IP]:8080
-   -----------------------------------------------------------------------
-
-   You can use these commands to get more information about the experiment
-   -----------------------------------------------------------------------
-            commands                       description
-   1. nnictl experiment show        show the information of experiments
-   2. nnictl trial ls               list all of trial jobs
-   3. nnictl top                    monitor the status of running experiments
-   4. nnictl log stderr             show stderr log content
-   5. nnictl log stdout             show stdout log content
-   6. nnictl stop                   stop an experiment
-   7. nnictl trial kill             kill a trial job by id
-   8. nnictl --help                 get help information about nnictl
-   -----------------------------------------------------------------------
-
-If you prepared ``trial``\ , ``search space``\ , and ``config`` according to the above steps and successfully created an NNI job, NNI will automatically tune the optimal hyper-parameters and run different hyper-parameter sets for each trial according to the defined search space. You can see its progress through the WebUI clearly.
-
-Step 5: View the Experiment
-^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-After starting the experiment successfully, you can find a message in the command-line interface that tells you the ``Web UI url`` like this:
-
-.. code-block:: text
-
-   The Web UI urls are: [Your IP]:8080
-
-Open the ``Web UI url`` (Here it's: ``[Your IP]:8080``\ ) in your browser, you can view detailed information about the experiment and all the submitted trial jobs as shown below. If you cannot open the WebUI link in your terminal, please refer to the `FAQ <FAQ.rst#could-not-open-webui-link>`__.
-
-
-View Overview Page
-******************
-
-Information about this experiment will be shown in the WebUI, including the experiment profile and search space message. NNI also supports downloading this information and the parameters through the **Experiment summary** button.
-
-.. image:: ../../img/webui-img/full-oview.png
-   :target: ../../img/webui-img/full-oview.png
-   :alt: overview
-
-
-View Trials Detail Page
-***********************
-
-You could see the best trial metrics and hyper-parameter graph in this page. And the table content includes more columns when you click the button ``Add/Remove columns``.
-
-.. image:: ../../img/webui-img/full-detail.png
-   :target: ../../img/webui-img/full-detail.png
-   :alt: detail
-
-
-View Experiments Management Page
-********************************
-
-On the ``All experiments`` page, you can see all the experiments on your machine. 
-
-.. image:: ../../img/webui-img/managerExperimentList/expList.png
-   :target: ../../img/webui-img/managerExperimentList/expList.png
-   :alt: Experiments list
-
-For more detailed usage of WebUI, please refer to `this doc <./WebUI.rst>`__.
-
-
-Related Topic
--------------
-
-* `How to debug? <HowToDebug.rst>`__
-* `How to write a trial? <../TrialExample/Trials.rst>`__
-* `How to try different Tuners? <../Tuner/BuiltinTuner.rst>`__
-* `How to try different Assessors? <../Assessor/BuiltinAssessor.rst>`__
-* `How to run an experiment on the different training platforms? <../training_services.rst>`__
-* `How to use Annotation? <AnnotationSpec.rst>`__
-* `How to use the command line tool nnictl? <Nnictl.rst>`__
-* `How to launch Tensorboard on WebUI? <Tensorboard.rst>`__
diff --git a/docs/source/Tutorial/QuickStart_zh.rst b/docs/source/Tutorial/QuickStart_zh.rst
deleted file mode 100644
index db4ff6f11..000000000
--- a/docs/source/Tutorial/QuickStart_zh.rst
+++ /dev/null
@@ -1,287 +0,0 @@
-.. 90b7c298df11d68ba419a1feaf453cfc
-
-快速入门
-==========
-
-安装
-----
-
-目前NNI支持了 Linux、macOS 和 Windows系统。 其中，Ubuntu 16.04 及更高版本、macOS 10.14.1 和 Windows 10.1809 均经过测试并支持。 在 ``python >= 3.6`` 环境中，只需运行 ``pip install`` 即可完成安装。
-
-Linux 和 macOS
-^^^^^^^^^^^^^^
-
-.. code-block:: bash
-
-   python3 -m pip install --upgrade nni
-
-Windows
-^^^^^^^
-
-.. code-block:: bash
-
-   python -m pip install --upgrade nni
-
-.. Note:: 在 Linux 和 macOS 上，如果要将 NNI 安装到当前用户的 home 目录中，可使用 ``--user`` ；这不需要特殊权限。
-
-.. Note:: 如果出现 ``Segmentation fault`` 这样的错误，参考 :doc:`常见问题 <FAQ>` 。
-
-.. Note:: NNI 的系统需求，参考 :doc:`Linux & Mac <InstallationLinux>` 或者 :doc:`Windows <InstallationWin>` 的安装教程。如果想要使用 docker, 参考 :doc:`如何使用 Docker <HowToUseDocker>` 。
-
-
-MNIST 上的 "Hello World"
-------------------------
-
-NNI 是一个能进行自动机器学习实验的工具包。 它可以自动进行获取超参、运行 Trial，测试结果，调优超参的循环。 在这里，将演示如何使用 NNI 帮助找到 MNIST 模型的最佳超参数。
-
-这是还 **没有 NNI** 的示例代码，用 CNN 在 MNIST 数据集上训练：
-
-.. code-block:: python
-
-    def main(args):
-        # 下载数据
-        train_loader = torch.utils.data.DataLoader(datasets.MNIST(...), batch_size=args['batch_size'], shuffle=True)
-        test_loader = torch.tuils.data.DataLoader(datasets.MNIST(...), batch_size=1000, shuffle=True)
-        # 构建模型
-        model = Net(hidden_size=args['hidden_size'])
-        optimizer = optim.SGD(model.parameters(), lr=args['lr'], momentum=args['momentum'])
-        # 训练
-        for epoch in range(10):
-            train(args, model, device, train_loader, optimizer, epoch)
-            test_acc = test(args, model, device, test_loader)
-            print(test_acc)
-        print('final accuracy:', test_acc)
-         
-    if __name__ == '__main__':
-        params = {
-            'batch_size': 32,
-            'hidden_size': 128,
-            'lr': 0.001,
-            'momentum': 0.5
-        }
-        main(params)
-
-上面的代码一次只能尝试一组参数，如果想要调优学习率，需要手工改动超参，并一次次尝试。
-
-NNI 用来帮助超参调优。它的流程如下：
-
-.. code-block:: text
-
-   输入: 搜索空间, Trial 代码, 配置文件
-   输出: 一组最优的参数配置
-
-   1: For t = 0, 1, 2, ..., maxTrialNum,
-   2:      hyperparameter = 从搜索空间选择一组参数
-   3:      final result = run_trial_and_evaluate(hyperparameter)
-   4:      返回最终结果给 NNI
-   5:      If 时间达到上限,
-   6:          停止实验
-   7: 返回最好的实验结果
-
-.. note::
-
-   如果需要使用 NNI 来自动训练模型，找到最佳超参，有两种实现方式：
-
-   1. 编写配置文件，然后使用命令行启动 experiment；
-   2. 直接从 Python 文件中配置并启动 experiment。
-
-   在本节中，我们将重点介绍第一种实现方式。如果希望使用第二种实现方式，请参考 `教程 <HowToLaunchFromPython.rst>`__\ 。
-
-
-第一步：修改 ``Trial`` 代码
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-修改 ``Trial`` 代码来从 NNI 获取超参，并向 NNI 报告训练结果。
-
-.. code-block:: diff
-
-    + import nni
-
-      def main(args):
-          # 下载数据
-          train_loader = torch.utils.data.DataLoader(datasets.MNIST(...), batch_size=args['batch_size'], shuffle=True)
-          test_loader = torch.tuils.data.DataLoader(datasets.MNIST(...), batch_size=1000, shuffle=True)
-          # 构造模型
-          model = Net(hidden_size=args['hidden_size'])
-          optimizer = optim.SGD(model.parameters(), lr=args['lr'], momentum=args['momentum'])
-          # 训练
-          for epoch in range(10):
-              train(args, model, device, train_loader, optimizer, epoch)
-              test_acc = test(args, model, device, test_loader)
-    -         print(test_acc)
-    +         nni.report_intermeidate_result(test_acc)
-    -     print('final accuracy:', test_acc)
-    +     nni.report_final_result(test_acc)
-           
-      if __name__ == '__main__':
-    -     params = {'batch_size': 32, 'hidden_size': 128, 'lr': 0.001, 'momentum': 0.5}
-    +     params = nni.get_next_parameter()
-          main(params)
-
-*示例：* :githublink:`mnist.py <examples/trials/mnist-pytorch/mnist.py>`
-
-
-第二步：定义搜索空间
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-编写 YAML 格式的 **搜索空间** 文件，包括所有需要搜索的超参的 **名称** 和 **分布** （离散和连续值均可）。
-
-.. code-block:: yaml
-
-   searchSpace:
-      batch_size:
-         _type: choice
-         _value: [16, 32, 64, 128]
-      hidden_size:
-         _type: choice
-         _value: [128, 256, 512, 1024]
-      lr:
-         _type: choice
-         _value: [0.0001, 0.001, 0.01, 0.1]
-      momentum:
-         _type: uniform
-         _value: [0, 1]
-
-*示例：* :githublink:`config_detailed.yml <examples/trials/mnist-pytorch/config_detailed.yml>`
-
-也可以使用 JSON 文件来编写搜索空间，并在配置中确认文件路径。关于如何编写搜索空间，可以参考 `教程 <SearchSpaceSpec.rst>`__.
-
-
-第三步：配置 experiment
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-除了在第二步中定义的搜索空间，还需要定义 YAML 格式的 **配置** 文件，声明 experiment 的关键信息，例如 Trail 文件，调优算法，最大 Trial 运行次数和最大持续时间等。
-
-.. code-block:: yaml
-
-   experimentName: MNIST               # 用于区分 experiment 的名字，可选项
-   trialCommand: python3 mnist.py      # 注意：如果使用 Windows，请将 "python3" 修改为 "python" 
-   trialConcurrency: 2                 # 同时运行 2 个 trial
-   maxTrialNumber: 10                  # 最多生成 10 个 trial
-   maxExperimentDuration: 1h           # 1 小时后停止生成 trial
-   tuner:                              # 配置调优算法
-      name: TPE
-      classArgs:                       # 算法特定参数
-         optimize_mode: maximize
-   trainingService:                    # 配置训练平台
-      platform: local
-
-Experiment 的配置文件可以参考 `文档 <../reference/experiment_config.rst>`__.
-
-.. _nniignore:
-
-.. Note:: 如果要使用远程服务器或集群作为 :doc:`训练平台 <../TrainingService/Overview>`，为了避免产生过大的网络压力，NNI 限制了文件的最大数量为 2000，大小为 300 MB。 如果代码目录中包含了过多的文件，可添加 ``.nniignore`` 文件来排除部分，与 ``.gitignore`` 文件用法类似。 参考 `git documentation <https://git-scm.com/docs/gitignore#_pattern_format>`__ ，了解更多如何编写此文件的详细信息。
-
-*示例：* :githublink:`config.yml <examples/trials/mnist-pytorch/config.yml>` 和 :githublink:`.nniignore <examples/trials/mnist-pytorch/.nniignore>`
-
-上面的代码都已准备好，并保存在 :githublink:`examples/trials/mnist-pytorch/ <examples/trials/mnist-pytorch>`。
-
-
-第四步：运行 experiment
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Linux 和 macOS
-**************
-
-从命令行使用 **config.yml** 文件启动 MNIST experiment 。
-
-.. code-block:: bash
-
-   nnictl create --config nni/examples/trials/mnist-pytorch/config_detailed.yml
-
-Windows
-*******
-
-在 **config_detailed.yml** 文件的 ``trialCommand`` 项中将 ``python3`` 修改为 ``python``，然后从命令行使用 **config_detailed.yml** 文件启动 MNIST experiment 。
-
-.. code-block:: bash
-
-   nnictl create --config nni\examples\trials\mnist-pytorch\config_detailed.yml
-
-.. Note:: ``nnictl`` 是一个命令行工具，用来控制 NNI experiment，如启动、停止、继续 experiment，启动、停止 NNIBoard 等等。 点击 :doc:`这里 <../reference/nnictl>` 查看 ``nnictl`` 的更多用法。
-
-在命令行中等待输出 ``INFO: Successfully started experiment!`` 。 此消息表明实验已成功启动。 期望的输出如下：
-
-.. code-block:: text
-
-   INFO: Starting restful server...
-   INFO: Successfully started Restful server!
-   INFO: Setting local config...
-   INFO: Successfully set local config!
-   INFO: Starting experiment...
-   INFO: Successfully started experiment!
-   -----------------------------------------------------------------------
-   The experiment id is egchD4qy
-   The Web UI urls are: [Your IP]:8080
-   -----------------------------------------------------------------------
-
-   You can use these commands to get more information about the experiment
-   -----------------------------------------------------------------------
-            commands                       description
-   1. nnictl experiment show        show the information of experiments
-   2. nnictl trial ls               list all of trial jobs
-   3. nnictl top                    monitor the status of running experiments
-   4. nnictl log stderr             show stderr log content
-   5. nnictl log stdout             show stdout log content
-   6. nnictl stop                   stop an experiment
-   7. nnictl trial kill             kill a trial job by id
-   8. nnictl --help                 get help information about nnictl
-   -----------------------------------------------------------------------
-
-如果根据上述步骤准备好了相应 ``Trial`` ， **搜索空间** 和 **配置** ，并成功创建的 NNI 任务。NNI 会自动开始通过配置的搜索空间来运行不同的超参集合，搜索最好的超参。 通过 Web 界面可看到 NNI 的进度。
-
-第五步：查看 experiment
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-启动 experiment 后，可以在命令行界面找到如下的 **Web 界面地址** ：
-
-.. code-block:: text
-
-   The Web UI urls are: [Your IP]:8080
-
-在浏览器中打开 **Web 界面地址** （即： ``[IP 地址]:8080`` ），就可以看到 experiment 的详细信息，以及所有的 Trial 任务。 如果无法打开终端中的 Web 界面链接，可以参考 `常见问题 <FAQ.rst>`__。
-
-
-查看概要页面
-******************
-
-Experiment 相关信息会显示在界面上，包括配置和搜索空间等。 NNI 还支持通过 **Experiment summary** 按钮下载这些信息和参数。
-
-.. image:: ../../img/webui-img/full-oview.png
-   :target: ../../img/webui-img/full-oview.png
-   :alt: overview
-
-
-查看 Trial 详情页面
-**********************************
-
-可以在此页面中看到最佳的 ``Trial`` 指标和超参数图。 您可以点击 ``Add/Remove columns`` 按钮向表格中添加更多列。
-
-.. image:: ../../img/webui-img/full-detail.png
-   :target: ../../img/webui-img/full-detail.png
-   :alt: detail
-
-
-查看 experiment 管理页面
-**********************************
-
-``All experiments`` 页面可以查看计算机上的所有实验。 
-
-.. image:: ../../img/webui-img/managerExperimentList/expList.png
-   :target: ../../img/webui-img/managerExperimentList/expList.png
-   :alt: Experiments list
-
-更多信息可参考 `此文档 <./WebUI.rst>`__。
-
-
-相关主题
--------------
-
-* `进行Debug <HowToDebug.rst>`__
-* `如何实现 Trial 代码 <../TrialExample/Trials.rst>`__
-* `尝试不同的 Tuner <../Tuner/BuiltinTuner.rst>`__
-* `尝试不同的 Assessor <../Assessor/BuiltinAssessor.rst>`__
-* `在不同训练平台上运行 experiment <../training_services.rst>`__
-* `如何使用 Annotation <AnnotationSpec.rst>`__
-* `如何使用命令行工具 nnictl <Nnictl.rst>`__
-* `在 Web 界面中启动 TensorBoard <Tensorboard.rst>`__
diff --git a/docs/source/Tutorial/SearchSpaceSpec.rst b/docs/source/Tutorial/SearchSpaceSpec.rst
deleted file mode 100644
index fc5f25efc..000000000
--- a/docs/source/Tutorial/SearchSpaceSpec.rst
+++ /dev/null
@@ -1,271 +0,0 @@
-.. role:: raw-html(raw)
-   :format: html
-
-Search Space
-============
-
-Overview
---------
-
-In NNI, tuner will sample parameters/architectures according to the search space.
-
-To define a search space, users should define the name of the variable, the type of sampling strategy and its parameters.
-
-* An example of a search space definition in a JSON file is as follow:
-
-.. code-block:: json 
-
-   {
-       "dropout_rate": {"_type": "uniform", "_value": [0.1, 0.5]},
-       "conv_size": {"_type": "choice", "_value": [2, 3, 5, 7]},
-       "hidden_size": {"_type": "choice", "_value": [124, 512, 1024]},
-       "batch_size": {"_type": "choice", "_value": [50, 250, 500]},
-       "learning_rate": {"_type": "uniform", "_value": [0.0001, 0.1]}
-   }
-
-Take the first line as an example. ``dropout_rate`` is defined as a variable whose prior distribution is a uniform distribution with a range from ``0.1`` to ``0.5``.
-
-.. note:: In the `experiment configuration (V2) schema <ExperimentConfig.rst>`_, NNI supports defining the search space directly in the configuration file, detailed usage can be found `here <QuickStart.rst#step-2-define-the-search-space>`__. When using Python API, users can write the search space in the Python file, refer `here <HowToLaunchFromPython.rst>`__. 
-
-Note that the available sampling strategies within a search space depend on the tuner you want to use. We list the supported types for each builtin tuner below. For a customized tuner, you don't have to follow our convention and you will have the flexibility to define any type you want.
-
-Types
------
-
-All types of sampling strategies and their parameter are listed here:
-
-
-* 
-  ``{"_type": "choice", "_value": options}``
-
-
-  * The variable's value is one of the options. Here ``options`` should be a list of **numbers** or a list of **strings**. Using arbitrary objects as members of this list (like sublists, a mixture of numbers and strings, or null values) should work in most cases, but may trigger undefined behaviors.
-  * ``options`` can also be a nested sub-search-space, this sub-search-space takes effect only when the corresponding element is chosen. The variables in this sub-search-space can be seen as conditional variables. Here is an simple :githublink:`example of nested search space definition <examples/trials/mnist-nested-search-space/search_space.json>`. If an element in the options list is a dict, it is a sub-search-space, and for our built-in tuners you have to add a ``_name`` key in this dict, which helps you to identify which element is chosen. Accordingly, here is a :githublink:`sample <examples/trials/mnist-nested-search-space/sample.json>` which users can get from nni with nested search space definition. See the table below for the tuners which support nested search spaces.
-
-* 
-  ``{"_type": "randint", "_value": [lower, upper]}``
-
-
-  * Choosing a random integer between ``lower`` (inclusive) and ``upper`` (exclusive).
-  * Note: Different tuners may interpret ``randint`` differently. Some (e.g., TPE, GridSearch) treat integers from lower
-    to upper as unordered ones, while others respect the ordering (e.g., SMAC). If you want all the tuners to respect
-    the ordering, please use ``quniform`` with ``q=1``.
-
-* 
-  ``{"_type": "uniform", "_value": [low, high]}``
-
-
-  * The variable value is uniformly sampled between low and high.
-  * When optimizing, this variable is constrained to a two-sided interval.
-
-* 
-  ``{"_type": "quniform", "_value": [low, high, q]}``
-
-
-  * The variable value is determined using ``clip(round(uniform(low, high) / q) * q, low, high)``\ , where the clip operation is used to constrain the generated value within the bounds. For example, for ``_value`` specified as [0, 10, 2.5], possible values are [0, 2.5, 5.0, 7.5, 10.0]; For ``_value`` specified as [2, 10, 5], possible values are [2, 5, 10].
-  * Suitable for a discrete value with respect to which the objective is still somewhat "smooth", but which should be bounded both above and below. If you want to uniformly choose an integer from a range [low, high], you can write ``_value`` like this: ``[low, high, 1]``.
-
-* 
-  ``{"_type": "loguniform", "_value": [low, high]}``
-
-
-  * The variable value is drawn from a range [low, high] according to a loguniform distribution like exp(uniform(log(low), log(high))), so that the logarithm of the return value is uniformly distributed.
-  * When optimizing, this variable is constrained to be positive.
-
-* 
-  ``{"_type": "qloguniform", "_value": [low, high, q]}``
-
-
-  * The variable value is determined using ``clip(round(loguniform(low, high) / q) * q, low, high)``\ , where the clip operation is used to constrain the generated value within the bounds.
-  * Suitable for a discrete variable with respect to which the objective is "smooth" and gets smoother with the size of the value, but which should be bounded both above and below.
-
-* 
-  ``{"_type": "normal", "_value": [mu, sigma]}``
-
-
-  * The variable value is a real value that's normally-distributed with mean mu and standard deviation sigma. When optimizing, this is an unconstrained variable.
-
-* 
-  ``{"_type": "qnormal", "_value": [mu, sigma, q]}``
-
-
-  * The variable value is determined using ``round(normal(mu, sigma) / q) * q``
-  * Suitable for a discrete variable that probably takes a value around mu, but is fundamentally unbounded.
-
-* 
-  ``{"_type": "lognormal", "_value": [mu, sigma]}``
-
-
-  * The variable value is drawn according to ``exp(normal(mu, sigma))`` so that the logarithm of the return value is normally distributed. When optimizing, this variable is constrained to be positive.
-
-* 
-  ``{"_type": "qlognormal", "_value": [mu, sigma, q]}``
-
-
-  * The variable value is determined using ``round(exp(normal(mu, sigma)) / q) * q``
-  * Suitable for a discrete variable with respect to which the objective is smooth and gets smoother with the size of the variable, which is bounded from one side.
-
-Search Space Types Supported by Each Tuner
-------------------------------------------
-
-.. list-table::
-   :header-rows: 1
-   :widths: auto
-
-   * - 
-     - choice
-     - choice(nested)
-     - randint
-     - uniform
-     - quniform
-     - loguniform
-     - qloguniform
-     - normal
-     - qnormal
-     - lognormal
-     - qlognormal
-   * - TPE Tuner
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-   * - Random Search Tuner
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-   * - Anneal Tuner
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-   * - Evolution Tuner
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-   * - SMAC Tuner
-     - :raw-html:`&#10003;`
-     - 
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - 
-     - 
-     - 
-     - 
-     - 
-   * - Batch Tuner
-     - :raw-html:`&#10003;`
-     - 
-     - 
-     - 
-     - 
-     - 
-     - 
-     - 
-     - 
-     - 
-     - 
-   * - Grid Search Tuner
-     - :raw-html:`&#10003;`
-     - 
-     - :raw-html:`&#10003;`
-     - 
-     - :raw-html:`&#10003;`
-     - 
-     - 
-     - 
-     - 
-     - 
-     - 
-   * - Hyperband Advisor
-     - :raw-html:`&#10003;`
-     - 
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-   * - Metis Tuner
-     - :raw-html:`&#10003;`
-     - 
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - 
-     - 
-     - 
-     - 
-     - 
-     - 
-   * - GP Tuner
-     - :raw-html:`&#10003;`
-     - 
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - 
-     - 
-     - 
-     - 
-   * - DNGO Tuner
-     - :raw-html:`&#10003;`
-     - 
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - 
-     - 
-     - 
-     - 
-
-
-Known Limitations:
-
-
-* 
-  GP Tuner, Metis Tuner and DNGO tuner support only **numerical values** in search space (\ ``choice`` type values can be no-numerical with other tuners, e.g. string values). Both GP Tuner and Metis Tuner use Gaussian Process Regressor(GPR). GPR make predictions based on a kernel function and the 'distance' between different points, it's hard to get the true distance between no-numerical values.
-
-* 
-  Note that for nested search space:
-
-
-  * Only Random Search/TPE/Anneal/Evolution/Grid Search tuner supports nested search space
diff --git a/docs/source/Tutorial/SetupNniDeveloperEnvironment.rst b/docs/source/Tutorial/SetupNniDeveloperEnvironment.rst
deleted file mode 100644
index b1adc5813..000000000
--- a/docs/source/Tutorial/SetupNniDeveloperEnvironment.rst
+++ /dev/null
@@ -1,68 +0,0 @@
-Setup NNI development environment
-=================================
-
-NNI development environment supports Ubuntu 1604 (or above), and Windows 10 with Python3 64bit.
-
-Installation
-------------
-
-1. Clone source code
-^^^^^^^^^^^^^^^^^^^^
-
-.. code-block:: bash
-
-   git clone https://github.com/Microsoft/nni.git
-
-Note, if you want to contribute code back, it needs to fork your own NNI repo, and clone from there.
-
-2. Install from source code
-^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. code-block:: bash
-
-   python3 -m pip install -U -r dependencies/setup.txt
-   python3 -m pip install -r dependencies/develop.txt
-   python3 setup.py develop
-
-This installs NNI in `development mode <https://setuptools.readthedocs.io/en/latest/userguide/development_mode.html>`__,
-so you don't need to reinstall it after edit.
-
-3. Check if the environment is ready
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Now, you can try to start an experiment to check if your environment is ready.
-For example, run the command
-
-.. code-block:: bash
-
-   nnictl create --config examples/trials/mnist-pytorch/config.yml
-
-And open WebUI to check if everything is OK
-
-4. Reload changes
-^^^^^^^^^^^^^^^^^
-
-Python
-******
-
-Nothing to do, the code is already linked to package folders.
-
-TypeScript (Linux and macOS)
-****************************
-
-* If ``ts/nni_manager`` is changed, run ``yarn watch`` under this folder. It will watch and build code continually. The ``nnictl`` need to be restarted to reload NNI manager.
-* If ``ts/webui`` is changed, run ``yarn dev``\ , which will run a mock API server and a webpack dev server simultaneously. Use ``EXPERIMENT`` environment variable (e.g., ``mnist-tfv1-running``\ ) to specify the mock data being used. Built-in mock experiments are listed in ``src/webui/mock``. An example of the full command is ``EXPERIMENT=mnist-tfv1-running yarn dev``.
-
-TypeScript (Windows)
-********************
-
-Currently you must rebuild TypeScript modules with `python3 setup.py build_ts` after edit.
-
-5. Submit Pull Request
-^^^^^^^^^^^^^^^^^^^^^^
-
-All changes are merged to master branch from your forked repo. The description of Pull Request must be meaningful, and useful.
-
-We will review the changes as soon as possible. Once it passes review, we will merge it to master branch.
-
-For more contribution guidelines and coding styles, you can refer to the `contributing document <Contributing.rst>`__.
diff --git a/docs/source/Tutorial/WebUI_zh.rst b/docs/source/Tutorial/WebUI_zh.rst
deleted file mode 100644
index 216189d58..000000000
--- a/docs/source/Tutorial/WebUI_zh.rst
+++ /dev/null
@@ -1,328 +0,0 @@
-.. bb68c969dbc2b3a2ec79d323cbd31401
-
-Web 界面
-==================
-
-Experiments 管理
------------------------
-
-点击导航栏上的 ``All experiments`` 标签。
-
-.. image:: ../../img/webui-img/managerExperimentList/experimentListNav.png
-   :target: ../../img/webui-img/managerExperimentList/experimentListNav.png
-   :alt: ExperimentList nav
-
-
-
-* 在 ``All experiments`` 页面，可以看到机器上的所有 Experiment。 
-
-.. image:: ../../img/webui-img/managerExperimentList/expList.png
-   :target: ../../img/webui-img/managerExperimentList/expList.png
-   :alt: Experiments list
-
-
-
-* 查看 Experiment 更多详细信息时，可以单击 trial ID 跳转至该 Experiment 详情页，如下所示：
-
-.. image:: ../../img/webui-img/managerExperimentList/toAnotherExp.png
-   :target: ../../img/webui-img/managerExperimentList/toAnotherExp.png
-   :alt: See this experiment detail
-
-
-
-* 如果表格里有很多 Experiment，可以使用 ``filter`` 按钮。
-
-.. image:: ../../img/webui-img/managerExperimentList/expFilter.png
-   :target: ../../img/webui-img/managerExperimentList/expFilter.png
-   :alt: filter button
-
-
-
-查看概要页面
------------------
-
-点击 ``Overview`` 标签。
-
-
-* 在 Overview 标签上，可看到 Experiment trial 的概况、搜索空间以及 ``top trials`` 的结果。
-
-
-.. image:: ../../img/webui-img/full-oview.png
-   :target: ../../img/webui-img/full-oview.png
-   :alt: overview
-
-
-
-如果想查看 Experiment 配置和搜索空间，点击右边的 ``Search space`` 和 ``Config`` 按钮。
-
-   1. 搜索空间文件：
-
-
-      .. image:: ../../img/webui-img/searchSpace.png
-         :target: ../../img/webui-img/searchSpace.png
-         :alt: searchSpace
-
-
-
-   2. 配置文件：
-
-
-      .. image:: ../../img/webui-img/config.png
-         :target: ../../img/webui-img/config.png
-         :alt: config
-
-
-
-* 你可以在这里查看和下载 ``nni-manager/dispatcher 日志文件``。
-
-
-.. image:: ../../img/webui-img/review-log.png
-   :target: ../../img/webui-img/review-log.png
-   :alt: logfile
-
-
-
-* 如果 Experiment 包含了较多 Trial，可改变刷新间隔。
-
-
-.. image:: ../../img/webui-img/refresh-interval.png
-   :target: ../../img/webui-img/refresh-interval.png
-   :alt: refresh
-
-
-
-
-* 单击按钮 ``Experiment summary`` ，可以查看和下载 Experiment 结果（``Experiment 配置``，``trial 信息`` 和 ``中间结果`` ）。
-
-
-.. image:: ../../img/webui-img/summary.png
-   :target: ../../img/webui-img/summary.png
-   :alt: summary
-
-
-
-* 在这里修改 Experiment 配置（例如 ``maxExecDuration``, ``maxTrialNum`` 和 ``trial concurrency``）。
-
-
-.. image:: ../../img/webui-img/edit-experiment-param.png
-   :target: ../../img/webui-img/edit-experiment-param.png
-   :alt: editExperimentParams
-
-
-
-* 通过单击 ``Learn about`` ，可以查看错误消息和 ``nni-manager/dispatcher 日志文件``
-
-
-.. image:: ../../img/webui-img/experimentError.png
-   :target: ../../img/webui-img/experimentError.png
-   :alt: experimentError
-
-
-
-
-* ``About`` 菜单内含有版本信息以及问题反馈渠道。
-
-查看 trial 最终结果
-----------------------------------------------
-
-
-* ``Default metric`` 是所有 trial 的最终结果图。 在每一个结果上悬停鼠标可以看到 trial 信息，比如 trial id、No.、超参等。
-
-
-.. image:: ../../img/webui-img/default-metric.png
-   :target: ../../img/webui-img/default-metric.png
-   :alt: defaultMetricGraph
-
-
-
-* 打开 ``Optimization curve`` 来查看 Experiment 的优化曲线。
-
-
-.. image:: ../../img/webui-img/best-curve.png
-   :target: ../../img/webui-img/best-curve.png
-   :alt: bestCurveGraph
-
-
-查看超参
---------------------
-
-单击 ``Hyper-parameter`` 标签查看平行坐标系图。
-
-
-* 可以点击 ``添加/删除`` 按钮来添加或删减纵坐标轴。
-* 直接在图上拖动轴线来交换轴线位置。
-* 通过调节百分比来查看 top trial。
-
-
-.. image:: ../../img/webui-img/hyperPara.png
-   :target: ../../img/webui-img/hyperPara.png
-   :alt: hyperParameterGraph
-
-
-
-查看 Trial 运行时间
--------------------
-
-点击 ``Trial Duration`` 标签来查看柱状图。
-
-
-.. image:: ../../img/webui-img/trial_duration.png
-   :target: ../../img/webui-img/trial_duration.png
-   :alt: trialDurationGraph
-
-
-
-查看 Trial 中间结果
-------------------------------------
-
-单击 ``Intermediate Result`` 标签查看折线图。
-
-
-.. image:: ../../img/webui-img/trials_intermeidate.png
-   :target: ../../img/webui-img/trials_intermeidate.png
-   :alt: trialIntermediateGraph
-
-
-
-Trial 在训练过程中可能有大量中间结果。 为了更清楚的理解一些 Trial 的趋势，可以为中间结果图设置过滤功能。
-
-这样可以发现 Trial 在某个中间结果上会变得更好或更差。 这表明它是一个重要的并相关的中间结果。 如果要仔细查看这个点，可以在 #Intermediate 中输入其 X 坐标。 并输入这个中间结果的指标范围。 在下图中，选择了第四个中间结果并将指标范围设置为了 0.8 -1。
-
-
-.. image:: ../../img/webui-img/filter-intermediate.png
-   :target: ../../img/webui-img/filter-intermediate.png
-   :alt: filterIntermediateGraph
-
-
-
-查看 Trial 状态
-------------------
-
-点击 ``Trials Detail`` 标签查看所有 Trial 的状态。具体如下：
-
-
-* Trial 详情：Trial id，持续时间，开始时间，结束时间，状态，精度和 search space 文件。
-
-
-.. image:: ../../img/webui-img/detail-local.png
-   :target: ../../img/webui-img/detail-local.png
-   :alt: detailLocalImage
-
-
-
-* 支持通过 id，状态，Trial 编号以及参数来搜索。  
-
-1. Trial id： 
-
-.. image:: ../../img/webui-img/detail/searchId.png
-   :target: ../../img/webui-img/detail/searchId.png
-   :alt: searchTrialId
-
-
-2. Trial No.： 
-
-.. image:: ../../img/webui-img/detail/searchNo.png
-   :target: ../../img/webui-img/detail/searchNo.png
-   :alt: searchTrialNo.
-
-
-3. Trial 状态：
-
-.. image:: ../../img/webui-img/detail/searchStatus.png
-   :target: ../../img/webui-img/detail/searchStatus.png
-   :alt: searchStatus
-
-4. Trial 参数：
-
-(1) 类型为 choice 的参数：
-
-.. image:: ../../img/webui-img/detail/searchParameterChoice.png
-   :target: ../../img/webui-img/detail/searchParameterChoice.png
-   :alt: searchParameterChoice
-
-(2) 类型不是 choice 的参数：
-
-.. image:: ../../img/webui-img/detail/searchParameterRange.png
-   :target: ../../img/webui-img/detail/searchParameterRange.png
-   :alt: searchParameterRange
-
-
-* ``Add column`` 按钮可选择在表格中显示的列。 如果 Experiment 的最终结果是 dict，则可以在表格中查看其它键。可选择 ``Intermediate count`` 列来查看 Trial 进度。
-
-
-.. image:: ../../img/webui-img/addColumn.png
-   :target: ../../img/webui-img/addColumn.png
-   :alt: addColumnGraph
-
-
-
-* 如果要比较某些 Trial，可选择并点击 ``Compare`` 来查看结果。
-
-
-.. image:: ../../img/webui-img/select-trial.png
-   :target: ../../img/webui-img/select-trial.png
-   :alt: selectTrialGraph
-
-
-.. image:: ../../img/webui-img/compare.png
-   :target: ../../img/webui-img/compare.png
-   :alt: compareTrialsGraph
-
-
-* ``Tensorboard`` 请参考 `此文档 <Tensorboard.rst>`__。
-
-
-* 可使用 ``Copy as python`` 按钮来拷贝 Trial 的参数。
-
-
-.. image:: ../../img/webui-img/copyParameter.png
-   :target: ../../img/webui-img/copyParameter.png
-   :alt: copyTrialParameters
-
-
-
-* 您可以在 ``Log`` 选项卡上看到 Trial 日志。 在本地模式下有 ``View trial log``, ``View trial error`` 和 ``View trial stdout`` 三个按钮。 * 如果在 OpenPAI 或 Kubeflow 平台上运行，还可以看到 hdfsLog。
-
-1. 本机模式
-
-.. image:: ../../img/webui-img/detail/log-local.png
-   :target: ../../img/webui-img/detail/log-local.png
-   :alt: logOnLocal
-
-
-2. OpenPAI、Kubeflow 等模式：
-
-.. image:: ../../img/webui-img/detail-pai.png
-   :target: ../../img/webui-img/detail-pai.png
-   :alt: detailPai
-
-
-* 中间结果图：可在此图中通过点击 intermediate 按钮来查看默认指标。
-
-
-.. image:: ../../img/webui-img/intermediate.png
-   :target: ../../img/webui-img/intermediate.png
-   :alt: intermeidateGraph
-
-
-
-* Kill: 可终止正在运行的任务。
-
-
-.. image:: ../../img/webui-img/kill-running.png
-   :target: ../../img/webui-img/kill-running.png
-   :alt: killTrial
-
-
-
-* 自定义 Trial：您可以更改此 Trial 参数，然后将其提交给 Experiment。如果您想重新运行失败的 Trial ，您可以向 Experiment 提交相同的参数。
-
-.. image:: ../../img/webui-img/detail/customizedTrialButton.png
-   :target: ../../img/webui-img/detail/customizedTrialButton.png
-   :alt: customizedTrialButton
-
-
-
-.. image:: ../../img/webui-img/detail/customizedTrial.png
-   :target: ../../img/webui-img/detail/customizedTrial.png
-   :alt: customizedTrial
diff --git a/docs/source/autotune_ref.rst b/docs/source/autotune_ref.rst
deleted file mode 100644
index a3e3261ff..000000000
--- a/docs/source/autotune_ref.rst
+++ /dev/null
@@ -1,92 +0,0 @@
-Python API Reference of Auto Tune
-=================================
-
-.. contents::
-
-Trial
------
-
-..  autofunction:: nni.get_next_parameter
-..  autofunction:: nni.get_current_parameter
-..  autofunction:: nni.report_intermediate_result
-..  autofunction:: nni.report_final_result
-..  autofunction:: nni.get_experiment_id
-..  autofunction:: nni.get_trial_id
-..  autofunction:: nni.get_sequence_id
-
-Tuner
------
-
-..  autoclass:: nni.tuner.Tuner
-    :members:
-
-..  autoclass:: nni.algorithms.hpo.tpe_tuner.TpeTuner
-    :members:
-
-..  autoclass:: nni.algorithms.hpo.random_tuner.RandomTuner
-    :members:
-
-..  autoclass:: nni.algorithms.hpo.hyperopt_tuner.HyperoptTuner
-    :members:
-
-..  autoclass:: nni.algorithms.hpo.evolution_tuner.EvolutionTuner
-    :members:
-
-..  autoclass:: nni.algorithms.hpo.smac_tuner.SMACTuner
-    :members:
-
-..  autoclass:: nni.algorithms.hpo.gridsearch_tuner.GridSearchTuner
-    :members:
-
-..  autoclass:: nni.algorithms.hpo.networkmorphism_tuner.NetworkMorphismTuner
-    :members:
-
-..  autoclass:: nni.algorithms.hpo.metis_tuner.MetisTuner
-    :members:
-
-..  autoclass:: nni.algorithms.hpo.ppo_tuner.PPOTuner
-    :members:
-
-..  autoclass:: nni.algorithms.hpo.batch_tuner.BatchTuner
-    :members:
-
-..  autoclass:: nni.algorithms.hpo.gp_tuner.GPTuner
-    :members:
-
-Assessor
---------
-
-..  autoclass:: nni.assessor.Assessor
-    :members:
-
-..  autoclass:: nni.assessor.AssessResult
-    :members:
-
-..  autoclass:: nni.algorithms.hpo.curvefitting_assessor.CurvefittingAssessor
-    :members:
-
-..  autoclass:: nni.algorithms.hpo.medianstop_assessor.MedianstopAssessor
-    :members:
-
-Advisor
--------
-
-..  autoclass:: nni.runtime.msg_dispatcher_base.MsgDispatcherBase
-    :members:
-
-..  autoclass:: nni.algorithms.hpo.hyperband_advisor.Hyperband
-    :members:
-
-..  autoclass:: nni.algorithms.hpo.bohb_advisor.BOHB
-    :members:
-
-Utilities
----------
-
-..  autofunction:: nni.utils.merge_parameter
-
-..  autofunction:: nni.trace
-
-..  autofunction:: nni.dump
-
-..  autofunction:: nni.load
diff --git a/docs/source/builtin_assessor.rst b/docs/source/builtin_assessor.rst
deleted file mode 100644
index 9b059f60f..000000000
--- a/docs/source/builtin_assessor.rst
+++ /dev/null
@@ -1,19 +0,0 @@
-Builtin-Assessors
-=================
-
-In order to save on computing resources, NNI supports an early stopping policy and has an interface called **Assessor** to do this job.
-
-Assessor receives the intermediate result from a trial and decides whether the trial should be killed using a specific algorithm. Once the trial experiment meets the early stopping conditions (which means Assessor is pessimistic about the final results), the assessor will kill the trial and the status of the trial will be `EARLY_STOPPED`.
-
-Here is an experimental result of MNIST after using the 'Curvefitting' Assessor in 'maximize' mode. You can see that Assessor successfully **early stopped** many trials with bad hyperparameters in advance. If you use Assessor, you may get better hyperparameters using the same computing resources.
-
-Implemented code directory: :githublink:`config_assessor.yml <examples/trials/mnist-pytorch/config_assessor.yml>`
-
-..  image:: ../img/Assessor.png
-
-..  toctree::
-    :maxdepth: 1
-
-    Overview<./Assessor/BuiltinAssessor>
-    Medianstop<./Assessor/MedianstopAssessor>
-    Curvefitting<./Assessor/CurvefittingAssessor>
diff --git a/docs/source/builtin_assessor_zh.rst b/docs/source/builtin_assessor_zh.rst
deleted file mode 100644
index e745109d0..000000000
--- a/docs/source/builtin_assessor_zh.rst
+++ /dev/null
@@ -1,21 +0,0 @@
-.. d5351e951811dcaeeda7f270427187fd
-
-内置 Assessor
-=================
-
-为了节省计算资源，NNI 支持提前终止策略，并且通过叫做 **Assessor** 的接口来执行此操作。
-
-Assessor 从 Trial 中接收中间结果，并通过指定的算法决定此 Trial 是否应该终止。 一旦 Trial 满足了提前终止策略（这表示 Assessor 认为最终结果不会太好），Assessor 会终止此 Trial，并将其状态标志为 `EARLY_STOPPED`。
-
-这是 MNIST 在 "最大化" 模式下使用 "曲线拟合" Assessor 的实验结果。 可以看到 Assessor 成功的 **提前终止** 了许多结果不好超参组合的 Trial。 使用 Assessor，能在相同的计算资源下，得到更好的结果。
-
-实验代码： :githublink:`config_assessor.yml <examples/trials/mnist-pytorch/config_assessor.yml>`
-
-..  image:: ../img/Assessor.png
-
-..  toctree::
-    :maxdepth: 1
-
-    概述<./Assessor/BuiltinAssessor>
-    Medianstop<./Assessor/MedianstopAssessor>
-    Curvefitting（曲线拟合）<./Assessor/CurvefittingAssessor>
diff --git a/docs/source/builtin_tuner.rst b/docs/source/builtin_tuner.rst
deleted file mode 100644
index b8867e825..000000000
--- a/docs/source/builtin_tuner.rst
+++ /dev/null
@@ -1,74 +0,0 @@
-Builtin-Tuners
-==============
-
-NNI provides an easy way to adopt an approach to set up parameter tuning algorithms, we call them **Tuner**.
-
-Tuner receives metrics from `Trial` to evaluate the performance of a specific parameters/architecture configuration. Tuner sends the next hyper-parameter or architecture configuration to Trial.
-
-The following table briefly describes the built-in tuners provided by NNI. Click the **Tuner's name** to get the Tuner's installation requirements, suggested scenario, and an example configuration. A link for a detailed description of each algorithm is located at the end of the suggested scenario for each tuner. Here is an `article <../CommunitySharings/HpoComparison.rst>`__ comparing different Tuners on several problems.
-
-.. list-table::
-   :header-rows: 1
-   :widths: auto
-
-   * - Tuner
-     - Brief Introduction of Algorithm
-
-   * - `TPE <./TpeTuner.rst>`__
-     - The Tree-structured Parzen Estimator (TPE) is a sequential model-based optimization (SMBO) approach. SMBO methods sequentially construct models to approximate the performance of hyperparameters based on historical measurements, and then subsequently choose new hyperparameters to test based on this model. `Reference Paper <https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf>`__
-
-   * - `Random Search <./RandomTuner.rst>`__
-     - In Random Search for Hyper-Parameter Optimization show that Random Search might be surprisingly simple and effective. We suggest that we could use Random Search as the baseline when we have no knowledge about the prior distribution of hyper-parameters. `Reference Paper <http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf>`__
-
-   * - `Anneal <./AnnealTuner.rst>`__
-     - This simple annealing algorithm begins by sampling from the prior, but tends over time to sample from points closer and closer to the best ones observed. This algorithm is a simple variation on the random search that leverages smoothness in the response surface. The annealing rate is not adaptive.
-
-   * - `Naïve Evolution <./EvolutionTuner.rst>`__
-     - Naïve Evolution comes from Large-Scale Evolution of Image Classifiers. It randomly initializes a population-based on search space. For each generation, it chooses better ones and does some mutation (e.g., change a hyperparameter, add/remove one layer) on them to get the next generation. Naïve Evolution requires many trials to work, but it's very simple and easy to expand new features. `Reference paper <https://arxiv.org/pdf/1703.01041.pdf>`__
-
-   * - `SMAC <./SmacTuner.rst>`__
-     - SMAC is based on Sequential Model-Based Optimization (SMBO). It adapts the most prominent previously used model class (Gaussian stochastic process models) and introduces the model class of random forests to SMBO, in order to handle categorical parameters. The SMAC supported by NNI is a wrapper on the SMAC3 GitHub repo.
-
-       Notice, SMAC needs to be installed by ``pip install nni[SMAC]`` command. `Reference Paper, <https://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf>`__ `GitHub Repo <https://github.com/automl/SMAC3>`__
-
-   * - `Batch tuner <./BatchTuner.rst>`__
-     - Batch tuner allows users to simply provide several configurations (i.e., choices of hyper-parameters) for their trial code. After finishing all the configurations, the experiment is done. Batch tuner only supports the type choice in search space spec.
-
-   * - `Grid Search <./GridsearchTuner.rst>`__
-     - Grid Search performs an exhaustive searching through the search space.
-
-   * - `Hyperband <./HyperbandAdvisor.rst>`__
-     - Hyperband tries to use limited resources to explore as many configurations as possible and returns the most promising ones as a final result. The basic idea is to generate many configurations and run them for a small number of trials. The half least-promising configurations are thrown out, the remaining are further trained along with a selection of new configurations. The size of these populations is sensitive to resource constraints (e.g. allotted search time). `Reference Paper <https://arxiv.org/pdf/1603.06560.pdf>`__
-
-   * - `Metis Tuner <./MetisTuner.rst>`__
-     - Metis offers the following benefits when it comes to tuning parameters: While most tools only predict the optimal configuration, Metis gives you two outputs: (a) current prediction of optimal configuration, and (b) suggestion for the next trial. No more guesswork. While most tools assume training datasets do not have noisy data, Metis actually tells you if you need to re-sample a particular hyper-parameter. `Reference Paper <https://www.microsoft.com/en-us/research/publication/metis-robustly-tuning-tail-latencies-cloud-systems/>`__
-
-   * - `BOHB <./BohbAdvisor.rst>`__
-     - BOHB is a follow-up work to Hyperband. It targets the weakness of Hyperband that new configurations are generated randomly without leveraging finished trials. For the name BOHB, HB means Hyperband, BO means Bayesian Optimization. BOHB leverages finished trials by building multiple TPE models, a proportion of new configurations are generated through these models. `Reference Paper <https://arxiv.org/abs/1807.01774>`__
-
-   * - `GP Tuner <./GPTuner.rst>`__
-     - Gaussian Process Tuner is a sequential model-based optimization (SMBO) approach with Gaussian Process as the surrogate. `Reference Paper <https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf>`__, `Github Repo <https://github.com/fmfn/BayesianOptimization>`__
-
-   * - `PBT Tuner <./PBTTuner.rst>`__
-     - PBT Tuner is a simple asynchronous optimization algorithm which effectively utilizes a fixed computational budget to jointly optimize a population of models and their hyperparameters to maximize performance. `Reference Paper <https://arxiv.org/abs/1711.09846v1>`__
-
-   * - `DNGO Tuner <./DngoTuner.rst>`__
-     - Use of neural networks as an alternative to GPs to model distributions over functions in bayesian optimization.
-
-..  toctree::
-    :maxdepth: 1
-
-    TPE <Tuner/TpeTuner>
-    Random Search <Tuner/RandomTuner>
-    Anneal <Tuner/AnnealTuner>
-    Naive Evolution <Tuner/EvolutionTuner>
-    SMAC <Tuner/SmacTuner>
-    Metis Tuner <Tuner/MetisTuner>
-    Batch Tuner <Tuner/BatchTuner>
-    Grid Search <Tuner/GridsearchTuner>
-    GP Tuner <Tuner/GPTuner>
-    Network Morphism <Tuner/NetworkmorphismTuner>
-    Hyperband <Tuner/HyperbandAdvisor>
-    BOHB <Tuner/BohbAdvisor>
-    PBT Tuner <Tuner/PBTTuner>
-    DNGO Tuner <Tuner/DngoTuner>
diff --git a/docs/source/builtin_tuner_zh.rst b/docs/source/builtin_tuner_zh.rst
deleted file mode 100644
index 813be5007..000000000
--- a/docs/source/builtin_tuner_zh.rst
+++ /dev/null
@@ -1,76 +0,0 @@
-.. 10b9097fcfec13f98bb6914b40bd0337
-
-内置 Tuner
-==========
-
-为了让机器学习和深度学习模型适应不同的任务和问题，我们需要进行超参数调优，而自动化调优依赖于优秀的调优算法。NNI 内置了先进的调优算法，并且提供了易于使用的 API。
-
-在 NNI 中，调优算法被称为“tuner”。Tuner 向 trial 发送超参数，接收运行结果从而评估这组超参的性能，然后将下一组超参发送给新的 trial。
-
-下表简要介绍了 NNI 内置的调优算法。点击 tuner 的名称可以查看其安装需求、推荐使用场景、示例配置文件等详细信息。`这篇文章 <../CommunitySharings/HpoComparison.rst>`__ 对比了各个 tuner 在不同场景下的性能。
-
-.. list-table::
-  :header-rows: 1
-  :widths: auto
-
-  * - Tuner
-    - 算法简介
-
-  * - `TPE <./TpeTuner.rst>`__
-    - Tree-structured Parzen Estimator (TPE) 是一种基于序列模型的优化方法 (sequential model-based optimization, SMBO)。SMBO方法根据历史数据来顺序地构造模型，从而预估超参性能，并基于此模型来选择新的超参。`参考论文 <https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf>`__
-
-  * - `Random Search (随机搜索) <./RandomTuner.rst>`__
-    - 随机搜索在超算优化中表现出了令人意外的性能。如果没有对超参分布的先验知识，我们推荐使用随机搜索作为基线方法。`参考论文 <http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf>`__
-
-  * - `Anneal (退火) <./AnnealTuner.rst>`__
-    - 朴素退火算法首先基于先验进行采样，然后逐渐逼近实际性能较好的采样点。该算法是随即搜索的变体，利用了反应曲面的平滑性。该实现中退火率不是自适应的。
-
-  * - `Naive Evolution（朴素进化） <./EvolutionTuner.rst>`__
-    - 朴素进化算法来自于 Large-Scale Evolution of Image Classifiers。它基于搜索空间随机生成一个种群，在每一代中选择较好的结果，并对其下一代进行变异。朴素进化算法需要很多 Trial 才能取得最优效果，但它也非常简单，易于扩展。`参考论文 <https://arxiv.org/pdf/1703.01041.pdf>`__
-
-  * - `SMAC <./SmacTuner.rst>`__
-    - SMAC 是基于序列模型的优化方法 (SMBO)。它利用使用过的最突出的模型（高斯随机过程模型），并将随机森林引入到SMBO中，来处理分类参数。NNI 的 SMAC tuner 封装了 GitHub 上的 `SMAC3 <https://github.com/automl/SMAC3>`__。`参考论文 <https://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf>`__
-
-      注意：SMAC 算法需要使用 ``pip install nni[SMAC]`` 安装依赖，暂不支持 Windows 操作系统。
-
-  * - `Batch（批处理） <./BatchTuner.rst>`__
-    - 批处理允许用户直接提供若干组配置，为每种配置运行一个 trial。
-
-  * - `Grid Search（网格遍历） <./GridsearchTuner.rst>`__
-    - 网格遍历会穷举搜索空间中的所有超参组合。
-
-  * - `Hyperband <./HyperbandAdvisor.rst>`__
-    - Hyperband 试图用有限的资源探索尽可能多的超参组合。该算法的思路是，首先生成大量超参配置，将每组超参运行较短的一段时间，随后抛弃其中效果较差的一半，让较好的超参继续运行，如此重复多轮。`参考论文 <https://arxiv.org/pdf/1603.06560.pdf>`__
-
-  * - `Metis <./MetisTuner.rst>`__
-    - 大多数调参工具仅仅预测最优配置，而 Metis 的优势在于它有两个输出：(a) 最优配置的当前预测结果， 以及 (b) 下一次 trial 的建议。大多数工具假设训练集没有噪声数据，但 Metis 会知道是否需要对某个超参重新采样。`参考论文 <https://www.microsoft.com/en-us/research/publication/metis-robustly-tuning-tail-latencies-cloud-systems/>`__
-
-  * - `BOHB <./BohbAdvisor.rst>`__
-    - BOHB 是 Hyperband 算法的后续工作。 Hyperband 在生成新的配置时，没有利用已有的 trial 结果，而本算法利用了 trial 结果。BOHB 中，HB 表示 Hyperband，BO 表示贝叶斯优化（Byesian Optimization）。 BOHB 会建立多个 TPE 模型，从而利用已完成的 Trial 生成新的配置。`参考论文 <https://arxiv.org/abs/1807.01774>`__
-
-  * - `GP (高斯过程) <./GPTuner.rst>`__
-    - GP Tuner 是基于序列模型的优化方法 (SMBO)，使用高斯过程进行 surrogate。`参考论文 <https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf>`__
-
-  * - `PBT <./PBTTuner.rst>`__
-    - PBT Tuner 是一种简单的异步优化算法，在固定的计算资源下，它能有效的联合优化一组模型及其超参来最优化性能。`参考论文 <https://arxiv.org/abs/1711.09846v1>`__
-
-  * - `DNGO <./DngoTuner.rst>`__
-    - DNGO 是基于序列模型的优化方法 (SMBO)，该算法使用神经网络（而不是高斯过程）去建模贝叶斯优化中所需要的函数分布。
-
-..  toctree::
-    :maxdepth: 1
-
-    TPE <Tuner/TpeTuner>
-    Random Search（随机搜索） <Tuner/RandomTuner>
-    Anneal（退火） <Tuner/AnnealTuner>
-    Naïve Evolution（朴素进化） <Tuner/EvolutionTuner>
-    SMAC <Tuner/SmacTuner>
-    Metis Tuner <Tuner/MetisTuner>
-    Batch Tuner（批处理） <Tuner/BatchTuner>
-    Grid Search（网格遍历） <Tuner/GridsearchTuner>
-    GP Tuner <Tuner/GPTuner>
-    Network Morphism <Tuner/NetworkmorphismTuner>
-    Hyperband <Tuner/HyperbandAdvisor>
-    BOHB <Tuner/BohbAdvisor>
-    PBT Tuner <Tuner/PBTTuner>
-    DNGO Tuner <Tuner/DngoTuner>
diff --git a/docs/source/compression/advanced_usage.rst b/docs/source/compression/advanced_usage.rst
new file mode 100644
index 000000000..b5f9c0de9
--- /dev/null
+++ b/docs/source/compression/advanced_usage.rst
@@ -0,0 +1,10 @@
+Advanced Usage
+==============
+
+..  toctree::
+    :maxdepth: 2
+
+    Customize Basic Pruner <../tutorials/pruning_customize>
+    Customize Quantizer <../tutorials/quantization_customize>
+    Customize Scheduled Pruning Process <pruning_scheduler>
+    Utilities <compression_utils>
diff --git a/docs/source/Compression/v2_pruning_config_list.rst b/docs/source/compression/compression_config_list.rst
similarity index 71%
rename from docs/source/Compression/v2_pruning_config_list.rst
rename to docs/source/compression/compression_config_list.rst
index 2f2f7b43e..db82704b3 100644
--- a/docs/source/Compression/v2_pruning_config_list.rst
+++ b/docs/source/compression/compression_config_list.rst
@@ -1,12 +1,12 @@
-Pruning Config Specification
-============================
-
-The Keys in Config List
------------------------
+Compression Config Specification
+================================
 
 Each sub-config in the config list is a dict, and the scope of each setting (key) is only internal to each sub-config.
 If multiple sub-configs are configured for the same layer, the later ones will overwrite the previous ones.
 
+Common Keys in Config
+---------------------
+
 op_types
 ^^^^^^^^
 
@@ -20,9 +20,20 @@ op_names
 The name of the layers targeted by this sub-config.
 If ``op_types`` is set in this sub-config, the selected layer should satisfy both type and name.
 
+exclude
+^^^^^^^
+
+The ``exclude`` and ``sparsity`` keyword are mutually exclusive and cannot exist in the same sub-config.
+If ``exclude`` is set in sub-config, the layers selected by this config will not be compressed.
+
+Special Keys for Pruning
+------------------------
+
 op_partial_names
 ^^^^^^^^^^^^^^^^
 
+This key will share with `Quantization Config` in the future.
+
 This key is for the layers to be pruned with names that have the same sub-string. NNI will find all names in the model,
 find names that contain one of ``op_partial_names``, and append them into the ``op_names``.
 
@@ -60,8 +71,25 @@ In ``total_sparsity`` example, there are 1200 parameters that need to be masked
 To avoid this situation, ``max_sparsity_per_layer`` can be set as 0.9, this means up to 450 parameters can be masked in ``layer_1``,
 and 900 parameters can be masked in ``layer_2``.
 
-exclude
-^^^^^^^
+Special Keys for Quantization
+-----------------------------
 
-The ``exclude`` and ``sparsity`` keyword are mutually exclusive and cannot exist in the same sub-config.
-If ``exclude`` is set in sub-config, the layers selected by this config will not be pruned.
+quant_types
+^^^^^^^^^^^
+
+Currently, nni support three kind of quantization types: 'weight', 'input', 'output'.
+It can be set as ``str`` or ``List[str]``.
+Note that 'weight' and 'input' are always quantize together, e.g., ``['input', 'weight']``.
+
+quant_bits
+^^^^^^^^^^
+
+Bits length of quantization, key is the quantization type set in ``quant_types``, value is the length,
+eg. {'weight': 8}, when the type is int, all quantization types share same bits length.
+
+quant_start_step
+^^^^^^^^^^^^^^^^
+
+Specific key for ``QAT Quantizer``. Disable quantization until model are run by certain number of steps,
+this allows the network to enter a more stable.
+State where output quantization ranges do not exclude a significant fraction of values, default value is 0.
diff --git a/docs/source/Compression/CompressionUtils.rst b/docs/source/compression/compression_utils.rst
similarity index 93%
rename from docs/source/Compression/CompressionUtils.rst
rename to docs/source/compression/compression_utils.rst
index 90215d5f6..974001df7 100644
--- a/docs/source/Compression/CompressionUtils.rst
+++ b/docs/source/compression/compression_utils.rst
@@ -1,8 +1,6 @@
 Analysis Utils for Model Compression
 ====================================
 
-.. contents::
-
 We provide several easy-to-use tools for users to analyze their model during model compression.
 
 Sensitivity Analysis
@@ -48,7 +46,7 @@ Futhermore, users can specify the sparsities values used to prune for each layer
 
 the SensitivityAnalysis will prune 25% 50% 75% weights gradually for each layer, and record the model's accuracy at the same time (SensitivityAnalysis only prune a layer once a time, the other layers are set to their original weights). If the sparsities is not set, SensitivityAnalysis will use the numpy.arange(0.1, 1.0, 0.1) as the default sparsity values.
 
-Users can also speed up the progress of sensitivity analysis by the early_stop_mode and early_stop_value option. By default, the SensitivityAnalysis will test the accuracy under all sparsities for each layer. In contrast, when the early_stop_mode and early_stop_value are set, the sensitivity analysis for a layer will stop, when the accuracy/loss has already met the threshold set by early_stop_value. We support four early stop modes:  minimize, maximize, dropped, raised.
+Users can also speedup the progress of sensitivity analysis by the early_stop_mode and early_stop_value option. By default, the SensitivityAnalysis will test the accuracy under all sparsities for each layer. In contrast, when the early_stop_mode and early_stop_value are set, the sensitivity analysis for a layer will stop, when the accuracy/loss has already met the threshold set by early_stop_value. We support four early stop modes:  minimize, maximize, dropped, raised.
 
 minimize: The analysis stops when the validation metric return by the val_func lower than ``early_stop_value``.
 
@@ -85,6 +83,8 @@ not have model accuracies/losses under all sparsities, for example, its accuracy
    features.8,0.55696,0.54194,0.48892,0.42986,0.33048,0.2266,0.09566,0.02348,0.0056
    features.10,0.55468,0.5394,0.49576,0.4291,0.3591,0.28138,0.14256,0.05446,0.01578
 
+.. _topology-analysis:
+
 Topology Analysis
 -----------------
 
@@ -160,6 +160,8 @@ If the output shape of the pruned conv layer is not divisible by 1024(for exampl
 
    not_safe = not_safe_to_prune(model, dummy_input)
 
+.. _flops-counter:
+
 Model FLOPs/Parameters Counter
 ------------------------------
 
@@ -172,7 +174,7 @@ Usage
 
 .. code-block:: python
 
-   from nni.compression.pytorch.utils.counter import count_flops_params
+   from nni.compression.pytorch.utils import count_flops_params
 
    # Given input size (1, 1, 28, 28)
    flops, params, results = count_flops_params(model, (1, 1, 28, 28)) 
@@ -180,10 +182,10 @@ Usage
    # Given input tensor with size (1, 1, 28, 28) and switch to full mode
    x = torch.randn(1, 1, 28, 28)
 
-   flops, params, results = count_flops_params(model, (x,) mode='full') # tuple of tensor as input
+   flops, params, results = count_flops_params(model, (x,), mode='full') # tuple of tensor as input
 
    # Format output size to M (i.e., 10^6)
-   print(f'FLOPs: {flops/1e6:.3f}M,  Params: {params/1e6:.3f}M)
+   print(f'FLOPs: {flops/1e6:.3f}M,  Params: {params/1e6:.3f}M')
    print(results)
    {
    'conv': {'flops': [60], 'params': [20], 'weight_size': [(5, 3, 1, 1)], 'input_size': [(1, 3, 2, 2)], 'output_size': [(1, 5, 2, 2)], 'module_type': ['Conv2d']}, 
diff --git a/docs/source/compression/overview.rst b/docs/source/compression/overview.rst
new file mode 100644
index 000000000..261cff80b
--- /dev/null
+++ b/docs/source/compression/overview.rst
@@ -0,0 +1,74 @@
+Overview of NNI Model Compression
+=================================
+
+Deep neural networks (DNNs) have achieved great success in many tasks like computer vision, nature launguage processing, speech processing.
+However, typical neural networks are both computationally expensive and energy-intensive,
+which can be difficult to be deployed on devices with low computation resources or with strict latency requirements.
+Therefore, a natural thought is to perform model compression to reduce model size and accelerate model training/inference without losing performance significantly.
+Model compression techniques can be divided into two categories: pruning and quantization.
+The pruning methods explore the redundancy in the model weights and try to remove/prune the redundant and uncritical weights.
+Quantization refers to compress models by reducing the number of bits required to represent weights or activations.
+We further elaborate on the two methods, pruning and quantization, in the following chapters. Besides, the figure below visualizes the difference between these two methods.
+
+.. image:: ../../img/prune_quant.jpg
+   :target: ../../img/prune_quant.jpg
+   :scale: 40%
+   :align: center
+   :alt:
+
+NNI provides an easy-to-use toolkit to help users design and use model pruning and quantization algorithms.
+For users to compress their models, they only need to add several lines in their code.
+There are some popular model compression algorithms built-in in NNI.
+On the other hand, users could easily customize their new compression algorithms using NNI’s interface.
+
+There are several core features supported by NNI model compression:
+
+* Support many popular pruning and quantization algorithms.
+* Automate model pruning and quantization process with state-of-the-art strategies and NNI's auto tuning power.
+* Speedup a compressed model to make it have lower inference latency and also make it smaller.
+* Provide friendly and easy-to-use compression utilities for users to dive into the compression process and results.
+* Concise interface for users to customize their own compression algorithms.
+
+
+Compression Pipeline
+--------------------
+
+.. image:: ../../img/compression_pipeline.png
+   :target: ../../img/compression_pipeline.png
+   :alt:
+   :align: center
+   :scale: 30%
+
+The overall compression pipeline in NNI is shown above. For compressing a pretrained model, pruning and quantization can be used alone or in combination.
+If users want to apply both, a sequential mode is recommended as common practise.
+
+.. note::
+  Note that NNI pruners or quantizers are not meant to physically compact the model but for simulating the compression effect. Whereas NNI speedup tool can truly compress model by changing the network architecture and therefore reduce latency.
+  To obtain a truly compact model, users should conduct :doc:`pruning speedup <../tutorials/pruning_speedup>` or :doc:`quantizaiton speedup <../tutorials/quantization_speedup>`. 
+  The interface and APIs are unified for both PyTorch and TensorFlow. Currently only PyTorch version has been supported, and TensorFlow version will be supported in future.
+
+
+Model Speedup
+-------------
+
+The final goal of model compression is to reduce inference latency and model size.
+However, existing model compression algorithms mainly use simulation to check the performance (e.g., accuracy) of compressed model.
+For example, using masks for pruning algorithms, and storing quantized values still in float32 for quantization algorithms.
+Given the output masks and quantization bits produced by those algorithms, NNI can really speedup the model.
+
+The following figure shows how NNI prunes and speeds up your models. 
+
+.. image:: ../../img/nni_prune_process.png
+   :target: ../../img/nni_prune_process.png
+   :scale: 30%
+   :align: center
+   :alt:
+
+The detailed tutorial of Speedup Model with Mask can be found :doc:`here <../tutorials/pruning_speedup>`.
+The detailed tutorial of Speedup Model with Calibration Config can be found :doc:`here <../tutorials/quantization_speedup>`.
+
+.. attention::
+
+  NNI's model pruning framework has been upgraded to a more powerful version (named pruning v2 before nni v2.6).
+  The old version (`named pruning before nni v2.6 <https://nni.readthedocs.io/en/v2.6/Compression/pruning.html>`_) will be out of maintenance. If for some reason you have to use the old pruning,
+  v2.6 is the last nni version to support old pruning version.
diff --git a/docs/source/compression/overview_zh.rst b/docs/source/compression/overview_zh.rst
new file mode 100644
index 000000000..9763d1a67
--- /dev/null
+++ b/docs/source/compression/overview_zh.rst
@@ -0,0 +1,81 @@
+.. b6bdf52910e2e2c72085d03482d45340
+
+模型压缩
+========
+
+深度神经网络（DNNs）在计算机视觉、自然语言处理、语音处理等领域取得了巨大的成功。   
+然而，典型的神经网络是计算和能源密集型的，很难将其部署在计算资源匮乏
+或具有严格延迟要求的设备上。 因此，一个自然的想法就是对模型进行压缩，
+以减小模型大小并加速模型训练/推断，同时不会显着降低模型性能。 
+模型压缩技术可以分为两类：剪枝和量化。 剪枝方法探索模型权重中的冗余，
+并尝试删除/修剪冗余和非关键的权重。 量化是指通过减少权重表示或激活所需的比特数来压缩模型。
+在接下来的章节中，我们将进一步阐述这两种方法: 剪枝和量化。 
+此外，下图直观地展示了这两种方法的区别。  
+
+.. image:: ../../img/prune_quant.jpg
+   :target: ../../img/prune_quant.jpg
+   :scale: 40%
+   :alt:
+
+NNI 提供了易于使用的工具包来帮助用户设计并使用剪枝和量化算法。
+其使用了统一的接口来支持 TensorFlow 和 PyTorch。
+对用户来说， 只需要添加几行代码即可压缩模型。
+NNI 中也内置了一些主流的模型压缩算法。
+用户可以进一步利用 NNI 的自动调优功能找到最佳的压缩模型，
+该功能在自动模型压缩部分有详细介绍。
+另一方面，用户可以使用 NNI 的接口自定义新的压缩算法。
+
+
+NNI 具备以下几个核心特性:
+* 内置许多流行的剪枝和量化算法。
+* 利用最先进的策略和NNI的自动调整能力，来自动化模型剪枝和量化过程。
+* 加速模型，使其有更低的推理延迟。
+* 提供友好和易于使用的压缩工具，让用户深入到压缩过程和结果。
+* 简洁的界面，供用户自定义自己的压缩算法。
+
+压缩流程
+---------
+
+.. image:: ../../img/compression_pipeline.png
+   :target: ../../img/compression_pipeline.png
+   :alt:
+   :align: center
+   :scale: 30%
+
+NNI中模型压缩的整体流程如上图所示。
+为了压缩一个预先训练好的模型，可以单独或联合使用修剪和量化。
+如果用户希望同时应用这两种模式，建议采用串行模式。
+
+
+.. note::
+  值得注意的是，NNI的pruner或quantizer并不能改变网络结构，只能模拟压缩的效果。
+  真正能够压缩模型、改变网络结构、降低推理延迟的是NNI的加速工具。
+  为了获得一个真正的压缩的模型，用户需要执行 :doc:`剪枝加速 <../tutorials/pruning_speedup>` or :doc:`量化加速 <../tutorials/quantization_speedup>`. 
+  PyTorch和TensorFlow的接口都是统一的。目前只支持PyTorch版本，未来将支持TensorFlow版本。
+
+
+模型加速
+---------
+
+模型压缩的最终目标是减少推理延迟和模型大小。
+然而，现有的模型压缩算法主要是通过仿真来检测压缩模型的性能。
+例如，修剪算法使用掩码，量化算法仍将值存储在float32中。
+如果能给定这些算法产生的输出掩码和量化位，NNI的加速工具就可以真正地压缩模型。
+
+下图显示了NNI如何修剪和加速您的模型。
+
+.. image:: ../../img/nni_prune_process.png
+   :target: ../../img/nni_prune_process.png
+   :scale: 30%
+   :align: center
+   :alt:
+
+关于用掩码进行模型加速的详细文档可以参考 :doc:`here <../tutorials/pruning_speedup>`.
+关于用校准配置进行模型加速的详细文档可以参考 :doc:`here <../tutorials/quantization_speedup>`.
+
+
+.. attention::
+
+  NNI的模型剪枝框架已经升级到更高级的版本 (在 nni 2.6 版本前称为pruning v2)。
+  旧版本 (`named pruning before nni v2.6 <https://nni.readthedocs.io/en/v2.6/Compression/pruning.html>`_) 不再进行维护. 
+  如果出于某些原因您不得不使用，v2.6 是最后的支持旧版剪枝算法的版本。
diff --git a/docs/source/compression/pruner.rst b/docs/source/compression/pruner.rst
new file mode 100644
index 000000000..8f8703d72
--- /dev/null
+++ b/docs/source/compression/pruner.rst
@@ -0,0 +1,46 @@
+Pruner in NNI
+=============
+
+NNI implements the main part of the pruning algorithm as pruner. All pruners are implemented as close as possible to what is described in the paper (if it has).
+The following table provides a brief introduction to the pruners implemented in nni, click the link in table to view a more detailed introduction and use cases.
+
+There are two kinds of pruners in NNI, please refer to :ref:`basic pruner <basic-pruner>` and :ref:`scheduled pruner <scheduled-pruner>` for details.
+
+.. list-table::
+   :header-rows: 1
+   :widths: auto
+
+   * - Name
+     - Brief Introduction of Algorithm
+   * - :ref:`level-pruner`
+     - Pruning the specified ratio on each weight element based on absolute value of weight element
+   * - :ref:`l1-norm-pruner`
+     - Pruning output channels with the smallest L1 norm of weights (Pruning Filters for Efficient Convnets) `Reference Paper <https://arxiv.org/abs/1608.08710>`__
+   * - :ref:`l2-norm-pruner`
+     - Pruning output channels with the smallest L2 norm of weights
+   * - :ref:`fpgm-pruner`
+     - Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration `Reference Paper <https://arxiv.org/abs/1811.00250>`__
+   * - :ref:`slim-pruner`
+     - Pruning output channels by pruning scaling factors in BN layers(Learning Efficient Convolutional Networks through Network Slimming) `Reference Paper <https://arxiv.org/abs/1708.06519>`__
+   * - :ref:`activation-apoz-rank-pruner`
+     - Pruning output channels based on the metric APoZ (average percentage of zeros) which measures the percentage of zeros in activations of (convolutional) layers. `Reference Paper <https://arxiv.org/abs/1607.03250>`__
+   * - :ref:`activation-mean-rank-pruner`
+     - Pruning output channels based on the metric that calculates the smallest mean value of output activations
+   * - :ref:`taylor-fo-weight-pruner`
+     - Pruning filters based on the first order taylor expansion on weights(Importance Estimation for Neural Network Pruning) `Reference Paper <http://jankautz.com/publications/Importance4NNPruning_CVPR19.pdf>`__
+   * - :ref:`admm-pruner`
+     - Pruning based on ADMM optimization technique `Reference Paper <https://arxiv.org/abs/1804.03294>`__
+   * - :ref:`linear-pruner`
+     - Sparsity ratio increases linearly during each pruning rounds, in each round, using a basic pruner to prune the model.
+   * - :ref:`agp-pruner`
+     - Automated gradual pruning (To prune, or not to prune: exploring the efficacy of pruning for model compression) `Reference Paper <https://arxiv.org/abs/1710.01878>`__
+   * - :ref:`lottery-ticket-pruner`
+     - The pruning process used by "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks". It prunes a model iteratively. `Reference Paper <https://arxiv.org/abs/1803.03635>`__
+   * - :ref:`simulated-annealing-pruner`
+     - Automatic pruning with a guided heuristic search method, Simulated Annealing algorithm `Reference Paper <https://arxiv.org/abs/1907.03141>`__
+   * - :ref:`auto-compress-pruner`
+     - Automatic pruning by iteratively call SimulatedAnnealing Pruner and ADMM Pruner `Reference Paper <https://arxiv.org/abs/1907.03141>`__
+   * - :ref:`amc-pruner`
+     - AMC: AutoML for Model Compression and Acceleration on Mobile Devices `Reference Paper <https://arxiv.org/abs/1802.03494>`__
+   * - :ref:`movement-pruner`
+     - Movement Pruning: Adaptive Sparsity by Fine-Tuning `Reference Paper <https://arxiv.org/abs/2005.07683>`__
diff --git a/docs/source/compression/pruning.rst b/docs/source/compression/pruning.rst
new file mode 100644
index 000000000..038e8a47c
--- /dev/null
+++ b/docs/source/compression/pruning.rst
@@ -0,0 +1,108 @@
+Overview of NNI Model Pruning
+=============================
+
+Pruning is a common technique to compress neural network models.
+The pruning methods explore the redundancy in the model weights(parameters) and try to remove/prune the redundant and uncritical weights.
+The redundant elements are pruned from the model, their values are zeroed and we make sure they don't take part in the back-propagation process.
+
+The following concepts can help you understand pruning in NNI.
+
+Pruning Target
+--------------
+
+Pruning target means where we apply the sparsity.
+Most pruning methods prune the weights to reduce the model size and accelerate the inference latency.
+Other pruning methods also apply sparsity on activations (e.g., inputs, outputs, or feature maps) to accelerate the inference latency.
+NNI supports pruning module weights right now, and will support other pruning targets in the future.
+
+.. _basic-pruner:
+
+Basic Pruner
+------------
+
+Basic pruner generates the masks for each pruning target (weights) for a determined sparsity ratio.
+It usually takes model and config as input arguments, then generates masks for each pruning target.
+
+.. _scheduled-pruner:
+
+Scheduled Pruner
+----------------
+
+Scheduled pruner decides how to allocate sparsity ratio to each pruning target,
+it also handles the model speedup (after each pruning iteration) and finetuning logic.
+From the implementation logic, the scheduled pruner is a combination of pruning scheduler, basic pruner and task generator.
+
+Task generator only cares about the pruning effect that should be achieved in each round, and uses a config list to express how to pruning.
+Basic pruner will reset with the model and config list given by task generator then generate the masks.
+
+For a clearer structure vision, please refer to the figure below.
+
+.. image:: ../../img/pruning_process.png
+   :target: ../../img/pruning_process.png
+   :scale: 30%
+   :align: center
+   :alt:
+
+More information about scheduled pruning process please refer to :doc:`Pruning Scheduler <pruning_scheduler>`.
+
+Granularity
+-----------
+
+Fine-grained pruning or unstructured pruning refers to pruning each individual weights separately.
+Coarse-grained pruning or structured pruning is pruning a regular group of weights, such as a convolutional filter.
+
+Only :ref:`level-pruner` and :ref:`admm-pruner` support fine-grained pruning, all other pruners do some kind of structured pruning on weights.
+
+.. _dependency-awareode-for-output-channel-pruning:
+
+Dependency-aware Mode for Output Channel Pruning
+------------------------------------------------
+
+Currently, we support dependency-aware mode in several ``pruner``: :ref:`l1-norm-pruner`, :ref:`l2-norm-pruner`, :ref:`fpgm-pruner`,
+:ref:`activation-apoz-rank-pruner`, :ref:`activation-mean-rank-pruner`, :ref:`taylor-fo-weight-pruner`.
+
+In these pruning algorithms, the pruner will prune each layer separately. While pruning a layer,
+the algorithm will quantify the importance of each filter based on some specific metrics(such as l1 norm), and prune the less important output channels.
+
+We use pruning convolutional layers as an example to explain dependency-aware mode.
+As :ref:`topology analysis utils <topology-analysis>` shows, if the output channels of two convolutional layers(conv1, conv2) are added together,
+then these two convolutional layers have channel dependency with each other (more details please see :ref:`ChannelDependency <topology-analysis>`).
+Take the following figure as an example.
+
+.. image:: ../../img/mask_conflict.jpg
+   :target: ../../img/mask_conflict.jpg
+   :scale: 80%
+   :align: center
+   :alt: 
+
+If we prune the first 50% of output channels (filters) for conv1, and prune the last 50% of output channels for conv2.
+Although both layers have pruned 50% of the filters, the speedup module still needs to add zeros to align the output channels.
+In this case, we cannot harvest the speed benefit from the model pruning.
+
+To better gain the speed benefit of the model pruning, we add a dependency-aware mode for the ``Pruner`` that can prune the output channels.
+In the dependency-aware mode, the pruner prunes the model not only based on the metric of each output channels, but also the topology of the whole network architecture.
+
+In the dependency-aware mode (``dependency_aware`` is set ``True``), the pruner will try to prune the same output channels for the layers that have the channel dependencies with each other, as shown in the following figure.
+
+.. image:: ../../img/dependency-aware.jpg
+   :target: ../../img/dependency-aware.jpg
+   :scale: 80%
+   :align: center
+   :alt: 
+
+Take the dependency-aware mode of :ref:`l1-norm-pruner` as an example.
+Specifically, the pruner will calculate the L1 norm (for example) sum of all the layers in the dependency set for each channel.
+Obviously, the number of channels that can actually be pruned of this dependency set in the end is determined by the minimum sparsity of layers in this dependency set (denoted by ``min_sparsity``).
+According to the L1 norm sum of each channel, the pruner will prune the same ``min_sparsity`` channels for all the layers.
+Next, the pruner will additionally prune ``sparsity`` - ``min_sparsity`` channels for each convolutional layer based on its own L1 norm of each channel.
+For example, suppose the output channels of ``conv1``, ``conv2`` are added together and the configured sparsities of ``conv1`` and ``conv2`` are 0.3, 0.2 respectively.
+In this case, the ``dependency-aware pruner`` will 
+
+* First, prune the same 20% of channels for `conv1` and `conv2` according to L1 norm sum of `conv1` and `conv2`.
+* Second, the pruner will additionally prune 10% channels for `conv1` according to the L1 norm of each channel of `conv1`.
+
+In addition, for the convolutional layers that have more than one filter group,
+``dependency-aware pruner`` will also try to prune the same number of the channels for each filter group.
+Overall, this pruner will prune the model according to the L1 norm of each filter and try to meet the topological constrains (channel dependency, etc) to improve the final speed gain after the speedup process. 
+
+In the dependency-aware mode, the pruner will provide a better speed gain from the model pruning.
diff --git a/docs/source/Compression/v2_scheduler.rst b/docs/source/compression/pruning_scheduler.rst
similarity index 94%
rename from docs/source/Compression/v2_scheduler.rst
rename to docs/source/compression/pruning_scheduler.rst
index 1f37af836..a1ed93f20 100644
--- a/docs/source/Compression/v2_scheduler.rst
+++ b/docs/source/compression/pruning_scheduler.rst
@@ -35,14 +35,14 @@ Using AGP Pruning as an example to explain how to implement an iterative pruning
 
     pruner = L1NormPruner(model=None, config_list=None, mode='dependency_aware', dummy_input=torch.rand(10, 3, 224, 224).to(device))
     task_generator = AGPTaskGenerator(total_iteration=10, origin_model=model, origin_config_list=config_list, log_dir='.', keep_intermediate_result=True)
-    scheduler = PruningScheduler(pruner, task_generator, finetuner=finetuner, speed_up=True, dummy_input=dummy_input, evaluator=None, reset_weight=False)
+    scheduler = PruningScheduler(pruner, task_generator, finetuner=finetuner, speedup=True, dummy_input=dummy_input, evaluator=None, reset_weight=False)
 
     scheduler.compress()
     _, model, masks, _, _ = scheduler.get_best_result()
 
-The full script can be found :githublink:`here <examples/model_compress/pruning/v2/scheduler_torch.py>`.
+The full script can be found :githublink:`here <examples/model_compress/pruning/scheduler_torch.py>`.
 
-In this example, we use ``dependency_aware`` mode L1 Norm Pruner as a basic pruner during each iteration.
+In this example, we use dependency-aware mode L1 Norm Pruner as a basic pruner during each iteration.
 Note we do not need to pass ``model`` and ``config_list`` to the pruner, because in each iteration the ``model`` and ``config_list`` used by the pruner are received from the task generator.
 Then we can use ``scheduler`` as an iterative pruner directly. In fact, this is the implementation of ``AGPPruner`` in NNI.
 
@@ -56,7 +56,8 @@ The pruning result will return to the ``TaskGenerator`` at the end of each itera
 
 The information included in the ``Task`` and ``TaskResult`` can be found :githublink:`here <nni/algorithms/compression/v2/pytorch/base/scheduler.py>`.
 
-A clearer iterative pruning flow chart can be found `here <v2_pruning.rst>`__.
+A clearer iterative pruning flow chart can be found :doc:`here <pruning>`.
+
 
 If you want to implement your own task generator, please following the ``TaskGenerator`` :githublink:`interface <nni/algorithms/compression/v2/pytorch/pruning/tools/base.py>`.
 Two main functions should be implemented, ``init_pending_tasks(self) -> List[Task]`` and ``generate_tasks(self, task_result: TaskResult) -> List[Task]``.
diff --git a/docs/source/Compression/quantization.rst b/docs/source/compression/quantization.rst
similarity index 60%
rename from docs/source/Compression/quantization.rst
rename to docs/source/compression/quantization.rst
index d909415f2..53f35a5bb 100644
--- a/docs/source/Compression/quantization.rst
+++ b/docs/source/compression/quantization.rst
@@ -1,6 +1,5 @@
-#################
-Quantization
-#################
+Overview of NNI Model Quantization
+==================================
 
 Quantization refers to compressing models by reducing the number of bits required to represent weights or activations,
 which can reduce the computations and the inference time. In the context of deep neural networks, the major numerical
@@ -8,11 +7,5 @@ format for model weights is 32-bit float, or FP32. Many research works have demo
 can be represented using 8-bit integers without significant loss in accuracy. Even lower bit-widths, such as 4/2/1 bits,
 is an active field of research.
 
-A quantizer is a quantization algorithm implementation in NNI, NNI provides multiple quantizers as below. You can also
-create your own quantizer using NNI model compression interface.
-
-..  toctree::
-    :maxdepth: 2
-
-    Quantizers <Quantizer>
-    Quantization Speedup <QuantizationSpeedup>
+A quantizer is a quantization algorithm implementation in NNI.
+You can also :doc:`create your own quantizer <../tutorials/quantization_customize>` using NNI model compression interface.
diff --git a/docs/source/compression/quantizer.rst b/docs/source/compression/quantizer.rst
new file mode 100644
index 000000000..50bde2fae
--- /dev/null
+++ b/docs/source/compression/quantizer.rst
@@ -0,0 +1,24 @@
+Quantizer in NNI
+================
+
+NNI implements the main part of the quantizaiton algorithm as quantizer. All quantizers are implemented as close as possible to what is described in the paper (if it has).
+The following table provides a brief introduction to the quantizers implemented in nni, click the link in table to view a more detailed introduction and use cases.
+
+.. list-table::
+   :header-rows: 1
+   :widths: auto
+
+   * - Name
+     - Brief Introduction of Algorithm
+   * - :ref:`naive-quantizer`
+     - Quantize weights to default 8 bits
+   * - :ref:`qat-quantizer`
+     - Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference. `Reference Paper <http://openaccess.thecvf.com/content_cvpr_2018/papers/Jacob_Quantization_and_Training_CVPR_2018_paper.pdf>`__
+   * - :ref:`dorefa-quantizer`
+     - DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients. `Reference Paper <https://arxiv.org/abs/1606.06160>`__
+   * - :ref:`bnn-quantizer`
+     - Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1. `Reference Paper <https://arxiv.org/abs/1602.02830>`__
+   * - :ref:`lsq-quantizer`
+     - Learned step size quantization. `Reference Paper <https://arxiv.org/pdf/1902.08153.pdf>`__
+   * - :ref:`observer-quantizer`
+     - Post training quantizaiton. Collect quantization information during calibration with observers.
diff --git a/docs/source/compression/toctree.rst b/docs/source/compression/toctree.rst
new file mode 100644
index 000000000..db179e06d
--- /dev/null
+++ b/docs/source/compression/toctree.rst
@@ -0,0 +1,12 @@
+Compression
+===========
+
+.. toctree::
+    :hidden:
+    :maxdepth: 2
+
+    Overview <overview>
+    Pruning <toctree_pruning>
+    Quantization <toctree_quantization>
+    Config Specification <compression_config_list>
+    Advanced Usage <advanced_usage>
diff --git a/docs/source/compression/toctree_pruning.rst b/docs/source/compression/toctree_pruning.rst
new file mode 100644
index 000000000..bc5f61c34
--- /dev/null
+++ b/docs/source/compression/toctree_pruning.rst
@@ -0,0 +1,11 @@
+Pruning
+=======
+
+.. toctree::
+    :hidden:
+    :maxdepth: 2
+
+    Overview <pruning>
+    Quickstart </tutorials/pruning_quick_start_mnist>
+    Pruner <pruner>
+    Speedup </tutorials/pruning_speedup>
diff --git a/docs/source/compression/toctree_quantization.rst b/docs/source/compression/toctree_quantization.rst
new file mode 100644
index 000000000..7b1bcecce
--- /dev/null
+++ b/docs/source/compression/toctree_quantization.rst
@@ -0,0 +1,11 @@
+Quantization
+============
+
+.. toctree::
+    :hidden:
+    :maxdepth: 2
+
+    Overview <quantization>
+    Quickstart </tutorials/quantization_quick_start_mnist>
+    Quantizer <quantizer>
+    SpeedUp </tutorials/quantization_speedup>
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 5dc8797a4..b812ec348 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -13,6 +13,7 @@
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 #
 import os
+import re
 import subprocess
 import sys
 sys.path.insert(0, os.path.abspath('../..'))
@@ -30,7 +31,7 @@ author = 'Microsoft'
 version = ''
 # The full version, including alpha/beta/rc tags
 # FIXME: this should be written somewhere globally
-release = 'v2.6'
+release = 'v2.7'
 
 # -- General configuration ---------------------------------------------------
 
@@ -44,6 +45,8 @@ release = 'v2.6'
 extensions = [
     'sphinx_gallery.gen_gallery',
     'sphinx.ext.autodoc',
+    'sphinx.ext.autosummary',
+    'sphinx.ext.intersphinx',
     'sphinx.ext.mathjax',
     'sphinxarg4nni.ext',
     'sphinx.ext.napoleon',
@@ -53,15 +56,64 @@ extensions = [
     # 'nbsphinx',  # nbsphinx has conflicts with sphinx-gallery.
     'sphinx.ext.extlinks',
     'IPython.sphinxext.ipython_console_highlighting',
+    'sphinx_tabs.tabs',
+    'sphinx_copybutton',
 
     # Custom extensions in extension/ folder.
+    'tutorial_links',  # this has to be after sphinx-gallery
+    'getpartialtext',
     'inplace_translation',
     'cardlinkitem',
-    'patch_docutils',
+    'codesnippetcard',
+    'patch_autodoc',
+    'toctree_check',
 ]
 
+# Autosummary related settings
+autosummary_imported_members = True
+autosummary_ignore_module_all = False
+
+# Auto-generate stub files before building docs
+autosummary_generate = True
+
 # Add mock modules
-autodoc_mock_imports = ['apex', 'nni_node', 'tensorrt', 'pycuda', 'nn_meter']
+autodoc_mock_imports = [
+    'apex', 'nni_node', 'tensorrt', 'pycuda', 'nn_meter', 'azureml',
+    'ConfigSpace', 'ConfigSpaceNNI', 'smac', 'statsmodels', 'pybnn',
+]
+
+# Some of our modules cannot generate summary
+autosummary_mock_imports = [
+    'nni.retiarii.codegen.tensorflow',
+    'nni.nas.benchmarks.nasbench101.db_gen',
+    'nni.tools.jupyter_extension.management',
+] + autodoc_mock_imports
+
+autodoc_typehints = 'description'
+autodoc_typehints_description_target = 'documented'
+autodoc_inherit_docstrings = False
+
+# Sphinx will warn about all references where the target cannot be found.
+nitpicky = False  # disabled for now
+
+# A list of regular expressions that match URIs that should not be checked.
+linkcheck_ignore = [
+    r'http://localhost:\d+',
+    r'.*://.*/#/',                          # Modern websites that has URLs like xxx.com/#/guide
+    r'https://github.com/JSong-Jia/Pic/',   # Community links can't be found any more
+]
+
+# Ignore all links located in release.rst
+linkcheck_exclude_documents = ['^release']
+
+# Bibliography files
+bibtex_bibfiles = ['refs.bib']
+
+# Add a heading to bibliography
+bibtex_footbibliography_header = '.. rubric:: Bibliography'
+
+# Set bibliography style
+bibtex_default_style = 'plain'
 
 # Bibliography files
 bibtex_bibfiles = ['refs.bib']
@@ -86,6 +138,41 @@ sphinx_gallery_conf = {
     'default_thumb_file': os.path.join(os.path.dirname(__file__), '../img/thumbnails/nni_icon_blue.png'),
 }
 
+# Copybutton: strip and configure input prompts for code cells.
+copybutton_prompt_text = r">>> |\.\.\. |\$ |In \[\d*\]: | {2,5}\.\.\.: | {5,8}: "
+copybutton_prompt_is_regexp = True
+
+# Copybutton: customize selector to exclude gallery outputs.
+copybutton_selector = ":not(div.sphx-glr-script-out) > div.highlight pre"
+
+# Allow additional builders to be considered compatible.
+sphinx_tabs_valid_builders = ['linkcheck']
+
+# Disallow the sphinx tabs css from loading.
+sphinx_tabs_disable_css_loading = True
+
+# Some tutorials might need to appear more than once in toc.
+# In this list, we make source/target tutorial pairs.
+# Each "source" tutorial rst will be copied to "target" tutorials.
+# The anchors will be replaced to avoid dupilcate labels.
+# Target should start with ``cp_`` to be properly ignored in git.
+tutorials_copy_list = [
+    # Seems that we don't need it for now.
+    # Add tuples back if we need it in future.
+]
+
+# Toctree ensures that toctree docs do not contain any other contents.
+# Home page should be an exception.
+toctree_check_whitelist = [
+    'index',
+
+    # FIXME: Other exceptions should be correctly handled.
+    'compression/index',
+    'compression/pruning',
+    'compression/quantization',
+    'hpo/hpo_benchmark',
+]
+
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ['../templates']
 
@@ -103,6 +190,18 @@ master_doc = 'index'
 # Usually you set "language" from the command line for these cases.
 language = None
 
+# Translation related settings
+locale_dir = ['locales']
+
+# Documents that requires translation: https://github.com/microsoft/nni/issues/4298
+gettext_documents = [
+    r'^index$',
+    r'^quickstart$',
+    r'^installation$',
+    r'^(nas|hpo|compression)/overview$',
+    r'^tutorials/(hello_nas|pruning_quick_start_mnist|hpo_quickstart_pytorch/main)$',
+]
+
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
 # This pattern also affects html_static_path and html_extra_path.
@@ -110,7 +209,6 @@ exclude_patterns = [
     '_build',
     'Thumbs.db',
     '.DS_Store',
-    'Release_v1.0.md',
     '**.ipynb_checkpoints',
     # Exclude translations. They will be added back via replacement later if language is set.
     '**_zh.rst',
@@ -151,17 +249,20 @@ html_theme_options = {
     'base_url': 'https://nni.readthedocs.io/',
 
     # Set the color and the accent color
-    # We can't have our customized themes currently
     # Remember to update static/css/material_custom.css when this is updated.
-    'color_primary': 'indigo',
-    'color_accent': 'pink',
+    # Set those colors in layout.html.
+    'color_primary': 'custom',
+    'color_accent': 'custom',
 
     # Set the repo location to get a badge with stats
     'repo_url': 'https://github.com/microsoft/nni/',
-    'repo_name': 'nni',
+    'repo_name': 'GitHub',
 
     # Visible levels of the global TOC; -1 means unlimited
-    'globaltoc_depth': 3,
+    'globaltoc_depth': 5,
+
+    # Expand all toc so that they can be dynamically collapsed
+    'globaltoc_collapse': False,
 
     'version_dropdown': True,
     # This is a placeholder, which should be replaced later.
@@ -171,8 +272,8 @@ html_theme_options = {
 
     # Text to appear at the top of the home page in a "hero" div.
     'heroes': {
-        # We can have heroes for the home pages of HPO, NAS, Compression in future.
-        'index': 'An open source AutoML toolkit for neural architecture search, model compression and hyper-parameter tuning.'
+        'index': 'An open source AutoML toolkit for hyperparameter optimization, neural architecture search, '
+                 'model compression and feature engineering.'
     }
 }
 
@@ -200,6 +301,7 @@ html_title = 'Neural Network Intelligence'
 
 # Add extra css files and js files
 html_css_files = [
+    'css/material_theme.css',
     'css/material_custom.css',
     'css/material_dropdown.css',
     'css/sphinx_gallery.css',
@@ -209,6 +311,7 @@ html_js_files = [
     'js/version.js',
     'js/github.js',
     'js/sphinx_gallery.js',
+    'js/misc.js'
 ]
 
 # HTML context that can be used in jinja templates
diff --git a/docs/source/contribution.rst b/docs/source/contribution.rst
deleted file mode 100644
index 6131b37a8..000000000
--- a/docs/source/contribution.rst
+++ /dev/null
@@ -1,7 +0,0 @@
-###############################
-Contribute to NNI
-###############################
-
-..  toctree::
-    Development Setup<./Tutorial/SetupNniDeveloperEnvironment>
-    Contribution Guide<./Tutorial/Contributing>
\ No newline at end of file
diff --git a/docs/source/contribution_zh.rst b/docs/source/contribution_zh.rst
deleted file mode 100644
index 71584e9fb..000000000
--- a/docs/source/contribution_zh.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-.. 24da49b25d3d36c476a69aceb825cb94
-
-###############################
-贡献代码
-###############################
-
-..  toctree::
-    设置开发环境<./Tutorial/SetupNniDeveloperEnvironment>
-    贡献指南<./Tutorial/Contributing>
\ No newline at end of file
diff --git a/docs/source/examples.rst b/docs/source/examples.rst
index c0e820c01..1d45863ea 100644
--- a/docs/source/examples.rst
+++ b/docs/source/examples.rst
@@ -1,12 +1,76 @@
-######################
 Examples
-######################
+========
 
-..  toctree::
-    :maxdepth: 2
+More examples can be found in our :githublink:`GitHub repository <examples>`.
 
-    MNIST<./TrialExample/MnistExamples>
-    Cifar10<./TrialExample/Cifar10Examples>
-    Scikit-learn<./TrialExample/SklearnExamples>
-    GBDT<./TrialExample/GbdtExample>
-    Pix2pix<./TrialExample/Pix2pixExample>
+.. cardlinkitem::
+   :header: HPO Quickstart with PyTorch
+   :description: Use HPO to tune a PyTorch FashionMNIST model
+   :link: tutorials/hpo_quickstart_pytorch/main
+   :image: ../img/thumbnails/hpo-pytorch.svg
+   :background: purple
+   :tags: HPO
+
+.. cardlinkitem::
+   :header: HPO Quickstart with TensorFlow
+   :description: Use HPO to tune a TensorFlow MNIST model
+   :link: tutorials/hpo_quickstart_tensorflow/main
+   :image: ../img/thumbnails/hpo-tensorflow.svg
+   :background: purple
+   :tags: HPO
+
+.. cardlinkitem::
+   :header: HPO using command line tool
+   :description: Run HPO experiment with nnictl
+   :link: tutorials/hpo_nnictl/nnictl
+   :image: ../img/thumbnails/hpo-pytorch.svg
+   :background: purple
+   :tags: HPO
+
+.. cardlinkitem::
+   :header: Hello, NAS!
+   :description: Beginners' NAS tutorial on how to search for neural architectures for MNIST dataset.
+   :link: tutorials/hello_nas
+   :image: ../img/thumbnails/nas-tutorial.svg
+   :background: cyan
+   :tags: NAS
+
+.. cardlinkitem::
+   :header: Use NAS Benchmarks as Datasets
+   :description: Query data from popular NAS benchmarks from our preprocessed benchmark database.
+   :link: tutorials/nasbench_as_dataset
+   :image: ../img/thumbnails/nas-benchmark.svg
+   :background: cyan
+   :tags: NAS
+
+.. cardlinkitem::
+   :header: Get Started with Model Pruning on MNIST
+   :description: Familiarize yourself with pruning to compress your model 
+   :link: tutorials/pruning_quick_start_mnist
+   :image: ../img/thumbnails/pruning-tutorial.svg
+   :background: blue
+   :tags: Compression
+
+.. cardlinkitem::
+   :header: Get Started with Model Quantization on MNIST
+   :description: Familiarize yourself with quantization to compress your model
+   :link: tutorials/quantization_quick_start_mnist
+   :image: ../img/thumbnails/quantization-tutorial.svg
+   :background: indigo
+   :tags: Compression
+
+.. cardlinkitem::
+   :header: Speedup Model with Mask
+   :description: Make your model real smaller and faster with speed-up after pruned by pruner
+   :link: tutorials/pruning_speedup
+   :image: ../img/thumbnails/pruning-speed-up.svg
+   :background: blue
+   :tags: Compression
+
+.. cardlinkitem::
+   :header: Speedup Model with Calibration Config
+   :description: Make your model real smaller and faster with speed-up after quantized by quantizer
+   :link: tutorials/quantization_speedup
+   :image: ../img/thumbnails/quantization-speed-up.svg
+   :background: indigo
+   :tags: Compression
diff --git a/docs/source/examples_zh.rst b/docs/source/examples_zh.rst
deleted file mode 100644
index c51da740a..000000000
--- a/docs/source/examples_zh.rst
+++ /dev/null
@@ -1,14 +0,0 @@
-.. d19a00598b8eca71c825d80c0a7106f2
-
-######################
-示例
-######################
-
-..  toctree::
-    :maxdepth: 2
-
-    MNIST<./TrialExample/MnistExamples>
-    Cifar10<./TrialExample/Cifar10Examples>
-    Scikit-learn<./TrialExample/SklearnExamples>
-    GBDT<./TrialExample/GbdtExample>
-    Pix2pix<./TrialExample/Pix2pixExample>
\ No newline at end of file
diff --git a/docs/source/experiment/experiment_management.rst b/docs/source/experiment/experiment_management.rst
new file mode 100644
index 000000000..feac35481
--- /dev/null
+++ b/docs/source/experiment/experiment_management.rst
@@ -0,0 +1,14 @@
+Experiment Management
+=====================
+
+An experiment can be created with command line tool ``nnictl`` or python APIs. NNI provides both command line tool ``nnictl`` and web Portal to manage the experiments, such as, creating, stopping, resuming, deleting, ranking, and comparing the experiments.
+
+Management with ``nnictl``
+--------------------------
+
+The ability of ``nnictl`` on experiment management is almost equivalent to :doc:`web_portal/web_portal`. Users can refer to :doc:`../reference/nnictl` for detailed usage. It is highly suggested when visualization is not well supported in your environment (e.g., web browser is not supported in your environment).
+
+Management with web portal
+--------------------------
+
+Experiment management on web potral gives an quick overview of all the experiment on users' machine. Users can easily switch to one experiment from this page. Users can refer to the :ref:`exp-manage-webportal` page for details. The experiment management on web portal is still under intensive development to bring more user-friendly features.
\ No newline at end of file
diff --git a/docs/source/experiment/overview.rst b/docs/source/experiment/overview.rst
new file mode 100644
index 000000000..c24738856
--- /dev/null
+++ b/docs/source/experiment/overview.rst
@@ -0,0 +1,18 @@
+Overview of NNI Experiment
+==========================
+
+An NNI experiment is a unit of one tuning process. For example, it is one run of hyper-parameter tuning on a specific search space, it is one run of neural architecture search on a search space, or it is one run of automatic model compression on user specified goal on latency and accuracy. Usually, the tuning process requires many trials to explore feasible and potentially good-performing models. Thus, an important component of NNI experiment is **training service**, which is a unified interface to abstract diverse computation resources (e.g., local machine, remote servers, AKS). Users can easily run the tuning process on their prefered computation resource and platform. On the other hand, NNI experiment provides **WebUI** to visualize the tuning process to users.
+
+During developing a DNN model, users need to manage the tuning process, such as, creating an experiment, adjusting an experiment, kill or rerun a trial in an experiment, dumping experiment data for customized analysis. Also, users may create a new experiment for comparison, or concurrently for new model developing tasks. Thus, NNI provides the functionality of **experiment management**. Users can use :doc:`../reference/nnictl` to interact with experiments.
+
+The relation of the components in NNI experiment is illustrated in the following figure. Hyper-parameter optimization (HPO), neural architecture search (NAS), and model compression are three key features in NNI that help users develop and tune their models. Training serivce provides the ability of parallel running trials on available computation resources. WebUI visualizes the tuning process. *nnictl* is for managing the experiments.
+
+.. image:: ../../img/experiment_arch.png
+   :scale: 80 %
+   :align: center
+
+Before reading the following content, you are recommended to go through either :doc:`the quickstart of HPO </tutorials/hpo_quickstart_pytorch/main>` or :doc:`quickstart of NAS </tutorials/hello_nas>` first.
+
+* :doc:`Overview of NNI training service <training_service/overview>`
+* :doc:`Introduction to Web Portal <web_portal/web_portal>`
+* :doc:`Manange Multiple Experiments <experiment_management>`
diff --git a/docs/source/experiment/toctree.rst b/docs/source/experiment/toctree.rst
new file mode 100644
index 000000000..172f08ea1
--- /dev/null
+++ b/docs/source/experiment/toctree.rst
@@ -0,0 +1,10 @@
+Experiment
+==========
+
+..  toctree::
+    :maxdepth: 2
+
+    Overview <overview>
+    Training Service <training_service/toctree>
+    Web Portal <web_portal/toctree>
+    Experiment Management <experiment_management>
diff --git a/docs/source/TrainingService/AdaptDLMode.rst b/docs/source/experiment/training_service/adaptdl.rst
similarity index 71%
rename from docs/source/TrainingService/AdaptDLMode.rst
rename to docs/source/experiment/training_service/adaptdl.rst
index b7f844a3c..17ee97e45 100644
--- a/docs/source/TrainingService/AdaptDLMode.rst
+++ b/docs/source/experiment/training_service/adaptdl.rst
@@ -1,83 +1,86 @@
-Run an Experiment on AdaptDL
-============================
-
-Now NNI supports running experiment on `AdaptDL <https://github.com/petuum/adaptdl>`__. Before starting to use NNI AdaptDL mode, you should have a Kubernetes cluster, either on-premises or `Azure Kubernetes Service(AKS) <https://azure.microsoft.com/en-us/services/kubernetes-service/>`__\ , a Ubuntu machine on which `kubeconfig <https://kubernetes.io/docs/concepts/configuration/organize-cluster-access-kubeconfig/>`__ is setup to connect to your Kubernetes cluster. In AdaptDL mode, your trial program will run as AdaptDL job in Kubernetes cluster.
+AdaptDL Training Service
+========================
 
+Now NNI supports running experiment on `AdaptDL <https://github.com/petuum/adaptdl>`__, which is a resource-adaptive deep learning training and scheduling framework. With AdaptDL training service, your trial program will run as AdaptDL job in Kubernetes cluster.
 AdaptDL aims to make distributed deep learning easy and efficient in dynamic-resource environments such as shared clusters and the cloud.
 
-Prerequisite for Kubernetes Service
------------------------------------
+.. note:: AdaptDL doesn't support :ref:`reuse mode <training-service-reuse>`.
+
+Prerequisite
+------------
 
+Before starting to use NNI AdaptDL training service, you should have a Kubernetes cluster, either on-premises or `Azure Kubernetes Service(AKS) <https://azure.microsoft.com/en-us/services/kubernetes-service/>`__\ , a Ubuntu machine on which `kubeconfig <https://kubernetes.io/docs/concepts/configuration/organize-cluster-access-kubeconfig/>`__ is setup to connect to your Kubernetes cluster.
 
 #. A **Kubernetes** cluster using Kubernetes 1.14 or later with storage. Follow this guideline to set up Kubernetes `on Azure <https://azure.microsoft.com/en-us/services/kubernetes-service/>`__\ , or `on-premise <https://kubernetes.io/docs/setup/>`__ with `cephfs <https://kubernetes.io/docs/concepts/storage/storage-classes/#ceph-rbd>`__\ , or `microk8s with storage add-on enabled <https://microk8s.io/docs/addons>`__.
 #. Helm install **AdaptDL Scheduler** to your Kubernetes cluster. Follow this `guideline <https://adaptdl.readthedocs.io/en/latest/installation/install-adaptdl.html>`__ to setup AdaptDL scheduler.
-#. Prepare a **kubeconfig** file, which will be used by NNI to interact with your Kubernetes API server. By default, NNI manager will use ``$(HOME)/.kube/config`` as kubeconfig file's path. You can also specify other kubeconfig files by setting the ** KUBECONFIG** environment variable. Refer this `guideline <https://kubernetes.io/docs/concepts/configuration/organize-cluster-access-kubeconfig>`__ to learn more about kubeconfig.
+#. Prepare a **kubeconfig** file, which will be used by NNI to interact with your Kubernetes API server. By default, NNI manager will use ``$(HOME)/.kube/config`` as kubeconfig file's path. You can also specify other kubeconfig files by setting the **KUBECONFIG** environment variable. Refer this `guideline <https://kubernetes.io/docs/concepts/configuration/organize-cluster-access-kubeconfig>`__ to learn more about kubeconfig.
 #. If your NNI trial job needs GPU resource, you should follow this `guideline <https://github.com/NVIDIA/k8s-device-plugin>`__ to configure **Nvidia device plugin for Kubernetes**.
 #. (Optional) Prepare a **NFS server** and export a general purpose mount as external storage.
-#. Install **NNI**\ , follow the install guide `here <../Tutorial/QuickStart.rst>`__.
+#. Install **NNI**.
 
-Verify Prerequisites
-^^^^^^^^^^^^^^^^^^^^
+Verify the Prerequisites
+^^^^^^^^^^^^^^^^^^^^^^^^
 
-.. code-block:: bash
+..  code-block:: bash
 
-   nnictl --version
-   # Expected: <version_number>
+    nnictl --version
+    # Expected: <version_number>
 
-.. code-block:: bash
+..  code-block:: bash
 
-   kubectl version
-   # Expected that the kubectl client version matches the server version.
+    kubectl version
+    # Expected that the kubectl client version matches the server version.
 
-.. code-block:: bash
+..  code-block:: bash
 
-   kubectl api-versions | grep adaptdl
-   # Expected: adaptdl.petuum.com/v1
+    kubectl api-versions | grep adaptdl
+    # Expected: adaptdl.petuum.com/v1
 
-Run an experiment
------------------
+Usage
+-----
 
-We have a CIFAR10 example that fully leverages the AdaptDL scheduler under ``examples/trials/cifar10_pytorch`` folder. (\ ``main_adl.py`` and ``config_adl.yaml``\ )
+We have a CIFAR10 example that fully leverages the AdaptDL scheduler under :githublink:`examples/trials/cifar10_pytorch` folder. (:githublink:`main_adl.py <examples/trials/cifar10_pytorch/main_adl.py>` and :githublink:`config_adl.yaml <examples/trials/cifar10_pytorch/config_adl.yml>`)
 
 Here is a template configuration specification to use AdaptDL as a training service.
 
-.. code-block:: yaml
-
-   authorName: default
-   experimentName: minimal_adl
-
-   trainingServicePlatform: adl
-   nniManagerIp: 10.1.10.11
-   logCollection: http
-
-   tuner:
-     builtinTunerName: GridSearch
-   searchSpacePath: search_space.json
-
-   trialConcurrency: 2
-   maxTrialNum: 2
-
-   trial:
-     adaptive: false # optional.
-     image: <image_tag>
-     imagePullSecrets:  # optional
-       - name: stagingsecret
-     codeDir: .
-     command: python main.py
-     gpuNum: 1
-     cpuNum: 1  # optional
-     memorySize: 8Gi  # optional
-     nfs: # optional
-       server: 10.20.41.55
-       path: /
-       containerMountPath: /nfs
-     checkpoint: # optional
-       storageClass: dfs
-       storageSize: 1Gi
-
-Those configs not mentioned below, are following the
-`default specs defined </Tutorial/ExperimentConfig.rst#configuration-spec>`__  in the NNI doc.
-
+..  code-block:: yaml
+
+    authorName: default
+    experimentName: minimal_adl
+
+    trainingServicePlatform: adl
+    nniManagerIp: 10.1.10.11
+    logCollection: http
+
+    tuner:
+      builtinTunerName: GridSearch
+    searchSpacePath: search_space.json
+
+    trialConcurrency: 2
+    maxTrialNum: 2
+
+    trial:
+      adaptive: false # optional.
+      image: <image_tag>
+      imagePullSecrets:  # optional
+        - name: stagingsecret
+      codeDir: .
+      command: python main.py
+      gpuNum: 1
+      cpuNum: 1  # optional
+      memorySize: 8Gi  # optional
+      nfs: # optional
+        server: 10.20.41.55
+        path: /
+        containerMountPath: /nfs
+      checkpoint: # optional
+        storageClass: dfs
+        storageSize: 1Gi
+
+..  warning::
+    This configuration is written following the specification of `legacy experiment configuration <https://nni.readthedocs.io/en/v2.6/Tutorial/ExperimentConfig.html>`__. It is still supported, and will be updated to the latest version in future release.
+
+The following explains the configuration fields of AdaptDL training service.
 
 * **trainingServicePlatform**\ : Choose ``adl`` to use the Kubernetes cluster with AdaptDL scheduler.
 * **nniManagerIp**\ : *Required* to get the correct info and metrics back from the cluster, for ``adl`` training service.
@@ -103,6 +106,9 @@ Those configs not mentioned below, are following the
     * **storageClass**\ : check `Kubernetes storage documentation <https://kubernetes.io/docs/concepts/storage/storage-classes/>`__ for how to use the appropriate ``storageClass``.
     * **storageSize**\ : this value should be large enough to fit your model's checkpoints, or it could cause "disk quota exceeded" error.
 
+More Features
+-------------
+
 NFS Storage
 ^^^^^^^^^^^
 
@@ -121,7 +127,6 @@ The ``adl`` training service can then mount it to the kubernetes for every trial
 
 Use cases:
 
-
 * If your training trials depend on a dataset of large size, you may want to download it first onto the NFS first,
   and mount it so that it can be shared across multiple trials.
 * The storage for containers are ephemeral and the trial containers will be deleted after a trial's lifecycle is over.
@@ -131,17 +136,17 @@ Use cases:
 In short, it is not limited how a trial wants to read from or write on the NFS storage, so you may use it flexibly as per your needs.
 
 Monitor via Log Stream
-----------------------
+^^^^^^^^^^^^^^^^^^^^^^
 
 Follow the log streaming of a certain trial:
 
 .. code-block:: bash
 
-   nnictl log trial --trial_id=<trial_id>
+   nnictl log trial --trial_id=TRIAL_ID
 
 .. code-block:: bash
 
-   nnictl log trial <experiment_id> --trial_id=<trial_id>
+   nnictl log trial EXPERIMENT_ID --trial_id=TRIAL_ID
 
 Note that *after* a trial has done and its pod has been deleted,
 no logs can be retrieved then via this command.
@@ -149,7 +154,7 @@ However you may still be able to access the past trial logs
 according to the following approach.
 
 Monitor via TensorBoard
------------------------
+^^^^^^^^^^^^^^^^^^^^^^^
 
 In the context of NNI, an experiment has multiple trials.
 For easy comparison across trials for a model tuning process,
@@ -192,7 +197,7 @@ If having multiple experiment running at the same time, you may use
 
 .. code-block:: bash
 
-   nnictl tensorboard start <experiment_id>
+   nnictl tensorboard start EXPERIMENT_ID
 
 It will provide you the web url to access the tensorboard.
 
diff --git a/docs/source/experiment/training_service/aml.rst b/docs/source/experiment/training_service/aml.rst
new file mode 100644
index 000000000..4a242b627
--- /dev/null
+++ b/docs/source/experiment/training_service/aml.rst
@@ -0,0 +1,79 @@
+AML Training Service
+====================
+
+To run your trials on `AzureML <https://azure.microsoft.com/en-us/services/machine-learning/>`__, you can use AML training service. AML training service can programmatically submit runs to AzureML platform and collect their metrics.
+
+Prerequisite
+------------
+
+1. Create an Azure account/subscription using this `link <https://azure.microsoft.com/en-us/free/services/machine-learning/>`__. If you already have an Azure account/subscription, skip this step.
+2. Install the Azure CLI on your machine, follow the install guide `here <https://docs.microsoft.com/en-us/cli/azure/install-azure-cli?view=azure-cli-latest>`__.
+3. Authenticate to your Azure subscription from the CLI. To authenticate interactively, open a command line or terminal and use the following command:
+
+   .. code-block:: bash
+
+      az login
+
+4. Log into your Azure account with a web browser and create a Machine Learning resource. You will need to choose a resource group and specific a workspace name. Then download ``config.json`` which will be used later.
+
+   .. image:: ../../../img/aml_workspace.png
+
+5. Create an AML cluster as the compute target.
+
+   .. image:: ../../../img/aml_cluster.png
+
+6. Open a command line and install AML package environment.
+
+   .. code-block:: bash
+
+      python3 -m pip install azureml
+      python3 -m pip install azureml-sdk
+
+Usage
+-----
+
+We show an example configuration here with YAML (Python configuration should be similar).
+
+.. code-block:: yaml
+
+   trialConcurrency: 1
+   maxTrialNumber: 10
+   ...
+   trainingService:
+     platform: aml
+     dockerImage: msranni/nni
+     subscriptionId: ${your subscription ID}
+     resourceGroup: ${your resource group}
+     workspaceName: ${your workspace name}
+     computeTarget: ${your compute target}
+
+Configuration References
+------------------------
+
+Compared with :doc:`local` and :doc:`remote`, OpenPAI training service supports the following additional configurations.
+
+.. list-table::
+   :header-rows: 1
+   :widths: auto
+
+   * - Field name
+     - Description
+   * - dockerImage
+     - Required field. The docker image name used in job. If you don't want to build your own, NNI has provided a docker image `msranni/nni <https://hub.docker.com/r/msranni/nni>`__, which is up-to-date with every NNI release.
+   * - subscriptionId
+     - Required field. The subscription id of your account, can be found in ``config.json`` described above.
+   * - resourceGroup
+     - Required field. The resource group of your account, can be found in ``config.json`` described above.
+   * - workspaceName
+     - Required field. The workspace name of your account, can be found in ``config.json`` described above.
+   * - computeTarget
+     - Required field. The compute cluster name you want to use in your AML workspace. See `reference <https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target>`__ and Step 5 above.
+   * - maxTrialNumberPerGpu
+     - Optional field. Default 1. Used to specify the max concurrency trial number on a GPU device.
+   * - useActiveGpu
+     - Optional field. Default false. Used to specify whether to use a GPU if there is another process. By default, NNI will use the GPU only if there is no other active process in the GPU. See :doc:`local` for details.
+
+Monitor your trial on the cloud by using AML studio
+---------------------------------------------------
+
+To see your trial job's detailed status on the cloud, you need to visit your studio which you create at Step 5 above. Once the job completes, go to the **Outputs + logs** tab. There you can see a ``70_driver_log.txt`` file, This file contains the standard output from a run and can be useful when you're debugging remote runs in the cloud. Learn more about aml from `here <https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-1st-experiment-hello-world>`__.
diff --git a/docs/source/TrainingService/HowToImplementTrainingService.rst b/docs/source/experiment/training_service/customize.rst
similarity index 91%
rename from docs/source/TrainingService/HowToImplementTrainingService.rst
rename to docs/source/experiment/training_service/customize.rst
index 502141002..bbc335988 100644
--- a/docs/source/TrainingService/HowToImplementTrainingService.rst
+++ b/docs/source/experiment/training_service/customize.rst
@@ -1,5 +1,5 @@
-How to Implement Training Service in NNI
-========================================
+Customize a Training Service
+============================
 
 Overview
 --------
@@ -10,12 +10,12 @@ System architecture
 -------------------
 
 
-.. image:: ../../img/NNIDesign.jpg
-   :target: ../../img/NNIDesign.jpg
+.. image:: ../../../img/NNIDesign.jpg
+   :target: ../../../img/NNIDesign.jpg
    :alt: 
 
 
-The brief system architecture of NNI is shown in the picture. NNIManager is the core management module of system, in charge of calling TrainingService to manage trial jobs and the communication between different modules. Dispatcher is a message processing center responsible for message dispatch. TrainingService is a module to manage trial jobs, it communicates with nniManager module, and has different instance according to different training platform. For the time being, NNI supports `local platfrom <LocalMode.rst>`__\ , `remote platfrom <RemoteMachineMode.rst>`__\ , `PAI platfrom <PaiMode.rst>`__\ , `kubeflow platform <KubeflowMode.rst>`__ and `FrameworkController platfrom <FrameworkControllerMode.rst>`__.
+The brief system architecture of NNI is shown in the picture. NNIManager is the core management module of system, in charge of calling TrainingService to manage trial jobs and the communication between different modules. Dispatcher is a message processing center responsible for message dispatch. TrainingService is a module to manage trial jobs, it communicates with nniManager module, and has different instance according to different training platform. For the time being, NNI supports :doc:`./local`, :doc:`./remote`, :doc:`./openpai`, :doc:`./kubeflow` and :doc:`./frameworkcontroller`.
 
 In this document, we introduce the brief design of TrainingService. If users want to add a new TrainingService instance, they just need to complete a child class to implement TrainingService, don't need to understand the code detail of NNIManager, Dispatcher or other modules.
 
@@ -24,7 +24,7 @@ Folder structure of code
 
 NNI's folder structure is shown below:
 
-.. code-block:: bash
+.. code-block:: text
 
    nni
      |- deployment
@@ -59,7 +59,7 @@ NNI's folder structure is shown below:
 Function annotation of TrainingService
 --------------------------------------
 
-.. code-block:: bash
+.. code-block:: typescript
 
    abstract class TrainingService {
        public abstract listTrialJobs(): Promise<TrialJobDetail[]>;
@@ -82,7 +82,7 @@ The parent class of TrainingService has a few abstract functions, users need to
 
 ClusterMetadata is the data related to platform details, for examples, the ClusterMetadata defined in remote machine server is:
 
-.. code-block:: bash
+.. code-block:: typescript
 
    export class RemoteMachineMeta {
        public readonly ip : string;
@@ -117,7 +117,7 @@ This function will return the metadata value according to the values, it could b
 
 SubmitTrialJob is a function to submit new trial jobs, users should generate a job instance in TrialJobDetail type. TrialJobDetail is defined as follow:
 
-.. code-block:: bash
+.. code-block:: typescript
 
    interface TrialJobDetail {
        readonly id: string;
@@ -175,8 +175,8 @@ NNI offers a TrialKeeper tool to help maintaining trial jobs. Users can find the
 The running architecture of TrialKeeper is show as follow:
 
 
-.. image:: ../../img/trialkeeper.jpg
-   :target: ../../img/trialkeeper.jpg
+.. image:: ../../../img/trialkeeper.jpg
+   :target: ../../../img/trialkeeper.jpg
    :alt: 
 
 
@@ -185,6 +185,4 @@ When users submit a trial job to cloud platform, they should wrap their trial co
 Reference
 ---------
 
-For more information about how to debug, please `refer <../Tutorial/HowToDebug.rst>`__.
-
-The guideline of how to contribute, please `refer <../Tutorial/Contributing.rst>`__.
+The guideline of how to contribute, please refer to :doc:`/notes/contributing`.
diff --git a/docs/source/experiment/training_service/frameworkcontroller.rst b/docs/source/experiment/training_service/frameworkcontroller.rst
new file mode 100644
index 000000000..4bbb65380
--- /dev/null
+++ b/docs/source/experiment/training_service/frameworkcontroller.rst
@@ -0,0 +1,138 @@
+FrameworkController Training Service
+====================================
+
+NNI supports running experiment using `FrameworkController <https://github.com/Microsoft/frameworkcontroller>`__,
+called frameworkcontroller mode.
+FrameworkController is built to orchestrate all kinds of applications on Kubernetes,
+you don't need to install Kubeflow for specific deep learning framework like tf-operator or pytorch-operator.
+Now you can use FrameworkController as the training service to run NNI experiment.
+
+Prerequisite for on-premises Kubernetes Service
+-----------------------------------------------
+
+1. A **Kubernetes** cluster using Kubernetes 1.8 or later.
+   Follow this `guideline <https://kubernetes.io/docs/setup/>`__ to set up Kubernetes.
+2. Prepare a **kubeconfig** file, which will be used by NNI to interact with your Kubernetes API server.
+   By default, NNI manager will use ``~/.kube/config`` as kubeconfig file's path.
+   You can also specify other kubeconfig files by setting the**KUBECONFIG** environment variable.
+   Refer this `guideline <https://kubernetes.io/docs/concepts/configuration/organize-cluster-access-kubeconfig>`__
+   to learn more about kubeconfig.
+3. If your NNI trial job needs GPU resource, you should follow this `guideline <https://github.com/NVIDIA/k8s-device-plugin>`__
+   to configure **Nvidia device plugin for Kubernetes**.
+4. Prepare a **NFS server** and export a general purpose mount
+   (we recommend to map your NFS server path in ``root_squash option``,
+   otherwise permission issue may raise when NNI copies files to NFS.
+   Refer this `page <https://linux.die.net/man/5/exports>`__ to learn what root_squash option is),
+   or **Azure File Storage**.
+5. Install **NFS client** on the machine where you install NNI and run nnictl to create experiment.
+   Run this command to install NFSv4 client:
+
+.. code-block:: bash
+
+    apt install nfs-common
+
+6. Install **NNI**:
+
+.. code-block:: bash
+
+    python -m pip install nni
+
+Prerequisite for Azure Kubernetes Service
+-----------------------------------------
+
+1. NNI support FrameworkController based on Azure Kubernetes Service,
+   follow the `guideline <https://azure.microsoft.com/en-us/services/kubernetes-service/>`__ to set up Azure Kubernetes Service.
+2. Install `Azure CLI <https://docs.microsoft.com/en-us/cli/azure/install-azure-cli?view=azure-cli-latest>`__ and **kubectl**.
+   Use ``az login`` to set azure account, and connect kubectl client to AKS,
+   refer this `guideline <https://docs.microsoft.com/en-us/azure/aks/kubernetes-walkthrough#connect-to-the-cluster>`__.
+3. Follow the `guideline <https://docs.microsoft.com/en-us/azure/storage/common/storage-quickstart-create-account?tabs=portal>`__
+   to create azure file storage account.
+   If you use Azure Kubernetes Service, NNI need Azure Storage Service to store code files and the output files.
+4. To access Azure storage service, NNI need the access key of the storage account,
+   and NNI uses `Azure Key Vault <https://azure.microsoft.com/en-us/services/key-vault/>`__ Service to protect your private key.
+   Set up Azure Key Vault Service, add a secret to Key Vault to store the access key of Azure storage account.
+   Follow this `guideline <https://docs.microsoft.com/en-us/azure/key-vault/quick-create-cli>`__ to store the access key.
+
+Setup FrameworkController
+-------------------------
+
+Follow the `guideline <https://github.com/Microsoft/frameworkcontroller/tree/master/example/run>`__
+to set up FrameworkController in the Kubernetes cluster, NNI supports FrameworkController by the stateful set mode.
+If your cluster enforces authorization, you need to create a service account with granted permission for FrameworkController,
+and then pass the name of the FrameworkController service account to the NNI Experiment Config.
+If the k8s cluster enforces Authorization, you also need to create a ServiceAccount with granted permission for FrameworkController.
+
+Design
+------
+
+Please refer the design of :doc:`Kubeflow training service <kubeflow>`,
+FrameworkController training service pipeline is similar.
+
+Example
+-------
+
+The FrameworkController config format is:
+
+.. code-block:: python
+
+    from nni.experiment import (
+        Experiment,
+        FrameworkAttemptCompletionPolicy,
+        FrameworkControllerRoleConfig,
+        K8sNfsConfig,
+    )
+
+    experiment = Experiment('frameworkcontroller')
+    experiment.config.trial_code_directory = '.'
+    experiment.config.search_space = search_space
+    experiment.config.tuner.name = 'TPE'
+    experiment.config.tuner.class_args['optimize_mode'] = 'maximize'
+    experiment.config.max_trial_number = 10
+    experiment.config.trial_concurrency = 2
+
+    experiment.config.training_service.storage = K8sNfsConfig()
+    experiment.config.training_service.storage.server = '10.20.30.40'
+    experiment.config.training_service.storage.path = '/mnt/nfs/nni'
+    experiment.config.training_service.task_roles = [FrameworkControllerRoleConfig()]
+    experiment.config.training_service.task_roles[0].name = 'worker'
+    experiment.config.training_service.task_roles[0].task_number = 1
+    experiment.config.training_service.task_roles[0].command = 'python3 model.py'
+    experiment.config.training_service.task_roles[0].gpuNumber = 1
+    experiment.config.training_service.task_roles[0].cpuNumber = 1
+    experiment.config.training_service.task_roles[0].memorySize = '4g'
+    experiment.config.training_service.task_roles[0].framework_attempt_completion_policy = \
+        FrameworkAttemptCompletionPolicy(min_failed_task_count = 1, min_succeed_task_count = 1)
+        
+If you use Azure Kubernetes Service, you should set storage config as follows:
+
+.. code-block:: python
+
+    experiment.config.training_service.storage = K8sAzureStorageConfig()
+    experiment.config.training_service.storage.azure_account = 'your_storage_account_name'
+    experiment.config.training_service.storage.azure_share = 'your_azure_share_name'
+    experiment.config.training_service.storage.key_vault_name = 'your_vault_name'
+    experiment.config.training_service.storage.key_vault_key = 'your_secret_name'
+
+If you set `ServiceAccount <https://kubernetes.io/docs/tasks/configure-pod-container/configure-service-account/>`__ in your k8s,
+please set ``serviceAccountName`` in your config:
+
+.. code-block:: python
+
+    experiment.config.training_service.service_account_name = 'frameworkcontroller'
+
+The trial's config format for NNI frameworkcontroller mode is a simple version of FrameworkController's official config,
+you could refer the `Tensorflow example of FrameworkController
+<https://github.com/microsoft/frameworkcontroller/blob/master/example/framework/scenario/tensorflow/ps/cpu/tensorflowdistributedtrainingwithcpu.yaml>`__
+for deep understanding.
+
+Once it's ready, run:
+
+.. code-block:: python
+
+    experiment.run(8080)
+
+Notice: In frameworkcontroller mode,
+NNIManager will start a rest server and listen on a port which is your NNI web portal's port plus 1.
+For example, if your web portal port is ``8080``, the rest server will listen on ``8081``,
+to receive metrics from trial job running in Kubernetes.
+So you should ``enable 8081`` TCP port in your firewall rule to allow incoming traffic.
diff --git a/docs/source/experiment/training_service/hybrid.rst b/docs/source/experiment/training_service/hybrid.rst
new file mode 100644
index 000000000..86d5de625
--- /dev/null
+++ b/docs/source/experiment/training_service/hybrid.rst
@@ -0,0 +1,31 @@
+Hybrid Training Service
+=======================
+
+Hybrid training service is for aggregating different types of computation resources into a virtually unified resource pool, in which trial jobs are dispatched. Hybrid training service is for collecting user's all available computation resources to jointly work on an AutoML task, it is flexibile enough to switch among different types of computation resources. For example, NNI could submit trial jobs to multiple remote machines and AML simultaneously.
+
+Prerequisite
+------------
+
+NNI has supported :doc:`./local`, :doc:`./remote`, :doc:`./openpai`, :doc:`./aml`, :doc:`./kubeflow`, :doc:`./frameworkcontroller`, for hybrid training service. Before starting an experiment using using hybrid training service, users should first setup their chosen (sub) training services (e.g., remote training service) according to each training service's own document page.
+
+.. note:: Reuse mode is disabled by default for local training service. But if you are using local training service in hybrid, :ref:`reuse mode <training-service-reuse>` is enabled by default.
+
+Usage
+-----
+
+Unlike other training services (e.g., ``platform: remote`` in remote training service), there is no dedicated keyword for hybrid training service, users can simply list the configurations of their chosen training services under the ``trainingService`` field. Below is an example of a hybrid training service containing remote training service and local training service in experiment configuration yaml.
+
+.. code-block:: yaml
+
+    # the experiment config yaml file
+    ...
+    trainingService:
+      - platform: remote
+        machineList:
+          - host: 127.0.0.1 # your machine's IP address
+            user: bob
+            password: bob
+      - platform: local
+    ...
+
+A complete example configuration file can be found in :githublink:`examples/trials/mnist-pytorch/config_hybrid.yml`.
\ No newline at end of file
diff --git a/docs/source/experiment/training_service/kubeflow.rst b/docs/source/experiment/training_service/kubeflow.rst
new file mode 100644
index 000000000..31f1a284d
--- /dev/null
+++ b/docs/source/experiment/training_service/kubeflow.rst
@@ -0,0 +1,187 @@
+Kubeflow Training Service
+=========================
+
+Now NNI supports running experiment on `Kubeflow <https://github.com/kubeflow/kubeflow>`__, called kubeflow mode.
+Before starting to use NNI kubeflow mode, you should have a Kubernetes cluster,
+either on-premises or `Azure Kubernetes Service(AKS) <https://azure.microsoft.com/en-us/services/kubernetes-service/>`__,
+a Ubuntu machine on which `kubeconfig <https://kubernetes.io/docs/concepts/configuration/organize-cluster-access-kubeconfig/>`__
+is setup to connect to your Kubernetes cluster.
+If you are not familiar with Kubernetes, `here <https://kubernetes.io/docs/tutorials/kubernetes-basics/>`__ is a good start.
+In kubeflow mode, your trial program will run as Kubeflow job in Kubernetes cluster.
+
+Prerequisite for on-premises Kubernetes Service
+-----------------------------------------------
+
+1. A **Kubernetes** cluster using Kubernetes 1.8 or later.
+   Follow this `guideline <https://kubernetes.io/docs/setup/>`__ to set up Kubernetes.
+2. Download, set up, and deploy **Kubeflow** to your Kubernetes cluster.
+   Follow this `guideline <https://www.kubeflow.org/docs/started/getting-started/>`__ to setup Kubeflow.
+3. Prepare a **kubeconfig** file, which will be used by NNI to interact with your Kubernetes API server.
+   By default, NNI manager will use ``~/.kube/config`` as kubeconfig file's path.
+   You can also specify other kubeconfig files by setting the **KUBECONFIG** environment variable.
+   Refer this `guideline <https://kubernetes.io/docs/concepts/configuration/organize-cluster-access-kubeconfig>`__
+   to learn more about kubeconfig.
+4. If your NNI trial job needs GPU resource, you should follow this `guideline <https://github.com/NVIDIA/k8s-device-plugin>`__
+   to configure **Nvidia device plugin for Kubernetes**.
+5. Prepare a **NFS server** and export a general purpose mount
+   (we recommend to map your NFS server path in ``root_squash option``,
+   otherwise permission issue may raise when NNI copy files to NFS.
+   Refer this `page <https://linux.die.net/man/5/exports>`__ to learn what root_squash option is),
+   or **Azure File Storage**.
+6. Install **NFS client** on the machine where you install NNI and run nnictl to create experiment.
+   Run this command to install NFSv4 client:
+
+.. code-block:: bash
+
+    apt install nfs-common
+
+7. Install **NNI**:
+
+.. code-block:: bash
+
+    python -m pip install nni
+
+Prerequisite for Azure Kubernetes Service
+-----------------------------------------
+
+1. NNI support Kubeflow based on Azure Kubernetes Service,
+   follow the `guideline <https://azure.microsoft.com/en-us/services/kubernetes-service/>`__ to set up Azure Kubernetes Service.
+2. Install `Azure CLI <https://docs.microsoft.com/en-us/cli/azure/install-azure-cli?view=azure-cli-latest>`__ and **kubectl**.
+   Use ``az login`` to set azure account, and connect kubectl client to AKS,
+   refer this `guideline <https://docs.microsoft.com/en-us/azure/aks/kubernetes-walkthrough#connect-to-the-cluster>`__.
+3. Deploy Kubeflow on Azure Kubernetes Service, follow the `guideline <https://www.kubeflow.org/docs/started/getting-started/>`__.
+4. Follow the `guideline <https://docs.microsoft.com/en-us/azure/storage/common/storage-quickstart-create-account?tabs=portal>`__
+   to create azure file storage account.
+   If you use Azure Kubernetes Service, NNI need Azure Storage Service to store code files and the output files.
+5. To access Azure storage service, NNI need the access key of the storage account,
+   and NNI use `Azure Key Vault <https://azure.microsoft.com/en-us/services/key-vault/>`__ Service to protect your private key.
+   Set up Azure Key Vault Service, add a secret to Key Vault to store the access key of Azure storage account.
+   Follow this `guideline <https://docs.microsoft.com/en-us/azure/key-vault/quick-create-cli>`__ to store the access key.
+
+Design
+------
+
+.. image:: ../../../img/kubeflow_training_design.png
+   :target: ../../../img/kubeflow_training_design.png
+   :alt: 
+
+Kubeflow training service instantiates a Kubernetes rest client to interact with your K8s cluster's API server.
+
+For each trial, we will upload all the files in your local ``trial_code_directory``
+together with NNI generated files like parameter.cfg into a storage volumn.
+Right now we support two kinds of storage volumes:
+`nfs <https://en.wikipedia.org/wiki/Network_File_System>`__
+and `azure file storage <https://azure.microsoft.com/en-us/services/storage/files/>`__,
+you should configure the storage volumn in experiment config.
+After files are prepared, Kubeflow training service will call K8S rest API to create Kubeflow jobs
+(`tf-operator <https://github.com/kubeflow/tf-operator>`__ job
+or `pytorch-operator <https://github.com/kubeflow/pytorch-operator>`__ job)
+in K8S, and mount your storage volume into the job's pod.
+Output files of Kubeflow job, like stdout, stderr, trial.log or model files, will also be copied back to the storage volumn.
+NNI will show the storage volumn's URL for each trial in web portal, to allow user browse the log files and job's output files.
+
+Supported operator
+------------------
+
+NNI only support tf-operator and pytorch-operator of Kubeflow, other operators are not tested.
+Users can set operator type in experiment config.
+The setting of tf-operator:
+
+.. code-block:: yaml
+
+    config.training_service.operator = 'tf-operator'
+
+The setting of pytorch-operator:
+
+.. code-block:: yaml
+
+    config.training_service.operator = 'pytorch-operator'
+
+If users want to use tf-operator, he could set ``ps`` and ``worker`` in trial config.
+If users want to use pytorch-operator, he could set ``master`` and ``worker`` in trial config.
+
+Supported storage type
+----------------------
+
+NNI support NFS and Azure Storage to store the code and output files,
+users could set storage type in config file and set the corresponding config.
+
+The setting for NFS storage are as follows:
+
+.. code-block:: python
+
+    config.training_service.storage = K8sNfsConfig(
+        server = '10.20.30.40', # your NFS server IP
+        path = '/mnt/nfs/nni'   # your NFS server export path
+    )
+
+If you use Azure storage, you should set ``storage`` in your config as follows:
+
+.. code-block:: python
+
+    config.training_service.storage = K8sAzureStorageConfig(
+        azure_account = your_azure_account_name,
+        azure_share = your_azure_share_name,
+        key_vault_name = your_vault_name,
+        key_vault_key = your_secret_name
+    )
+
+Run an experiment
+-----------------
+
+Use :doc:`PyTorch quickstart </tutorials/hpo_quickstart_pytorch/main>` as an example.
+This is a PyTorch job, and use pytorch-operator of Kubeflow.
+The experiment config is like:
+
+.. code-block:: python
+
+    from nni.experiment import Experiment, K8sNfsConfig, KubeflowRowConfig
+
+    experiment = Experiment('kubeflow')
+    experiment.config.search_space = search_space
+    experiment.config.tuner.name = 'TPE'
+    experiment.config.tuner.class_args['optimize_mode'] = 'maximize'
+    experiment.config.max_trial_number = 10
+    experiment.config.trial_concurrency = 2
+
+    experiment.config.operator = 'pytorch-operator'
+    experiment.config.api_version = 'v1alpha2'
+
+    experiment.config.training_service.storage = K8sNfsConfig()
+    experiment.config.training_service.storage.server = '10.20.30.40'
+    experiment.config.training_service.storage.path = '/mnt/nfs/nni'
+
+    experiment.config.training_service.worker = KubeflowRowConfig()
+    experiment.config.training_service.worker.replicas = 2
+    experiment.config.training_service.worker.command = 'python3 model.py'
+    experiment.config.training_service.worker.gpuNumber = 1
+    experiment.config.training_service.worker.cpuNumber = 1
+    experiment.config.training_service.worker.memorySize = '4g'
+    experiment.config.training_service.worker.code_directory = '.'
+
+    experiment.config.training_service.master = KubeflowRowConfig()
+    experiment.config.training_service.master.replicas = 1
+    experiment.config.training_service.master.command = 'python3 model.py'
+    experiment.config.training_service.master.gpuNumber = 0
+    experiment.config.training_service.master.cpuNumber = 1
+    experiment.config.training_service.master.memorySize = '4g'
+    experiment.config.training_service.master.code_directory = '.'
+    experiment.config.training_service.worker.docker_image = 'msranni/nni:latest'  # default
+
+Once it's ready, run:
+
+.. code-block:: python
+
+    experiment.run(8080)
+
+NNI will create Kubeflow pytorchjob for each trial,
+and the job name format is something like ``nni_exp_{experiment_id}_trial_{trial_id}``.
+You can see the Kubeflow jobs created by NNI in your Kubernetes dashboard.
+
+Notice: In kubeflow mode, NNIManager will start a rest server and listen on a port which is your NNI web portal's port plus 1.
+For example, if your web portal port is ``8080``, the rest server will listen on ``8081``,
+to receive metrics from trial job running in Kubernetes.
+So you should ``enable 8081`` TCP port in your firewall rule to allow incoming traffic.
+
+Once a trial job is completed, you can go to NNI web portal's overview page (like http://localhost:8080/oview)
+to check trials' information.
diff --git a/docs/source/experiment/training_service/local.rst b/docs/source/experiment/training_service/local.rst
new file mode 100644
index 000000000..97d0f668b
--- /dev/null
+++ b/docs/source/experiment/training_service/local.rst
@@ -0,0 +1,43 @@
+Local Training Service
+======================
+
+With local training service, the whole experiment (e.g., tuning algorithms, trials) runs on a single machine, i.e., user's dev machine. The generated trials run on this machine following ``trialConcurrency`` set in the configuration yaml file. If GPUs are used by trial, local training service will allocate required number of GPUs for each trial, like a resource scheduler.
+
+.. note:: Currently, :ref:`reuse mode <training-service-reuse>` remains disabled by default in local training service.
+
+Prerequisite
+------------
+
+You are recommended to go through quick start first, as this document page only explains the configuration of local training service, one part of the experiment configuration yaml file.
+
+
+Usage
+-----
+
+..  code-block:: yaml
+
+    # the experiment config yaml file
+    ...
+    trainingService:
+      platform: local
+      useActiveGpu: false # optional
+    ...
+
+There are other supported fields for local training service, such as ``maxTrialNumberPerGpu``, ``gpuIndices``, for concurrently running multiple trials on one GPU, and running trials on a subset of GPUs on your machine. Please refer to :ref:`reference-local-config-label` in reference for detailed usage.
+
+..  note::
+    Users should set **useActiveGpu** to `true`, if the local machine has GPUs and your trial uses GPU, but generated trials keep waiting. This is usually the case when you are using graphical OS like Windows 10 and Ubuntu desktop.
+
+Then we explain how local training service works with different configurations of ``trialGpuNumber`` and ``trialConcurrency``. Suppose user's local machine has 4 GPUs, with configuration ``trialGpuNumber: 1`` and ``trialConcurrency: 4``, there will be 4 trials run on this machine concurrently, each of which uses 1 GPU. If the configuration is ``trialGpuNumber: 2`` and ``trialConcurrency: 2``, there will be 2 trials run on this machine concurrently, each of which uses 2 GPUs. Which GPU is allocated to which trial is decided by local training service, users do not need to worry about it. An exmaple configuration below.
+
+..  code-block:: yaml
+
+    ...
+    trialGpuNumber: 1
+    trialConcurrency: 4
+    ...
+    trainingService:
+      platform: local
+      useActiveGpu: false
+
+A complete example configuration file can be found :githublink:`examples/trials/mnist-pytorch/config.yml`.
\ No newline at end of file
diff --git a/docs/source/experiment/training_service/openpai.rst b/docs/source/experiment/training_service/openpai.rst
new file mode 100644
index 000000000..f817af166
--- /dev/null
+++ b/docs/source/experiment/training_service/openpai.rst
@@ -0,0 +1,143 @@
+OpenPAI Training Service
+========================
+
+NNI supports running an experiment on `OpenPAI <https://github.com/Microsoft/pai>`__. OpenPAI manages computing resources and is optimized for deep learning. Through docker technology, the computing hardware are decoupled with software, so that it's easy to run distributed jobs, switch with different deep learning frameworks, or run other kinds of jobs on consistent environments.
+
+Prerequisite
+------------
+
+1. Before starting to use OpenPAI training service, you should have an account to access an `OpenPAI <https://github.com/Microsoft/pai>`__ cluster. See `here <https://github.com/Microsoft/pai>`__ if you don't have any OpenPAI account and want to deploy an OpenPAI cluster. Please note that, on OpenPAI, your trial program will run in Docker containers.
+
+2. Get token. Open web portal of OpenPAI, and click ``My profile`` button in the top-right side.
+
+   .. image:: ../../../img/pai_profile.jpg
+      :scale: 80%
+
+   Click ``copy`` button in the page to copy a jwt token.
+
+   .. image:: ../../../img/pai_token.jpg
+      :scale: 67%
+
+3. Mount NFS storage to local machine. If you don't know where to find the NFS storage, please click ``Submit job`` button in web portal.
+
+   .. image:: ../../../img/pai_job_submission_page.jpg
+      :scale: 50%
+
+   Find the data management region in job submission page.
+
+   .. image:: ../../../img/pai_data_management_page.jpg
+      :scale: 33%  
+
+   The ``Preview container paths`` is the NFS host and path that OpenPAI provided, you need to mount the corresponding host and path to your local machine first, then NNI could use the OpenPAI's NFS storage to upload data/code to or download from OpenPAI cluster. To mount the storage, please use ``mount`` command, for example:
+
+   .. code-block:: bash
+
+      sudo mount -t nfs4 gcr-openpai-infra02:/pai/data /local/mnt
+
+   Then the ``/data`` folder in container will be mounted to ``/local/mnt`` folder in your local machine. Please keep in mind that ``localStorageMountPoint`` should be set to ``/local/mnt`` in this case.
+
+4. Get OpenPAI's storage config name and ``containerStorageMountPoint``. They can also be found in data management region in job submission page. Please find the ``Name`` and ``Path`` of your ``Team share storage``. They should be put into ``storageConfigName`` and ``containerStorageMountPoint``. For example,
+
+   .. code-block:: yaml
+
+      storageConfigName: confignfs-data
+      containerStorageMountPoint: /mnt/confignfs-data
+
+Usage
+-----
+
+We show an example configuration here with YAML (Python configuration should be similar).
+
+.. code-block:: yaml
+
+   trialGpuNumber: 0
+   trialConcurrency: 1
+   ...
+   trainingService:
+     platform: openpai
+     host: http://123.123.123.123
+     username: ${your user name}
+     token: ${your token}
+     dockerImage: msranni/nni
+     trialCpuNumber: 1
+     trialMemorySize: 8GB
+     storageConfigName: confignfs-data
+     localStorageMountPoint: /local/mnt
+     containerStorageMountPoint: /mnt/confignfs-data
+
+Once completing the configuration and run nnictl / use Python to launch the experiment. NNI will start to spawn trials to your specified OpenPAI platform.
+
+The job name format is something like ``nni_exp_{experiment_id}_trial_{trial_id}``. You can see jobs created by NNI on the OpenPAI cluster's web portal, like:
+
+.. image:: ../../../img/nni_pai_joblist.jpg
+
+.. note:: For OpenPAI training service, NNI will start an additional rest server and listen on a port which is your NNI WebUI's port plus 1. For example, if your WebUI port is ``8080``, the rest server will listen on ``8081``, to receive metrics from trial job running in Kubernetes. So you should ``enable 8081`` TCP port in your firewall rule to allow incoming traffic.
+
+Once a trial job is completed, you can go to NNI WebUI's overview page (like ``http://localhost:8080/oview``) to check trial's information. For example, you can expand a trial information in trial list view, click the logPath link like:
+
+.. image:: ../../../img/nni_webui_joblist.png
+    :scale: 30%
+
+Configuration References
+------------------------
+
+Compared with :doc:`local` and :doc:`remote`, OpenPAI training service supports the following additional configurations.
+
+.. list-table::
+   :header-rows: 1
+   :widths: auto
+
+   * - Field name
+     - Description
+   * - username
+     - Required field. User name of OpenPAI platform.
+   * - token
+     - Required field. Authentication key of OpenPAI platform.
+   * - host
+     - Required field. The host of OpenPAI platform. It's PAI's job submission page URI, like ``10.10.5.1``. The default protocol in NNI is HTTPS. If your PAI's cluster has disabled https, please use the URI in ``http://10.10.5.1`` format.
+   * - trialCpuNumber
+     - Optional field. Should be positive number based on your trial program's CPU requirement. If it's not set in trial configuration, it should be set in the config specified in ``openpaiConfig`` or ``openpaiConfigFile`` field.
+   * - trialMemorySize
+     - Optional field. Should be in format like ``2gb`` based on your trial program's memory requirement. If it's not set in trial configuration, it should be set in the config specified in ``openpaiConfig`` or ``openpaiConfigFile`` field.
+   * - dockerImage
+     - Optional field. In OpenPAI training service, your trial program will be scheduled by OpenPAI to run in `Docker container <https://www.docker.com/>`__. This key is used to specify the Docker image used to create the container in which your trial will run. Upon every NNI release, we build `a docker image <https://hub.docker.com/r/msranni/nni>`__ with `this Dockerfile <https://hub.docker.com/r/msranni/nni>`__. You can either use this image directly in your config file, or build your own image. If it's not set in trial configuration, it should be set in the config specified in ``openpaiConfig`` or ``openpaiConfigFile`` field.
+   * - virtualCluster
+     - Optional field. Set the virtualCluster of OpenPAI. If omitted, the job will run on ``default`` virtual cluster.
+   * - localStorageMountPoint
+     - Required field. Set the mount path in the machine you start the experiment.
+   * - containerStorageMountPoint
+     - Optional field. Set the mount path in your container used in OpenPAI.
+   * - storageConfigName
+     - Optional field. Set the storage name used in OpenPAI. If it's not set in trial configuration, it should be set in the config specified in ``openpaiConfig`` or ``openpaiConfigFile`` field.
+   * - openpaiConfigFile
+     - Optional field. Set the file path of OpenPAI job configuration, the file is in yaml format. If users set ``openpaiConfigFile`` in NNI's configuration file, there's no need to specify the fields ``storageConfigName``, ``virtualCluster``, ``dockerImage``, ``trialCpuNumber``, ``trialGpuNumber``, ``trialMemorySize`` in configuration. These fields will use the values from the config file specified by  ``openpaiConfigFile``.
+   * - openpaiConfig
+     - Optional field. Similar to ``openpaiConfigFile``, but instead of referencing an external file, using this field you embed the content into NNI's config YAML.
+
+.. note::
+
+   #. The job name in OpenPAI's configuration file will be replaced by a new job name, the new job name is created by NNI, the name format is ``nni_exp_{this.experimentId}_trial_{trialJobId}`` .
+   #. If users set multiple taskRoles in OpenPAI's configuration file, NNI will wrap all of these taskRoles and start multiple tasks in one trial job, users should ensure that only one taskRole report metric to NNI, otherwise there might be some conflict error.
+
+Data management
+---------------
+
+Before using NNI to start your experiment, users should set the corresponding mount data path in your nniManager machine. OpenPAI has their own storage (NFS, AzureBlob ...), and the storage will used in OpenPAI will be mounted to the container when it start a job. Users should set the OpenPAI storage type by ``paiStorageConfigName`` field to choose a storage in OpenPAI. Then users should mount the storage to their nniManager machine, and set the ``nniManagerNFSMountPath`` field in configuration file, NNI will generate bash files and copy data in ``codeDir`` to the ``nniManagerNFSMountPath`` folder, then NNI will start a trial job. The data in ``nniManagerNFSMountPath`` will be sync to OpenPAI storage, and will be mounted to OpenPAI's container. The data path in container is set in ``containerNFSMountPath``, NNI will enter this folder first, and then run scripts to start a trial job. 
+
+Version check
+-------------
+
+NNI support version check feature in since version 0.6. It is a policy to insure the version of NNIManager is consistent with trialKeeper, and avoid errors caused by version incompatibility.
+Check policy:
+
+#. NNIManager before v0.6 could run any version of trialKeeper, trialKeeper support backward compatibility.
+#. Since version 0.6, NNIManager version should keep same with triakKeeper version. For example, if NNIManager version is 0.6, trialKeeper version should be 0.6 too.
+#. Note that the version check feature only check first two digits of version.For example, NNIManager v0.6.1 could use trialKeeper v0.6 or trialKeeper v0.6.2, but could not use trialKeeper v0.5.1 or trialKeeper v0.7.
+
+If you could not run your experiment and want to know if it is caused by version check, you could check your webUI, and there will be an error message about version check.
+
+
+.. image:: ../../../img/webui-img/experimentError.png
+   :scale: 80%
+
+With local training service, the whole experiment (e.g., tuning algorithms, trials) runs on a single machine, i.e., user's dev machine. The generated trials run on this machine following ``trialConcurrency`` set in the configuration yaml file. If GPUs are used by trial, local training service will allocate required number of GPUs for each trial, like a resource scheduler.
diff --git a/docs/source/experiment/training_service/overview.rst b/docs/source/experiment/training_service/overview.rst
new file mode 100644
index 000000000..31734997f
--- /dev/null
+++ b/docs/source/experiment/training_service/overview.rst
@@ -0,0 +1,38 @@
+Overview
+========
+
+NNI has supported many training services listed below. Users can go through each page to learning how to configure the corresponding training service. NNI has high extensibility by design, users can customize new training service for their special resource, platform or needs.
+
+
+..  list-table::
+    :header-rows: 1
+
+    * - Training Service
+      - Description
+    * - :doc:`Local <local>`
+      - The whole experiment runs on your dev machine (i.e., a single local machine)
+    * - :doc:`Remote <remote>`
+      - The trials are dispatched to your configured SSH servers
+    * - :doc:`OpenPAI <openpai>`
+      - Running trials on OpenPAI, a DNN model training platform based on Kubernetes
+    * - :doc:`Kubeflow <kubeflow>`
+      - Running trials with Kubeflow, a DNN model training framework based on Kubernetes
+    * - :doc:`AdaptDL <adaptdl>`
+      - Running trials on AdaptDL, an elastic DNN model training platform
+    * - :doc:`FrameworkController <frameworkcontroller>`
+      - Running trials with FrameworkController, a DNN model training framework on Kubernetes
+    * - :doc:`AML <aml>`
+      - Running trials on Azure Machine Learning (AML) cloud service
+    * - :doc:`PAI-DLC <paidlc>`
+      - Running trials on PAI-DLC, which is deep learning containers based on Alibaba ACK
+    * - :doc:`Hybrid <hybrid>`
+      - Support jointly using multiple above training services
+
+.. _training-service-reuse:
+
+Training Service Under Reuse Mode
+---------------------------------
+
+Since NNI v2.0, there are two sets of training service implementations in NNI. The new one is called *reuse mode*. When reuse mode is enabled, a cluster, such as a remote machine or a computer instance on AML, will launch a long-running environment, so that NNI will submit trials to these environments iteratively, which saves the time to create new jobs. For instance, using OpenPAI training platform under reuse mode can avoid the overhead of pulling docker images, creating containers, and downloading data repeatedly.
+
+.. note:: In the reuse mode, users need to make sure each trial can run independently in the same job (e.g., avoid loading checkpoints from previous trials).
diff --git a/docs/source/TrainingService/DLCMode.rst b/docs/source/experiment/training_service/paidlc.rst
similarity index 84%
rename from docs/source/TrainingService/DLCMode.rst
rename to docs/source/experiment/training_service/paidlc.rst
index d51125f11..dbddd7bac 100644
--- a/docs/source/TrainingService/DLCMode.rst
+++ b/docs/source/experiment/training_service/paidlc.rst
@@ -1,14 +1,14 @@
-**Run an Experiment on Aliyun PAI-DSW + PAI-DLC**
-===================================================
+PAI-DLC Training Service
+========================
 
-NNI supports running an experiment on `PAI-DSW <https://help.aliyun.com/document_detail/194831.html>`__ , submit trials to `PAI-DLC <https://help.aliyun.com/document_detail/165137.html>`__ called dlc mode.
+NNI supports running an experiment on `PAI-DSW <https://help.aliyun.com/document_detail/194831.html>`__ , submit trials to `PAI-DLC <https://help.aliyun.com/document_detail/165137.html>`__ which is deep learning containers based on Alibaba ACK.
 
 PAI-DSW server performs the role to submit a job while PAI-DLC is where the training job runs.
 
-Setup environment
------------------
+Prerequisite
+------------
 
-Step 1. Install NNI, follow the install guide `here <../Tutorial/QuickStart.rst>`__.
+Step 1. Install NNI, follow the :doc:`install guide </installation>`.
 
 Step 2. Create PAI-DSW server following this `link <https://help.aliyun.com/document_detail/163684.html?section-2cw-lsi-es9#title-ji9-re9-88x>`__. Note as the training service will be run on PAI-DLC, it won't cost many resources to run and you may just need a PAI-DSW server with CPU.
 
@@ -24,8 +24,8 @@ Step 4. Open your PAI-DSW server command line, download and install PAI-DLC pyth
    pip install ./pai-dlc-20201203  # pai-dlc-20201203 refer to unzipped sdk file name, replace it accordingly.
 
 
-Run an experiment
------------------
+Usage
+-----
 
 Use ``examples/trials/mnist-pytorch`` as an example. The NNI config YAML file's content is like:
 
@@ -60,7 +60,7 @@ Use ``examples/trials/mnist-pytorch`` as an example. The NNI config YAML file's
 
 Note: You should set ``platform: dlc`` in NNI config YAML file if you want to start experiment in dlc mode.
 
-Compared with `LocalMode <LocalMode.rst>`__ training service configuration in dlc mode have these additional keys like ``type/image/jobType/podCount/ecsSpec/region/nasDataSourceId/accessKeyId/accessKeySecret``, for detailed explanation ref to this `link <https://help.aliyun.com/document_detail/203111.html#h2-url-3>`__.
+Compared with :doc:`local`, training service configuration in dlc mode have these additional keys like ``type/image/jobType/podCount/ecsSpec/region/nasDataSourceId/accessKeyId/accessKeySecret``, for detailed explanation ref to this `link <https://help.aliyun.com/document_detail/203111.html#h2-url-3>`__.
 
 Also, as dlc mode requires DSW/DLC to mount the same NAS disk to share information, there are two extra keys related to this: ``localStorageMountPoint`` and ``containerStorageMountPoint``.
 
@@ -78,6 +78,6 @@ Run the following commands to start the example experiment:
 Replace ``${NNI_VERSION}`` with a released version name or branch name, e.g., ``v2.3``.
 
 Monitor your job
-----------------
+^^^^^^^^^^^^^^^^
 
 To monitor your job on DLC, you need to visit `DLC  <https://pai-dlc.console.aliyun.com/#/jobs>`__ to check job status.
diff --git a/docs/source/experiment/training_service/remote.rst b/docs/source/experiment/training_service/remote.rst
new file mode 100644
index 000000000..84fff93e6
--- /dev/null
+++ b/docs/source/experiment/training_service/remote.rst
@@ -0,0 +1,114 @@
+Remote Training Service
+=======================
+
+NNI can run one experiment on multiple remote machines through SSH, called ``remote`` mode. It's like a lightweight training platform. In this mode, NNI can be started from your computer, and dispatch trials to remote machines in parallel.
+
+The OS of remote machines supports ``Linux``\ , ``Windows 10``\ , and ``Windows Server 2019``.
+
+Prerequisite
+------------
+
+
+1. Make sure the default environment of remote machines meets requirements of your trial code. If the default environment does not meet the requirements, the setup script can be added into ``command`` field of NNI config.
+
+2. Make sure remote machines can be accessed through SSH from the machine which runs ``nnictl`` command. It supports both password and key authentication of SSH. For advanced usage, please refer to :ref:`reference-remote-config-label` in reference for detailed usage.
+
+3. Make sure the NNI version on each machine is consistent. Follow the install guide :doc:`here </installation>` to install NNI.
+
+4. Make sure the command of Trial is compatible with remote OSes, if you want to use remote Linux and Windows together. For example, the default python 3.x executable called ``python3`` on Linux, and ``python`` on Windows.
+
+In addition, there are several steps for Windows server.
+
+1. Install and start ``OpenSSH Server``.
+
+  1) Open ``Settings`` app on Windows.
+
+  2) Click ``Apps``\ , then click ``Optional features``.
+
+  3) Click ``Add a feature``\ , search and select ``OpenSSH Server``\ , and then click ``Install``.
+
+  4) Once it's installed, run below command to start and set to automatic start.
+
+  .. code-block:: bat
+
+      sc config sshd start=auto
+      net start sshd
+
+2. Make sure remote account is administrator, so that it can stop running trials.
+
+3. Make sure there is no welcome message more than default, since it causes ssh2 failed in NodeJs. For example, if you're using Data Science VM on Azure, it needs to remove extra echo commands in ``C:\dsvm\tools\setup\welcome.bat``.
+
+  The output like below is ok, when opening a new command window.
+
+  .. code-block:: text
+
+     Microsoft Windows [Version 10.0.17763.1192]
+     (c) 2018 Microsoft Corporation. All rights reserved.
+
+     (py37_default) C:\Users\AzureUser>
+
+Usage
+-----
+
+Use ``examples/trials/mnist-pytorch`` as the example. Suppose there are two machines, which can be logged in with username and password or key authentication of SSH. Here is a template configuration specification.
+
+.. code-block:: yaml
+
+   searchSpaceFile: search_space.json
+   trialCommand: python3 mnist.py
+   trialGpuNumber: 0
+   trialConcurrency: 4
+   maxTrialNumber: 20
+   tuner:
+     name: TPE
+     classArgs:
+       optimize_mode: maximize
+   trainingService:
+     platform: remote
+     machineList:
+       - host: 192.0.2.1
+         user: alice
+         ssh_key_file: ~/.ssh/id_rsa
+       - host: 192.0.2.2
+         port: 10022
+         user: bob
+         password: bob123
+
+The example configuration is saved in ``examples/trials/mnist-pytorch/config_remote.yml``.
+
+You can run below command on Windows, Linux, or macOS to spawn trials on remote Linux machines:
+
+.. code-block:: bash
+
+   nnictl create --config examples/trials/mnist-pytorch/config_remote.yml
+
+
+.. _nniignore:
+
+.. Note:: If you are planning to use remote machines or clusters as your training service, to avoid too much pressure on network, NNI limits the number of files to 2000 and total size to 300MB. If your trial code directory contains too many files, you can choose which files and subfolders should be excluded by adding a ``.nniignore`` file that works like a ``.gitignore`` file. For more details on how to write this file, see the `git documentation <https://git-scm.com/docs/gitignore#_pattern_format>`__.
+
+*Example:* :githublink:`config_detailed.yml <examples/trials/mnist-pytorch/config_detailed.yml>` and :githublink:`.nniignore <examples/trials/mnist-pytorch/.nniignore>`
+
+More features
+-------------
+
+Configure python environment
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+By default, commands and scripts will be executed in the default environment in remote machine. If there are multiple python virtual environments in your remote machine, and you want to run experiments in a specific environment, then use **pythonPath** to specify a python environment on your remote machine. 
+
+For example, with anaconda you can specify:
+
+.. code-block:: yaml
+
+   pythonPath: /home/bob/.conda/envs/ENV-NAME/bin
+
+Configure shared storage
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Remote training service support shared storage, which can help use your own storage during using NNI. Follow the guide `here <./shared_storage.rst>`__ to learn how to use shared storage.
+
+Monitor via TensorBoard
+^^^^^^^^^^^^^^^^^^^^^^^
+
+Remote training service support trial visualization via TensorBoard. Follow the guide :doc:`/experiment/web_portal/tensorboard` to learn how to use TensorBoard.
diff --git a/docs/source/Tutorial/HowToUseSharedStorage.rst b/docs/source/experiment/training_service/shared_storage.rst
similarity index 94%
rename from docs/source/Tutorial/HowToUseSharedStorage.rst
rename to docs/source/experiment/training_service/shared_storage.rst
index 45eb37a75..5d145acd1 100644
--- a/docs/source/Tutorial/HowToUseSharedStorage.rst
+++ b/docs/source/experiment/training_service/shared_storage.rst
@@ -1,5 +1,5 @@
-**How to Use Shared Storage**
-=============================
+How to Use Shared Storage
+=========================
 
 If you want to use your own storage during using NNI, shared storage can satisfy you.
 Instead of using training service native storage, shared storage can bring you more convenience.
@@ -7,7 +7,7 @@ All the information generated by the experiment will be stored under ``/nni`` fo
 All the output produced by the trial will be located under ``/nni/{EXPERIMENT_ID}/trials/{TRIAL_ID}/nnioutput`` folder in your shared storage.
 This saves you from finding for experiment-related information in various places.
 Remember that your trial working directory is ``/nni/{EXPERIMENT_ID}/trials/{TRIAL_ID}``, so if you upload your data in this shared storage, you can open it like a local file in your trial code without downloading it.
-And we will develop more practical features in the future based on shared storage. The config reference can be found `here <../reference/experiment_config.html#sharedstorageconfig>`_.
+And we will develop more practical features in the future based on shared storage. The config reference can be found :ref:`here <reference-sharedstorage-config-label>`.
 
 .. note::
     Shared storage is currently in the experimental stage. We suggest use AzureBlob under Ubuntu/CentOS/RHEL, and NFS under Ubuntu/CentOS/RHEL/Fedora/Debian for remote.
@@ -43,8 +43,8 @@ If you want to use AzureBlob, add below to your config. Full config file see :gi
 
 You can find ``storageAccountName``, ``storageAccountKey``, ``containerName`` on azure storage account portal.
 
-.. image:: ../../img/azure_storage.png
-   :target: ../../img/azure_storage.png
+.. image:: ../../../img/azure_storage.png
+   :target: ../../../img/azure_storage.png
    :alt: 
 
 If you want to use NFS, add below to your config. Full config file see :githublink:`mnist-sharedstorage/config_nfs.yml <examples/trials/mnist-sharedstorage/config_nfs.yml>`.
diff --git a/docs/source/experiment/training_service/toctree.rst b/docs/source/experiment/training_service/toctree.rst
new file mode 100644
index 000000000..246d34297
--- /dev/null
+++ b/docs/source/experiment/training_service/toctree.rst
@@ -0,0 +1,18 @@
+Training Service
+================
+
+..  toctree::
+    :hidden:
+
+    Overview <overview>
+    Local <local>
+    Remote <remote>
+    OpenPAI <openpai>
+    Kubeflow <kubeflow>
+    AdaptDL <adaptdl>
+    FrameworkController <frameworkcontroller>
+    AML <aml>
+    PAI-DLC <paidlc>
+    Hybrid <hybrid>
+    Customize a Training Service <customize>
+    Shared Storage <shared_storage>
diff --git a/docs/source/Tutorial/Tensorboard.rst b/docs/source/experiment/web_portal/tensorboard.rst
similarity index 51%
rename from docs/source/Tutorial/Tensorboard.rst
rename to docs/source/experiment/web_portal/tensorboard.rst
index 2fe34f7e6..64ccc11cc 100644
--- a/docs/source/Tutorial/Tensorboard.rst
+++ b/docs/source/experiment/web_portal/tensorboard.rst
@@ -1,7 +1,8 @@
-How to Use Tensorboard within WebUI
-===================================
+Visualize Trial with TensorBoard
+================================
 
-You can launch a tensorboard process cross one or multi trials within webui since NNI v2.2. This feature supports local training service and reuse mode training service with shared storage for now, and will support more scenarios in later nni version.
+
+You can launch a tensorboard process cross one or multi trials within webportal since NNI v2.2. This feature supports local training service and reuse mode training service with shared storage for now, and will support more scenarios in later nni version.
 
 Preparation
 -----------
@@ -11,8 +12,8 @@ Make sure tensorboard installed in your environment. If you never used tensorboa
 Use WebUI Launch Tensorboard
 ----------------------------
 
-1. Save Logs
-^^^^^^^^^^^^
+Save Logs
+^^^^^^^^^
 
 NNI will automatically fetch the ``tensorboard`` subfolder under trial's output folder as tensorboard logdir. So in trial's source code, you need to save the tensorboard logs under ``NNI_OUTPUT_DIR/tensorboard``. This log path can be joined as:
 
@@ -20,32 +21,35 @@ NNI will automatically fetch the ``tensorboard`` subfolder under trial's output
 
     log_dir = os.path.join(os.environ["NNI_OUTPUT_DIR"], 'tensorboard')
 
-2. Launch Tensorboard
-^^^^^^^^^^^^^^^^^^^^^
+Launch Tensorboard
+^^^^^^^^^^^^^^^^^^
 
-Like compare, select the trials you want to combine to launch the tensorboard at first, then click the ``Tensorboard`` button.
+* Like compare, select the trials you want to combine to launch the tensorboard at first, then click the ``Tensorboard`` button.
 
-.. image:: ../../img/Tensorboard_1.png
-   :target: ../../img/Tensorboard_1.png
+.. image:: ../../../img/Tensorboard_1.png
+   :target: ../../../img/Tensorboard_1.png
    :alt: 
 
-After click the ``OK`` button in the pop-up box, you will jump to the tensorboard portal.
+* After click the ``OK`` button in the pop-up box, you will jump to the tensorboard portal.
 
-.. image:: ../../img/Tensorboard_2.png
-   :target: ../../img/Tensorboard_2.png
+.. image:: ../../../img/Tensorboard_2.png
+   :target: ../../../img/Tensorboard_2.png
    :alt: 
 
-You can see the ``SequenceID-TrialID`` on the tensorboard portal.
+* You can see the ``SequenceID-TrialID`` on the tensorboard portal.
 
-.. image:: ../../img/Tensorboard_3.png
-   :target: ../../img/Tensorboard_3.png
+.. image:: ../../../img/Tensorboard_3.png
+   :target: ../../../img/Tensorboard_3.png
    :alt: 
 
-3. Stop All
-^^^^^^^^^^^^
+Stop All
+^^^^^^^^
+
 
 If you want to open the portal you have already launched, click the tensorboard id. If you don't need the tensorboard anymore, click ``Stop all tensorboard`` button.
 
-.. image:: ../../img/Tensorboard_4.png
-   :target: ../../img/Tensorboard_4.png
+.. image:: ../../../img/Tensorboard_4.png
+   :target: ../../../img/Tensorboard_4.png
    :alt: 
+
+
diff --git a/docs/source/experiment/web_portal/toctree.rst b/docs/source/experiment/web_portal/toctree.rst
new file mode 100644
index 000000000..35355f523
--- /dev/null
+++ b/docs/source/experiment/web_portal/toctree.rst
@@ -0,0 +1,8 @@
+Web Portal
+==========
+
+..  toctree::
+    :hidden:
+
+    Experiment Web Portal <web_portal>
+    Visualize with TensorBoard <tensorboard>
diff --git a/docs/source/experiment/web_portal/web_portal.rst b/docs/source/experiment/web_portal/web_portal.rst
new file mode 100644
index 000000000..09c32773f
--- /dev/null
+++ b/docs/source/experiment/web_portal/web_portal.rst
@@ -0,0 +1,418 @@
+Web Portal
+==========
+
+Web portal is for users to conveniently visualize their NNI experiments, tuning and training progress, detailed metrics, and error logs. Web portal also allows users to control their NNI experiments, trials, such as updating an experiment of its concurrency, duration, rerunning trials.
+
+.. image:: ../../../static/img/webui.gif
+   :width: 100%
+
+Q&A
+---
+
+There are many trials in the detail table but ``Default Metric`` chart is empty
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. note::
+   First you should know that ``Default metric`` and ``Hyper parameter`` chart only show succeeded trials.
+
+
+What should you do when you think the chart is strange, such as ``Default metric``, ``Hyper parameter``...
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+* Download the experiment results(``experiment config``, ``trial message`` and ``intermeidate metrics``) from ``Experiment summary`` and then upload these results in your issue.
+
+
+
+.. image:: ../../../img/webui-img/summary.png
+   :target: ../../../img/webui-img/summary.png
+   :alt: summary
+
+
+
+What should you do when your experiment has error
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+* Click the icon in the right of ``experiment status`` and screenshot the error message. 
+* And then click the ``learn about`` to download ``nni-manager`` and ``dispatcher`` logfile.
+* Please file an issue from the `Feedback` in the `About` and upload above message.
+
+
+
+.. image:: ../../../img/webui-img/experimentError.png
+   :target: ../../../img/webui-img/experimentError.png
+   :alt: experimentError
+
+
+
+What should you do when your trial fails
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+* ``Customized trial`` could be used in here. Just submit the same parameters to the experiment to rerun the trial.
+
+
+
+.. image:: ../../../img/webui-img/detail/customizedTrialButton.png
+   :target: ../../../img/webui-img/detail/customizedTrialButton.png
+   :alt: customizedTrialButton
+
+
+
+.. image:: ../../../img/webui-img/detail/customizedTrial.png
+   :target: ../../../img/webui-img/detail/customizedTrial.png
+   :alt: customizedTrial
+
+
+
+
+* ``Log model`` will help you find the error reason. There are three buttons ``View trial log``, ``View trial error`` and ``View trial stdout`` on local mode. If you run on the OpenPAI or Kubeflow platform, you could see trial stdout and nfs log.
+  If you have any question you could tell us in the issue.
+
+   **local mode:**
+
+
+
+   .. image:: ../../../img/webui-img/detail/log-local.png
+      :target: ../../../img/webui-img/detail/log-local.png
+      :alt: logOnLocal
+
+
+
+   **OpenPAI, Kubeflow and other mode:**
+
+
+
+   .. image:: ../../../img/webui-img/detail-pai.png
+      :target: ../../../img/webui-img/detail-pai.png
+      :alt: detailPai
+
+
+
+How to use dict intermediate result
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+`The discussion <https://github.com/microsoft/nni/discussions/4289>`_ could help you.
+
+
+.. _exp-manage-webportal:
+
+Experiments management
+----------------------
+
+Experiments management page could manage many experiments on your machine. 
+
+
+
+.. image:: ../../../img/webui-img/managerExperimentList/experimentListNav.png
+   :target: ../../../img/webui-img/managerExperimentList/experimentListNav.png
+   :alt: ExperimentList nav
+
+
+
+* On the ``All experiments`` page, you can see all the experiments on your machine. 
+
+
+
+.. image:: ../../../img/webui-img/managerExperimentList/expList.png
+   :target: ../../../img/webui-img/managerExperimentList/expList.png
+   :alt: Experiments list
+
+
+
+* When you want to see more details about an experiment you could click the trial id, look that:
+
+
+
+.. image:: ../../../img/webui-img/managerExperimentList/toAnotherExp.png
+   :target: ../../../img/webui-img/managerExperimentList/toAnotherExp.png
+   :alt: See this experiment detail
+
+
+
+* If has many experiments on the table, you can use the ``filter`` button.
+
+
+
+.. image:: ../../../img/webui-img/managerExperimentList/expFilter.png
+   :target: ../../../img/webui-img/managerExperimentList/expFilter.png
+   :alt: filter button
+
+
+
+Experiment details
+------------------
+
+
+View overview page
+^^^^^^^^^^^^^^^^^^
+
+
+* On the overview tab, you can see the experiment information and status and the performance of ``top trials``.
+
+
+
+.. image:: ../../../img/webui-img/full-oview.png
+   :target: ../../../img/webui-img/full-oview.png
+   :alt: overview
+
+
+
+* If you want to see experiment search space and config, please click the right button ``Search space`` and ``Config`` (when you hover on this button).
+
+   **Search space file:**
+
+
+
+   .. image:: ../../../img/webui-img/searchSpace.png
+      :target: ../../../img/webui-img/searchSpace.png
+      :alt: searchSpace
+
+
+
+   **Config file:**
+
+
+
+   .. image:: ../../../img/webui-img/config.png
+      :target: ../../../img/webui-img/config.png
+      :alt: config
+
+
+
+* You can view and download ``nni-manager/dispatcher log files`` on here.
+
+
+
+.. image:: ../../../img/webui-img/review-log.png
+   :target: ../../../img/webui-img/review-log.png
+   :alt: logfile
+
+
+
+* If your experiment has many trials, you can change the refresh interval here.
+
+
+
+.. image:: ../../../img/webui-img/refresh-interval.png
+   :target: ../../../img/webui-img/refresh-interval.png
+   :alt: refresh
+
+
+
+* You can change some experiment configurations such as ``maxExecDuration``, ``maxTrialNum`` and ``trial concurrency`` on here.
+
+
+
+.. image:: ../../../img/webui-img/edit-experiment-param.png
+   :target: ../../../img/webui-img/edit-experiment-param.png
+   :alt: editExperimentParams
+
+
+
+View job default metric
+^^^^^^^^^^^^^^^^^^^^^^^
+
+* Click the tab ``Default metric`` to see the point chart of all trials. Hover to see its specific default metric and search space message.
+
+
+
+.. image:: ../../../img/webui-img/default-metric.png
+   :target: ../../../img/webui-img/default-metric.png
+   :alt: defaultMetricGraph
+
+
+
+* Turn on the switch named ``Optimization curve`` to see the experiment's optimization curve.
+
+
+
+.. image:: ../../../img/webui-img/best-curve.png
+   :target: ../../../img/webui-img/best-curve.png
+   :alt: bestCurveGraph
+
+
+
+View hyper parameter
+^^^^^^^^^^^^^^^^^^^^
+
+Click the tab ``Hyper-parameter`` to see the parallel chart.
+
+
+* You can click the ``add/remove`` button to add or remove axes.
+* Drag the axes to swap axes on the chart.
+* You can select the percentage to see top trials.
+
+
+
+.. image:: ../../../img/webui-img/hyperPara.png
+   :target: ../../../img/webui-img/hyperPara.png
+   :alt: hyperParameterGraph
+
+
+
+View Trial Duration
+^^^^^^^^^^^^^^^^^^^
+
+Click the tab ``Trial Duration`` to see the bar chart.
+
+
+
+.. image:: ../../../img/webui-img/trial_duration.png
+   :target: ../../../img/webui-img/trial_duration.png
+   :alt: trialDurationGraph
+
+
+
+View Trial Intermediate Result chart
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Click the tab ``Intermediate Result`` to see the line chart.
+
+
+
+.. image:: ../../../img/webui-img/trials_intermeidate.png
+   :target: ../../../img/webui-img/trials_intermeidate.png
+   :alt: trialIntermediateGraph
+
+
+
+The trial may have many intermediate results in the training process. In order to see the trend of some trials more clearly, we set a filtering function for the intermediate result chart.
+
+You may find that these trials will get better or worse at an intermediate result. This indicates that it is an important and relevant intermediate result. To take a closer look at the point here, you need to enter its corresponding X-value at #Intermediate. Then input the range of metrics on this intermedia result. In the picture below, we choose the No. 4 intermediate result and set the range of metrics to 0.8-1.
+
+
+
+.. image:: ../../../img/webui-img/filter-intermediate.png
+   :target: ../../../img/webui-img/filter-intermediate.png
+   :alt: filterIntermediateGraph
+
+
+
+View trials status
+^^^^^^^^^^^^^^^^^^
+
+Click the tab ``Trials Detail`` to see the status of all trials. Specifically:
+
+
+* Trial detail: trial's id, trial's duration, start time, end time, status, accuracy, and search space file.
+
+
+
+.. image:: ../../../img/webui-img/detail-local.png
+   :target: ../../../img/webui-img/detail-local.png
+   :alt: detailLocalImage
+
+
+
+* Support searching for a specific trial by its id, status, Trial No. and trial parameters.
+
+   **Trial id:**
+   
+
+
+   .. image:: ../../../img/webui-img/detail/searchId.png
+      :target: ../../../img/webui-img/detail/searchId.png
+      :alt: searchTrialId
+
+
+
+   **Trial No.:**
+
+
+
+   .. image:: ../../../img/webui-img/detail/searchNo.png
+      :target: ../../../img/webui-img/detail/searchNo.png
+      :alt: searchTrialNo.
+
+
+
+   **Trial status:**
+
+
+
+   .. image:: ../../../img/webui-img/detail/searchStatus.png
+      :target: ../../../img/webui-img/detail/searchStatus.png
+      :alt: searchStatus
+
+
+
+   **Trial parameters:**
+
+      ``parameters whose type is choice:``
+      
+
+
+      .. image:: ../../../img/webui-img/detail/searchParameterChoice.png
+         :target: ../../../img/webui-img/detail/searchParameterChoice.png
+         :alt: searchParameterChoice
+
+
+
+      ``parameters whose type is not choice:``
+      
+
+
+      .. image:: ../../../img/webui-img/detail/searchParameterRange.png
+         :target: ../../../img/webui-img/detail/searchParameterRange.png
+         :alt: searchParameterRange
+
+
+
+* The button named ``Add column`` can select which column to show on the table. If you run an experiment whose final result is a dict, you can see other keys in the table. You can choose the column ``Intermediate count`` to watch the trial's progress.
+
+
+
+.. image:: ../../../img/webui-img/addColumn.png
+   :target: ../../../img/webui-img/addColumn.png
+   :alt: addColumnGraph
+
+
+
+* If you want to compare some trials, you can select them and then click ``Compare`` to see the results.
+
+
+
+.. image:: ../../../img/webui-img/select-trial.png
+   :target: ../../../img/webui-img/select-trial.png
+   :alt: selectTrialGraph
+
+
+
+.. image:: ../../../img/webui-img/compare.png
+   :target: ../../../img/webui-img/compare.png
+   :alt: compareTrialsGraph
+
+
+
+
+* You can use the button named ``Copy as python`` to copy the trial's parameters.
+
+
+
+.. image:: ../../../img/webui-img/copyParameter.png
+   :target: ../../../img/webui-img/copyParameter.png
+   :alt: copyTrialParameters
+
+
+
+
+* Intermediate Result chart: you can see the default metric in this chart by clicking the intermediate button.
+
+
+
+.. image:: ../../../img/webui-img/intermediate.png
+   :target: ../../../img/webui-img/intermediate.png
+   :alt: intermeidateGraph
+
+
+
+
+* Kill: you can kill a job that status is running.
+
+
+
+.. image:: ../../../img/webui-img/kill-running.png
+   :target: ../../../img/webui-img/kill-running.png
+   :alt: killTrial
+
+
+
diff --git a/docs/source/experiment/web_portal/web_portal_zh.rst b/docs/source/experiment/web_portal/web_portal_zh.rst
new file mode 100644
index 000000000..f69cd96e5
--- /dev/null
+++ b/docs/source/experiment/web_portal/web_portal_zh.rst
@@ -0,0 +1,415 @@
+.. 424a57ff9c92c3f4738a9beabc4cfb50
+
+Web 界面
+========
+
+
+Q&A
+---
+
+在 detail 页面的表格里明明有很多 trial 但是 Default Metric 图是空的没有数据
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. note::
+   首先你要明白 ``Default metric`` 和 ``Hyper parameter`` 图只展示成功 trial。
+
+
+当你觉得 ``Default metric``、``Hyper parameter`` 图有问题的时候应该做什么
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+* 从 Experiment summary 下载实验结果（实验配置，trial 信息，中间值），并把这些结果上传进 issue 里。
+
+
+
+.. image:: ../../../img/webui-img/summary.png
+   :target: ../../../img/webui-img/summary.png
+   :alt: summary
+
+
+
+当你的实验有故障时应该做什么
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+* 点击实验状态右边的小图标把 error 信息截屏。
+* 然后点击 learn about 去下载 log 文件。And then click the ``learn about`` to download ``nni-manager`` and ``dispatcher`` logfile.
+* 点击页面导航栏的 About 按钮点 Feedback 开一个 issue，附带上以上的截屏和 log 信息。
+
+
+
+.. image:: ../../../img/webui-img/experimentError.png
+   :target: ../../../img/webui-img/experimentError.png
+   :alt: experimentError
+
+
+
+当你的 trial 跑失败了你应该怎么做
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+* 使用 Customized trial 功能。向实验提交相同的 trial 参数即可。
+
+
+
+.. image:: ../../../img/webui-img/detail/customizedTrialButton.png
+   :target: ../../../img/webui-img/detail/customizedTrialButton.png
+   :alt: customizedTrialButton
+
+
+
+.. image:: ../../../img/webui-img/detail/customizedTrial.png
+   :target: ../../../img/webui-img/detail/customizedTrial.png
+   :alt: customizedTrial
+
+
+
+
+* ``Log 模块`` 能帮助你找到错误原因。 有三个按钮： ``View trial log``, ``View trial error`` 和 ``View trial stdout`` 可查 log。如果你用 OpenPai 或者 Kubeflow，你能看到 trial stdout 和 nfs log。
+    有任何问题请在 issue 里联系我们。
+
+**local mode：**
+
+
+
+.. image:: ../../../img/webui-img/detail/log-local.png
+   :target: ../../../img/webui-img/detail/log-local.png
+   :alt: logOnLocal
+
+
+
+**OpenPAI, Kubeflow and other mode：**
+
+
+
+.. image:: ../../../img/webui-img/detail-pai.png
+   :target: ../../../img/webui-img/detail-pai.png
+   :alt: detailPai
+
+
+
+怎样去使用 dict intermediate result
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+`The discussion <https://github.com/microsoft/nni/discussions/4289>`_ 能帮助你。
+
+.. _exp-manage-webportal:
+
+实验管理
+--------
+
+实验管理页面能统筹你机器上的所有实验。 
+
+
+
+.. image:: ../../../img/webui-img/managerExperimentList/experimentListNav.png
+   :target: ../../../img/webui-img/managerExperimentList/experimentListNav.png
+   :alt: ExperimentList nav
+
+
+
+* 在 ``All experiments`` 页面，可以看到机器上的所有 Experiment。 
+
+
+
+.. image:: ../../../img/webui-img/managerExperimentList/expList.png
+   :target: ../../../img/webui-img/managerExperimentList/expList.png
+   :alt: Experiments list
+
+
+
+* 查看 Experiment 更多详细信息时，可以单击 trial ID 跳转至该 Experiment 详情页，如下所示：
+
+
+
+.. image:: ../../../img/webui-img/managerExperimentList/toAnotherExp.png
+   :target: ../../../img/webui-img/managerExperimentList/toAnotherExp.png
+   :alt: See this experiment detail
+
+
+
+* 如果表格里有很多 Experiment，可以使用 ``filter`` 按钮。
+
+
+
+.. image:: ../../../img/webui-img/managerExperimentList/expFilter.png
+   :target: ../../../img/webui-img/managerExperimentList/expFilter.png
+   :alt: filter button
+
+
+
+实验详情
+--------
+
+
+查看实验 overview 页面
+^^^^^^^^^^^^^^^^^^^^^^^
+
+
+* 在 Overview 标签上，可看到 Experiment trial 的概况、搜索空间以及 ``top trials`` 的结果。
+
+
+
+.. image:: ../../../img/webui-img/full-oview.png
+   :target: ../../../img/webui-img/full-oview.png
+   :alt: overview
+
+
+
+* 如果想查看 Experiment 配置和搜索空间，点击右边的 ``Search space`` 和 ``Config`` 按钮。
+
+   **搜索空间文件：**
+
+
+
+   .. image:: ../../../img/webui-img/searchSpace.png
+      :target: ../../../img/webui-img/searchSpace.png
+      :alt: searchSpace
+
+
+
+   **配置文件：**
+
+
+
+   .. image:: ../../../img/webui-img/config.png
+      :target: ../../../img/webui-img/config.png
+      :alt: config
+
+
+
+* 你可以在这里查看和下载 ``nni-manager/dispatcher 日志文件``。
+
+
+
+.. image:: ../../../img/webui-img/review-log.png
+   :target: ../../../img/webui-img/review-log.png
+   :alt: logfile
+
+
+
+* 如果 Experiment 包含了较多 Trial，可改变刷新间隔。
+
+
+
+.. image:: ../../../img/webui-img/refresh-interval.png
+   :target: ../../../img/webui-img/refresh-interval.png
+   :alt: refresh
+
+
+
+* 在这里修改 Experiment 配置（例如 ``maxExecDuration``, ``maxTrialNum`` 和 ``trial concurrency``）。
+
+
+
+.. image:: ../../../img/webui-img/edit-experiment-param.png
+   :target: ../../../img/webui-img/edit-experiment-param.png
+   :alt: editExperimentParams
+
+
+
+查看 trial 最终结果
+^^^^^^^^^^^^^^^^^^^^^
+
+* ``Default metric`` 是所有 trial 的最终结果图。 在每一个结果上悬停鼠标可以看到 trial 信息，比如 trial id、No. 超参等。
+
+
+
+.. image:: ../../../img/webui-img/default-metric.png
+   :target: ../../../img/webui-img/default-metric.png
+   :alt: defaultMetricGraph
+
+
+
+* 打开 ``Optimization curve`` 来查看 Experiment 的优化曲线。
+
+
+
+.. image:: ../../../img/webui-img/best-curve.png
+   :target: ../../../img/webui-img/best-curve.png
+   :alt: bestCurveGraph
+
+
+
+查看超参
+^^^^^^^^^^
+
+单击 ``Hyper-parameter`` 标签查看平行坐标系图。
+
+
+* 可以点击 ``添加/删除`` 按钮来添加或删减纵坐标轴。
+* 直接在图上拖动轴线来交换轴线位置。
+* 通过调节百分比来查看 top trial。
+
+
+
+.. image:: ../../../img/webui-img/hyperPara.png
+   :target: ../../../img/webui-img/hyperPara.png
+   :alt: hyperParameterGraph
+
+
+
+查看 Trial 运行时间
+^^^^^^^^^^^^^^^^^^^^^^
+
+点击 ``Trial Duration`` 标签来查看柱状图。
+
+
+
+.. image:: ../../../img/webui-img/trial_duration.png
+   :target: ../../../img/webui-img/trial_duration.png
+   :alt: trialDurationGraph
+
+
+
+查看 Trial 中间结果
+^^^^^^^^^^^^^^^^^^^^^^
+
+单击 ``Intermediate Result`` 标签查看折线图。
+
+
+
+.. image:: ../../../img/webui-img/trials_intermeidate.png
+   :target: ../../../img/webui-img/trials_intermeidate.png
+   :alt: trialIntermediateGraph
+
+
+
+Trial 在训练过程中可能有大量中间结果。 为了更清楚的理解一些 Trial 的趋势，可以为中间结果图设置过滤功能。
+
+这样可以发现 Trial 在某个中间结果上会变得更好或更差。 这表明它是一个重要的并相关的中间结果。 如果要仔细查看这个点，可以在 #Intermediate 中输入其 X 坐标。 并输入这个中间结果的指标范围。 在下图中，选择了第四个中间结果并将指标范围设置为了 0.8 -1。
+
+
+
+.. image:: ../../../img/webui-img/filter-intermediate.png
+   :target: ../../../img/webui-img/filter-intermediate.png
+   :alt: filterIntermediateGraph
+
+
+
+查看 Trial 状态
+^^^^^^^^^^^^^^^^^^
+
+点击 ``Trials Detail`` 标签查看所有 Trial 的状态。具体如下：
+
+
+* Trial 详情：Trial id，持续时间，开始时间，结束时间，状态，精度和 search space 文件。
+
+
+
+.. image:: ../../../img/webui-img/detail-local.png
+   :target: ../../../img/webui-img/detail-local.png
+   :alt: detailLocalImage
+
+
+
+* * 支持通过 id，状态，Trial 编号以及参数来搜索。
+
+   **Trial id：**
+   
+
+
+   .. image:: ../../../img/webui-img/detail/searchId.png
+      :target: ../../../img/webui-img/detail/searchId.png
+      :alt: searchTrialId
+
+
+
+   **Trial No.：**
+
+
+
+   .. image:: ../../../img/webui-img/detail/searchNo.png
+      :target: ../../../img/webui-img/detail/searchNo.png
+      :alt: searchTrialNo.
+
+
+
+   **Trial status：**
+
+
+
+   .. image:: ../../../img/webui-img/detail/searchStatus.png
+      :target: ../../../img/webui-img/detail/searchStatus.png
+      :alt: searchStatus
+
+
+
+   **Trial parameters：**
+
+      ``类型为 choice 的参数：``
+      
+
+
+      .. image:: ../../../img/webui-img/detail/searchParameterChoice.png
+         :target: ../../../img/webui-img/detail/searchParameterChoice.png
+         :alt: searchParameterChoice
+
+
+
+      ``类型不是 choice 的参数：``
+      
+
+
+      .. image:: ../../../img/webui-img/detail/searchParameterRange.png
+         :target: ../../../img/webui-img/detail/searchParameterRange.png
+         :alt: searchParameterRange
+
+
+
+* ``Add column`` 按钮可选择在表格中显示的列。 如果 Experiment 的最终结果是 dict，则可以在表格中查看其它键。可选择 ``Intermediate count`` 列来查看 Trial 进度。
+
+
+
+.. image:: ../../../img/webui-img/addColumn.png
+   :target: ../../../img/webui-img/addColumn.png
+   :alt: addColumnGraph
+
+
+
+* 如果要比较某些 Trial，可选择并点击 ``Compare`` 来查看结果。
+
+
+
+.. image:: ../../../img/webui-img/select-trial.png
+   :target: ../../../img/webui-img/select-trial.png
+   :alt: selectTrialGraph
+
+
+
+.. image:: ../../../img/webui-img/compare.png
+   :target: ../../../img/webui-img/compare.png
+   :alt: compareTrialsGraph
+
+
+
+
+* 可使用 ``Copy as python`` 按钮来拷贝 Trial 的参数。
+
+
+
+.. image:: ../../../img/webui-img/copyParameter.png
+   :target: ../../../img/webui-img/copyParameter.png
+   :alt: copyTrialParameters
+
+
+
+
+* 中间结果图：可在此图中通过点击 intermediate 按钮来查看默认指标。
+
+
+
+.. image:: ../../../img/webui-img/intermediate.png
+   :target: ../../../img/webui-img/intermediate.png
+   :alt: intermeidateGraph
+
+
+
+
+* Kill: 可终止正在运行的 trial。
+
+
+
+.. image:: ../../../img/webui-img/kill-running.png
+   :target: ../../../img/webui-img/kill-running.png
+   :alt: killTrial
+
+
+
diff --git a/docs/source/feature_engineering.rst b/docs/source/feature_engineering.rst
deleted file mode 100644
index a2b2afda2..000000000
--- a/docs/source/feature_engineering.rst
+++ /dev/null
@@ -1,16 +0,0 @@
-###################
-Feature Engineering
-###################
-
-We are glad to introduce Feature Engineering toolkit on top of NNI,
-it's still in the experiment phase which might evolve based on usage feedback.
-We'd like to invite you to use, feedback and even contribute.
-
-For details, please refer to the following tutorials:
-
-..  toctree::
-    :maxdepth: 2
-
-    Overview <FeatureEngineering/Overview>
-    GradientFeatureSelector <FeatureEngineering/GradientFeatureSelector>
-    GBDTSelector <FeatureEngineering/GBDTSelector>
diff --git a/docs/source/FeatureEngineering/GBDTSelector.rst b/docs/source/feature_engineering/gbdt_selector.rst
similarity index 100%
rename from docs/source/FeatureEngineering/GBDTSelector.rst
rename to docs/source/feature_engineering/gbdt_selector.rst
diff --git a/docs/source/FeatureEngineering/GradientFeatureSelector.rst b/docs/source/feature_engineering/gradient_feature_selector.rst
similarity index 100%
rename from docs/source/FeatureEngineering/GradientFeatureSelector.rst
rename to docs/source/feature_engineering/gradient_feature_selector.rst
diff --git a/docs/source/FeatureEngineering/Overview.rst b/docs/source/feature_engineering/overview.rst
similarity index 85%
rename from docs/source/FeatureEngineering/Overview.rst
rename to docs/source/feature_engineering/overview.rst
index e6bba3af8..5a8289b97 100644
--- a/docs/source/FeatureEngineering/Overview.rst
+++ b/docs/source/feature_engineering/overview.rst
@@ -1,13 +1,15 @@
 Feature Engineering with NNI
 ============================
 
-We are glad to announce the alpha release for Feature Engineering toolkit on top of NNI, it's still in the experiment phase which might evolve based on user feedback. We'd like to invite you to use, feedback and even contribute.
+.. note::
+
+    We are glad to announce the alpha release for Feature Engineering toolkit on top of NNI, it's still in the experiment phase which might evolve based on user feedback. We'd like to invite you to use, feedback and even contribute.
 
 For now, we support the following feature selector:
 
 
-* `GradientFeatureSelector <./GradientFeatureSelector.rst>`__
-* `GBDTSelector <./GBDTSelector.rst>`__
+* :doc:`GradientFeatureSelector <./gradient_feature_selector>`
+* :doc:`GBDTSelector <./gbdt_selector>`
 
 These selectors are suitable for tabular data(which means it doesn't include image, speech and text data).
 
@@ -61,8 +63,8 @@ Here is an example:
    from nni.feature_engineering.feature_selector import FeatureSelector
 
    class CustomizedSelector(FeatureSelector):
-       def __init__(self, ...):
-       ...
+       def __init__(self, *args, **kwargs):
+           ...
 
 **2. Implement fit and _get_selected features Function**
 
@@ -73,8 +75,8 @@ Here is an example:
    from nni.feature_engineering.feature_selector import FeatureSelector
 
    class CustomizedSelector(FeatureSelector):
-       def __init__(self, ...):
-       ...
+       def __init__(self, *args, **kwargs):
+           ...
 
        def fit(self, X, y, **kwargs):
            """
@@ -126,16 +128,15 @@ Here is an example:
    from nni.feature_engineering.feature_selector import FeatureSelector
 
    class CustomizedSelector(FeatureSelector, BaseEstimator):
-       def __init__(self, ...):
-       ...
+       def __init__(self, *args, **kwargs):
+           ...
 
-       def get_params(self, ...):
+       def get_params(self, *args, **kwargs):
            """
            Get parameters for this estimator.
            """
            params = self.__dict__
-           params = {key: val for (key, val) in params.items()
-           if not key.endswith('_')}
+           params = {key: val for (key, val) in params.items() if not key.endswith('_')}
            return params
 
        def set_params(self, **params):
@@ -143,8 +144,8 @@ Here is an example:
            Set the parameters of this estimator.
            """
            for param in params:
-           if hasattr(self, param):
-           setattr(self, param, params[param])
+               if hasattr(self, param):
+                   setattr(self, param, params[param])
            return self
 
 **2. Inherit the SelectorMixin Class and its Function**
@@ -157,10 +158,10 @@ Here is an example:
    from nni.feature_engineering.feature_selector import FeatureSelector
 
    class CustomizedSelector(FeatureSelector, BaseEstimator, SelectorMixin):
-       def __init__(self, ...):
+       def __init__(self, *args, **kwargs):
            ...
 
-       def get_params(self, ...):
+       def get_params(self, *args, **kwargs):
            """
            Get parameters for this estimator.
            """
@@ -174,8 +175,8 @@ Here is an example:
            Set the parameters of this estimator.
            """
            for param in params:
-           if hasattr(self, param):
-           setattr(self, param, params[param])
+               if hasattr(self, param):
+                   setattr(self, param, params[param])
            return self
 
        def get_support(self, indices=False):
@@ -308,13 +309,3 @@ Benchmark
 The dataset of benchmark could be download in `here <https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/>`__
 
 The code could be refenrence ``/examples/feature_engineering/gradient_feature_selector/benchmark_test.py``.
-
-Reference and Feedback
-----------------------
-
-
-* To `report a bug <https://github.com/microsoft/nni/issues/new?template=bug-report.rst>`__ for this feature in GitHub;
-* To `file a feature or improvement request <https://github.com/microsoft/nni/issues/new?template=enhancement.rst>`__ for this feature in GitHub;
-* To know more about :githublink:`Neural Architecture Search with NNI <docs/en_US/NAS/Overview.rst>`\ ;
-* To know more about :githublink:`Model Compression with NNI <docs/en_US/Compression/Overview.rst>`\ ;
-* To know more about :githublink:`Hyperparameter Tuning with NNI <docs/en_US/Tuner/BuiltinTuner.rst>`\ ;
diff --git a/docs/source/feature_engineering/toctree.rst b/docs/source/feature_engineering/toctree.rst
new file mode 100644
index 000000000..9d4d3f70f
--- /dev/null
+++ b/docs/source/feature_engineering/toctree.rst
@@ -0,0 +1,9 @@
+Feature Engineering
+===================
+
+..  toctree::
+    :maxdepth: 2
+
+    Overview <overview>
+    GradientFeatureSelector <gradient_feature_selector>
+    GBDTSelector <gbdt_selector>
diff --git a/docs/source/feature_engineering_zh.rst b/docs/source/feature_engineering_zh.rst
deleted file mode 100644
index 53634d10c..000000000
--- a/docs/source/feature_engineering_zh.rst
+++ /dev/null
@@ -1,18 +0,0 @@
-.. 0958703dcd6f8078a1ad1bcaef9c7199
-
-###################
-特征工程
-###################
-
-很高兴在 NNI 上引入了特征工程工具包，
-其仍处于试验阶段，会根据使用反馈来演化。
-诚挚邀请您使用、反馈，或更多贡献。
-
-详细信息，参考以下教程：
-
-..  toctree::
-    :maxdepth: 2
-
-    概述 <FeatureEngineering/Overview>
-    GradientFeatureSelector <FeatureEngineering/GradientFeatureSelector>
-    GBDTSelector <FeatureEngineering/GBDTSelector>
diff --git a/docs/source/hpo/advanced_usage.rst b/docs/source/hpo/advanced_usage.rst
new file mode 100644
index 000000000..1150d1e09
--- /dev/null
+++ b/docs/source/hpo/advanced_usage.rst
@@ -0,0 +1,11 @@
+Advanced Usage
+==============
+
+.. toctree::
+    :hidden:
+
+    Command Line Tool Example </tutorials/hpo_nnictl/nnictl>
+    Implement Custom Tuners and Assessors <custom_algorithm>
+    Install Custom or 3rd-party Tuners and Assessors <custom_algorithm_installation>
+    Tuner Benchmark <hpo_benchmark>
+    Tuner Benchmark Example Statistics <hpo_benchmark_stats>
diff --git a/docs/source/hpo/assessors.rst b/docs/source/hpo/assessors.rst
new file mode 100644
index 000000000..be53eb20f
--- /dev/null
+++ b/docs/source/hpo/assessors.rst
@@ -0,0 +1,45 @@
+Assessor: Early Stopping
+========================
+
+In HPO, some hyperparameter sets may have obviously poor performance and it will be unnecessary to finish the evaluation.
+This is called *early stopping*, and in NNI early stopping algorithms are called *assessors*.
+
+An assessor monitors *intermediate results* of each *trial*.
+If a trial is predicted to produce suboptimal final result, the assessor will stop that trial immediately,
+to save computing resources for other hyperparameter sets.
+
+As introduced in quickstart tutorial, a trial is the evaluation process of a hyperparameter set,
+and intermediate results are reported with :func:`nni.report_intermediate_result` API in trial code.
+Typically, intermediate results are accuracy or loss metrics of each epoch.
+
+Using an assessor will increase the efficiency of computing resources,
+but may slightly reduce the predicition accuracy of tuners.
+It is recommended to use an assessor when computing resources are insufficient.
+
+Common Usage
+------------
+
+The usage of assessors are similar to tuners.
+
+To use a built-in assessor you need to specify its name and arguments:
+
+.. code-block:: python
+
+    config.assessor.name = 'Medianstop'
+    config.tuner.class_args = {'optimize_mode': 'maximize'}
+
+Built-in Assessors
+------------------
+
+.. list-table::
+    :header-rows: 1
+    :widths: auto
+ 
+    * - Assessor
+      - Brief Introduction of Algorithm
+ 
+    * - :class:`Median Stop <nni.algorithms.hpo.medianstop_assessor.MedianstopAssessor>`
+      - Stop if the hyperparameter set performs worse than median at any step.
+
+    * - :class:`Curve Fitting <nni.algorithms.hpo.curvefitting_assessor.CurvefittingAssessor>`
+      - Stop if the learning curve will likely converge to suboptimal result.
diff --git a/docs/source/Tuner/CustomizeTuner.rst b/docs/source/hpo/custom_algorithm.rst
similarity index 68%
rename from docs/source/Tuner/CustomizeTuner.rst
rename to docs/source/hpo/custom_algorithm.rst
index b79563662..6304438c3 100644
--- a/docs/source/Tuner/CustomizeTuner.rst
+++ b/docs/source/hpo/custom_algorithm.rst
@@ -1,5 +1,8 @@
-Customize-Tuner
-===============
+Customizing Algorithms
+======================
+
+Customize Tuner
+---------------
 
 NNI provides state-of-the-art tuning algorithm in builtin-tuners. NNI supports to build a tuner by yourself for tuning demand.
 
@@ -19,7 +22,7 @@ Here is an example:
    from nni.tuner import Tuner
 
    class CustomizedTuner(Tuner):
-       def __init__(self, ...):
+       def __init__(self, *args, **kwargs):
            ...
 
 **2. Implement receive_trial_result, generate_parameter and update_search_space function**
@@ -29,7 +32,7 @@ Here is an example:
    from nni.tuner import Tuner
 
    class CustomizedTuner(Tuner):
-       def __init__(self, ...):
+       def __init__(self, *args, **kwargs):
            ...
 
        def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
@@ -122,4 +125,69 @@ More detail example you could see:
 Write a more advanced automl algorithm
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-The methods above are usually enough to write a general tuner. However, users may also want more methods, for example, intermediate results, trials' state (e.g., the methods in assessor), in order to have a more powerful automl algorithm. Therefore, we have another concept called ``advisor`` which directly inherits from ``MsgDispatcherBase`` in :githublink:`msg_dispatcher_base.py <nni/runtime/msg_dispatcher_base.py>`. Please refer to `here <CustomizeAdvisor.rst>`__ for how to write a customized advisor.
+The methods above are usually enough to write a general tuner. However, users may also want more methods, for example, intermediate results, trials' state (e.g., the methods in assessor), in order to have a more powerful automl algorithm. Therefore, we have another concept called ``advisor`` which directly inherits from ``MsgDispatcherBase`` in :githublink:`msg_dispatcher_base.py <nni/runtime/msg_dispatcher_base.py>`.
+
+Customize Assessor
+------------------
+
+NNI supports to build an assessor by yourself for tuning demand.
+
+If you want to implement a customized Assessor, there are three things to do:
+
+
+#. Inherit the base Assessor class
+#. Implement assess_trial function
+#. Configure your customized Assessor in experiment YAML config file
+
+**1. Inherit the base Assessor class**
+
+.. code-block:: python
+
+   from nni.assessor import Assessor
+
+   class CustomizedAssessor(Assessor):
+       def __init__(self, *args, **kwargs):
+           ...
+
+**2. Implement assess trial function**
+
+.. code-block:: python
+
+   from nni.assessor import Assessor, AssessResult
+
+   class CustomizedAssessor(Assessor):
+       def __init__(self, *args, **kwargs):
+           ...
+
+       def assess_trial(self, trial_history):
+           """
+           Determines whether a trial should be killed. Must override.
+           trial_history: a list of intermediate result objects.
+           Returns AssessResult.Good or AssessResult.Bad.
+           """
+           # you code implement here.
+           ...
+
+**3. Configure your customized Assessor in experiment YAML config file**
+
+NNI needs to locate your customized Assessor class and instantiate the class, so you need to specify the location of the customized Assessor class and pass literal values as parameters to the __init__ constructor.
+
+.. code-block:: yaml
+
+   assessor:
+     codeDir: /home/abc/myassessor
+     classFileName: my_customized_assessor.py
+     className: CustomizedAssessor
+     # Any parameter need to pass to your Assessor class __init__ constructor
+     # can be specified in this optional classArgs field, for example
+     classArgs:
+       arg1: value1
+
+Please noted in **2**. The object ``trial_history`` are exact the object that Trial send to Assessor by using SDK ``report_intermediate_result`` function.
+
+The working directory of your assessor is ``<home>/nni-experiments/<experiment_id>/log``\ , which can be retrieved with environment variable ``NNI_LOG_DIRECTORY``\ ,
+
+More detail example you could see:
+
+* :githublink:`medianstop-assessor <nni/algorithms/hpo/medianstop_assessor.py>`
+* :githublink:`curvefitting-assessor <nni/algorithms/hpo/curvefitting_assessor/>`
diff --git a/docs/source/Tutorial/InstallCustomizedAlgos.rst b/docs/source/hpo/custom_algorithm_installation.rst
similarity index 87%
rename from docs/source/Tutorial/InstallCustomizedAlgos.rst
rename to docs/source/hpo/custom_algorithm_installation.rst
index ce3a2d03f..5ab5c38d1 100644
--- a/docs/source/Tutorial/InstallCustomizedAlgos.rst
+++ b/docs/source/hpo/custom_algorithm_installation.rst
@@ -1,15 +1,12 @@
-
-**How to register customized algorithms as builtin tuners, assessors and advisors**
-=======================================================================================
-
-.. contents::
+How to register customized algorithms as builtin tuners, assessors and advisors
+===============================================================================
 
 Overview
 --------
 
-NNI provides a lot of `builtin tuners <../Tuner/BuiltinTuner.rst>`_, `advisors <../Tuner/HyperbandAdvisor.rst>`__ and `assessors <../Assessor/BuiltinAssessor.rst>`__ can be used directly for Hyper Parameter Optimization, and some extra algorithms can be registered via ``nnictl algo register --meta <path_to_meta_file>`` after NNI is installed. You can check builtin algorithms via ``nnictl algo list`` command.
+NNI provides a lot of :doc:`builtin tuners <tuners>`, and :doc:`assessors <assessors>` can be used directly for Hyper Parameter Optimization, and some extra algorithms can be registered via ``nnictl algo register --meta <path_to_meta_file>`` after NNI is installed. You can check builtin algorithms via ``nnictl algo list`` command.
 
-NNI also provides the ability to build your own customized tuners, advisors and assessors. To use the customized algorithm, users can simply follow the spec in experiment config file to properly reference the algorithm, which has been illustrated in the tutorials of `customized tuners <../Tuner/CustomizeTuner.rst>`_ / `advisors <../Tuner/CustomizeAdvisor.rst>`__ / `assessors <../Assessor/CustomizeAssessor.rst>`__.
+NNI also provides the ability to build your own customized tuners, advisors and assessors. To use the customized algorithm, users can simply follow the spec in experiment config file to properly reference the algorithm, which has been illustrated in the tutorials of :doc:`customized algorithms <custom_algorithm>`.
 
 NNI also allows users to install the customized algorithm as a builtin algorithm, in order for users to use the algorithm in the same way as NNI builtin tuners/advisors/assessors. More importantly, it becomes much easier for users to share or distribute their implemented algorithm to others. Customized tuners/advisors/assessors can be installed into NNI as builtin algorithms, once they are installed into NNI, you can use your customized algorithms the same way as builtin tuners/advisors/assessors in your experiment configuration file. For example, you built a customized tuner and installed it into NNI using a builtin name ``mytuner``, then you can use this tuner in your configuration file like below:
 
@@ -18,20 +15,15 @@ NNI also allows users to install the customized algorithm as a builtin algorithm
    tuner:
      builtinTunerName: mytuner
 
-Register customized algorithms as builtin tuners, assessors and advisors
-------------------------------------------------------------------------
+Register customized algorithms like builtin tuners, assessors and advisors
+--------------------------------------------------------------------------
 
 You can follow below steps to build a customized tuner/assessor/advisor, and register it into NNI as builtin algorithm.
 
 1. Create a customized tuner/assessor/advisor
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-Reference following instructions to create:
-
-
-* `customized tuner <../Tuner/CustomizeTuner.rst>`_
-* `customized assessor <../Assessor/CustomizeAssessor.rst>`_
-* `customized advisor <../Tuner/CustomizeAdvisor.rst>`_
+Reference following instruction: :doc:`custom_algorithm`
 
 2. (Optional) Create a validator to validate classArgs
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -101,9 +93,9 @@ Run following command to register the customized algorithms as builtin algorithm
 
 .. code-block:: bash
 
-   nnictl algo register --meta <path_to_meta_file>
+   nnictl algo register --meta PATH_TO_META_FILE
 
-The ``<path_to_meta_file>`` is the path to the yaml file your created in above section.
+The ``PATH_TO_META_FILE`` is the path to the yaml file your created in above section.
 
 Reference `customized tuner example <#example-register-a-customized-tuner-as-a-builtin-tuner>`_ for a full example.
 
@@ -128,7 +120,7 @@ List builtin algorithms
 
 Run following command to list the registered builtin algorithms:
 
-.. code-block:: bash
+.. code-block:: text
 
    nnictl algo list
    +-----------------+------------+-----------+--------=-------------+------------------------------------------+
@@ -167,7 +159,8 @@ For example:
 Porting customized algorithms from v1.x to v2.x
 -----------------------------------------------
 
-All that needs to be modified is to delete ``NNI Package :: tuner`` metadata in ``setup.py`` and add a meta file mentioned in `4. Prepare meta file`_. Then you can follow `Register customized algorithms as builtin tuners, assessors and advisors`_ to register your customized algorithms.
+All that needs to be modified is to delete ``NNI Package :: tuner`` metadata in ``setup.py`` and add a meta file mentioned in `4. Prepare meta file`_.
+Then you can follow `Register customized algorithms like builtin tuners, assessors and advisors`_ to register your customized algorithms.
 
 Example: Register a customized tuner as a builtin tuner
 -------------------------------------------------------
@@ -213,7 +206,7 @@ Check the registered builtin algorithms
 
 Then run command ``nnictl algo list``\ , you should be able to see that demotuner is installed:
 
-.. code-block:: bash
+.. code-block:: text
 
    +-----------------+------------+-----------+--------=-------------+------------------------------------------+
    |      Name       |    Type    |   source  |      Class Name      |               Module Name                |
diff --git a/docs/source/hpo_benchmark.rst b/docs/source/hpo/hpo_benchmark.rst
similarity index 94%
rename from docs/source/hpo_benchmark.rst
rename to docs/source/hpo/hpo_benchmark.rst
index 413f6d457..b43f7f8d6 100644
--- a/docs/source/hpo_benchmark.rst
+++ b/docs/source/hpo/hpo_benchmark.rst
@@ -1,13 +1,8 @@
 HPO Benchmarks
 ==============
 
-..  toctree::
-    :hidden:
-
-    HPO Benchmark Example Statistics <hpo_benchmark_stats>
-
 We provide a benchmarking tool to compare the performances of tuners provided by NNI (and users' custom tuners) on different
-types of tasks. This tool uses the `automlbenchmark repository <https://github.com/openml/automlbenchmark)>`_  to run different *benchmarks* on the NNI *tuners*.
+types of tasks. This tool uses the `automlbenchmark repository <https://github.com/openml/automlbenchmark>`_  to run different *benchmarks* on the NNI *tuners*.
 The tool is located in ``examples/trials/benchmarking/automlbenchmark``. This document provides a brief introduction to the tool, its usage, and currently available benchmarks.
 
 Overview and Terminologies
@@ -29,7 +24,7 @@ and handle the repeated trial-evaluate-feedback loop in the **framework** abstra
 contains two main components: a **benchmark** from the automlbenchmark library, and an **architecture** which defines the search
 space and the evaluator. To further clarify, we provide the definition for the terminologies used in this document.
 
-* **tuner**\ : a `tuner or advisor provided by NNI <https://nni.readthedocs.io/en/stable/builtin_tuner.html>`_, or a custom tuner provided by the user.
+* **tuner**\ : a :doc:`tuner or advisor provided by NNI <tuners>`, or a custom tuner provided by the user.
 * **task**\ : an abstraction used by automlbenchmark. A task can be thought of as a tuple (dataset, metric). It provides train and test datasets to the frameworks. Then, based on the returns predictions on the test set, the task evaluates the metric (e.g., mse for regression, f1 for classification) and reports the score.
 * **benchmark**\ : an abstraction used by automlbenchmark. A benchmark is a set of tasks, along with other external constraints such as time limits.
 * **framework**\ : an abstraction used by automlbenchmark. Given a task, a framework solves the proposed regression or classification problem using train data and produces predictions on the test set. In our implementation, each framework is an architecture, which defines a search space. To evaluate a task given by the benchmark on a specific tuner, we let the tuner continuously tune the hyperparameters (by giving it cross-validation score on the train data as feedback) until the time or trial limit is reached. Then, the architecture is retrained on the entire train set using the best set of hyperparameters.
@@ -153,7 +148,7 @@ By default, the script runs the specified tuners against the specified benchmark
 all tuners simultaneously in the background, set the "serialize" flag to false in ``runbenchmark_nni.sh``.
 
 Note: the SMAC tuner, DNGO tuner, and the BOHB advisor has to be manually installed before running benchmarks on them.
-Please refer to `this page <https://nni.readthedocs.io/en/stable/Tuner/BuiltinTuner.html?highlight=nni>`_ for more details
+Please refer to :doc:`this page <tuners>` for more details
 on installation.
 
 Run customized benchmarks on existing tuners
@@ -168,10 +163,10 @@ Run benchmarks on custom tuners
 
 You may also use the benchmark to compare a custom tuner written by yourself with the NNI built-in tuners. To use custom
 tuners, first make sure that the tuner inherits from ``nni.tuner.Tuner`` and correctly implements the required APIs. For
-more information on implementing a custom tuner, please refer to `here <https://nni.readthedocs.io/en/stable/Tuner/CustomizeTuner.html>`_.
+more information on implementing a custom tuner, please refer to :doc:`here <custom_algorithm>`.
 Next, perform the following steps:
 
-#. Install the custom tuner via the command ``nnictl algo register``. Check `this document <https://nni.readthedocs.io/en/stable/Tutorial/Nnictl.html>`_ for details. 
+#. Install the custom tuner via the command ``nnictl algo register``. Check :doc:`this document <../reference/nnictl>` for details. 
 #. In ``./nni/frameworks.yaml``\ , add a new framework extending the base framework NNI. Make sure that the parameter ``tuner_type`` corresponds to the "builtinName" of tuner installed in step 1.
 #. Run the following command
 
diff --git a/docs/source/hpo_benchmark_stats.rst b/docs/source/hpo/hpo_benchmark_stats.rst
similarity index 83%
rename from docs/source/hpo_benchmark_stats.rst
rename to docs/source/hpo/hpo_benchmark_stats.rst
index 1c4e01cf4..419397bbf 100644
--- a/docs/source/hpo_benchmark_stats.rst
+++ b/docs/source/hpo/hpo_benchmark_stats.rst
@@ -28,8 +28,8 @@ classification tasks, the metric "auc" and "logloss" were used for evaluation, w
 After the script finishes, the final scores of each tuner are summarized in the file ``results[time]/reports/performances.txt``.
 Since the file is large, we only show the following screenshot and summarize other important statistics instead.
 
-.. image:: ../img/hpo_benchmark/performances.png
-   :target: ../img/hpo_benchmark/performances.png
+.. image:: ../../img/hpo_benchmark/performances.png
+   :target: ../../img/hpo_benchmark/performances.png
    :alt:
 
 When the results are parsed, the tuners are also ranked based on their final performance. The following three tables show
@@ -154,52 +154,52 @@ To view the same data in another way, for each tuner, we present the average ran
 
 Besides these reports, our script also generates two graphs for each fold of each task: one graph presents the best score received by each tuner until trial x, and another graph shows the score that each tuner receives in trial x. These two graphs can give some information regarding how the tuners are "converging" to their final solution. We found that for "nnismall", tuners on the random forest model with search space defined in ``/examples/trials/benchmarking/automlbenchmark/nni/extensions/NNI/architectures/run_random_forest.py`` generally converge to the final solution after 40 to 60 trials. As there are too much graphs to incldue in a single report (96 graphs in total), we only present 10 graphs here.
 
-.. image:: ../img/hpo_benchmark/car_fold1_1.jpg
-   :target: ../img/hpo_benchmark/car_fold1_1.jpg
+.. image:: ../../img/hpo_benchmark/car_fold1_1.jpg
+   :target: ../../img/hpo_benchmark/car_fold1_1.jpg
    :alt:
 
 
-.. image:: ../img/hpo_benchmark/car_fold1_2.jpg
-   :target: ../img/hpo_benchmark/car_fold1_2.jpg
+.. image:: ../../img/hpo_benchmark/car_fold1_2.jpg
+   :target: ../../img/hpo_benchmark/car_fold1_2.jpg
    :alt:
 
 The previous two graphs are generated for fold 1 of the task "car". In the first graph, we observe that most tuners find a relatively good solution within 40 trials. In this experiment, among all tuners, the DNGOTuner converges fastest to the best solution (within 10 trials). Its best score improved for three times in the entire experiment. In the second graph, we observe that most tuners have their score flucturate between 0.8 and 1 throughout the experiment. However, it seems that the Anneal tuner (green line) is more unstable (having more fluctuations) while the GPTuner has a more stable pattern. This may be interpreted as the Anneal tuner explores more aggressively than the GPTuner and thus its scores for different trials vary a lot. Regardless, although this pattern can to some extent hint a tuner's position on the explore-exploit tradeoff, it is not a comprehensive evaluation of a tuner's effectiveness.
 
-.. image:: ../img/hpo_benchmark/christine_fold0_1.jpg
-   :target: ../img/hpo_benchmark/christine_fold0_1.jpg
+.. image:: ../../img/hpo_benchmark/christine_fold0_1.jpg
+   :target: ../../img/hpo_benchmark/christine_fold0_1.jpg
    :alt:
 
 
-.. image:: ../img/hpo_benchmark/christine_fold0_2.jpg
-   :target: ../img/hpo_benchmark/christine_fold0_2.jpg
+.. image:: ../../img/hpo_benchmark/christine_fold0_2.jpg
+   :target: ../../img/hpo_benchmark/christine_fold0_2.jpg
    :alt:
 
 
-.. image:: ../img/hpo_benchmark/cnae-9_fold0_1.jpg
-   :target: ../img/hpo_benchmark/cnae-9_fold0_1.jpg
+.. image:: ../../img/hpo_benchmark/cnae-9_fold0_1.jpg
+   :target: ../../img/hpo_benchmark/cnae-9_fold0_1.jpg
    :alt:
 
 
-.. image:: ../img/hpo_benchmark/cnae-9_fold0_2.jpg
-   :target: ../img/hpo_benchmark/cnae-9_fold0_2.jpg
+.. image:: ../../img/hpo_benchmark/cnae-9_fold0_2.jpg
+   :target: ../../img/hpo_benchmark/cnae-9_fold0_2.jpg
    :alt:
 
 
-.. image:: ../img/hpo_benchmark/credit-g_fold1_1.jpg
-   :target: ../img/hpo_benchmark/credit-g_fold1_1.jpg
+.. image:: ../../img/hpo_benchmark/credit-g_fold1_1.jpg
+   :target: ../../img/hpo_benchmark/credit-g_fold1_1.jpg
    :alt:
 
 
-.. image:: ../img/hpo_benchmark/credit-g_fold1_2.jpg
-   :target: ../img/hpo_benchmark/credit-g_fold1_2.jpg
+.. image:: ../../img/hpo_benchmark/credit-g_fold1_2.jpg
+   :target: ../../img/hpo_benchmark/credit-g_fold1_2.jpg
    :alt:
 
 
-.. image:: ../img/hpo_benchmark/titanic_2_fold1_1.jpg
-   :target: ../img/hpo_benchmark/titanic_2_fold1_1.jpg
+.. image:: ../../img/hpo_benchmark/titanic_2_fold1_1.jpg
+   :target: ../../img/hpo_benchmark/titanic_2_fold1_1.jpg
    :alt:
 
 
-.. image:: ../img/hpo_benchmark/titanic_2_fold1_2.jpg
-   :target: ../img/hpo_benchmark/titanic_2_fold1_2.jpg
+.. image:: ../../img/hpo_benchmark/titanic_2_fold1_2.jpg
+   :target: ../../img/hpo_benchmark/titanic_2_fold1_2.jpg
    :alt:
diff --git a/docs/source/Tutorial/AnnotationSpec.rst b/docs/source/hpo/nni_annotation.rst
similarity index 92%
rename from docs/source/Tutorial/AnnotationSpec.rst
rename to docs/source/hpo/nni_annotation.rst
index ed3a2918a..8a748b82f 100644
--- a/docs/source/Tutorial/AnnotationSpec.rst
+++ b/docs/source/hpo/nni_annotation.rst
@@ -1,3 +1,5 @@
+:orphan:
+
 NNI Annotation
 ==============
 
@@ -32,7 +34,7 @@ In NNI, there are mainly four types of annotation:
 **Arguments**
 
 
-* **sampling_algo**\ : Sampling algorithm that specifies a search space. User should replace it with a built-in NNI sampling function whose name consists of an ``nni.`` identification and a search space type specified in `SearchSpaceSpec <SearchSpaceSpec.rst>`__ such as ``choice`` or ``uniform``.
+* **sampling_algo**\ : Sampling algorithm that specifies a search space. User should replace it with a built-in NNI sampling function whose name consists of an ``nni.`` identification and a search space type specified in :doc:`SearchSpaceSpec <search_space>` such as ``choice`` or ``uniform``.
 * **name**\ : The name of the variable that the selected value will be assigned to. Note that this argument should be the same as the left value of the following assignment statement.
 
 There are 10 types to express your search space as follows:
@@ -91,11 +93,11 @@ An example here is:
 
    ``'''@nni.report_intermediate_result(metrics)'''``
 
-``@nni.report_intermediate_result`` is used to report intermediate result, whose usage is the same as ``nni.report_intermediate_result`` in the doc of `Write a trial run on NNI <../TrialExample/Trials.rst>`__
+``@nni.report_intermediate_result`` is used to report intermediate result, whose usage is the same as :func:`nni.report_intermediate_result`.
 
 4. Annotate final result
 ^^^^^^^^^^^^^^^^^^^^^^^^
 
    ``'''@nni.report_final_result(metrics)'''``
 
-``@nni.report_final_result`` is used to report the final result of the current trial, whose usage is the same as ``nni.report_final_result`` in the doc of `Write a trial run on NNI <../TrialExample/Trials.rst>`__
+``@nni.report_final_result`` is used to report the final result of the current trial, whose usage is the same as :func:`nni.report_final_result`.
diff --git a/docs/source/hpo/overview.rst b/docs/source/hpo/overview.rst
new file mode 100644
index 000000000..a053ab97f
--- /dev/null
+++ b/docs/source/hpo/overview.rst
@@ -0,0 +1,115 @@
+Hyperparameter Optimization Overview
+====================================
+
+Auto hyperparameter optimization (HPO), or auto tuning, is one of the key features of NNI.
+
+Introduction to HPO
+-------------------
+
+In machine learning, a hyperparameter is a parameter whose value is used to control learning process,
+and HPO is the problem of choosing a set of optimal hyperparameters for a learning algorithm.
+(`From <https://en.wikipedia.org/wiki/Hyperparameter_(machine_learning)>`__
+`Wikipedia <https://en.wikipedia.org/wiki/Hyperparameter_optimization>`__)
+
+Following code snippet demonstrates a naive HPO process:
+
+.. code-block:: python
+
+    best_hyperparameters = None
+    best_accuracy = 0
+
+    for learning_rate in [0.1, 0.01, 0.001, 0.0001]:
+        for momentum in [i / 10 for i in range(10)]:
+            for activation_type in ['relu', 'tanh', 'sigmoid']:
+                model = build_model(activation_type)
+                train_model(model, learning_rate, momentum)
+                accuracy = evaluate_model(model)
+
+                if accuracy > best_accuracy:
+                    best_accuracy = accuracy
+                    best_hyperparameters = (learning_rate, momentum, activation_type)
+
+    print('Best hyperparameters:', best_hyperparameters)
+
+You may have noticed, the example will train 4×10×3=120 models in total.
+Since it consumes so much computing resources, you may want to:
+
+1. :ref:`Find the best hyperparameter set with less iterations. <hpo-overview-tuners>`
+2. :ref:`Train the models on distributed platforms. <hpo-overview-platforms>`
+3. :ref:`Have a portal to monitor and control the process. <hpo-overview-portal>`
+
+NNI will do them for you.
+
+Key Features of NNI HPO
+-----------------------
+
+.. _hpo-overview-tuners:
+
+Tuning Algorithms
+^^^^^^^^^^^^^^^^^
+
+NNI provides *tuners* to speed up the process of finding best hyperparameter set.
+
+A tuner, or a tuning algorithm, decides the order in which hyperparameter sets are evaluated.
+Based on the results of historical hyperparameter sets, an efficient tuner can predict where the best hyperparameters locates around,
+and finds them in much fewer attempts.
+
+The naive example above evaluates all possible hyperparameter sets in constant order, ignoring the historical results.
+This is the brute-force tuning algorithm called *grid search*.
+
+NNI has out-of-the-box support for a variety of popular tuners.
+It includes naive algorithms like random search and grid search, Bayesian-based algorithms like TPE and SMAC,
+RL based algorithms like PPO, and much more.
+
+Main article: :doc:`tuners`
+
+.. _hpo-overview-platforms:
+
+Training Platforms
+^^^^^^^^^^^^^^^^^^
+
+If you are not interested in distributed platforms, you can simply run NNI HPO with current computer,
+just like any ordinary Python library.
+
+And when you want to leverage more computing resources, NNI provides built-in integration for training platforms
+from simple on-premise servers to scalable commercial clouds.
+
+With NNI you can write one piece of model code, and concurrently evaluate hyperparameter sets on local machine, SSH servers,
+Kubernetes-based clusters, AzureML service, and much more.
+
+Main article: :doc:`/experiment/training_service/overview`
+
+.. _hpo-overview-portal:
+
+Web Portal
+^^^^^^^^^^
+
+NNI provides a web portal to monitor training progress, to visualize hyperparameter performance,
+to manually customize hyperparameters, and to manage multiple HPO experiments.
+
+Main article: :doc:`/experiment/web_portal/web_portal`
+
+.. image:: ../../static/img/webui.gif
+    :width: 100%
+
+Tutorials
+---------
+
+To start using NNI HPO, choose the quickstart tutorial of your favorite framework:
+
+* :doc:`PyTorch tutorial </tutorials/hpo_quickstart_pytorch/main>`
+* :doc:`TensorFlow tutorial </tutorials/hpo_quickstart_tensorflow/main>`
+
+Extra Features
+--------------
+
+After you are familiar with basic usage, you can explore more HPO features:
+
+* :doc:`Use command line tool to create and manage experiments (nnictl) </reference/nnictl>`
+
+  * :doc:`nnictl example </tutorials/hpo_nnictl/nnictl>`
+
+* :doc:`Early stop non-optimal models (assessor) <assessors>`
+* :doc:`TensorBoard integration </experiment/web_portal/tensorboard>`
+* :doc:`Implement your own algorithm <custom_algorithm>`
+* :doc:`Benchmark tuners <hpo_benchmark>`
diff --git a/docs/source/hpo/overview_zh.rst b/docs/source/hpo/overview_zh.rst
new file mode 100644
index 000000000..c4d0fc33b
--- /dev/null
+++ b/docs/source/hpo/overview_zh.rst
@@ -0,0 +1,103 @@
+.. c74f6d072f5f8fa93eadd214bba992b4
+
+超参调优
+========
+
+自动超参调优（hyperparameter optimization, HPO）是 NNI 的主要功能之一。
+
+超参调优简介
+------------
+
+在机器学习中，用来控制学习过程的参数被称为“超参数”或“超参”，而为一种机器学习算法选择最优超参组合的问题被称为“超参调优”。
+
+以下代码片段演示了一次朴素的超参调优：
+
+.. code-block:: python
+
+    best_hyperparameters = None
+    best_accuracy = 0
+
+    for learning_rate in [0.1, 0.01, 0.001, 0.0001]:
+        for momentum in [i / 10 for i in range(10)]:
+            for activation_type in ['relu', 'tanh', 'sigmoid']:
+                model = build_model(activation_type)
+                train_model(model, learning_rate, momentum)
+                accuracy = evaluate_model(model)
+
+                if accuracy > best_accuracy:
+                    best_accuracy = accuracy
+                    best_hyperparameters = (learning_rate, momentum, activation_type)
+
+    print('最优超参：', best_hyperparameters)
+
+可以看到，这段超参调优代码总计训练4×10×3=120个模型，要消耗大量的计算资源，因此您可能会有以下需求：
+
+1. :ref:`通过较少的尝试次数找到最优超参组合 <zh-hpo-overview-tuners>`
+2. :ref:`利用分布式平台进行训练 <zh-hpo-overview-platforms>`
+3. :ref:`使用网页控制台来监控调参过程 <zh-hpo-overview-portal>`
+
+NNI 可以满足您的这些需求。
+
+NNI 超参调优的主要功能
+----------------------
+
+.. _zh-hpo-overview-tuners:
+
+调优算法
+^^^^^^^^
+
+NNI 通过调优算法来更快地找到最优超参组合，这些算法被称为“tuner”（调参器）。
+
+调优算法会决定需要运行、评估哪些超参组合，以及应该以何种顺序评估超参组合。
+高效的算法可以通过已评估超参组合的结果去预测最优超参的取值，从而减少找到最优超参所需的评估次数。
+
+开头的示例以固定顺序评估所有可能的超参组合，无视了超参的评估结果，这种朴素方法被称为“grid search”（网格搜索）。
+
+NNI 内建了很多流行的调优算法，包括朴素算法如随机搜索、网格搜索，贝叶斯优化类算法如 TPE、SMAC，强化学习算法如 PPO 等等。
+
+完整内容： :doc:`tuners`
+
+.. _zh-hpo-overview-platforms:
+
+训练平台
+^^^^^^^^
+
+如果您不准备使用分布式训练平台，您可以像使用普通 Python 函数库一样，在自己的电脑上直接运行 NNI 超参调优。
+
+如果想利用更多计算资源加速调优过程，您也可以使用 NNI 内建的训练平台集成，从简单的 SSH 服务器到可扩容的 Kubernetes 集群 NNI 都提供支持。
+
+完整内容： :doc:`/experiment/training_service/overview`
+
+.. _zh-hpo-overview-portal:
+
+网页控制台
+^^^^^^^^^^
+
+您可以使用 NNI 的网页控制台来监控超参调优实验，它支持实时显示实验进度、对超参性能进行可视化、人工修改超参数值、同时管理多个实验等诸多功能。
+
+完整内容： :doc:`/experiment/web_portal/web_portal`
+
+.. image:: ../../static/img/webui.gif
+    :width: 100%
+
+教程
+----
+
+我们提供了以下教程帮助您上手 NNI 超参调优，您可以选择最熟悉的机器学习框架：
+
+* :doc:`使用PyTorch的超参调优教程 </tutorials/hpo_quickstart_pytorch/main>`
+* :doc:`使用TensorFlow的超参调优教程（英文） </tutorials/hpo_quickstart_tensorflow/main>`
+
+更多功能
+--------
+
+在掌握了 NNI 超参调优的基础用法之后，您可以尝试以下更多功能：
+
+* :doc:`Use command line tool to create and manage experiments (nnictl) </reference/nnictl>`
+
+  * :doc:`nnictl example </tutorials/hpo_nnictl/nnictl>`
+
+* :doc:`Early stop non-optimal models (assessor) <assessors>`
+* :doc:`TensorBoard integration </experiment/web_portal/tensorboard>`
+* :doc:`Implement your own algorithm <custom_algorithm>`
+* :doc:`Benchmark tuners <hpo_benchmark>`
diff --git a/docs/source/hpo/quickstart.rst b/docs/source/hpo/quickstart.rst
new file mode 100644
index 000000000..bdf451b0c
--- /dev/null
+++ b/docs/source/hpo/quickstart.rst
@@ -0,0 +1,7 @@
+Quickstart
+==========
+
+.. toctree::
+
+    PyTorch </tutorials/hpo_quickstart_pytorch/main>
+    TensorFlow </tutorials/hpo_quickstart_tensorflow/main>
diff --git a/docs/source/hpo/search_space.rst b/docs/source/hpo/search_space.rst
new file mode 100644
index 000000000..98bab896b
--- /dev/null
+++ b/docs/source/hpo/search_space.rst
@@ -0,0 +1,340 @@
+Search Space
+============
+
+Overview
+--------
+
+In NNI, tuner will sample hyperparameters according to the search space.
+
+To define a search space, users should define the name of the variable, the type of sampling strategy and its parameters.
+
+* An example of a search space definition in JSON format is as follow:
+
+.. code-block:: json 
+
+   {
+       "dropout_rate": {"_type": "uniform", "_value": [0.1, 0.5]},
+       "conv_size": {"_type": "choice", "_value": [2, 3, 5, 7]},
+       "hidden_size": {"_type": "choice", "_value": [124, 512, 1024]},
+       "batch_size": {"_type": "choice", "_value": [50, 250, 500]},
+       "learning_rate": {"_type": "uniform", "_value": [0.0001, 0.1]}
+   }
+
+Take the first line as an example.
+``dropout_rate`` is defined as a variable whose prior distribution is a uniform distribution with a range from ``0.1`` to ``0.5``.
+
+.. attention::
+
+    The available sampling strategies within a search space depend on the tuner you want to use.
+    We list the supported types for each built-in tuner :ref:`below <hpo-space-support>`.
+
+    For a customized tuner, you don't have to follow our convention and you will have the flexibility to define any type you want.
+
+Types
+-----
+
+All types of sampling strategies and their parameter are listed here:
+
+choice
+^^^^^^
+
+.. code-block:: python 
+
+    {"_type": "choice", "_value": options}
+
+* The variable's value is one of the options. Here ``options`` should be a list of **numbers** or a list of **strings**. Using arbitrary objects as members of this list (like sublists, a mixture of numbers and strings, or null values) should work in most cases, but may trigger undefined behaviors.
+* ``options`` can also be a nested sub-search-space, this sub-search-space takes effect only when the corresponding element is chosen. The variables in this sub-search-space can be seen as conditional variables. Here is an simple :githublink:`example of nested search space definition <examples/trials/mnist-nested-search-space/search_space.json>`. If an element in the options list is a dict, it is a sub-search-space, and for our built-in tuners you have to add a ``_name`` key in this dict, which helps you to identify which element is chosen. Accordingly, here is a :githublink:`sample <examples/trials/mnist-nested-search-space/sample.json>` which users can get from nni with nested search space definition. See the table below for the tuners which support nested search spaces.
+
+randint
+^^^^^^^
+
+.. code-block:: python 
+
+    {"_type": "randint", "_value": [lower, upper]}
+
+* Choosing a random integer between ``lower`` (inclusive) and ``upper`` (exclusive).
+* Note: Different tuners may interpret ``randint`` differently. Some (e.g., TPE, GridSearch) treat integers from lower
+  to upper as unordered ones, while others respect the ordering (e.g., SMAC). If you want all the tuners to respect
+  the ordering, please use ``quniform`` with ``q=1``.
+
+uniform
+^^^^^^^
+
+.. code-block:: python 
+
+    {"_type": "uniform", "_value": [low, high]}
+
+* The variable value is uniformly sampled between low and high.
+* When optimizing, this variable is constrained to a two-sided interval.
+
+quniform
+^^^^^^^^
+
+.. code-block:: python 
+
+    {"_type": "quniform", "_value": [low, high, q]}
+
+* The variable value is determined using ``clip(round(uniform(low, high) / q) * q, low, high)``\ , where the clip operation is used to constrain the generated value within the bounds. For example, for ``_value`` specified as [0, 10, 2.5], possible values are [0, 2.5, 5.0, 7.5, 10.0]; For ``_value`` specified as [2, 10, 5], possible values are [2, 5, 10].
+* Suitable for a discrete value with respect to which the objective is still somewhat "smooth", but which should be bounded both above and below. If you want to uniformly choose an integer from a range [low, high], you can write ``_value`` like this: ``[low, high, 1]``.
+
+loguniform
+^^^^^^^^^^
+
+.. code-block:: python 
+
+    {"_type": "loguniform", "_value": [low, high]}
+
+* The variable value is drawn from a range [low, high] according to a loguniform distribution like exp(uniform(log(low), log(high))), so that the logarithm of the return value is uniformly distributed.
+* When optimizing, this variable is constrained to be positive.
+
+qloguniform
+^^^^^^^^^^^
+
+.. code-block:: python 
+
+    {"_type": "qloguniform", "_value": [low, high, q]}
+
+* The variable value is determined using ``clip(round(loguniform(low, high) / q) * q, low, high)``\ , where the clip operation is used to constrain the generated value within the bounds.
+* Suitable for a discrete variable with respect to which the objective is "smooth" and gets smoother with the size of the value, but which should be bounded both above and below.
+
+normal
+^^^^^^
+
+.. code-block:: python 
+
+    {"_type": "normal", "_value": [mu, sigma]}
+
+* The variable value is a real value that's normally-distributed with mean mu and standard deviation sigma. When optimizing, this is an unconstrained variable.
+
+qnormal
+^^^^^^^
+
+.. code-block:: python 
+
+    {"_type": "qnormal", "_value": [mu, sigma, q]}
+
+* The variable value is determined using ``round(normal(mu, sigma) / q) * q``
+* Suitable for a discrete variable that probably takes a value around mu, but is fundamentally unbounded.
+
+lognormal
+^^^^^^^^^
+
+.. code-block:: python 
+
+    {"_type": "lognormal", "_value": [mu, sigma]}
+
+* The variable value is drawn according to ``exp(normal(mu, sigma))`` so that the logarithm of the return value is normally distributed. When optimizing, this variable is constrained to be positive.
+
+qlognormal
+^^^^^^^^^^
+
+.. code-block:: python 
+
+    {"_type": "qlognormal", "_value": [mu, sigma, q]}
+
+* The variable value is determined using ``round(exp(normal(mu, sigma)) / q) * q``
+* Suitable for a discrete variable with respect to which the objective is smooth and gets smoother with the size of the variable, which is bounded from one side.
+
+.. _hpo-space-support:
+
+Search Space Types Supported by Each Tuner
+------------------------------------------
+
+.. list-table::
+   :header-rows: 1
+   :widths: auto
+
+   * - 
+     - choice
+     - choice(nested)
+     - randint
+     - uniform
+     - quniform
+     - loguniform
+     - qloguniform
+     - normal
+     - qnormal
+     - lognormal
+     - qlognormal
+
+   * - :class:`TPE <nni.algorithms.hpo.tpe_tuner.TpeTuner>`
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+
+   * - :class:`Random <nni.algorithms.hpo.random_tuner.RandomTuner>`
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+
+   * - :class:`Grid Search <nni.algorithms.hpo.gridsearch_tuner.GridSearchTuner>`
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+
+   * - :class:`Anneal <nni.algorithms.hpo.hyperopt_tuner.HyperoptTuner>`
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+
+   * - :class:`Evolution <nni.algorithms.hpo.evolution_tuner.EvolutionTuner>`
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+
+   * - :class:`SMAC <nni.algorithms.hpo.smac_tuner.SMACTuner>`
+     - ✓
+     - 
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - 
+     - 
+     - 
+     - 
+     - 
+
+   * - :class:`Batch <nni.algorithms.hpo.batch_tuner.BatchTuner>`
+     - ✓
+     - 
+     - 
+     - 
+     - 
+     - 
+     - 
+     - 
+     - 
+     - 
+     - 
+
+   * - :class:`Hyperband <nni.algorithms.hpo.hyperband_advisor.Hyperband>`
+     - ✓
+     - 
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+
+   * - :class:`Metis <nni.algorithms.hpo.metis_tuner.MetisTuner>`
+     - ✓
+     - 
+     - ✓
+     - ✓
+     - ✓
+     - 
+     - 
+     - 
+     - 
+     - 
+     - 
+
+   * - :class:`BOHB <nni.algorithms.hpo.bohb_advisor.BOHB>`
+     - ✓
+     -
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+
+   * - :class:`GP <nni.algorithms.hpo.gp_tuner.GPTuner>`
+     - ✓
+     - 
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - 
+     - 
+     - 
+     - 
+
+   * - :class:`PBT <nni.algorithms.hpo.pbt_tuner.PBTTuner>`
+     - ✓
+     -
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+
+   * - :class:`DNGO <nni.algorithms.hpo.dngo_tuner.DNGOTuner>`
+     - ✓
+     - 
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - ✓
+     - 
+     - 
+     - 
+     - 
+
+
+Known Limitations:
+
+* GP Tuner, Metis Tuner and DNGO tuner support only **numerical values** in search space
+  (``choice`` type values can be no-numerical with other tuners, e.g. string values).
+  Both GP Tuner and Metis Tuner use Gaussian Process Regressor(GPR).
+  GPR make predictions based on a kernel function and the 'distance' between different points,
+  it's hard to get the true distance between no-numerical values.
+
+* Note that for nested search space:
+
+  * Only TPE/Random/Grid Search/Anneal/Evolution tuners support nested search space.
diff --git a/docs/source/hpo/toctree.rst b/docs/source/hpo/toctree.rst
new file mode 100644
index 000000000..9ed2de6e0
--- /dev/null
+++ b/docs/source/hpo/toctree.rst
@@ -0,0 +1,12 @@
+Hyperparameter Optimization
+===========================
+
+.. toctree::
+    :hidden:
+
+    Overview <overview>
+    quickstart
+    Search Space <search_space>
+    Tuners <tuners>
+    Assessors <assessors>
+    advanced_usage
diff --git a/docs/source/hpo/toctree_zh.rst b/docs/source/hpo/toctree_zh.rst
new file mode 100644
index 000000000..7bdc97a82
--- /dev/null
+++ b/docs/source/hpo/toctree_zh.rst
@@ -0,0 +1,14 @@
+.. 21e9c3e0f6b182cf42a99a7f6c4ecf98
+
+超参调优
+========
+
+.. toctree::
+    :hidden:
+
+    概述 <overview>
+    教程 <quickstart>
+    搜索空间 <search_space>
+    Tuners <tuners>
+    Assessors <assessors>
+    高级用法 <advanced_usage>
diff --git a/docs/source/hpo/tuners.rst b/docs/source/hpo/tuners.rst
new file mode 100644
index 000000000..a4268d98d
--- /dev/null
+++ b/docs/source/hpo/tuners.rst
@@ -0,0 +1,125 @@
+Tuner: Tuning Algorithms
+========================
+
+The tuner decides which hyperparameter sets will be evaluated. It is a most important part of NNI HPO.
+
+A tuner works like following pseudocode:
+
+.. code-block:: python
+
+    space = get_search_space()
+    history = []
+    while not experiment_end:
+        hp = suggest_hyperparameter_set(space, history)
+        result = run_trial(hp)
+        history.append((hp, result))
+
+NNI has out-of-the-box support for many popular tuning algorithms. 
+They should be sufficient to cover most typical machine learning scenarios.
+
+However, if you have a very specific demand, or if you have designed an algorithm yourself,
+you can also implement your own tuner: :doc:`custom_algorithm`
+
+Common Usage
+------------
+
+All built-in tuners have similar usage.
+
+To use a built-in tuner, you need to specify its name and arguments in experiment config,
+and provides a standard :doc:`search_space`.
+Some tuners, like SMAC and DNGO, have extra dependencies that need to be installed separately.
+Please check each tuner's reference page for what arguments it supports and whether it needs extra dependencies.
+
+As a general example, random tuner can be configured as follow:
+
+.. code-block:: python
+
+    config.search_space = {
+        'x': {'_type': 'uniform', '_value': [0, 1]},
+        'y': {'_type': 'choice', '_value': ['a', 'b', 'c']}
+    }
+    config.tuner.name = 'Random'
+    config.tuner.class_args = {'seed': 0}
+
+Built-in Tuners
+---------------
+
+.. list-table::
+    :header-rows: 1
+    :widths: auto
+
+    * - Tuner
+      - Category
+      - Brief Introduction
+
+    * - :class:`TPE <nni.algorithms.hpo.tpe_tuner.TpeTuner>`
+      - Bayesian
+      - Tree-structured Parzen Estimator, a classic Bayesian optimization algorithm.
+        (`paper <https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf>`__)
+
+        TPE is a lightweight tuner that has no extra dependency and supports all search space types.
+        Good to start with.
+
+        The drawback is that TPE cannot discover relationship between different hyperparameters.
+
+    * - :class:`Random <nni.algorithms.hpo.random_tuner.RandomTuner>`
+      - Basic
+      - Naive random search, the baseline. It supports all search space types.
+
+    * - :class:`Grid Search <nni.algorithms.hpo.gridsearch_tuner.GridSearchTuner>`
+      - Basic
+      - Divides search space into evenly spaced grid, and performs brute-force traverse. Another baseline.
+
+        It supports all search space types.
+        Recommended when the search space is small, and when you want to find the strictly optimal hyperparameters.
+
+    * - :class:`Anneal <nni.algorithms.hpo.hyperopt_tuner.HyperoptTuner>`
+      - Heuristic
+      - This simple annealing algorithm begins by sampling from the prior, but tends over time to sample from points closer and closer to the best ones observed. This algorithm is a simple variation on the random search that leverages smoothness in the response surface. The annealing rate is not adaptive.
+
+    * - :class:`Evolution <nni.algorithms.hpo.evolution_tuner.EvolutionTuner>`
+      - Heuristic
+      - Naive Evolution comes from Large-Scale Evolution of Image Classifiers. It randomly initializes a population-based on search space. For each generation, it chooses better ones and does some mutation (e.g., change a hyperparameter, add/remove one layer) on them to get the next generation. Naïve Evolution requires many trials to work, but it's very simple and easy to expand new features. `Reference paper <https://arxiv.org/pdf/1703.01041.pdf>`__
+
+    * - :class:`SMAC <nni.algorithms.hpo.smac_tuner.SMACTuner>`
+      - Bayesian
+      - SMAC is based on Sequential Model-Based Optimization (SMBO). It adapts the most prominent previously used model class (Gaussian stochastic process models) and introduces the model class of random forests to SMBO, in order to handle categorical parameters. The SMAC supported by NNI is a wrapper on the SMAC3 GitHub repo.
+
+        Notice, SMAC needs to be installed by ``pip install nni[SMAC]`` command. `Reference Paper, <https://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf>`__ `GitHub Repo <https://github.com/automl/SMAC3>`__
+
+    * - :class:`Batch <nni.algorithms.hpo.batch_tuner.BatchTuner>`
+      - Basic
+      - Batch tuner allows users to simply provide several configurations (i.e., choices of hyper-parameters) for their trial code. After finishing all the configurations, the experiment is done. Batch tuner only supports the type choice in search space spec.
+
+    * - :class:`Hyperband <nni.algorithms.hpo.hyperband_advisor.Hyperband>`
+      - Heuristic
+      - Hyperband tries to use limited resources to explore as many configurations as possible and returns the most promising ones as a final result. The basic idea is to generate many configurations and run them for a small number of trials. The half least-promising configurations are thrown out, the remaining are further trained along with a selection of new configurations. The size of these populations is sensitive to resource constraints (e.g. allotted search time). `Reference Paper <https://arxiv.org/pdf/1603.06560.pdf>`__
+
+    * - :class:`Metis <nni.algorithms.hpo.metis_tuner.MetisTuner>`
+      - Bayesian
+      - Metis offers the following benefits when it comes to tuning parameters: While most tools only predict the optimal configuration, Metis gives you two outputs: (a) current prediction of optimal configuration, and (b) suggestion for the next trial. No more guesswork. While most tools assume training datasets do not have noisy data, Metis actually tells you if you need to re-sample a particular hyper-parameter. `Reference Paper <https://www.microsoft.com/en-us/research/publication/metis-robustly-tuning-tail-latencies-cloud-systems/>`__
+
+    * - :class:`BOHB <nni.algorithms.hpo.bohb_advisor.BOHB>`
+      - Bayesian
+      - BOHB is a follow-up work to Hyperband. It targets the weakness of Hyperband that new configurations are generated randomly without leveraging finished trials. For the name BOHB, HB means Hyperband, BO means Bayesian Optimization. BOHB leverages finished trials by building multiple TPE models, a proportion of new configurations are generated through these models. `Reference Paper <https://arxiv.org/abs/1807.01774>`__
+
+    * - :class:`GP <nni.algorithms.hpo.gp_tuner.GPTuner>`
+      - Bayesian
+      - Gaussian Process Tuner is a sequential model-based optimization (SMBO) approach with Gaussian Process as the surrogate. `Reference Paper <https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf>`__, `Github Repo <https://github.com/fmfn/BayesianOptimization>`__
+
+    * - :class:`PBT <nni.algorithms.hpo.pbt_tuner.PBTTuner>`
+      - Heuristic
+      - PBT Tuner is a simple asynchronous optimization algorithm which effectively utilizes a fixed computational budget to jointly optimize a population of models and their hyperparameters to maximize performance. `Reference Paper <https://arxiv.org/abs/1711.09846v1>`__
+
+    * - :class:`DNGO <nni.algorithms.hpo.dngo_tuner.DNGOTuner>`
+      - Bayesian
+      - Use of neural networks as an alternative to GPs to model distributions over functions in bayesian optimization.
+
+Comparison
+----------
+
+These articles have compared built-in tuners' performance on some different tasks:
+
+:doc:`hpo_benchmark_stats`
+
+:doc:`/sharings/hpo_comparison`
diff --git a/docs/source/hpo_advanced.rst b/docs/source/hpo_advanced.rst
deleted file mode 100644
index f2c4529ac..000000000
--- a/docs/source/hpo_advanced.rst
+++ /dev/null
@@ -1,11 +0,0 @@
-Advanced Features
-=================
-
-..  toctree::
-    :maxdepth: 2
-
-    Write a New Tuner <Tuner/CustomizeTuner>
-    Write a New Assessor <Assessor/CustomizeAssessor>
-    Write a New Advisor <Tuner/CustomizeAdvisor>
-    Write a New Training Service <TrainingService/HowToImplementTrainingService>
-    Install Customized Algorithms as Builtin Tuners/Assessors/Advisors <Tutorial/InstallCustomizedAlgos>
diff --git a/docs/source/hpo_advanced_zh.rst b/docs/source/hpo_advanced_zh.rst
deleted file mode 100644
index 401b43ec9..000000000
--- a/docs/source/hpo_advanced_zh.rst
+++ /dev/null
@@ -1,13 +0,0 @@
-.. 43bb394b1e25458a948c134058ec68ac
-
-高级功能
-=================
-
-..  toctree::
-    :maxdepth: 2
-
-    编写新的 Tuner <Tuner/CustomizeTuner>
-    编写新的 Assessor <Assessor/CustomizeAssessor>
-    编写新的 Advisor <Tuner/CustomizeAdvisor>
-    编写新的训练平台 <TrainingService/HowToImplementTrainingService>
-    安装自定义的 Tuners/Assessors/Advisors <Tutorial/InstallCustomizedAlgos>
diff --git a/docs/source/hyperparameter_tune.rst b/docs/source/hyperparameter_tune.rst
deleted file mode 100644
index e0f613677..000000000
--- a/docs/source/hyperparameter_tune.rst
+++ /dev/null
@@ -1,28 +0,0 @@
-#############################
-Auto (Hyper-parameter) Tuning
-#############################
-
-Auto tuning is one of the key features provided by NNI; a main application scenario being
-hyper-parameter tuning. Tuning specifically applies to trial code. We provide a lot of popular
-auto tuning algorithms (called Tuner), and some early stop algorithms (called Assessor).
-NNI supports running trials on various training platforms, for example, on a local machine,
-on several servers in a distributed manner, or on platforms such as OpenPAI, Kubernetes, etc.
-
-Other key features of NNI, such as model compression, feature engineering, can also be further
-enhanced by auto tuning, which we'll described when introducing those features.
-
-NNI has high extensibility, advanced users can customize their own Tuner, Assessor, and Training Service
-according to their needs.
-
-..  toctree::
-    :maxdepth: 2
-
-    Write Trial <TrialExample/Trials>
-    Tuners <builtin_tuner>
-    Assessors <builtin_assessor>
-    Training Platform <training_services>
-    Examples <examples>
-    WebUI <Tutorial/WebUI>
-    How to Debug <Tutorial/HowToDebug>
-    Advanced <hpo_advanced>
-    HPO Benchmarks <hpo_benchmark>
diff --git a/docs/source/hyperparameter_tune_zh.rst b/docs/source/hyperparameter_tune_zh.rst
deleted file mode 100644
index c8e379ab8..000000000
--- a/docs/source/hyperparameter_tune_zh.rst
+++ /dev/null
@@ -1,30 +0,0 @@
-.. 6ed30d3a87dbc4c1c4650cf56f074045
-
-##############
-自动超参数调优
-##############
-
-自动调优是 NNI 的主要功能之一。它的工作模式是
-反复运行 trial 代码，每次向其提供不同的超参组合，从而对 trial 的运行结果进行调优。
-NNI 提供了很多流行的自动调优算法（称为 Tuner）和一些提前终止算法（称为 Assessor）。
-NNI 支持在多种训练平台上运行 trial，包括本机、
-远程服务器、Azure Machine Learning、基于 Kubernetes 的集群（如 OpenPAI、Kubeflow）等等。
-
-其他的功能，例如模型压缩、特征工程，也可以
-使用自动调优。这些我们在介绍相应功能的时候会具体介绍。
-
-NNI 具有高扩展性，
-用户可以根据需求实现自己的 Tuner 算法和训练平台。
-
-..  toctree::
-    :maxdepth: 2
-
-    实现 Trial <./TrialExample/Trials>
-    Tuners <builtin_tuner>
-    Assessors <builtin_assessor>
-    训练平台 <training_services>
-    示例 <examples>
-    Web 界面 <Tutorial/WebUI>
-    如何调试 <Tutorial/HowToDebug>
-    高级功能 <hpo_advanced>
-    Tuner 基准测试 <hpo_benchmark>
diff --git a/docs/source/index.rst b/docs/source/index.rst
index bb40524d1..5e8a96f58 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -1,584 +1,293 @@
-.. modified from index.html
-.. replace \{\{ pathto\('(.*)'\) \}\} -> $1.html
+NNI Documentation
+=================
 
-###########################
-Neural Network Intelligence
-###########################
+.. toctree::
+   :maxdepth: 2
+   :caption: Get Started
+   :hidden:
 
+   installation
+   quickstart
 
-..  toctree::
-    :maxdepth: 2
-    :caption: Get Started
-    :hidden:
+.. toctree::
+   :maxdepth: 2
+   :caption: User Guide
+   :hidden:
 
-    Installation <installation>
-    QuickStart <Tutorial/QuickStart>
-    Tutorials <tutorials>
+   hpo/toctree
+   nas/toctree
+   Model Compression <compression/toctree>
+   feature_engineering/toctree
+   experiment/toctree
 
-..  toctree::
-    :maxdepth: 2
-    :caption: Advanced Materials
-    :hidden:
+.. toctree::
+   :maxdepth: 2
+   :caption: References
+   :hidden:
 
-    Overview
-    Auto (Hyper-parameter) Tuning <hyperparameter_tune>
-    Neural Architecture Search <nas>
-    Model Compression <model_compression>
-    Feature Engineering <feature_engineering>
+   Python API <reference/python_api>
+   reference/experiment_config
+   reference/nnictl
 
-..  toctree::
-    :maxdepth: 2
-    :caption: References
-    :hidden:
+.. toctree::
+   :maxdepth: 2
+   :caption: Misc
+   :hidden:
 
-    References <reference>
+   examples
+   sharings/community_sharings
+   notes/research_publications
+   notes/build_from_source
+   notes/contributing
+   release
 
-..  toctree::
-    :maxdepth: 2
-    :caption: Misc
-    :hidden:
+**NNI (Neural Network Intelligence)** is a lightweight but powerful toolkit to help users **automate**:
 
-    Use Cases and Solutions <CommunitySharings/community_sharings>
-    Research and Publications <ResearchPublications>
-    FAQ <Tutorial/FAQ>
-    How to Contribute <contribution>
-    Change Log <Release>
+* :doc:`Hyperparameter Optimization </hpo/overview>`
+* :doc:`Neural Architecture Search </nas/overview>`
+* :doc:`Model Compression </compression/overview>`
+* :doc:`Feature Engineering </feature_engineering/overview>`
 
+Get Started
+-----------
 
+To install the current release:
+
+.. code-block:: bash
+
+   $ pip install nni
+
+See the :doc:`installation guide </installation>` if you need additional help on installation.
+
+Try your first NNI experiment
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. code-block:: shell
+
+   $ nnictl hello
+
+.. note:: You need to have `PyTorch <https://pytorch.org/>`_ (as well as `torchvision <https://pytorch.org/vision/stable/index.html>`_) installed to run this experiment.
+
+To start your journey now, please follow the :doc:`absolute quickstart of NNI <quickstart>`!
+
+Why choose NNI?
+---------------
+
+NNI makes AutoML techniques plug-and-play
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. raw:: html
+
+   <div class="codesnippet-card-container">
+
+.. codesnippetcard::
+   :icon: ../img/thumbnails/hpo-small.svg
+   :title: Hyperparameter Tuning
+   :link: tutorials/hpo_quickstart_pytorch/main
+
+   .. code-block::
+
+      params = nni.get_next_parameter()
+
+      class Net(nn.Module):
+          ...
+
+      model = Net()
+      optimizer = optim.SGD(model.parameters(),
+                            params['lr'],
+                            params['momentum'])
+
+      for epoch in range(10):
+          train(...)
+
+      accuracy = test(model)
+      nni.report_final_result(accuracy)
+
+.. codesnippetcard::
+   :icon: ../img/thumbnails/pruning-small.svg
+   :title: Model Pruning
+   :link: tutorials/pruning_quick_start_mnist
+
+   .. code-block::
+
+      # define a config_list
+      config = [{
+          'sparsity': 0.8,
+          'op_types': ['Conv2d']
+      }]
+
+      # generate masks for simulated pruning
+      wrapped_model, masks = \
+          L1NormPruner(model, config). \
+          compress()
+
+      # apply the masks for real speedup
+      ModelSpeedup(unwrapped_model, input, masks). \
+          speedup_model()
+
+.. codesnippetcard::
+   :icon: ../img/thumbnails/quantization-small.svg
+   :title: Quantization
+   :link: tutorials/quantization_quick_start_mnist
+
+   .. code-block::
+
+      # define a config_list
+      config = [{
+          'quant_types': ['input', 'weight'],
+          'quant_bits': {'input': 8, 'weight': 8},
+          'op_types': ['Conv2d']
+      }]
+
+      # in case quantizer needs a extra training
+      quantizer = QAT_Quantizer(model, config)
+      quantizer.compress()
+      # Training...
+
+      # export calibration config and
+      # generate TensorRT engine for real speedup
+      calibration_config = quantizer.export_model(
+          model_path, calibration_path)
+      engine = ModelSpeedupTensorRT(
+          model, input_shape, config=calib_config)
+      engine.compress()
+
+.. codesnippetcard::
+   :icon: ../img/thumbnails/multi-trial-nas-small.svg
+   :title: Neural Architecture Search
+   :link: tutorials/hello_nas
+
+   .. code-block:: python
+
+      # define model space
+      class Model(nn.Module):
+          self.conv2 = nn.LayerChoice([
+              nn.Conv2d(32, 64, 3, 1),
+              DepthwiseSeparableConv(32, 64)
+          ])
+      model_space = Model()
+      # search strategy + evaluator
+      strategy = RegularizedEvolution()
+      evaluator = FunctionalEvaluator(
+          train_eval_fn)
+
+      # run experiment
+      RetiariiExperiment(model_space,
+          evaluator, strategy).run()
+
+.. codesnippetcard::
+   :icon: ../img/thumbnails/one-shot-nas-small.svg
+   :title: One-shot NAS
+   :link: nas/exploration_strategy
+
+   .. code-block::
+
+      # define model space
+      space = AnySearchSpace()
+
+      # get a darts trainer
+      trainer = DartsTrainer(space, loss, metrics)
+      trainer.fit()
+
+      # get final searched architecture
+      arch = trainer.export()
+
+.. codesnippetcard::
+   :icon: ../img/thumbnails/feature-engineering-small.svg
+   :title: Feature Engineering
+   :link: feature_engineering/overview
+
+   .. code-block::
+
+      selector = GBDTSelector()
+      selector.fit(
+          X_train, y_train,
+          lgb_params=lgb_params,
+          eval_ratio=eval_ratio,
+          early_stopping_rounds=10,
+          importance_type='gain',
+          num_boost_round=1000)
+
+      # get selected features
+      features = selector.get_selected_features()
+
+.. End of code snippet card
 
 .. raw:: html
 
-  <div class="rowHeight">
-    <div class="chinese"><a href="https://nni.readthedocs.io/zh/stable/">简体中文</a></div>
-    <b>NNI (Neural Network Intelligence)</b> is a lightweight but powerful toolkit to
-    help users <b>automate</b>
-    <a href="FeatureEngineering/Overview.html">Feature Engineering</a>,
-    <a href="NAS/Overview.html">Neural Architecture Search</a>,
-    <a href="Tuner/BuiltinTuner.html">Hyperparameter Tuning</a> and
-    <a href="Compression/Overview.html">Model Compression</a>.
-  </div>
-  <p class="gap rowHeight">
-    The tool manages automated machine learning (AutoML) experiments,
-    <b>dispatches and runs</b>
-    experiments' trial jobs generated by tuning algorithms to search the best neural
-    architecture and/or hyper-parameters in
-    <b>different training environments</b> like
-    <a href="TrainingService/LocalMode.html">Local Machine</a>,
-    <a href="TrainingService/RemoteMachineMode.html">Remote Servers</a>,
-    <a href="TrainingService/PaiMode.html">OpenPAI</a>,
-    <a href="TrainingService/KubeflowMode.html">Kubeflow</a>,
-    <a href="TrainingService/FrameworkControllerMode.html">FrameworkController on K8S (AKS etc.)</a>,
-    <a href="TrainingService/DLTSMode.html">DLWorkspace (aka. DLTS)</a>,
-    <a href="TrainingService/AMLMode.html">AML (Azure Machine Learning)</a>, 
-    <a href="TrainingService/AdaptDLMode.html">AdaptDL (aka. ADL)</a>, other cloud options and even <a href="TrainingService/HybridMode.html">Hybrid mode</a>.
-  </p>
-  <!-- Who should consider using NNI -->
-  <div>
-    <h2 class="title">Who should consider using NNI</h2>
-    <ul>
-      <li>Those who want to <b>try different AutoML algorithms</b> in their training code/model.</li>
-      <li>Those who want to run AutoML trial jobs <b>in different environments</b> to speed up search.</li>
-      <li class="rowHeight">Researchers and data scientists who want to easily <b>implement and experiement new AutoML
-          algorithms</b>
-        , may it be: hyperparameter tuning algorithm,
-        neural architect search algorithm or model compression algorithm.
-      </li>
-      <li>ML Platform owners who want to <b>support AutoML in their platform</b></li>
-    </ul>
-  </div>
-  <!-- what's new -->
-  <div>
-    <div class="inline gap">
-      <h2>What's NEW! </h2>
-      <img width="48" src="_static/img/release_icon.png">
-    </div>
-    <hr class="whatNew"/>
-    <ul>
-      <li><b>New release:</b> <a href='https://github.com/microsoft/nni/releases/tag/v2.6'>v2.6 is available.  <i>- released on Jan-18-2022</i></a></li>
-      <li><b>New demo available:</b> <a href="https://www.youtube.com/channel/UCKcafm6861B2mnYhPbZHavw">Youtube entry</a> | <a href="https://space.bilibili.com/1649051673">Bilibili</a> 入口 <i>- last updated on May-26-2021</i></li>
-      <li><b>New webinar:</b> <a href="https://note.microsoft.com/MSR-Webinar-Retiarii-Registration-On-Demand.html">
-        Introducing Retiarii: A deep learning exploratory-training framework on NNI
-        </a> <i>- scheduled on June-24-2021</i>
-      </li>
-      <li><b>New community channel:</b> <a href="https://github.com/microsoft/nni/discussions">Discussions</a></li>
-      <li>
-        <div><b>New emoticons release:</b> <a href="nnSpider.html">nnSpider</a></div>
-        <img class="gap" src="_static/img/home.svg"></img>
-      </li>
-    </ul>
-  </div>
-  <!-- NNI capabilities in a glance -->
-  <div class="gap">
-    <h2 class="title">NNI capabilities in a glance</h2>
-    <p class="rowHeight">
-      NNI provides CommandLine Tool as well as an user friendly WebUI to manage training experiements.
-      With the extensible API, you can customize your own AutoML algorithms and training services.
-      To make it easy for new users, NNI also provides a set of build-in stat-of-the-art
-      AutoML algorithms and out of box support for popular training platforms.
-    </p>
-    <p class="rowHeight">
-      Within the following table, we summarized the current NNI capabilities,
-      we are gradually adding new capabilities and we'd love to have your contribution.
-    </p>
-  </div>
-
-  <p align="center">
-    <a href="#overview"><img src="_static/img/overview.svg" /></a>
-  </p>
-
-  <table class="main-table">
-    <tbody>
-      <tr align="center" valign="bottom" class="column">
-        <td></td>
-        <td class="framework">
-          <b>Frameworks & Libraries</b>
-        </td>
-        <td>
-          <b>Algorithms</b>
-        </td>
-        <td>
-          <b>Training Services</b>
-        </td>
-      </tr>
-      </tr>
-      <tr>
-        <td class="verticalMiddle"><b>Built-in</b></td>
-        <td>
-          <ul class="firstUl">
-            <li><b>Supported Frameworks</b></li>
-            <ul class="circle">
-              <li>PyTorch</li>
-              <li>Keras</li>
-              <li>TensorFlow</li>
-              <li>MXNet</li>
-              <li>Caffe2</li>
-              <a href="SupportedFramework_Library.html">More...</a><br />
-            </ul>
-          </ul>
-          <ul class="firstUl">
-            <li><b>Supported Libraries</b></li>
-            <ul class="circle">
-              <li>Scikit-learn</li>
-              <li>XGBoost</li>
-              <li>LightGBM</li>
-              <a href="SupportedFramework_Library.html">More...</a><br />
-            </ul>
-          </ul>
-          <ul class="firstUl">
-            <li><b>Examples</b></li>
-            <ul class="circle">
-              <li><a href="https://github.com/microsoft/nni/tree/master/examples/trials/mnist-pytorch">MNIST-pytorch</li>
-              </a>
-              <li><a href="https://github.com/microsoft/nni/tree/master/examples/trials/mnist-tfv2">MNIST-tensorflow</li>
-              </a>
-              <li><a href="https://github.com/microsoft/nni/tree/master/examples/trials/mnist-keras">MNIST-keras</li></a>
-              <li><a href="TrialExample/GbdtExample.html">Auto-gbdt</a></li>
-              <li><a href="TrialExample/Cifar10Examples.html">Cifar10-pytorch</li></a>
-              <li><a href="TrialExample/SklearnExamples.html">Scikit-learn</a></li>
-              <li><a href="TrialExample/EfficientNet.html">EfficientNet</a></li>
-              <li><a href="TrialExample/OpEvoExamples.html">Kernel Tunning</li></a>
-              <a href="SupportedFramework_Library.html">More...</a><br />
-            </ul>
-          </ul>
-        </td>
-        <td align="left">
-          <a href="Tuner/BuiltinTuner.html">Hyperparameter Tuning</a>
-          <ul class="firstUl">
-            <div><b>Exhaustive search</b></div>
-            <ul class="circle">
-              <li><a href="Tuner/BuiltinTuner.html#Random">Random Search</a></li>
-              <li><a href="Tuner/BuiltinTuner.html#GridSearch">Grid Search</a></li>
-              <li><a href="Tuner/BuiltinTuner.html#Batch">Batch</a></li>
-            </ul>
-            <div><b>Heuristic search</b></div>
-            <ul class="circle">
-              <li><a href="Tuner/BuiltinTuner.html#Evolution">Naïve Evolution</a></li>
-              <li><a href="Tuner/BuiltinTuner.html#Anneal">Anneal</a></li>
-              <li><a href="Tuner/BuiltinTuner.html#Hyperband">Hyperband</a></li>
-              <li><a href="Tuner/BuiltinTuner.html#PBTTuner">PBT</a></li>
-            </ul>
-            <div><b>Bayesian optimization</b></div>
-            <ul class="circle">
-              <li><a href="Tuner/BuiltinTuner.html#BOHB">BOHB</a></li>
-              <li><a href="Tuner/BuiltinTuner.html#TPE">TPE</a></li>
-              <li><a href="Tuner/BuiltinTuner.html#SMAC">SMAC</a></li>
-              <li><a href="Tuner/BuiltinTuner.html#MetisTuner">Metis Tuner</a></li>
-              <li><a href="Tuner/BuiltinTuner.html#GPTuner">GP Tuner</a> </li>
-              <li><a href="Tuner/BuiltinTuner.html#DNGOTuner">DNGO Tuner</a></li>
-            </ul>
-          </ul>
-          <a href="NAS/Overview.html">Neural Architecture Search (Retiarii)</a>
-          <ul class="firstUl">
-            <ul class="circle">
-              <li><a href="NAS/ENAS.html">ENAS</a></li>
-              <li><a href="NAS/DARTS.html">DARTS</a></li>
-              <li><a href="NAS/SPOS.html">SPOS</a></li>
-              <li><a href="NAS/Proxylessnas.html">ProxylessNAS</a></li>
-              <li><a href="NAS/FBNet.html">FBNet</a></li>
-              <li><a href="NAS/ExplorationStrategies.html">Reinforcement Learning</a></li>
-              <li><a href="NAS/ExplorationStrategies.html">Regularized Evolution</a></li>
-              <li><a href="NAS/Overview.html">More...</a></li>
-            </ul>
-          </ul>
-          <a href="Compression/Overview.html">Model Compression</a>
-          <ul class="firstUl">
-            <div><b>Pruning</b></div>
-            <ul class="circle">
-              <li><a href="Compression/Pruner.html#agp-pruner">AGP Pruner</a></li>
-              <li><a href="Compression/Pruner.html#slim-pruner">Slim Pruner</a></li>
-              <li><a href="Compression/Pruner.html#fpgm-pruner">FPGM Pruner</a></li>
-              <li><a href="Compression/Pruner.html#netadapt-pruner">NetAdapt Pruner</a></li>
-              <li><a href="Compression/Pruner.html#simulatedannealing-pruner">SimulatedAnnealing Pruner</a></li>
-              <li><a href="Compression/Pruner.html#admm-pruner">ADMM Pruner</a></li>
-              <li><a href="Compression/Pruner.html#autocompress-pruner">AutoCompress Pruner</a></li>
-              <li><a href="Compression/Overview.html">More...</a></li>
-            </ul>
-            <div><b>Quantization</b></div>
-            <ul class="circle">
-              <li><a href="Compression/Quantizer.html#qat-quantize">QAT Quantizer</a></li>
-              <li><a href="Compression/Quantizer.html#dorefa-quantizer">DoReFa Quantizer</a></li>
-              <li><a href="Compression/Quantizer.html#bnn-quantizer">BNN Quantizer</a></li>
-            </ul>
-          </ul>
-          <a href="FeatureEngineering/Overview.html">Feature Engineering (Beta)</a>
-          <ul class="circle">
-            <li><a href="FeatureEngineering/GradientFeatureSelector.html">GradientFeatureSelector</a></li>
-            <li><a href="FeatureEngineering/GBDTSelector.html">GBDTSelector</a></li>
-          </ul>
-          <a href="Assessor/BuiltinAssessor.html">Early Stop Algorithms</a>
-          <ul class="circle">
-            <li><a href="Assessor/BuiltinAssessor.html#MedianStop">Median Stop</a></li>
-            <li><a href="Assessor/BuiltinAssessor.html#Curvefitting">Curve Fitting</a></li>
-          </ul>
-        </td>
-        <td>
-          <ul class="firstUl">
-            <li><a href="TrainingService/LocalMode.html">Local Machine</a></li>
-            <li><a href="TrainingService/RemoteMachineMode.html">Remote Servers</a></li>
-            <li><a href="TrainingService/HybridMode.html">Hybrid mode</a></li>
-            <li><a href="TrainingService/AMLMode.html">AML(Azure Machine Learning)</a></li>
-            <li><b>Kubernetes based services</b></li>
-            <ul>
-              <li><a href="TrainingService/PaiMode.html">OpenPAI</a></li>
-              <li><a href="TrainingService/KubeflowMode.html">Kubeflow</a></li>
-              <li><a href="TrainingService/FrameworkControllerMode.html">FrameworkController on K8S (AKS etc.)</a></li>
-              <li><a href="TrainingService/DLTSMode.html">DLWorkspace (aka. DLTS)</a></li>
-              <li><a href="TrainingService/AdaptDLMode.html">AdaptDL (aka. ADL)</a></li>
-            </ul>
-          </ul>
-        </td>
-      </tr>
-      <tr valign="top">
-        <td class="verticalMiddle"><b>References</b></td>
-        <td>
-          <ul class="firstUl">
-            <li><a href="Tutorial/HowToLaunchFromPython.html">Python API</a></li>
-            <li><a href="Tutorial/AnnotationSpec.html">NNI Annotation</a></li>
-            <li><a href="installation.html">Supported OS</a></li>
-          </ul>
-        </td>
-        <td>
-          <ul class="firstUl">
-            <li><a href="Tuner/CustomizeTuner.html">CustomizeTuner</a></li>
-            <li><a href="Assessor/CustomizeAssessor.html">CustomizeAssessor</a></li>
-            <li><a href="Tutorial/InstallCustomizedAlgos.html">Install Customized Algorithms as Builtin Tuners/Assessors/Advisors</a></li>
-            <li><a href="NAS/QuickStart.html">Define NAS Model Space</a></li>
-            <li><a href="NAS/ApiReference.html">NAS/Retiarii APIs</a></li>
-          </ul>
-        </td>
-        <td>
-          <ul class="firstUl">
-            <li><a href="TrainingService/Overview.html">Support TrainingService</a></li>
-            <li><a href="TrainingService/HowToImplementTrainingService.html">Implement TrainingService</a></li>
-          </ul>
-        </td>
-      </tr>
-    </tbody>
-  </table>
-
-  <!-- Installation -->
-  <div class="gap">
-    <h2 class="title">Installation</h2>
-    <div>
-      <h3 class="second-title">Install</h3>
-      <div class="gap2">
-        NNI supports and is tested on Ubuntu >= 16.04, macOS >= 10.14.1,
-        and Windows 10 >= 1809. Simply run the following <code>pip install</code>
-        in an environment that has <code>python 64-bit >= 3.6</code>.
-      </div>
-      <div class="command-intro">Linux or macOS</div>
-      <div class="command">python3 -m pip install --upgrade nni</div>
-      <div class="command-intro">Windows</div>
-      <div class="command">python -m pip install --upgrade nni</div>
-      <div class="command-intro">If you want to try latest code, please <a href="installation.html">install
-          NNI</a> from source code.
-      </div>
-      <div class="chinese">For detail system requirements of NNI, please refer to <a href="Tutorial/InstallationLinux.html">here</a>
-        for Linux & macOS, and <a href="Tutorial/InstallationWin.html">here</a> for Windows.</div>
-    </div>
-    <div>
-      <p>Note:</p>
-      <ul>
-        <li>If there is any privilege issue, add --user to install NNI in the user directory.</li>
-        <li class="rowHeight">Currently NNI on Windows supports local, remote and pai mode. Anaconda or Miniconda is highly
-          recommended to install <a href="Tutorial/InstallationWin.html">NNI on Windows</a>.</li>
-        <li>If there is any error like Segmentation fault, please refer to <a
-            href="installation.html">FAQ</a>. For FAQ on Windows, please refer
-          to <a href="Tutorial/InstallationWin.html">NNI on Windows</a>.</li>
-      </ul>
-    </div>
-    <div>
-      <h3 class="second-title gap">Verify installation</h3>
-      <div>
-        The following example is built on TensorFlow 1.x. Make sure <b>TensorFlow 1.x is used</b> when running
-        it.
-      </div>
-      <ul>
-        <li>
-          <div class="command-intro">Download the examples via clone the source code.</div>
-          <div class="command">git clone -b v2.6 https://github.com/Microsoft/nni.git</div>
-        </li>
-        <li>
-          <div>Run the MNIST example.</div>
-          <div class="command-intro">Linux or macOS</div>
-          <div class="command">nnictl create --config nni/examples/trials/mnist-pytorch/config.yml</div>
-          <div class="command-intro">Windows</div>
-          <div class="command">nnictl create --config nni\examples\trials\mnist-pytorch\config_windows.yml</div>
-        </li>
-        <li>
-          <div class="rowHeight">
-            Wait for the message INFO: Successfully started experiment! in the command line.
-            This message indicates that your experiment has been successfully started.
-            You can explore the experiment using the Web UI url.
-          </div>
-          <!-- Indentation affects style！ -->
-          <pre class="main-code">
-  INFO: Starting restful server...
-  INFO: Successfully started Restful server!
-  INFO: Setting local config...
-  INFO: Successfully set local config!
-  INFO: Starting experiment...
-  INFO: Successfully started experiment!
-  -----------------------------------------------------------------------
-  The experiment id is egchD4qy
-  The Web UI urls are: http://223.255.255.1:8080   http://127.0.0.1:8080
-  -----------------------------------------------------------------------
-
-  You can use these commands to get more information about the experiment
-  -----------------------------------------------------------------------
-    commands                       description
-  1. nnictl experiment show        show the information of experiments
-  2. nnictl trial ls               list all of trial jobs
-  3. nnictl top                    monitor the status of running experiments
-  4. nnictl log stderr             show stderr log content
-  5. nnictl log stdout             show stdout log content
-  6. nnictl stop                   stop an experiment
-  7. nnictl trial kill             kill a trial job by id
-  8. nnictl --help                 get help information about nnictl
-  -----------------------------------------------------------------------
-  </pre>
-        </li>
-        <li class="rowHeight">
-          Open the Web UI url in your browser, you can view detail information of the experiment and
-          all the submitted trial jobs as shown below. <a href="Tutorial/WebUI.html">Here</a> are more Web UI
-          pages.
-          <img class="gap" src="_static/img/webui.gif" width="100%"/>
-    </div>
-    </li>
-    </ul>
-  </div>
-
-  <!-- Releases and Contributing -->
-  <div class="gap">
-    <h2 class="title">Releases and Contributing</h2>
-    <div>NNI has a monthly release cycle (major releases). Please let us know if you encounter a bug by filling an issue.</div>
-    <br/>
-    <div>We appreciate all contributions. If you are planning to contribute any bug-fixes, please do so without further discussions.</div>
-    <br/>
-    <div class="rowHeight">If you plan to contribute new features, new tuners, new training services, etc. please first open an issue or reuse an exisiting issue, and discuss the feature with us. We will discuss with you on the issue timely or set up conference calls if needed.</div>
-    <br/>
-    <div>To learn more about making a contribution to NNI, please refer to our <a href="contribution.html"">How-to contribution page</a>.</div>
-    <br/>
-    <div>We appreciate all contributions and thank all the contributors!</div>
-    <img class="gap" src="_static/img/contributors.png"></img>
-  </div>
-  <!-- feedback -->
-  <div class="gap">
-    <h2 class="title">Feedback</h2>
-    <ul>
-      <li><a href="https://github.com/microsoft/nni/issues/new/choose">File an issue</a> on GitHub.</li>
-      <li>Open or participate in a <a href="https://github.com/microsoft/nni/discussions">discussion</a>.</li>
-      <li>Discuss on the <a href="https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge">NNI Gitter</a> in NNI.</li>
-    </ul>
-    <div>
-      <div class="rowHeight">Join IM discussion groups:</div>
-      <table class="gap" border=1 style="border-collapse: collapse;">
-        <tbody>
-          <tr style="line-height: 30px;">
-            <th>Gitter</th>
-            <td></td>
-            <th>WeChat</th>
-          </tr>
-          <tr>
-            <td class="QR">
-              <img src="https://user-images.githubusercontent.com/39592018/80665738-e0574a80-8acc-11ea-91bc-0836dc4cbf89.png" alt="Gitter" />
-            </td>
-            <td width="80" align="center" class="or">OR</td>
-            <td class="QR">
-              <img src="https://github.com/scarlett2018/nniutil/raw/master/wechat.png" alt="NNI Wechat" />
-            </td>
-          </tr>
-        </tbody>
-      </table>
-    </div>
-  </div>
-  <!-- Test status -->
-  <div class="gap">
-    <h2 class="title">Test status</h2>
-    <h3>Essentials</h3>
-    <table class="pipeline">
-      <tr>
-        <th>Type</th>
-        <th>Status</th>
-      </tr>
-      <tr>
-        <td>Fast test</td>
-        <td>
-          <a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=54&branchName=master">
-            <img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/fast%20test?branchName=master"/>
-          </a>
-        </td>
-      </tr>
-      <tr>
-        <td>Full linux</td>
-        <td>
-          <a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=62&repoName=microsoft%2Fnni&branchName=master">
-            <img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/full%20test%20-%20linux?repoName=microsoft%2Fnni&branchName=master"/>
-          </a>
-        </td>
-      </tr>
-      <tr>
-        <td>Full windows</td>
-        <td>
-          <a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=63&branchName=master">
-            <img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/full%20test%20-%20windows?branchName=master"/>
-          </a>
-        </td>
-      </tr>
-    </table>
-    <h3 class="gap">Training services</h3>
-    <table class="pipeline">
-      <tr>
-        <th>Type</th>
-        <th>Status</th>
-      </th>
-      <tr>
-        <td>Remote - linux to linux</td>
-        <td>
-          <a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=64&branchName=master">
-            <img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20remote%20-%20linux%20to%20linux?branchName=master"/>
-          </a>
-        </td>
-      </tr>
-      <tr>
-        <td>Remote - linux to windows</td>
-        <td>
-          <a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=67&branchName=master">
-            <img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20remote%20-%20linux%20to%20windows?branchName=master"/>
-          </a>
-        </td>
-      </tr>
-      <tr>
-        <td>Remote - windows to linux</td>
-        <td>
-          <a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=68&branchName=master">
-            <img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20remote%20-%20windows%20to%20linux?branchName=master"/>
-          </a>
-        </td>
-      </tr>
-      <tr>
-        <td>OpenPAI</td>
-        <td>
-          <a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=65&branchName=master">
-            <img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20openpai%20-%20linux?branchName=master"/>
-          </a>
-        </td>
-      </tr>
-      <tr>
-        <td>Frameworkcontroller</td>
-        <td>
-          <a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=70&branchName=master">
-            <img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20frameworkcontroller?branchName=master"/>
-          </a>
-        </td>
-      </tr>
-      <tr>
-        <td>Kubeflow</td>
-        <td>
-          <a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=69&branchName=master">
-            <img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20kubeflow?branchName=master"/>
-          </a>
-        </td>
-      </tr>
-      <tr>
-        <td>Hybrid</td>
-        <td>
-          <a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=79&branchName=master">
-            <img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20hybrid?branchName=master"/>
-          </a>
-        </td>
-      </tr>
-      <tr>
-        <td>AzureML</td>
-        <td>
-          <a href="https://msrasrg.visualstudio.com/NNIOpenSource/_build/latest?definitionId=78&branchName=master">
-            <img src="https://msrasrg.visualstudio.com/NNIOpenSource/_apis/build/status/integration%20test%20-%20aml?branchName=master"/>
-          </a>
-        </td>
-      </tr>
-    </table>
-  </div>
-  <!-- Related Projects -->
-  <div class="gap">
-    <h2 class="title">Related Projects</h2>
-    <p class="rowHeight">
-      Targeting at openness and advancing state-of-art technology,
-      <a href="https://www.microsoft.com/en-us/research/group/systems-and-networking-research-group-asia/">Microsoft Research (MSR)</a>
-      had also released few
-      other open source projects.</p>
-    <ul id="relatedProject">
-      <li class="rowHeight">
-        <a href="https://github.com/Microsoft/pai">OpenPAI</a> : an open source platform that provides complete AI model
-        training and resource management
-        capabilities, it is easy to extend and supports on-premise,
-        cloud and hybrid environments in various scale.
-      </li>
-      <li class="rowHeight">
-        <a href="https://github.com/Microsoft/frameworkcontroller">FrameworkController</a> : an open source
-        general-purpose Kubernetes Pod Controller that orchestrate
-        all kinds of applications on Kubernetes by a single controller.
-      </li>
-      <li class="rowHeight">
-        <a href="https://github.com/Microsoft/MMdnn">MMdnn</a> : A comprehensive, cross-framework solution to convert,
-        visualize and diagnose deep neural network
-        models. The "MM" in MMdnn stands for model management
-        and "dnn" is an acronym for deep neural network.
-      </li>
-      <li class="rowHeight">
-        <a href="https://github.com/Microsoft/SPTAG">SPTAG</a> : Space Partition Tree And Graph (SPTAG) is an open
-        source library
-        for large scale vector approximate nearest neighbor search scenario.
-      </li>
-      <li class="rowHeight">
-        <a href="https://github.com/Microsoft/SPTAG">nn-Meter</a> : An accurate inference latency predictor for DNN models on diverse edge devices.
-      </li>
-    </ul>
-    <p>We encourage researchers and students leverage these projects to accelerate the AI development and research.</p>
-  </div>
-
-  <!-- License -->
-  <div>
-    <h2 class="title">License</h2>
-    <p>The entire codebase is under <a href="https://github.com/microsoft/nni/blob/master/LICENSE">MIT license</a></p>
-  </div>
-  </div>
+   </div>
+
+NNI eases the effort to scale and manage AutoML experiments
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. codesnippetcard::
+   :icon: ../img/thumbnails/training-service-small.svg
+   :title: Training Service
+   :link: experiment/training_service/overview
+   :seemore: See more here.
+
+   An AutoML experiment requires many trials to explore feasible and potentially good-performing models.
+   **Training service** aims to make the tuning process easily scalable in a distributed platforms.
+   It provides a unified user experience for diverse computation resources (e.g., local machine, remote servers, AKS).
+   Currently, NNI supports **more than 9** kinds of training services.
+
+.. codesnippetcard::
+   :icon: ../img/thumbnails/web-portal-small.svg
+   :title: Web Portal
+   :link: experiment/web_portal/web_portal
+   :seemore: See more here.
+
+   Web portal visualizes the tuning process, exposing the ability to inspect, monitor and control the experiment.
+
+   .. image:: ../static/img/webui.gif
+      :width: 100%
+
+.. codesnippetcard::
+   :icon: ../img/thumbnails/experiment-management-small.svg
+   :title: Experiment Management
+   :link: experiment/experiment_management
+   :seemore: See more here.
+
+   The DNN model tuning often requires more than one experiment.
+   Users might try different tuning algorithms, fine-tune their search space, or switch to another training service.
+   **Experiment management** provides the power to aggregate and compare tuning results from multiple experiments,
+   so that the tuning workflow becomes clean and organized.
+
+Get Support and Contribute Back
+-------------------------------
+
+NNI is maintained on the `NNI GitHub repository <https://github.com/microsoft/nni>`_. We collect feedbacks and new proposals/ideas on GitHub. You can:
+
+* Open a `GitHub issue <https://github.com/microsoft/nni/issues>`_ for bugs and feature requests.
+* Open a `pull request <https://github.com/microsoft/nni/pulls>`_ to contribute code (make sure to read the :doc:`contribution guide <notes/contributing>` before doing this).
+* Participate in `NNI Discussion <https://github.com/microsoft/nni/discussions>`_ for general questions and new ideas.
+* Join the following IM groups.
+
+.. list-table::
+   :header-rows: 1
+   :widths: auto
+
+   * - Gitter
+     - WeChat
+   * -
+       .. image:: https://user-images.githubusercontent.com/39592018/80665738-e0574a80-8acc-11ea-91bc-0836dc4cbf89.png
+     -
+       .. image:: https://github.com/scarlett2018/nniutil/raw/master/wechat.png
+
+Citing NNI
+----------
+
+If you use NNI in a scientific publication, please consider citing NNI in your references.
+
+   Microsoft. Neural Network Intelligence (version |release|). https://github.com/microsoft/nni
+
+Bibtex entry (please replace the version with the particular version you are using): ::
+
+   @software{nni2021,
+      author = {{Microsoft}},
+      month = {1},
+      title = {{Neural Network Intelligence}},
+      url = {https://github.com/microsoft/nni},
+      version = {2.0},
+      year = {2021}
+   }
diff --git a/docs/source/index_zh.rst b/docs/source/index_zh.rst
index e57bd56a1..f87d2dee1 100644
--- a/docs/source/index_zh.rst
+++ b/docs/source/index_zh.rst
@@ -1,483 +1,298 @@
-.. 1c1500ed177d6b4badecd72037a24a30
-
-###########################
-Neural Network Intelligence
-###########################
-
-
-..  toctree::
-    :maxdepth: 2
-    :titlesonly:
-    :hidden:
-
-    概述<Overview>
-    安装 <installation>
-    入门<Tutorial/QuickStart>
-    教程<tutorials>
-    自动（超参数）调优 <hyperparameter_tune>
-    神经网络架构搜索<nas>
-    模型压缩<model_compression>
-    特征工程<feature_engineering>
-    参考<reference>
-    示例与解决方案<CommunitySharings/community_sharings>
-    研究和出版物 <ResearchPublications>
-    常见问题 <Tutorial/FAQ>
-    如何贡献 <contribution>
-    更改日志 <Release>
+.. dbd41cab307bcd76cc747b3d478709b8
 
 
+NNI 文档
+=================
+
+.. toctree::
+   :maxdepth: 2
+   :caption: 开始使用
+   :hidden:
+
+   安装 <installation>
+   快速入门 <quickstart>
+
+.. toctree::
+   :maxdepth: 2
+   :caption: 用户指南
+   :hidden:
+
+   超参调优 <hpo/toctree>
+   架构搜索 <nas/toctree>
+   模型压缩 <compression/toctree>
+   特征工程 <feature_engineering/toctree>
+   实验管理 <experiment/toctree>
+
+.. toctree::
+   :maxdepth: 2
+   :caption: 参考
+   :hidden:
+
+   Python API <reference/python_api>
+   实验配置 <reference/experiment_config>
+   nnictl 命令 <reference/nnictl>
+
+.. toctree::
+   :maxdepth: 2
+   :caption: 杂项
+   :hidden:
+
+   示例 <examples>
+   社区分享 <sharings/community_sharings>
+   研究发布 <notes/research_publications>
+   源码安装 <notes/build_from_source>
+   贡献指南 <notes/contributing>
+   版本说明 <release>
+
+**NNI (Neural Network Intelligence)** 是一个轻量而强大的工具，可以帮助用户 **自动化**：
+
+* :doc:`超参调优 </hpo/overview>`
+* :doc:`架构搜索 </nas/overview>`
+* :doc:`模型压缩 </compression/overview>`
+* :doc:`特征工程 </feature_engineering/overview>`
+
+开始使用
+-----------
+
+安装最新的版本，可执行以下命令：
+
+.. code-block:: bash
+
+   $ pip install nni
+
+如果在安装上遇到问题，可参考 :doc:`安装指南 </installation>`。
+
+开始你的第一个 NNI 实验
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. code-block:: shell
+
+   $ nnictl hello
+
+.. note:: 你需要预先安装 `PyTorch <https://pytorch.org/>`_ （以及 `torchvision <https://pytorch.org/vision/stable/index.html>`_ ）才能运行这个实验。
+
+请阅读 :doc:`NNI 快速入门 <quickstart>` 以开启你的 NNI 旅程！
+
+为什么选择 NNI？
+--------------------
+
+NNI 使得自动机器学习技术即插即用
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
 .. raw:: html
 
-  <div class="rowHeight">
-    <div class="chinese"><a href="https://nni.readthedocs.io/zh/stable/">English</a></div>
-    <b>NNI (Neural Network Intelligence)</b> 是一个轻量但强大的工具包，帮助用户<b>自动</b>的进行
-    <a href="FeatureEngineering/Overview.html">特征工程</a>，<a href="NAS/Overview.html">神经网络架构搜索</a>， <a href="Tuner/BuiltinTuner.html">超参调优</a>以及<a href="Compression/Overview.html">模型压缩</a>。
-  </div>
-  <p class="gap rowHeight">
-    NNI 管理自动机器学习 (AutoML) 的 Experiment，
-    <b>调度运行</b>
-  由调优算法生成的 Trial 任务来找到最好的神经网络架构和/或超参，支持
-    <b>各种训练环境</b>，如
-    <a href="TrainingService/LocalMode.html">本机</a>,
-    <a href="TrainingService/RemoteMachineMode.html">远程服务器</a>,
-    <a href="TrainingService/PaiMode.html">OpenPAI</a>,
-    <a href="TrainingService/KubeflowMode.html">Kubeflow</a>,
-    <a href="TrainingService/FrameworkControllerMode.html">基于 K8S 的 FrameworkController（如，AKS 等)</a>,
-    <a href="TrainingService/DLTSMode.html">DLWorkspace (又称 DLTS)</a>,
-    <a href="TrainingService/AMLMode.html">AML (Azure Machine Learning)</a>
-  以及其它云服务。
-  </p>
-  <!-- Who should consider using NNI -->
-  <div>
-    <h1 class="title">使用场景</h1>
-    <ul>
-      <li>想要在自己的代码、模型中试验<b>不同的自动机器学习算法</b>。</li>
-      <li>想要在<b>不同的环境中</b>加速运行自动机器学习。</li>
-      <li>想要更容易<b>实现或试验新的自动机器学习算法</b>的研究员或数据科学家，包括：超参调优算法，神经网络搜索算法以及模型压缩算法。
-      </li>
-      <li>在机器学习平台中<b>支持自动机器学习</b>。</li>
-    </ul>
-  </div>
-  <!-- nni release to version -->
-  <div class="inline gap">
-    <h3><a href="https://github.com/microsoft/nni/releases">NNI v2.6 已发布！</a></h3>
-    <img width="48" src="_static/img/release_icon.png">
-  </div>
-  <!-- NNI capabilities in a glance -->
-  <div class="gap">
-    <h1 class="title">NNI 功能一览</h1>
-    <p class="rowHeight">
-      NNI 提供命令行工具以及友好的 WebUI 来管理训练的 Experiment。
-      通过可扩展的 API，可定制自动机器学习算法和训练平台。
-      为了方便新用户，NNI 内置了最新的自动机器学习算法，并为流行的训练平台提供了开箱即用的支持。
-    </p>
-    <p class="rowHeight">
-      下表中，包含了 NNI 的功能，同时在不断地增添新功能，也非常希望您能贡献其中。
-    </p>
-  </div>
-
-  <p align="center">
-    <a href="#overview"><img src="_static/img/overview.svg" /></a>
-  </p>
-
-  <table class="main-table">
-    <tbody>
-      <tr align="center" valign="bottom" class="column">
-        <td></td>
-        <td class="framework">
-          <b>框架和库</b>
-        </td>
-        <td>
-          <b>算法</b>
-        </td>
-        <td>
-          <b>训练平台</b>
-        </td>
-      </tr>
-      </tr>
-      <tr>
-        <td class="verticalMiddle"><b>内置</b></td>
-        <td>
-          <ul class="firstUl">
-            <li><b>支持的框架</b></li>
-            <ul class="circle">
-              <li>PyTorch</li>
-              <li>Keras</li>
-              <li>TensorFlow</li>
-              <li>MXNet</li>
-              <li>Caffe2</li>
-              <a href="SupportedFramework_Library.html">更多...</a><br />
-            </ul>
-          </ul>
-          <ul class="firstUl">
-            <li><b>支持的库</b></li>
-            <ul class="circle">
-              <li>Scikit-learn</li>
-              <li>XGBoost</li>
-              <li>LightGBM</li>
-              <a href="SupportedFramework_Library.html">更多...</a><br />
-            </ul>
-          </ul>
-          <ul class="firstUl">
-            <li><b>示例</b></li>
-            <ul class="circle">
-              <li><a href="https://github.com/microsoft/nni/tree/master/examples/trials/mnist-pytorch">MNIST-pytorch</li>
-              </a>
-              <li><a href="https://github.com/microsoft/nni/tree/master/examples/trials/mnist-tfv2">MNIST-tensorflow</li>
-              </a>
-              <li><a href="https://github.com/microsoft/nni/tree/master/examples/trials/mnist-keras">MNIST-keras</li></a>
-              <li><a href="TrialExample/GbdtExample.html">Auto-gbdt</a></li>
-              <li><a href="TrialExample/Cifar10Examples.html">Cifar10-pytorch</li></a>
-              <li><a href="TrialExample/SklearnExamples.html">Scikit-learn</a></li>
-              <li><a href="TrialExample/EfficientNet.html">EfficientNet</a></li>
-              <li><a href="TrialExample/OpEvoExamples.html">GPU Kernel 调优</li></a>
-              <a href="SupportedFramework_Library.html">更多...</a><br />
-            </ul>
-          </ul>
-        </td>
-        <td align="left">
-          <a href="Tuner/BuiltinTuner.html">超参调优</a>
-          <ul class="firstUl">
-            <div><b>穷举搜索</b></div>
-            <ul class="circle">
-              <li><a href="Tuner/BuiltinTuner.html#Random">Random Search（随机搜索）</a></li>
-              <li><a href="Tuner/BuiltinTuner.html#GridSearch">Grid Search（遍历搜索）</a></li>
-              <li><a href="Tuner/BuiltinTuner.html#Batch">Batch（批处理）</a></li>
-            </ul>
-            <div><b>启发式搜索</b></div>
-            <ul class="circle">
-              <li><a href="Tuner/BuiltinTuner.html#Evolution">Naïve Evolution（朴素进化）</a></li>
-              <li><a href="Tuner/BuiltinTuner.html#Anneal">Anneal（退火算法）</a></li>
-              <li><a href="Tuner/BuiltinTuner.html#Hyperband">Hyperband</a></li>
-              <li><a href="Tuner/BuiltinTuner.html#PBTTuner">P-DARTS</a></li>
-            </ul>
-            <div><b>贝叶斯优化</b></div>
-            <ul class="circle">
-              <li><a href="Tuner/BuiltinTuner.html#BOHB">BOHB</a></li>
-              <li><a href="Tuner/BuiltinTuner.html#TPE">TPE</a></li>
-              <li><a href="Tuner/BuiltinTuner.html#SMAC">SMAC</a></li>
-              <li><a href="Tuner/BuiltinTuner.html#MetisTuner">Metis Tuner</a></li>
-              <li><a href="Tuner/BuiltinTuner.html#GPTuner">GP Tuner</a> </li>
-              <li><a href="Tuner/BuiltinTuner.html#DNGOTuner">PPO Tuner</a></li>
-            </ul>
-          </ul>
-          <a href="NAS/Overview.html">神经网络架构搜索</a>
-          <ul class="firstUl">
-            <ul class="circle">
-              <li><a href="NAS/ENAS.html">ENAS</a></li>
-              <li><a href="NAS/DARTS.html">DARTS</a></li>
-              <li><a href="NAS/SPOS.html">SPOS</a></li>
-              <li><a href="NAS/Proxylessnas.html">ProxylessNAS</a></li>
-              <li><a href="NAS/FBNet.html">微信</a></li>
-              <li><a href="NAS/ExplorationStrategies.html">基于强化学习</a></li>
-              <li><a href="NAS/ExplorationStrategies.html">Network Morphism</a></li>
-              <li><a href="NAS/Overview.html">TextNAS</a></li>
-            </ul>
-          </ul>
-          <a href="Compression/Overview.html">模型压缩</a>
-          <ul class="firstUl">
-            <div><b>剪枝</b></div>
-            <ul class="circle">
-              <li><a href="Compression/Pruner.html#agp-pruner">AGP Pruner</a></li>
-              <li><a href="Compression/Pruner.html#slim-pruner">Slim Pruner</a></li>
-              <li><a href="Compression/Pruner.html#fpgm-pruner">FPGM Pruner</a></li>
-              <li><a href="Compression/Pruner.html#netadapt-pruner">NetAdapt Pruner</a></li>
-              <li><a href="Compression/Pruner.html#simulatedannealing-pruner">SimulatedAnnealing Pruner</a></li>
-              <li><a href="Compression/Pruner.html#admm-pruner">ADMM Pruner</a></li>
-              <li><a href="Compression/Pruner.html#autocompress-pruner">AutoCompress Pruner</a></li>
-              <li><a href="Compression/Overview.html">更多...</a></li>
-            </ul>
-            <div><b>量化</b></div>
-            <ul class="circle">
-              <li><a href="Compression/Quantizer.html#qat-quantize">QAT Quantizer</a></li>
-              <li><a href="Compression/Quantizer.html#dorefa-quantizer">DoReFa Quantizer</a></li>
-              <li><a href="Compression/Quantizer.html#bnn-quantizer">BNN Quantizer</a></li>
-            </ul>
-          </ul>
-          <a href="FeatureEngineering/Overview.html">特征工程（测试版）</a>
-          <ul class="circle">
-            <li><a href="FeatureEngineering/GradientFeatureSelector.html">GradientFeatureSelector</a></li>
-            <li><a href="FeatureEngineering/GBDTSelector.html">GBDTSelector</a></li>
-          </ul>
-          <a href="Assessor/BuiltinAssessor.html">提前终止算法</a>
-          <ul class="circle">
-            <li><a href="Assessor/BuiltinAssessor.html#MedianStop">Median Stop（中位数终止）</a></li>
-            <li><a href="Assessor/BuiltinAssessor.html#Curvefitting">Curve Fitting（曲线拟合）</a></li>
-          </ul>
-        </td>
-        <td>
-          <ul class="firstUl">
-            <li><a href="TrainingService/LocalMode.html">本机</a></li>
-            <li><a href="TrainingService/RemoteMachineMode.html">远程计算机</a></li>
-            <li><a href="TrainingService/HybridMode.html">混合模式</a></li>
-            <li><a href="TrainingService/AMLMode.html">AML(Azure Machine Learning)</a></li>
-            <li><b>基于 Kubernetes 的平台</b></li>
-            <ul>
-              <li><a href="TrainingService/PaiMode.html">OpenPAI</a></li>
-              <li><a href="TrainingService/KubeflowMode.html">Kubeflow</a></li>
-              <li><a href="TrainingService/FrameworkControllerMode.html">基于 K8S 的 FrameworkController (如 AKS 等)</a></li>
-              <li><a href="TrainingService/DLTSMode.html">DLWorkspace (又称 DLTS)</a></li>
-              <li><a href="TrainingService/AdaptDLMode.html">AML (Azure Machine Learning)</a></li>
-            </ul>
-          </ul>
-        </td>
-      </tr>
-      <tr valign="top">
-        <td class="verticalMiddle"><b>参考</b></td>
-        <td>
-          <ul class="firstUl">
-            <li><a href="Tutorial/HowToLaunchFromPython.html">Python API</a></li>
-            <li><a href="Tutorial/AnnotationSpec.html">NNI Annotation</a></li>
-            <li><a href="installation.html">支持的操作系统</a></li>
-          </ul>
-        </td>
-        <td>
-          <ul class="firstUl">
-            <li><a href="Tuner/CustomizeTuner.html">自定义 Tuner</a></li>
-            <li><a href="Assessor/CustomizeAssessor.html">自定义 Assessor</a></li>
-            <li><a href="Tutorial/InstallCustomizedAlgos.html">安装自定义的 Tuner，Assessor，Advisor</a></li>
-            <li><a href="NAS/QuickStart.html">定义 NAS 模型空间</a></li>
-            <li><a href="NAS/ApiReference.html">NAS/Retiarii APIs</a></li>
-          </ul>
-        </td>
-        <td>
-          <ul class="firstUl">
-            <li><a href="TrainingService/Overview.html">支持训练平台</a></li>
-            <li><a href="TrainingService/HowToImplementTrainingService.html">实现训练平台</a></li>
-          </ul>
-        </td>
-      </tr>
-    </tbody>
-  </table>
-
-  <!-- Installation -->
-  <div>
-    <h1 class="title">安装</h1>
-    <div>
-      <h2 class="second-title">安装</h2>
-      <p>
-        NNI 支持并在 Ubuntu >= 16.04, macOS >= 10.14.1, 和 Windows 10 >= 1809 通过了测试。 在 <code>python 64-bit >= 3.6</code> 的环境中，只需要运行 <code>pip install</code> 即可完成安装。
-      </p>
-      <div class="command-intro">Linux 或 macOS</div>
-      <div class="command">python3 -m pip install --upgrade nni</div>
-      <div class="command-intro">Windows</div>
-      <div class="command">python -m pip install --upgrade nni</div>
-      <p class="topMargin">如果想要尝试最新代码，可通过源代码<a href="installation.html">安装
-          NNI</a>。
-      </p>
-      <p>Linux 和 macOS 下 NNI 系统需求<a href="Tutorial/InstallationLinux.html">参考这里</a>，Windows <a href="Tutorial/InstallationWin.html">参考这里</a>。</p>
-    </div>
-    <div>
-      <p>注意：</p>
-      <ul>
-        <li>如果遇到任何权限问题，可添加 --user 在用户目录中安装 NNI。</li>
-        <li>目前，Windows 上的 NNI 支持本机，远程和 OpenPAI 模式。 强烈推荐使用 Anaconda 或 Miniconda <a href="Tutorial/InstallationWin.html">在 Windows 上安装 NNI</a>。</li>
-        <li>如果遇到如 Segmentation fault 这样的任何错误请参考 <a
-            href="installation.html">常见问题</a>。 Windows 上的常见问题，参考在 <a href="Tutorial/InstallationWin.html">Windows 上使用 NNI</a>。 Windows 上的常见问题，参考在 <a href="Tutorial/InstallationWin.html">Windows 上使用 NNI</a>。</li>
-      </ul>
-    </div>
-    <div>
-      <h2 class="second-title">验证安装</h2>
-      <p>
-        以下示例基于 TensorFlow 1.x 构建。 确保运行环境中使用的是 <b>TensorFlow 1.x</b>。
-      </p>
-      <ul>
-        <li>
-          <p>通过克隆源代码下载示例。</p>
-          <div class="command">git clone -b v2.6 https://github.com/Microsoft/nni.git</div>
-        </li>
-        <li>
-          <p>运行 MNIST 示例。</p>
-          <div class="command-intro">Linux 或 macOS</div>
-          <div class="command">nnictl create --config nni/examples/trials/mnist-tfv1/config.yml</div>
-          <div class="command-intro">Windows</div>
-          <div class="command">nnictl create --config nni\examples\trials\mnist-tfv1\config_windows.yml</div>
-        </li>
-        <li>
-          <p>
-            在命令行中等待输出 INFO: Successfully started experiment!  
-            此消息表明 Experiment 已成功启动。
-            通过命令行输出的 Web UI url 来访问 Experiment 的界面。
-          </p>
-          <!-- Indentation affects style！ -->
-          <pre class="main-code">
-  INFO: Starting restful server...
-  INFO: Successfully started Restful server!
-  INFO: Setting local config...
-  INFO: Successfully set local config!
-  INFO: Starting experiment...
-  INFO: Successfully started experiment!
-  -----------------------------------------------------------------------
-  The experiment id is egchD4qy
-  The Web UI urls are: http://223.255.255.1:8080   http://127.0.0.1:8080
-  -----------------------------------------------------------------------
-
-  You can use these commands to get more information about the experiment
-  -----------------------------------------------------------------------
-    commands                       description
-  1. nnictl experiment show        show the information of experiments
-  2. nnictl trial ls               list all of trial jobs
-  3. nnictl top                    monitor the status of running experiments
-  4. nnictl log stderr             show stderr log content
-  5. nnictl log stdout             show stdout log content
-  6. nnictl stop                   stop an experiment
-  7. nnictl trial kill             kill a trial job by id
-  8. nnictl --help                 get help information about nnictl
-  -----------------------------------------------------------------------
-  </pre>
-        </li>
-        <li class="rowHeight">
-          在浏览器中打开 Web UI 地址，可看到下图的 Experiment 详细信息，以及所有的 Trial 任务。 查看<a href="Tutorial/WebUI.html">这里的</a>更多页面示例。
-          <img src="_static/img/webui.gif" width="100%"/>
-    </div>
-    </li>
-    </ul>
-  </div>
-
-  <!-- Documentation -->
-  <div>
-    <h1 class="title">文档</h1>
-    <ul>
-      <li>要了解 NNI，请阅读 <a href="Overview.html">NNI 概述</a>。</li>
-      <li>要熟悉如何使用 NNI，请阅读<a href="index.html">文档</a>。</li>
-      <li>要安装 NNI，请参阅<a href="installation.html">安装 NNI</a>。</li>
-    </ul>
-  </div>
-
-  <!-- Contributing -->
-  <div>
-    <h1 class="title">贡献</h1>
-    <p>
-      本项目欢迎任何贡献和建议。 大多数贡献都需要你同意参与者许可协议（CLA），来声明你有权，并实际上授予我们有权使用你的贡献。
-      有关详细信息，请访问 <a href="https://cla.microsoft.com">https://cla.microsoft.com</a>。
-    </p>
-    <p>
-      当你提交拉取请求时，CLA 机器人会自动检查你是否需要提供 CLA，并修饰这个拉取请求（例如，标签、注释）。 只需要按照机器人提供的说明进行操作即可。 CLA 只需要同意一次，就能应用到所有的代码仓库上。
-    </p>
-    <p>
-      该项目采用了 <a href="https://opensource.microsoft.com/codeofconduct/">Microsoft 开源行为准则 </a>。 有关详细信息,请参阅<a href="https://opensource.microsoft.com/codeofconduct/faq/">行为守则常见问题解答</a>或联系 <a
-        href="mailto:opencode@microsoft.com">opencode@microsoft.com</a> 咨询问题或评论。
-    </p>
-    <p>
-      熟悉贡献协议后，即可按照 NNI 开发人员教程，创建第一个 PR =) 了：
-    </p>
-    <ul>
-      <li>推荐新贡献者先从简单的问题开始：<a
-          href="https://github.com/Microsoft/nni/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22">'good first issue'</a> 或 <a
-          href="https://github.com/microsoft/nni/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22">'help-wanted'</a>。
-      </li>
-      <li><a href="Tutorial/SetupNniDeveloperEnvironment.html">NNI 开发环境安装教程</a></li>
-      <li><a href="Tutorial/HowToDebug.html">如何调试</a></li>
-      <li>
-        如果有使用上的问题，可先查看<a href="Tutorial/FAQ.html">常见问题解答</a>。如果没能解决问题，可通过 <a
-          href="https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge">Gitter</a> 
-  联系 NNI 开发团队或在 GitHub 上<a href="https://github.com/microsoft/nni/issues/new/choose">报告问题</a>。
-      </li>
-      <li><a href="Tuner/CustomizeTuner.html">自定义 Tuner</a></li>
-      <li><a href="TrainingService/HowToImplementTrainingService.html">实现定制的训练平台</a>
-      </li>
-      <li><a href="NAS/Advanced.html">在 NNI 上实现新的 NAS Trainer</a></li>
-      <li><a href="Tuner/CustomizeAdvisor.html">自定义 Advisor</a></li>
-    </ul>
-  </div>
-
-  <!-- External Repositories and References -->
-  <div>
-    <h1 class="title">其它代码库和参考</h1>
-    <p>经作者许可的一些 NNI 用法示例和相关文档。</p>
-    <ul>
-      <h2>外部代码库</h2>
-      <li>在 NNI 中运行 <a href="NAS/ENAS.html">ENAS</a></li>
-      <li>
-        https://github.com/microsoft/nni/blob/master/examples/feature_engineering/auto-feature-engineering/README_zh_CN.md
-      </li>
-      <li>使用 NNI 的 <a
-          href="https://github.com/microsoft/recommenders/blob/master/examples/04_model_select_and_optimize/nni_surprise_svd.ipynb">矩阵分解超参调优</a></li>
-      <li><a href="https://github.com/ksachdeva/scikit-nni">scikit-nni</a> 使用 NNI 为 scikit-learn 开发的超参搜索。</li>
-    </ul>
-
-    <!-- Relevant Articles -->
-    <ul>
-      <h2>相关文章</h2>
-      <li><a href="CommunitySharings/HpoComparison.html">超参数优化的对比</a></li>
-      <li><a href="CommunitySharings/NasComparison.html">神经网络结构搜索的对比</a></li>
-      <li><a href="CommunitySharings/ParallelizingTpeSearch.html">并行化顺序算法：TPE</a>
-      </li>
-      <li><a href="CommunitySharings/RecommendersSvd.html">使用 NNI 为 SVD 自动调参</a></li>
-      <li><a href="CommunitySharings/SptagAutoTune.html">使用 NNI 为 SPTAG 自动调参</a></li>
-      <li><a
-          href="https://towardsdatascience.com/find-thy-hyper-parameters-for-scikit-learn-pipelines-using-microsoft-nni-f1015b1224c1">
-          使用 NNI 为 scikit-learn 查找超参
-        </a></li>
-      <li>
-        <strong>博客</strong> - <a
-          href="http://gaocegege.com/Blog/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/katib-new#%E6%80%BB%E7%BB%93%E4%B8%8E%E5%88%86%E6%9E%90">AutoML 工具（Advisor，NNI 与 Google Vizier）的对比</a> 作者：@gaocegege - kubeflow/katib 的设计与实现的总结与分析章节
-      </li>
-      <li>
-        Blog (中文) - <a href="https://mp.weixin.qq.com/s/7_KRT-rRojQbNuJzkjFMuA">NNI 2019 新功能汇总</a> by @squirrelsc
-      </li>
-    </ul>
-  </div>
-
-  <!-- feedback -->
-  <div>
-    <h1 class="title">反馈</h1>
-    <ul>
-      <li><a href="https://github.com/microsoft/nni/issues/new/choose">在 GitHub 上提交问题</a>。</li>
-      <li>在 <a
-          href="https://stackoverflow.com/questions/tagged/nni?sort=Newest&edited=true">Stack Overflow</a> 上使用 nni 标签提问。
-      </li>
-      <li>在 <a
-          href="https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge">Gitter</a> 中参与讨论。</li>
-    </ul>
-    <div>
-      <div>加入聊天组：</div>
-      <table border=1 style="border-collapse: collapse;">
-        <tbody>
-          <tr style="line-height: 30px;">
-            <th>Gitter</th>
-            <td></td>
-            <th>微信</th>
-          </tr>
-          <tr>
-            <td class="QR">
-              <img src="https://user-images.githubusercontent.com/39592018/80665738-e0574a80-8acc-11ea-91bc-0836dc4cbf89.png" alt="Gitter" />
-            </td>
-            <td width="80" align="center" class="or">或</td>
-            <td class="QR">
-              <img src="https://github.com/scarlett2018/nniutil/raw/master/wechat.png" alt="NNI 微信" />
-            </td>
-          </tr>
-        </tbody>
-      </table>
-    </div>
-  </div>
-
-  <!-- Related Projects -->
-  <div>
-    <h1 class="title">相关项目</h1>
-    <p>
-      以探索先进技术和开放为目标，<a href="https://www.microsoft.com/zh-cn/research/group/systems-and-networking-research-group-asia/">Microsoft Research (MSR)</a> 还发布了一些相关的开源项目。</p>
-    <ul id="relatedProject">
-      <li>
-        <a href="https://github.com/Microsoft/pai">OpenPAI</a>：作为开源平台，提供了完整的 AI 模型训练和资源管理能力，能轻松扩展，并支持各种规模的私有部署、云和混合环境。
-      </li>
-      <li>
-        <a href="https://github.com/Microsoft/frameworkcontroller">FrameworkController</a>：开源的通用 Kubernetes Pod 控制器，通过单个控制器来编排 Kubernetes 上所有类型的应用。
-      </li>
-      <li>
-        <a href="https://github.com/Microsoft/MMdnn">MMdnn</a>：一个完整、跨框架的解决方案，能够转换、可视化、诊断深度神经网络模型。 MMdnn 中的 "MM" 表示 model management（模型管理），而 "dnn" 是 deep neural network（深度神经网络）的缩写。
-      </li>
-      <li>
-        <a href="https://github.com/Microsoft/SPTAG">SPTAG</a> : Space Partition Tree And Graph (SPTAG) 是用于大规模向量的最近邻搜索场景的开源库。
-      </li>
-    </ul>
-    <p>我们鼓励研究人员和学生利用这些项目来加速 AI 开发和研究。</p>
-  </div>
-
-  <!-- License -->
-  <div>
-    <h1 class="title">许可协议</h1>
-    <p>代码库遵循 <a href="https://github.com/microsoft/nni/blob/master/LICENSE">MIT 许可协议</a></p>
-  </div>
-  </div>
\ No newline at end of file
+   <div class="codesnippet-card-container">
+
+.. codesnippetcard::
+   :icon: ../img/thumbnails/hpo-small.svg
+   :title: 超参调优
+   :link: tutorials/hpo_quickstart_pytorch/main
+   :seemore: 点这里阅读完整教程
+
+   .. code-block::
+
+      params = nni.get_next_parameter()
+
+      class Net(nn.Module):
+          ...
+
+      model = Net()
+      optimizer = optim.SGD(model.parameters(),
+                            params['lr'],
+                            params['momentum'])
+
+      for epoch in range(10):
+          train(...)
+
+      accuracy = test(model)
+      nni.report_final_result(accuracy)
+
+.. codesnippetcard::
+   :icon: ../img/thumbnails/pruning-small.svg
+   :title: 模型剪枝
+   :link: tutorials/pruning_quick_start_mnist
+   :seemore: 点这里阅读完整教程
+
+   .. code-block::
+
+      # define a config_list
+      config = [{
+          'sparsity': 0.8,
+          'op_types': ['Conv2d']
+      }]
+
+      # generate masks for simulated pruning
+      wrapped_model, masks = \
+          L1NormPruner(model, config). \
+          compress()
+
+      # apply the masks for real speedup
+      ModelSpeedup(unwrapped_model, input, masks). \
+          speedup_model()
+
+.. codesnippetcard::
+   :icon: ../img/thumbnails/quantization-small.svg
+   :title: 模型量化
+   :link: tutorials/quantization_speedup
+   :seemore: 点这里阅读完整教程
+
+   .. code-block::
+
+      # define a config_list
+      config = [{
+          'quant_types': ['input', 'weight'],
+          'quant_bits': {'input': 8, 'weight': 8},
+          'op_types': ['Conv2d']
+      }]
+
+      # in case quantizer needs a extra training
+      quantizer = QAT_Quantizer(model, config)
+      quantizer.compress()
+      # Training...
+
+      # export calibration config and
+      # generate TensorRT engine for real speedup
+      calibration_config = quantizer.export_model(
+          model_path, calibration_path)
+      engine = ModelSpeedupTensorRT(
+          model, input_shape, config=calib_config)
+      engine.compress()
+
+.. codesnippetcard::
+   :icon: ../img/thumbnails/multi-trial-nas-small.svg
+   :title: 神经网络架构搜索
+   :link: tutorials/hello_nas
+   :seemore: 点这里阅读完整教程
+
+   .. code-block::
+
+      # define model space
+      -   self.conv2 = nn.Conv2d(32, 64, 3, 1)
+      +   self.conv2 = nn.LayerChoice([
+      +       nn.Conv2d(32, 64, 3, 1),
+      +       DepthwiseSeparableConv(32, 64)
+      +   ])
+      # search strategy + evaluator
+      strategy = RegularizedEvolution()
+      evaluator = FunctionalEvaluator(
+          train_eval_fn)
+
+      # run experiment
+      RetiariiExperiment(model_space,
+          evaluator, strategy).run()
+
+.. codesnippetcard::
+   :icon: ../img/thumbnails/one-shot-nas-small.svg
+   :title: 单尝试 (One-shot) NAS
+   :link: nas/exploration_strategy
+   :seemore: 点这里阅读完整教程
+
+   .. code-block::
+
+      # define model space
+      space = AnySearchSpace()
+
+      # get a darts trainer
+      trainer = DartsTrainer(space, loss, metrics)
+      trainer.fit()
+
+      # get final searched architecture
+      arch = trainer.export()
+
+.. codesnippetcard::
+   :icon: ../img/thumbnails/feature-engineering-small.svg
+   :title: 特征工程
+   :link: feature_engineering/overview
+   :seemore: 点这里阅读完整教程
+
+   .. code-block::
+
+      selector = GBDTSelector()
+      selector.fit(
+          X_train, y_train,
+          lgb_params=lgb_params,
+          eval_ratio=eval_ratio,
+          early_stopping_rounds=10,
+          importance_type='gain',
+          num_boost_round=1000)
+
+      # get selected features
+      features = selector.get_selected_features()
+
+.. End of code snippet card
+
+.. raw:: html
+
+   </div>
+
+NNI 可降低自动机器学习实验管理的成本
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. codesnippetcard::
+   :icon: ../img/thumbnails/training-service-small.svg
+   :title: 训练平台
+   :link: experiment/training_service/overview
+   :seemore: 点这里了解更多
+
+   一个自动机器学习实验通常需要很多次尝试，来找到合适且具有潜力的模型。
+   **训练平台** 的目标便是让整个调优过程可以轻松的扩展到分布式平台上，为不同的计算资源（例如本地机器、远端服务器、集群等）提供的统一的用户体验。
+   目前，NNI 已经支持 **超过九种** 训练平台。
+
+.. codesnippetcard::
+   :icon: ../img/thumbnails/web-portal-small.svg
+   :title: 网页控制台
+   :link: experiment/web_portal/web_portal
+   :seemore: 点这里了解更多
+
+   网页控制台提供了可视化调优过程的能力，让你可以轻松检查、跟踪、控制实验流程。
+
+   .. image:: ../static/img/webui.gif
+      :width: 100%
+
+.. codesnippetcard::
+   :icon: ../img/thumbnails/experiment-management-small.svg
+   :title: 多实验管理
+   :link: experiment/experiment_management
+   :seemore: 点这里了解更多
+
+   深度学习模型往往需要多个实验不断迭代，例如用户可能想尝试不同的调优算法，优化他们的搜索空间，或者切换到其他的计算资源。
+   **多实验管理** 提供了对多个实验的结果进行聚合和比较的强大能力，极大程度上简化了开发者的开发流程。
+
+获取帮助或参与贡献
+-------------------------------
+
+NNI 使用 `NNI GitHub 仓库 <https://github.com/microsoft/nni>`_ 进行维护。我们在 GitHub 上收集反馈，以及新需求和想法。你可以：
+
+* 新建一个 `GitHub issue <https://github.com/microsoft/nni/issues>`_ 反馈一个 bug 或者需求。
+* 新建一个 `pull request <https://github.com/microsoft/nni/pulls>`_ 以贡献代码（在此之前，请务必确保你已经阅读过 :doc:`贡献指南 <notes/contributing>`）。
+* 如果你有任何问题，都可以加入 `NNI 讨论 <https://github.com/microsoft/nni/discussions>`_。
+* 加入即时聊天群组：
+
+.. list-table::
+   :header-rows: 1
+   :widths: auto
+
+   * - Gitter
+     - 微信
+   * -
+       .. image:: https://user-images.githubusercontent.com/39592018/80665738-e0574a80-8acc-11ea-91bc-0836dc4cbf89.png
+     -
+       .. image:: https://github.com/scarlett2018/nniutil/raw/master/wechat.png
+
+引用 NNI
+----------
+
+如果你在你的文献中用到了 NNI，请考虑引用我们：
+
+   Microsoft. Neural Network Intelligence (version |release|). https://github.com/microsoft/nni
+
+Bibtex 格式如下（请将版本号替换成你在使用的特定版本）： ::
+
+   @software{nni2021,
+      author = {{Microsoft}},
+      month = {1},
+      title = {{Neural Network Intelligence}},
+      url = {https://github.com/microsoft/nni},
+      version = {2.0},
+      year = {2021}
+   }
diff --git a/docs/source/installation.rst b/docs/source/installation.rst
index 5688b3b4c..8e90c7371 100644
--- a/docs/source/installation.rst
+++ b/docs/source/installation.rst
@@ -1,12 +1,94 @@
-############
-Installation
-############
+Install NNI
+===========
 
-Currently we support installation on Linux, Mac and Windows. We also allow you to use docker.
+NNI requires Python >= 3.7.
+It is tested and supported on Ubuntu >= 18.04,
+Windows 10 >= 21H2, and macOS >= 11.
 
-..  toctree::
-    :maxdepth: 2
+There are 3 ways to install NNI:
 
-    Linux & Mac <Tutorial/InstallationLinux>
-    Windows <Tutorial/InstallationWin>
-    Use Docker <Tutorial/HowToUseDocker>
\ No newline at end of file
+* :ref:`Using pip <installation-pip>`
+* :ref:`Build source code <installation-source>`
+* :ref:`Using Docker <installation-docker>`
+
+.. _installation-pip:
+
+Using pip
+---------
+
+NNI provides official packages for x86-64 CPUs. They can be installed with pip:
+
+.. code-block:: text
+
+    pip install nni
+
+Or to upgrade to latest version:
+
+.. code-block:: text
+
+    pip install --latest nni
+
+You can check installation with:
+
+.. code-block:: text
+
+    nnictl --version
+
+On Linux systems without Conda, you may encounter ``bash: nnictl: command not found`` error.
+In this case you need to add pip script directory to ``PATH``:
+
+.. code-block:: bash
+
+    echo 'export PATH=${PATH}:${HOME}/.local/bin' >> ~/.bashrc
+    source ~/.bashrc
+
+.. _installation-source:
+
+Installing from Source Code
+---------------------------
+
+NNI hosts source code on `GitHub <https://github.com/microsoft/nni>`__.
+
+NNI has experimental support for ARM64 CPUs, including Apple M1.
+It requires to install from source code.
+
+See :doc:`/notes/build_from_source`.
+
+.. _installation-docker:
+
+Using Docker
+------------
+
+NNI provides official Docker image on `Docker Hub <https://hub.docker.com/r/msranni/nni>`__.
+
+.. code-block:: text
+
+    docker pull msranni/nni
+
+Installing Extra Dependencies
+-----------------------------
+
+Some built-in algorithms of NNI requires extra packages.
+Use ``nni[<algorithm-name>]`` to install their dependencies.
+
+For example, to install dependencies of :class:`DNGO tuner<nni.algorithms.hpo.dngo_tuner.DNGOTuner>` :
+
+.. code-block:: text
+
+    pip install nni[DNGO]
+
+This command will not reinstall NNI itself, even if it was installed in development mode.
+
+Alternatively, you may install all extra dependencies at once:
+
+.. code-block:: text
+
+    pip install nni[all]
+
+**NOTE**: SMAC tuner depends on swig3, which requires a manual downgrade on Ubuntu:
+
+.. code-block:: bash
+
+    sudo apt install swig3.0
+    sudo rm /usr/bin/swig
+    sudo ln -s swig3.0 /usr/bin/swig
diff --git a/docs/source/installation_zh.rst b/docs/source/installation_zh.rst
index cf0e8ccc4..89ef9d6f7 100644
--- a/docs/source/installation_zh.rst
+++ b/docs/source/installation_zh.rst
@@ -1,14 +1,90 @@
-.. c62173d7147a43a13bf2cdf945b82d07
+.. b4703fc8c8e8dc1babdb38ba9ebcd4a6
 
-############
-安装
-############
+安装 NNI
+========
 
-当前支持在 Linux，macOS 和 Windows 下安装。 还可使用 Docker。
+NNI 依赖于 Python 3.7 或以上版本。
 
-..  toctree::
-    :maxdepth: 2
+您可以通过以下三种方式之一安装 NNI：
 
-    Linux 和 macOS <Tutorial/InstallationLinux>
-    Windows <Tutorial/InstallationWin>
-    使用 Docker <Tutorial/HowToUseDocker>
\ No newline at end of file
+* :ref:`通过 pip 安装<zh-installation-pip>`
+* :ref:`从源代码编译安装<zh-installation-source>`
+* :ref:`使用 Docker 容器<zh-installation-docker>`
+
+.. _zh-installation-pip:
+
+pip 安装
+--------
+
+NNI 为 x86-64 平台提供预编译的安装包，您可以使用 pip 进行安装：
+
+.. code-block:: text
+
+    pip install nni
+
+您也可以升级已安装的旧版本 NNI：
+
+.. code-block:: text
+
+    pip install --latest nni
+
+安装完成后，请运行以下命令进行检查：
+
+.. code-block:: text
+
+    nnictl --version
+
+如果您使用的是 Linux 系统并且没有使用 Conda，您可能会遇到 ``bash: nnictl: command not found`` 错误，
+此时您需要将 pip 安装的可执行文件添加到 ``PATH`` 环境变量：
+
+.. code-block:: bash
+
+    echo 'export PATH=${PATH}:${HOME}/.local/bin' >> ~/.bashrc
+    source ~/.bashrc
+
+.. _zh-installation-source:
+
+编译安装
+--------
+
+NNI 项目使用 `GitHub <https://github.com/microsoft/nni>`__ 托管源代码。
+
+NNI 对 ARM64 平台（包括苹果 M1）提供实验性支持，如果您希望在此类平台上使用 NNI，请从源代码编译安装。
+
+编译步骤请参见英文文档： :doc:`/notes/build_from_source`
+
+.. _zh-installation-docker:
+
+Docker 镜像
+-----------
+
+NNI 在 `Docker Hub <https://hub.docker.com/r/msranni/nni>`__ 上提供了官方镜像。
+
+.. code-block:: text
+
+    docker pull msranni/nni
+
+安装额外依赖
+------------
+
+有一些算法依赖于额外的 pip 包，在使用前需要先指定 ``nni[算法名]`` 安装依赖。以 DNGO 算法为例，使用前请运行以下命令：
+
+.. code-block:: text
+
+    pip install nni[DNGO]
+
+如果您已经通过任一种方式安装了 NNI，以上命令不会重新安装或改变 NNI 版本，只会安装 DNGO 算法的额外依赖。
+
+您也可以一次性安装所有可选依赖：
+
+.. code-block:: text
+
+    pip install nni[all]
+
+**注意**：SMAC 算法依赖于 swig3，在 Ubuntu 系统中需要手动进行降级：
+
+.. code-block:: bash
+
+    sudo apt install swig3.0
+    sudo rm /usr/bin/swig
+    sudo ln -s swig3.0 /usr/bin/swig
diff --git a/docs/source/locales/zh/LC_MESSAGES/compression.po b/docs/source/locales/zh/LC_MESSAGES/compression.po
new file mode 100644
index 000000000..0ac8fc277
--- /dev/null
+++ b/docs/source/locales/zh/LC_MESSAGES/compression.po
@@ -0,0 +1,144 @@
+# SOME DESCRIPTIVE TITLE.
+# Copyright (C) 2022, Microsoft
+# This file is distributed under the same license as the NNI package.
+# FIRST AUTHOR <EMAIL@ADDRESS>, 2022.
+#
+#, fuzzy
+msgid ""
+msgstr ""
+"Project-Id-Version: NNI \n"
+"Report-Msgid-Bugs-To: \n"
+"POT-Creation-Date: 2022-04-13 03:14+0000\n"
+"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
+"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
+"Language-Team: LANGUAGE <LL@li.org>\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=utf-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Generated-By: Babel 2.9.1\n"
+
+#: ../../source/compression/overview.rst:2
+msgid "Overview of NNI Model Compression"
+msgstr ""
+
+#: ../../source/compression/overview.rst:4
+msgid ""
+"Deep neural networks (DNNs) have achieved great success in many tasks "
+"like computer vision, nature launguage processing, speech processing. "
+"However, typical neural networks are both computationally expensive and "
+"energy-intensive, which can be difficult to be deployed on devices with "
+"low computation resources or with strict latency requirements. Therefore,"
+" a natural thought is to perform model compression to reduce model size "
+"and accelerate model training/inference without losing performance "
+"significantly. Model compression techniques can be divided into two "
+"categories: pruning and quantization. The pruning methods explore the "
+"redundancy in the model weights and try to remove/prune the redundant and"
+" uncritical weights. Quantization refers to compress models by reducing "
+"the number of bits required to represent weights or activations. We "
+"further elaborate on the two methods, pruning and quantization, in the "
+"following chapters. Besides, the figure below visualizes the difference "
+"between these two methods."
+msgstr ""
+
+#: ../../source/compression/overview.rst:19
+msgid ""
+"NNI provides an easy-to-use toolkit to help users design and use model "
+"pruning and quantization algorithms. For users to compress their models, "
+"they only need to add several lines in their code. There are some popular"
+" model compression algorithms built-in in NNI. On the other hand, users "
+"could easily customize their new compression algorithms using NNI’s "
+"interface."
+msgstr ""
+
+#: ../../source/compression/overview.rst:24
+msgid "There are several core features supported by NNI model compression:"
+msgstr ""
+
+#: ../../source/compression/overview.rst:26
+msgid "Support many popular pruning and quantization algorithms."
+msgstr ""
+
+#: ../../source/compression/overview.rst:27
+msgid ""
+"Automate model pruning and quantization process with state-of-the-art "
+"strategies and NNI's auto tuning power."
+msgstr ""
+
+#: ../../source/compression/overview.rst:28
+msgid ""
+"Speedup a compressed model to make it have lower inference latency and "
+"also make it smaller."
+msgstr ""
+
+#: ../../source/compression/overview.rst:29
+msgid ""
+"Provide friendly and easy-to-use compression utilities for users to dive "
+"into the compression process and results."
+msgstr ""
+
+#: ../../source/compression/overview.rst:30
+msgid "Concise interface for users to customize their own compression algorithms."
+msgstr ""
+
+#: ../../source/compression/overview.rst:34
+msgid "Compression Pipeline"
+msgstr ""
+
+#: ../../source/compression/overview.rst:42
+msgid ""
+"The overall compression pipeline in NNI is shown above. For compressing a"
+" pretrained model, pruning and quantization can be used alone or in "
+"combination. If users want to apply both, a sequential mode is "
+"recommended as common practise."
+msgstr ""
+
+#: ../../source/compression/overview.rst:46
+msgid ""
+"Note that NNI pruners or quantizers are not meant to physically compact "
+"the model but for simulating the compression effect. Whereas NNI speedup "
+"tool can truly compress model by changing the network architecture and "
+"therefore reduce latency. To obtain a truly compact model, users should "
+"conduct :doc:`pruning speedup <../tutorials/pruning_speedup>` or "
+":doc:`quantizaiton speedup <../tutorials/quantization_speedup>`. The "
+"interface and APIs are unified for both PyTorch and TensorFlow. Currently"
+" only PyTorch version has been supported, and TensorFlow version will be "
+"supported in future."
+msgstr ""
+
+#: ../../source/compression/overview.rst:52
+msgid "Model Speedup"
+msgstr ""
+
+#: ../../source/compression/overview.rst:54
+msgid ""
+"The final goal of model compression is to reduce inference latency and "
+"model size. However, existing model compression algorithms mainly use "
+"simulation to check the performance (e.g., accuracy) of compressed model."
+" For example, using masks for pruning algorithms, and storing quantized "
+"values still in float32 for quantization algorithms. Given the output "
+"masks and quantization bits produced by those algorithms, NNI can really "
+"speedup the model."
+msgstr ""
+
+#: ../../source/compression/overview.rst:59
+msgid "The following figure shows how NNI prunes and speeds up your models."
+msgstr ""
+
+#: ../../source/compression/overview.rst:67
+msgid ""
+"The detailed tutorial of Speedup Model with Mask can be found :doc:`here "
+"<../tutorials/pruning_speedup>`. The detailed tutorial of Speedup Model "
+"with Calibration Config can be found :doc:`here "
+"<../tutorials/quantization_speedup>`."
+msgstr ""
+
+#: ../../source/compression/overview.rst:72
+msgid ""
+"NNI's model pruning framework has been upgraded to a more powerful "
+"version (named pruning v2 before nni v2.6). The old version (`named "
+"pruning before nni v2.6 "
+"<https://nni.readthedocs.io/en/v2.6/Compression/pruning.html>`_) will be "
+"out of maintenance. If for some reason you have to use the old pruning, "
+"v2.6 is the last nni version to support old pruning version."
+msgstr ""
+
diff --git a/docs/source/locales/zh/LC_MESSAGES/hpo.po b/docs/source/locales/zh/LC_MESSAGES/hpo.po
new file mode 100644
index 000000000..c3b4da488
--- /dev/null
+++ b/docs/source/locales/zh/LC_MESSAGES/hpo.po
@@ -0,0 +1,211 @@
+# SOME DESCRIPTIVE TITLE.
+# Copyright (C) 2022, Microsoft
+# This file is distributed under the same license as the NNI package.
+# FIRST AUTHOR <EMAIL@ADDRESS>, 2022.
+#
+#, fuzzy
+msgid ""
+msgstr ""
+"Project-Id-Version: NNI \n"
+"Report-Msgid-Bugs-To: \n"
+"POT-Creation-Date: 2022-04-20 05:50+0000\n"
+"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
+"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
+"Language-Team: LANGUAGE <LL@li.org>\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=utf-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Generated-By: Babel 2.9.1\n"
+
+#: ../../source/hpo/overview.rst:2
+msgid "Hyperparameter Optimization Overview"
+msgstr ""
+
+#: ../../source/hpo/overview.rst:4
+msgid ""
+"Auto hyperparameter optimization (HPO), or auto tuning, is one of the key"
+" features of NNI."
+msgstr ""
+
+#: ../../source/hpo/overview.rst:7
+msgid "Introduction to HPO"
+msgstr ""
+
+#: ../../source/hpo/overview.rst:9
+msgid ""
+"In machine learning, a hyperparameter is a parameter whose value is used "
+"to control learning process, and HPO is the problem of choosing a set of "
+"optimal hyperparameters for a learning algorithm. (`From "
+"<https://en.wikipedia.org/wiki/Hyperparameter_(machine_learning)>`__ "
+"`Wikipedia "
+"<https://en.wikipedia.org/wiki/Hyperparameter_optimization>`__)"
+msgstr ""
+
+#: ../../source/hpo/overview.rst:14
+msgid "Following code snippet demonstrates a naive HPO process:"
+msgstr ""
+
+#: ../../source/hpo/overview.rst:34
+msgid ""
+"You may have noticed, the example will train 4×10×3=120 models in total. "
+"Since it consumes so much computing resources, you may want to:"
+msgstr ""
+
+#: ../../source/hpo/overview.rst:37
+msgid ""
+":ref:`Find the best hyperparameter set with less iterations. <hpo-"
+"overview-tuners>`"
+msgstr ""
+
+#: ../../source/hpo/overview.rst:38
+msgid ":ref:`Train the models on distributed platforms. <hpo-overview-platforms>`"
+msgstr ""
+
+#: ../../source/hpo/overview.rst:39
+msgid ""
+":ref:`Have a portal to monitor and control the process. <hpo-overview-"
+"portal>`"
+msgstr ""
+
+#: ../../source/hpo/overview.rst:41
+msgid "NNI will do them for you."
+msgstr ""
+
+#: ../../source/hpo/overview.rst:44
+msgid "Key Features of NNI HPO"
+msgstr ""
+
+#: ../../source/hpo/overview.rst:49
+msgid "Tuning Algorithms"
+msgstr ""
+
+#: ../../source/hpo/overview.rst:51
+msgid ""
+"NNI provides *tuners* to speed up the process of finding best "
+"hyperparameter set."
+msgstr ""
+
+#: ../../source/hpo/overview.rst:53
+msgid ""
+"A tuner, or a tuning algorithm, decides the order in which hyperparameter"
+" sets are evaluated. Based on the results of historical hyperparameter "
+"sets, an efficient tuner can predict where the best hyperparameters "
+"locates around, and finds them in much fewer attempts."
+msgstr ""
+
+#: ../../source/hpo/overview.rst:57
+msgid ""
+"The naive example above evaluates all possible hyperparameter sets in "
+"constant order, ignoring the historical results. This is the brute-force "
+"tuning algorithm called *grid search*."
+msgstr ""
+
+#: ../../source/hpo/overview.rst:60
+msgid ""
+"NNI has out-of-the-box support for a variety of popular tuners. It "
+"includes naive algorithms like random search and grid search, Bayesian-"
+"based algorithms like TPE and SMAC, RL based algorithms like PPO, and "
+"much more."
+msgstr ""
+
+#: ../../source/hpo/overview.rst:64
+msgid "Main article: :doc:`tuners`"
+msgstr ""
+
+#: ../../source/hpo/overview.rst:69
+msgid "Training Platforms"
+msgstr ""
+
+#: ../../source/hpo/overview.rst:71
+msgid ""
+"If you are not interested in distributed platforms, you can simply run "
+"NNI HPO with current computer, just like any ordinary Python library."
+msgstr ""
+
+#: ../../source/hpo/overview.rst:74
+msgid ""
+"And when you want to leverage more computing resources, NNI provides "
+"built-in integration for training platforms from simple on-premise "
+"servers to scalable commercial clouds."
+msgstr ""
+
+#: ../../source/hpo/overview.rst:77
+msgid ""
+"With NNI you can write one piece of model code, and concurrently evaluate"
+" hyperparameter sets on local machine, SSH servers, Kubernetes-based "
+"clusters, AzureML service, and much more."
+msgstr ""
+
+#: ../../source/hpo/overview.rst:80
+msgid "Main article: :doc:`/experiment/training_service/overview`"
+msgstr ""
+
+#: ../../source/hpo/overview.rst:85
+msgid "Web Portal"
+msgstr ""
+
+#: ../../source/hpo/overview.rst:87
+msgid ""
+"NNI provides a web portal to monitor training progress, to visualize "
+"hyperparameter performance, to manually customize hyperparameters, and to"
+" manage multiple HPO experiments."
+msgstr ""
+
+#: ../../source/hpo/overview.rst:90
+msgid "Main article: :doc:`/experiment/web_portal/web_portal`"
+msgstr ""
+
+#: ../../source/hpo/overview.rst:96
+msgid "Tutorials"
+msgstr ""
+
+#: ../../source/hpo/overview.rst:98
+msgid ""
+"To start using NNI HPO, choose the quickstart tutorial of your favorite "
+"framework:"
+msgstr ""
+
+#: ../../source/hpo/overview.rst:100
+msgid ":doc:`PyTorch tutorial </tutorials/hpo_quickstart_pytorch/main>`"
+msgstr ""
+
+#: ../../source/hpo/overview.rst:101
+msgid ":doc:`TensorFlow tutorial </tutorials/hpo_quickstart_tensorflow/main>`"
+msgstr ""
+
+#: ../../source/hpo/overview.rst:104
+msgid "Extra Features"
+msgstr ""
+
+#: ../../source/hpo/overview.rst:106
+msgid ""
+"After you are familiar with basic usage, you can explore more HPO "
+"features:"
+msgstr ""
+
+#: ../../source/hpo/overview.rst:108
+msgid ""
+":doc:`Use command line tool to create and manage experiments (nnictl) "
+"</reference/nnictl>`"
+msgstr ""
+
+#: ../../source/hpo/overview.rst:110
+msgid ":doc:`nnictl example </tutorials/hpo_nnictl/nnictl>`"
+msgstr ""
+
+#: ../../source/hpo/overview.rst:112
+msgid ":doc:`Early stop non-optimal models (assessor) <assessors>`"
+msgstr ""
+
+#: ../../source/hpo/overview.rst:113
+msgid ":doc:`TensorBoard integration </experiment/web_portal/tensorboard>`"
+msgstr ""
+
+#: ../../source/hpo/overview.rst:114
+msgid ":doc:`Implement your own algorithm <custom_algorithm>`"
+msgstr ""
+
+#: ../../source/hpo/overview.rst:115
+msgid ":doc:`Benchmark tuners <hpo_benchmark>`"
+msgstr ""
+
diff --git a/docs/source/locales/zh/LC_MESSAGES/index.po b/docs/source/locales/zh/LC_MESSAGES/index.po
new file mode 100644
index 000000000..b7024287a
--- /dev/null
+++ b/docs/source/locales/zh/LC_MESSAGES/index.po
@@ -0,0 +1,219 @@
+# SOME DESCRIPTIVE TITLE.
+# Copyright (C) 2022, Microsoft
+# This file is distributed under the same license as the NNI package.
+# FIRST AUTHOR <EMAIL@ADDRESS>, 2022.
+#
+#, fuzzy
+msgid ""
+msgstr ""
+"Project-Id-Version: NNI \n"
+"Report-Msgid-Bugs-To: \n"
+"POT-Creation-Date: 2022-04-20 05:50+0000\n"
+"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
+"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
+"Language-Team: LANGUAGE <LL@li.org>\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=utf-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Generated-By: Babel 2.9.1\n"
+
+#: ../../source/index.rst:4 ../../source/index.rst:52
+msgid "Get Started"
+msgstr ""
+
+#: ../../source/index.rst:12
+msgid "Model Compression"
+msgstr ""
+
+#: ../../source/index.rst:12
+msgid "User Guide"
+msgstr ""
+
+#: ../../source/index.rst:23
+msgid "Python API"
+msgstr ""
+
+#: ../../source/index.rst:23
+msgid "References"
+msgstr ""
+
+#: ../../source/index.rst:32
+msgid "Misc"
+msgstr ""
+
+#: ../../source/index.rst:2
+msgid "NNI Documentation"
+msgstr ""
+
+#: ../../source/index.rst:44
+msgid ""
+"**NNI (Neural Network Intelligence)** is a lightweight but powerful "
+"toolkit to help users **automate**:"
+msgstr ""
+
+#: ../../source/index.rst:46
+msgid ":doc:`Hyperparameter Optimization </hpo/overview>`"
+msgstr ""
+
+#: ../../source/index.rst:47
+msgid ":doc:`Neural Architecture Search </nas/overview>`"
+msgstr ""
+
+#: ../../source/index.rst:48
+msgid ":doc:`Model Compression </compression/overview>`"
+msgstr ""
+
+#: ../../source/index.rst:49
+msgid ":doc:`Feature Engineering </feature_engineering/overview>`"
+msgstr ""
+
+#: ../../source/index.rst:54
+msgid "To install the current release:"
+msgstr ""
+
+#: ../../source/index.rst:60
+msgid ""
+"See the :doc:`installation guide </installation>` if you need additional "
+"help on installation."
+msgstr ""
+
+#: ../../source/index.rst:63
+msgid "Try your first NNI experiment"
+msgstr ""
+
+#: ../../source/index.rst:69
+msgid ""
+"You need to have `PyTorch <https://pytorch.org/>`_ (as well as "
+"`torchvision <https://pytorch.org/vision/stable/index.html>`_) installed "
+"to run this experiment."
+msgstr ""
+
+#: ../../source/index.rst:71
+msgid ""
+"To start your journey now, please follow the :doc:`absolute quickstart of"
+" NNI <quickstart>`!"
+msgstr ""
+
+#: ../../source/index.rst:74
+msgid "Why choose NNI?"
+msgstr ""
+
+#: ../../source/index.rst:77
+msgid "NNI makes AutoML techniques plug-and-play"
+msgstr ""
+
+#: ../../source/index.rst:221
+msgid "NNI eases the effort to scale and manage AutoML experiments"
+msgstr ""
+
+#: ../../source/index.rst:229
+msgid ""
+"An AutoML experiment requires many trials to explore feasible and "
+"potentially good-performing models. **Training service** aims to make the"
+" tuning process easily scalable in a distributed platforms. It provides a"
+" unified user experience for diverse computation resources (e.g., local "
+"machine, remote servers, AKS). Currently, NNI supports **more than 9** "
+"kinds of training services."
+msgstr ""
+
+#: ../../source/index.rst:240
+msgid ""
+"Web portal visualizes the tuning process, exposing the ability to "
+"inspect, monitor and control the experiment."
+msgstr ""
+
+#: ../../source/index.rst:251
+msgid ""
+"The DNN model tuning often requires more than one experiment. Users might"
+" try different tuning algorithms, fine-tune their search space, or switch"
+" to another training service. **Experiment management** provides the "
+"power to aggregate and compare tuning results from multiple experiments, "
+"so that the tuning workflow becomes clean and organized."
+msgstr ""
+
+#: ../../source/index.rst:257
+msgid "Get Support and Contribute Back"
+msgstr ""
+
+#: ../../source/index.rst:259
+msgid ""
+"NNI is maintained on the `NNI GitHub repository "
+"<https://github.com/microsoft/nni>`_. We collect feedbacks and new "
+"proposals/ideas on GitHub. You can:"
+msgstr ""
+
+#: ../../source/index.rst:261
+msgid ""
+"Open a `GitHub issue <https://github.com/microsoft/nni/issues>`_ for bugs"
+" and feature requests."
+msgstr ""
+
+#: ../../source/index.rst:262
+msgid ""
+"Open a `pull request <https://github.com/microsoft/nni/pulls>`_ to "
+"contribute code (make sure to read the :doc:`contribution guide "
+"<notes/contributing>` before doing this)."
+msgstr ""
+
+#: ../../source/index.rst:263
+msgid ""
+"Participate in `NNI Discussion "
+"<https://github.com/microsoft/nni/discussions>`_ for general questions "
+"and new ideas."
+msgstr ""
+
+#: ../../source/index.rst:264
+msgid "Join the following IM groups."
+msgstr ""
+
+#: ../../source/index.rst:270
+msgid "Gitter"
+msgstr ""
+
+#: ../../source/index.rst:271
+msgid "WeChat"
+msgstr ""
+
+#: ../../source/index.rst:278
+msgid "Citing NNI"
+msgstr ""
+
+#: ../../source/index.rst:280
+msgid ""
+"If you use NNI in a scientific publication, please consider citing NNI in"
+" your references."
+msgstr ""
+
+#: ../../source/index.rst:282
+msgid ""
+"Microsoft. Neural Network Intelligence (version |release|). "
+"https://github.com/microsoft/nni"
+msgstr ""
+
+#: ../../source/index.rst:284
+msgid ""
+"Bibtex entry (please replace the version with the particular version you "
+"are using): ::"
+msgstr ""
+
+#~ msgid "Hyperparameter Optimization"
+#~ msgstr ""
+
+#~ msgid "To run your first NNI experiment:"
+#~ msgstr ""
+
+#~ msgid ""
+#~ "you need to have `PyTorch "
+#~ "<https://pytorch.org/>`_ (as well as "
+#~ "`torchvision <https://pytorch.org/vision/stable/index.html>`_)"
+#~ " installed to run this experiment."
+#~ msgstr ""
+
+#~ msgid ""
+#~ "Open a `pull request "
+#~ "<https://github.com/microsoft/nni/pulls>`_ to contribute"
+#~ " code (make sure to read the "
+#~ "`contribution guide </contribution>` before "
+#~ "doing this)."
+#~ msgstr ""
+
diff --git a/docs/source/locales/zh/LC_MESSAGES/installation.po b/docs/source/locales/zh/LC_MESSAGES/installation.po
new file mode 100644
index 000000000..8960bf0f9
--- /dev/null
+++ b/docs/source/locales/zh/LC_MESSAGES/installation.po
@@ -0,0 +1,130 @@
+# SOME DESCRIPTIVE TITLE.
+# Copyright (C) 2022, Microsoft
+# This file is distributed under the same license as the NNI package.
+# FIRST AUTHOR <EMAIL@ADDRESS>, 2022.
+#
+#, fuzzy
+msgid ""
+msgstr ""
+"Project-Id-Version: NNI \n"
+"Report-Msgid-Bugs-To: \n"
+"POT-Creation-Date: 2022-04-13 03:14+0000\n"
+"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
+"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
+"Language-Team: LANGUAGE <LL@li.org>\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=utf-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Generated-By: Babel 2.9.1\n"
+
+#: ../../source/installation.rst:2
+msgid "Install NNI"
+msgstr ""
+
+#: ../../source/installation.rst:4
+msgid ""
+"NNI requires Python >= 3.7. It is tested and supported on Ubuntu >= "
+"18.04, Windows 10 >= 21H2, and macOS >= 11."
+msgstr ""
+
+#: ../../source/installation.rst:8
+msgid "There are 3 ways to install NNI:"
+msgstr ""
+
+#: ../../source/installation.rst:10
+msgid ":ref:`Using pip <installation-pip>`"
+msgstr ""
+
+#: ../../source/installation.rst:11
+msgid ":ref:`Build source code <installation-source>`"
+msgstr ""
+
+#: ../../source/installation.rst:12
+msgid ":ref:`Using Docker <installation-docker>`"
+msgstr ""
+
+#: ../../source/installation.rst:17
+msgid "Using pip"
+msgstr ""
+
+#: ../../source/installation.rst:19
+msgid ""
+"NNI provides official packages for x86-64 CPUs. They can be installed "
+"with pip:"
+msgstr ""
+
+#: ../../source/installation.rst:25
+msgid "Or to upgrade to latest version:"
+msgstr ""
+
+#: ../../source/installation.rst:31
+msgid "You can check installation with:"
+msgstr ""
+
+#: ../../source/installation.rst:37
+msgid ""
+"On Linux systems without Conda, you may encounter ``bash: nnictl: command"
+" not found`` error. In this case you need to add pip script directory to "
+"``PATH``:"
+msgstr ""
+
+#: ../../source/installation.rst:48
+msgid "Installing from Source Code"
+msgstr ""
+
+#: ../../source/installation.rst:50
+msgid "NNI hosts source code on `GitHub <https://github.com/microsoft/nni>`__."
+msgstr ""
+
+#: ../../source/installation.rst:52
+msgid ""
+"NNI has experimental support for ARM64 CPUs, including Apple M1. It "
+"requires to install from source code."
+msgstr ""
+
+#: ../../source/installation.rst:55
+msgid "See :doc:`/notes/build_from_source`."
+msgstr ""
+
+#: ../../source/installation.rst:60
+msgid "Using Docker"
+msgstr ""
+
+#: ../../source/installation.rst:62
+msgid ""
+"NNI provides official Docker image on `Docker Hub "
+"<https://hub.docker.com/r/msranni/nni>`__."
+msgstr ""
+
+#: ../../source/installation.rst:69
+msgid "Installing Extra Dependencies"
+msgstr ""
+
+#: ../../source/installation.rst:71
+msgid ""
+"Some built-in algorithms of NNI requires extra packages. Use ``nni"
+"[<algorithm-name>]`` to install their dependencies."
+msgstr ""
+
+#: ../../source/installation.rst:74
+msgid ""
+"For example, to install dependencies of :class:`DNGO "
+"tuner<nni.algorithms.hpo.dngo_tuner.DNGOTuner>` :"
+msgstr ""
+
+#: ../../source/installation.rst:80
+msgid ""
+"This command will not reinstall NNI itself, even if it was installed in "
+"development mode."
+msgstr ""
+
+#: ../../source/installation.rst:82
+msgid "Alternatively, you may install all extra dependencies at once:"
+msgstr ""
+
+#: ../../source/installation.rst:88
+msgid ""
+"**NOTE**: SMAC tuner depends on swig3, which requires a manual downgrade "
+"on Ubuntu:"
+msgstr ""
+
diff --git a/docs/source/locales/zh/LC_MESSAGES/nas.po b/docs/source/locales/zh/LC_MESSAGES/nas.po
new file mode 100644
index 000000000..094225265
--- /dev/null
+++ b/docs/source/locales/zh/LC_MESSAGES/nas.po
@@ -0,0 +1,318 @@
+# SOME DESCRIPTIVE TITLE.
+# Copyright (C) 2022, Microsoft
+# This file is distributed under the same license as the NNI package.
+# FIRST AUTHOR <EMAIL@ADDRESS>, 2022.
+#
+#, fuzzy
+msgid ""
+msgstr ""
+"Project-Id-Version: NNI \n"
+"Report-Msgid-Bugs-To: \n"
+"POT-Creation-Date: 2022-04-20 05:50+0000\n"
+"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
+"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
+"Language-Team: LANGUAGE <LL@li.org>\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=utf-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Generated-By: Babel 2.9.1\n"
+
+#: ../../source/nas/overview.rst:2
+msgid "Overview"
+msgstr ""
+
+#: ../../source/nas/overview.rst:4
+msgid ""
+"NNI's latest NAS supports are all based on Retiarii Framework, users who "
+"are still on `early version using NNI NAS v1.0 "
+"<https://nni.readthedocs.io/en/v2.2/nas.html>`__ shall migrate your work "
+"to Retiarii as soon as possible. We plan to remove the legacy NAS "
+"framework in the next few releases."
+msgstr ""
+
+#: ../../source/nas/overview.rst:6
+msgid ""
+"PyTorch is the **only supported framework on Retiarii**. Inquiries of NAS"
+" support on Tensorflow is in `this discussion "
+"<https://github.com/microsoft/nni/discussions/4605>`__. If you intend to "
+"run NAS with DL frameworks other than PyTorch and Tensorflow, please "
+"`open new issues <https://github.com/microsoft/nni/issues>`__ to let us "
+"know."
+msgstr ""
+
+#: ../../source/nas/overview.rst:9
+msgid "Basics"
+msgstr ""
+
+#: ../../source/nas/overview.rst:11
+msgid ""
+"Automatic neural architecture search is playing an increasingly important"
+" role in finding better models. Recent research has proven the "
+"feasibility of automatic NAS and has led to models that beat many "
+"manually designed and tuned models. Representative works include `NASNet "
+"<https://arxiv.org/abs/1707.07012>`__, `ENAS "
+"<https://arxiv.org/abs/1802.03268>`__, `DARTS "
+"<https://arxiv.org/abs/1806.09055>`__, `Network Morphism "
+"<https://arxiv.org/abs/1806.10282>`__, and `Evolution "
+"<https://arxiv.org/abs/1703.01041>`__. In addition, new innovations "
+"continue to emerge."
+msgstr ""
+
+#: ../../source/nas/overview.rst:13
+msgid ""
+"High-level speaking, aiming to solve any particular task with neural "
+"architecture search typically requires: search space design, search "
+"strategy selection, and performance evaluation. The three components work"
+" together with the following loop (from the famous `NAS survey "
+"<https://arxiv.org/abs/1808.05377>`__):"
+msgstr ""
+
+#: ../../source/nas/overview.rst:19
+msgid "In this figure:"
+msgstr ""
+
+#: ../../source/nas/overview.rst:21
+msgid ""
+"*Model search space*  means a set of models from which the best model is "
+"explored/searched. Sometimes we use *search space* or *model space* in "
+"short."
+msgstr ""
+
+#: ../../source/nas/overview.rst:22
+msgid ""
+"*Exploration strategy* is the algorithm that is used to explore a model "
+"search space. Sometimes we also call it *search strategy*."
+msgstr ""
+
+#: ../../source/nas/overview.rst:23
+msgid ""
+"*Model evaluator* is responsible for training a model and evaluating its "
+"performance."
+msgstr ""
+
+#: ../../source/nas/overview.rst:25
+msgid ""
+"The process is similar to :doc:`Hyperparameter Optimization "
+"</hpo/overview>`, except that the target is the best architecture rather "
+"than hyperparameter. Concretely, an exploration strategy selects an "
+"architecture from a predefined search space. The architecture is passed "
+"to a performance evaluation to get a score, which represents how well "
+"this architecture performs on a particular task. This process is repeated"
+" until the search process is able to find the best architecture."
+msgstr ""
+
+#: ../../source/nas/overview.rst:28
+msgid "Key Features"
+msgstr ""
+
+#: ../../source/nas/overview.rst:30
+msgid ""
+"The current NAS framework in NNI is powered by the research of `Retiarii:"
+" A Deep Learning Exploratory-Training Framework "
+"<https://www.usenix.org/system/files/osdi20-zhang_quanlu.pdf>`__, where "
+"we highlight the following features:"
+msgstr ""
+
+#: ../../source/nas/overview.rst:32
+msgid ":doc:`Simple APIs to construct search space easily <construct_space>`"
+msgstr ""
+
+#: ../../source/nas/overview.rst:33
+msgid ":doc:`SOTA NAS algorithms to explore search space <exploration_strategy>`"
+msgstr ""
+
+#: ../../source/nas/overview.rst:34
+msgid ""
+":doc:`Experiment backend support to scale up experiments on large-scale "
+"AI platforms </experiment/overview>`"
+msgstr ""
+
+#: ../../source/nas/overview.rst:37
+msgid "Why NAS with NNI"
+msgstr ""
+
+#: ../../source/nas/overview.rst:39
+msgid ""
+"We list out the three perspectives where NAS can be particularly "
+"challegning without NNI. NNI provides solutions to relieve users' "
+"engineering effort when they want to try NAS techniques in their own "
+"scenario."
+msgstr ""
+
+#: ../../source/nas/overview.rst:42
+msgid "Search Space Design"
+msgstr ""
+
+#: ../../source/nas/overview.rst:44
+msgid ""
+"The search space defines which architectures can be represented in "
+"principle. Incorporating prior knowledge about typical properties of "
+"architectures well-suited for a task can reduce the size of the search "
+"space and simplify the search. However, this also introduces a human "
+"bias, which may prevent finding novel architectural building blocks that "
+"go beyond the current human knowledge. Search space design can be very "
+"challenging for beginners, who might not possess the experience to "
+"balance the richness and simplicity."
+msgstr ""
+
+#: ../../source/nas/overview.rst:46
+msgid ""
+"In NNI, we provide a wide range of APIs to build the search space. There "
+"are :doc:`high-level APIs <construct_space>`, that enables the "
+"possibility to incorporate human knowledge about what makes a good "
+"architecture or search space. There are also :doc:`low-level APIs "
+"<mutator>`, that is a list of primitives to construct a network from "
+"operation to operation."
+msgstr ""
+
+#: ../../source/nas/overview.rst:49
+msgid "Exploration strategy"
+msgstr ""
+
+#: ../../source/nas/overview.rst:51
+msgid ""
+"The exploration strategy details how to explore the search space (which "
+"is often exponentially large). It encompasses the classical exploration-"
+"exploitation trade-off since, on the one hand, it is desirable to find "
+"well-performing architectures quickly, while on the other hand, premature"
+" convergence to a region of suboptimal architectures should be avoided. "
+"The \"best\" exploration strategy for a particular scenario is usually "
+"found via trial-and-error. As many state-of-the-art strategies are "
+"implemented with their own code-base, it becomes very troublesome to "
+"switch from one to another."
+msgstr ""
+
+#: ../../source/nas/overview.rst:53
+msgid ""
+"In NNI, we have also provided :doc:`a list of strategies "
+"<exploration_strategy>`. Some of them are powerful yet time consuming, "
+"while others might be suboptimal but really efficient. Given that all "
+"strategies are implemented with a unified interface, users can always "
+"find one that matches their need."
+msgstr ""
+
+#: ../../source/nas/overview.rst:56
+msgid "Performance estimation"
+msgstr ""
+
+#: ../../source/nas/overview.rst:58
+msgid ""
+"The objective of NAS is typically to find architectures that achieve high"
+" predictive performance on unseen data. Performance estimation refers to "
+"the process of estimating this performance. The problem with performance "
+"estimation is mostly its scalability, i.e., how can I run and manage "
+"multiple trials simultaneously."
+msgstr ""
+
+#: ../../source/nas/overview.rst:60
+msgid ""
+"In NNI, we standardize this process is implemented with :doc:`evaluator "
+"<evaluator>`, which is responsible of estimating a model's performance. "
+"NNI has quite a few built-in supports of evaluators, ranging from the "
+"simplest option, e.g., to perform a standard training and validation of "
+"the architecture on data, to complex configurations and implementations. "
+"Evaluators are run in *trials*, where trials can be spawn onto "
+"distributed platforms with our powerful :doc:`training service "
+"</experiment/training_service/overview>`."
+msgstr ""
+
+#: ../../source/nas/overview.rst:63
+msgid "Tutorials"
+msgstr ""
+
+#: ../../source/nas/overview.rst:65
+msgid ""
+"To start using NNI NAS framework, we recommend at least going through the"
+" following tutorials:"
+msgstr ""
+
+#: ../../source/nas/overview.rst:67
+msgid ":doc:`Quickstart </tutorials/hello_nas>`"
+msgstr ""
+
+#: ../../source/nas/overview.rst:68
+msgid ":doc:`construct_space`"
+msgstr ""
+
+#: ../../source/nas/overview.rst:69
+msgid ":doc:`exploration_strategy`"
+msgstr ""
+
+#: ../../source/nas/overview.rst:70
+msgid ":doc:`evaluator`"
+msgstr ""
+
+#: ../../source/nas/overview.rst:73
+msgid "Resources"
+msgstr ""
+
+#: ../../source/nas/overview.rst:75
+msgid ""
+"The following articles will help with a better understanding of the "
+"current arts of NAS:"
+msgstr ""
+
+#: ../../source/nas/overview.rst:77
+msgid ""
+"`Neural Architecture Search: A Survey "
+"<https://arxiv.org/abs/1808.05377>`__"
+msgstr ""
+
+#: ../../source/nas/overview.rst:78
+msgid ""
+"`A Comprehensive Survey of Neural Architecture Search: Challenges and "
+"Solutions <https://arxiv.org/abs/2006.02903>`__"
+msgstr ""
+
+#~ msgid "Basics"
+#~ msgstr ""
+
+#~ msgid "Basic Concepts"
+#~ msgstr ""
+
+#~ msgid ""
+#~ "The process is similar to "
+#~ ":doc:`Hyperparameter Optimization </hpo/index>`, "
+#~ "except that the target is the best"
+#~ " architecture rather than hyperparameter. "
+#~ "Concretely, an exploration strategy selects"
+#~ " an architecture from a predefined "
+#~ "search space. The architecture is passed"
+#~ " to a performance evaluation to get"
+#~ " a score, which represents how well"
+#~ " this architecture performs on a "
+#~ "particular task. This process is "
+#~ "repeated until the search process is "
+#~ "able to find the best architecture."
+#~ msgstr ""
+
+#~ msgid ""
+#~ "In NNI, we provide a wide range"
+#~ " of APIs to build the search "
+#~ "space. There are :doc:`high-level APIs"
+#~ " <construct_space>`, that enables incorporating"
+#~ " human knowledge about what makes a"
+#~ " good architecture or search space. "
+#~ "There are also :doc:`low-level APIs "
+#~ "<mutator>`, that is a list of "
+#~ "primitives to construct a network from"
+#~ " operator to operator."
+#~ msgstr ""
+
+#~ msgid ""
+#~ "In NNI, we standardize this process "
+#~ "is implemented with :doc:`evaluator "
+#~ "<evaluator>`, which is responsible of "
+#~ "estimating a model's performance. The "
+#~ "choices of evaluators also range from"
+#~ " the simplest option, e.g., to "
+#~ "perform a standard training and "
+#~ "validation of the architecture on data,"
+#~ " to complex configurations and "
+#~ "implementations. Evaluators are run in "
+#~ "*trials*, where trials can be spawn "
+#~ "onto distributed platforms with our "
+#~ "powerful :doc:`training service "
+#~ "</experiment/training_service/overview>`."
+#~ msgstr ""
+
diff --git a/docs/source/locales/zh/LC_MESSAGES/quickstart.po b/docs/source/locales/zh/LC_MESSAGES/quickstart.po
new file mode 100644
index 000000000..38bf53498
--- /dev/null
+++ b/docs/source/locales/zh/LC_MESSAGES/quickstart.po
@@ -0,0 +1,23 @@
+# SOME DESCRIPTIVE TITLE.
+# Copyright (C) 2022, Microsoft
+# This file is distributed under the same license as the NNI package.
+# FIRST AUTHOR <EMAIL@ADDRESS>, 2022.
+#
+#, fuzzy
+msgid ""
+msgstr ""
+"Project-Id-Version: NNI \n"
+"Report-Msgid-Bugs-To: \n"
+"POT-Creation-Date: 2022-04-13 03:14+0000\n"
+"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
+"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
+"Language-Team: LANGUAGE <LL@li.org>\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=utf-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Generated-By: Babel 2.9.1\n"
+
+#: ../../source/quickstart.rst:2
+msgid "Quickstart"
+msgstr ""
+
diff --git a/docs/source/locales/zh/LC_MESSAGES/sphinx.po b/docs/source/locales/zh/LC_MESSAGES/sphinx.po
new file mode 100644
index 000000000..b059911f1
--- /dev/null
+++ b/docs/source/locales/zh/LC_MESSAGES/sphinx.po
@@ -0,0 +1,23 @@
+# SOME DESCRIPTIVE TITLE.
+# Copyright (C) 2022, Microsoft
+# This file is distributed under the same license as the NNI package.
+# FIRST AUTHOR <EMAIL@ADDRESS>, 2022.
+#
+#, fuzzy
+msgid ""
+msgstr ""
+"Project-Id-Version: NNI \n"
+"Report-Msgid-Bugs-To: \n"
+"POT-Creation-Date: 2022-04-12 17:35+0000\n"
+"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
+"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
+"Language-Team: LANGUAGE <LL@li.org>\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=utf-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Generated-By: Babel 2.9.1\n"
+
+#: ../../templates/globaltoc.html:6
+msgid "Overview"
+msgstr ""
+
diff --git a/docs/source/locales/zh/LC_MESSAGES/tutorials.po b/docs/source/locales/zh/LC_MESSAGES/tutorials.po
new file mode 100644
index 000000000..ad12c7bcc
--- /dev/null
+++ b/docs/source/locales/zh/LC_MESSAGES/tutorials.po
@@ -0,0 +1,773 @@
+# SOME DESCRIPTIVE TITLE.
+# Copyright (C) 2022, Microsoft
+# This file is distributed under the same license as the NNI package.
+# FIRST AUTHOR <EMAIL@ADDRESS>, 2022.
+#
+#, fuzzy
+msgid ""
+msgstr ""
+"Project-Id-Version: NNI \n"
+"Report-Msgid-Bugs-To: \n"
+"POT-Creation-Date: 2022-04-20 05:50+0000\n"
+"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
+"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
+"Language-Team: LANGUAGE <LL@li.org>\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=utf-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Generated-By: Babel 2.9.1\n"
+
+#: ../../source/tutorials/hello_nas.rst:13
+msgid ""
+"Click :ref:`here <sphx_glr_download_tutorials_hello_nas.py>` to download "
+"the full example code"
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:22
+msgid "Hello, NAS!"
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:24
+msgid ""
+"This is the 101 tutorial of Neural Architecture Search (NAS) on NNI. In "
+"this tutorial, we will search for a neural architecture on MNIST dataset "
+"with the help of NAS framework of NNI, i.e., *Retiarii*. We use multi-"
+"trial NAS as an example to show how to construct and explore a model "
+"space."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:28
+msgid ""
+"There are mainly three crucial components for a neural architecture "
+"search task, namely,"
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:30
+msgid "Model search space that defines a set of models to explore."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:31
+msgid "A proper strategy as the method to explore this model space."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:32
+msgid ""
+"A model evaluator that reports the performance of every model in the "
+"space."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:34
+msgid ""
+"Currently, PyTorch is the only supported framework by Retiarii, and we "
+"have only tested **PyTorch 1.7 to 1.10**. This tutorial assumes PyTorch "
+"context but it should also apply to other frameworks, which is in our "
+"future plan."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:38
+msgid "Define your Model Space"
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:40
+msgid ""
+"Model space is defined by users to express a set of models that users "
+"want to explore, which contains potentially good-performing models. In "
+"this framework, a model space is defined with two parts: a base model and"
+" possible mutations on the base model."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:46
+msgid "Define Base Model"
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:48
+msgid ""
+"Defining a base model is almost the same as defining a PyTorch (or "
+"TensorFlow) model. Usually, you only need to replace the code ``import "
+"torch.nn as nn`` with ``import nni.retiarii.nn.pytorch as nn`` to use our"
+" wrapped PyTorch modules."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:52
+msgid "Below is a very simple example of defining a base model."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:93
+msgid ""
+"Always keep in mind that you should use ``import nni.retiarii.nn.pytorch "
+"as nn`` and :meth:`nni.retiarii.model_wrapper`. Many mistakes are a "
+"result of forgetting one of those. Also, please use ``torch.nn`` for "
+"submodules of ``nn.init``, e.g., ``torch.nn.init`` instead of "
+"``nn.init``."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:98
+msgid "Define Model Mutations"
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:100
+msgid ""
+"A base model is only one concrete model not a model space. We provide "
+":doc:`API and Primitives </nas/construct_space>` for users to express how"
+" the base model can be mutated. That is, to build a model space which "
+"includes many models."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:103
+msgid "Based on the above base model, we can define a model space as below."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:134
+msgid "This results in the following code:"
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:189
+#: ../../source/tutorials/hello_nas.rst:259
+#: ../../source/tutorials/hello_nas.rst:471
+#: ../../source/tutorials/hello_nas.rst:564
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:244
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:281
+#: ../../source/tutorials/pruning_quick_start_mnist.rst:65
+#: ../../source/tutorials/pruning_quick_start_mnist.rst:107
+#: ../../source/tutorials/pruning_quick_start_mnist.rst:172
+#: ../../source/tutorials/pruning_quick_start_mnist.rst:218
+#: ../../source/tutorials/pruning_quick_start_mnist.rst:255
+#: ../../source/tutorials/pruning_quick_start_mnist.rst:283
+msgid "Out:"
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:210
+msgid ""
+"This example uses two mutation APIs, :class:`nn.LayerChoice "
+"<nni.retiarii.nn.pytorch.LayerChoice>` and :class:`nn.InputChoice "
+"<nni.retiarii.nn.pytorch.ValueChoice>`. :class:`nn.LayerChoice "
+"<nni.retiarii.nn.pytorch.LayerChoice>` takes a list of candidate modules "
+"(two in this example), one will be chosen for each sampled model. It can "
+"be used like normal PyTorch module. :class:`nn.InputChoice "
+"<nni.retiarii.nn.pytorch.ValueChoice>` takes a list of candidate values, "
+"one will be chosen to take effect for each sampled model."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:219
+msgid ""
+"More detailed API description and usage can be found :doc:`here "
+"</nas/construct_space>`."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:223
+msgid ""
+"We are actively enriching the mutation APIs, to facilitate easy "
+"construction of model space. If the currently supported mutation APIs "
+"cannot express your model space, please refer to :doc:`this doc "
+"</nas/mutator>` for customizing mutators."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:228
+msgid "Explore the Defined Model Space"
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:230
+msgid ""
+"There are basically two exploration approaches: (1) search by evaluating "
+"each sampled model independently, which is the search approach in :ref"
+":`multi-trial NAS <multi-trial-nas>` and (2) one-shot weight-sharing "
+"based search, which is used in one-shot NAS. We demonstrate the first "
+"approach in this tutorial. Users can refer to :ref:`here <one-shot-nas>` "
+"for the second approach."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:235
+msgid ""
+"First, users need to pick a proper exploration strategy to explore the "
+"defined model space. Second, users need to pick or customize a model "
+"evaluator to evaluate the performance of each explored model."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:239
+msgid "Pick an exploration strategy"
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:241
+msgid ""
+"Retiarii supports many :doc:`exploration strategies "
+"</nas/exploration_strategy>`."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:243
+msgid "Simply choosing (i.e., instantiate) an exploration strategy as below."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:273
+msgid "Pick or customize a model evaluator"
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:275
+msgid ""
+"In the exploration process, the exploration strategy repeatedly generates"
+" new models. A model evaluator is for training and validating each "
+"generated model to obtain the model's performance. The performance is "
+"sent to the exploration strategy for the strategy to generate better "
+"models."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:279
+msgid ""
+"Retiarii has provided :doc:`built-in model evaluators </nas/evaluator>`, "
+"but to start with, it is recommended to use :class:`FunctionalEvaluator "
+"<nni.retiarii.evaluator.FunctionalEvaluator>`, that is, to wrap your own "
+"training and evaluation code with one single function. This function "
+"should receive one single model class and uses "
+":func:`nni.report_final_result` to report the final score of this model."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:284
+msgid ""
+"An example here creates a simple evaluator that runs on MNIST dataset, "
+"trains for 2 epochs, and reports its validation accuracy."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:367
+msgid "Create the evaluator"
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:386
+msgid ""
+"The ``train_epoch`` and ``test_epoch`` here can be any customized "
+"function, where users can write their own training recipe."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:389
+msgid ""
+"It is recommended that the ``evaluate_model`` here accepts no additional "
+"arguments other than ``model_cls``. However, in the :doc:`advanced "
+"tutorial </nas/evaluator>`, we will show how to use additional arguments "
+"in case you actually need those. In future, we will support mutation on "
+"the arguments of evaluators, which is commonly called \"Hyper-parmeter "
+"tuning\"."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:394
+msgid "Launch an Experiment"
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:396
+msgid ""
+"After all the above are prepared, it is time to start an experiment to do"
+" the model search. An example is shown below."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:417
+msgid ""
+"The following configurations are useful to control how many trials to run"
+" at most / at the same time."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:436
+msgid ""
+"Remember to set the following config if you want to GPU. "
+"``use_active_gpu`` should be set true if you wish to use an occupied GPU "
+"(possibly running a GUI)."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:456
+msgid ""
+"Launch the experiment. The experiment should take several minutes to "
+"finish on a workstation with 2 GPUs."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:495
+msgid ""
+"Users can also run Retiarii Experiment with :doc:`different training "
+"services </experiment/training_service/overview>` besides ``local`` "
+"training service."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:499
+msgid "Visualize the Experiment"
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:501
+msgid ""
+"Users can visualize their experiment in the same way as visualizing a "
+"normal hyper-parameter tuning experiment. For example, open "
+"``localhost:8081`` in your browser, 8081 is the port that you set in "
+"``exp.run``. Please refer to :doc:`here "
+"</experiment/web_portal/web_portal>` for details."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:505
+msgid ""
+"We support visualizing models with 3rd-party visualization engines (like "
+"`Netron <https://netron.app/>`__). This can be used by clicking "
+"``Visualization`` in detail panel for each trial. Note that current "
+"visualization is based on `onnx <https://onnx.ai/>`__ , thus "
+"visualization is not feasible if the model cannot be exported into onnx."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:510
+msgid ""
+"Built-in evaluators (e.g., Classification) will automatically export the "
+"model into a file. For your own evaluator, you need to save your file "
+"into ``$NNI_OUTPUT_DIR/model.onnx`` to make this work. For instance,"
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:541
+msgid "Relaunch the experiment, and a button is shown on Web portal."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:546
+msgid "Export Top Models"
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:548
+msgid ""
+"Users can export top models after the exploration is done using "
+"``export_top_models``."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:575
+msgid ""
+"The output is ``json`` object which records the mutation actions of the "
+"top model. If users want to output source code of the top model, they can"
+" use :ref:`graph-based execution engine <graph-based-execution-engine>` "
+"for the experiment, by simply adding the following two lines."
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:597
+msgid "**Total running time of the script:** ( 2 minutes  4.499 seconds)"
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:612
+msgid ":download:`Download Python source code: hello_nas.py <hello_nas.py>`"
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:618
+msgid ":download:`Download Jupyter notebook: hello_nas.ipynb <hello_nas.ipynb>`"
+msgstr ""
+
+#: ../../source/tutorials/hello_nas.rst:625
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:335
+#: ../../source/tutorials/pruning_quick_start_mnist.rst:357
+msgid "`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_"
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:13
+msgid ""
+"Click :ref:`here "
+"<sphx_glr_download_tutorials_hpo_quickstart_pytorch_main.py>` to download"
+" the full example code"
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:22
+msgid "HPO Quickstart with PyTorch"
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:23
+msgid ""
+"This tutorial optimizes the model in `official PyTorch quickstart`_ with "
+"auto-tuning."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:25
+msgid "The tutorial consists of 4 steps:"
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:27
+msgid "Modify the model for auto-tuning."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:28
+msgid "Define hyperparameters' search space."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:29
+msgid "Configure the experiment."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:30
+msgid "Run the experiment."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:37
+msgid "Step 1: Prepare the model"
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:38
+msgid "In first step, we need to prepare the model to be tuned."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:40
+msgid ""
+"The model should be put in a separate script. It will be evaluated many "
+"times concurrently, and possibly will be trained on distributed "
+"platforms."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:44
+msgid "In this tutorial, the model is defined in :doc:`model.py <model>`."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:46
+msgid "In short, it is a PyTorch model with 3 additional API calls:"
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:48
+msgid ""
+"Use :func:`nni.get_next_parameter` to fetch the hyperparameters to be "
+"evalutated."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:49
+msgid ""
+"Use :func:`nni.report_intermediate_result` to report per-epoch accuracy "
+"metrics."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:50
+msgid "Use :func:`nni.report_final_result` to report final accuracy."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:52
+msgid "Please understand the model code before continue to next step."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:57
+msgid "Step 2: Define search space"
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:58
+msgid ""
+"In model code, we have prepared 3 hyperparameters to be tuned: "
+"*features*, *lr*, and *momentum*."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:61
+msgid ""
+"Here we need to define their *search space* so the tuning algorithm can "
+"sample them in desired range."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:63
+msgid "Assuming we have following prior knowledge for these hyperparameters:"
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:65
+msgid "*features* should be one of 128, 256, 512, 1024."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:66
+msgid ""
+"*lr* should be a float between 0.0001 and 0.1, and it follows exponential"
+" distribution."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:67
+msgid "*momentum* should be a float between 0 and 1."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:69
+msgid ""
+"In NNI, the space of *features* is called ``choice``; the space of *lr* "
+"is called ``loguniform``; and the space of *momentum* is called "
+"``uniform``. You may have noticed, these names are derived from "
+"``numpy.random``."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:74
+msgid ""
+"For full specification of search space, check :doc:`the reference "
+"</hpo/search_space>`."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:76
+msgid "Now we can define the search space as follow:"
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:99
+msgid "Step 3: Configure the experiment"
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:100
+msgid ""
+"NNI uses an *experiment* to manage the HPO process. The *experiment "
+"config* defines how to train the models and how to explore the search "
+"space."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:103
+msgid ""
+"In this tutorial we use a *local* mode experiment, which means models "
+"will be trained on local machine, without using any special training "
+"platform."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:122
+msgid "Now we start to configure the experiment."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:125
+msgid "Configure trial code"
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:126
+msgid ""
+"In NNI evaluation of each hyperparameter set is called a *trial*. So the "
+"model script is called *trial code*."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:144
+msgid ""
+"When ``trial_code_directory`` is a relative path, it relates to current "
+"working directory. To run ``main.py`` in a different path, you can set "
+"trial code directory to ``Path(__file__).parent``. (`__file__ "
+"<https://docs.python.org/3.10/reference/datamodel.html#index-43>`__ is "
+"only available in standard Python, not in Jupyter Notebook.)"
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:151
+msgid ""
+"If you are using Linux system without Conda, you may need to change "
+"``\"python model.py\"`` to ``\"python3 model.py\"``."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:157
+msgid "Configure search space"
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:175
+msgid "Configure tuning algorithm"
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:176
+msgid "Here we use :doc:`TPE tuner </hpo/tuners>`."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:195
+msgid "Configure how many trials to run"
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:196
+msgid ""
+"Here we evaluate 10 sets of hyperparameters in total, and concurrently "
+"evaluate 2 sets at a time."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:213
+msgid "You may also set ``max_experiment_duration = '1h'`` to limit running time."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:215
+msgid ""
+"If neither ``max_trial_number`` nor ``max_experiment_duration`` are set, "
+"the experiment will run forever until you press Ctrl-C."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:220
+msgid ""
+"``max_trial_number`` is set to 10 here for a fast example. In real world "
+"it should be set to a larger number. With default config TPE tuner "
+"requires 20 trials to warm up."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:227
+msgid "Step 4: Run the experiment"
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:228
+msgid ""
+"Now the experiment is ready. Choose a port and launch it. (Here we use "
+"port 8080.)"
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:230
+msgid ""
+"You can use the web portal to view experiment status: "
+"http://localhost:8080."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:260
+msgid "After the experiment is done"
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:261
+msgid "Everything is done and it is safe to exit now. The following are optional."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:263
+msgid ""
+"If you are using standard Python instead of Jupyter Notebook, you can add"
+" ``input()`` or ``signal.pause()`` to prevent Python from exiting, "
+"allowing you to view the web portal after the experiment is done."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:293
+msgid ""
+":meth:`nni.experiment.Experiment.stop` is automatically invoked when "
+"Python exits, so it can be omitted in your code."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:296
+msgid ""
+"After the experiment is stopped, you can run "
+":meth:`nni.experiment.Experiment.view` to restart web portal."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:300
+msgid ""
+"This example uses :doc:`Python API </reference/experiment>` to create "
+"experiment."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:302
+msgid ""
+"You can also create and manage experiments with :doc:`command line tool "
+"<../hpo_nnictl/nnictl>`."
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:307
+msgid "**Total running time of the script:** ( 1 minutes  24.367 seconds)"
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:322
+msgid ":download:`Download Python source code: main.py <main.py>`"
+msgstr ""
+
+#: ../../source/tutorials/hpo_quickstart_pytorch/main.rst:328
+msgid ":download:`Download Jupyter notebook: main.ipynb <main.ipynb>`"
+msgstr ""
+
+#: ../../source/tutorials/pruning_quick_start_mnist.rst:13
+msgid ""
+"Click :ref:`here "
+"<sphx_glr_download_tutorials_pruning_quick_start_mnist.py>` to download "
+"the full example code"
+msgstr ""
+
+#: ../../source/tutorials/pruning_quick_start_mnist.rst:22
+msgid "Pruning Quickstart"
+msgstr ""
+
+#: ../../source/tutorials/pruning_quick_start_mnist.rst:24
+msgid ""
+"Model pruning is a technique to reduce the model size and computation by "
+"reducing model weight size or intermediate state size. There are three "
+"common practices for pruning a DNN model:"
+msgstr ""
+
+#: ../../source/tutorials/pruning_quick_start_mnist.rst:27
+msgid "Pre-training a model -> Pruning the model -> Fine-tuning the pruned model"
+msgstr ""
+
+#: ../../source/tutorials/pruning_quick_start_mnist.rst:28
+msgid ""
+"Pruning a model during training (i.e., pruning aware training) -> Fine-"
+"tuning the pruned model"
+msgstr ""
+
+#: ../../source/tutorials/pruning_quick_start_mnist.rst:29
+msgid "Pruning a model -> Training the pruned model from scratch"
+msgstr ""
+
+#: ../../source/tutorials/pruning_quick_start_mnist.rst:31
+msgid ""
+"NNI supports all of the above pruning practices by working on the key "
+"pruning stage. Following this tutorial for a quick look at how to use NNI"
+" to prune a model in a common practice."
+msgstr ""
+
+#: ../../source/tutorials/pruning_quick_start_mnist.rst:37
+msgid "Preparation"
+msgstr ""
+
+#: ../../source/tutorials/pruning_quick_start_mnist.rst:39
+msgid ""
+"In this tutorial, we use a simple model and pre-trained on MNIST dataset."
+" If you are familiar with defining a model and training in pytorch, you "
+"can skip directly to `Pruning Model`_."
+msgstr ""
+
+#: ../../source/tutorials/pruning_quick_start_mnist.rst:121
+msgid "Pruning Model"
+msgstr ""
+
+#: ../../source/tutorials/pruning_quick_start_mnist.rst:123
+msgid ""
+"Using L1NormPruner to prune the model and generate the masks. Usually, a "
+"pruner requires original model and ``config_list`` as its inputs. "
+"Detailed about how to write ``config_list`` please refer "
+":doc:`compression config specification "
+"<../compression/compression_config_list>`."
+msgstr ""
+
+#: ../../source/tutorials/pruning_quick_start_mnist.rst:127
+msgid ""
+"The following `config_list` means all layers whose type is `Linear` or "
+"`Conv2d` will be pruned, except the layer named `fc3`, because `fc3` is "
+"`exclude`. The final sparsity ratio for each layer is 50%. The layer "
+"named `fc3` will not be pruned."
+msgstr ""
+
+#: ../../source/tutorials/pruning_quick_start_mnist.rst:153
+msgid "Pruners usually require `model` and `config_list` as input arguments."
+msgstr ""
+
+#: ../../source/tutorials/pruning_quick_start_mnist.rst:232
+msgid ""
+"Speedup the original model with masks, note that `ModelSpeedup` requires "
+"an unwrapped model. The model becomes smaller after speedup, and reaches "
+"a higher sparsity ratio because `ModelSpeedup` will propagate the masks "
+"across layers."
+msgstr ""
+
+#: ../../source/tutorials/pruning_quick_start_mnist.rst:269
+msgid "the model will become real smaller after speedup"
+msgstr ""
+
+#: ../../source/tutorials/pruning_quick_start_mnist.rst:307
+msgid "Fine-tuning Compacted Model"
+msgstr ""
+
+#: ../../source/tutorials/pruning_quick_start_mnist.rst:308
+msgid ""
+"Note that if the model has been sped up, you need to re-initialize a new "
+"optimizer for fine-tuning. Because speedup will replace the masked big "
+"layers with dense small ones."
+msgstr ""
+
+#: ../../source/tutorials/pruning_quick_start_mnist.rst:329
+msgid "**Total running time of the script:** ( 0 minutes  58.337 seconds)"
+msgstr ""
+
+#: ../../source/tutorials/pruning_quick_start_mnist.rst:344
+msgid ""
+":download:`Download Python source code: pruning_quick_start_mnist.py "
+"<pruning_quick_start_mnist.py>`"
+msgstr ""
+
+#: ../../source/tutorials/pruning_quick_start_mnist.rst:350
+msgid ""
+":download:`Download Jupyter notebook: pruning_quick_start_mnist.ipynb "
+"<pruning_quick_start_mnist.ipynb>`"
+msgstr ""
+
+#~ msgid "**Total running time of the script:** ( 2 minutes  15.810 seconds)"
+#~ msgstr ""
+
+#~ msgid "NNI HPO Quickstart with PyTorch"
+#~ msgstr ""
+
+#~ msgid ""
+#~ "There is also a :doc:`TensorFlow "
+#~ "version<../hpo_quickstart_tensorflow/main>` if you "
+#~ "prefer it."
+#~ msgstr ""
+
+#~ msgid ""
+#~ "You can also create and manage "
+#~ "experiments with :doc:`command line tool "
+#~ "</reference/nnictl>`."
+#~ msgstr ""
+
+#~ msgid "**Total running time of the script:** ( 1 minutes  24.393 seconds)"
+#~ msgstr ""
+
diff --git a/docs/source/model_compression.rst b/docs/source/model_compression.rst
deleted file mode 100644
index 04be65b3a..000000000
--- a/docs/source/model_compression.rst
+++ /dev/null
@@ -1,34 +0,0 @@
-#################
-Model Compression
-#################
-
-Deep neural networks (DNNs) have achieved great success in many tasks. However, typical neural networks are both
-computationally expensive and energy intensive, can be difficult to be deployed on devices with low computation
-resources or with strict latency requirements. Therefore, a natural thought is to perform model compression to
-reduce model size and accelerate model training/inference without losing performance significantly. Model compression
-techniques can be divided into two categories: pruning and quantization. The pruning methods explore the redundancy
-in the model weights and try to remove/prune the redundant and uncritical weights. Quantization refers to compressing
-models by reducing the number of bits required to represent weights or activations.
-
-NNI provides an easy-to-use toolkit to help user design and use model pruning and quantization algorithms.
-It supports Tensorflow and PyTorch with unified interface.
-For users to compress their models, they only need to add several lines in their code.
-There are some popular model compression algorithms built-in in NNI.
-Users could further use NNI's auto tuning power to find the best compressed model,
-which is detailed in Auto Model Compression.
-On the other hand, users could easily customize their new compression algorithms using NNI's interface.
-
-For details, please refer to the following tutorials:
-
-..  toctree::
-    :maxdepth: 2
-
-    Overview <Compression/Overview>
-    Quick Start <Compression/QuickStart>
-    Tutorial <Compression/Tutorial>
-    Pruning <Compression/pruning>
-    Pruning V2 <Compression/v2_pruning>
-    Quantization <Compression/quantization>
-    Utilities <Compression/CompressionUtils>
-    Advanced Usage <Compression/advanced>
-    API Reference <Compression/CompressionReference>
diff --git a/docs/source/model_compression_zh.rst b/docs/source/model_compression_zh.rst
deleted file mode 100644
index 01f4a37e1..000000000
--- a/docs/source/model_compression_zh.rst
+++ /dev/null
@@ -1,36 +0,0 @@
-.. da97b4cdd507bd8fad43d640f3d2bfef
-
-#################
-模型压缩
-#################
-
-深度神经网络（DNNs）在许多领域都取得了巨大的成功。 然而，典型的神经网络是
-计算和能源密集型的，很难将其部署在计算资源匮乏
-或具有严格延迟要求的设备上。 因此，一个自然的想法就是对模型进行压缩
-以减小模型大小并加速模型训练/推断，同时不会显着降低模型性能。 模型压缩
-技术可以分为两类：剪枝和量化。 剪枝方法探索模型权重中的冗余，
-并尝试删除/修剪冗余和非关键的权重。 量化是指通过减少
-权重表示或激活所需的比特数来压缩模型。
-
-NNI 提供了易于使用的工具包来帮助用户设计并使用剪枝和量化算法。
-其使用了统一的接口来支持 TensorFlow 和 PyTorch。
-对用户来说， 只需要添加几行代码即可压缩模型。
-NNI 中也内置了一些主流的模型压缩算法。
-用户可以进一步利用 NNI 的自动调优功能找到最佳的压缩模型，
-该功能在自动模型压缩部分有详细介绍。
-另一方面，用户可以使用 NNI 的接口自定义新的压缩算法。
-
-详细信息，参考以下教程：
-
-..  toctree::
-    :maxdepth: 2
-
-    概述 <Compression/Overview>
-    快速入门 <Compression/QuickStart>
-    教程 <Compression/Tutorial>
-    剪枝 <Compression/pruning>
-    剪枝（V2版本） <Compression/v2_pruning>
-    量化 <Compression/quantization>
-    工具 <Compression/CompressionUtils>
-    高级用法 <Compression/advanced>
-    API 参考 <Compression/CompressionReference>
diff --git a/docs/source/nas.rst b/docs/source/nas.rst
deleted file mode 100644
index 0c9860388..000000000
--- a/docs/source/nas.rst
+++ /dev/null
@@ -1,34 +0,0 @@
-#############################################
-Retiarii for Neural Architecture Search (NAS)
-#############################################
-
-Automatic neural architecture search is taking an increasingly important role on finding better models.
-Recent research works have proved the feasibility of automatic NAS, and also found some models that could beat manually tuned models.
-Some of representative works are NASNet, ENAS, DARTS, Network Morphism, and Evolution. Moreover, new innovations keep emerging.
-
-However, it takes great efforts to implement NAS algorithms, and it is hard to reuse code base of existing algorithms in a new one.
-To facilitate NAS innovations (e.g., design and implement new NAS models, compare different NAS models side-by-side),
-an easy-to-use and flexible programming interface is crucial.
-
-Thus, we design `Retiarii <https://www.usenix.org/system/files/osdi20-zhang_quanlu.pdf>`__. It is a deep learning framework that supports the exploratory training on a neural network model space, rather than on a single neural network model.
-Exploratory training with Retiarii allows user to express various search spaces for *Neural Architecture Search* and *Hyper-Parameter Tuning* with high flexibility.
-
-Some frequently used terminologies in this document:
-
-* *Model search space*: it means a set of models from which the best model is explored/searched. Sometimes we use *search space* or *model space* in short.
-* *Exploration strategy*: the algorithm that is used to explore a model search space.
-* *Model evaluator*: it is used to train a model and evaluate the model's performance.
-
-Follow the instructions below to start your journey with Retiarii.
-
-..  toctree::
-    :maxdepth: 2
-
-    Overview <NAS/Overview>
-    Quick Start <NAS/QuickStart>
-    Construct Model Space <NAS/construct_space>
-    Multi-trial NAS <NAS/multi_trial_nas>
-    One-shot NAS <NAS/one_shot_nas>
-    Hardware-aware NAS <NAS/HardwareAwareNAS>
-    NAS Benchmarks <NAS/Benchmarks>
-    NAS API References <NAS/ApiReference>
diff --git a/docs/source/nas/advanced_usage.rst b/docs/source/nas/advanced_usage.rst
new file mode 100644
index 000000000..7cce6fcf1
--- /dev/null
+++ b/docs/source/nas/advanced_usage.rst
@@ -0,0 +1,12 @@
+Advanced Usage
+==============
+
+..  toctree::
+    :maxdepth: 2
+
+    execution_engine
+    hardware_aware_nas
+    mutator
+    customize_strategy
+    serialization
+    benchmarks_toctree
diff --git a/docs/source/nas/benchmarks.rst b/docs/source/nas/benchmarks.rst
new file mode 100644
index 000000000..7c0ceddf9
--- /dev/null
+++ b/docs/source/nas/benchmarks.rst
@@ -0,0 +1,85 @@
+NAS Benchmark
+=============
+
+.. note:: :doc:`Example usage of NAS benchmarks </tutorials/nasbench_as_dataset>`.
+
+To improve the reproducibility of NAS algorithms as well as reducing computing resource requirements, researchers proposed a series of NAS benchmarks such as `NAS-Bench-101 <https://arxiv.org/abs/1902.09635>`__, `NAS-Bench-201 <https://arxiv.org/abs/2001.00326>`__, `NDS <https://arxiv.org/abs/1905.13214>`__, etc. NNI provides a query interface for users to acquire these benchmarks. Within just a few lines of code, researcher are able to evaluate their NAS algorithms easily and fairly by utilizing these benchmarks.
+
+Prerequisites
+-------------
+
+* Please prepare a folder to household all the benchmark databases. By default, it can be found at ``${HOME}/.cache/nni/nasbenchmark``. Or you can place it anywhere you like, and specify it in ``NASBENCHMARK_DIR`` via ``export NASBENCHMARK_DIR=/path/to/your/nasbenchmark`` before importing NNI.
+* Please install ``peewee`` via ``pip3 install peewee``, which NNI uses to connect to database.
+
+Data Preparation
+----------------
+
+Option 1 (Recommended)
+^^^^^^^^^^^^^^^^^^^^^^
+
+You can download the preprocessed benchmark files via ``python -m nni.nas.benchmarks.download <benchmark_name>``, where ``<benchmark_name>`` can be ``nasbench101``, ``nasbench201``, and etc. Add ``--help`` to the command for supported command line arguments.
+
+Option 2
+^^^^^^^^
+
+.. note:: If you have files that are processed before v2.5, it is recommended that you delete them and try option 1.
+
+#. Clone NNI to your machine and enter ``examples/nas/benchmarks`` directory.
+
+   .. code-block:: bash
+
+      git clone -b ${NNI_VERSION} https://github.com/microsoft/nni
+      cd nni/examples/nas/benchmarks
+
+   Replace ``${NNI_VERSION}`` with a released version name or branch name, e.g., ``v2.4``.
+
+#. Install dependencies via ``pip3 install -r xxx.requirements.txt``. ``xxx`` can be ``nasbench101``, ``nasbench201`` or ``nds``.
+
+#. Generate the database via ``./xxx.sh``. The directory that stores the benchmark file can be configured with ``NASBENCHMARK_DIR`` environment variable, which defaults to ``~/.nni/nasbenchmark``. Note that the NAS-Bench-201 dataset will be downloaded from a google drive.
+
+Please make sure there is at least 10GB free disk space and note that the conversion process can take up to hours to complete.
+
+Example Usages
+--------------
+
+Please refer to :doc:`examples usages of Benchmarks API </tutorials/nasbench_as_dataset>`.
+
+NAS-Bench-101
+-------------
+
+* `Paper link <https://arxiv.org/abs/1902.09635>`__ 
+* `Open-source <https://github.com/google-research/nasbench>`__
+
+NAS-Bench-101 contains 423,624 unique neural networks, combined with 4 variations in number of epochs (4, 12, 36, 108), each of which is trained 3 times. It is a cell-wise search space, which constructs and stacks a cell by enumerating DAGs with at most 7 operators, and no more than 9 connections. All operators can be chosen from ``CONV3X3_BN_RELU``, ``CONV1X1_BN_RELU`` and ``MAXPOOL3X3``, except the first operator (always ``INPUT``\ ) and last operator (always ``OUTPUT``\ ).
+
+Notably, NAS-Bench-101 eliminates invalid cells (e.g., there is no path from input to output, or there is redundant computation). Furthermore, isomorphic cells are de-duplicated, i.e., all the remaining cells are computationally unique.
+
+See :doc:`example usages </tutorials/nasbench_as_dataset>` and :ref:`API references <nas-bench-101-reference>`.
+
+NAS-Bench-201
+-------------
+
+* `Paper link <https://arxiv.org/abs/2001.00326>`__ 
+* `Open-source API <https://github.com/D-X-Y/NAS-Bench-201>`__ 
+* `Implementations <https://github.com/D-X-Y/AutoDL-Projects>`__
+
+NAS-Bench-201 is a cell-wise search space that views nodes as tensors and edges as operators. The search space contains all possible densely-connected DAGs with 4 nodes, resulting in 15,625 candidates in total. Each operator (i.e., edge) is selected from a pre-defined operator set (\ ``NONE``, ``SKIP_CONNECT``, ``CONV_1X1``, ``CONV_3X3`` and ``AVG_POOL_3X3``\ ). Training appraoches vary in the dataset used (CIFAR-10, CIFAR-100, ImageNet) and number of epochs scheduled (12 and 200). Each combination of architecture and training approach is repeated 1 - 3 times with different random seeds.
+
+See :doc:`example usages </tutorials/nasbench_as_dataset>` and :ref:`API references <nas-bench-201-reference>`.
+
+NDS
+---
+
+* `Paper link <https://arxiv.org/abs/1905.13214>`__ 
+* `Open-source <https://github.com/facebookresearch/nds>`__
+
+*On Network Design Spaces for Visual Recognition* released trial statistics of over 100,000 configurations (models + hyper-parameters) sampled from multiple model families, including vanilla (feedforward network loosely inspired by VGG), ResNet and ResNeXt (residual basic block and residual bottleneck block) and NAS cells (following popular design from NASNet, Ameoba, PNAS, ENAS and DARTS). Most configurations are trained only once with a fixed seed, except a few that are trained twice or three times.
+
+Instead of storing results obtained with different configurations in separate files, we dump them into one single database to enable comparison in multiple dimensions. Specifically, we use ``model_family`` to distinguish model types, ``model_spec`` for all hyper-parameters needed to build this model, ``cell_spec`` for detailed information on operators and connections if it is a NAS cell, ``generator`` to denote the sampling policy through which this configuration is generated. Refer to API documentation for details.
+
+Here is a list of available operators used in NDS.
+
+.. automodule:: nni.nas.benchmarks.nds.constants
+   :noindex:
+
+See :doc:`example usages </tutorials/nasbench_as_dataset>` and :ref:`API references <nds-reference>`.
diff --git a/docs/source/nas/benchmarks_toctree.rst b/docs/source/nas/benchmarks_toctree.rst
new file mode 100644
index 000000000..e586da641
--- /dev/null
+++ b/docs/source/nas/benchmarks_toctree.rst
@@ -0,0 +1,8 @@
+NAS Benchmark
+=============
+
+.. toctree::
+   :hidden:
+
+   Overview <benchmarks>
+   Examples </tutorials/nasbench_as_dataset>
diff --git a/docs/source/nas/construct_space.rst b/docs/source/nas/construct_space.rst
new file mode 100644
index 000000000..f8c4f3822
--- /dev/null
+++ b/docs/source/nas/construct_space.rst
@@ -0,0 +1,45 @@
+Construct Model Space
+=====================
+
+NNI provides powerful (and multi-level) APIs for users to easily express model space (or search space).
+
+* *Mutation Primitives*: high-level APIs (e.g., ValueChoice, LayerChoice) that are utilities to build blocks in search space. In most cases, mutation pritimives should be straightforward yet expressive enough. **We strongly recommend users to try them first,** and report issues if those APIs are not satisfying.
+* *Hyper-module Library*: plug-and-play modules that are proved useful. They are usually well studied in research, and comes with pre-searched results. (For example, the optimal activation function in `AutoActivation <https://arxiv.org/abs/1710.05941>`__ is reported to be `Swish <https://pytorch.org/docs/stable/generated/torch.nn.SiLU.html>`__).
+* *Mutator*: for advanced users only. NNI provides interface to customize new mutators for expressing more complicated model spaces.
+
+The following table summarizes all the APIs we have provided for constructing search space.
+
+.. list-table::
+   :header-rows: 1
+   :widths: auto
+
+   * - Name
+     - Category
+     - Brief Description
+   * - :class:`LayerChoice <nni.retiarii.nn.pytorch.LayerChoice>`
+     - :ref:`Mutation Primitives <mutation-primitives>`
+     - Select from some PyTorch modules
+   * - :class:`InputChoice <nni.retiarii.nn.pytorch.InputChoice>`
+     - :ref:`Mutation Primitives <mutation-primitives>`
+     - Select from some inputs (tensors)
+   * - :class:`ValueChoice <nni.retiarii.nn.pytorch.ValueChoice>`
+     - :ref:`Mutation Primitives <mutation-primitives>`
+     - Select from some candidate values
+   * - :class:`Repeat <nni.retiarii.nn.pytorch.Repeat>`
+     - :ref:`Mutation Primitives <mutation-primitives>`
+     - Repeat a block by a variable number of times
+   * - :class:`Cell <nni.retiarii.nn.pytorch.Cell>`
+     - :ref:`Mutation Primitives <mutation-primitives>`
+     - Cell structure popularly used in literature
+   * - :class:`NasBench101Cell <nni.retiarii.nn.pytorch.NasBench101Cell>`
+     - :ref:`Mutation Primitives <mutation-primitives>`
+     - Cell structure (variant) proposed by NAS-Bench-101
+   * - :class:`NasBench201Cell <nni.retiarii.nn.pytorch.NasBench201Cell>`
+     - :ref:`Mutation Primitives <mutation-primitives>`
+     - Cell structure (variant) proposed by NAS-Bench-201
+   * - :class:`AutoActivation <nni.retiarii.nn.pytorch.AutoActivation>`
+     - :ref:`Hyper-modules Library <hyper-modules>`
+     - Searching for activation functions
+   * - :class:`Mutator <nni.retiarii.Mutator>`
+     - :doc:`Mutator <mutator>`
+     - Flexible mutations on graphs. :doc:`See tutorial here <mutator>`
diff --git a/docs/source/nas/customize_strategy.rst b/docs/source/nas/customize_strategy.rst
new file mode 100644
index 000000000..636e439bc
--- /dev/null
+++ b/docs/source/nas/customize_strategy.rst
@@ -0,0 +1,98 @@
+Customize Exploration Strategy
+==============================
+
+Customize Multi-trial Strategy
+------------------------------
+
+If users want to innovate a new exploration strategy, they can easily customize a new one following the interface provided by NNI. Specifically, users should inherit the base strategy class :class:`nni.retiarii.strategy.BaseStrategy`, then implement the member function ``run``. This member function takes ``base_model`` and ``applied_mutators`` as its input arguments. It can simply apply the user specified mutators in ``applied_mutators`` onto ``base_model`` to generate a new model. When a mutator is applied, it should be bound with a sampler (e.g., ``RandomSampler``). Every sampler implements the ``choice`` function which chooses value(s) from candidate values. The ``choice`` functions invoked in mutators are executed with the sampler.
+
+Below is a very simple random strategy, which makes the choices completely random.
+
+.. code-block:: python
+
+    from nni.retiarii import Sampler
+
+    class RandomSampler(Sampler):
+        def choice(self, candidates, mutator, model, index):
+            return random.choice(candidates)
+
+    class RandomStrategy(BaseStrategy):
+        def __init__(self):
+            self.random_sampler = RandomSampler()
+
+        def run(self, base_model, applied_mutators):
+            _logger.info('stargety start...')
+            while True:
+                avail_resource = query_available_resources()
+                if avail_resource > 0:
+                    model = base_model
+                    _logger.info('apply mutators...')
+                    _logger.info('mutators: %s', str(applied_mutators))
+                    for mutator in applied_mutators:
+                        mutator.bind_sampler(self.random_sampler)
+                        model = mutator.apply(model)
+                    # run models
+                    submit_models(model)
+                else:
+                    time.sleep(2)
+
+You can find that this strategy does not know the search space beforehand, it passively makes decisions every time ``choice`` is invoked from mutators. If a strategy wants to know the whole search space before making any decision (e.g., TPE, SMAC), it can use ``dry_run`` function provided by ``Mutator`` to obtain the space. An example strategy can be found :githublink:`here <nni/retiarii/strategy/tpe_strategy.py>`.
+
+After generating a new model, the strategy can use our provided APIs (e.g., :func:`nni.retiarii.execution.submit_models`, :func:`nni.retiarii.execution.is_stopped_exec`) to submit the model and get its reported results.
+
+Customize a New One-shot Trainer (legacy)
+-----------------------------------------
+
+One-shot trainers should inherit :class:`nni.retiarii.oneshot.BaseOneShotTrainer`, and need to implement ``fit()`` (used to conduct the fitting and searching process) and ``export()`` method (used to return the searched best architecture).
+
+Writing a one-shot trainer is very different to single-arch evaluator. First of all, there are no more restrictions on init method arguments, any Python arguments are acceptable. Secondly, the model fed into one-shot trainers might be a model with Retiarii-specific modules, such as LayerChoice and InputChoice. Such model cannot directly forward-propagate and trainers need to decide how to handle those modules.
+
+A typical example is DartsTrainer, where learnable-parameters are used to combine multiple choices in LayerChoice. Retiarii provides ease-to-use utility functions for module-replace purposes, namely :meth:`nni.retiarii.oneshot.pytorch.utils.replace_layer_choice`, :meth:`nni.retiarii.oneshot.pytorch.utils.replace_input_choice`. A simplified example is as follows: 
+
+.. code-block:: python
+
+    from nni.retiarii.oneshot import BaseOneShotTrainer
+    from nni.retiarii.oneshot.pytorch.utils import replace_layer_choice, replace_input_choice
+
+
+    class DartsLayerChoice(nn.Module):
+        def __init__(self, layer_choice):
+            super(DartsLayerChoice, self).__init__()
+            self.name = layer_choice.label
+            self.op_choices = nn.ModuleDict(layer_choice.named_children())
+            self.alpha = nn.Parameter(torch.randn(len(self.op_choices)) * 1e-3)
+
+        def forward(self, *args, **kwargs):
+            op_results = torch.stack([op(*args, **kwargs) for op in self.op_choices.values()])
+            alpha_shape = [-1] + [1] * (len(op_results.size()) - 1)
+            return torch.sum(op_results * F.softmax(self.alpha, -1).view(*alpha_shape), 0)
+
+
+    class DartsTrainer(BaseOneShotTrainer):
+
+        def __init__(self, model, loss, metrics, optimizer):
+            self.model = model
+            self.loss = loss
+            self.metrics = metrics
+            self.num_epochs = 10
+
+            self.nas_modules = []
+            replace_layer_choice(self.model, DartsLayerChoice, self.nas_modules)
+
+            ... # init dataloaders and optimizers
+
+        def fit(self):
+            for i in range(self.num_epochs):
+                for (trn_X, trn_y), (val_X, val_y) in zip(self.train_loader, self.valid_loader):
+                    self.train_architecture(val_X, val_y)
+                    self.train_model_weight(trn_X, trn_y)
+
+        @torch.no_grad()
+        def export(self):
+            result = dict()
+            for name, module in self.nas_modules:
+                if name not in result:
+                    result[name] = select_best_of_module(module)
+            return result
+
+The full code of DartsTrainer is available to Retiarii source code. Please have a check at :githublink:`DartsTrainer <nni/retiarii/oneshot/pytorch/darts.py>`.
diff --git a/docs/source/NAS/ModelEvaluators.rst b/docs/source/nas/evaluator.rst
similarity index 71%
rename from docs/source/NAS/ModelEvaluators.rst
rename to docs/source/nas/evaluator.rst
index 670014d97..78a1f2e95 100644
--- a/docs/source/NAS/ModelEvaluators.rst
+++ b/docs/source/nas/evaluator.rst
@@ -1,12 +1,14 @@
-Model Evaluators
-================
+Model Evaluator
+===============
 
 A model evaluator is for training and validating each generated model. They are necessary to evaluate the performance of new explored models.
 
+.. _functional-evaluator:
+
 Customize Evaluator with Any Function
 -------------------------------------
 
-The simplest way to customize a new evaluator is with functional APIs, which is very easy when training code is already available. Users only need to write a fit function that wraps everything, which usually includes training, validating and testing of a single model. This function takes one positional arguments (``model_cls``) and possible keyword arguments. The keyword arguments (other than ``model_cls``) are fed to FunctionEvaluator as its initialization parameters (note that they will be `serialized <./Serialization.rst>`__). In this way, users get everything under their control, but expose less information to the framework and as a result, further optimizations like `CGO <./ExecutionEngines.rst#cgo-execution-engine-experimental>`__ might be not feasible. An example is as belows:
+The simplest way to customize a new evaluator is with :class:`FunctionalEvaluator <nni.retiarii.evaluator.FunctionalEvaluator>`, which is very easy when training code is already available. Users only need to write a fit function that wraps everything, which usually includes training, validating and testing of a single model. This function takes one positional arguments (``model_cls``) and possible keyword arguments. The keyword arguments (other than ``model_cls``) are fed to :class:`FunctionalEvaluator <nni.retiarii.evaluator.FunctionalEvaluator>` as its initialization parameters (note that they will be :doc:`serialized <./serialization>`). In this way, users get everything under their control, but expose less information to the framework and as a result, further optimizations like :ref:`CGO <cgo-execution-engine>` might be not feasible. An example is as belows:
 
 .. code-block:: python
 
@@ -46,12 +48,15 @@ Evaluators with PyTorch-Lightning
 Use Built-in Evaluators
 ^^^^^^^^^^^^^^^^^^^^^^^
 
-NNI provides some commonly used model evaluators for users' convenience. These evaluators are built upon the awesome library PyTorch-Lightning.
+NNI provides some commonly used model evaluators for users' convenience. These evaluators are built upon the awesome library PyTorch-Lightning. Read the :doc:`reference </reference/nas/evaluator>` for their detailed usages.
+
+* :class:`nni.retiarii.evaluator.pytorch.Classification`: for classification tasks.
+* :class:`nni.retiarii.evaluator.pytorch.Regression`: for regression tasks.
 
-We recommend to read the `serialization tutorial <./Serialization.rst>`__ before using these evaluators. A few notes to summarize the tutorial:
+We recommend to read the :doc:`serialization tutorial <serialization>` before using these evaluators. A few notes to summarize the tutorial:
 
-1. ``pl.DataLoader`` should be used in place of ``torch.utils.data.DataLoader``.
-2. The datasets used in data-loader should be decorated with ``nni.trace`` recursively.
+1. :class:`nni.retiarii.evaluator.pytorch.DataLoader` should be used in place of ``torch.utils.data.DataLoader``.
+2. The datasets used in data-loader should be decorated with :meth:`nni.trace` recursively.
 
 For example,
 
@@ -69,18 +74,12 @@ For example,
                                 val_dataloaders=pl.DataLoader(test_dataset, batch_size=100),
                                 max_epochs=10)
 
-..  autoclass:: nni.retiarii.evaluator.pytorch.lightning.Classification
-    :noindex:
-
-..  autoclass:: nni.retiarii.evaluator.pytorch.lightning.Regression
-    :noindex:
-
 Customize Evaluator with PyTorch-Lightning
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Another approach is to write training code in PyTorch-Lightning style, that is, to write a LightningModule that defines all elements needed for training (e.g., loss function, optimizer) and to define a trainer that takes (optional) dataloaders to execute the training. Before that, please read the `document of PyTorch-lightning <https://pytorch-lightning.readthedocs.io/>`__ to learn the basic concepts and components provided by PyTorch-lightning.
 
-In practice, writing a new training module in Retiarii should inherit ``nni.retiarii.evaluator.pytorch.lightning.LightningModule``, which has a ``set_model`` that will be called after ``__init__`` to save the candidate model (generated by strategy) as ``self.model``. The rest of the process (like ``training_step``) should be the same as writing any other lightning module. Evaluators should also communicate with strategies via two API calls (``nni.report_intermediate_result`` for periodical metrics and ``nni.report_final_result`` for final metrics), added in ``on_validation_epoch_end`` and ``teardown`` respectively. 
+In practice, writing a new training module in Retiarii should inherit :class:`nni.retiarii.evaluator.pytorch.LightningModule`, which has a ``set_model`` that will be called after ``__init__`` to save the candidate model (generated by strategy) as ``self.model``. The rest of the process (like ``training_step``) should be the same as writing any other lightning module. Evaluators should also communicate with strategies via two API calls (:meth:`nni.report_intermediate_result` for periodical metrics and :meth:`nni.report_final_result` for final metrics), added in ``on_validation_epoch_end`` and ``teardown`` respectively. 
 
 An example is as follows:
 
@@ -133,7 +132,7 @@ An example is as follows:
             if stage == 'fit':
                 nni.report_final_result(self.trainer.callback_metrics['val_loss'].item())
 
-Then, users need to wrap everything (including LightningModule, trainer and dataloaders) into a ``Lightning`` object, and pass this object into a Retiarii experiment.
+Then, users need to wrap everything (including LightningModule, trainer and dataloaders) into a :class:`nni.retiarii.evaluator.pytorch.Lightning` object, and pass this object into a Retiarii experiment.
 
 .. code-block:: python
 
diff --git a/docs/source/NAS/ExecutionEngines.rst b/docs/source/nas/execution_engine.rst
similarity index 73%
rename from docs/source/NAS/ExecutionEngines.rst
rename to docs/source/nas/execution_engine.rst
index e0e2bb5f5..242d03a95 100644
--- a/docs/source/NAS/ExecutionEngines.rst
+++ b/docs/source/nas/execution_engine.rst
@@ -1,35 +1,37 @@
 Execution Engines
 =================
 
-Execution engine is for running Retiarii Experiment. NNI supports three execution engines, users can choose a speicific engine according to the type of their model mutation definition and their requirements for cross-model optimizations. 
+Execution engine is for running Retiarii Experiment. NNI supports three execution engines, users can choose a specific engine according to the type of their model mutation definition and their requirements for cross-model optimizations. 
 
-* **Pure-python execution engine** is the default engine, it supports the model space expressed by `inline mutation API <./MutationPrimitives.rst>`__. 
+* **Pure-python execution engine** is the default engine, it supports the model space expressed by :doc:`mutation primitives <construct_space>`.
 
-* **Graph-based execution engine** supports the use of `inline mutation APIs <./MutationPrimitives.rst>`__ and model spaces represented by `mutators <./Mutators.rst>`__. It requires the user's model to be parsed by `TorchScript <https://pytorch.org/docs/stable/jit.html>`__.
+* **Graph-based execution engine** supports the use of :doc:`mutation primitives <construct_space>` and model spaces represented by :doc:`mutators <mutator>`. It requires the user's model to be parsed by `TorchScript <https://pytorch.org/docs/stable/jit.html>`__.
 
 * **CGO execution engine** has the same requirements and capabilities as the **Graph-based execution engine**. But further enables cross-model optimizations, which makes model space exploration faster.
 
+.. _pure-python-execution-engine:
+
 Pure-python Execution Engine
 ----------------------------
 
 Pure-python Execution Engine is the default engine, we recommend users to keep using this execution engine, if they are new to NNI NAS. Pure-python execution engine plays magic within the scope of inline mutation APIs, while does not touch the rest of user model. Thus, it has minimal requirement on user model. 
 
-One steps are needed to use this engine now.
-
-1. Add ``@nni.retiarii.model_wrapper`` decorator outside the whole PyTorch model.
+Rememeber to add :meth:`nni.retiarii.model_wrapper` decorator outside the whole PyTorch model before using this engine.
 
 .. note:: You should always use ``super().__init__()`` instead of ``super(MyNetwork, self).__init__()`` in the PyTorch model, because the latter one has issues with model wrapper.
 
+.. _graph-based-execution-engine:
+
 Graph-based Execution Engine
 ----------------------------
 
 For graph-based execution engine, it converts user-defined model to a graph representation (called graph IR) using `TorchScript <https://pytorch.org/docs/stable/jit.html>`__, each instantiated module in the model is converted to a subgraph. Then mutations are applied to the graph to generate new graphs. Each new graph is then converted back to PyTorch code and executed on the user specified training service.
 
-Users may find ``@basic_unit`` helpful in some cases. ``@basic_unit`` here means the module will not be converted to a subgraph, instead, it is converted to a single graph node as a basic unit.
+Users may find ``@basic_unit`` helpful in some cases. :meth:`nni.retiarii.basic_unit` here means the module will not be converted to a subgraph, instead, it is converted to a single graph node as a basic unit.
 
 ``@basic_unit`` is usually used in the following cases:
 
-* When users want to tune initialization parameters of a module using ``ValueChoice``, then decorate the module with ``@basic_unit``. For example, ``self.conv = MyConv(kernel_size=nn.ValueChoice([1, 3, 5]))``, here ``MyConv`` should be decorated.
+* When users want to tune initialization parameters of a module using :class:`nni.retiarii.nn.pytorch.ValueChoice`, then decorate the module with ``@basic_unit``. For example, ``self.conv = MyConv(kernel_size=nn.ValueChoice([1, 3, 5]))``, here ``MyConv`` should be decorated.
 
 * When a module cannot be successfully parsed to a subgraph, decorate the module with ``@basic_unit``. The parse failure could be due to complex control flow. Currently Retiarii does not support adhoc loop, if there is adhoc loop in a module's forward, this class should be decorated as serializable module. For example, the following ``MyModule`` should be decorated.
 
@@ -43,23 +45,25 @@ Users may find ``@basic_unit`` helpful in some cases. ``@basic_unit`` here means
         for i in range(10): # <- adhoc loop
           ...
 
-* Some inline mutation APIs require their handled module to be decorated with ``@basic_unit``. For example, user-defined module that is provided to ``LayerChoice`` as a candidate op should be decorated.
+* Some inline mutation APIs require their handled module to be decorated with ``@basic_unit``. For example, user-defined module that is provided to :class:`nni.retiarii.nn.pytorch.LayerChoice` as a candidate op should be decorated.
 
 Three steps are need to use graph-based execution engine.
 
 1. Remove ``@nni.retiarii.model_wrapper`` if there is any in your model.
-2. Add ``config.execution_engine = 'base'`` to ``RetiariiExeConfig``. The default value of ``execution_engine`` is 'py', which means pure-python execution engine.
+2. Add ``config.execution_engine = 'base'`` to :class:`nni.retiarii.experiment.pytorch.RetiariiExeConfig`. The default value of ``execution_engine`` is 'py', which means pure-python execution engine.
 3. Add ``@basic_unit`` when necessary following the above guidelines.
 
 For exporting top models, graph-based execution engine supports exporting source code for top models by running ``exp.export_top_models(formatter='code')``.
 
+.. _cgo-execution-engine:
+
 CGO Execution Engine (experimental)
 -----------------------------------
 
-CGO（Cross-Graph Optimization) execution engine does cross-model optimizations based on the graph-based execution engine. In CGO execution engine, multiple models could be merged and trained together in one trial.
+CGO (Cross-Graph Optimization) execution engine does cross-model optimizations based on the graph-based execution engine. In CGO execution engine, multiple models could be merged and trained together in one trial.
 Currently, it only supports ``DedupInputOptimizer`` that can merge graphs sharing the same dataset to only loading and pre-processing each batch of data once, which can avoid bottleneck on data loading. 
 
-.. note :: To use CGO engine, PyTorch-lightning above version 1.4.2 is required.
+.. note :: To use CGO engine, PyTorch Lightning of 1.5.x is required.
 
 To enable CGO execution engine, you need to follow these steps:
 
@@ -67,7 +71,7 @@ To enable CGO execution engine, you need to follow these steps:
 2. Add configurations for remote training service
 3. Add configurations for CGO engine
 
-  .. code-block:: python
+.. code-block:: python
   
     exp = RetiariiExperiment(base_model, trainer, mutators, strategy)
     config = RetiariiExeConfig('remote')
@@ -103,4 +107,4 @@ We have already implemented two trainers: :class:`nni.retiarii.evaluator.pytorch
 Advanced users can also implement their own trainers by inheriting ``MultiModelSupervisedLearningModule``.
 
 Sometimes, a mutated model cannot be executed (e.g., due to shape mismatch). When a trial running multiple models contains 
-a bad model, CGO execution engine will re-run each model independently in seperate trials without cross-model optimizations.
+a bad model, CGO execution engine will re-run each model independently in separate trials without cross-model optimizations.
diff --git a/docs/source/nas/exploration_strategy.rst b/docs/source/nas/exploration_strategy.rst
new file mode 100644
index 000000000..0b29976d6
--- /dev/null
+++ b/docs/source/nas/exploration_strategy.rst
@@ -0,0 +1,99 @@
+Exploration Strategy
+====================
+
+There are two types of model space exploration approach: **Multi-trial strategy** and **One-shot strategy**. When the model space has been constructed, users can use either exploration approach to explore the model space. 
+
+* :ref:`Mutli-trial strategy <multi-trial-nas>` trains each sampled model in the model space independently.
+* :ref:`One-shot strategy <one-shot-nas>` samples the model from a super model.
+
+Here is the list of exploration strategies that NNI has supported.
+
+.. list-table::
+   :header-rows: 1
+   :widths: auto
+
+   * - Name
+     - Category
+     - Brief Description
+   * - :class:`Random <nni.retiarii.strategy.Random>`
+     - :ref:`Multi-trial <multi-trial-nas>`
+     - Randomly sample an architecture each time
+   * - :class:`GridSearch <nni.retiarii.strategy.GridSearch>`
+     - :ref:`Multi-trial <multi-trial-nas>`
+     - Traverse the search space and try all possibilities
+   * - :class:`RegularizedEvolution <nni.retiarii.strategy.RegularizedEvolution>`
+     - :ref:`Multi-trial <multi-trial-nas>`
+     - Evolution algorithm for NAS. `Reference <https://arxiv.org/abs/1802.01548>`__
+   * - :class:`TPE <nni.retiarii.strategy.TPE>`
+     - :ref:`Multi-trial <multi-trial-nas>`
+     - Tree-structured Parzen Estimator (TPE). `Reference <https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf>`__
+   * - :class:`PolicyBasedRL <nni.retiarii.strategy.PolicyBasedRL>`
+     - :ref:`Multi-trial <multi-trial-nas>`
+     - Policy-based reinforcement learning, based on implementation of tianshou. `Reference <https://arxiv.org/abs/1611.01578>`__
+   * - :ref:`darts-strategy`
+     - :ref:`One-shot <one-shot-nas>`
+     - Continuous relaxation of the architecture representation, allowing efficient search of the architecture using gradient descent. `Reference <https://arxiv.org/abs/1806.09055>`__
+   * - :ref:`enas-strategy`
+     - :ref:`One-shot <one-shot-nas>`
+     - RL controller learns to generate the best network on a super-net. `Reference <https://arxiv.org/abs/1802.03268>`__
+   * - :ref:`fbnet-strategy`
+     - :ref:`One-shot <one-shot-nas>`
+     - Choose the best block by using Gumbel Softmax random sampling and differentiable training. `Reference <https://arxiv.org/abs/1812.03443>`__
+   * - :ref:`spos-strategy`
+     - :ref:`One-shot <one-shot-nas>`
+     - Train a super-net with uniform path sampling. `Reference <https://arxiv.org/abs/1904.00420>`__
+   * - :ref:`proxylessnas-strategy`
+     - :ref:`One-shot <one-shot-nas>`
+     - A low-memory-consuming optimized version of differentiable architecture search. `Reference <https://arxiv.org/abs/1812.00332>`__
+
+.. _multi-trial-nas:
+
+Multi-trial strategy
+--------------------
+
+Multi-trial NAS means each sampled model from model space is trained independently. A typical multi-trial NAS is `NASNet <https://arxiv.org/abs/1707.07012>`__. In multi-trial NAS, users need model evaluator to evaluate the performance of each sampled model, and need an exploration strategy to sample models from a defined model space. Here, users could use NNI provided model evaluators or write their own model evalutor. They can simply choose a exploration strategy. Advanced users can also customize new exploration strategy.
+
+To use an exploration strategy, users simply instantiate an exploration strategy and pass the instantiated object to :class:`RetiariiExperiment <nni.retiarii.experiment.pytorch.RetiariiExperiment>`. Below is a simple example.
+
+.. code-block:: python
+
+   import nni.retiarii.strategy as strategy
+   exploration_strategy = strategy.Random(dedup=True)
+
+Rather than using :class:`strategy.Random <nni.retiarii.strategy.Random>`, users can choose one of the strategies from the table above.
+
+.. _one-shot-nas:
+
+One-shot strategy
+-----------------
+
+One-shot NAS algorithms leverage weight sharing among models in neural architecture search space to train a supernet, and use this supernet to guide the selection of better models. This type of algorihtms greatly reduces computational resource compared to independently training each model from scratch (which we call "Multi-trial NAS").
+
+Currently, the usage of one-shot NAS strategy is a little different from multi-trial strategy. One-shot strategy is implemented with a special type of objects named *Trainer*. Following the common practice of one-shot NAS, *Trainer* trains the super-net and searches for the optimal architecture in a single run. For example,
+
+.. code-block:: python
+
+   from nni.retiarii.oneshot.pytorch import DartsTrainer
+
+   trainer = DartsTrainer(
+      model=model,
+      loss=criterion,
+      metrics=lambda output, target: accuracy(output, target, topk=(1,)),
+      optimizer=optim,
+      dataset=dataset_train,
+      batch_size=32,
+      log_frequency=50
+   )
+   trainer.fit()
+
+One-shot strategy can be used without :class:`RetiariiExperiment <nni.retiarii.experiment.pytorch.RetiariiExperiment>`. Thus, the ``trainer.fit()`` here runs the experiment locally.
+
+After ``trainer.fit()`` completes, we can use ``trainer.export()`` to export the searched architecture (a dict of choices) to a file.
+
+.. code-block:: python
+
+   final_architecture = trainer.export()
+   print('Final architecture:', trainer.export())
+   json.dump(trainer.export(), open('checkpoint.json', 'w'))
+
+.. tip:: The trained super-net (neither the weights or exported JSON) can't be used directly. It's only an intermediate result used for deriving the final architecture. The exported architecture (can be retrieved with :meth:`nni.retiarii.fixed_arch`) needs to be *retrained* with a standard training recipe to get the final model.
diff --git a/docs/source/NAS/HardwareAwareNAS.rst b/docs/source/nas/hardware_aware_nas.rst
similarity index 65%
rename from docs/source/NAS/HardwareAwareNAS.rst
rename to docs/source/nas/hardware_aware_nas.rst
index afcadb8c6..3b95db919 100644
--- a/docs/source/NAS/HardwareAwareNAS.rst
+++ b/docs/source/nas/hardware_aware_nas.rst
@@ -1,7 +1,7 @@
 Hardware-aware NAS
 ==================
 
-.. contents::
+.. This file should be rewritten as a tutorial
 
 End-to-end Multi-trial SPOS Demo
 --------------------------------
@@ -18,17 +18,21 @@ Then run multi-trail SPOS demo:
 
 .. code-block:: bash
 
-  python ${NNI_ROOT}/examples/nas/oneshot/spos/multi_trial.py
+  cd ${NNI_ROOT}/examples/nas/oneshot/spos/
+  python search.py --latency-filter cortexA76cpu_tflite21
 
 
 How the demo works
 ^^^^^^^^^^^^^^^^^^
 
-To support hardware-aware NAS, you first need a ``Strategy`` that supports filtering the models by latency. We provide such a filter named ``LatencyFilter`` in NNI and initialize a ``Random`` strategy with the filter:
+To support hardware-aware NAS, you first need a ``Strategy`` that supports filtering the models by latency. We provide such a filter named ``LatencyFilter`` in NNI and initialize a ``RegularizedEvolution`` strategy with the filter:
 
 .. code-block:: python
 
-  simple_strategy = strategy.Random(model_filter=LatencyFilter(threshold=100, predictor=base_predictor))
+  evolution_strategy = strategy.RegularizedEvolution(
+        model_filter=latency_filter,
+        sample_size=args.evolution_sample_size, population_size=args.evolution_population_size, cycles=args.evolution_cycles
+        )
 
 ``LatencyFilter`` will predict the models\' latency by using nn-Meter and filter out the models whose latency are larger than the threshold (i.e., ``100`` in this example).
 You can also build your own strategies and filters to support more flexible NAS such as sorting the models according to latency.
@@ -37,21 +41,21 @@ Then, pass this strategy to ``RetiariiExperiment``:
 
 .. code-block:: python
 
-  exp = RetiariiExperiment(base_model, trainer, strategy=simple_strategy)
+  exp = RetiariiExperiment(base_model, evaluator, strategy=evolution_strategy)
 
   exp_config = RetiariiExeConfig('local')
   ...
-  exp_config.dummy_input = [1, 3, 32, 32]
+  exp_config.dummy_input = [1, 3, 224, 224]
 
-  exp.run(exp_config, port)
+  exp.run(exp_config, args.port)
 
-In ``exp_config``, ``dummy_input`` is required for tracing shape info.
+In ``exp_config``, ``dummy_input`` is required for tracing shape info in latency predictor.
 
 
 End-to-end ProxylessNAS with Latency Constraints
 ------------------------------------------------
 
-`ProxylessNAS <https://arxiv.org/pdf/1812.00332.pdf>`__ is a hardware-aware one-shot NAS algorithm. ProxylessNAS applies the expected latency of the model to build a differentiable metric and design efficient neural network architectures for hardware. The latency loss is added as a regularization term for architecture parameter optimization. In this example, nn-Meter provides a latency estimator to predict expected latency for the mixed operation on other types of mobile and edge hardware. 
+`ProxylessNAS <https://arxiv.org/abs/1812.00332>`__ is a hardware-aware one-shot NAS algorithm. ProxylessNAS applies the expected latency of the model to build a differentiable metric and design efficient neural network architectures for hardware. The latency loss is added as a regularization term for architecture parameter optimization. In this example, nn-Meter provides a latency estimator to predict expected latency for the mixed operation on other types of mobile and edge hardware. 
 
 To run the one-shot ProxylessNAS demo, first install nn-Meter by running:
 
@@ -61,14 +65,14 @@ To run the one-shot ProxylessNAS demo, first install nn-Meter by running:
 
 Then run one-shot ProxylessNAS demo:
 
-```bash
-python ${NNI_ROOT}/examples/nas/oneshot/proxylessnas/main.py --applied_hardware <hardware> --reference_latency <reference latency (ms)>
-```
+.. code-block:: bash
+
+   python ${NNI_ROOT}/examples/nas/oneshot/proxylessnas/main.py --applied_hardware HARDWARE --reference_latency REFERENCE_LATENCY_MS
 
 How the demo works
 ^^^^^^^^^^^^^^^^^^
 
-In the implementation of ProxylessNAS ``trainer``, we provide a ``HardwareLatencyEstimator`` which currently builds a lookup table, that stores the measured latency of each candidate building block in the search space. The latency sum of all building blocks in a candidate model will be treated as the model inference latency. The latency prediction is obtained by ``nn-Meter``. ``HardwareLatencyEstimator`` predicts expected latency for the mixed operation based on the path weight of `ProxylessLayerChoice`. With leveraging ``nn-Meter`` in NNI, users can apply ProxylessNAS to search efficient DNN models on more types of edge devices. 
+In the implementation of ProxylessNAS ``trainer``, we provide a ``HardwareLatencyEstimator`` which currently builds a lookup table, that stores the measured latency of each candidate building block in the search space. The latency sum of all building blocks in a candidate model will be treated as the model inference latency. The latency prediction is obtained by ``nn-Meter``. ``HardwareLatencyEstimator`` predicts expected latency for the mixed operation based on the path weight of ``ProxylessLayerChoice``. With leveraging ``nn-Meter`` in NNI, users can apply ProxylessNAS to search efficient DNN models on more types of edge devices. 
 
 Despite of ``applied_hardware`` and ``reference_latency``, There are some other parameters related to hardware-aware ProxylessNAS training in this :githublink:`example <examples/nas/oneshot/proxylessnas/main.py>`:
 
diff --git a/docs/source/NAS/Mutators.rst b/docs/source/nas/mutator.rst
similarity index 59%
rename from docs/source/NAS/Mutators.rst
rename to docs/source/nas/mutator.rst
index 3e02f89d4..710dbd179 100644
--- a/docs/source/NAS/Mutators.rst
+++ b/docs/source/nas/mutator.rst
@@ -1,9 +1,9 @@
-Express Mutations with Mutators
-===============================
+Construct Space with Mutator
+============================
 
-Besides the inline mutation APIs demonstrated `here <./MutationPrimitives.rst>`__, NNI provides a more general approach to express a model space, i.e., *Mutator*, to cover more complex model spaces. Those inline mutation APIs are also implemented with mutator in the underlying system, which can be seen as a special case of model mutation.
+Besides the mutation primitives demonstrated in the :doc:`basic tutorial <construct_space>`, NNI provides a more general approach to express a model space, i.e., *Mutator*, to cover more complex model spaces. The high-level APIs are also implemented with mutator in the underlying system, which can be seen as a special case of model mutation.
 
-.. note:: Mutator and inline mutation APIs cannot be used together.
+.. warning:: Mutator and inline mutation APIs can NOT be used together.
 
 A mutator is a piece of logic to express how to mutate a given model. Users are free to write their own mutators. Then a model space is expressed with a base model and a list of mutators. A model in the model space is sampled by applying the mutators on the base model one after another. An example is shown below.
 
@@ -18,7 +18,7 @@ A mutator is a piece of logic to express how to mutate a given model. Users are
 Write a mutator
 ---------------
 
-User-defined mutator should inherit ``Mutator`` class, and implement mutation logic in the member function ``mutate``.
+User-defined mutator should inherit :class:`nni.retiarii.Mutator` class, and implement mutation logic in the member function :meth:`nni.retiarii.Mutator.mutate`.
 
 .. code-block:: python
 
@@ -35,9 +35,9 @@ User-defined mutator should inherit ``Mutator`` class, and implement mutation lo
         chosen_op = self.choice(self.candidate_op_list)
         node.update_operation(chosen_op.type, chosen_op.params)
 
-The input of ``mutate`` is graph IR (Intermediate Representation) of the base model (please refer to `here <./ApiReference.rst>`__ for the format and APIs of the IR), users can mutate the graph using the graph's member functions (e.g., ``get_nodes_by_label``, ``update_operation``). The mutation operations can be combined with the API ``self.choice``, in order to express a set of possible mutations. In the above example, the node's operation can be changed to any operation from ``candidate_op_list``.
+The input of :meth:`nni.retiarii.Mutator.mutate` is graph IR (Intermediate Representation) of the base model, users can mutate the graph using the graph's member functions (e.g., :meth:`nni.retiarii.Model.get_nodes_by_label`). The mutation operations can be combined with the API ``self.choice``, in order to express a set of possible mutations. In the above example, the node's operation can be changed to any operation from ``candidate_op_list``.
 
-Use placehoder to make mutation easier: ``nn.Placeholder``. If you want to mutate a subgraph or node of your model, you can define a placeholder in this model to represent the subgraph or node. Then, use mutator to mutate this placeholder to make it real modules.
+Use placeholder to make mutation easier: :class:`nni.retiarii.nn.pytorch.Placeholder`. If you want to mutate a subgraph or node of your model, you can define a placeholder in this model to represent the subgraph or node. Then, use mutator to mutate this placeholder to make it real modules.
 
 .. code-block:: python
 
@@ -51,7 +51,7 @@ Use placehoder to make mutation easier: ``nn.Placeholder``. If you want to mutat
 
 ``label`` is used by mutator to identify this placeholder. The other parameters are the information that is required by mutator. They can be accessed from ``node.operation.parameters`` as a dict, it could include any information that users want to put to pass it to user defined mutator. The complete example code can be found in :githublink:`Mnasnet base model <examples/nas/multi-trial/mnasnet/base_mnasnet.py>`.
 
-Starting an experiment is almost the same as using inline mutation APIs. The only difference is that the applied mutators should be passed to ``RetiariiExperiment``. Below is a simple example.
+Starting an experiment is almost the same as using inline mutation APIs. The only difference is that the applied mutators should be passed to :class:`nni.retiarii.experiment.pytorch.RetiariiExperiment`. Below is a simple example.
 
 .. code-block:: python
 
diff --git a/docs/source/nas/overview.rst b/docs/source/nas/overview.rst
new file mode 100644
index 000000000..14251ac53
--- /dev/null
+++ b/docs/source/nas/overview.rst
@@ -0,0 +1,78 @@
+Overview
+========
+
+.. attention:: NNI's latest NAS supports are all based on Retiarii Framework, users who are still on `early version using NNI NAS v1.0 <https://nni.readthedocs.io/en/v2.2/nas.html>`__ shall migrate your work to Retiarii as soon as possible. We plan to remove the legacy NAS framework in the next few releases.
+
+.. note:: PyTorch is the **only supported framework on Retiarii**. Inquiries of NAS support on Tensorflow is in `this discussion <https://github.com/microsoft/nni/discussions/4605>`__. If you intend to run NAS with DL frameworks other than PyTorch and Tensorflow, please `open new issues <https://github.com/microsoft/nni/issues>`__ to let us know.
+
+Basics
+------
+
+Automatic neural architecture search is playing an increasingly important role in finding better models. Recent research has proven the feasibility of automatic NAS and has led to models that beat many manually designed and tuned models. Representative works include `NASNet <https://arxiv.org/abs/1707.07012>`__, `ENAS <https://arxiv.org/abs/1802.03268>`__, `DARTS <https://arxiv.org/abs/1806.09055>`__, `Network Morphism <https://arxiv.org/abs/1806.10282>`__, and `Evolution <https://arxiv.org/abs/1703.01041>`__. In addition, new innovations continue to emerge.
+
+High-level speaking, aiming to solve any particular task with neural architecture search typically requires: search space design, search strategy selection, and performance evaluation. The three components work together with the following loop (from the famous `NAS survey <https://arxiv.org/abs/1808.05377>`__):
+
+.. image:: ../../img/nas_abstract_illustration.png
+   :align: center
+   :width: 700
+
+In this figure:
+
+* *Model search space*  means a set of models from which the best model is explored/searched. Sometimes we use *search space* or *model space* in short.
+* *Exploration strategy* is the algorithm that is used to explore a model search space. Sometimes we also call it *search strategy*.
+* *Model evaluator* is responsible for training a model and evaluating its performance.
+
+The process is similar to :doc:`Hyperparameter Optimization </hpo/overview>`, except that the target is the best architecture rather than hyperparameter. Concretely, an exploration strategy selects an architecture from a predefined search space. The architecture is passed to a performance evaluation to get a score, which represents how well this architecture performs on a particular task. This process is repeated until the search process is able to find the best architecture.
+
+Key Features
+------------
+
+The current NAS framework in NNI is powered by the research of `Retiarii: A Deep Learning Exploratory-Training Framework <https://www.usenix.org/system/files/osdi20-zhang_quanlu.pdf>`__, where we highlight the following features:
+
+* :doc:`Simple APIs to construct search space easily <construct_space>`
+* :doc:`SOTA NAS algorithms to explore search space <exploration_strategy>`
+* :doc:`Experiment backend support to scale up experiments on large-scale AI platforms </experiment/overview>`
+
+Why NAS with NNI
+----------------
+
+We list out the three perspectives where NAS can be particularly challegning without NNI. NNI provides solutions to relieve users' engineering effort when they want to try NAS techniques in their own scenario.
+
+Search Space Design
+^^^^^^^^^^^^^^^^^^^
+
+The search space defines which architectures can be represented in principle. Incorporating prior knowledge about typical properties of architectures well-suited for a task can reduce the size of the search space and simplify the search. However, this also introduces a human bias, which may prevent finding novel architectural building blocks that go beyond the current human knowledge. Search space design can be very challenging for beginners, who might not possess the experience to balance the richness and simplicity.
+
+In NNI, we provide a wide range of APIs to build the search space. There are :doc:`high-level APIs <construct_space>`, that enables the possibility to incorporate human knowledge about what makes a good architecture or search space. There are also :doc:`low-level APIs <mutator>`, that is a list of primitives to construct a network from operation to operation.
+
+Exploration strategy
+^^^^^^^^^^^^^^^^^^^^
+
+The exploration strategy details how to explore the search space (which is often exponentially large). It encompasses the classical exploration-exploitation trade-off since, on the one hand, it is desirable to find well-performing architectures quickly, while on the other hand, premature convergence to a region of suboptimal architectures should be avoided. The "best" exploration strategy for a particular scenario is usually found via trial-and-error. As many state-of-the-art strategies are implemented with their own code-base, it becomes very troublesome to switch from one to another.
+
+In NNI, we have also provided :doc:`a list of strategies <exploration_strategy>`. Some of them are powerful yet time consuming, while others might be suboptimal but really efficient. Given that all strategies are implemented with a unified interface, users can always find one that matches their need.
+
+Performance estimation
+^^^^^^^^^^^^^^^^^^^^^^
+
+The objective of NAS is typically to find architectures that achieve high predictive performance on unseen data. Performance estimation refers to the process of estimating this performance. The problem with performance estimation is mostly its scalability, i.e., how can I run and manage multiple trials simultaneously.
+
+In NNI, we standardize this process is implemented with :doc:`evaluator <evaluator>`, which is responsible of estimating a model's performance. NNI has quite a few built-in supports of evaluators, ranging from the simplest option, e.g., to perform a standard training and validation of the architecture on data, to complex configurations and implementations. Evaluators are run in *trials*, where trials can be spawn onto distributed platforms with our powerful :doc:`training service </experiment/training_service/overview>`.
+
+Tutorials
+---------
+
+To start using NNI NAS framework, we recommend at least going through the following tutorials:
+
+* :doc:`Quickstart </tutorials/hello_nas>`
+* :doc:`construct_space`
+* :doc:`exploration_strategy`
+* :doc:`evaluator`
+
+Resources
+---------
+
+The following articles will help with a better understanding of the current arts of NAS:
+
+* `Neural Architecture Search: A Survey <https://arxiv.org/abs/1808.05377>`__
+* `A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions <https://arxiv.org/abs/2006.02903>`__
diff --git a/docs/source/nas/overview_zh.rst b/docs/source/nas/overview_zh.rst
new file mode 100644
index 000000000..b71913cbd
--- /dev/null
+++ b/docs/source/nas/overview_zh.rst
@@ -0,0 +1,89 @@
+.. 48c39585a539a877461aadef63078c48
+
+神经架构搜索
+===========================
+
+.. toctree::
+   :hidden:
+
+   快速入门 </tutorials/hello_nas>
+   构建搜索空间 <construct_space>
+   探索策略 <exploration_strategy>
+   评估器 <evaluator>
+   高级用法 <advanced_usage>
+
+.. attention:: NNI 最新的架构搜索支持都是基于 Retiarii 框架，还在使用 `NNI 架构搜索的早期版本 <https://nni.readthedocs.io/en/v2.2/nas.html>`__ 的用户应尽快将您的工作迁移到 Retiarii。我们计划在接下来的几个版本中删除旧的架构搜索框架。
+
+.. attention:: PyTorch 是 **Retiarii 唯一支持的框架**。有关 Tensorflow 上架构搜索支持的需求在 `此讨论 <https://github.com/microsoft/nni/discussions/4605>`__ 中。另外，如果您打算使用 PyTorch 和 Tensorflow 以外的 DL 框架运行 NAS，请 `创建新 issue <https://github.com/microsoft/nni/issues>`__ 让我们知道。
+
+概述
+------
+
+自动神经架构搜索 (Neural Architecture Search, NAS）在寻找更好的模型方面发挥着越来越重要的作用。最近的研究证明了自动架构搜索的可行性，并导致模型击败了许多手动设计和调整的模型。其中具有代表性的有 `NASNet <https://arxiv.org/abs/1707.07012>`__、 `ENAS <https://arxiv.org/abs/1802.03268>`__、 `DARTS <https://arxiv.org/ abs/1806.09055>`__、 `Network Morphism <https://arxiv.org/abs/1806.10282>`__ 和 `进化算法 <https://arxiv.org/abs/1703.01041>`__。此外，新的创新正不断涌现。
+
+总的来说，使用神经架构搜索解决任何特定任务通常需要：搜索空间设计、搜索策略选择和性能评估。这三个组件形成如下的循环（图来自于 `架构搜索综述 <https://arxiv.org/abs/1808.05377>`__）：
+
+.. image:: ../../img/nas_abstract_illustration.png
+   :align: center
+   :width: 700
+
+在这个图中：
+
+* *模型搜索空间* 是指一组模型，从中探索/搜索最佳模型，简称为 *搜索空间* 或 *模型空间*。
+* *探索策略* 是用于探索模型搜索空间的算法。有时我们也称它为 *搜索策略*。
+* *模型评估者* 负责训练模型并评估其性能。
+
+该过程类似于 :doc:`超参数优化 </hpo/overview>`，只不过目标是最佳网络结构而不是最优超参数。具体来说，探索策略从预定义的搜索空间中选择架构。该架构被传递给性能评估以获得评分，该评分表示这个网络结构在特定任务上的表现。重复此过程，直到搜索过程能够找到最优的网络结构。
+
+主要特点
+------------
+
+NNI 中当前的架构搜索框架由 `Retiarii: A Deep Learning Exploratory-Training Framework <https://www.usenix.org/system/files/osdi20-zhang_quanlu.pdf>`__ 的研究支撑，具有以下特点：
+
+* :doc:`简单的 API，让您轻松构建搜索空间 <construct_space>`
+* :doc:`SOTA 架构搜索算法，以高效探索搜索空间 <exploration_strategy>`
+* :doc:`后端支持，在大规模 AI 平台上运行实验 </experiment/overview>`
+
+为什么使用 NNI 的架构搜索
+-------------------------------
+
+若没有 NNI，实现架构搜索将极具挑战性，主要包含以下三个方面。当用户想在自己的场景中尝试架构搜索技术时，NNI 提供的解决方案可以极大程度上减轻用户的工作量。
+
+搜索空间设计
+^^^^^^^^^^^^^^^^^^^
+
+搜索空间定义了架构的可行域集合。为了简化搜索，我们通常需要结合任务相关的先验知识，减小搜索空间的规模。然而，这也引入了人类的偏见，在某种程度上可能会丧失突破人类认知的可能性。无论如何，对于初学者来说，搜索空间设计是一个极具挑战性的任务，因为他们可能无法在简单的空间和丰富的想象力之间取得平衡。
+
+在 NNI 中，我们提供了不同层级的 API 来构建搜索空间。有 :doc:`高层 API <construct_space>`，引入大量先验，帮助用户迅速了解什么是好的架构或搜索空间；也有 :doc:`底层 API <mutator>`，提供了最底层的算子和图变换原语。
+
+探索策略
+^^^^^^^^^^^^^^^^^^^^
+
+探索策略定义了如何探索搜索空间（通常是指数级规模的）。它包含经典的探索-利用权衡。一方面，我们希望快速找到性能良好的架构；而另一方面，我们也应避免过早收敛到次优架构的区域。我们往往需要通常通过反复试验找到特定场景的“最佳”探索策略。由于许多近期发表的探索策略都是使用自己的代码库实现的，因此从一个切换到另一个变得非常麻烦。
+
+在 NNI 中，我们还提供了 :doc:`一系列的探索策略 <exploration_strategy>`。其中一些功能强大但耗时，而另一些可能不能找到最优架构但非常高效。鉴于所有策略都使用统一的用户接口实现，用户可以轻松找到符合他们需求的策略。
+
+性能评估
+^^^^^^^^^^^^^^^^^^^^^^
+
+架构搜索的目标通常是找到能够在测试数据集表现理想的网络结构。性能评估的作用便是量化每个网络的好坏。其主要难点在于可扩展性，即如何在大规模训练平台上同时运行和管理多个试验。
+
+在 NNI 中，我们使用 :doc:`evaluator <evaluator>` 来标准化性能评估流程。它负责估计模型的性能。NNI 内建了不少性能评估器，从最简单的交叉验证，到复杂的自定义配置。评估器在 *试验 (trials)* 中运行，可以通过我们强大的 :doc:`训练平台 </experiment/training_service/overview>` 将试验分发到大规模训练平台上。
+
+教程
+---------
+
+要开始使用 NNI 架构搜索框架，我们建议至少阅读以下教程：
+
+* :doc:`快速入门 </tutorials/hello_nas>`
+* :doc:`构建搜索空间 <construct_space>`
+* :doc:`探索策略 <exploration_strategy>`
+* :doc:`评估器 <evaluator>`
+
+资源
+---------
+
+以下文章将有助于更好地了解 NAS 的最新发展：
+
+* `神经架构搜索：综述 <https://arxiv.org/abs/1808.05377>`__
+* `神经架构搜索的综述：挑战和解决方案 <https://arxiv.org/abs/2006.02903>`__
diff --git a/docs/source/NAS/Serialization.rst b/docs/source/nas/serialization.rst
similarity index 53%
rename from docs/source/NAS/Serialization.rst
rename to docs/source/nas/serialization.rst
index fbe69059e..7f7ae0838 100644
--- a/docs/source/NAS/Serialization.rst
+++ b/docs/source/nas/serialization.rst
@@ -1,22 +1,22 @@
 Serialization
 =============
 
-In multi-trial NAS, a sampled model should be able to be executed on a remote machine or a training platform (e.g., AzureML, OpenPAI). "Serialization" enables re-instantiation of model evaluator in another process or machine, such that, both the model and its model evaluator should be correctly serialized. To make NNI correctly serialize model evaluator, users should apply ``nni.trace`` on some of their functions and objects. API references can be found in :func:`nni.trace`.
+In multi-trial NAS, a sampled model should be able to be executed on a remote machine or a training platform (e.g., AzureML, OpenPAI). "Serialization" enables re-instantiation of model evaluator in another process or machine, such that, both the model and its model evaluator should be correctly serialized. To make NNI correctly serialize model evaluator, users should apply :func:`nni.trace <nni.common.serializer.trace>` on some of their functions and objects. API references can be found in :func:`nni.trace <nni.common.serializer.trace>`.
 
 Serialization is implemented as a combination of `json-tricks <https://json-tricks.readthedocs.io/en/latest/>`_ and `cloudpickle <https://github.com/cloudpipe/cloudpickle>`_. Essentially, it is json-tricks, that is a enhanced version of Python JSON, enabling handling of serialization of numpy arrays, date/times, decimal, fraction and etc. The difference lies in the handling of class instances. Json-tricks deals with class instances with ``__dict__`` and ``__class__``, which in most of our cases are not reliable (e.g., datasets, dataloaders). Rather, our serialization deals with class instances with two methods:
 
-1. If the class / factory that creates the object is decorated with ``nni.trace``, we can serialize the class / factory function, along with the parameters, such that the instance can be re-instantiated.
+1. If the class / factory that creates the object is decorated with :func:`nni.trace <nni.common.serializer.trace>`, we can serialize the class / factory function, along with the parameters, such that the instance can be re-instantiated.
 2. Otherwise, cloudpickle is used to serialize the object into a binary.
 
-The recommendation is, unless you are absolutely certain that there is no problem and extra burden to serialize the object into binary, always add ``nni.trace``. In most cases, it will be more clean and neat, and enables possibilities such as mutation of parameters (will be supported in future).
+The recommendation is, unless you are absolutely certain that there is no problem and extra burden to serialize the object into binary, always add :func:`nni.trace <nni.common.serializer.trace>`. In most cases, it will be more clean and neat, and enables possibilities such as mutation of parameters (will be supported in future).
 
 .. warning::
 
     **What will happen if I forget to "trace" my objects?**
 
-    It is likely that the program can still run. NNI will try to serialize the untraced object into a binary. It might fail in complex cases. For example, when the object is too large. Even if it succeeds, the result might be a substantially large object. For example, if you forgot to add ``nni.trace`` on ``MNIST``, the MNIST dataset object wil be serialized into binary, which will be dozens of megabytes because the object has the whole 60k images stored inside. You might see warnings and even errors when running experiments. To avoid such issues, the easiest way is to always remember to add ``nni.trace`` to non-primitive objects.
+    It is likely that the program can still run. NNI will try to serialize the untraced object into a binary. It might fail in complex cases. For example, when the object is too large. Even if it succeeds, the result might be a substantially large object. For example, if you forgot to add :func:`nni.trace <nni.common.serializer.trace>` on ``MNIST``, the MNIST dataset object wil be serialized into binary, which will be dozens of megabytes because the object has the whole 60k images stored inside. You might see warnings and even errors when running experiments. To avoid such issues, the easiest way is to always remember to add :func:`nni.trace <nni.common.serializer.trace>` to non-primitive objects.
 
-.. note:: In Retiarii, serializer will throw exception when one of an single object in the recursive serialization is larger than 64 KB when binary serialized. This indicates that such object needs to be wrapped by ``nni.trace``. In rare cases, if you insist on pickling large data, the limit can be overridden by setting an environment variable ``PICKLE_SIZE_LIMIT``, whose unit is byte. Please note that even if the experiment might be able to run, this can still cause performance issues and even the crash of NNI experiment.
+.. note:: In Retiarii, serializer will throw exception when one of an single object in the recursive serialization is larger than 64 KB when binary serialized. This indicates that such object needs to be wrapped by :func:`nni.trace <nni.common.serializer.trace>`. In rare cases, if you insist on pickling large data, the limit can be overridden by setting an environment variable ``PICKLE_SIZE_LIMIT``, whose unit is byte. Please note that even if the experiment might be able to run, this can still cause performance issues and even the crash of NNI experiment.
 
 To trace a function or class, users can use decorator like,
 
@@ -26,11 +26,15 @@ To trace a function or class, users can use decorator like,
     class MyClass:
         ...
 
-Inline trace that traces instantly on the object instantiation or function invoke is also acceptable: ``nni.trace(MyClass)(parameters)``.
+Inline trace that traces instantly on the object instantiation or function invoke is also acceptable:
 
-Assuming a class ``cls`` is already traced, when it is serialized, its class type along with initialization parameters will be dumped. As the parameters are possibly class instances (if not primitive types like ``int`` and ``str``), their serialization will be a similar problem. We recommend decorate them with ``nni.trace`` as well. In other words, ``nni.trace`` should be applied recursively if necessary.
+.. code-block:: python
+
+   nni.trace(MyClass)(parameters)
+
+Assuming a class ``cls`` is already traced, when it is serialized, its class type along with initialization parameters will be dumped. As the parameters are possibly class instances (if not primitive types like ``int`` and ``str``), their serialization will be a similar problem. We recommend decorate them with :func:`nni.trace <nni.common.serializer.trace>` as well. In other words, :func:`nni.trace <nni.common.serializer.trace>` should be applied recursively if necessary.
 
-Below is an example, ``transforms.Compose``, ``transforms.Normalize``, and ``MNIST`` are serialized manually using ``nni.trace``. ``nni.trace`` takes a class / function as its argument, and returns a wrapped class and function that has the same behavior with the original class / function. The usage of the wrapped class / function is also identical to the original one, except that the arguments are recorded. No need to apply ``nni.trace`` to ``pl.Classification`` and ``pl.DataLoader`` because they are already traced.
+Below is an example, ``transforms.Compose``, ``transforms.Normalize``, and ``MNIST`` are serialized manually using :func:`nni.trace <nni.common.serializer.trace>`. :func:`nni.trace <nni.common.serializer.trace>` takes a class / function as its argument, and returns a wrapped class and function that has the same behavior with the original class / function. The usage of the wrapped class / function is also identical to the original one, except that the arguments are recorded. No need to apply :func:`nni.trace <nni.common.serializer.trace>` to :class:`pl.Classification <nni.retiarii.evaluator.pytorch.Classification>` and :class:`pl.DataLoader <nni.retiarii.evaluator.pytorch.DataLoader>` because they are already traced.
 
 .. code-block:: python
 
@@ -57,6 +61,6 @@ Below is an example, ``transforms.Compose``, ``transforms.Normalize``, and ``MNI
 
     **What's the relationship between model_wrapper, basic_unit and nni.trace?**
 
-    They are fundamentally different. ``model_wrapper`` is used to wrap a base model (search space), ``basic_unit`` to annotate a module as primitive. ``nni.trace`` is to enable serialization of general objects. Though they share similar underlying implementations, but do keep in mind that you will experience errors if you mix them up.
+    They are fundamentally different. :func:`model_wrapper <nni.retiarii.model_wrapper>` is used to wrap a base model (search space), :func:`basic_unit <nni.retiarii.basic_unit>` to annotate a module as primitive. :func:`nni.trace <nni.common.serializer.trace>` is to enable serialization of general objects. Though they share similar underlying implementations, but do keep in mind that you will experience errors if you mix them up.
 
-    .. seealso:: Please refer to API reference of :meth:`nni.retiarii.model_wrapper`, :meth:`nni.retiarii.basic_unit`, and :meth:`nni.trace`.
+    Please refer to API reference of :meth:`nni.retiarii.model_wrapper`, :meth:`nni.retiarii.basic_unit`, and :func:`nni.trace <nni.common.serializer.trace>`.
diff --git a/docs/source/nas/toctree.rst b/docs/source/nas/toctree.rst
new file mode 100644
index 000000000..0307bea2f
--- /dev/null
+++ b/docs/source/nas/toctree.rst
@@ -0,0 +1,12 @@
+Neural Architecture Search
+==========================
+
+.. toctree::
+   :hidden:
+
+   overview
+   Quickstart </tutorials/hello_nas>
+   construct_space
+   exploration_strategy
+   evaluator
+   advanced_usage
diff --git a/docs/source/nas_zh.rst b/docs/source/nas_zh.rst
deleted file mode 100644
index 42484805f..000000000
--- a/docs/source/nas_zh.rst
+++ /dev/null
@@ -1,36 +0,0 @@
-.. 0b36fb7844fd9cc88c4e74ad2c6b9ece
-
-##########################
-神经网络架构搜索
-##########################
-
-自动化的神经网络架构（NAS）搜索在寻找更好的模型方面发挥着越来越重要的作用。
-最近的研究工作证明了自动化 NAS 的可行性，并发现了一些超越手动调整的模型。
-代表工作有 NASNet, ENAS, DARTS, Network Morphism, 以及 Evolution 等。 此外，新的创新不断涌现。
-
-但是，要实现 NAS 算法需要花费大量的精力，并且很难在新算法中重用现有算法的代码。
-为了促进 NAS 创新 (如, 设计实现新的 NAS 模型，比较不同的 NAS 模型)，
-易于使用且灵活的编程接口非常重要。
-
-因此，NNI 设计了 `Retiarii <https://www.usenix.org/system/files/osdi20-zhang_quanlu.pdf>`__， 它是一个深度学习框架，支持在神经网络模型空间，而不是单个神经网络模型上进行探索性训练。
-Retiarii 的探索性训练允许用户以高度灵活的方式表达 *神经网络架构搜索* 和 *超参数调整* 的各种搜索空间。
-
-本文档中的一些常用术语：
-
-* *Model search space（模型搜索空间）* ：它意味着一组模型，用于从中探索/搜索出最佳模型。 有时我们简称为 *search space（搜索空间）* 或 *model space（模型空间）* 。
-* *Exploration strategy（探索策略）*：用于探索模型搜索空间的算法。
-* *Model evaluator（模型评估器）*：用于训练模型并评估模型的性能。
-
-按照以下说明开始您的 Retiarii 之旅。
-
-..  toctree::
-    :maxdepth: 2
-
-    概述 <NAS/Overview>
-    快速入门 <NAS/QuickStart>
-    构建模型空间 <NAS/construct_space>
-    Multi-trial NAS <NAS/multi_trial_nas>
-    One-Shot NAS <NAS/one_shot_nas>
-    硬件相关 NAS <NAS/HardwareAwareNAS>
-    NAS 基准测试 <NAS/Benchmarks>
-    NAS API 参考 <NAS/ApiReference>
diff --git a/docs/source/nnSpider.rst b/docs/source/nnSpider.rst
deleted file mode 100644
index 0290bce29..000000000
--- a/docs/source/nnSpider.rst
+++ /dev/null
@@ -1,90 +0,0 @@
-:orphan:
-
-
-.. raw:: html
-
-    <h2 class="center">nnSpider emoticons</h2>
-    <ul class="emotion">
-        <li class="first">
-            <div>
-                <a href="{{ pathto('nnSpider/nobug') }}">
-                    <img src="_static/img/NoBug.png" alt="NoBug" />
-                </a>
-            </div>
-            <p class="center">NoBug</p>
-        </li>
-        <li class="first">
-            <div>
-                <a href="{{ pathto('nnSpider/holiday') }}">
-                    <img src="_static/img/Holiday.png" alt="Holiday" />
-                </a>
-            </div>
-            <p class="center">Holiday</p>
-        </li>
-        <li class="first">
-            <div>
-                <a href="{{ pathto('nnSpider/errorEmotion') }}">
-                    <img src="_static/img/Error.png" alt="Error" />
-                </a>
-            </div>
-            <p class="center">Error</p>
-        </li>
-        <li class="second">
-            <div>
-                <a href="{{ pathto('nnSpider/working') }}">
-                    <img class="working" src="_static/img/Working.png" alt="Working" />
-                </a>
-            </div>
-            <p class="center">Working</p>
-        </li>
-        <li class="second">
-            <div>
-                <a href="{{ pathto('nnSpider/sign') }}">
-                    <img class="sign" src="_static/img/Sign.png" alt="Sign" />
-                </a>
-            </div>
-            <p class="center">Sign</p>
-        </li>
-        <li class="second">
-            <div>
-                <a href="{{ pathto('nnSpider/crying') }}">
-                    <img class="crying" src="_static/img/Crying.png" alt="Crying" />
-                </a>
-            </div>
-            <p class="center">Crying</p>
-        </li>
-        <li class="three">
-            <div>
-                <a href="{{ pathto('nnSpider/cut') }}">
-                    <img src="_static/img/Cut.png" alt="Crying" />
-                </a>
-            </div>
-            <p class="center">Cut</p>
-        </li>
-        <li class="three">
-            <div>
-                <a href="{{ pathto('nnSpider/weaving') }}">
-                    <img class="weaving" src="_static/img/Weaving.png" alt="Weaving" />
-                </a>
-            </div>
-            <p class="center">weaving</p>
-        </li>
-        <li class="three">
-            <div class="comfort">
-                <a href="{{ pathto('nnSpider/comfort') }}">
-                    <img src="_static/img/Comfort.png" alt="Weaving" />
-                </a>
-            </div>
-            <p class="center">comfort</p>
-        </li>
-        <li class="four">
-            <div>
-                <a href="{{ pathto('nnSpider/sweat') }}">
-                    <img src="_static/img/Sweat.png" alt="Sweat" />
-                </a>
-            </div>
-            <p class="center">Sweat</p>
-        </li>
-    <div class="clear"></div>
-
-    </ul>
diff --git a/docs/source/nnSpider/comfort.rst b/docs/source/nnSpider/comfort.rst
deleted file mode 100644
index 39b266079..000000000
--- a/docs/source/nnSpider/comfort.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-:orphan:
-
-
-.. raw:: html
-
-    <h2>Comfort</h2>
-    <div class="details-container">
-        <img src="../_static/img/Comfort.png" alt="Comfort" />
-    </div>
diff --git a/docs/source/nnSpider/crying.rst b/docs/source/nnSpider/crying.rst
deleted file mode 100644
index 614fff697..000000000
--- a/docs/source/nnSpider/crying.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-:orphan:
-
-
-.. raw:: html
-
-    <h2>Crying</h2>
-    <div class="details-container">
-        <img src="../_static/img/Crying.png" alt="Crying" />
-    </div>
diff --git a/docs/source/nnSpider/cut.rst b/docs/source/nnSpider/cut.rst
deleted file mode 100644
index b0bc4775b..000000000
--- a/docs/source/nnSpider/cut.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-:orphan:
-
-
-.. raw:: html
-
-    <h2>Cut</h2>
-    <div class="details-container">
-        <img src="../_static/img/Cut.png" alt="Cut" />
-    </div>
diff --git a/docs/source/nnSpider/errorEmotion.rst b/docs/source/nnSpider/errorEmotion.rst
deleted file mode 100644
index 0bb8992cf..000000000
--- a/docs/source/nnSpider/errorEmotion.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-:orphan:
-
-
-.. raw:: html
-
-    <h2>Error</h2>
-    <div class="details-container">
-        <img src="../_static/img/Error.png" alt="Error" />
-    </div>
diff --git a/docs/source/nnSpider/holiday.rst b/docs/source/nnSpider/holiday.rst
deleted file mode 100644
index 758e33069..000000000
--- a/docs/source/nnSpider/holiday.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-:orphan:
-
-
-.. raw:: html
-
-    <h2>Holiday</h2>
-    <div class="details-container">
-        <img src="../_static/img/Holiday.png" alt="NoBug" />
-    </div>
diff --git a/docs/source/nnSpider/nobug.rst b/docs/source/nnSpider/nobug.rst
deleted file mode 100644
index 9fe2b3219..000000000
--- a/docs/source/nnSpider/nobug.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-:orphan:
-
-
-.. raw:: html
-
-    <h2>NoBug</h2>
-    <div class="details-container">
-        <img src="../_static/img/NoBug.png" alt="NoBug" />
-    </div>
diff --git a/docs/source/nnSpider/sign.rst b/docs/source/nnSpider/sign.rst
deleted file mode 100644
index 2b4aa2455..000000000
--- a/docs/source/nnSpider/sign.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-:orphan:
-
-
-.. raw:: html
-
-    <h2>Sign</h2>
-    <div class="details-container">
-        <img src="../_static/img/Sign.png" alt="Sign" />
-    </div>
diff --git a/docs/source/nnSpider/sweat.rst b/docs/source/nnSpider/sweat.rst
deleted file mode 100644
index 9d81ee62a..000000000
--- a/docs/source/nnSpider/sweat.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-:orphan:
-
-
-.. raw:: html
-
-    <h2>Sweat</h2>
-    <div class="details-container">
-        <img src="../_static/img/Sweat.png" alt="Sweat" />
-    </div>
diff --git a/docs/source/nnSpider/weaving.rst b/docs/source/nnSpider/weaving.rst
deleted file mode 100644
index d6e75334d..000000000
--- a/docs/source/nnSpider/weaving.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-:orphan:
-
-
-.. raw:: html
-
-    <h2>Weaving</h2>
-    <div class="details-container">
-        <img src="../_static/img/Weaving.png" alt="Weaving" />
-    </div>
diff --git a/docs/source/nnSpider/working.rst b/docs/source/nnSpider/working.rst
deleted file mode 100644
index 92d91e64c..000000000
--- a/docs/source/nnSpider/working.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-:orphan:
-
-
-.. raw:: html
-
-    <h2>Working</h2>
-    <div class="details-container">
-        <img src="../_static/img/Working.png" alt="Working" />
-    </div>
diff --git a/docs/source/notes/architecture_overview.rst b/docs/source/notes/architecture_overview.rst
new file mode 100644
index 000000000..472676eb8
--- /dev/null
+++ b/docs/source/notes/architecture_overview.rst
@@ -0,0 +1,85 @@
+:orphan:
+
+Architecture Overview
+=====================
+
+NNI (Neural Network Intelligence) is a toolkit to help users design and tune machine learning models (e.g., hyperparameters), neural network architectures, or complex system's parameters, in an efficient and automatic way. NNI has several appealing properties: ease-of-use, scalability, flexibility, and efficiency.
+
+* **Ease-of-use**: NNI can be easily installed through python pip. Only several lines need to be added to your code in order to use NNI's power. You can use both the commandline tool and WebUI to work with your experiments.
+* **Scalability**: Tuning hyperparameters or the neural architecture often demands a large number of computational resources, while NNI is designed to fully leverage different computation resources, such as remote machines, training platforms (e.g., OpenPAI, Kubernetes). Hundreds of trials could run in parallel by depending on the capacity of your configured training platforms.
+* **Flexibility**: Besides rich built-in algorithms, NNI allows users to customize various hyperparameter tuning algorithms, neural architecture search algorithms, early stopping algorithms, etc. Users can also extend NNI with more training platforms, such as virtual machines, kubernetes service on the cloud. Moreover, NNI can connect to external environments to tune special applications/models on them.
+* **Efficiency**: We are intensively working on more efficient model tuning on both the system and algorithm level. For example, we leverage early feedback to speedup the tuning procedure.
+
+The figure below shows high-level architecture of NNI.
+
+
+.. image:: https://user-images.githubusercontent.com/16907603/92089316-94147200-ee00-11ea-9944-bf3c4544257f.png
+   :width: 700
+
+Key Concepts
+------------
+
+* *Experiment*: One task of, for example, finding out the best hyperparameters of a model, finding out the best neural network architecture, etc. It consists of trials and AutoML algorithms.
+
+* *Search Space*: The feasible region for tuning the model. For example, the value range of each hyperparameter.
+
+* *Configuration*: An instance from the search space, that is, each hyperparameter has a specific value.
+
+* *Trial*: An individual attempt at applying a new configuration (e.g., a set of hyperparameter values, a specific neural architecture, etc.). Trial code should be able to run with the provided configuration.
+
+* *Tuner*: An AutoML algorithm, which generates a new configuration for the next try. A new trial will run with this configuration.
+
+* *Assessor*: Analyze a trial's intermediate results (e.g., periodically evaluated accuracy on test dataset) to tell whether this trial can be early stopped or not.
+
+* *Training Platform*: Where trials are executed. Depending on your experiment's configuration, it could be your local machine, or remote servers, or large-scale training platform (e.g., OpenPAI, Kubernetes).
+
+Basically, an experiment runs as follows: Tuner receives search space and generates configurations. These configurations will be submitted to training platforms, such as the local machine, remote machines, or training clusters. Their performances are reported back to Tuner. Then, new configurations are generated and submitted.
+
+For each experiment, the user only needs to define a search space and update a few lines of code, and then leverage NNI built-in Tuner/Assessor and training platforms to search the best hyperparameters and/or neural architecture. There are basically 3 steps:
+
+* Step 1: :doc:`Define search space <../hpo/search_space>`
+
+* Step 2: Update model codes
+
+* Step 3: :doc:`Define Experiment <../reference/experiment_config>`
+
+.. image:: https://user-images.githubusercontent.com/23273522/51816627-5d13db80-2302-11e9-8f3e-627e260203d5.jpg
+
+For more details about how to run an experiment, please refer to :doc:`Quickstart <../tutorials/hpo_quickstart_pytorch/main>`.
+
+Core Features
+-------------
+
+NNI provides a key capacity to run multiple instances in parallel to find the best combinations of parameters. This feature can be used in various domains, like finding the best hyperparameters for a deep learning model or finding the best configuration for database and other complex systems with real data.
+
+NNI also provides algorithm toolkits for machine learning and deep learning, especially neural architecture search (NAS) algorithms, model compression algorithms, and feature engineering algorithms.
+
+Hyperparameter Tuning
+^^^^^^^^^^^^^^^^^^^^^
+
+This is a core and basic feature of NNI, we provide many popular :doc:`automatic tuning algorithms <../hpo/tuners>` (i.e., tuner) and :doc:`early stop algorithms <../hpo/assessors>` (i.e., assessor). You can follow :doc:`Quickstart <../tutorials/hpo_quickstart_pytorch/main>` to tune your model (or system). Basically, there are the above three steps and then starting an NNI experiment.
+
+General NAS Framework
+^^^^^^^^^^^^^^^^^^^^^
+
+This NAS framework is for users to easily specify candidate neural architectures, for example, one can specify multiple candidate operations (e.g., separable conv, dilated conv) for a single layer, and specify possible skip connections. NNI will find the best candidate automatically. On the other hand, the NAS framework provides a simple interface for another type of user (e.g., NAS algorithm researchers) to implement new NAS algorithms. A detailed description of NAS and its usage can be found :doc:`here </nas/overview>`.
+
+NNI has support for many one-shot NAS algorithms such as ENAS and DARTS through NNI trial SDK. To use these algorithms you do not have to start an NNI experiment. Instead, import an algorithm in your trial code and simply run your trial code. If you want to tune the hyperparameters in the algorithms or want to run multiple instances, you can choose a tuner and start an NNI experiment.
+
+Other than one-shot NAS, NAS can also run in a classic mode where each candidate architecture runs as an independent trial job. In this mode, similar to hyperparameter tuning, users have to start an NNI experiment and choose a tuner for NAS.
+
+Model Compression
+^^^^^^^^^^^^^^^^^
+
+NNI provides an easy-to-use model compression framework to compress deep neural networks, the compressed networks typically have much smaller model size and much faster
+inference speed without losing performance significantlly. Model compression on NNI includes pruning algorithms and quantization algorithms. NNI provides many pruning and
+quantization algorithms through NNI trial SDK. Users can directly use them in their trial code and run the trial code without starting an NNI experiment. Users can also use NNI model compression framework to customize their own pruning and quantization algorithms.
+
+A detailed description of model compression and its usage can be found :doc:`here <../compression/overview>`.
+
+Automatic Feature Engineering
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Automatic feature engineering is for users to find the best features for their tasks. A detailed description of automatic feature engineering and its usage can be found :doc:`here <../feature_engineering/overview>`. It is supported through NNI trial SDK, which means you do not have to create an NNI experiment. Instead, simply import a built-in auto-feature-engineering algorithm in your trial code and directly run your trial code. 
+
+The auto-feature-engineering algorithms usually have a bunch of hyperparameters themselves. If you want to automatically tune those hyperparameters, you can leverage hyperparameter tuning of NNI, that is, choose a tuning algorithm (i.e., tuner) and start an NNI experiment for it.
diff --git a/docs/source/notes/build_from_source.rst b/docs/source/notes/build_from_source.rst
new file mode 100644
index 000000000..ea3f9aae6
--- /dev/null
+++ b/docs/source/notes/build_from_source.rst
@@ -0,0 +1,123 @@
+Build from Source
+=================
+
+This article describes how to build and install NNI from `source code <https://github.com/microsoft/nni>`__.
+
+Preparation
+-----------
+
+Fetch source code from GitHub:
+
+.. code-block:: bash
+
+    git clone https://github.com/microsoft/nni.git
+    cd nni
+
+Upgrade to latest toolchain:
+
+.. code-block:: text
+
+    pip install --upgrade setuptools pip wheel
+
+.. note::
+
+    Please make sure ``python`` and ``pip`` executables have correct Python version.
+
+    For Apple Silicon M1, if ``python`` command is not available, you may need to manually fix dependency building issues.
+    (`GitHub issue <https://github.com/mapbox/node-sqlite3/issues/1413>`__ |
+    `Stack Overflow question <https://stackoverflow.com/questions/70874412/sqlite3-on-m1-chip-npm-is-failing>`__)
+
+Development Build
+-----------------
+
+If you want to build NNI for your own use, we recommend using `development mode`_.
+
+.. code-block:: text
+
+    python setup.py develop
+
+This will install NNI as symlink, and the version number will be ``999.dev0``.
+
+.. _development mode: https://setuptools.pypa.io/en/latest/userguide/development_mode.html
+
+Then if you want to modify NNI source code, please check :doc:`contribution guide <contributing>`.
+
+Release Build
+-------------
+
+To install in release mode, you must first build a wheel.
+NNI does not support setuptools' "install" command.
+
+A release package requires jupyterlab to build the extension:
+
+.. code-block:: text
+
+    pip install jupyterlab==3.0.9
+
+You need to set ``NNI_RELEASE`` environment variable to the version number,
+and compile TypeScript modules before "bdist_wheel".
+
+In bash:
+
+.. code-block:: bash
+
+    export NNI_RELEASE=2.0
+    python setup.py build_ts
+    python bdist_wheel
+
+In PowerShell:
+
+.. code-block:: powershell
+
+    $env:NNI_RELEASE=2.0
+    python setup.py build_ts
+    python bdist_wheel
+
+If successful, you will find the wheel in ``dist`` directory.
+
+.. note::
+
+    NNI's build process is somewhat complicated.
+    This is due to setuptools and TypeScript not working well together.
+
+    Setuptools require to provide ``package_data``, the full list of package files, before running any command.
+    However it is nearly impossible to predict what files will be generated before invoking TypeScript compiler.
+
+    If you have any solution for this problem, please open an issue to let us know.
+
+Build Docker Image
+------------------
+
+You can build a Docker image with :githublink:`Dockerfile <Dockerfile>`:
+
+.. code-block:: bash
+
+    export NNI_RELEASE=2.7
+    python setup.py build_ts
+    python setup.py bdist_wheel -p manylinux1_x86_64
+    docker build --build-arg NNI_RELEASE=${NNI_RELEASE} -t my/nni .
+
+To build image for other platforms, please edit Dockerfile yourself.
+
+Other Commands and Options
+--------------------------
+
+Clean
+^^^^^
+
+If the build fails, please clean up and try again:
+
+.. code:: text
+
+    python setup.py clean
+
+Skip compiling TypeScript modules
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+This is useful when you have uninstalled NNI from development mode and want to install again.
+
+It will not work if you have never built TypeScript modules before.
+
+.. code:: text
+
+    python setup.py develop --skip-ts
diff --git a/docs/source/notes/contributing.rst b/docs/source/notes/contributing.rst
new file mode 100644
index 000000000..73ecd54c6
--- /dev/null
+++ b/docs/source/notes/contributing.rst
@@ -0,0 +1,344 @@
+Contribution Guide
+==================
+
+Great! We are always on the lookout for more contributors to our code base.
+
+Firstly, if you are unsure or afraid of anything, just ask or submit the issue or pull request anyways. You won't be yelled at for giving your best effort. The worst that can happen is that you'll be politely asked to change something. We appreciate any sort of contributions and don't want a wall of rules to get in the way of that.
+
+However, for those individuals who want a bit more guidance on the best way to contribute to the project, read on. This document will cover all the points we're looking for in your contributions, raising your chances of quickly merging or addressing your contributions.
+
+There are a few simple guidelines that you need to follow before providing your hacks.
+
+Bug Reports and Feature Requests
+--------------------------------
+
+If you encountered a problem when using NNI, or have an idea for a new feature, your feedbacks are always welcome. Here are some possible channels:
+
+*  `File an issue <https://github.com/microsoft/nni/issues/new/choose>`_ on GitHub.
+*  Open or participate in a `discussion <https://github.com/microsoft/nni/discussions>`_.
+*  Discuss on the NNI `Gitter <https://gitter.im/Microsoft/nni?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge>`_ in NNI.
+*  Join IM discussion groups:
+
+   .. list-table::
+      :widths: 50 50
+      :header-rows: 1
+
+      * - Gitter
+        - WeChat
+      * - .. image:: https://user-images.githubusercontent.com/39592018/80665738-e0574a80-8acc-11ea-91bc-0836dc4cbf89.png
+        - .. image:: https://github.com/scarlett2018/nniutil/raw/master/wechat.png
+
+Looking for an existing issue
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Before you create a new issue, please do a search in `open issues <https://github.com/microsoft/nni/issues>`_ to see if the issue or feature request has already been filed.
+
+Be sure to scan through the `most popular <https://github.com/microsoft/nni/issues?q=is%3Aopen+is%3Aissue+label%3AFAQ+sort%3Areactions-%2B1-desc>`_ feature requests.
+
+If you find your issue already exists, make relevant comments and add your `reaction <https://github.com/blog/2119-add-reactions-to-pull-requests-issues-and-comments>`_. Use a reaction in place of a "+1" comment:
+
+* 👍 - upvote
+* 👎 - downvote
+
+If you cannot find an existing issue that describes your bug or feature, create a new issue following the guidelines below.
+
+Writing good bug reports or feature requests
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+* File a single issue per problem and feature request. Do not enumerate multiple bugs or feature requests in the same issue.
+
+* Provide as much information as you think might relevant to the context (thinking the issue is assigning to you, what kinds of info you will need to debug it!!!). To give you a general idea about what kinds of info are useful for developers to dig out the issue, we had provided issue template for you.
+
+* Once you had submitted an issue, be sure to follow it for questions and discussions. 
+
+* Once the bug is fixed or feature is addressed, be sure to close the issue.
+
+Writing code
+------------
+
+There is always something more that is required, to make it easier to suit your use-cases.
+Before starting to write code, we recommend checking for `issues <https://github.com/microsoft/nni/issues>`_ on GitHub or open a new issue to initiate a discussion. There could be cases where people are already working on a fix, or similar features have already been under discussion.
+
+To contribute code, you first need to find the NNI code repo located on `GitHub <https://github.com/microsoft/nni>`_. Firstly, fork the repository under your own GitHub handle. After cloning the repository, add, commit, push and squash (if necessary) the changes with detailed commit messages to your fork. From where you can proceed to making a pull request. The pull request will then be reviewed by our core maintainers before merging into master branch. `Here <https://github.com/firstcontributions/first-contributions>`_ is a step-by-step guide for this process.
+
+Contributions to NNI should follow our code of conduct. Please see details :ref:`here <code-of-conduct>`.
+
+Find the code snippet that concerns you
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The NNI repository is large code-base. High-level speaking, it can be decomposed into several core parts:
+
+* ``nni``: the core Python package that contains most features of hyper-parameter tuner, neural architecture search, model compression.
+* ``ts``: contains ``nni_manager`` that manages experiments and training services, and ``webui`` for visualization.
+* ``pipelines`` and ``test``: unit test and integration test, alongside their configurations.
+
+See :doc:`./architecture_overview` if you are interested in details.
+
+.. _get-started-dev:
+
+Get started with development
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+NNI development environment supports Ubuntu 1604 (or above), and Windows 10 with Python 3.7+ (documentation build requires Python 3.8+). We recommend using `conda <https://docs.conda.io/>`_ on Windows.
+
+1. Fork the NNI's GitHub repository and clone the forked repository to your machine.
+
+   .. code-block:: bash
+
+      git clone https://github.com/<your_github_handle>/nni.git
+
+2. Create a new working branch. Use any name you like.
+
+   .. code-block:: bash
+
+      cd nni
+      git checkout -b feature-xyz
+
+3. Install NNI from source code if you need to modify the source code, and test it.
+
+   .. code-block:: bash
+
+      python3 -m pip install -U -r dependencies/setup.txt
+      python3 -m pip install -r dependencies/develop.txt
+      python3 setup.py develop
+
+   This installs NNI in `development mode <https://setuptools.readthedocs.io/en/latest/userguide/development_mode.html>`_,
+   so you don't need to reinstall it after edit.
+
+4. Try to start an experiment to check if your environment is ready. For example, run the command
+
+   .. code-block:: bash
+
+      nnictl create --config examples/trials/mnist-pytorch/config.yml
+
+   And open WebUI to check if everything is OK. Or check the version of installed NNI,
+
+   .. code-block:: python
+
+      >>> import nni
+      >>> nni.__version__
+      '999.dev0'
+
+   .. note:: Please don't run test under the same folder where the NNI repository is located. As the repository is probably also called ``nni``, it could import the wrong ``nni`` package.
+
+5. Write your code along with tests to verify whether the bug is fixed, or the feature works as expected.
+
+6. Reload changes. For Python, nothing needs to be done, because the code is already linked to package folders. For TypeScript on Linux and MacOS,
+
+   * If ``ts/nni_manager`` is changed, run ``yarn watch`` under this folder. It will watch and build code continually. The ``nnictl`` need to be restarted to reload NNI manager.
+   * If ``ts/webui`` is changed, run ``yarn dev``\ , which will run a mock API server and a webpack dev server simultaneously. Use ``EXPERIMENT`` environment variable (e.g., ``mnist-tfv1-running``\ ) to specify the mock data being used. Built-in mock experiments are listed in ``src/webui/mock``. An example of the full command is ``EXPERIMENT=mnist-tfv1-running yarn dev``.
+
+   For TypeScript on Windows, currently you must rebuild TypeScript modules with `python3 setup.py build_ts` after edit.
+
+7. Commit and push your changes, and submit your pull request!
+
+Coding Tips
+-----------
+
+We expect all contributors to respect the following coding styles and naming conventions upon their contribution.
+
+Python
+^^^^^^
+
+* We follow `PEP8 <https://www.python.org/dev/peps/pep-0008/>`__ for Python code and naming conventions, do try to adhere to the same when making a pull request. Our pull request has a mandatory code scan with ``pylint`` and ``flake8``.
+
+  .. note:: To scan your own code locally, run
+
+     .. code-block:: bash
+
+         python -m pylint --rcfile pylintrc nni
+
+  .. tip:: One can also take the help of auto-format tools such as `autopep8 <https://code.visualstudio.com/docs/python/editing#_formatting>`_, which will automatically resolve most of the styling issues.
+
+* We recommend documenting all the methods and classes in your code. Follow `NumPy Docstring Style <https://numpydoc.readthedocs.io/en/latest/format.html>`__ for Python Docstring Conventions.
+
+  * For function docstring, **description**, **Parameters**, and **Returns** are mandatory.
+  * For class docstring, **description** is mandatory. Optionally **Parameters** and **Attributes**. The parameters of ``__init__`` should be documented in the docstring of class.
+  * For docstring to describe ``dict``, which is commonly used in our hyper-parameter format description, please refer to `Internal Guideline on Writing Standards <https://ribokit.github.io/docs/text/>`_.
+
+  .. tip:: Basically, you can use :ref:`ReStructuredText <restructuredtext-intro>` syntax in docstrings, without some exceptions. For example, custom headings are not allowed in docstrings.
+
+TypeScript
+^^^^^^^^^^
+
+TypeScript code checks can be done with,
+
+.. code-block:: bash
+
+   # for nni manager
+   cd ts/nni_manager
+   yarn eslint
+
+   # for webui
+   cd ts/webui
+   yarn sanity-check
+
+Tests
+-----
+
+When a new feature is added or a bug is fixed, tests are highly recommended to make sure that the fix is effective or the feature won't break in future. There are two types of tests in NNI:
+
+* Unit test (**UT**): each test targets at a specific class / function / module.
+* Integration test (**IT**): each test is an end-to-end example / demo.
+
+Unit test (Python)
+^^^^^^^^^^^^^^^^^^
+
+Python UT are located in ``test/ut/`` folder. We use `pytest <https://docs.pytest.org/>`_ to launch the tests, and the working directory is ``test/ut/``.
+
+.. tip:: pytest can be used on a single file or a single test function.
+
+   .. code-block:: bash
+
+      pytest sdk/test_tuner.py
+      pytest sdk/test_tuner.py::test_tpe
+
+Unit test (TypeScript)
+^^^^^^^^^^^^^^^^^^^^^^
+
+TypeScript UT are paired with TypeScript code. Use ``yarn test`` to run them.
+
+Integration test
+^^^^^^^^^^^^^^^^
+
+The integration tests can be found in ``pipelines/`` folder. 
+
+The integration tests are run on Azure DevOps platform on a daily basis, in order to make sure that our examples and training service integrations work properly. However, for critical changes that have impacts on the core functionalities of NNI, we recommend to `trigger the pipeline on the pull request branch <https://stackoverflow.com/questions/60157818/azure-pipeline-run-build-on-pull-request-branch>`_.
+
+The integration tests won't be automatically triggered on pull requests. You might need to contact the core developers to help you trigger the tests.
+
+Documentation
+-------------
+
+Build and check documentation
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Our documentation is located under ``docs/`` folder. The following command can be used to build the documentation.
+
+.. code-block:: bash
+
+   cd docs
+   make html
+
+.. note::
+
+   If you experience issues in building documentation, and see errors like:
+      
+   * ``Could not import extension xxx (exception: No module named 'xxx')`` : please check your development environment and make sure dependencies have been properly installed: :ref:`get-started-dev`.
+   * ``unsupported pickle protocol: 5``: please upgrade to Python 3.8.
+   * ``autodoc: No module named 'xxx'``: some dependencies in ``dependencies/`` are not installed. In this case, documentation can be still mostly successfully built, but some API reference could be missing.
+
+It's also highly recommended taking care of **every WARNING** during the build, which is very likely the signal of a **deadlink** and other annoying issues. Our code check will also make sure that the documentation build completes with no warning.
+
+The built documentation can be found in ``docs/build/html`` folder.
+
+.. attention:: Always use your web browser to check the documentation before committing your change.
+
+.. tip:: `Live Server <https://github.com/ritwickdey/vscode-live-server>`_ is a great extension if you are looking for a static-files server to serve contents in ``docs/build/html``.
+
+Writing new documents
+^^^^^^^^^^^^^^^^^^^^^
+
+.. |link_example| raw:: html
+
+   <code class="docutils literal notranslate">`Link text &lt;https://domain.invalid/&gt;`_</code>
+
+.. |link_example_2| raw:: html
+
+   <code class="docutils literal notranslate">`Link text &lt;https://domain.invalid/&gt;`__</code>
+
+.. |link_example_3| raw:: html
+
+   <code class="docutils literal notranslate">:doc:`./relative/to/my_doc`</code>
+
+.. |githublink_example| raw:: html
+
+   <code class="docutils literal notranslate">:githublink:`path/to/file.ext`</code>
+
+.. |githublink_example_2| raw:: html
+
+   <code class="docutils literal notranslate">:githublink:`text &lt;path/to/file.ext&gt;`</code>
+
+.. _restructuredtext-intro:
+
+`ReStructuredText <https://docutils.sourceforge.io/docs/user/rst/quickstart.html>`_ is our documentation language. Please find the reference of RST `here <https://docutils.sourceforge.io/docs/ref/rst/restructuredtext.html>`__.
+
+.. tip:: Sphinx has `an excellent cheatsheet of rst <https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html>`_ which contains almost everything you might need to know to write a elegant document.
+
+**Dealing with sections.** ``=`` for sections. ``-`` for subsections. ``^`` for subsubsections. ``"`` for paragraphs.
+
+**Dealing with images.** Images should be put into ``docs/img`` folder. Then, reference the image in the document with relative links. For example, ``.. image:: ../../img/example.png``.
+
+**Dealing with codes.** We recommend using ``.. code-block:: python`` to start a code block. The ``python`` here annotates the syntax highlighting.
+
+**Dealing with links.** Use |link_example_3| for links to another doc (no suffix like ``.rst``). To reference a specific section, please use ``:ref:`` (see `Cross-referencing arbitrary locations <https://www.sphinx-doc.org/en/master/usage/restructuredtext/roles.html#cross-referencing-arbitrary-locations>`_). For general links that ``:doc:`` and ``:ref:`` can't handle, you can also use |link_example| for inline web links. Note that use one underline might cause `"duplicated target name" error <https://stackoverflow.com/questions/27420317/restructured-text-rst-http-links-underscore-vs-use>`_ when multiple targets share the same name. In that case, use double-underline to avoid the error: |link_example_2|.
+
+Other than built-in directives provided by Sphinx, we also provide some custom directives:
+
+* ``.. cardlinkitem::``: A tutorial card, useful in :doc:`/examples`.
+* |githublink_example| or |githublink_example_2|: reference a file on the GitHub. Linked to the same commit id as where the documentation is built.
+
+Writing new tutorials
+^^^^^^^^^^^^^^^^^^^^^
+
+Our tutorials are powered by `sphinx-gallery <https://sphinx-gallery.github.io/>`. Sphinx-gallery is an extension that builds an HTML gallery of examples from any set of Python scripts.
+
+To contribute a new tutorial, here are the steps to follow:
+
+1. Create a notebook styled python file. If you want it executed while inserted into documentation, save the file under ``examples/tutorials/``. If your tutorial contains other auxiliary scripts which are not intended to be included into documentation, save them under ``examples/tutorials/scripts/``.
+
+   .. tip:: The syntax to write a "notebook styled python file" is very simple. In essence, you only need to write a slightly well formatted python file. Here is a useful guide of `how to structure your Python scripts for Sphinx-Gallery <https://sphinx-gallery.github.io/stable/syntax.html>`_.
+
+2. Put the tutorials into ``docs/source/tutorials.rst``. You should add it both in ``toctree`` (to make it appear in the sidebar content table), and ``cardlinkitem`` (to create a card link), and specify the appropriate ``header``, ``description``, ``link``, ``image``, ``background`` (for image) and ``tags``.
+
+   ``link`` are the generated link, which is usually ``tutorials/<your_python_file_name>.html``. Some useful images can be found in ``docs/img/thumbnails``, but you can always use your own. Available background colors are: ``red``, ``pink``, ``purple``, ``deep-purple``, ``blue``, ``light-blue``, ``cyan``, ``teal``, ``green``, ``deep-orange``, ``brown``, ``indigo``.
+
+   In case you prefer to write your tutorial in jupyter, you can use `this script <https://gist.github.com/chsasank/7218ca16f8d022e02a9c0deb94a310fe>`_ to convert the notebook to python file. After conversion and addition to the project, please make sure the sections headings etc are in logical order.
+
+3. Build the tutorials. Since some of the tutorials contain complex AutoML examples, it's very inefficient to build them over and over again. Therefore, we cache the built tutorials in ``docs/source/tutorials``, so that the unchanged tutorials won't be rebuilt. To trigger the build, run ``make html``. This will execute the tutorials and convert the scripts into HTML files. How long it takes depends on your tutorial. As ``make html`` is not very debug-friendly, we suggest making the script runnable by itself before using this building tool.
+
+.. note::
+
+   Some useful HOW-TOs in writing new tutorials:
+
+   * `How to force rebuilding one tutorial <https://sphinx-gallery.github.io/stable/configuration.html#rerunning-stale-examples>`_.
+   * `How to add images to notebooks <https://sphinx-gallery.github.io/stable/configuration.html#adding-images-to-notebooks>`_.
+   * `How to reference a tutorial in documentation <https://sphinx-gallery.github.io/stable/advanced.html#cross-referencing>`_.
+
+Translation (i18n)
+^^^^^^^^^^^^^^^^^^
+
+We only maintain `a partial set of documents <https://github.com/microsoft/nni/issues/4298>`_ with translation. Currently, translation is provided in Simplified Chinese only.
+
+* If you want to update the translation of an existing document, please update messages in ``docs/source/locales``.
+* If you have updated a translated English document, we require that the corresponding translated documents to be updated (at least the update should be triggered). Please follow these steps:
+
+  1. Run ``make i18n`` under ``docs`` folder.
+  2. Verify that there are new messages in ``docs/source/locales``.
+  3. Translate the messages.
+
+* If you intend to translate a new document:
+
+  1. Update ``docs/source/conf.py`` to make ``gettext_documents`` include your document (probably adding a new regular expression).
+  2. See the steps above.
+
+
+To build the translated documentation (for example Chinese documentation), please run:
+
+.. code-block:: bash
+
+   make -e SPHINXOPTS="-D language='zh'" html
+
+If you ever encountered problems for translation builds, try to remove the previous build via ``rm -r docs/build/``.
+
+.. _code-of-conduct:
+
+Code of Conduct
+---------------
+
+This project has adopted the `Microsoft Open Source Code of Conduct <https://opensource.microsoft.com/codeofconduct/>`_.
+For more information see the `Code of Conduct FAQ <https://opensource.microsoft.com/codeofconduct/faq/>`_ or contact `opencode@microsoft.com <mailto:opencode@microsoft.com>`_ with any additional questions or comments.
+
+Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
+
+When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
diff --git a/docs/source/ResearchPublications.rst b/docs/source/notes/research_publications.rst
similarity index 100%
rename from docs/source/ResearchPublications.rst
rename to docs/source/notes/research_publications.rst
diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst
new file mode 100644
index 000000000..1abd8bdae
--- /dev/null
+++ b/docs/source/quickstart.rst
@@ -0,0 +1,23 @@
+Quickstart
+==========
+
+.. cardlinkitem::
+   :header: Hyperparameter Optimization Quickstart with PyTorch
+   :description: Use Hyperparameter Optimization (HPO) to tune a PyTorch FashionMNIST model.
+   :link: tutorials/hpo_quickstart_pytorch/main
+   :image: ../img/thumbnails/hpo-pytorch.svg
+   :background: purple
+
+.. cardlinkitem::
+   :header: Neural Architecture Search Quickstart
+   :description: Beginners' NAS tutorial on how to search for neural architectures for MNIST dataset.
+   :link: tutorials/hello_nas
+   :image: ../img/thumbnails/nas-tutorial.svg
+   :background: cyan
+
+.. cardlinkitem::
+   :header: Model Compression Quickstart
+   :description: Familiarize yourself with pruning to compress your model.
+   :link: tutorials/pruning_quick_start_mnist
+   :image: ../img/thumbnails/pruning-tutorial.svg
+   :background: blue
diff --git a/docs/source/quickstart_zh.rst b/docs/source/quickstart_zh.rst
new file mode 100644
index 000000000..1396ca11f
--- /dev/null
+++ b/docs/source/quickstart_zh.rst
@@ -0,0 +1,25 @@
+.. ccd00e2e56b44cf452b0afb81e8cecff
+
+快速入门
+==========
+
+.. cardlinkitem::
+   :header: 超参调优快速入门（以 PyTorch 框架为例）
+   :description: 使用超参数调优 (HPO) 为一个 PyTorch FashionMNIST 模型调参.
+   :link: tutorials/hpo_quickstart_pytorch/main
+   :image: ../img/thumbnails/hpo-pytorch.svg
+   :background: purple
+
+.. cardlinkitem::
+   :header: 神经架构搜索快速入门
+   :description: 为初学者讲解如何使用 NNI 在 MNIST 数据集上搜索一个网络结构。
+   :link: tutorials/hello_nas
+   :image: ../img/thumbnails/nas-tutorial.svg
+   :background: cyan
+
+.. cardlinkitem::
+   :header: 模型压缩快速入门
+   :description: 学习剪枝以压缩您的模型。
+   :link: tutorials/pruning_quick_start_mnist
+   :image: ../img/thumbnails/pruning-tutorial.svg
+   :background: blue
diff --git a/docs/source/reference.rst b/docs/source/reference.rst
deleted file mode 100644
index 75214cbde..000000000
--- a/docs/source/reference.rst
+++ /dev/null
@@ -1,16 +0,0 @@
-References
-==================
-
-..  toctree::
-    :maxdepth: 2
-
-    nnictl Commands <reference/nnictl>
-    Experiment Configuration <reference/experiment_config>
-    Experiment Configuration (legacy) <Tutorial/ExperimentConfig>
-    Search Space <Tutorial/SearchSpaceSpec>
-    NNI Annotation <Tutorial/AnnotationSpec>
-    SDK API References <sdk_reference>
-    Supported Framework Library <SupportedFramework_Library>
-    Launch from Python <Tutorial/HowToLaunchFromPython>
-    Shared Storage <Tutorial/HowToUseSharedStorage>
-    Tensorboard <Tutorial/Tensorboard>
diff --git a/docs/source/reference/compression/framework.rst b/docs/source/reference/compression/framework.rst
new file mode 100644
index 000000000..5ab0105c3
--- /dev/null
+++ b/docs/source/reference/compression/framework.rst
@@ -0,0 +1,67 @@
+Framework Related
+=================
+
+Pruner
+------
+
+.. autoclass:: nni.algorithms.compression.v2.pytorch.base.Pruner
+    :members:
+
+PrunerModuleWrapper
+-------------------
+
+.. autoclass:: nni.algorithms.compression.v2.pytorch.base.PrunerModuleWrapper
+
+BasicPruner
+-----------
+
+.. autoclass:: nni.algorithms.compression.v2.pytorch.pruning.basic_pruner.BasicPruner
+    :members:
+
+DataCollector
+-------------
+
+.. autoclass:: nni.algorithms.compression.v2.pytorch.pruning.tools.DataCollector
+    :members:
+
+MetricsCalculator
+-----------------
+
+.. autoclass:: nni.algorithms.compression.v2.pytorch.pruning.tools.MetricsCalculator
+    :members:
+
+SparsityAllocator
+-----------------
+
+.. autoclass:: nni.algorithms.compression.v2.pytorch.pruning.tools.SparsityAllocator
+    :members:
+
+BasePruningScheduler
+--------------------
+
+.. autoclass:: nni.algorithms.compression.v2.pytorch.base.BasePruningScheduler
+    :members:
+
+TaskGenerator
+-------------
+
+.. autoclass:: nni.algorithms.compression.v2.pytorch.pruning.tools.TaskGenerator
+    :members:
+
+Quantizer
+---------
+
+.. autoclass:: nni.compression.pytorch.compressor.Quantizer
+    :members:
+
+QuantizerModuleWrapper
+----------------------
+
+.. autoclass:: nni.compression.pytorch.compressor.QuantizerModuleWrapper
+    :members:
+
+QuantGrad
+---------
+
+.. autoclass:: nni.compression.pytorch.compressor.QuantGrad
+    :members:
diff --git a/docs/source/reference/compression/pruner.rst b/docs/source/reference/compression/pruner.rst
new file mode 100644
index 000000000..8088cbacf
--- /dev/null
+++ b/docs/source/reference/compression/pruner.rst
@@ -0,0 +1,123 @@
+Pruner
+======
+
+Basic Pruner
+------------
+
+.. _level-pruner:
+
+Level Pruner
+^^^^^^^^^^^^
+
+..  autoclass:: nni.compression.pytorch.pruning.LevelPruner
+
+.. _l1-norm-pruner:
+
+L1 Norm Pruner
+^^^^^^^^^^^^^^
+
+.. autoclass:: nni.compression.pytorch.pruning.L1NormPruner
+
+.. _l2-norm-pruner:
+
+L2 Norm Pruner
+^^^^^^^^^^^^^^
+
+.. autoclass:: nni.compression.pytorch.pruning.L2NormPruner
+
+.. _fpgm-pruner:
+
+FPGM Pruner
+^^^^^^^^^^^
+
+.. autoclass:: nni.compression.pytorch.pruning.FPGMPruner
+
+.. _slim-pruner:
+
+Slim Pruner
+^^^^^^^^^^^
+
+.. autoclass:: nni.compression.pytorch.pruning.SlimPruner
+
+.. _activation-apoz-rank-pruner:
+
+Activation APoZ Rank Pruner
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autoclass:: nni.compression.pytorch.pruning.ActivationAPoZRankPruner
+
+.. _activation-mean-rank-pruner:
+
+Activation Mean Rank Pruner
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autoclass:: nni.compression.pytorch.pruning.ActivationMeanRankPruner
+
+.. _taylor-fo-weight-pruner:
+
+Taylor FO Weight Pruner
+^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autoclass:: nni.compression.pytorch.pruning.TaylorFOWeightPruner
+
+.. _admm-pruner:
+
+ADMM Pruner
+^^^^^^^^^^^
+
+.. autoclass:: nni.compression.pytorch.pruning.ADMMPruner
+
+Scheduled Pruners
+-----------------
+
+.. _linear-pruner:
+
+Linear Pruner
+^^^^^^^^^^^^^
+
+.. autoclass:: nni.compression.pytorch.pruning.LinearPruner
+
+.. _agp-pruner:
+
+AGP Pruner
+^^^^^^^^^^
+
+.. autoclass:: nni.compression.pytorch.pruning.AGPPruner
+
+.. _lottery-ticket-pruner:
+
+Lottery Ticket Pruner
+^^^^^^^^^^^^^^^^^^^^^
+
+.. autoclass:: nni.compression.pytorch.pruning.LotteryTicketPruner
+
+.. _simulated-annealing-pruner:
+
+Simulated Annealing Pruner
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autoclass:: nni.compression.pytorch.pruning.SimulatedAnnealingPruner
+
+.. _auto-compress-pruner:
+
+Auto Compress Pruner
+^^^^^^^^^^^^^^^^^^^^
+
+.. autoclass:: nni.compression.pytorch.pruning.AutoCompressPruner
+
+.. _amc-pruner:
+
+AMC Pruner
+^^^^^^^^^^
+
+..  autoclass:: nni.compression.pytorch.pruning.AMCPruner
+
+Other Pruner
+------------
+
+.. _movement-pruner:
+
+Movement Pruner
+^^^^^^^^^^^^^^^
+
+.. autoclass:: nni.compression.pytorch.pruning.MovementPruner
\ No newline at end of file
diff --git a/docs/source/reference/compression/pruning_speedup.rst b/docs/source/reference/compression/pruning_speedup.rst
new file mode 100644
index 000000000..f0c322aae
--- /dev/null
+++ b/docs/source/reference/compression/pruning_speedup.rst
@@ -0,0 +1,5 @@
+Pruning Speedup
+===============
+
+.. autoclass:: nni.compression.pytorch.speedup.ModelSpeedup
+    :members:
diff --git a/docs/source/reference/compression/quantization_speedup.rst b/docs/source/reference/compression/quantization_speedup.rst
new file mode 100644
index 000000000..5b8ffa224
--- /dev/null
+++ b/docs/source/reference/compression/quantization_speedup.rst
@@ -0,0 +1,5 @@
+Quantization Speedup
+====================
+
+.. autoclass:: nni.compression.pytorch.quantization_speedup.ModelSpeedupTensorRT
+    :members:
diff --git a/docs/source/reference/compression/quantizer.rst b/docs/source/reference/compression/quantizer.rst
new file mode 100644
index 000000000..d56c3b1f8
--- /dev/null
+++ b/docs/source/reference/compression/quantizer.rst
@@ -0,0 +1,44 @@
+Quantizer
+=========
+
+.. _naive-quantizer:
+
+Naive Quantizer
+^^^^^^^^^^^^^^^
+
+..  autoclass:: nni.algorithms.compression.pytorch.quantization.NaiveQuantizer
+
+.. _qat-quantizer:
+
+QAT Quantizer
+^^^^^^^^^^^^^
+
+..  autoclass:: nni.algorithms.compression.pytorch.quantization.QAT_Quantizer
+
+.. _dorefa-quantizer:
+
+DoReFa Quantizer
+^^^^^^^^^^^^^^^^
+
+..  autoclass:: nni.algorithms.compression.pytorch.quantization.DoReFaQuantizer
+
+.. _bnn-quantizer:
+
+BNN Quantizer
+^^^^^^^^^^^^^
+
+..  autoclass:: nni.algorithms.compression.pytorch.quantization.BNNQuantizer
+
+.. _lsq-quantizer:
+
+LSQ Quantizer
+^^^^^^^^^^^^^
+
+..  autoclass:: nni.algorithms.compression.pytorch.quantization.LsqQuantizer
+
+.. _observer-quantizer:
+
+Observer Quantizer
+^^^^^^^^^^^^^^^^^^
+
+..  autoclass:: nni.algorithms.compression.pytorch.quantization.ObserverQuantizer
diff --git a/docs/source/reference/compression/toctree.rst b/docs/source/reference/compression/toctree.rst
new file mode 100644
index 000000000..72e7e425a
--- /dev/null
+++ b/docs/source/reference/compression/toctree.rst
@@ -0,0 +1,12 @@
+Compression API Reference
+=========================
+
+..  toctree::
+    :maxdepth: 1
+
+    Pruner <pruner>
+    Quantizer <quantizer>
+    Pruning Speedup <pruning_speedup>
+    Quantization Speedup <quantization_speedup>
+    Compression Utilities <utils>
+    Framework Related <framework>
diff --git a/docs/source/reference/compression/utils.rst b/docs/source/reference/compression/utils.rst
new file mode 100644
index 000000000..7b0e8459e
--- /dev/null
+++ b/docs/source/reference/compression/utils.rst
@@ -0,0 +1,42 @@
+Compression Utilities
+=====================
+
+SensitivityAnalysis
+-------------------
+
+.. autoclass:: nni.compression.pytorch.utils.SensitivityAnalysis
+    :members:
+
+ChannelDependency
+-----------------
+
+.. autoclass:: nni.compression.pytorch.utils.ChannelDependency
+    :members:
+
+GroupDependency
+---------------
+
+.. autoclass:: nni.compression.pytorch.utils.GroupDependency
+    :members:
+
+ChannelMaskConflict
+-------------------
+
+.. autoclass:: nni.compression.pytorch.utils.ChannelMaskConflict
+    :members:
+
+GroupMaskConflict
+-----------------
+
+.. autoclass:: nni.compression.pytorch.utils.GroupMaskConflict
+    :members:
+
+count_flops_params
+------------------
+
+.. autofunction:: nni.compression.pytorch.utils.count_flops_params
+
+compute_sparsity
+----------------
+
+.. autofunction:: nni.algorithms.compression.v2.pytorch.utils.pruning.compute_sparsity
diff --git a/docs/source/reference/experiment.rst b/docs/source/reference/experiment.rst
new file mode 100644
index 000000000..5d99324da
--- /dev/null
+++ b/docs/source/reference/experiment.rst
@@ -0,0 +1,5 @@
+Experiment API Reference
+========================
+
+.. autoclass:: nni.experiment.Experiment
+    :members:
diff --git a/docs/source/reference/experiment_config.rst b/docs/source/reference/experiment_config.rst
index 8bc52e71c..76541573c 100644
--- a/docs/source/reference/experiment_config.rst
+++ b/docs/source/reference/experiment_config.rst
@@ -20,11 +20,6 @@ A config file is needed when creating an experiment. This document describes the
 
     4. Setting a field to ``None`` or ``null`` is equivalent to not setting the field.
 
-.. contents:: Contents
-   :local:
-   :depth: 3
- 
-
 Examples
 ========
 
@@ -120,13 +115,13 @@ ExperimentConfig
     * - searchSpaceFile
       - ``str``, optional
       - Path_ to the JSON file containing the search space.
-        Search space format is determined by tuner. The common format for built-in tuners is documented  `here <../Tutorial/SearchSpaceSpec.rst>`__.
+        Search space format is determined by tuner. The common format for built-in tuners is documented :doc:`here </hpo/search_space>`.
         Mutually exclusive to ``searchSpace``.
 
     * - searchSpace
       - ``JSON``, optional
       - Search space object.
-        The format is determined by tuner. Common format for built-in tuners is documented `here <../Tutorial/SearchSpaceSpec.rst>`__.
+        The format is determined by tuner. Common format for built-in tuners is documented :doc:`here </hpo/search_space>`.
         Note that ``None`` means "no such field" so empty search space should be written as ``{}``.
         Mutually exclusive to ``searchSpaceFile``.
 
@@ -151,7 +146,7 @@ ExperimentConfig
       - ``int`` or ``None``, optional
       - Default: None. This field might have slightly different meanings for various training services,
         especially when set to ``0`` or ``None``.
-        See `training service's document <../training_services.rst>`__ for details.
+        See :doc:`training service's document </experiment/training_service/overview>` for details.
 
         In local mode, setting the field to ``0`` will prevent trials from accessing GPU (by empty ``CUDA_VISIBLE_DEVICES``).
         And when set to ``None``, trials will be created and scheduled as if they did not use GPU,
@@ -183,7 +178,7 @@ ExperimentConfig
 
     * - useAnnotation
       - ``bool``, optional
-      - Default: ``False``. Enable `annotation <../Tutorial/AnnotationSpec.rst>`__.
+      - Default: ``False``. Enable :doc:`annotation </hpo/nni_annotation>`.
         When using annotation, ``searchSpace`` and ``searchSpaceFile`` should not be specified manually.
 
     * - debug
@@ -215,25 +210,25 @@ ExperimentConfig
     * - tuner
       - ``AlgorithmConfig``, optional
       - Specify the tuner.
-        The built-in tuners can be found `here <../builtin_tuner.rst>`__ and you can follow `this tutorial <../Tuner/CustomizeTuner.rst>`__ to customize a new tuner.
+        The built-in tuners can be found :doc:`here </hpo/tuners>` and you can follow :doc:`this tutorial </hpo/custom_algorithm>` to customize a new tuner.
 
     * - assessor
       - ``AlgorithmConfig``, optional
       - Specify the assessor.
-        The built-in assessors can be found `here <../builtin_assessor.rst>`__ and you can follow `this tutorial <../Assessor/CustomizeAssessor.rst>`__ to customize a new assessor.
+        The built-in assessors can be found :doc:`here </hpo/assessors>` and you can follow :doc:`this tutorial </hpo/custom_algorithm>` to customize a new assessor.
 
     * - advisor
       - ``AlgorithmConfig``, optional
       - Specify the advisor.
-        NNI provides two built-in advisors: `BOHB <../Tuner/BohbAdvisor.rst>`__ and `Hyperband <../Tuner/HyperbandAdvisor.rst>`__, and you can follow `this tutorial <../Tuner/CustomizeAdvisor.rst>`__ to customize a new advisor.
+        NNI provides two built-in advisors: :class:`BOHB <nni.algorithms.hpo.bohb_advisor.BOHB>` and :class:`Hyperband <nni.algorithms.hpo.hyperband_advisor.Hyperband>`.
 
     * - trainingService
       - ``TrainingServiceConfig``
-      - Specify the `training service <../TrainingService/Overview.rst>`__.
+      - Specify the :doc:`training service </experiment/training_service/overview>`.
 
     * - sharedStorage
       - ``SharedStorageConfig``, optional
-      - Configure the shared storage, detailed usage can be found `here <../Tutorial/HowToUseSharedStorage.rst>`__.
+      - Configure the shared storage, detailed usage can be found :doc:`here </experiment/training_service/shared_storage>`.
 
 AlgorithmConfig
 ^^^^^^^^^^^^^^^
@@ -242,9 +237,9 @@ AlgorithmConfig
 
 For customized algorithms, there are two ways to describe them:
 
-  1. `Register the algorithm <../Tutorial/InstallCustomizedAlgos.rst>`__ to use it like built-in. (preferred)
+1. :doc:`Register the algorithm </hpo/custom_algorithm_installation>` to use it like built-in. (preferred)
 
-  2. Specify code directory and class name directly.
+2. Specify code directory and class name directly.
 
 .. list-table::
     :widths: 10 10 80
@@ -286,13 +281,15 @@ One of the following:
 - `AmlConfig`_
 - `DlcConfig`_
 - `HybridConfig`_
+- :doc:`FrameworkControllerConfig </experiment/training_service/frameworkcontroller>`
+- :doc:`KubeflowConfig </experiment/training_service/kubeflow>`
 
-For `Kubeflow <../TrainingService/KubeflowMode.rst>`_, `FrameworkController <../TrainingService/FrameworkControllerMode.rst>`_, and `AdaptDL <../TrainingService/AdaptDLMode.rst>`_ training platforms, it is suggested to use `v1 config schema <../Tutorial/ExperimentConfig.rst>`_ for now.
+.. _reference-local-config-label:
 
 LocalConfig
 -----------
 
-Detailed usage can be found `here <../TrainingService/LocalMode.rst>`__.
+Introduction of the corresponding local training service can be found :doc:`/experiment/training_service/local`.
 
 .. list-table::
     :widths: 10 10 80
@@ -330,10 +327,12 @@ Detailed usage can be found `here <../TrainingService/LocalMode.rst>`__.
         If ``trialGpuNumber`` is less than the length of this value, only a subset will be visible to each trial.
         This will be used as ``CUDA_VISIBLE_DEVICES`` environment variable.
 
+.. _reference-remote-config-label:
+
 RemoteConfig
 ------------
 
-Detailed usage can be found `here <../TrainingService/RemoteMachineMode.rst>`__.
+Detailed usage can be found :doc:`/experiment/training_service/remote`.
 
 .. list-table::
     :widths: 10 10 80
@@ -353,7 +352,7 @@ Detailed usage can be found `here <../TrainingService/RemoteMachineMode.rst>`__.
 
     * - reuseMode
       - ``bool``, optional
-      - Default: ``True``. Enable `reuse mode <../TrainingService/Overview.rst#training-service-under-reuse-mode>`__.
+      - Default: ``True``. Enable :ref:`reuse mode <training-service-reuse>`.
 
 RemoteMachineConfig
 """""""""""""""""""
@@ -433,7 +432,7 @@ RemoteMachineConfig
 OpenpaiConfig
 -------------
 
-Detailed usage can be found `here <../TrainingService/PaiMode.rst>`__.
+Detailed usage can be found :doc:`here </experiment/training_service/openpai>`.
 
 .. list-table::
     :widths: 10 10 80
@@ -491,7 +490,7 @@ Detailed usage can be found `here <../TrainingService/PaiMode.rst>`__.
 
     * - reuseMode
       - ``bool``, optional
-      - Default: ``True``. Enable `reuse mode <../TrainingService/Overview.rst#training-service-under-reuse-mode>`__.
+      - Default: ``True``. Enable :ref:`reuse mode <training-service-reuse>`.
 
     * - openpaiConfig
       - ``JSON``, optional
@@ -505,7 +504,7 @@ Detailed usage can be found `here <../TrainingService/PaiMode.rst>`__.
 AmlConfig
 ---------
 
-Detailed usage can be found `here <../TrainingService/AMLMode.rst>`__.
+Detailed usage can be found :doc:`here </experiment/training_service/aml>`.
 
 .. list-table::
     :widths: 10 10 80
@@ -542,7 +541,7 @@ Detailed usage can be found `here <../TrainingService/AMLMode.rst>`__.
 DlcConfig
 ---------
 
-Detailed usage can be found `here <../TrainingService/DlcMode.rst>`__.
+Detailed usage can be found :doc:`here </experiment/training_service/paidlc>`.
 
 .. list-table::
     :widths: 10 10 80
@@ -607,14 +606,16 @@ Detailed usage can be found `here <../TrainingService/DlcMode.rst>`__.
 HybridConfig
 ------------
 
-Currently only support `LocalConfig`_, `RemoteConfig`_, `OpenpaiConfig`_ and `AmlConfig`_ . Detailed usage can be found `here <../TrainingService/HybridMode.rst>`__.
+Currently only support `LocalConfig`_, `RemoteConfig`_, `OpenpaiConfig`_ and `AmlConfig`_ . Detailed usage can be found :doc:`here </experiment/training_service/hybrid>`.
+
+.. _reference-sharedstorage-config-label:
 
 SharedStorageConfig
 ^^^^^^^^^^^^^^^^^^^
 
-Detailed usage can be found `here <../Tutorial/HowToUseSharedStorage.rst>`__.
+Detailed usage can be found :doc:`here </experiment/training_service/shared_storage>`.
 
-nfsConfig
+NfsConfig
 ---------
 
 .. list-table::
@@ -653,7 +654,7 @@ nfsConfig
       - ``str``
       - Exported directory of NFS server, detailed `here <https://www.ibm.com/docs/en/aix/7.2?topic=system-nfs-exporting-mounting>`_.
 
-azureBlobConfig
+AzureBlobConfig
 ---------------
 
 .. list-table::
diff --git a/docs/source/reference/hpo.rst b/docs/source/reference/hpo.rst
new file mode 100644
index 000000000..7bd6b93a0
--- /dev/null
+++ b/docs/source/reference/hpo.rst
@@ -0,0 +1,93 @@
+HPO API Reference
+=================
+
+Trial APIs
+----------
+
+.. autofunction:: nni.get_experiment_id
+.. autofunction:: nni.get_next_parameter
+.. autofunction:: nni.get_sequence_id
+.. autofunction:: nni.get_trial_id
+.. autofunction:: nni.report_final_result
+.. autofunction:: nni.report_intermediate_result
+
+Tuners
+------
+
+Batch Tuner
+^^^^^^^^^^^
+.. autoclass:: nni.algorithms.hpo.batch_tuner.BatchTuner
+
+BOHB Tuner
+^^^^^^^^^^
+.. autoclass:: nni.algorithms.hpo.bohb_advisor.BOHB
+
+DNGO Tuner
+^^^^^^^^^^
+.. autoclass:: nni.algorithms.hpo.dngo_tuner.DNGOTuner
+
+Evolution Tuner
+^^^^^^^^^^^^^^^
+.. autoclass:: nni.algorithms.hpo.evolution_tuner.EvolutionTuner
+
+GP Tuner
+^^^^^^^^
+.. autoclass:: nni.algorithms.hpo.gp_tuner.GPTuner
+
+Grid Search Tuner
+^^^^^^^^^^^^^^^^^
+.. autoclass:: nni.algorithms.hpo.gridsearch_tuner.GridSearchTuner
+
+Hyperband Tuner
+^^^^^^^^^^^^^^^
+.. autoclass:: nni.algorithms.hpo.hyperband_advisor.Hyperband
+
+Hyperopt Tuner
+^^^^^^^^^^^^^^
+.. autoclass:: nni.algorithms.hpo.hyperopt_tuner.HyperoptTuner
+
+Metis Tuner
+^^^^^^^^^^^
+.. autoclass:: nni.algorithms.hpo.metis_tuner.MetisTuner
+
+PBT Tuner
+^^^^^^^^^
+.. autoclass:: nni.algorithms.hpo.pbt_tuner.PBTTuner
+
+PPO Tuner
+^^^^^^^^^
+.. autoclass:: nni.algorithms.hpo.ppo_tuner.PPOTuner
+
+Random Tuner
+^^^^^^^^^^^^
+.. autoclass:: nni.algorithms.hpo.random_tuner.RandomTuner
+
+SMAC Tuner
+^^^^^^^^^^
+.. autoclass:: nni.algorithms.hpo.smac_tuner.SMACTuner
+
+TPE Tuner
+^^^^^^^^^
+.. autoclass:: nni.algorithms.hpo.tpe_tuner.TpeTuner
+.. autoclass:: nni.algorithms.hpo.tpe_tuner.TpeArguments
+
+Assessors
+---------
+
+Curve Fitting Assessor
+^^^^^^^^^^^^^^^^^^^^^^
+.. autoclass:: nni.algorithms.hpo.curvefitting_assessor.CurvefittingAssessor
+
+Median Stop Assessor
+^^^^^^^^^^^^^^^^^^^^
+.. autoclass:: nni.algorithms.hpo.medianstop_assessor.MedianstopAssessor
+
+Customization
+-------------
+
+.. autoclass:: nni.assessor.AssessResult
+    :members:
+.. autoclass:: nni.assessor.Assessor
+    :members:
+.. autoclass:: nni.tuner.Tuner
+    :members:
diff --git a/docs/source/reference/nas/evaluator.rst b/docs/source/reference/nas/evaluator.rst
new file mode 100644
index 000000000..deae73f70
--- /dev/null
+++ b/docs/source/reference/nas/evaluator.rst
@@ -0,0 +1,46 @@
+Evaluator
+=========
+
+FunctionalEvaluator
+-------------------
+
+..  autoclass:: nni.retiarii.evaluator.FunctionalEvaluator
+    :members:
+
+Classification
+--------------
+
+..  autoclass:: nni.retiarii.evaluator.pytorch.Classification
+    :members:
+
+Regression
+----------
+
+..  autoclass:: nni.retiarii.evaluator.pytorch.Regression
+    :members:
+
+Utilities
+---------
+
+..  autoclass:: nni.retiarii.evaluator.pytorch.Trainer
+
+..  autoclass:: nni.retiarii.evaluator.pytorch.DataLoader
+
+Customization
+-------------
+
+..  autoclass:: nni.retiarii.evaluator.pytorch.Lightning
+
+..  autoclass:: nni.retiarii.evaluator.pytorch.LightningModule
+
+Cross-graph Optimization (experimental)
+---------------------------------------
+
+..  autoclass:: nni.retiarii.evaluator.pytorch.cgo.evaluator.MultiModelSupervisedLearningModule
+    :members:
+
+..  autoclass:: nni.retiarii.evaluator.pytorch.cgo.evaluator.Classification
+    :members:
+
+..  autoclass:: nni.retiarii.evaluator.pytorch.cgo.evaluator.Regression
+    :members:
diff --git a/docs/source/reference/nas/others.rst b/docs/source/reference/nas/others.rst
new file mode 100644
index 000000000..409cdc662
--- /dev/null
+++ b/docs/source/reference/nas/others.rst
@@ -0,0 +1,60 @@
+Uncategorized Modules
+=====================
+
+Experiment
+----------
+
+..  autoclass:: nni.retiarii.experiment.pytorch.RetiariiExeConfig
+    :members:
+
+..  autoclass:: nni.retiarii.experiment.pytorch.RetiariiExperiment
+    :members:
+
+NAS Benchmarks
+--------------
+
+.. _nas-bench-101-reference:
+
+NAS-Bench-101
+^^^^^^^^^^^^^
+
+.. automodule:: nni.nas.benchmarks.nasbench101
+   :members:
+   :imported-members:
+
+.. _nas-bench-201-reference:
+
+NAS-Bench-201
+^^^^^^^^^^^^^
+
+.. automodule:: nni.nas.benchmarks.nasbench201
+   :members:
+   :imported-members:
+
+.. _nds-reference:
+
+NDS
+^^^
+
+.. automodule:: nni.nas.benchmarks.nds
+   :members:
+   :imported-members:
+
+Retrain (Architecture Evaluation)
+---------------------------------
+
+..  autofunction:: nni.retiarii.fixed_arch
+
+Utilities
+---------
+
+..  autofunction:: nni.retiarii.basic_unit
+
+..  autofunction:: nni.retiarii.model_wrapper
+
+..  automodule:: nni.retiarii.nn.pytorch.mutation_utils
+    :imported-members:
+    :members:
+
+..  automodule:: nni.retiarii.utils
+    :members:
diff --git a/docs/source/reference/nas/search_space.rst b/docs/source/reference/nas/search_space.rst
new file mode 100644
index 000000000..8269c01d6
--- /dev/null
+++ b/docs/source/reference/nas/search_space.rst
@@ -0,0 +1,111 @@
+Search Space
+============
+
+.. _mutation-primitives:
+
+Mutation Pritimives
+-------------------
+
+LayerChoice
+^^^^^^^^^^^
+
+.. autoclass:: nni.retiarii.nn.pytorch.LayerChoice
+   :members:
+
+
+InputChoice
+^^^^^^^^^^^
+
+.. autoclass:: nni.retiarii.nn.pytorch.InputChoice
+   :members:
+
+.. autoclass:: nni.retiarii.nn.pytorch.ChosenInputs
+   :members:
+
+ValueChoice
+^^^^^^^^^^^
+
+.. autoclass:: nni.retiarii.nn.pytorch.ValueChoice
+   :members:
+   :inherited-members: Module
+
+ModelParameterChoice
+^^^^^^^^^^^^^^^^^^^^
+
+.. autoclass:: nni.retiarii.nn.pytorch.ModelParameterChoice
+   :members:
+   :inherited-members: Module
+
+Repeat
+^^^^^^
+
+.. autoclass:: nni.retiarii.nn.pytorch.Repeat
+   :members:
+
+Cell
+^^^^
+
+.. autoclass:: nni.retiarii.nn.pytorch.Cell
+   :members:
+
+NasBench101Cell
+^^^^^^^^^^^^^^^
+
+.. autoclass:: nni.retiarii.nn.pytorch.NasBench101Cell
+   :members:
+
+NasBench201Cell
+^^^^^^^^^^^^^^^
+
+.. autoclass:: nni.retiarii.nn.pytorch.NasBench201Cell
+   :members:
+
+.. _hyper-modules:
+
+Hyper-module Library (experimental)
+-----------------------------------
+
+AutoActivation
+^^^^^^^^^^^^^^
+
+..  autoclass:: nni.retiarii.nn.pytorch.AutoActivation
+    :members:
+
+Mutators (advanced)
+-------------------
+
+Mutator
+^^^^^^^
+
+..  autoclass:: nni.retiarii.Mutator
+    :members:
+
+..  autoclass:: nni.retiarii.Sampler
+    :members:
+
+..  autoclass:: nni.retiarii.InvalidMutation
+    :members:
+
+Placeholder
+^^^^^^^^^^^
+
+..  autoclass:: nni.retiarii.nn.pytorch.Placeholder
+    :members:
+
+Graph
+^^^^^
+
+..  autoclass:: nni.retiarii.Model
+    :members:
+
+..  autoclass:: nni.retiarii.Graph
+    :members:
+
+..  autoclass:: nni.retiarii.Node
+    :members:
+
+..  autoclass:: nni.retiarii.Edge
+    :members:
+
+..  autoclass:: nni.retiarii.Operation
+    :members:
diff --git a/docs/source/reference/nas/strategy.rst b/docs/source/reference/nas/strategy.rst
new file mode 100644
index 000000000..2393ab23e
--- /dev/null
+++ b/docs/source/reference/nas/strategy.rst
@@ -0,0 +1,426 @@
+Strategy
+========
+
+.. _multi-trial-nas-reference:
+
+Multi-trial Strategy
+--------------------
+
+Random
+^^^^^^
+
+.. autoclass:: nni.retiarii.strategy.Random
+   :members:
+
+GridSearch
+^^^^^^^^^^
+
+.. autoclass:: nni.retiarii.strategy.GridSearch
+   :members:
+
+RegularizedEvolution
+^^^^^^^^^^^^^^^^^^^^
+
+.. autoclass:: nni.retiarii.strategy.RegularizedEvolution
+   :members:
+
+TPE
+^^^
+
+.. autoclass:: nni.retiarii.strategy.TPE
+   :members:
+
+PolicyBasedRL
+^^^^^^^^^^^^^
+
+.. autoclass:: nni.retiarii.strategy.PolicyBasedRL
+   :members:
+
+.. _one-shot-strategy-reference:
+
+One-shot Strategy
+-----------------
+
+.. _darts-strategy:
+
+DARTS
+^^^^^
+
+The paper `DARTS: Differentiable Architecture Search <https://arxiv.org/abs/1806.09055>`__ addresses the scalability challenge of architecture search by formulating the task in a differentiable manner. Their method is based on the continuous relaxation of the architecture representation, allowing efficient search of the architecture using gradient descent.
+
+Authors' code optimizes the network weights and architecture weights alternatively in mini-batches. They further explore the possibility that uses second order optimization (unroll) instead of first order, to improve the performance.
+
+Implementation on NNI is based on the `official implementation <https://github.com/quark0/darts>`__ and a `popular 3rd-party repo <https://github.com/khanrc/pt.darts>`__. DARTS on NNI is designed to be general for arbitrary search space. A CNN search space tailored for CIFAR10, same as the original paper, is implemented as a use case of DARTS.
+
+..  autoclass:: nni.retiarii.oneshot.pytorch.DartsTrainer
+
+Reproduction Results
+""""""""""""""""""""
+
+The above-mentioned example is meant to reproduce the results in the paper, we do experiments with first and second order optimization. Due to the time limit, we retrain *only the best architecture* derived from the search phase and we repeat the experiment *only once*. Our results is currently on par with the results reported in paper. We will add more results later when ready.
+
+.. list-table::
+   :header-rows: 1
+   :widths: auto
+
+   * - 
+     - In paper
+     - Reproduction
+   * - First order (CIFAR10)
+     - 3.00 +/- 0.14
+     - 2.78
+   * - Second order (CIFAR10)
+     - 2.76 +/- 0.09
+     - 2.80
+
+Examples
+""""""""
+
+:githublink:`Example code <examples/nas/oneshot/darts>`
+
+.. code-block:: bash
+
+   # In case NNI code is not cloned. If the code is cloned already, ignore this line and enter code folder.
+   git clone https://github.com/Microsoft/nni.git
+
+   # search the best architecture
+   cd examples/nas/oneshot/darts
+   python3 search.py
+
+   # train the best architecture
+   python3 retrain.py --arc-checkpoint ./checkpoints/epoch_49.json
+
+Limitations
+"""""""""""
+
+* DARTS doesn't support DataParallel and needs to be customized in order to support DistributedDataParallel.
+
+.. _enas-strategy:
+
+ENAS
+^^^^
+
+The paper `Efficient Neural Architecture Search via Parameter Sharing <https://arxiv.org/abs/1802.03268>`__ uses parameter sharing between child models to accelerate the NAS process. In ENAS, a controller learns to discover neural network architectures by searching for an optimal subgraph within a large computational graph. The controller is trained with policy gradient to select a subgraph that maximizes the expected reward on the validation set. Meanwhile the model corresponding to the selected subgraph is trained to minimize a canonical cross entropy loss.
+
+Implementation on NNI is based on the `official implementation in Tensorflow <https://github.com/melodyguan/enas>`__, including a general-purpose Reinforcement-learning controller and a trainer that trains target network and this controller alternatively. Following paper, we have also implemented macro and micro search space on CIFAR10 to demonstrate how to use these trainers. Since code to train from scratch on NNI is not ready yet, reproduction results are currently unavailable.
+
+..  autoclass:: nni.retiarii.oneshot.pytorch.EnasTrainer
+
+Examples
+""""""""
+
+:githublink:`Example code <examples/nas/oneshot/enas>`
+
+.. code-block:: bash
+
+   # In case NNI code is not cloned. If the code is cloned already, ignore this line and enter code folder.
+   git clone https://github.com/Microsoft/nni.git
+
+   # search the best architecture
+   cd examples/nas/oneshot/enas
+
+   # search in macro search space
+   python3 search.py --search-for macro
+
+   # search in micro search space
+   python3 search.py --search-for micro
+
+   # view more options for search
+   python3 search.py -h
+
+.. _fbnet-strategy:
+
+FBNet
+^^^^^
+
+.. note:: This one-shot NAS is still implemented under NNI NAS 1.0, and will `be migrated to Retiarii framework in near future <https://github.com/microsoft/nni/issues/3814>`__.
+
+For the mobile application of facial landmark, based on the basic architecture of PFLD model, we have applied the FBNet (Block-wise DNAS) to design an concise model with the trade-off between latency and accuracy. References are listed as below:
+
+* `FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search <https://arxiv.org/abs/1812.03443>`__
+* `PFLD: A Practical Facial Landmark Detector <https://arxiv.org/abs/1902.10859>`__
+
+FBNet is a block-wise differentiable NAS method (Block-wise DNAS), where the best candidate building blocks can be chosen by using Gumbel Softmax random sampling and differentiable training. At each layer (or stage) to be searched, the diverse candidate blocks are side by side planned (just like the effectiveness of structural re-parameterization), leading to sufficient pre-training of the supernet. The pre-trained supernet is further sampled for finetuning of the subnet, to achieve better performance.
+
+.. image:: ../../../img/fbnet.png
+   :width: 800
+   :align: center
+
+PFLD is a lightweight facial landmark model for realtime application. The architecture of PLFD is firstly simplified for acceleration, by using the stem block of PeleeNet, average pooling with depthwise convolution and eSE module.
+
+To achieve better trade-off between latency and accuracy, the FBNet is further applied on the simplified PFLD for searching the best block at each specific layer. The search space is based on the FBNet space, and optimized for mobile deployment by using the average pooling with depthwise convolution and eSE module etc.
+
+Experiments
+"""""""""""
+
+To verify the effectiveness of FBNet applied on PFLD, we choose the open source dataset with 106 landmark points as the benchmark:
+
+* `Grand Challenge of 106-Point Facial Landmark Localization <https://arxiv.org/abs/1905.03469>`__
+
+The baseline model is denoted as MobileNet-V3 PFLD (`Reference baseline <https://github.com/Hsintao/pfld_106_face_landmarks>`__), and the searched model is denoted as Subnet. The experimental results are listed as below, where the latency is tested on Qualcomm 625 CPU (ARMv8):
+
+.. list-table::
+   :header-rows: 1
+   :widths: auto
+
+   * - Model
+     - Size
+     - Latency
+     - Validation NME
+   * - MobileNet-V3 PFLD
+     - 1.01MB
+     - 10ms
+     - 6.22%
+   * - Subnet
+     - 693KB
+     - 1.60ms
+     - 5.58%
+
+Example
+"""""""
+
+`Example code <https://github.com/microsoft/nni/tree/master/examples/nas/oneshot/pfld>`__
+
+Please run the following scripts at the example directory.
+
+The Python dependencies used here are listed as below:
+
+.. code-block:: bash
+
+   numpy==1.18.5
+   opencv-python==4.5.1.48
+   torch==1.6.0
+   torchvision==0.7.0
+   onnx==1.8.1
+   onnx-simplifier==0.3.5
+   onnxruntime==1.7.0
+
+To run the tutorial, follow the steps below:
+
+1. **Data Preparation**: Firstly, you should download the dataset `106points dataset <https://drive.google.com/file/d/1I7QdnLxAlyG2Tq3L66QYzGhiBEoVfzKo/view?usp=sharing>`__ to the path ``./data/106points`` . The dataset includes the train-set and test-set:
+
+   .. code-block:: bash
+
+      ./data/106points/train_data/imgs
+      ./data/106points/train_data/list.txt
+      ./data/106points/test_data/imgs
+      ./data/106points/test_data/list.txt
+
+2. **Search**: Based on the architecture of simplified PFLD, the setting of multi-stage search space and hyper-parameters for searching should be firstly configured to construct the supernet. For example,
+
+   .. code-block::
+
+      from lib.builder import search_space
+      from lib.ops import PRIMITIVES
+      from lib.supernet import PFLDInference, AuxiliaryNet
+      from nni.algorithms.nas.pytorch.fbnet import LookUpTable, NASConfig,
+
+      # configuration of hyper-parameters
+      # search_space defines the multi-stage search space
+      nas_config = NASConfig(
+         model_dir="./ckpt_save",
+         nas_lr=0.01,
+         mode="mul",
+         alpha=0.25,
+         beta=0.6,
+         search_space=search_space,
+      )
+      # lookup table to manage the information
+      lookup_table = LookUpTable(config=nas_config, primitives=PRIMITIVES)
+      # created supernet
+      pfld_backbone = PFLDInference(lookup_table)
+
+   After creation of the supernet with the specification of search space and hyper-parameters, we can run below command to start searching and training of the supernet:
+
+   .. code-block:: bash
+
+      python train.py --dev_id "0,1" --snapshot "./ckpt_save" --data_root "./data/106points"
+
+   The validation accuracy will be shown during training, and the model with best accuracy will be saved as ``./ckpt_save/supernet/checkpoint_best.pth``.
+
+3. **Finetune**: After pre-training of the supernet, we can run below command to sample the subnet and conduct the finetuning:
+
+   .. code-block:: bash
+
+      python retrain.py --dev_id "0,1" --snapshot "./ckpt_save" --data_root "./data/106points" \
+                        --supernet "./ckpt_save/supernet/checkpoint_best.pth"
+
+   The validation accuracy will be shown during training, and the model with best accuracy will be saved as ``./ckpt_save/subnet/checkpoint_best.pth``.
+
+4. **Export**: After the finetuning of subnet, we can run below command to export the ONNX model:
+
+   .. code-block:: bash
+
+      python export.py --supernet "./ckpt_save/supernet/checkpoint_best.pth" \
+                       --resume "./ckpt_save/subnet/checkpoint_best.pth"
+
+   ONNX model is saved as ``./output/subnet.onnx``, which can be further converted to the mobile inference engine by using `MNN <https://github.com/alibaba/MNN>`__ .
+   The checkpoints of pre-trained supernet and subnet are offered as below:
+
+   * `Supernet <https://drive.google.com/file/d/1TCuWKq8u4_BQ84BWbHSCZ45N3JGB9kFJ/view?usp=sharing>`__
+   * `Subnet <https://drive.google.com/file/d/160rkuwB7y7qlBZNM3W_T53cb6MQIYHIE/view?usp=sharing>`__
+   * `ONNX model <https://drive.google.com/file/d/1s-v-aOiMv0cqBspPVF3vSGujTbn_T_Uo/view?usp=sharing>`__
+
+.. _spos-strategy:
+
+SPOS
+^^^^
+
+Proposed in `Single Path One-Shot Neural Architecture Search with Uniform Sampling <https://arxiv.org/abs/1904.00420>`__ is a one-shot NAS method that addresses the difficulties in training One-Shot NAS models by constructing a simplified supernet trained with an uniform path sampling method, so that all underlying architectures (and their weights) get trained fully and equally. An evolutionary algorithm is then applied to efficiently search for the best-performing architectures without any fine tuning.
+
+Implementation on NNI is based on `official repo <https://github.com/megvii-model/SinglePathOneShot>`__. We implement a trainer that trains the supernet and a evolution tuner that leverages the power of NNI framework that speeds up the evolutionary search phase.
+
+..  autoclass:: nni.retiarii.oneshot.pytorch.SinglePathTrainer
+
+Examples
+""""""""
+
+Here is a use case, which is the search space in paper. However, we applied latency limit instead of flops limit to perform the architecture search phase.
+
+:githublink:`Example code <examples/nas/oneshot/spos>`
+
+**Requirements:** Prepare ImageNet in the standard format (follow the script `here <https://gist.github.com/BIGBALLON/8a71d225eff18d88e469e6ea9b39cef4>`__). Linking it to ``data/imagenet`` will be more convenient. Download the checkpoint file from `here <https://1drv.ms/u/s!Am_mmG2-KsrnajesvSdfsq_cN48?e=aHVppN>`__ (maintained by `Megvii <https://github.com/megvii-model>`__) if you don't want to retrain the supernet. Put ``checkpoint-150000.pth.tar`` under ``data`` directory. After preparation, it's expected to have the following code structure:
+
+.. code-block:: bash
+
+   spos
+   ├── architecture_final.json
+   ├── blocks.py
+   ├── data
+   │   ├── imagenet
+   │   │   ├── train
+   │   │   └── val
+   │   └── checkpoint-150000.pth.tar
+   ├── network.py
+   ├── readme.md
+   ├── supernet.py
+   ├── evaluation.py
+   ├── search.py
+   └── utils.py
+
+Then follow the 3 steps:
+
+1. **Train Supernet**:
+
+   .. code-block:: bash
+
+      python supernet.py
+
+   This will export the checkpoint to ``checkpoints`` directory, for the next step.
+
+   .. note:: The data loading used in the official repo is `slightly different from usual <https://github.com/megvii-model/SinglePathOneShot/issues/5>`__, as they use BGR tensor and keep the values between 0 and 255 intentionally to align with their own DL framework. The option ``--spos-preprocessing`` will simulate the behavior used originally and enable you to use the checkpoints pretrained.
+
+2. **Evolution Search**: Single Path One-Shot leverages evolution algorithm to search for the best architecture. In the paper, the search module, which is responsible for testing the sampled architecture, recalculates all the batch norm for a subset of training images, and evaluates the architecture on the full validation set.
+   In this example, it will inherit the ``state_dict`` of supernet from `./data/checkpoint-150000.pth.tar`, and search the best architecture with the regularized evolution strategy. Search in the supernet with the following command
+
+   .. code-block:: bash
+
+      python search.py
+
+   NNI support a latency filter to filter unsatisfied model from search phase. Latency is predicted by Microsoft nn-Meter (https://github.com/microsoft/nn-Meter). To apply the latency filter, users could run search.py with additional arguments ``--latency-filter``. Here is an example:
+
+   .. code-block:: bash
+
+      python search.py --latency-filter cortexA76cpu_tflite21
+
+   Note that the latency filter is only supported for base execution engine.
+
+   The final architecture exported from every epoch of evolution can be found in ``trials`` under the working directory of your tuner, which, by default, is ``$HOME/nni-experiments/your_experiment_id/trials``.
+
+3. **Train for Evaluation**:
+
+   .. code-block:: bash
+
+      python evaluation.py
+
+   By default, it will use ``architecture_final.json``. This architecture is provided by the official repo (converted into NNI format). You can use any architecture (e.g., the architecture found in step 2) with ``--fixed-arc`` option.
+
+Known Limitations
+"""""""""""""""""
+
+* Block search only. Channel search is not supported yet.
+
+Current Reproduction Results
+""""""""""""""""""""""""""""
+
+Reproduction is still undergoing. Due to the gap between official release and original paper, we compare our current results with official repo (our run) and paper.
+
+* Evolution phase is almost aligned with official repo. Our evolution algorithm shows a converging trend and reaches ~65% accuracy at the end of search. Nevertheless, this result is not on par with paper. For details, please refer to `this issue <https://github.com/megvii-model/SinglePathOneShot/issues/6>`__.
+* Retrain phase is not aligned. Our retraining code, which uses the architecture released by the authors, reaches 72.14% accuracy, still having a gap towards 73.61% by official release and 74.3% reported in original paper.
+
+.. _proxylessnas-strategy:
+
+ProxylessNAS
+^^^^^^^^^^^^
+
+The paper `ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware <https://arxiv.org/abs/1812.00332>`__ removes proxy, it directly learns the architectures for large-scale target tasks and target hardware platforms. They address high memory consumption issue of differentiable NAS and reduce the computational cost to the same level of regular training while still allowing a large candidate set. Please refer to the paper for the details.
+
+..  autoclass:: nni.retiarii.oneshot.pytorch.ProxylessTrainer
+
+To use ProxylessNAS training/searching approach, users need to specify search space in their model using :doc:`NNI NAS interface </nas/construct_space>`, e.g., ``LayerChoice``, ``InputChoice``. After defining and instantiating the model, the following work can be leaved to ProxylessNasTrainer by instantiating the trainer and passing the model to it.
+
+.. code-block:: python
+
+   trainer = ProxylessTrainer(model,
+                              loss=LabelSmoothingLoss(),
+                              dataset=None,
+                              optimizer=optimizer,
+                              metrics=lambda output, target: accuracy(output, target, topk=(1, 5,)),
+                              num_epochs=120,
+                              log_frequency=10,
+                              grad_reg_loss_type=args.grad_reg_loss_type, 
+                              grad_reg_loss_params=grad_reg_loss_params, 
+                              applied_hardware=args.applied_hardware, dummy_input=(1, 3, 224, 224),
+                              ref_latency=args.reference_latency)
+   trainer.train()
+   trainer.export(args.arch_path)
+
+The complete example code can be found :githublink:`here <examples/nas/oneshot/proxylessnas>`.
+
+Implementation
+""""""""""""""
+
+The implementation on NNI is based on the `offical implementation <https://github.com/mit-han-lab/ProxylessNAS>`__. The official implementation supports two training approaches: gradient descent and RL based. In our current implementation on NNI, gradient descent training approach is supported. The complete support of ProxylessNAS is ongoing.
+
+The official implementation supports different targeted hardware, including 'mobile', 'cpu', 'gpu8', 'flops'.  In NNI repo, the hardware latency prediction is supported by `Microsoft nn-Meter <https://github.com/microsoft/nn-Meter>`__. nn-Meter is an accurate inference latency predictor for DNN models on diverse edge devices. nn-Meter support four hardwares up to now, including ``cortexA76cpu_tflite21``, ``adreno640gpu_tflite21``, ``adreno630gpu_tflite21``, and ``myriadvpu_openvino2019r2``. Users can find more information about nn-Meter on its website. More hardware will be supported in the future. Users could find more details about applying ``nn-Meter`` :doc:`here </nas/hardware_aware_nas>`.
+
+Below we will describe implementation details. Like other one-shot NAS algorithms on NNI, ProxylessNAS is composed of two parts: *search space* and *training approach*. For users to flexibly define their own search space and use built-in ProxylessNAS training approach, please refer to :githublink:`example code <examples/nas/oneshot/proxylessnas>` for a reference.
+
+.. image:: ../../../img/proxylessnas.png
+   :width: 450
+   :align: center
+
+ProxylessNAS training approach is composed of ProxylessLayerChoice and ProxylessNasTrainer. ProxylessLayerChoice instantiates MixedOp for each mutable (i.e., LayerChoice), and manage architecture weights in MixedOp. **For DataParallel**, architecture weights should be included in user model. Specifically, in ProxylessNAS implementation, we add MixedOp to the corresponding mutable (i.e., LayerChoice) as a member variable. The ProxylessLayerChoice class also exposes two member functions, i.e., ``resample``, ``finalize_grad``, for the trainer to control the training of architecture weights.
+
+Reproduction Results
+""""""""""""""""""""
+
+To reproduce the result, we first run the search, we found that though it runs many epochs the chosen architecture converges at the first several epochs. This is probably induced by hyper-parameters or the implementation, we are working on it.
+
+Customization
+-------------
+
+Multi-trial
+^^^^^^^^^^^
+
+..  autoclass:: nni.retiarii.Sampler
+    :noindex:
+    :members:
+
+..  autoclass:: nni.retiarii.strategy.BaseStrategy
+    :members:
+
+..  automodule:: nni.retiarii.execution
+    :members:
+    :imported-members:
+    :undoc-members:
+
+One-shot
+^^^^^^^^
+
+..  autoclass:: nni.retiarii.oneshot.BaseOneShotTrainer
+    :members:
+
+..  autofunction:: nni.retiarii.oneshot.pytorch.utils.replace_layer_choice
+
+..  autofunction:: nni.retiarii.oneshot.pytorch.utils.replace_input_choice
diff --git a/docs/source/reference/nas/toctree.rst b/docs/source/reference/nas/toctree.rst
new file mode 100644
index 000000000..6dbd07014
--- /dev/null
+++ b/docs/source/reference/nas/toctree.rst
@@ -0,0 +1,10 @@
+NAS API Reference
+=================
+
+.. toctree::
+   :maxdepth: 2
+
+   search_space
+   strategy
+   evaluator
+   Others <others>
diff --git a/docs/source/reference/nnictl.rst b/docs/source/reference/nnictl.rst
index af313f8fa..1452fa181 100644
--- a/docs/source/reference/nnictl.rst
+++ b/docs/source/reference/nnictl.rst
@@ -1,5 +1,5 @@
-nnictl
-======
+nnictl Commands
+===============
 
 .. argparse::
    :module: nni.tools.nnictl.nnictl
diff --git a/docs/source/reference/others.rst b/docs/source/reference/others.rst
new file mode 100644
index 000000000..a2b5d2a27
--- /dev/null
+++ b/docs/source/reference/others.rst
@@ -0,0 +1,14 @@
+Uncategorized Modules
+=====================
+
+nni.common.serializer
+---------------------
+
+.. automodule:: nni.common.serializer
+    :members:
+
+nni.typehint
+------------
+
+.. automodule:: nni.typehint
+    :members:
diff --git a/docs/source/reference/python_api.rst b/docs/source/reference/python_api.rst
new file mode 100644
index 000000000..34959e3c1
--- /dev/null
+++ b/docs/source/reference/python_api.rst
@@ -0,0 +1,11 @@
+Python API Reference
+====================
+
+..  toctree::
+    :maxdepth: 1
+
+    Hyperparameter Optimization <hpo>
+    Neural Architecture Search <nas/toctree>
+    Model Compression <compression/toctree>
+    Experiment <experiment>
+    Others <others>
diff --git a/docs/source/reference_zh.rst b/docs/source/reference_zh.rst
deleted file mode 100644
index c8911b662..000000000
--- a/docs/source/reference_zh.rst
+++ /dev/null
@@ -1,18 +0,0 @@
-.. 19ce4f2ee1d3c4f1be277ab09ba40092
-
-参考
-==================
-
-..  toctree::
-    :maxdepth: 2
-
-    nnictl 命令 <reference/nnictl>
-    Experiment 配置 <reference/experiment_config>
-    Experiment 配置（遗产） <Tutorial/ExperimentConfig>
-    搜索空间<Tutorial/SearchSpaceSpec>
-    NNI Annotation<Tutorial/AnnotationSpec>
-    SDK API 参考 <sdk_reference>
-    支持的框架和库 <SupportedFramework_Library>
-    从 Python 发起实验 <Tutorial/HowToLaunchFromPython>
-    共享存储 <Tutorial/HowToUseSharedStorage>
-    Tensorboard <Tutorial/Tensorboard>
diff --git a/docs/source/refs.bib b/docs/source/refs.bib
index d6c642b27..7a70a4c58 100644
--- a/docs/source/refs.bib
+++ b/docs/source/refs.bib
@@ -1,3 +1,67 @@
+/* HPO */
+
+@article{bergstra2011algorithms,
+  title={Algorithms for hyper-parameter optimization},
+  author={Bergstra, James and Bardenet, R{\'e}mi and Bengio, Yoshua and K{\'e}gl, Bal{\'a}zs},
+  journal={Advances in neural information processing systems},
+  volume={24},
+  year={2011}
+}
+
+@inproceedings{li2018metis,
+  title={Metis: Robustly tuning tail latencies of cloud systems},
+  author={Li, Zhao Lucis and Liang, Chieh-Jan Mike and He, Wenjia and Zhu, Lianjie and Dai, Wenjun and Jiang, Jin and Sun, Guangzhong},
+  booktitle={2018 USENIX Annual Technical Conference (USENIX ATC 18)},
+  pages={981--992},
+  year={2018}
+}
+
+@inproceedings{hutter2011sequential,
+  title={Sequential model-based optimization for general algorithm configuration},
+  author={Hutter, Frank and Hoos, Holger H and Leyton-Brown, Kevin},
+  booktitle={International conference on learning and intelligent optimization},
+  pages={507--523},
+  year={2011},
+  organization={Springer}
+}
+
+@article{li2017hyperband,
+  title={Hyperband: A novel bandit-based approach to hyperparameter optimization},
+  author={Li, Lisha and Jamieson, Kevin and DeSalvo, Giulia and Rostamizadeh, Afshin and Talwalkar, Ameet},
+  journal={The Journal of Machine Learning Research},
+  volume={18},
+  number={1},
+  pages={6765--6816},
+  year={2017},
+  publisher={JMLR. org}
+}
+
+@inproceedings{falkner2018bohb,
+  title={BOHB: Robust and efficient hyperparameter optimization at scale},
+  author={Falkner, Stefan and Klein, Aaron and Hutter, Frank},
+  booktitle={International Conference on Machine Learning},
+  pages={1437--1446},
+  year={2018},
+  organization={PMLR}
+}
+
+/* NAS */
+
+@inproceedings{zoph2017neural,
+  title={Neural Architecture Search with Reinforcement Learning},
+  author={Zoph, Barret and Le, Quoc V},
+  booktitle={International Conference on Learning Representations},
+  year={2017}
+}
+
+@inproceedings{zoph2018learning,
+  title={Learning transferable architectures for scalable image recognition},
+  author={Zoph, Barret and Vasudevan, Vijay and Shlens, Jonathon and Le, Quoc V},
+  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
+  pages={8697--8710},
+  year={2018}
+}
+
 @inproceedings{liu2018darts,
   title={DARTS: Differentiable Architecture Search},
   author={Liu, Hanxiao and Simonyan, Karen and Yang, Yiming},
@@ -27,3 +91,27 @@
   year={2018},
   organization={PMLR}
 }
+
+@inproceedings{radosavovic2019network,
+  title={On network design spaces for visual recognition},
+  author={Radosavovic, Ilija and Johnson, Justin and Xie, Saining and Lo, Wan-Yen and Doll{\'a}r, Piotr},
+  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
+  pages={1882--1890},
+  year={2019}
+}
+
+@inproceedings{ying2019bench,
+  title={Nas-bench-101: Towards reproducible neural architecture search},
+  author={Ying, Chris and Klein, Aaron and Christiansen, Eric and Real, Esteban and Murphy, Kevin and Hutter, Frank},
+  booktitle={International Conference on Machine Learning},
+  pages={7105--7114},
+  year={2019},
+  organization={PMLR}
+}
+
+@inproceedings{dong2019bench,
+  title={NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search},
+  author={Dong, Xuanyi and Yang, Yi},
+  booktitle={International Conference on Learning Representations},
+  year={2019}
+}
diff --git a/docs/source/Release.rst b/docs/source/release.rst
similarity index 94%
rename from docs/source/Release.rst
rename to docs/source/release.rst
index 1bb467f74..1d7cb5395 100644
--- a/docs/source/Release.rst
+++ b/docs/source/release.rst
@@ -5,6 +5,61 @@
 Change Log
 ==========
 
+Release 2.7 - 4/18/2022
+-----------------------
+
+Documentation
+^^^^^^^^^^^^^
+
+A full-size upgrade of the documentation, with the following significant improvements in the reading experience, practical tutorials, and examples:
+
+* Reorganized the document structure with a new document template. (`Upgraded doc entry <https://nni.readthedocs.io/en/v2.7>`__)
+* Add more friendly tutorials with jupyter notebook. (`New Quick Starts <https://nni.readthedocs.io/en/v2.7/quickstart.html>`__)
+* New model pruning demo available. (`Youtube entry <https://www.youtube.com/channel/UCKcafm6861B2mnYhPbZHavw>`__, `Bilibili entry <https://space.bilibili.com/1649051673>`__)
+
+Hyper-Parameter Optimization
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+* [Improvement] TPE and random tuners will not generate duplicate hyperparameters anymore.
+* [Improvement] Most Python APIs now have type annotations.
+
+Neural Architecture Search
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+* Jointly search for architecture and hyper-parameters: ValueChoice in evaluator. (`doc <https://nni.readthedocs.io/en/v2.7/reference/nas/search_space.html#valuechoice>`__)
+* Support composition (transformation) of one or several value choices. (`doc <https://nni.readthedocs.io/en/v2.7/reference/nas/search_space.html#valuechoice>`__)
+* Enhanced Cell API (``merge_op``, preprocessor, postprocessor). (`doc <https://nni.readthedocs.io/en/v2.7/reference/nas/search_space.html#cell>`__)
+* The argument ``depth`` in the ``Repeat`` API allows ValueChoice. (`doc <https://nni.readthedocs.io/en/v2.7/reference/nas/search_space.html#repeat>`__)
+* Support loading ``state_dict`` between sub-net and super-net. (`doc <https://nni.readthedocs.io/en/v2.7/reference/nas/others.html#nni.retiarii.utils.original_state_dict_hooks>`__, `example in spos <https://nni.readthedocs.io/en/v2.7/reference/nas/strategy.html#spos>`__)
+* Support BN fine-tuning and evaluation in SPOS example. (`doc <https://nni.readthedocs.io/en/v2.7/reference/nas/strategy.html#spos>`__)
+* *Experimental* Model hyper-parameter choice. (`doc <https://nni.readthedocs.io/en/v2.7/reference/nas/search_space.html#modelparameterchoice>`__)
+* *Preview* Lightning implementation for Retiarii including DARTS, ENAS, ProxylessNAS and RandomNAS. (`example usage <https://github.com/microsoft/nni/blob/v2.7/test/ut/retiarii/test_oneshot.py>`__)
+* *Preview* A search space hub that contains 10 search spaces. (`code <https://github.com/microsoft/nni/tree/v2.7/nni/retiarii/hub>`__)
+
+Model Compression
+^^^^^^^^^^^^^^^^^
+
+* Pruning V2 is promoted as default pruning framework, old pruning is legacy and keeps for a few releases.(`doc <https://nni.readthedocs.io/en/v2.7/reference/compression/pruner.html>`__)
+* A new pruning mode ``balance`` is supported in ``LevelPruner``.(`doc <https://nni.readthedocs.io/en/v2.7/reference/compression/pruner.html#level-pruner>`__)
+* Support coarse-grained pruning in ``ADMMPruner``.(`doc <https://nni.readthedocs.io/en/v2.7/reference/compression/pruner.html#admm-pruner>`__)
+* [Improvement] Support more operation types in pruning speedup.
+* [Improvement] Optimize performance of some pruners.
+
+Experiment
+^^^^^^^^^^
+
+* [Improvement] Experiment.run() no longer stops web portal on return.
+
+Notable Bugfixes
+^^^^^^^^^^^^^^^^
+
+* Fixed: experiment list could not open experiment with prefix.
+* Fixed: serializer for complex kinds of arguments.
+* Fixed: some typos in code. (thanks @a1trl9 @mrshu)
+* Fixed: dependency issue across layer in pruning speedup. 
+* Fixed: uncheck trial doesn't work bug in the detail table.
+* Fixed: filter name | id bug in the experiment management page.
+
 Release 2.6 - 1/19/2022
 -----------------------
 
@@ -1506,7 +1561,7 @@ NNICTL new features and updates
 
   Before v0.3, NNI only supports running single experiment once a time. After this release, users are able to run multiple experiments simultaneously. Each experiment will require a unique port, the 1st experiment will be set to the default port as previous versions. You can specify a unique port for the rest experiments as below:
 
-  .. code-block:: bash
+  .. code-block:: text
 
      nnictl create --port 8081 --config <config file path>
 
diff --git a/docs/source/sdk_reference.rst b/docs/source/sdk_reference.rst
deleted file mode 100644
index 4d5a64353..000000000
--- a/docs/source/sdk_reference.rst
+++ /dev/null
@@ -1,12 +0,0 @@
-####################
-Python API Reference
-####################
-
-
-..  toctree::
-    :maxdepth: 1
-
-    Auto Tune <autotune_ref>
-    NAS <NAS/ApiReference>
-    Compression <Compression/CompressionReference>
-    Python API <Tutorial/HowToLaunchFromPython>
\ No newline at end of file
diff --git a/docs/source/sdk_reference_zh.rst b/docs/source/sdk_reference_zh.rst
deleted file mode 100644
index fbf85cad7..000000000
--- a/docs/source/sdk_reference_zh.rst
+++ /dev/null
@@ -1,14 +0,0 @@
-.. 60cb924d0ec522b7709acf4f8cff3f16
-
-####################
-Python API 参考
-####################
-
-
-..  toctree::
-    :maxdepth: 1
-
-    自动调优 <autotune_ref>
-    NAS <NAS/ApiReference>
-    模型压缩 <Compression/CompressionReference>
-    Python API <Tutorial/HowToLaunchFromPython>
\ No newline at end of file
diff --git a/docs/source/sharings/automodel_toctree.rst b/docs/source/sharings/automodel_toctree.rst
new file mode 100644
index 000000000..b49cabc51
--- /dev/null
+++ b/docs/source/sharings/automodel_toctree.rst
@@ -0,0 +1,10 @@
+Automatic Model Tuning
+======================
+
+..  toctree::
+    :maxdepth: 1
+
+    Tuning SVD automatically <recommenders_svd>
+    EfficientNet on NNI <efficientnet>
+    Automatic Model Architecture Search for Reading Comprehension <squad_evolution_examples>
+    Parallelizing Optimization for TPE <parallelizing_tpe_search>
\ No newline at end of file
diff --git a/docs/source/sharings/autosys_toctree.rst b/docs/source/sharings/autosys_toctree.rst
new file mode 100644
index 000000000..0f00d8beb
--- /dev/null
+++ b/docs/source/sharings/autosys_toctree.rst
@@ -0,0 +1,9 @@
+Automatic System Tuning
+=======================
+
+..  toctree::
+    :maxdepth: 1
+
+    Tuning SPTAG (Space Partition Tree And Graph) automatically <sptag_auto_tune>
+    Tuning the performance of RocksDB <rocksdb_examples>
+    Tuning Tensor Operators automatically <op_evo_examples>
\ No newline at end of file
diff --git a/docs/source/sharings/community_sharings.rst b/docs/source/sharings/community_sharings.rst
new file mode 100644
index 000000000..8de66dcb4
--- /dev/null
+++ b/docs/source/sharings/community_sharings.rst
@@ -0,0 +1,14 @@
+Use Cases and Solutions
+=======================
+
+..  toctree::
+    :maxdepth: 1
+
+    Overview <overview>
+    Automatic Model Tuning (HPO/NAS) <automodel_toctree>
+    Automatic System Tuning (AutoSys) <autosys_toctree>
+    Model Compression <model_compression_toctree>
+    Feature Engineering <feature_engineering_toctree>
+    Performance measurement, comparison and analysis <perf_compare_toctree>
+    Use NNI on Google Colab <nni_colab_support>
+    nnSpider Emoticons <nn_spider>
diff --git a/docs/source/TrialExample/EfficientNet.rst b/docs/source/sharings/efficientnet.rst
similarity index 86%
rename from docs/source/TrialExample/EfficientNet.rst
rename to docs/source/sharings/efficientnet.rst
index b544f8831..0c010f6f0 100644
--- a/docs/source/TrialExample/EfficientNet.rst
+++ b/docs/source/sharings/efficientnet.rst
@@ -15,7 +15,7 @@ Instructions
 #. Run ``git clone https://github.com/ultmaster/EfficientNet-PyTorch`` to clone the `ultmaster modified version <https://github.com/ultmaster/EfficientNet-PyTorch>`__ of the original `EfficientNet-PyTorch <https://github.com/lukemelas/EfficientNet-PyTorch>`__. The modifications were done to adhere to the original `Tensorflow version <https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet>`__ as close as possible (including EMA, label smoothing and etc.); also added are the part which gets parameters from tuner and reports intermediate/final results. Clone it into ``EfficientNet-PyTorch``\ ; the files like ``main.py``\ , ``train_imagenet.sh`` will appear inside, as specified in the configuration files.
 #. Run ``nnictl create --config config_local.yml`` (use ``config_pai.yml`` for OpenPAI) to find the best EfficientNet-B1. Adjust the training service (PAI/local/remote), batch size in the config files according to the environment.
 
-For training on ImageNet, read ``EfficientNet-PyTorch/train_imagenet.sh``. Download ImageNet beforehand and extract it adhering to `PyTorch format <https://pytorch.org/docs/stable/torchvision/datasets.html#imagenet>`__ and then replace ``/mnt/data/imagenet`` in with the location of the ImageNet storage. This file should also be a good example to follow for mounting ImageNet into the container on OpenPAI.
+For training on ImageNet, read ``EfficientNet-PyTorch/train_imagenet.sh``. Download ImageNet beforehand and extract it adhering to `PyTorch format <https://pytorch.org/vision/stable/generated/torchvision.datasets.ImageNet.html>`__ and then replace ``/mnt/data/imagenet`` in with the location of the ImageNet storage. This file should also be a good example to follow for mounting ImageNet into the container on OpenPAI.
 
 Results
 -------
diff --git a/docs/source/sharings/feature_engineering_toctree.rst b/docs/source/sharings/feature_engineering_toctree.rst
new file mode 100644
index 000000000..10a86cf9d
--- /dev/null
+++ b/docs/source/sharings/feature_engineering_toctree.rst
@@ -0,0 +1,7 @@
+Feature Engineering
+===================
+
+..  toctree::
+    :maxdepth: 1
+
+    NNI review article from Zhihu: - By Garvin Li <nni_autofeatureeng>
diff --git a/docs/source/CommunitySharings/HpoComparison.rst b/docs/source/sharings/hpo_comparison.rst
similarity index 92%
rename from docs/source/CommunitySharings/HpoComparison.rst
rename to docs/source/sharings/hpo_comparison.rst
index 5a95e42f8..88f4622f1 100644
--- a/docs/source/CommunitySharings/HpoComparison.rst
+++ b/docs/source/sharings/hpo_comparison.rst
@@ -5,24 +5,13 @@ Hyper Parameter Optimization Comparison
 
 Comparison of Hyperparameter Optimization (HPO) algorithms on several problems.
 
-Hyperparameter Optimization algorithms are list below:
-
-
-* `Random Search <../Tuner/BuiltinTuner.rst>`__
-* `Grid Search <../Tuner/BuiltinTuner.rst>`__
-* `Evolution <../Tuner/BuiltinTuner.rst>`__
-* `Anneal <../Tuner/BuiltinTuner.rst>`__
-* `Metis <../Tuner/BuiltinTuner.rst>`__
-* `TPE <../Tuner/BuiltinTuner.rst>`__
-* `SMAC <../Tuner/BuiltinTuner.rst>`__
-* `HyperBand <../Tuner/BuiltinTuner.rst>`__
-* `BOHB <../Tuner/BuiltinTuner.rst>`__
+Hyperparameter Optimization algorithms are listed in :doc:`/hpo/tuners`.
 
 All algorithms run in NNI local environment.
 
-Machine Environment：
+Machine Environment:
 
-.. code-block:: bash
+.. code-block:: text
 
    OS: Linux Ubuntu 16.04 LTS
    CPU: Intel(R) Xeon(R) CPU E5-2690 v3 @ 2.60GHz 2600 MHz
@@ -39,7 +28,7 @@ AutoGBDT Example
 Problem Description
 ^^^^^^^^^^^^^^^^^^^
 
-Nonconvex problem on the hyper-parameter search of `AutoGBDT <../TrialExample/GbdtExample.rst>`__ example.
+Nonconvex problem on the hyper-parameter search of :githublink:`AutoGBDT example <examples/trials/auto-gbdt>`.
 
 Search Space
 ^^^^^^^^^^^^
@@ -215,7 +204,7 @@ The performance of ``DB_Bench`` is associated with the machine configuration and
 Machine configuration
 ^^^^^^^^^^^^^^^^^^^^^
 
-.. code-block:: bash
+.. code-block:: text
 
    RocksDB:    version 6.1
    CPU:        6 * Intel(R) Xeon(R) CPU E5-2690 v4 @ 2.60GHz
diff --git a/docs/img/emoicons/Comfort.png b/docs/source/sharings/images/nn_spider/comfort.png
similarity index 100%
rename from docs/img/emoicons/Comfort.png
rename to docs/source/sharings/images/nn_spider/comfort.png
diff --git a/docs/img/emoicons/Crying.png b/docs/source/sharings/images/nn_spider/crying.png
similarity index 100%
rename from docs/img/emoicons/Crying.png
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similarity index 100%
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similarity index 100%
rename from docs/img/emoicons/Error.png
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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similarity index 100%
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diff --git a/docs/img/emoicons/Weaving.png b/docs/source/sharings/images/nn_spider/weaving.png
similarity index 100%
rename from docs/img/emoicons/Weaving.png
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diff --git a/docs/img/emoicons/Working.png b/docs/source/sharings/images/nn_spider/working.png
similarity index 100%
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diff --git a/docs/source/TrialExample/KDExample.rst b/docs/source/sharings/kd_example.rst
similarity index 74%
rename from docs/source/TrialExample/KDExample.rst
rename to docs/source/sharings/kd_example.rst
index 29a23ae02..20d4e25eb 100644
--- a/docs/source/TrialExample/KDExample.rst
+++ b/docs/source/sharings/kd_example.rst
@@ -35,12 +35,12 @@ PyTorch code
          loss.backward()
 
 
-The complete code for fine-tuning the pruned model can be found :githublink:`here <examples/model_compress/pruning/finetune_kd_torch.py>`
+The complete code for fine-tuning the pruned model can be found :githublink:`here <examples/model_compress/pruning/legacy/finetune_kd_torch.py>`
 
-.. code-block:: python
+.. code-block:: bash
 
-      python finetune_kd_torch.py --model [model name] --teacher-model-dir [pretrained checkpoint path]  --student-model-dir [pruned checkpoint path] --mask-path [mask file path]
+   python finetune_kd_torch.py --model [model name] --teacher-model-dir [pretrained checkpoint path]  --student-model-dir [pruned checkpoint path] --mask-path [mask file path]
 
-Note that: for fine-tuning a pruned model, run :githublink:`basic_pruners_torch.py <examples/model_compress/pruning/basic_pruners_torch.py>` first to get the mask file, then pass the mask path as argument to the script.
+Note that: for fine-tuning a pruned model, run :githublink:`basic_pruners_torch.py <examples/model_compress/pruning/legacy/basic_pruners_torch.py>` first to get the mask file, then pass the mask path as argument to the script.
 
 
diff --git a/docs/source/CommunitySharings/ModelCompressionComparison.rst b/docs/source/sharings/model_compress_comp.rst
similarity index 72%
rename from docs/source/CommunitySharings/ModelCompressionComparison.rst
rename to docs/source/sharings/model_compress_comp.rst
index de22b8639..ec610774d 100644
--- a/docs/source/CommunitySharings/ModelCompressionComparison.rst
+++ b/docs/source/sharings/model_compress_comp.rst
@@ -13,7 +13,7 @@ The experiments are performed with the following pruners/datasets/models:
 
 
 * 
-  Models: :githublink:`VGG16, ResNet18, ResNet50 <examples/model_compress/pruning/models/cifar10>`
+  Models: :githublink:`VGG16, ResNet18, ResNet50 <examples/model_compress/models/cifar10>`
 
 * 
   Datasets: CIFAR-10
@@ -35,7 +35,7 @@ The experiments are performed with the following pruners/datasets/models:
     For the pruners with scheduling, ``L1Filter Pruner`` is used as the base algorithm. That is to say, after the sparsities distribution is decided by the scheduling algorithm, ``L1Filter Pruner`` is used to performn real pruning.
 
   * 
-    All the pruners listed above are implemented in :githublink:`nni <docs/en_US/Compression/Overview.rst>`.
+    All the pruners listed above are implemented in :doc:`nni </compression/overview>`.
 
 Experiment Result
 -----------------
@@ -50,24 +50,24 @@ The experiment result are shown in the following figures:
 CIFAR-10, VGG16:
 
 
-.. image:: ../../../examples/model_compress/pruning/comparison_of_pruners/img/performance_comparison_vgg16.png
-   :target: ../../../examples/model_compress/pruning/comparison_of_pruners/img/performance_comparison_vgg16.png
+.. image:: ../../../examples/model_compress/pruning/legacy/comparison_of_pruners/img/performance_comparison_vgg16.png
+   :target: ../../../examples/model_compress/pruning/legacy/comparison_of_pruners/img/performance_comparison_vgg16.png
    :alt: 
 
 
 CIFAR-10, ResNet18:
 
 
-.. image:: ../../../examples/model_compress/pruning/comparison_of_pruners/img/performance_comparison_resnet18.png
-   :target: ../../../examples/model_compress/pruning/comparison_of_pruners/img/performance_comparison_resnet18.png
+.. image:: ../../../examples/model_compress/pruning/legacy/comparison_of_pruners/img/performance_comparison_resnet18.png
+   :target: ../../../examples/model_compress/pruning/legacy/comparison_of_pruners/img/performance_comparison_resnet18.png
    :alt: 
 
 
 CIFAR-10, ResNet50:
 
 
-.. image:: ../../../examples/model_compress/pruning/comparison_of_pruners/img/performance_comparison_resnet50.png
-   :target: ../../../examples/model_compress/pruning/comparison_of_pruners/img/performance_comparison_resnet50.png
+.. image:: ../../../examples/model_compress/pruning/legacy/comparison_of_pruners/img/performance_comparison_resnet50.png
+   :target: ../../../examples/model_compress/pruning/legacy/comparison_of_pruners/img/performance_comparison_resnet50.png
    :alt: 
 
 
@@ -88,22 +88,19 @@ Implementation Details
 ^^^^^^^^^^^^^^^^^^^^^^
 
 
-* 
-  The experiment results are all collected with the default configuration of the pruners in nni, which means that when we call a pruner class in nni, we don't change any default class arguments.
+* The experiment results are all collected with the default configuration of the pruners in nni, which means that when we call a pruner class in nni, we don't change any default class arguments.
 
-* 
-  Both FLOPs and the number of parameters are counted with :githublink:`Model FLOPs/Parameters Counter <docs/en_US/Compression/CompressionUtils.md#model-flopsparameters-counter>` after :githublink:`model speed up <docs/en_US/Compression/ModelSpeedup.rst>`.
+* Both FLOPs and the number of parameters are counted with :ref:`Model FLOPs/Parameters Counter <flops-counter>` after :doc:`model speedup </tutorials/pruning_speedup>`.
   This avoids potential issues of counting them of masked models.
 
-* 
-  The experiment code can be found :githublink:`here <examples/model_compress/pruning/auto_pruners_torch.py>`.
+* The experiment code can be found :githublink:`here <examples/model_compress/pruning/legacy/auto_pruners_torch.py>`.
 
 Experiment Result Rendering
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 
 * 
-  If you follow the practice in the :githublink:`example <examples/model_compress/pruning/auto_pruners_torch.py>`\ , for every single pruning experiment, the experiment result will be saved in JSON format as follows:
+  If you follow the practice in the :githublink:`example <examples/model_compress/pruning/legacy/auto_pruners_torch.py>`\ , for every single pruning experiment, the experiment result will be saved in JSON format as follows:
 
   .. code-block:: json
 
@@ -114,8 +111,8 @@ Experiment Result Rendering
        }
 
 * 
-  The experiment results are saved :githublink:`here <examples/model_compress/pruning/comparison_of_pruners>`. 
-  You can refer to :githublink:`analyze <examples/model_compress/pruning/comparison_of_pruners/analyze.py>` to plot new performance comparison figures.
+  The experiment results are saved :githublink:`here <examples/model_compress/pruning/legacy/comparison_of_pruners>`. 
+  You can refer to :githublink:`analyze <examples/model_compress/pruning/legacy/comparison_of_pruners/analyze.py>` to plot new performance comparison figures.
 
 Contribution
 ------------
diff --git a/docs/source/sharings/model_compression_toctree.rst b/docs/source/sharings/model_compression_toctree.rst
new file mode 100644
index 000000000..a678654aa
--- /dev/null
+++ b/docs/source/sharings/model_compression_toctree.rst
@@ -0,0 +1,7 @@
+Model Compression
+=================
+
+..  toctree::
+    :maxdepth: 1
+
+    Knowledge distillation with NNI model compression <kd_example>
\ No newline at end of file
diff --git a/docs/source/CommunitySharings/NasComparison.rst b/docs/source/sharings/nas_comparison.rst
similarity index 100%
rename from docs/source/CommunitySharings/NasComparison.rst
rename to docs/source/sharings/nas_comparison.rst
diff --git a/docs/source/sharings/nn_spider.rst b/docs/source/sharings/nn_spider.rst
new file mode 100644
index 000000000..9be0a2e52
--- /dev/null
+++ b/docs/source/sharings/nn_spider.rst
@@ -0,0 +1,52 @@
+nnSpider Emoticons
+==================
+
+* Comfort
+
+  .. image:: images/nn_spider/comfort.png
+     :width: 400
+
+* Crying
+
+  .. image:: images/nn_spider/crying.png
+     :width: 400
+
+* Cut
+
+  .. image:: images/nn_spider/cut.png
+     :width: 400
+
+* Error
+
+  .. image:: images/nn_spider/error.png
+     :width: 400
+
+* Holiday
+
+  .. image:: images/nn_spider/holiday.png
+     :width: 400
+
+* No bug
+
+  .. image:: images/nn_spider/nobug.png
+     :width: 400
+
+* Sign
+
+  .. image:: images/nn_spider/sign.png
+     :width: 400
+
+* Sweat
+
+  .. image:: images/nn_spider/sweat.png
+     :width: 400
+
+* Weaving
+
+  .. image:: images/nn_spider/weaving.png
+     :width: 400
+
+* Working
+
+  .. image:: images/nn_spider/working.png
+     :width: 400
diff --git a/docs/source/CommunitySharings/NNI_AutoFeatureEng.rst b/docs/source/sharings/nni_autofeatureeng.rst
similarity index 100%
rename from docs/source/CommunitySharings/NNI_AutoFeatureEng.rst
rename to docs/source/sharings/nni_autofeatureeng.rst
diff --git a/docs/source/CommunitySharings/NNI_colab_support.rst b/docs/source/sharings/nni_colab_support.rst
similarity index 52%
rename from docs/source/CommunitySharings/NNI_colab_support.rst
rename to docs/source/sharings/nni_colab_support.rst
index 438f66bb2..99ca87d6b 100644
--- a/docs/source/CommunitySharings/NNI_colab_support.rst
+++ b/docs/source/sharings/nni_colab_support.rst
@@ -6,40 +6,42 @@ NNI can easily run on Google Colab platform. However, Colab doesn't expose its p
 How to Open NNI's Web UI on Google Colab
 ----------------------------------------
 
-
 #. Install required packages and softwares.
 
-.. code-block:: bash
+   .. code-block:: bash
 
-   ! pip install nni # install nni
-   ! wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip # download ngrok and unzip it
-   ! unzip ngrok-stable-linux-amd64.zip
-   ! mkdir -p nni_repo
-   ! git clone https://github.com/microsoft/nni.git nni_repo/nni # clone NNI's offical repo to get examples
+      ! pip install nni # install nni
+      ! wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip # download ngrok and unzip it
+      ! unzip ngrok-stable-linux-amd64.zip
+      ! mkdir -p nni_repo
+      ! git clone https://github.com/microsoft/nni.git nni_repo/nni # clone NNI's offical repo to get examples
 
 
-#. Register a ngrok account `here <https://ngrok.com/>`__\ , then connect to your account using your authtoken.
+#. Register a ngrok account `here <https://ngrok.com/>`__, then connect to your account using your authtoken.
 
-.. code-block:: bash
+   .. code-block:: bash
 
-   ! ./ngrok authtoken <your-authtoken>
+      ! ./ngrok authtoken YOUR_AUTH_TOKEN
 
 
 #. Start an NNI example on a port bigger than 1024, then start ngrok with the same port. If you want to use gpu, make sure gpuNum >= 1 in config.yml. Use ``get_ipython()`` to start ngrok since it will be stuck if you use ``! ngrok http 5000 &``.
 
-.. code-block:: bash
+   .. code-block:: bash
+
+      ! nnictl create --config nni_repo/nni/examples/trials/mnist-pytorch/config.yml --port 5000 &
+
+   .. code-block:: python
 
-   ! nnictl create --config nni_repo/nni/examples/trials/mnist-pytorch/config.yml --port 5000 &
-   get_ipython().system_raw('./ngrok http 5000 &')
+      get_ipython().system_raw('./ngrok http 5000 &')
 
 
 #. Check the public url.
 
-.. code-block:: bash
+   .. code-block:: bash
 
-   ! curl -s http://localhost:4040/api/tunnels # don't change the port number 4040
+      ! curl -s http://localhost:4040/api/tunnels # don't change the port number 4040
 
-You will see an url like http://xxxx.ngrok.io after step 4, open this url and you will find NNI's Web UI. Have fun :)
+   You will see an url like ``http://xxxx.ngrok.io`` after step 4, open this url and you will find NNI's Web UI. Have fun :)
 
 Access Web UI with frp
 ----------------------
diff --git a/docs/source/TrialExample/OpEvoExamples.rst b/docs/source/sharings/op_evo_examples.rst
similarity index 99%
rename from docs/source/TrialExample/OpEvoExamples.rst
rename to docs/source/sharings/op_evo_examples.rst
index 870bdd212..f4984728b 100644
--- a/docs/source/TrialExample/OpEvoExamples.rst
+++ b/docs/source/sharings/op_evo_examples.rst
@@ -118,7 +118,7 @@ Citing OpEvo
 
 If you feel OpEvo is helpful, please consider citing the paper as follows:
 
-.. code-block:: bash
+.. code-block:: bib
 
    @misc{gao2020opevo,
        title={OpEvo: An Evolutionary Method for Tensor Operator Optimization},
diff --git a/docs/source/sharings/overview.rst b/docs/source/sharings/overview.rst
new file mode 100644
index 000000000..a4b7c9350
--- /dev/null
+++ b/docs/source/sharings/overview.rst
@@ -0,0 +1,46 @@
+Use Cases and Solutions
+=======================
+
+Different from the tutorials and examples in the rest of the document which show the usage of a feature, this part mainly introduces end-to-end scenarios and use cases to help users further understand how NNI can help them. NNI can be widely adopted in various scenarios. We also encourage community contributors to share their AutoML practices especially the NNI usage practices from their experience.
+
+Automatic Model Tuning
+----------------------
+
+NNI can be applied on various model tuning tasks. Some state-of-the-art model search algorithms, such as EfficientNet, can be easily built on NNI. Popular models, e.g., recommendation models, can be tuned with NNI. The following are some use cases to illustrate how to leverage NNI in your model tuning tasks and how to build your own pipeline with NNI.
+
+* :doc:`Tuning SVD automatically <recommenders_svd>`
+* :doc:`EfficientNet on NNI <efficientnet>`
+* :doc:`Automatic Model Architecture Search for Reading Comprehension <squad_evolution_examples>`
+* :doc:`Parallelizing Optimization for TPE <parallelizing_tpe_search>`
+
+Automatic System Tuning
+-----------------------
+
+The performance of systems, such as database, tensor operator implementaion, often need to be tuned to adapt to specific hardware configuration, targeted workload, etc. Manually tuning a system is complicated and often requires detailed understanding of hardware and workload. NNI can make such tasks much easier and help system owners find the best configuration to the system automatically. The detailed design philosophy of automatic system tuning can be found in this `paper <https://dl.acm.org/doi/10.1145/3352020.3352031>`__ . The following are some typical cases that NNI can help.
+
+* :doc:`Tuning SPTAG (Space Partition Tree And Graph) automatically <sptag_auto_tune>`
+* :doc:`Tuning the performance of RocksDB <rocksdb_examples>`
+* :doc:`Tuning Tensor Operators automatically <op_evo_examples>`
+
+Model Compression
+-----------------
+
+The following one shows how to apply knowledge distillation on NNI model compression. More use cases and solutions will be added in the future.
+
+* :doc:`Knowledge distillation with NNI model compression <kd_example>`
+
+Feature Engineering
+-------------------
+
+The following is an article about how NNI helps in auto feature engineering shared by a community contributor. More use cases and solutions will be added in the future.
+
+* :doc:`NNI review article from Zhihu: - By Garvin Li <nni_autofeatureeng>`
+
+Performance Measurement, Comparison and Analysis
+------------------------------------------------
+
+Performance comparison and analysis can help users decide a proper algorithm (e.g., tuner, NAS algorithm) for their scenario. The following are some measurement and comparison data for users' reference.
+
+* :doc:`Neural Architecture Search Comparison <nas_comparison>`
+* :doc:`Hyper-parameter Tuning Algorithm Comparsion <hpo_comparison>`
+* :doc:`Model Compression Algorithm Comparsion <model_compress_comp>`
diff --git a/docs/source/CommunitySharings/ParallelizingTpeSearch.rst b/docs/source/sharings/parallelizing_tpe_search.rst
similarity index 100%
rename from docs/source/CommunitySharings/ParallelizingTpeSearch.rst
rename to docs/source/sharings/parallelizing_tpe_search.rst
diff --git a/docs/source/sharings/perf_compare_toctree.rst b/docs/source/sharings/perf_compare_toctree.rst
new file mode 100644
index 000000000..e27714b7b
--- /dev/null
+++ b/docs/source/sharings/perf_compare_toctree.rst
@@ -0,0 +1,9 @@
+Performance Measurement, Comparison and Analysis
+================================================
+
+..  toctree::
+    :maxdepth: 1
+
+    Neural Architecture Search Comparison <nas_comparison>
+    Hyper-parameter Tuning Algorithm Comparsion <hpo_comparison>
+    Model Compression Algorithm Comparsion <model_compress_comp>
\ No newline at end of file
diff --git a/docs/source/CommunitySharings/RecommendersSvd.rst b/docs/source/sharings/recommenders_svd.rst
similarity index 100%
rename from docs/source/CommunitySharings/RecommendersSvd.rst
rename to docs/source/sharings/recommenders_svd.rst
diff --git a/docs/source/TrialExample/RocksdbExamples.rst b/docs/source/sharings/rocksdb_examples.rst
similarity index 91%
rename from docs/source/TrialExample/RocksdbExamples.rst
rename to docs/source/sharings/rocksdb_examples.rst
index b91719485..aaeb877a5 100644
--- a/docs/source/TrialExample/RocksdbExamples.rst
+++ b/docs/source/sharings/rocksdb_examples.rst
@@ -8,7 +8,7 @@ Overview
 
 The performance of RocksDB is highly contingent on its tuning. However, because of the complexity of its underlying technology and a large number of configurable parameters, a good configuration is sometimes hard to obtain. NNI can help to address this issue. NNI supports many kinds of tuning algorithms to search the best configuration of RocksDB, and support many kinds of environments like local machine, remote servers and cloud. 
 
-This example illustrates how to use NNI to search the best configuration of RocksDB for a ``fillrandom`` benchmark supported by a benchmark tool ``db_bench``\ , which is an official benchmark tool provided by RocksDB itself. Therefore, before running this example, please make sure NNI is installed and `db_bench <https://github.com/facebook/rocksdb/wiki/Benchmarking-tools>`__ is in your ``PATH``. Please refer to `here <../Tutorial/QuickStart.rst>`__ for detailed information about installation and preparing of NNI environment, and `here <https://github.com/facebook/rocksdb/blob/master/INSTALL.md>`__ for compiling RocksDB as well as ``db_bench``.
+This example illustrates how to use NNI to search the best configuration of RocksDB for a ``fillrandom`` benchmark supported by a benchmark tool ``db_bench``\ , which is an official benchmark tool provided by RocksDB itself. Therefore, before running this example, please make sure NNI is installed and `db_bench <https://github.com/facebook/rocksdb/wiki/Benchmarking-tools>`__ is in your ``PATH``. Please refer to :doc:`here </installation>` for detailed information about installation and preparing of NNI environment, and `here <https://github.com/facebook/rocksdb/blob/master/INSTALL.md>`__ for compiling RocksDB as well as ``db_bench``.
 
 We also provide a simple script :githublink:`db_bench_installation.sh <examples/trials/systems_auto_tuning/rocksdb-fillrandom/db_bench_installation.sh>` helping to compile and install ``db_bench`` as well as its dependencies on Ubuntu. Installing RocksDB on other systems can follow the same procedure.
 
@@ -24,7 +24,7 @@ Search Space
 
 For simplicity, this example tunes three parameters, ``write_buffer_size``\ , ``min_write_buffer_num`` and ``level0_file_num_compaction_trigger``\ , for writing 16M keys with 20 Bytes of key size and 100 Bytes of value size randomly, based on writing operations per second (OPS). ``write_buffer_size`` sets the size of a single memtable. Once memtable exceeds this size, it is marked immutable and a new one is created. ``min_write_buffer_num`` is the minimum number of memtables to be merged before flushing to storage. Once the number of files in level 0 reaches ``level0_file_num_compaction_trigger``\ , level 0 to level 1 compaction is triggered.
 
-In this example, the search space is specified by a ``search_space.json`` file as shown below. Detailed explanation of search space could be found `here <../Tutorial/SearchSpaceSpec.rst>`__.
+In this example, the search space is specified by a ``search_space.json`` file as shown below. Detailed explanation of search space could be found :doc:`here </hpo/search_space>`.
 
 .. code-block:: json
 
@@ -48,8 +48,7 @@ In this example, the search space is specified by a ``search_space.json`` file a
 Benchmark code
 ^^^^^^^^^^^^^^
 
-Benchmark code should receive a configuration from NNI manager, and report the corresponding benchmark result back. Following NNI APIs are designed for this purpose. In this example, writing operations per second (OPS) is used as a performance metric. Please refer to `here <Trials.rst>`__ for detailed information.
-
+Benchmark code should receive a configuration from NNI manager, and report the corresponding benchmark result back. Following NNI APIs are designed for this purpose. In this example, writing operations per second (OPS) is used as a performance metric.
 
 * Use ``nni.get_next_parameter()`` to get next system configuration.
 * Use ``nni.report_final_result(metric)`` to report the benchmark result.
@@ -59,7 +58,7 @@ Benchmark code should receive a configuration from NNI manager, and report the c
 Config file
 ^^^^^^^^^^^
 
-One could start a NNI experiment with a config file. A config file for NNI is a ``yaml`` file usually including experiment settings (\ ``trialConcurrency``\ , ``trialGpuNumber``\ , etc.), platform settings (\ ``trainingService``\ ), path settings (\ ``searchSpaceFile``\ , ``trialCodeDirectory``\ , etc.) and tuner settings (\ ``tuner``\ , ``tuner optimize_mode``\ , etc.). Please refer to `here <../Tutorial/QuickStart.rst>`__ for more information.
+One could start a NNI experiment with a config file. A config file for NNI is a ``yaml`` file usually including experiment settings (\ ``trialConcurrency``\ , ``trialGpuNumber``\ , etc.), platform settings (\ ``trainingService``\ ), path settings (\ ``searchSpaceFile``\ , ``trialCodeDirectory``\ , etc.) and tuner settings (\ ``tuner``\ , ``tuner optimize_mode``\ , etc.). Please refer to :doc:`/reference/experiment_config`.
 
 Here is an example of tuning RocksDB with SMAC algorithm:
 
@@ -69,7 +68,7 @@ Here is an example of tuning RocksDB with TPE algorithm:
 
 :githublink:`code directory <examples/trials/systems_auto_tuning/rocksdb-fillrandom/config_tpe.yml>`
 
-Other tuners can be easily adopted in the same way. Please refer to `here <../Tuner/BuiltinTuner.rst>`__ for more information.
+Other tuners can be easily adopted in the same way. Please refer to :doc:`here </hpo/tuners>` for more information.
 
 Finally, we could enter the example folder and start the experiment using following commands:
 
diff --git a/docs/source/CommunitySharings/SptagAutoTune.rst b/docs/source/sharings/sptag_auto_tune.rst
similarity index 100%
rename from docs/source/CommunitySharings/SptagAutoTune.rst
rename to docs/source/sharings/sptag_auto_tune.rst
diff --git a/docs/source/TrialExample/SquadEvolutionExamples.rst b/docs/source/sharings/squad_evolution_examples.rst
similarity index 99%
rename from docs/source/TrialExample/SquadEvolutionExamples.rst
rename to docs/source/sharings/squad_evolution_examples.rst
index 69cc7e474..49f121598 100644
--- a/docs/source/TrialExample/SquadEvolutionExamples.rst
+++ b/docs/source/sharings/squad_evolution_examples.rst
@@ -146,9 +146,9 @@ Among those files, ``trial.py`` and ``graph_to_tf.py`` are special.
            if topo_i == '|':
                continue
            if graph.layers[topo_i].graph_type == LayerType.input.value:
-               # ......
+               ...
            elif graph.layers[topo_i].graph_type == LayerType.attention.value:
-               # ......
+               ...
            # More layers to handle
 
 As we can see, this function is actually a compiler, that converts the internal model DAG configuration (which will be introduced in the ``Model configuration format`` section) ``graph``\ , to a Tensorflow computation graph.
@@ -162,6 +162,7 @@ performs topological sorting on the internal graph representation, and the code
 .. code-block:: python
 
    for _, topo_i in enumerate(topology):
+       ...
 
 performs actually conversion that maps each layer to a part in Tensorflow computation graph.
 
diff --git a/docs/source/training_services_zh.rst b/docs/source/training_services_zh.rst
deleted file mode 100644
index 761536b24..000000000
--- a/docs/source/training_services_zh.rst
+++ /dev/null
@@ -1,17 +0,0 @@
-.. 4e054d96c7d211dc514c99d673415d8e
-
-NNI 支持的训练平台介绍
-=====================================
-
-..  toctree::
-    Overview <./TrainingService/Overview>
-    本机<./TrainingService/LocalMode>
-    远程<./TrainingService/RemoteMachineMode>
-    OpenPAI<./TrainingService/PaiMode>
-    Kubeflow<./TrainingService/KubeflowMode>
-    AdaptDL<./TrainingService/AdaptDLMode>
-    FrameworkController<./TrainingService/FrameworkControllerMode>
-    DLTS<./TrainingService/DLTSMode>
-    AML<./TrainingService/AMLMode>
-    PAI-DLC<./TrainingService/DLCMode>
-    混合模式 <./TrainingService/HybridMode>
diff --git a/docs/source/tutorials.rst b/docs/source/tutorials.rst
deleted file mode 100644
index 9759089cf..000000000
--- a/docs/source/tutorials.rst
+++ /dev/null
@@ -1,28 +0,0 @@
-Tutorials
-=========
-
-.. TOC
-
-.. toctree::
-   :maxdepth: 2
-   :hidden:
-
-   tutorials/nni_experiment
-   tutorials/nas_quick_start_mnist
-
-.. ----------------------
-
-.. cardlinkitem::
-   :header: Start and Manage a New Experiment
-   :description: Familiarize yourself with Pythonic API to manage a hyper-parameter tuning experiment
-   :link: tutorials/nni_experiment.html
-   :image: ../img/thumbnails/overview-31.png
-   :tags: Experiment/HPO
-
-.. cardlinkitem::
-   :header: Get started with NAS on MNIST
-   :description: bla bla bla bla
-   :link: tutorials/nas_quick_start_mnist.html
-   :image: ../img/thumbnails/overview-30.png
-   :background: cyan
-   :tags: NAS
diff --git a/docs/source/tutorials/hello_nas.ipynb b/docs/source/tutorials/hello_nas.ipynb
new file mode 100644
index 000000000..d7e1f9f88
--- /dev/null
+++ b/docs/source/tutorials/hello_nas.ipynb
@@ -0,0 +1,259 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Hello, NAS!\n\nThis is the 101 tutorial of Neural Architecture Search (NAS) on NNI.\nIn this tutorial, we will search for a neural architecture on MNIST dataset with the help of NAS framework of NNI, i.e., *Retiarii*.\nWe use multi-trial NAS as an example to show how to construct and explore a model space.\n\nThere are mainly three crucial components for a neural architecture search task, namely,\n\n* Model search space that defines a set of models to explore.\n* A proper strategy as the method to explore this model space.\n* A model evaluator that reports the performance of every model in the space.\n\nCurrently, PyTorch is the only supported framework by Retiarii, and we have only tested **PyTorch 1.7 to 1.10**.\nThis tutorial assumes PyTorch context but it should also apply to other frameworks, which is in our future plan.\n\n## Define your Model Space\n\nModel space is defined by users to express a set of models that users want to explore, which contains potentially good-performing models.\nIn this framework, a model space is defined with two parts: a base model and possible mutations on the base model.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Define Base Model\n\nDefining a base model is almost the same as defining a PyTorch (or TensorFlow) model.\nUsually, you only need to replace the code ``import torch.nn as nn`` with\n``import nni.retiarii.nn.pytorch as nn`` to use our wrapped PyTorch modules.\n\nBelow is a very simple example of defining a base model.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import torch\nimport torch.nn.functional as F\nimport nni.retiarii.nn.pytorch as nn\nfrom nni.retiarii import model_wrapper\n\n\n@model_wrapper      # this decorator should be put on the out most\nclass Net(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.conv1 = nn.Conv2d(1, 32, 3, 1)\n        self.conv2 = nn.Conv2d(32, 64, 3, 1)\n        self.dropout1 = nn.Dropout(0.25)\n        self.dropout2 = nn.Dropout(0.5)\n        self.fc1 = nn.Linear(9216, 128)\n        self.fc2 = nn.Linear(128, 10)\n\n    def forward(self, x):\n        x = F.relu(self.conv1(x))\n        x = F.max_pool2d(self.conv2(x), 2)\n        x = torch.flatten(self.dropout1(x), 1)\n        x = self.fc2(self.dropout2(F.relu(self.fc1(x))))\n        output = F.log_softmax(x, dim=1)\n        return output"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        ".. tip:: Always keep in mind that you should use ``import nni.retiarii.nn.pytorch as nn`` and :meth:`nni.retiarii.model_wrapper`.\n         Many mistakes are a result of forgetting one of those.\n         Also, please use ``torch.nn`` for submodules of ``nn.init``, e.g., ``torch.nn.init`` instead of ``nn.init``.\n\n### Define Model Mutations\n\nA base model is only one concrete model not a model space. We provide :doc:`API and Primitives </nas/construct_space>`\nfor users to express how the base model can be mutated. That is, to build a model space which includes many models.\n\nBased on the above base model, we can define a model space as below.\n\n.. code-block:: diff\n\n  @model_wrapper\n  class Net(nn.Module):\n    def __init__(self):\n      super().__init__()\n      self.conv1 = nn.Conv2d(1, 32, 3, 1)\n  -   self.conv2 = nn.Conv2d(32, 64, 3, 1)\n  +   self.conv2 = nn.LayerChoice([\n  +       nn.Conv2d(32, 64, 3, 1),\n  +       DepthwiseSeparableConv(32, 64)\n  +   ])\n  -   self.dropout1 = nn.Dropout(0.25)\n  +   self.dropout1 = nn.Dropout(nn.ValueChoice([0.25, 0.5, 0.75]))\n      self.dropout2 = nn.Dropout(0.5)\n  -   self.fc1 = nn.Linear(9216, 128)\n  -   self.fc2 = nn.Linear(128, 10)\n  +   feature = nn.ValueChoice([64, 128, 256])\n  +   self.fc1 = nn.Linear(9216, feature)\n  +   self.fc2 = nn.Linear(feature, 10)\n\n    def forward(self, x):\n      x = F.relu(self.conv1(x))\n      x = F.max_pool2d(self.conv2(x), 2)\n      x = torch.flatten(self.dropout1(x), 1)\n      x = self.fc2(self.dropout2(F.relu(self.fc1(x))))\n      output = F.log_softmax(x, dim=1)\n      return output\n\nThis results in the following code:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "class DepthwiseSeparableConv(nn.Module):\n    def __init__(self, in_ch, out_ch):\n        super().__init__()\n        self.depthwise = nn.Conv2d(in_ch, in_ch, kernel_size=3, groups=in_ch)\n        self.pointwise = nn.Conv2d(in_ch, out_ch, kernel_size=1)\n\n    def forward(self, x):\n        return self.pointwise(self.depthwise(x))\n\n\n@model_wrapper\nclass ModelSpace(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.conv1 = nn.Conv2d(1, 32, 3, 1)\n        # LayerChoice is used to select a layer between Conv2d and DwConv.\n        self.conv2 = nn.LayerChoice([\n            nn.Conv2d(32, 64, 3, 1),\n            DepthwiseSeparableConv(32, 64)\n        ])\n        # ValueChoice is used to select a dropout rate.\n        # ValueChoice can be used as parameter of modules wrapped in `nni.retiarii.nn.pytorch`\n        # or customized modules wrapped with `@basic_unit`.\n        self.dropout1 = nn.Dropout(nn.ValueChoice([0.25, 0.5, 0.75]))  # choose dropout rate from 0.25, 0.5 and 0.75\n        self.dropout2 = nn.Dropout(0.5)\n        feature = nn.ValueChoice([64, 128, 256])\n        self.fc1 = nn.Linear(9216, feature)\n        self.fc2 = nn.Linear(feature, 10)\n\n    def forward(self, x):\n        x = F.relu(self.conv1(x))\n        x = F.max_pool2d(self.conv2(x), 2)\n        x = torch.flatten(self.dropout1(x), 1)\n        x = self.fc2(self.dropout2(F.relu(self.fc1(x))))\n        output = F.log_softmax(x, dim=1)\n        return output\n\n\nmodel_space = ModelSpace()\nmodel_space"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This example uses two mutation APIs,\n:class:`nn.LayerChoice <nni.retiarii.nn.pytorch.LayerChoice>` and\n:class:`nn.InputChoice <nni.retiarii.nn.pytorch.ValueChoice>`.\n:class:`nn.LayerChoice <nni.retiarii.nn.pytorch.LayerChoice>`\ntakes a list of candidate modules (two in this example), one will be chosen for each sampled model.\nIt can be used like normal PyTorch module.\n:class:`nn.InputChoice <nni.retiarii.nn.pytorch.ValueChoice>` takes a list of candidate values,\none will be chosen to take effect for each sampled model.\n\nMore detailed API description and usage can be found :doc:`here </nas/construct_space>`.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>We are actively enriching the mutation APIs, to facilitate easy construction of model space.\n    If the currently supported mutation APIs cannot express your model space,\n    please refer to :doc:`this doc </nas/mutator>` for customizing mutators.</p></div>\n\n## Explore the Defined Model Space\n\nThere are basically two exploration approaches: (1) search by evaluating each sampled model independently,\nwhich is the search approach in `multi-trial NAS <multi-trial-nas>`\nand (2) one-shot weight-sharing based search, which is used in one-shot NAS.\nWe demonstrate the first approach in this tutorial. Users can refer to `here <one-shot-nas>` for the second approach.\n\nFirst, users need to pick a proper exploration strategy to explore the defined model space.\nSecond, users need to pick or customize a model evaluator to evaluate the performance of each explored model.\n\n### Pick an exploration strategy\n\nRetiarii supports many :doc:`exploration strategies </nas/exploration_strategy>`.\n\nSimply choosing (i.e., instantiate) an exploration strategy as below.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import nni.retiarii.strategy as strategy\nsearch_strategy = strategy.Random(dedup=True)  # dedup=False if deduplication is not wanted"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Pick or customize a model evaluator\n\nIn the exploration process, the exploration strategy repeatedly generates new models. A model evaluator is for training\nand validating each generated model to obtain the model's performance.\nThe performance is sent to the exploration strategy for the strategy to generate better models.\n\nRetiarii has provided :doc:`built-in model evaluators </nas/evaluator>`, but to start with,\nit is recommended to use :class:`FunctionalEvaluator <nni.retiarii.evaluator.FunctionalEvaluator>`,\nthat is, to wrap your own training and evaluation code with one single function.\nThis function should receive one single model class and uses :func:`nni.report_final_result` to report the final score of this model.\n\nAn example here creates a simple evaluator that runs on MNIST dataset, trains for 2 epochs, and reports its validation accuracy.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import nni\n\nfrom torchvision import transforms\nfrom torchvision.datasets import MNIST\nfrom torch.utils.data import DataLoader\n\n\ndef train_epoch(model, device, train_loader, optimizer, epoch):\n    loss_fn = torch.nn.CrossEntropyLoss()\n    model.train()\n    for batch_idx, (data, target) in enumerate(train_loader):\n        data, target = data.to(device), target.to(device)\n        optimizer.zero_grad()\n        output = model(data)\n        loss = loss_fn(output, target)\n        loss.backward()\n        optimizer.step()\n        if batch_idx % 10 == 0:\n            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n                epoch, batch_idx * len(data), len(train_loader.dataset),\n                100. * batch_idx / len(train_loader), loss.item()))\n\n\ndef test_epoch(model, device, test_loader):\n    model.eval()\n    test_loss = 0\n    correct = 0\n    with torch.no_grad():\n        for data, target in test_loader:\n            data, target = data.to(device), target.to(device)\n            output = model(data)\n            pred = output.argmax(dim=1, keepdim=True)\n            correct += pred.eq(target.view_as(pred)).sum().item()\n\n    test_loss /= len(test_loader.dataset)\n    accuracy = 100. * correct / len(test_loader.dataset)\n\n    print('\\nTest set: Accuracy: {}/{} ({:.0f}%)\\n'.format(\n          correct, len(test_loader.dataset), accuracy))\n\n    return accuracy\n\n\ndef evaluate_model(model_cls):\n    # \"model_cls\" is a class, need to instantiate\n    model = model_cls()\n\n    device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n    model.to(device)\n\n    optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n    transf = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])\n    train_loader = DataLoader(MNIST('data/mnist', download=True, transform=transf), batch_size=64, shuffle=True)\n    test_loader = DataLoader(MNIST('data/mnist', download=True, train=False, transform=transf), batch_size=64)\n\n    for epoch in range(3):\n        # train the model for one epoch\n        train_epoch(model, device, train_loader, optimizer, epoch)\n        # test the model for one epoch\n        accuracy = test_epoch(model, device, test_loader)\n        # call report intermediate result. Result can be float or dict\n        nni.report_intermediate_result(accuracy)\n\n    # report final test result\n    nni.report_final_result(accuracy)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Create the evaluator\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from nni.retiarii.evaluator import FunctionalEvaluator\nevaluator = FunctionalEvaluator(evaluate_model)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The ``train_epoch`` and ``test_epoch`` here can be any customized function,\nwhere users can write their own training recipe.\n\nIt is recommended that the ``evaluate_model`` here accepts no additional arguments other than ``model_cls``.\nHowever, in the :doc:`advanced tutorial </nas/evaluator>`, we will show how to use additional arguments in case you actually need those.\nIn future, we will support mutation on the arguments of evaluators, which is commonly called \"Hyper-parmeter tuning\".\n\n## Launch an Experiment\n\nAfter all the above are prepared, it is time to start an experiment to do the model search. An example is shown below.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from nni.retiarii.experiment.pytorch import RetiariiExperiment, RetiariiExeConfig\nexp = RetiariiExperiment(model_space, evaluator, [], search_strategy)\nexp_config = RetiariiExeConfig('local')\nexp_config.experiment_name = 'mnist_search'"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The following configurations are useful to control how many trials to run at most / at the same time.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "exp_config.max_trial_number = 4   # spawn 4 trials at most\nexp_config.trial_concurrency = 2  # will run two trials concurrently"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Remember to set the following config if you want to GPU.\n``use_active_gpu`` should be set true if you wish to use an occupied GPU (possibly running a GUI).\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "exp_config.trial_gpu_number = 1\nexp_config.training_service.use_active_gpu = True"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Launch the experiment. The experiment should take several minutes to finish on a workstation with 2 GPUs.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "exp.run(exp_config, 8081)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Users can also run Retiarii Experiment with :doc:`different training services </experiment/training_service/overview>`\nbesides ``local`` training service.\n\n## Visualize the Experiment\n\nUsers can visualize their experiment in the same way as visualizing a normal hyper-parameter tuning experiment.\nFor example, open ``localhost:8081`` in your browser, 8081 is the port that you set in ``exp.run``.\nPlease refer to :doc:`here </experiment/web_portal/web_portal>` for details.\n\nWe support visualizing models with 3rd-party visualization engines (like `Netron <https://netron.app/>`__).\nThis can be used by clicking ``Visualization`` in detail panel for each trial.\nNote that current visualization is based on `onnx <https://onnx.ai/>`__ ,\nthus visualization is not feasible if the model cannot be exported into onnx.\n\nBuilt-in evaluators (e.g., Classification) will automatically export the model into a file.\nFor your own evaluator, you need to save your file into ``$NNI_OUTPUT_DIR/model.onnx`` to make this work.\nFor instance,\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import os\nfrom pathlib import Path\n\n\ndef evaluate_model_with_visualization(model_cls):\n    model = model_cls()\n    # dump the model into an onnx\n    if 'NNI_OUTPUT_DIR' in os.environ:\n        dummy_input = torch.zeros(1, 3, 32, 32)\n        torch.onnx.export(model, (dummy_input, ),\n                          Path(os.environ['NNI_OUTPUT_DIR']) / 'model.onnx')\n    evaluate_model(model_cls)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Relaunch the experiment, and a button is shown on Web portal.\n\n<img src=\"file://../../img/netron_entrance_webui.png\">\n\n## Export Top Models\n\nUsers can export top models after the exploration is done using ``export_top_models``.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "for model_dict in exp.export_top_models(formatter='dict'):\n    print(model_dict)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The output is ``json`` object which records the mutation actions of the top model.\nIf users want to output source code of the top model,\nthey can use `graph-based execution engine <graph-based-execution-engine>` for the experiment,\nby simply adding the following two lines.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "exp_config.execution_engine = 'base'\nexport_formatter = 'code'"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.8"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/source/tutorials/hello_nas.py b/docs/source/tutorials/hello_nas.py
new file mode 100644
index 000000000..ca0b580aa
--- /dev/null
+++ b/docs/source/tutorials/hello_nas.py
@@ -0,0 +1,364 @@
+"""
+Hello, NAS!
+===========
+
+This is the 101 tutorial of Neural Architecture Search (NAS) on NNI.
+In this tutorial, we will search for a neural architecture on MNIST dataset with the help of NAS framework of NNI, i.e., *Retiarii*.
+We use multi-trial NAS as an example to show how to construct and explore a model space.
+
+There are mainly three crucial components for a neural architecture search task, namely,
+
+* Model search space that defines a set of models to explore.
+* A proper strategy as the method to explore this model space.
+* A model evaluator that reports the performance of every model in the space.
+
+Currently, PyTorch is the only supported framework by Retiarii, and we have only tested **PyTorch 1.7 to 1.10**.
+This tutorial assumes PyTorch context but it should also apply to other frameworks, which is in our future plan.
+
+Define your Model Space
+-----------------------
+
+Model space is defined by users to express a set of models that users want to explore, which contains potentially good-performing models.
+In this framework, a model space is defined with two parts: a base model and possible mutations on the base model.
+"""
+
+# %%
+#
+# Define Base Model
+# ^^^^^^^^^^^^^^^^^
+#
+# Defining a base model is almost the same as defining a PyTorch (or TensorFlow) model.
+# Usually, you only need to replace the code ``import torch.nn as nn`` with
+# ``import nni.retiarii.nn.pytorch as nn`` to use our wrapped PyTorch modules.
+#
+# Below is a very simple example of defining a base model.
+
+import torch
+import torch.nn.functional as F
+import nni.retiarii.nn.pytorch as nn
+from nni.retiarii import model_wrapper
+
+
+@model_wrapper      # this decorator should be put on the out most
+class Net(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv1 = nn.Conv2d(1, 32, 3, 1)
+        self.conv2 = nn.Conv2d(32, 64, 3, 1)
+        self.dropout1 = nn.Dropout(0.25)
+        self.dropout2 = nn.Dropout(0.5)
+        self.fc1 = nn.Linear(9216, 128)
+        self.fc2 = nn.Linear(128, 10)
+
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = F.max_pool2d(self.conv2(x), 2)
+        x = torch.flatten(self.dropout1(x), 1)
+        x = self.fc2(self.dropout2(F.relu(self.fc1(x))))
+        output = F.log_softmax(x, dim=1)
+        return output
+
+# %%
+# .. tip:: Always keep in mind that you should use ``import nni.retiarii.nn.pytorch as nn`` and :meth:`nni.retiarii.model_wrapper`.
+#          Many mistakes are a result of forgetting one of those.
+#          Also, please use ``torch.nn`` for submodules of ``nn.init``, e.g., ``torch.nn.init`` instead of ``nn.init``.
+#
+# Define Model Mutations
+# ^^^^^^^^^^^^^^^^^^^^^^
+#
+# A base model is only one concrete model not a model space. We provide :doc:`API and Primitives </nas/construct_space>`
+# for users to express how the base model can be mutated. That is, to build a model space which includes many models.
+#
+# Based on the above base model, we can define a model space as below.
+#
+# .. code-block:: diff
+#
+#   @model_wrapper
+#   class Net(nn.Module):
+#     def __init__(self):
+#       super().__init__()
+#       self.conv1 = nn.Conv2d(1, 32, 3, 1)
+#   -   self.conv2 = nn.Conv2d(32, 64, 3, 1)
+#   +   self.conv2 = nn.LayerChoice([
+#   +       nn.Conv2d(32, 64, 3, 1),
+#   +       DepthwiseSeparableConv(32, 64)
+#   +   ])
+#   -   self.dropout1 = nn.Dropout(0.25)
+#   +   self.dropout1 = nn.Dropout(nn.ValueChoice([0.25, 0.5, 0.75]))
+#       self.dropout2 = nn.Dropout(0.5)
+#   -   self.fc1 = nn.Linear(9216, 128)
+#   -   self.fc2 = nn.Linear(128, 10)
+#   +   feature = nn.ValueChoice([64, 128, 256])
+#   +   self.fc1 = nn.Linear(9216, feature)
+#   +   self.fc2 = nn.Linear(feature, 10)
+#
+#     def forward(self, x):
+#       x = F.relu(self.conv1(x))
+#       x = F.max_pool2d(self.conv2(x), 2)
+#       x = torch.flatten(self.dropout1(x), 1)
+#       x = self.fc2(self.dropout2(F.relu(self.fc1(x))))
+#       output = F.log_softmax(x, dim=1)
+#       return output
+#
+# This results in the following code:
+
+
+class DepthwiseSeparableConv(nn.Module):
+    def __init__(self, in_ch, out_ch):
+        super().__init__()
+        self.depthwise = nn.Conv2d(in_ch, in_ch, kernel_size=3, groups=in_ch)
+        self.pointwise = nn.Conv2d(in_ch, out_ch, kernel_size=1)
+
+    def forward(self, x):
+        return self.pointwise(self.depthwise(x))
+
+
+@model_wrapper
+class ModelSpace(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv1 = nn.Conv2d(1, 32, 3, 1)
+        # LayerChoice is used to select a layer between Conv2d and DwConv.
+        self.conv2 = nn.LayerChoice([
+            nn.Conv2d(32, 64, 3, 1),
+            DepthwiseSeparableConv(32, 64)
+        ])
+        # ValueChoice is used to select a dropout rate.
+        # ValueChoice can be used as parameter of modules wrapped in `nni.retiarii.nn.pytorch`
+        # or customized modules wrapped with `@basic_unit`.
+        self.dropout1 = nn.Dropout(nn.ValueChoice([0.25, 0.5, 0.75]))  # choose dropout rate from 0.25, 0.5 and 0.75
+        self.dropout2 = nn.Dropout(0.5)
+        feature = nn.ValueChoice([64, 128, 256])
+        self.fc1 = nn.Linear(9216, feature)
+        self.fc2 = nn.Linear(feature, 10)
+
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = F.max_pool2d(self.conv2(x), 2)
+        x = torch.flatten(self.dropout1(x), 1)
+        x = self.fc2(self.dropout2(F.relu(self.fc1(x))))
+        output = F.log_softmax(x, dim=1)
+        return output
+
+
+model_space = ModelSpace()
+model_space
+
+# %%
+# This example uses two mutation APIs,
+# :class:`nn.LayerChoice <nni.retiarii.nn.pytorch.LayerChoice>` and
+# :class:`nn.InputChoice <nni.retiarii.nn.pytorch.ValueChoice>`.
+# :class:`nn.LayerChoice <nni.retiarii.nn.pytorch.LayerChoice>`
+# takes a list of candidate modules (two in this example), one will be chosen for each sampled model.
+# It can be used like normal PyTorch module.
+# :class:`nn.InputChoice <nni.retiarii.nn.pytorch.ValueChoice>` takes a list of candidate values,
+# one will be chosen to take effect for each sampled model.
+#
+# More detailed API description and usage can be found :doc:`here </nas/construct_space>`.
+#
+# .. note::
+#
+#     We are actively enriching the mutation APIs, to facilitate easy construction of model space.
+#     If the currently supported mutation APIs cannot express your model space,
+#     please refer to :doc:`this doc </nas/mutator>` for customizing mutators.
+#
+# Explore the Defined Model Space
+# -------------------------------
+#
+# There are basically two exploration approaches: (1) search by evaluating each sampled model independently,
+# which is the search approach in :ref:`multi-trial NAS <multi-trial-nas>`
+# and (2) one-shot weight-sharing based search, which is used in one-shot NAS.
+# We demonstrate the first approach in this tutorial. Users can refer to :ref:`here <one-shot-nas>` for the second approach.
+#
+# First, users need to pick a proper exploration strategy to explore the defined model space.
+# Second, users need to pick or customize a model evaluator to evaluate the performance of each explored model.
+#
+# Pick an exploration strategy
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# Retiarii supports many :doc:`exploration strategies </nas/exploration_strategy>`.
+#
+# Simply choosing (i.e., instantiate) an exploration strategy as below.
+
+import nni.retiarii.strategy as strategy
+search_strategy = strategy.Random(dedup=True)  # dedup=False if deduplication is not wanted
+
+# %%
+# Pick or customize a model evaluator
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# In the exploration process, the exploration strategy repeatedly generates new models. A model evaluator is for training
+# and validating each generated model to obtain the model's performance.
+# The performance is sent to the exploration strategy for the strategy to generate better models.
+#
+# Retiarii has provided :doc:`built-in model evaluators </nas/evaluator>`, but to start with,
+# it is recommended to use :class:`FunctionalEvaluator <nni.retiarii.evaluator.FunctionalEvaluator>`,
+# that is, to wrap your own training and evaluation code with one single function.
+# This function should receive one single model class and uses :func:`nni.report_final_result` to report the final score of this model.
+#
+# An example here creates a simple evaluator that runs on MNIST dataset, trains for 2 epochs, and reports its validation accuracy.
+
+import nni
+
+from torchvision import transforms
+from torchvision.datasets import MNIST
+from torch.utils.data import DataLoader
+
+
+def train_epoch(model, device, train_loader, optimizer, epoch):
+    loss_fn = torch.nn.CrossEntropyLoss()
+    model.train()
+    for batch_idx, (data, target) in enumerate(train_loader):
+        data, target = data.to(device), target.to(device)
+        optimizer.zero_grad()
+        output = model(data)
+        loss = loss_fn(output, target)
+        loss.backward()
+        optimizer.step()
+        if batch_idx % 10 == 0:
+            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+                epoch, batch_idx * len(data), len(train_loader.dataset),
+                100. * batch_idx / len(train_loader), loss.item()))
+
+
+def test_epoch(model, device, test_loader):
+    model.eval()
+    test_loss = 0
+    correct = 0
+    with torch.no_grad():
+        for data, target in test_loader:
+            data, target = data.to(device), target.to(device)
+            output = model(data)
+            pred = output.argmax(dim=1, keepdim=True)
+            correct += pred.eq(target.view_as(pred)).sum().item()
+
+    test_loss /= len(test_loader.dataset)
+    accuracy = 100. * correct / len(test_loader.dataset)
+
+    print('\nTest set: Accuracy: {}/{} ({:.0f}%)\n'.format(
+          correct, len(test_loader.dataset), accuracy))
+
+    return accuracy
+
+
+def evaluate_model(model_cls):
+    # "model_cls" is a class, need to instantiate
+    model = model_cls()
+
+    device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+    model.to(device)
+
+    optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
+    transf = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
+    train_loader = DataLoader(MNIST('data/mnist', download=True, transform=transf), batch_size=64, shuffle=True)
+    test_loader = DataLoader(MNIST('data/mnist', download=True, train=False, transform=transf), batch_size=64)
+
+    for epoch in range(3):
+        # train the model for one epoch
+        train_epoch(model, device, train_loader, optimizer, epoch)
+        # test the model for one epoch
+        accuracy = test_epoch(model, device, test_loader)
+        # call report intermediate result. Result can be float or dict
+        nni.report_intermediate_result(accuracy)
+
+    # report final test result
+    nni.report_final_result(accuracy)
+
+
+# %%
+# Create the evaluator
+
+from nni.retiarii.evaluator import FunctionalEvaluator
+evaluator = FunctionalEvaluator(evaluate_model)
+
+# %%
+#
+# The ``train_epoch`` and ``test_epoch`` here can be any customized function,
+# where users can write their own training recipe.
+#
+# It is recommended that the ``evaluate_model`` here accepts no additional arguments other than ``model_cls``.
+# However, in the :doc:`advanced tutorial </nas/evaluator>`, we will show how to use additional arguments in case you actually need those.
+# In future, we will support mutation on the arguments of evaluators, which is commonly called "Hyper-parmeter tuning".
+#
+# Launch an Experiment
+# --------------------
+#
+# After all the above are prepared, it is time to start an experiment to do the model search. An example is shown below.
+
+from nni.retiarii.experiment.pytorch import RetiariiExperiment, RetiariiExeConfig
+exp = RetiariiExperiment(model_space, evaluator, [], search_strategy)
+exp_config = RetiariiExeConfig('local')
+exp_config.experiment_name = 'mnist_search'
+
+# %%
+# The following configurations are useful to control how many trials to run at most / at the same time.
+
+exp_config.max_trial_number = 4   # spawn 4 trials at most
+exp_config.trial_concurrency = 2  # will run two trials concurrently
+
+# %%
+# Remember to set the following config if you want to GPU.
+# ``use_active_gpu`` should be set true if you wish to use an occupied GPU (possibly running a GUI).
+
+exp_config.trial_gpu_number = 1
+exp_config.training_service.use_active_gpu = True
+
+# %%
+# Launch the experiment. The experiment should take several minutes to finish on a workstation with 2 GPUs.
+
+exp.run(exp_config, 8081)
+
+# %%
+# Users can also run Retiarii Experiment with :doc:`different training services </experiment/training_service/overview>`
+# besides ``local`` training service.
+#
+# Visualize the Experiment
+# ------------------------
+#
+# Users can visualize their experiment in the same way as visualizing a normal hyper-parameter tuning experiment.
+# For example, open ``localhost:8081`` in your browser, 8081 is the port that you set in ``exp.run``.
+# Please refer to :doc:`here </experiment/web_portal/web_portal>` for details.
+#
+# We support visualizing models with 3rd-party visualization engines (like `Netron <https://netron.app/>`__).
+# This can be used by clicking ``Visualization`` in detail panel for each trial.
+# Note that current visualization is based on `onnx <https://onnx.ai/>`__ ,
+# thus visualization is not feasible if the model cannot be exported into onnx.
+#
+# Built-in evaluators (e.g., Classification) will automatically export the model into a file.
+# For your own evaluator, you need to save your file into ``$NNI_OUTPUT_DIR/model.onnx`` to make this work.
+# For instance,
+
+import os
+from pathlib import Path
+
+
+def evaluate_model_with_visualization(model_cls):
+    model = model_cls()
+    # dump the model into an onnx
+    if 'NNI_OUTPUT_DIR' in os.environ:
+        dummy_input = torch.zeros(1, 3, 32, 32)
+        torch.onnx.export(model, (dummy_input, ),
+                          Path(os.environ['NNI_OUTPUT_DIR']) / 'model.onnx')
+    evaluate_model(model_cls)
+
+# %%
+# Relaunch the experiment, and a button is shown on Web portal.
+#
+# .. image:: ../../img/netron_entrance_webui.png
+#
+# Export Top Models
+# -----------------
+#
+# Users can export top models after the exploration is done using ``export_top_models``.
+
+for model_dict in exp.export_top_models(formatter='dict'):
+    print(model_dict)
+
+# %%
+# The output is ``json`` object which records the mutation actions of the top model.
+# If users want to output source code of the top model,
+# they can use :ref:`graph-based execution engine <graph-based-execution-engine>` for the experiment,
+# by simply adding the following two lines.
+
+exp_config.execution_engine = 'base'
+export_formatter = 'code'
diff --git a/docs/source/tutorials/hello_nas.py.md5 b/docs/source/tutorials/hello_nas.py.md5
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+0e49e3aef98633744807b814786f6b31
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diff --git a/docs/source/tutorials/hello_nas.rst b/docs/source/tutorials/hello_nas.rst
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+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "tutorials/hello_nas.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        Click :ref:`here <sphx_glr_download_tutorials_hello_nas.py>`
+        to download the full example code
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorials_hello_nas.py:
+
+
+Hello, NAS!
+===========
+
+This is the 101 tutorial of Neural Architecture Search (NAS) on NNI.
+In this tutorial, we will search for a neural architecture on MNIST dataset with the help of NAS framework of NNI, i.e., *Retiarii*.
+We use multi-trial NAS as an example to show how to construct and explore a model space.
+
+There are mainly three crucial components for a neural architecture search task, namely,
+
+* Model search space that defines a set of models to explore.
+* A proper strategy as the method to explore this model space.
+* A model evaluator that reports the performance of every model in the space.
+
+Currently, PyTorch is the only supported framework by Retiarii, and we have only tested **PyTorch 1.7 to 1.10**.
+This tutorial assumes PyTorch context but it should also apply to other frameworks, which is in our future plan.
+
+Define your Model Space
+-----------------------
+
+Model space is defined by users to express a set of models that users want to explore, which contains potentially good-performing models.
+In this framework, a model space is defined with two parts: a base model and possible mutations on the base model.
+
+.. GENERATED FROM PYTHON SOURCE LINES 26-34
+
+Define Base Model
+^^^^^^^^^^^^^^^^^
+
+Defining a base model is almost the same as defining a PyTorch (or TensorFlow) model.
+Usually, you only need to replace the code ``import torch.nn as nn`` with
+``import nni.retiarii.nn.pytorch as nn`` to use our wrapped PyTorch modules.
+
+Below is a very simple example of defining a base model.
+
+.. GENERATED FROM PYTHON SOURCE LINES 35-61
+
+.. code-block:: default
+
+
+    import torch
+    import torch.nn.functional as F
+    import nni.retiarii.nn.pytorch as nn
+    from nni.retiarii import model_wrapper
+
+
+    @model_wrapper      # this decorator should be put on the out most
+    class Net(nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.conv1 = nn.Conv2d(1, 32, 3, 1)
+            self.conv2 = nn.Conv2d(32, 64, 3, 1)
+            self.dropout1 = nn.Dropout(0.25)
+            self.dropout2 = nn.Dropout(0.5)
+            self.fc1 = nn.Linear(9216, 128)
+            self.fc2 = nn.Linear(128, 10)
+
+        def forward(self, x):
+            x = F.relu(self.conv1(x))
+            x = F.max_pool2d(self.conv2(x), 2)
+            x = torch.flatten(self.dropout1(x), 1)
+            x = self.fc2(self.dropout2(F.relu(self.fc1(x))))
+            output = F.log_softmax(x, dim=1)
+            return output
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 62-104
+
+.. tip:: Always keep in mind that you should use ``import nni.retiarii.nn.pytorch as nn`` and :meth:`nni.retiarii.model_wrapper`.
+         Many mistakes are a result of forgetting one of those.
+         Also, please use ``torch.nn`` for submodules of ``nn.init``, e.g., ``torch.nn.init`` instead of ``nn.init``.
+
+Define Model Mutations
+^^^^^^^^^^^^^^^^^^^^^^
+
+A base model is only one concrete model not a model space. We provide :doc:`API and Primitives </nas/construct_space>`
+for users to express how the base model can be mutated. That is, to build a model space which includes many models.
+
+Based on the above base model, we can define a model space as below.
+
+.. code-block:: diff
+
+  @model_wrapper
+  class Net(nn.Module):
+    def __init__(self):
+      super().__init__()
+      self.conv1 = nn.Conv2d(1, 32, 3, 1)
+  -   self.conv2 = nn.Conv2d(32, 64, 3, 1)
+  +   self.conv2 = nn.LayerChoice([
+  +       nn.Conv2d(32, 64, 3, 1),
+  +       DepthwiseSeparableConv(32, 64)
+  +   ])
+  -   self.dropout1 = nn.Dropout(0.25)
+  +   self.dropout1 = nn.Dropout(nn.ValueChoice([0.25, 0.5, 0.75]))
+      self.dropout2 = nn.Dropout(0.5)
+  -   self.fc1 = nn.Linear(9216, 128)
+  -   self.fc2 = nn.Linear(128, 10)
+  +   feature = nn.ValueChoice([64, 128, 256])
+  +   self.fc1 = nn.Linear(9216, feature)
+  +   self.fc2 = nn.Linear(feature, 10)
+
+    def forward(self, x):
+      x = F.relu(self.conv1(x))
+      x = F.max_pool2d(self.conv2(x), 2)
+      x = torch.flatten(self.dropout1(x), 1)
+      x = self.fc2(self.dropout2(F.relu(self.fc1(x))))
+      output = F.log_softmax(x, dim=1)
+      return output
+
+This results in the following code:
+
+.. GENERATED FROM PYTHON SOURCE LINES 104-147
+
+.. code-block:: default
+
+
+
+    class DepthwiseSeparableConv(nn.Module):
+        def __init__(self, in_ch, out_ch):
+            super().__init__()
+            self.depthwise = nn.Conv2d(in_ch, in_ch, kernel_size=3, groups=in_ch)
+            self.pointwise = nn.Conv2d(in_ch, out_ch, kernel_size=1)
+
+        def forward(self, x):
+            return self.pointwise(self.depthwise(x))
+
+
+    @model_wrapper
+    class ModelSpace(nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.conv1 = nn.Conv2d(1, 32, 3, 1)
+            # LayerChoice is used to select a layer between Conv2d and DwConv.
+            self.conv2 = nn.LayerChoice([
+                nn.Conv2d(32, 64, 3, 1),
+                DepthwiseSeparableConv(32, 64)
+            ])
+            # ValueChoice is used to select a dropout rate.
+            # ValueChoice can be used as parameter of modules wrapped in `nni.retiarii.nn.pytorch`
+            # or customized modules wrapped with `@basic_unit`.
+            self.dropout1 = nn.Dropout(nn.ValueChoice([0.25, 0.5, 0.75]))  # choose dropout rate from 0.25, 0.5 and 0.75
+            self.dropout2 = nn.Dropout(0.5)
+            feature = nn.ValueChoice([64, 128, 256])
+            self.fc1 = nn.Linear(9216, feature)
+            self.fc2 = nn.Linear(feature, 10)
+
+        def forward(self, x):
+            x = F.relu(self.conv1(x))
+            x = F.max_pool2d(self.conv2(x), 2)
+            x = torch.flatten(self.dropout1(x), 1)
+            x = self.fc2(self.dropout2(F.relu(self.fc1(x))))
+            output = F.log_softmax(x, dim=1)
+            return output
+
+
+    model_space = ModelSpace()
+    model_space
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+
+    ModelSpace(
+      (conv1): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))
+      (conv2): LayerChoice([Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1)), DepthwiseSeparableConv(
+        (depthwise): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), groups=32)
+        (pointwise): Conv2d(32, 64, kernel_size=(1, 1), stride=(1, 1))
+      )], label='model_1')
+      (dropout1): Dropout(p=0.25, inplace=False)
+      (dropout2): Dropout(p=0.5, inplace=False)
+      (fc1): Linear(in_features=9216, out_features=64, bias=True)
+      (fc2): Linear(in_features=64, out_features=10, bias=True)
+    )
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 148-182
+
+This example uses two mutation APIs,
+:class:`nn.LayerChoice <nni.retiarii.nn.pytorch.LayerChoice>` and
+:class:`nn.InputChoice <nni.retiarii.nn.pytorch.ValueChoice>`.
+:class:`nn.LayerChoice <nni.retiarii.nn.pytorch.LayerChoice>`
+takes a list of candidate modules (two in this example), one will be chosen for each sampled model.
+It can be used like normal PyTorch module.
+:class:`nn.InputChoice <nni.retiarii.nn.pytorch.ValueChoice>` takes a list of candidate values,
+one will be chosen to take effect for each sampled model.
+
+More detailed API description and usage can be found :doc:`here </nas/construct_space>`.
+
+.. note::
+
+    We are actively enriching the mutation APIs, to facilitate easy construction of model space.
+    If the currently supported mutation APIs cannot express your model space,
+    please refer to :doc:`this doc </nas/mutator>` for customizing mutators.
+
+Explore the Defined Model Space
+-------------------------------
+
+There are basically two exploration approaches: (1) search by evaluating each sampled model independently,
+which is the search approach in :ref:`multi-trial NAS <multi-trial-nas>`
+and (2) one-shot weight-sharing based search, which is used in one-shot NAS.
+We demonstrate the first approach in this tutorial. Users can refer to :ref:`here <one-shot-nas>` for the second approach.
+
+First, users need to pick a proper exploration strategy to explore the defined model space.
+Second, users need to pick or customize a model evaluator to evaluate the performance of each explored model.
+
+Pick an exploration strategy
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Retiarii supports many :doc:`exploration strategies </nas/exploration_strategy>`.
+
+Simply choosing (i.e., instantiate) an exploration strategy as below.
+
+.. GENERATED FROM PYTHON SOURCE LINES 182-186
+
+.. code-block:: default
+
+
+    import nni.retiarii.strategy as strategy
+    search_strategy = strategy.Random(dedup=True)  # dedup=False if deduplication is not wanted
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+
+    /home/yugzhan/miniconda3/envs/cu102/lib/python3.8/site-packages/ray/autoscaler/_private/cli_logger.py:57: FutureWarning: Not all Ray CLI dependencies were found. In Ray 1.4+, the Ray CLI, autoscaler, and dashboard will only be usable via `pip install 'ray[default]'`. Please update your install command.
+      warnings.warn(
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 187-200
+
+Pick or customize a model evaluator
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+In the exploration process, the exploration strategy repeatedly generates new models. A model evaluator is for training
+and validating each generated model to obtain the model's performance.
+The performance is sent to the exploration strategy for the strategy to generate better models.
+
+Retiarii has provided :doc:`built-in model evaluators </nas/evaluator>`, but to start with,
+it is recommended to use :class:`FunctionalEvaluator <nni.retiarii.evaluator.FunctionalEvaluator>`,
+that is, to wrap your own training and evaluation code with one single function.
+This function should receive one single model class and uses :func:`nni.report_final_result` to report the final score of this model.
+
+An example here creates a simple evaluator that runs on MNIST dataset, trains for 2 epochs, and reports its validation accuracy.
+
+.. GENERATED FROM PYTHON SOURCE LINES 200-268
+
+.. code-block:: default
+
+
+    import nni
+
+    from torchvision import transforms
+    from torchvision.datasets import MNIST
+    from torch.utils.data import DataLoader
+
+
+    def train_epoch(model, device, train_loader, optimizer, epoch):
+        loss_fn = torch.nn.CrossEntropyLoss()
+        model.train()
+        for batch_idx, (data, target) in enumerate(train_loader):
+            data, target = data.to(device), target.to(device)
+            optimizer.zero_grad()
+            output = model(data)
+            loss = loss_fn(output, target)
+            loss.backward()
+            optimizer.step()
+            if batch_idx % 10 == 0:
+                print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+                    epoch, batch_idx * len(data), len(train_loader.dataset),
+                    100. * batch_idx / len(train_loader), loss.item()))
+
+
+    def test_epoch(model, device, test_loader):
+        model.eval()
+        test_loss = 0
+        correct = 0
+        with torch.no_grad():
+            for data, target in test_loader:
+                data, target = data.to(device), target.to(device)
+                output = model(data)
+                pred = output.argmax(dim=1, keepdim=True)
+                correct += pred.eq(target.view_as(pred)).sum().item()
+
+        test_loss /= len(test_loader.dataset)
+        accuracy = 100. * correct / len(test_loader.dataset)
+
+        print('\nTest set: Accuracy: {}/{} ({:.0f}%)\n'.format(
+              correct, len(test_loader.dataset), accuracy))
+
+        return accuracy
+
+
+    def evaluate_model(model_cls):
+        # "model_cls" is a class, need to instantiate
+        model = model_cls()
+
+        device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+        model.to(device)
+
+        optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
+        transf = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
+        train_loader = DataLoader(MNIST('data/mnist', download=True, transform=transf), batch_size=64, shuffle=True)
+        test_loader = DataLoader(MNIST('data/mnist', download=True, train=False, transform=transf), batch_size=64)
+
+        for epoch in range(3):
+            # train the model for one epoch
+            train_epoch(model, device, train_loader, optimizer, epoch)
+            # test the model for one epoch
+            accuracy = test_epoch(model, device, test_loader)
+            # call report intermediate result. Result can be float or dict
+            nni.report_intermediate_result(accuracy)
+
+        # report final test result
+        nni.report_final_result(accuracy)
+
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 269-270
+
+Create the evaluator
+
+.. GENERATED FROM PYTHON SOURCE LINES 270-274
+
+.. code-block:: default
+
+
+    from nni.retiarii.evaluator import FunctionalEvaluator
+    evaluator = FunctionalEvaluator(evaluate_model)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 275-286
+
+The ``train_epoch`` and ``test_epoch`` here can be any customized function,
+where users can write their own training recipe.
+
+It is recommended that the ``evaluate_model`` here accepts no additional arguments other than ``model_cls``.
+However, in the :doc:`advanced tutorial </nas/evaluator>`, we will show how to use additional arguments in case you actually need those.
+In future, we will support mutation on the arguments of evaluators, which is commonly called "Hyper-parmeter tuning".
+
+Launch an Experiment
+--------------------
+
+After all the above are prepared, it is time to start an experiment to do the model search. An example is shown below.
+
+.. GENERATED FROM PYTHON SOURCE LINES 287-293
+
+.. code-block:: default
+
+
+    from nni.retiarii.experiment.pytorch import RetiariiExperiment, RetiariiExeConfig
+    exp = RetiariiExperiment(model_space, evaluator, [], search_strategy)
+    exp_config = RetiariiExeConfig('local')
+    exp_config.experiment_name = 'mnist_search'
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 294-295
+
+The following configurations are useful to control how many trials to run at most / at the same time.
+
+.. GENERATED FROM PYTHON SOURCE LINES 295-299
+
+.. code-block:: default
+
+
+    exp_config.max_trial_number = 4   # spawn 4 trials at most
+    exp_config.trial_concurrency = 2  # will run two trials concurrently
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 300-302
+
+Remember to set the following config if you want to GPU.
+``use_active_gpu`` should be set true if you wish to use an occupied GPU (possibly running a GUI).
+
+.. GENERATED FROM PYTHON SOURCE LINES 302-306
+
+.. code-block:: default
+
+
+    exp_config.trial_gpu_number = 1
+    exp_config.training_service.use_active_gpu = True
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 307-308
+
+Launch the experiment. The experiment should take several minutes to finish on a workstation with 2 GPUs.
+
+.. GENERATED FROM PYTHON SOURCE LINES 308-311
+
+.. code-block:: default
+
+
+    exp.run(exp_config, 8081)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    INFO:nni.experiment:Creating experiment, Experiment ID: z8ns5fv7
+    INFO:nni.experiment:Connecting IPC pipe...
+    INFO:nni.experiment:Starting web server...
+    INFO:nni.experiment:Setting up...
+    INFO:nni.runtime.msg_dispatcher_base:Dispatcher started
+    INFO:nni.retiarii.experiment.pytorch:Web UI URLs: http://127.0.0.1:8081 http://10.190.172.35:8081 http://192.168.49.1:8081 http://172.17.0.1:8081
+    INFO:nni.retiarii.experiment.pytorch:Start strategy...
+    INFO:root:Successfully update searchSpace.
+    INFO:nni.retiarii.strategy.bruteforce:Random search running in fixed size mode. Dedup: on.
+    INFO:nni.retiarii.experiment.pytorch:Stopping experiment, please wait...
+    INFO:nni.retiarii.experiment.pytorch:Strategy exit
+    INFO:nni.retiarii.experiment.pytorch:Waiting for experiment to become DONE (you can ctrl+c if there is no running trial jobs)...
+    INFO:nni.runtime.msg_dispatcher_base:Dispatcher exiting...
+    INFO:nni.retiarii.experiment.pytorch:Experiment stopped
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 312-330
+
+Users can also run Retiarii Experiment with :doc:`different training services </experiment/training_service/overview>`
+besides ``local`` training service.
+
+Visualize the Experiment
+------------------------
+
+Users can visualize their experiment in the same way as visualizing a normal hyper-parameter tuning experiment.
+For example, open ``localhost:8081`` in your browser, 8081 is the port that you set in ``exp.run``.
+Please refer to :doc:`here </experiment/web_portal/web_portal>` for details.
+
+We support visualizing models with 3rd-party visualization engines (like `Netron <https://netron.app/>`__).
+This can be used by clicking ``Visualization`` in detail panel for each trial.
+Note that current visualization is based on `onnx <https://onnx.ai/>`__ ,
+thus visualization is not feasible if the model cannot be exported into onnx.
+
+Built-in evaluators (e.g., Classification) will automatically export the model into a file.
+For your own evaluator, you need to save your file into ``$NNI_OUTPUT_DIR/model.onnx`` to make this work.
+For instance,
+
+.. GENERATED FROM PYTHON SOURCE LINES 330-344
+
+.. code-block:: default
+
+
+    import os
+    from pathlib import Path
+
+
+    def evaluate_model_with_visualization(model_cls):
+        model = model_cls()
+        # dump the model into an onnx
+        if 'NNI_OUTPUT_DIR' in os.environ:
+            dummy_input = torch.zeros(1, 3, 32, 32)
+            torch.onnx.export(model, (dummy_input, ),
+                              Path(os.environ['NNI_OUTPUT_DIR']) / 'model.onnx')
+        evaluate_model(model_cls)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 345-353
+
+Relaunch the experiment, and a button is shown on Web portal.
+
+.. image:: ../../img/netron_entrance_webui.png
+
+Export Top Models
+-----------------
+
+Users can export top models after the exploration is done using ``export_top_models``.
+
+.. GENERATED FROM PYTHON SOURCE LINES 353-357
+
+.. code-block:: default
+
+
+    for model_dict in exp.export_top_models(formatter='dict'):
+        print(model_dict)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    {'model_1': '0', 'model_2': 0.25, 'model_3': 64}
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 358-362
+
+The output is ``json`` object which records the mutation actions of the top model.
+If users want to output source code of the top model,
+they can use :ref:`graph-based execution engine <graph-based-execution-engine>` for the experiment,
+by simply adding the following two lines.
+
+.. GENERATED FROM PYTHON SOURCE LINES 362-365
+
+.. code-block:: default
+
+
+    exp_config.execution_engine = 'base'
+    export_formatter = 'code'
+
+
+
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 2 minutes  4.499 seconds)
+
+
+.. _sphx_glr_download_tutorials_hello_nas.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+    :class: sphx-glr-footer-example
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-python
+
+     :download:`Download Python source code: hello_nas.py <hello_nas.py>`
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+     :download:`Download Jupyter notebook: hello_nas.ipynb <hello_nas.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/source/tutorials/hello_nas_codeobj.pickle b/docs/source/tutorials/hello_nas_codeobj.pickle
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diff --git a/docs/source/tutorials/hello_nas_zh.rst b/docs/source/tutorials/hello_nas_zh.rst
new file mode 100644
index 000000000..aa325fddf
--- /dev/null
+++ b/docs/source/tutorials/hello_nas_zh.rst
@@ -0,0 +1,617 @@
+.. 8a873f2c9cb0e8e3ed2d66b9d16c330f
+
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "tutorials/hello_nas.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        Click :ref:`here <sphx_glr_download_tutorials_hello_nas.py>`
+        to download the full example code
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorials_hello_nas.py:
+
+
+架构搜索入门教程
+================
+
+这是 NNI 上的神经架构搜索（NAS）的入门教程。
+在本教程中，我们将借助 NNI 的 NAS 框架，即 *Retiarii*，在 MNIST 数据集上实现网络结构搜索。
+我们以多尝试的架构搜索为例来展示如何构建和探索模型空间。
+
+神经架构搜索任务主要有三个关键组成部分，即
+
+* 模型搜索空间，定义了一个要探索的模型的集合。
+* 一个合适的策略作为探索这个模型空间的方法。
+* 一个模型评估器，用于为搜索空间中每个模型评估性能。
+
+目前，Retiarii 只支持 PyTorch，并对 **PyTorch 1.7 到 1.10** 进行了测试。
+所以本教程假定您使用 PyTorch 作为深度学习框架。未来我们会支持更多框架。
+
+定义您的模型空间
+----------------------
+
+模型空间是由用户定义的，用来表达用户想要探索的一组模型，其中包含有潜力的好模型。
+在 NNI 的框架中，模型空间由两部分定义：基本模型和基本模型上可能的变化。
+
+.. GENERATED FROM PYTHON SOURCE LINES 26-34
+
+定义基本模型
+^^^^^^^^^^^^^^^^^
+
+定义基本模型与定义 PyTorch（或 TensorFlow）模型几乎相同。
+通常，您只需将代码 ``import torch.nn as nn`` 替换为
+``import nni.retiarii.nn.pytorch as nn`` 以使用我们打包的 PyTorch 模块。
+
+下面是定义基本模型的一个非常简单的示例。
+
+.. GENERATED FROM PYTHON SOURCE LINES 35-61
+
+.. code-block:: default
+
+
+    import torch
+    import torch.nn.functional as F
+    import nni.retiarii.nn.pytorch as nn
+    from nni.retiarii import model_wrapper
+
+
+    @model_wrapper      # this decorator should be put on the out most
+    class Net(nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.conv1 = nn.Conv2d(1, 32, 3, 1)
+            self.conv2 = nn.Conv2d(32, 64, 3, 1)
+            self.dropout1 = nn.Dropout(0.25)
+            self.dropout2 = nn.Dropout(0.5)
+            self.fc1 = nn.Linear(9216, 128)
+            self.fc2 = nn.Linear(128, 10)
+
+        def forward(self, x):
+            x = F.relu(self.conv1(x))
+            x = F.max_pool2d(self.conv2(x), 2)
+            x = torch.flatten(self.dropout1(x), 1)
+            x = self.fc2(self.dropout2(F.relu(self.fc1(x))))
+            output = F.log_softmax(x, dim=1)
+            return output
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 62-104
+
+.. tip:: 记住，您应该使用 ``import nni.retiarii.nn.pytorch as nn`` 和 :meth:`nni.retiarii.model_wrapper`。
+    许多错误都是因为忘记使用某一个。
+    另外，要使用 ``nn.init`` 的子模块，可以使用 ``torch.nn``，例如， ``torch.nn.init`` 而不是 ``nn.init``。
+
+定义模型变化
+^^^^^^^^^^^^^^^^^^^^^^
+
+基本模型只是一个具体模型，而不是模型空间。 我们提供 :doc:`模型变化的 API </nas/construct_space>`
+让用户表达如何改变基本模型。 即构建一个包含许多模型的搜索空间。
+
+基于上述基本模型，我们可以定义如下模型空间。
+
+.. code-block:: diff
+
+  @model_wrapper
+  class Net(nn.Module):
+    def __init__(self):
+      super().__init__()
+      self.conv1 = nn.Conv2d(1, 32, 3, 1)
+  -   self.conv2 = nn.Conv2d(32, 64, 3, 1)
+  +   self.conv2 = nn.LayerChoice([
+  +       nn.Conv2d(32, 64, 3, 1),
+  +       DepthwiseSeparableConv(32, 64)
+  +   ])
+  -   self.dropout1 = nn.Dropout(0.25)
+  +   self.dropout1 = nn.Dropout(nn.ValueChoice([0.25, 0.5, 0.75]))
+      self.dropout2 = nn.Dropout(0.5)
+  -   self.fc1 = nn.Linear(9216, 128)
+  -   self.fc2 = nn.Linear(128, 10)
+  +   feature = nn.ValueChoice([64, 128, 256])
+  +   self.fc1 = nn.Linear(9216, feature)
+  +   self.fc2 = nn.Linear(feature, 10)
+
+    def forward(self, x):
+      x = F.relu(self.conv1(x))
+      x = F.max_pool2d(self.conv2(x), 2)
+      x = torch.flatten(self.dropout1(x), 1)
+      x = self.fc2(self.dropout2(F.relu(self.fc1(x))))
+      output = F.log_softmax(x, dim=1)
+      return output
+
+结果是以下代码：
+
+.. GENERATED FROM PYTHON SOURCE LINES 104-147
+
+.. code-block:: default
+
+
+
+    class DepthwiseSeparableConv(nn.Module):
+        def __init__(self, in_ch, out_ch):
+            super().__init__()
+            self.depthwise = nn.Conv2d(in_ch, in_ch, kernel_size=3, groups=in_ch)
+            self.pointwise = nn.Conv2d(in_ch, out_ch, kernel_size=1)
+
+        def forward(self, x):
+            return self.pointwise(self.depthwise(x))
+
+
+    @model_wrapper
+    class ModelSpace(nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.conv1 = nn.Conv2d(1, 32, 3, 1)
+            # LayerChoice is used to select a layer between Conv2d and DwConv.
+            self.conv2 = nn.LayerChoice([
+                nn.Conv2d(32, 64, 3, 1),
+                DepthwiseSeparableConv(32, 64)
+            ])
+            # ValueChoice is used to select a dropout rate.
+            # ValueChoice can be used as parameter of modules wrapped in `nni.retiarii.nn.pytorch`
+            # or customized modules wrapped with `@basic_unit`.
+            self.dropout1 = nn.Dropout(nn.ValueChoice([0.25, 0.5, 0.75]))  # choose dropout rate from 0.25, 0.5 and 0.75
+            self.dropout2 = nn.Dropout(0.5)
+            feature = nn.ValueChoice([64, 128, 256])
+            self.fc1 = nn.Linear(9216, feature)
+            self.fc2 = nn.Linear(feature, 10)
+
+        def forward(self, x):
+            x = F.relu(self.conv1(x))
+            x = F.max_pool2d(self.conv2(x), 2)
+            x = torch.flatten(self.dropout1(x), 1)
+            x = self.fc2(self.dropout2(F.relu(self.fc1(x))))
+            output = F.log_softmax(x, dim=1)
+            return output
+
+
+    model_space = ModelSpace()
+    model_space
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+
+    ModelSpace(
+      (conv1): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))
+      (conv2): LayerChoice([Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1)), DepthwiseSeparableConv(
+        (depthwise): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), groups=32)
+        (pointwise): Conv2d(32, 64, kernel_size=(1, 1), stride=(1, 1))
+      )], label='model_1')
+      (dropout1): Dropout(p=0.25, inplace=False)
+      (dropout2): Dropout(p=0.5, inplace=False)
+      (fc1): Linear(in_features=9216, out_features=64, bias=True)
+      (fc2): Linear(in_features=64, out_features=10, bias=True)
+    )
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 148-182
+
+这个例子使用了两个模型变化的 API， :class:`nn.LayerChoice <nni.retiarii.nn.pytorch.LayerChoice>` 和 :class:`nn.InputChoice <nni.retiarii.nn.pytorch.ValueChoice>`。
+:class:`nn.LayerChoice <nni.retiarii.nn.pytorch.LayerChoice>` 可以从一系列的候选子模块中（在本例中为两个），为每个采样模型选择一个。
+它可以像原来的 PyTorch 子模块一样使用。
+:class:`nn.InputChoice <nni.retiarii.nn.pytorch.ValueChoice>` 的参数是一个候选值列表，语义是为每个采样模型选择一个值。
+
+更详细的 API 描述和用法可以在 :doc:`这里 </nas/construct_space>` 找到。
+
+.. note::
+
+    我们正在积极丰富模型变化的 API，使得您可以轻松构建模型空间。
+    如果当前支持的模型变化的 API 不能表达您的模型空间，
+    请参考 :doc:`这篇文档 </nas/mutator>` 来自定义突变。
+
+探索定义的模型空间
+-------------------------------------------
+
+简单来讲，有两种探索方法：
+(1) 独立评估每个采样到的模型，这是 :ref:`多尝试 NAS <multi-trial-nas>` 中的搜索方法。
+(2) 单尝试共享权重型的搜索，简称单尝试 NAS。
+我们在本教程中演示了第一种方法。第二种方法用户可以参考 :ref:`这里 <one-shot-nas>`。
+
+首先，用户需要选择合适的探索策略来探索定义好的模型空间。
+其次，用户需要选择或自定义模型性能评估来评估每个探索模型的性能。
+
+选择探索策略
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Retiarii 支持许多 :doc:`探索策略</nas/exploration_strategy>`。
+
+只需选择（即实例化）探索策略，就如下面的代码演示的一样：
+
+.. GENERATED FROM PYTHON SOURCE LINES 182-186
+
+.. code-block:: default
+
+
+    import nni.retiarii.strategy as strategy
+    search_strategy = strategy.Random(dedup=True)  # dedup=False if deduplication is not wanted
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+
+    /home/yugzhan/miniconda3/envs/cu102/lib/python3.8/site-packages/ray/autoscaler/_private/cli_logger.py:57: FutureWarning: Not all Ray CLI dependencies were found. In Ray 1.4+, the Ray CLI, autoscaler, and dashboard will only be usable via `pip install 'ray[default]'`. Please update your install command.
+      warnings.warn(
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 187-200
+
+挑选或自定义模型评估器
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+在探索过程中，探索策略反复生成新模型。模型评估器负责训练并验证每个生成的模型以获得模型的性能。
+该性能作为模型的得分被发送到探索策略以帮助其生成更好的模型。
+
+Retiarii 提供了 :doc:`内置模型评估器 </nas/evaluator>`，但在此之前，
+我们建议使用 :class:`FunctionalEvaluator <nni.retiarii.evaluator.FunctionalEvaluator>`，即用一个函数包装您自己的训练和评估代码。
+这个函数应该接收一个单一的模型类并使用 :func:`nni.report_final_result` 报告这个模型的最终分数。
+
+此处的示例创建了一个简单的评估器，该评估器在 MNIST 数据集上运行，训练 2 个 epoch，并报告其在验证集上的准确率。
+
+.. GENERATED FROM PYTHON SOURCE LINES 200-268
+
+.. code-block:: default
+
+
+    import nni
+
+    from torchvision import transforms
+    from torchvision.datasets import MNIST
+    from torch.utils.data import DataLoader
+
+
+    def train_epoch(model, device, train_loader, optimizer, epoch):
+        loss_fn = torch.nn.CrossEntropyLoss()
+        model.train()
+        for batch_idx, (data, target) in enumerate(train_loader):
+            data, target = data.to(device), target.to(device)
+            optimizer.zero_grad()
+            output = model(data)
+            loss = loss_fn(output, target)
+            loss.backward()
+            optimizer.step()
+            if batch_idx % 10 == 0:
+                print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+                    epoch, batch_idx * len(data), len(train_loader.dataset),
+                    100. * batch_idx / len(train_loader), loss.item()))
+
+
+    def test_epoch(model, device, test_loader):
+        model.eval()
+        test_loss = 0
+        correct = 0
+        with torch.no_grad():
+            for data, target in test_loader:
+                data, target = data.to(device), target.to(device)
+                output = model(data)
+                pred = output.argmax(dim=1, keepdim=True)
+                correct += pred.eq(target.view_as(pred)).sum().item()
+
+        test_loss /= len(test_loader.dataset)
+        accuracy = 100. * correct / len(test_loader.dataset)
+
+        print('\nTest set: Accuracy: {}/{} ({:.0f}%)\n'.format(
+              correct, len(test_loader.dataset), accuracy))
+
+        return accuracy
+
+
+    def evaluate_model(model_cls):
+        # "model_cls" is a class, need to instantiate
+        model = model_cls()
+
+        device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+        model.to(device)
+
+        optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
+        transf = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
+        train_loader = DataLoader(MNIST('data/mnist', download=True, transform=transf), batch_size=64, shuffle=True)
+        test_loader = DataLoader(MNIST('data/mnist', download=True, train=False, transform=transf), batch_size=64)
+
+        for epoch in range(3):
+            # train the model for one epoch
+            train_epoch(model, device, train_loader, optimizer, epoch)
+            # test the model for one epoch
+            accuracy = test_epoch(model, device, test_loader)
+            # call report intermediate result. Result can be float or dict
+            nni.report_intermediate_result(accuracy)
+
+        # report final test result
+        nni.report_final_result(accuracy)
+
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 269-270
+
+创建评估器
+
+.. GENERATED FROM PYTHON SOURCE LINES 270-274
+
+.. code-block:: default
+
+
+    from nni.retiarii.evaluator import FunctionalEvaluator
+    evaluator = FunctionalEvaluator(evaluate_model)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 275-286
+
+这里的 ``train_epoch`` 和 ``test_epoch`` 可以是任何自定义函数，用户可以在其中编写自己的训练逻辑。
+
+建议这里的 ``evaluate_model`` 不接受除 ``model_cls`` 之外的其他参数。
+但是，在 `高级教程 </nas/evaluator>` 中，我们将展示如何使用其他参数，以免您确实需要这些参数。
+未来，我们将支持对评估器的参数进行变化（通常称为“超参数调优”）。
+
+启动实验
+--------------------
+
+一切都已准备就绪，现在就可以开始做模型搜索的实验了。如下所示。
+
+.. GENERATED FROM PYTHON SOURCE LINES 287-293
+
+.. code-block:: default
+
+
+    from nni.retiarii.experiment.pytorch import RetiariiExperiment, RetiariiExeConfig
+    exp = RetiariiExperiment(model_space, evaluator, [], search_strategy)
+    exp_config = RetiariiExeConfig('local')
+    exp_config.experiment_name = 'mnist_search'
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 294-295
+
+以下配置可以用于控制最多/同时运行多少试验。
+
+.. GENERATED FROM PYTHON SOURCE LINES 295-299
+
+.. code-block:: default
+
+
+    exp_config.max_trial_number = 4   # 最多运行 4 个实验
+    exp_config.trial_concurrency = 2  # 最多同时运行 2 个试验
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 300-302
+
+如果要使用 GPU，请设置以下配置。
+如果您希望使用被占用了的 GPU（比如 GPU 上可能正在运行 GUI），则 ``use_active_gpu`` 应设置为 true。
+
+.. GENERATED FROM PYTHON SOURCE LINES 302-306
+
+.. code-block:: default
+
+
+    exp_config.trial_gpu_number = 1
+    exp_config.training_service.use_active_gpu = True
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 307-308
+
+启动实验。 在一个有两块 GPU 的工作站上完成整个实验大约需要几分钟时间。
+
+.. GENERATED FROM PYTHON SOURCE LINES 308-311
+
+.. code-block:: default
+
+
+    exp.run(exp_config, 8081)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    INFO:nni.experiment:Creating experiment, Experiment ID: z8ns5fv7
+    INFO:nni.experiment:Connecting IPC pipe...
+    INFO:nni.experiment:Starting web server...
+    INFO:nni.experiment:Setting up...
+    INFO:nni.runtime.msg_dispatcher_base:Dispatcher started
+    INFO:nni.retiarii.experiment.pytorch:Web UI URLs: http://127.0.0.1:8081 http://10.190.172.35:8081 http://192.168.49.1:8081 http://172.17.0.1:8081
+    INFO:nni.retiarii.experiment.pytorch:Start strategy...
+    INFO:root:Successfully update searchSpace.
+    INFO:nni.retiarii.strategy.bruteforce:Random search running in fixed size mode. Dedup: on.
+    INFO:nni.retiarii.experiment.pytorch:Stopping experiment, please wait...
+    INFO:nni.retiarii.experiment.pytorch:Strategy exit
+    INFO:nni.retiarii.experiment.pytorch:Waiting for experiment to become DONE (you can ctrl+c if there is no running trial jobs)...
+    INFO:nni.runtime.msg_dispatcher_base:Dispatcher exiting...
+    INFO:nni.retiarii.experiment.pytorch:Experiment stopped
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 312-330
+
+除了 ``local`` 训练平台，用户还可以使用 :doc:`不同的训练平台 </experiment/training_service/overview>` 来运行 Retiarii 试验。
+
+可视化实验
+----------------------
+
+用户可以可视化他们的架构搜索实验，就像可视化超参调优实验一样。
+例如，在浏览器中打开 ``localhost:8081``，8081 是您在 ``exp.run`` 中设置的端口。
+详情请参考 :doc:`这里</experiment/web_portal/web_portal>`。
+
+我们支持使用第三方可视化引擎（如 `Netron <https://netron.app/>`__）对模型进行可视化。
+这可以通过单击每个试验的详细面板中的“可视化”来使用。
+请注意，当前的可视化是基于 `onnx <https://onnx.ai/>`__，
+因此，如果模型不能导出为 onnx，可视化是不可行的。
+
+内置评估器（例如 Classification）会将模型自动导出到文件中。
+对于您自己的评估器，您需要将文件保存到 ``$NNI_OUTPUT_DIR/model.onnx``。
+例如，
+
+.. GENERATED FROM PYTHON SOURCE LINES 330-344
+
+.. code-block:: default
+
+
+    import os
+    from pathlib import Path
+
+
+    def evaluate_model_with_visualization(model_cls):
+        model = model_cls()
+        # dump the model into an onnx
+        if 'NNI_OUTPUT_DIR' in os.environ:
+            dummy_input = torch.zeros(1, 3, 32, 32)
+            torch.onnx.export(model, (dummy_input, ),
+                              Path(os.environ['NNI_OUTPUT_DIR']) / 'model.onnx')
+        evaluate_model(model_cls)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 345-353
+
+重新启动实验，Web 界面上会显示一个按钮。
+
+.. image:: ../../img/netron_entrance_webui.png
+
+导出最优模型
+-----------------
+
+搜索完成后，用户可以使用 ``export_top_models`` 导出最优模型。
+
+.. GENERATED FROM PYTHON SOURCE LINES 353-357
+
+.. code-block:: default
+
+
+    for model_dict in exp.export_top_models(formatter='dict'):
+        print(model_dict)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    {'model_1': '0', 'model_2': 0.25, 'model_3': 64}
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 358-362
+
+输出是一个 JSON 对象，记录了最好的模型的每一个选择都选了什么。
+如果用户想要搜出来的模型的源代码，他们可以使用 :ref:`基于图的引擎 <graph-based-execution-engine>`，只需增加如下两行。
+
+.. GENERATED FROM PYTHON SOURCE LINES 362-365
+
+.. code-block:: default
+
+
+    exp_config.execution_engine = 'base'
+    export_formatter = 'code'
+
+
+
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 2 minutes  4.499 seconds)
+
+
+.. _sphx_glr_download_tutorials_hello_nas.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+    :class: sphx-glr-footer-example
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-python
+
+     :download:`Download Python source code: hello_nas.py <hello_nas.py>`
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+     :download:`Download Jupyter notebook: hello_nas.ipynb <hello_nas.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/source/tutorials/hpo_nnictl/model.ipynb b/docs/source/tutorials/hpo_nnictl/model.ipynb
new file mode 100644
index 000000000..f1acdfc84
--- /dev/null
+++ b/docs/source/tutorials/hpo_nnictl/model.ipynb
@@ -0,0 +1,162 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Port PyTorch Quickstart to NNI\nThis is a modified version of `PyTorch quickstart`_.\n\nIt can be run directly and will have the exact same result as original version.\n\nFurthermore, it enables the ability of auto tuning with an NNI *experiment*, which will be detailed later.\n\nIt is recommended to run this script directly first to verify the environment.\n\nThere are 2 key differences from the original version:\n\n1. In `Get optimized hyperparameters`_ part, it receives generated hyperparameters.\n2. In `Train model and report accuracy`_ part, it reports accuracy metrics to NNI.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import nni\nimport torch\nfrom torch import nn\nfrom torch.utils.data import DataLoader\nfrom torchvision import datasets\nfrom torchvision.transforms import ToTensor"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Hyperparameters to be tuned\nThese are the hyperparameters that will be tuned.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "params = {\n    'features': 512,\n    'lr': 0.001,\n    'momentum': 0,\n}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Get optimized hyperparameters\nIf run directly, :func:`nni.get_next_parameter` is a no-op and returns an empty dict.\nBut with an NNI *experiment*, it will receive optimized hyperparameters from tuning algorithm.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "optimized_params = nni.get_next_parameter()\nparams.update(optimized_params)\nprint(params)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Load dataset\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "training_data = datasets.FashionMNIST(root=\"data\", train=True, download=True, transform=ToTensor())\ntest_data = datasets.FashionMNIST(root=\"data\", train=False, download=True, transform=ToTensor())\n\nbatch_size = 64\n\ntrain_dataloader = DataLoader(training_data, batch_size=batch_size)\ntest_dataloader = DataLoader(test_data, batch_size=batch_size)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Build model with hyperparameters\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\nprint(f\"Using {device} device\")\n\nclass NeuralNetwork(nn.Module):\n    def __init__(self):\n        super(NeuralNetwork, self).__init__()\n        self.flatten = nn.Flatten()\n        self.linear_relu_stack = nn.Sequential(\n            nn.Linear(28*28, params['features']),\n            nn.ReLU(),\n            nn.Linear(params['features'], params['features']),\n            nn.ReLU(),\n            nn.Linear(params['features'], 10)\n        )\n\n    def forward(self, x):\n        x = self.flatten(x)\n        logits = self.linear_relu_stack(x)\n        return logits\n\nmodel = NeuralNetwork().to(device)\n\nloss_fn = nn.CrossEntropyLoss()\noptimizer = torch.optim.SGD(model.parameters(), lr=params['lr'], momentum=params['momentum'])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Define train and test\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "def train(dataloader, model, loss_fn, optimizer):\n    size = len(dataloader.dataset)\n    model.train()\n    for batch, (X, y) in enumerate(dataloader):\n        X, y = X.to(device), y.to(device)\n        pred = model(X)\n        loss = loss_fn(pred, y)\n        optimizer.zero_grad()\n        loss.backward()\n        optimizer.step()\n\ndef test(dataloader, model, loss_fn):\n    size = len(dataloader.dataset)\n    num_batches = len(dataloader)\n    model.eval()\n    test_loss, correct = 0, 0\n    with torch.no_grad():\n        for X, y in dataloader:\n            X, y = X.to(device), y.to(device)\n            pred = model(X)\n            test_loss += loss_fn(pred, y).item()\n            correct += (pred.argmax(1) == y).type(torch.float).sum().item()\n    test_loss /= num_batches\n    correct /= size\n    return correct"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Train model and report accuracy\nReport accuracy metrics to NNI so the tuning algorithm can suggest better hyperparameters.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "epochs = 5\nfor t in range(epochs):\n    print(f\"Epoch {t+1}\\n-------------------------------\")\n    train(train_dataloader, model, loss_fn, optimizer)\n    accuracy = test(test_dataloader, model, loss_fn)\n    nni.report_intermediate_result(accuracy)\nnni.report_final_result(accuracy)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/source/tutorials/hpo_nnictl/model.py b/docs/source/tutorials/hpo_nnictl/model.py
new file mode 100644
index 000000000..1bb07bf14
--- /dev/null
+++ b/docs/source/tutorials/hpo_nnictl/model.py
@@ -0,0 +1,125 @@
+"""
+Port PyTorch Quickstart to NNI
+==============================
+This is a modified version of `PyTorch quickstart`_.
+
+It can be run directly and will have the exact same result as original version.
+
+Furthermore, it enables the ability of auto tuning with an NNI *experiment*, which will be detailed later.
+
+It is recommended to run this script directly first to verify the environment.
+
+There are 2 key differences from the original version:
+
+1. In `Get optimized hyperparameters`_ part, it receives generated hyperparameters.
+2. In `Train model and report accuracy`_ part, it reports accuracy metrics to NNI.
+
+.. _PyTorch quickstart: https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
+"""
+
+# %%
+import nni
+import torch
+from torch import nn
+from torch.utils.data import DataLoader
+from torchvision import datasets
+from torchvision.transforms import ToTensor
+
+# %%
+# Hyperparameters to be tuned
+# ---------------------------
+# These are the hyperparameters that will be tuned.
+params = {
+    'features': 512,
+    'lr': 0.001,
+    'momentum': 0,
+}
+
+# %%
+# Get optimized hyperparameters
+# -----------------------------
+# If run directly, :func:`nni.get_next_parameter` is a no-op and returns an empty dict.
+# But with an NNI *experiment*, it will receive optimized hyperparameters from tuning algorithm.
+optimized_params = nni.get_next_parameter()
+params.update(optimized_params)
+print(params)
+
+# %%
+# Load dataset
+# ------------
+training_data = datasets.FashionMNIST(root="data", train=True, download=True, transform=ToTensor())
+test_data = datasets.FashionMNIST(root="data", train=False, download=True, transform=ToTensor())
+
+batch_size = 64
+
+train_dataloader = DataLoader(training_data, batch_size=batch_size)
+test_dataloader = DataLoader(test_data, batch_size=batch_size)
+
+# %%
+# Build model with hyperparameters
+# --------------------------------
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Using {device} device")
+
+class NeuralNetwork(nn.Module):
+    def __init__(self):
+        super(NeuralNetwork, self).__init__()
+        self.flatten = nn.Flatten()
+        self.linear_relu_stack = nn.Sequential(
+            nn.Linear(28*28, params['features']),
+            nn.ReLU(),
+            nn.Linear(params['features'], params['features']),
+            nn.ReLU(),
+            nn.Linear(params['features'], 10)
+        )
+
+    def forward(self, x):
+        x = self.flatten(x)
+        logits = self.linear_relu_stack(x)
+        return logits
+
+model = NeuralNetwork().to(device)
+
+loss_fn = nn.CrossEntropyLoss()
+optimizer = torch.optim.SGD(model.parameters(), lr=params['lr'], momentum=params['momentum'])
+
+# %%
+# Define train and test
+# ---------------------
+def train(dataloader, model, loss_fn, optimizer):
+    size = len(dataloader.dataset)
+    model.train()
+    for batch, (X, y) in enumerate(dataloader):
+        X, y = X.to(device), y.to(device)
+        pred = model(X)
+        loss = loss_fn(pred, y)
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+def test(dataloader, model, loss_fn):
+    size = len(dataloader.dataset)
+    num_batches = len(dataloader)
+    model.eval()
+    test_loss, correct = 0, 0
+    with torch.no_grad():
+        for X, y in dataloader:
+            X, y = X.to(device), y.to(device)
+            pred = model(X)
+            test_loss += loss_fn(pred, y).item()
+            correct += (pred.argmax(1) == y).type(torch.float).sum().item()
+    test_loss /= num_batches
+    correct /= size
+    return correct
+
+# %%
+# Train model and report accuracy
+# -------------------------------
+# Report accuracy metrics to NNI so the tuning algorithm can suggest better hyperparameters.
+epochs = 5
+for t in range(epochs):
+    print(f"Epoch {t+1}\n-------------------------------")
+    train(train_dataloader, model, loss_fn, optimizer)
+    accuracy = test(test_dataloader, model, loss_fn)
+    nni.report_intermediate_result(accuracy)
+nni.report_final_result(accuracy)
diff --git a/docs/source/tutorials/hpo_nnictl/model.py.md5 b/docs/source/tutorials/hpo_nnictl/model.py.md5
new file mode 100644
index 000000000..4559246a4
--- /dev/null
+++ b/docs/source/tutorials/hpo_nnictl/model.py.md5
@@ -0,0 +1 @@
+ed8bfc27e3d555d842fc4eec2635e619
\ No newline at end of file
diff --git a/docs/source/tutorials/hpo_nnictl/model.rst b/docs/source/tutorials/hpo_nnictl/model.rst
new file mode 100644
index 000000000..9c978807e
--- /dev/null
+++ b/docs/source/tutorials/hpo_nnictl/model.rst
@@ -0,0 +1,303 @@
+:orphan:
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "tutorials/hpo_nnictl/model.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        Click :ref:`here <sphx_glr_download_tutorials_hpo_nnictl_model.py>`
+        to download the full example code
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorials_hpo_nnictl_model.py:
+
+
+Port PyTorch Quickstart to NNI
+==============================
+This is a modified version of `PyTorch quickstart`_.
+
+It can be run directly and will have the exact same result as original version.
+
+Furthermore, it enables the ability of auto tuning with an NNI *experiment*, which will be detailed later.
+
+It is recommended to run this script directly first to verify the environment.
+
+There are 2 key differences from the original version:
+
+1. In `Get optimized hyperparameters`_ part, it receives generated hyperparameters.
+2. In `Train model and report accuracy`_ part, it reports accuracy metrics to NNI.
+
+.. _PyTorch quickstart: https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
+
+.. GENERATED FROM PYTHON SOURCE LINES 21-28
+
+.. code-block:: default
+
+    import nni
+    import torch
+    from torch import nn
+    from torch.utils.data import DataLoader
+    from torchvision import datasets
+    from torchvision.transforms import ToTensor
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 29-32
+
+Hyperparameters to be tuned
+---------------------------
+These are the hyperparameters that will be tuned.
+
+.. GENERATED FROM PYTHON SOURCE LINES 32-38
+
+.. code-block:: default
+
+    params = {
+        'features': 512,
+        'lr': 0.001,
+        'momentum': 0,
+    }
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 39-43
+
+Get optimized hyperparameters
+-----------------------------
+If run directly, :func:`nni.get_next_parameter` is a no-op and returns an empty dict.
+But with an NNI *experiment*, it will receive optimized hyperparameters from tuning algorithm.
+
+.. GENERATED FROM PYTHON SOURCE LINES 43-47
+
+.. code-block:: default
+
+    optimized_params = nni.get_next_parameter()
+    params.update(optimized_params)
+    print(params)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    {'features': 512, 'lr': 0.001, 'momentum': 0}
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 48-50
+
+Load dataset
+------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 50-58
+
+.. code-block:: default
+
+    training_data = datasets.FashionMNIST(root="data", train=True, download=True, transform=ToTensor())
+    test_data = datasets.FashionMNIST(root="data", train=False, download=True, transform=ToTensor())
+
+    batch_size = 64
+
+    train_dataloader = DataLoader(training_data, batch_size=batch_size)
+    test_dataloader = DataLoader(test_data, batch_size=batch_size)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 59-61
+
+Build model with hyperparameters
+--------------------------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 61-86
+
+.. code-block:: default
+
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    print(f"Using {device} device")
+
+    class NeuralNetwork(nn.Module):
+        def __init__(self):
+            super(NeuralNetwork, self).__init__()
+            self.flatten = nn.Flatten()
+            self.linear_relu_stack = nn.Sequential(
+                nn.Linear(28*28, params['features']),
+                nn.ReLU(),
+                nn.Linear(params['features'], params['features']),
+                nn.ReLU(),
+                nn.Linear(params['features'], 10)
+            )
+
+        def forward(self, x):
+            x = self.flatten(x)
+            logits = self.linear_relu_stack(x)
+            return logits
+
+    model = NeuralNetwork().to(device)
+
+    loss_fn = nn.CrossEntropyLoss()
+    optimizer = torch.optim.SGD(model.parameters(), lr=params['lr'], momentum=params['momentum'])
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    Using cpu device
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 87-89
+
+Define train and test
+---------------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 89-115
+
+.. code-block:: default
+
+    def train(dataloader, model, loss_fn, optimizer):
+        size = len(dataloader.dataset)
+        model.train()
+        for batch, (X, y) in enumerate(dataloader):
+            X, y = X.to(device), y.to(device)
+            pred = model(X)
+            loss = loss_fn(pred, y)
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+    def test(dataloader, model, loss_fn):
+        size = len(dataloader.dataset)
+        num_batches = len(dataloader)
+        model.eval()
+        test_loss, correct = 0, 0
+        with torch.no_grad():
+            for X, y in dataloader:
+                X, y = X.to(device), y.to(device)
+                pred = model(X)
+                test_loss += loss_fn(pred, y).item()
+                correct += (pred.argmax(1) == y).type(torch.float).sum().item()
+        test_loss /= num_batches
+        correct /= size
+        return correct
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 116-119
+
+Train model and report accuracy
+-------------------------------
+Report accuracy metrics to NNI so the tuning algorithm can suggest better hyperparameters.
+
+.. GENERATED FROM PYTHON SOURCE LINES 119-126
+
+.. code-block:: default
+
+    epochs = 5
+    for t in range(epochs):
+        print(f"Epoch {t+1}\n-------------------------------")
+        train(train_dataloader, model, loss_fn, optimizer)
+        accuracy = test(test_dataloader, model, loss_fn)
+        nni.report_intermediate_result(accuracy)
+    nni.report_final_result(accuracy)
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    Epoch 1
+    -------------------------------
+    [2022-03-21 01:09:37] INFO (nni/MainThread) Intermediate result: 0.461  (Index 0)
+    Epoch 2
+    -------------------------------
+    [2022-03-21 01:09:42] INFO (nni/MainThread) Intermediate result: 0.5529  (Index 1)
+    Epoch 3
+    -------------------------------
+    [2022-03-21 01:09:47] INFO (nni/MainThread) Intermediate result: 0.6155  (Index 2)
+    Epoch 4
+    -------------------------------
+    [2022-03-21 01:09:52] INFO (nni/MainThread) Intermediate result: 0.6345  (Index 3)
+    Epoch 5
+    -------------------------------
+    [2022-03-21 01:09:56] INFO (nni/MainThread) Intermediate result: 0.6505  (Index 4)
+    [2022-03-21 01:09:56] INFO (nni/MainThread) Final result: 0.6505
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 0 minutes  24.441 seconds)
+
+
+.. _sphx_glr_download_tutorials_hpo_nnictl_model.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+    :class: sphx-glr-footer-example
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-python
+
+     :download:`Download Python source code: model.py <model.py>`
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+     :download:`Download Jupyter notebook: model.ipynb <model.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/source/tutorials/hpo_nnictl/model_codeobj.pickle b/docs/source/tutorials/hpo_nnictl/model_codeobj.pickle
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diff --git a/docs/source/tutorials/hpo_nnictl/nnictl.rst b/docs/source/tutorials/hpo_nnictl/nnictl.rst
new file mode 100644
index 000000000..c50322072
--- /dev/null
+++ b/docs/source/tutorials/hpo_nnictl/nnictl.rst
@@ -0,0 +1,207 @@
+Run HPO Experiment with nnictl
+==============================
+
+This tutorial has exactly the same effect as :doc:`../hpo_quickstart_pytorch/main`.
+
+Both tutorials optimize the model in `official PyTorch quickstart
+<https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html>`__ with auto-tuning,
+while this one manages the experiment with command line tool and YAML config file, instead of pure Python code.
+
+The tutorial consists of 4 steps: 
+
+1. Modify the model for auto-tuning.
+2. Define hyperparameters' search space.
+3. Create config file.
+4. Run the experiment.
+
+The first two steps are identical to quickstart.
+
+Step 1: Prepare the model
+-------------------------
+In first step, we need to prepare the model to be tuned.
+
+The model should be put in a separate script.
+It will be evaluated many times concurrently,
+and possibly will be trained on distributed platforms.
+
+In this tutorial, the model is defined in :doc:`model.py <model>`.
+
+In short, it is a PyTorch model with 3 additional API calls:
+
+1. Use :func:`nni.get_next_parameter` to fetch the hyperparameters to be evalutated.
+2. Use :func:`nni.report_intermediate_result` to report per-epoch accuracy metrics.
+3. Use :func:`nni.report_final_result` to report final accuracy.
+
+Please understand the model code before continue to next step.
+
+Step 2: Define search space
+---------------------------
+In model code, we have prepared 3 hyperparameters to be tuned:
+*features*, *lr*, and *momentum*.
+
+Here we need to define their *search space* so the tuning algorithm can sample them in desired range.
+
+Assuming we have following prior knowledge for these hyperparameters:
+
+1. *features* should be one of 128, 256, 512, 1024.
+2. *lr* should be a float between 0.0001 and 0.1, and it follows exponential distribution.
+3. *momentum* should be a float between 0 and 1.
+
+In NNI, the space of *features* is called ``choice``;
+the space of *lr* is called ``loguniform``;
+and the space of *momentum* is called ``uniform``.
+You may have noticed, these names are derived from ``numpy.random``.
+
+For full specification of search space, check :doc:`the reference </hpo/search_space>`.
+
+Now we can define the search space as follow:
+
+.. code-block:: yaml
+
+    search_space:
+      features:
+        _type: choice
+        _value: [ 128, 256, 512, 1024 ]
+      lr:
+        _type: loguniform
+        _value: [ 0.0001, 0.1 ]
+      momentum:
+        _type: uniform
+        _value: [ 0, 1 ]
+
+Step 3: Configure the experiment
+--------------------------------
+NNI uses an *experiment* to manage the HPO process.
+The *experiment config* defines how to train the models and how to explore the search space.
+
+In this tutorial we use a YAML file ``config.yaml`` to define the experiment.
+
+Configure trial code
+^^^^^^^^^^^^^^^^^^^^
+In NNI evaluation of each hyperparameter set is called a *trial*.
+So the model script is called *trial code*.
+
+.. code-block:: yaml
+
+    trial_command: python model.py
+    trial_code_directory: .
+
+When ``trial_code_directory`` is a relative path, it relates to the config file.
+So in this case we need to put ``config.yaml`` and ``model.py`` in the same directory.
+
+.. attention::
+
+    The rules for resolving relative path are different in YAML config file and :doc:`Python experiment API </reference/experiment>`.
+    In Python experiment API relative paths are relative to current working directory.
+
+Configure how many trials to run
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Here we evaluate 10 sets of hyperparameters in total, and concurrently evaluate 2 sets at a time.
+
+.. code-block:: yaml
+
+    max_trial_number: 10
+    trial_concurrency: 2
+
+You may also set ``max_experiment_duration = '1h'`` to limit running time.
+
+If neither ``max_trial_number`` nor ``max_experiment_duration`` are set,
+the experiment will run forever until you stop it.
+
+.. note::
+
+    ``max_trial_number`` is set to 10 here for a fast example.
+    In real world it should be set to a larger number.
+    With default config TPE tuner requires 20 trials to warm up.
+
+
+Configure tuning algorithm
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+Here we use :doc:`TPE tuner </hpo/tuners>`.
+
+.. code-block:: yaml
+
+    name: TPE
+    class_args:
+      optimize_mode: maximize
+
+Configure training service
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+In this tutorial we use *local* mode,
+which means models will be trained on local machine, without using any special training platform.
+
+.. code-block:: yaml
+
+    training_service:
+      platform: local
+
+Wrap up
+^^^^^^^
+
+The full content of ``config.yaml`` is as follow:
+
+.. code-block:: yaml
+
+    search_space:
+      features:
+        _type: choice
+        _value: [ 128, 256, 512, 1024 ]
+      lr:
+        _type: loguniform
+        _value: [ 0.0001, 0.1 ]
+      momentum:
+        _type: uniform
+        _value: [ 0, 1 ]
+    
+    trial_command: python model.py
+    trial_code_directory: .
+
+    trial_concurrency: 2
+    max_trial_number: 10
+    
+    tuner:
+      name: TPE
+      class_args:
+        optimize_mode: maximize
+    
+    training_service:
+      platform: local
+
+Step 4: Run the experiment
+--------------------------
+Now the experiment is ready. Launch it with ``nnictl create`` command:
+
+.. code-block:: bash
+
+    $ nnictl create --config config.yaml --port 8080
+
+You can use the web portal to view experiment status: http://localhost:8080.
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    [2022-04-01 12:00:00] Creating experiment, Experiment ID: p43ny6ew
+    [2022-04-01 12:00:00] Starting web server...
+    [2022-04-01 12:00:01] Setting up...
+    [2022-04-01 12:00:01] Web portal URLs: http://127.0.0.1:8080 http://192.168.1.1:8080
+    [2022-04-01 12:00:01] To stop experiment run "nnictl stop p43ny6ew" or "nnictl stop --all"
+    [2022-04-01 12:00:01] Reference: https://nni.readthedocs.io/en/stable/reference/nnictl.html
+
+When the experiment is done, use ``nnictl stop`` command to stop it.
+
+.. code-block:: bash
+
+    $ nnictl stop p43ny6ew
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    INFO:  Stopping experiment 7u8yg9zw
+    INFO:  Stop experiment success.
diff --git a/docs/source/tutorials/hpo_quickstart_pytorch/images/thumb/sphx_glr_main_thumb.png b/docs/source/tutorials/hpo_quickstart_pytorch/images/thumb/sphx_glr_main_thumb.png
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diff --git a/docs/source/tutorials/hpo_quickstart_pytorch/main.ipynb b/docs/source/tutorials/hpo_quickstart_pytorch/main.ipynb
new file mode 100644
index 000000000..a464395de
--- /dev/null
+++ b/docs/source/tutorials/hpo_quickstart_pytorch/main.ipynb
@@ -0,0 +1,215 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# HPO Quickstart with PyTorch\nThis tutorial optimizes the model in `official PyTorch quickstart`_ with auto-tuning.\n\nThe tutorial consists of 4 steps: \n\n1. Modify the model for auto-tuning.\n2. Define hyperparameters' search space.\n3. Configure the experiment.\n4. Run the experiment.\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Step 1: Prepare the model\nIn first step, we need to prepare the model to be tuned.\n\nThe model should be put in a separate script.\nIt will be evaluated many times concurrently,\nand possibly will be trained on distributed platforms.\n\nIn this tutorial, the model is defined in :doc:`model.py <model>`.\n\nIn short, it is a PyTorch model with 3 additional API calls:\n\n1. Use :func:`nni.get_next_parameter` to fetch the hyperparameters to be evalutated.\n2. Use :func:`nni.report_intermediate_result` to report per-epoch accuracy metrics.\n3. Use :func:`nni.report_final_result` to report final accuracy.\n\nPlease understand the model code before continue to next step.\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Step 2: Define search space\nIn model code, we have prepared 3 hyperparameters to be tuned:\n*features*, *lr*, and *momentum*.\n\nHere we need to define their *search space* so the tuning algorithm can sample them in desired range.\n\nAssuming we have following prior knowledge for these hyperparameters:\n\n1. *features* should be one of 128, 256, 512, 1024.\n2. *lr* should be a float between 0.0001 and 0.1, and it follows exponential distribution.\n3. *momentum* should be a float between 0 and 1.\n\nIn NNI, the space of *features* is called ``choice``;\nthe space of *lr* is called ``loguniform``;\nand the space of *momentum* is called ``uniform``.\nYou may have noticed, these names are derived from ``numpy.random``.\n\nFor full specification of search space, check :doc:`the reference </hpo/search_space>`.\n\nNow we can define the search space as follow:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "search_space = {\n    'features': {'_type': 'choice', '_value': [128, 256, 512, 1024]},\n    'lr': {'_type': 'loguniform', '_value': [0.0001, 0.1]},\n    'momentum': {'_type': 'uniform', '_value': [0, 1]},\n}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Step 3: Configure the experiment\nNNI uses an *experiment* to manage the HPO process.\nThe *experiment config* defines how to train the models and how to explore the search space.\n\nIn this tutorial we use a *local* mode experiment,\nwhich means models will be trained on local machine, without using any special training platform.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from nni.experiment import Experiment\nexperiment = Experiment('local')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we start to configure the experiment.\n\n### Configure trial code\nIn NNI evaluation of each hyperparameter set is called a *trial*.\nSo the model script is called *trial code*.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "experiment.config.trial_command = 'python model.py'\nexperiment.config.trial_code_directory = '.'"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "When ``trial_code_directory`` is a relative path, it relates to current working directory.\nTo run ``main.py`` in a different path, you can set trial code directory to ``Path(__file__).parent``.\n(`__file__ <https://docs.python.org/3.10/reference/datamodel.html#index-43>`__\nis only available in standard Python, not in Jupyter Notebook.)\n\n.. attention::\n\n    If you are using Linux system without Conda,\n    you may need to change ``\"python model.py\"`` to ``\"python3 model.py\"``.\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Configure search space\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "experiment.config.search_space = search_space"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Configure tuning algorithm\nHere we use :doc:`TPE tuner </hpo/tuners>`.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "experiment.config.tuner.name = 'TPE'\nexperiment.config.tuner.class_args['optimize_mode'] = 'maximize'"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Configure how many trials to run\nHere we evaluate 10 sets of hyperparameters in total, and concurrently evaluate 2 sets at a time.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "experiment.config.max_trial_number = 10\nexperiment.config.trial_concurrency = 2"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You may also set ``max_experiment_duration = '1h'`` to limit running time.\n\nIf neither ``max_trial_number`` nor ``max_experiment_duration`` are set,\nthe experiment will run forever until you press Ctrl-C.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>``max_trial_number`` is set to 10 here for a fast example.\n    In real world it should be set to a larger number.\n    With default config TPE tuner requires 20 trials to warm up.</p></div>\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Step 4: Run the experiment\nNow the experiment is ready. Choose a port and launch it. (Here we use port 8080.)\n\nYou can use the web portal to view experiment status: http://localhost:8080.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "experiment.run(8080)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## After the experiment is done\nEverything is done and it is safe to exit now. The following are optional.\n\nIf you are using standard Python instead of Jupyter Notebook,\nyou can add ``input()`` or ``signal.pause()`` to prevent Python from exiting,\nallowing you to view the web portal after the experiment is done.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# input('Press enter to quit')\nexperiment.stop()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        ":meth:`nni.experiment.Experiment.stop` is automatically invoked when Python exits,\nso it can be omitted in your code.\n\nAfter the experiment is stopped, you can run :meth:`nni.experiment.Experiment.view` to restart web portal.\n\n.. tip::\n\n    This example uses :doc:`Python API </reference/experiment>` to create experiment.\n\n    You can also create and manage experiments with :doc:`command line tool <../hpo_nnictl/nnictl>`.\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.4"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/source/tutorials/hpo_quickstart_pytorch/main.py b/docs/source/tutorials/hpo_quickstart_pytorch/main.py
new file mode 100644
index 000000000..648e03be8
--- /dev/null
+++ b/docs/source/tutorials/hpo_quickstart_pytorch/main.py
@@ -0,0 +1,155 @@
+"""
+HPO Quickstart with PyTorch
+===========================
+This tutorial optimizes the model in `official PyTorch quickstart`_ with auto-tuning.
+
+The tutorial consists of 4 steps: 
+
+1. Modify the model for auto-tuning.
+2. Define hyperparameters' search space.
+3. Configure the experiment.
+4. Run the experiment.
+
+.. _official PyTorch quickstart: https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
+"""
+
+# %%
+# Step 1: Prepare the model
+# -------------------------
+# In first step, we need to prepare the model to be tuned.
+#
+# The model should be put in a separate script.
+# It will be evaluated many times concurrently,
+# and possibly will be trained on distributed platforms.
+#
+# In this tutorial, the model is defined in :doc:`model.py <model>`.
+#
+# In short, it is a PyTorch model with 3 additional API calls:
+#
+# 1. Use :func:`nni.get_next_parameter` to fetch the hyperparameters to be evalutated.
+# 2. Use :func:`nni.report_intermediate_result` to report per-epoch accuracy metrics.
+# 3. Use :func:`nni.report_final_result` to report final accuracy.
+#
+# Please understand the model code before continue to next step.
+
+# %%
+# Step 2: Define search space
+# ---------------------------
+# In model code, we have prepared 3 hyperparameters to be tuned:
+# *features*, *lr*, and *momentum*.
+#
+# Here we need to define their *search space* so the tuning algorithm can sample them in desired range.
+#
+# Assuming we have following prior knowledge for these hyperparameters:
+#
+# 1. *features* should be one of 128, 256, 512, 1024.
+# 2. *lr* should be a float between 0.0001 and 0.1, and it follows exponential distribution.
+# 3. *momentum* should be a float between 0 and 1.
+#
+# In NNI, the space of *features* is called ``choice``;
+# the space of *lr* is called ``loguniform``;
+# and the space of *momentum* is called ``uniform``.
+# You may have noticed, these names are derived from ``numpy.random``.
+#
+# For full specification of search space, check :doc:`the reference </hpo/search_space>`.
+#
+# Now we can define the search space as follow:
+
+search_space = {
+    'features': {'_type': 'choice', '_value': [128, 256, 512, 1024]},
+    'lr': {'_type': 'loguniform', '_value': [0.0001, 0.1]},
+    'momentum': {'_type': 'uniform', '_value': [0, 1]},
+}
+
+# %%
+# Step 3: Configure the experiment
+# --------------------------------
+# NNI uses an *experiment* to manage the HPO process.
+# The *experiment config* defines how to train the models and how to explore the search space.
+# 
+# In this tutorial we use a *local* mode experiment,
+# which means models will be trained on local machine, without using any special training platform.
+from nni.experiment import Experiment
+experiment = Experiment('local')
+
+# %%
+# Now we start to configure the experiment.
+#
+# Configure trial code
+# ^^^^^^^^^^^^^^^^^^^^
+# In NNI evaluation of each hyperparameter set is called a *trial*.
+# So the model script is called *trial code*.
+experiment.config.trial_command = 'python model.py'
+experiment.config.trial_code_directory = '.'
+# %%
+# When ``trial_code_directory`` is a relative path, it relates to current working directory.
+# To run ``main.py`` in a different path, you can set trial code directory to ``Path(__file__).parent``.
+# (`__file__ <https://docs.python.org/3.10/reference/datamodel.html#index-43>`__
+# is only available in standard Python, not in Jupyter Notebook.)
+#
+# .. attention::
+#
+#     If you are using Linux system without Conda,
+#     you may need to change ``"python model.py"`` to ``"python3 model.py"``.
+
+# %%
+# Configure search space
+# ^^^^^^^^^^^^^^^^^^^^^^
+experiment.config.search_space = search_space
+
+# %%
+# Configure tuning algorithm
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^
+# Here we use :doc:`TPE tuner </hpo/tuners>`.
+experiment.config.tuner.name = 'TPE'
+experiment.config.tuner.class_args['optimize_mode'] = 'maximize'
+
+# %%
+# Configure how many trials to run
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# Here we evaluate 10 sets of hyperparameters in total, and concurrently evaluate 2 sets at a time.
+experiment.config.max_trial_number = 10
+experiment.config.trial_concurrency = 2
+# %%
+# You may also set ``max_experiment_duration = '1h'`` to limit running time.
+#
+# If neither ``max_trial_number`` nor ``max_experiment_duration`` are set,
+# the experiment will run forever until you press Ctrl-C.
+#
+# .. note::
+#
+#     ``max_trial_number`` is set to 10 here for a fast example.
+#     In real world it should be set to a larger number.
+#     With default config TPE tuner requires 20 trials to warm up.
+
+# %%
+# Step 4: Run the experiment
+# --------------------------
+# Now the experiment is ready. Choose a port and launch it. (Here we use port 8080.)
+#
+# You can use the web portal to view experiment status: http://localhost:8080.
+experiment.run(8080)
+
+# %%
+# After the experiment is done
+# ----------------------------
+# Everything is done and it is safe to exit now. The following are optional.
+#
+# If you are using standard Python instead of Jupyter Notebook,
+# you can add ``input()`` or ``signal.pause()`` to prevent Python from exiting,
+# allowing you to view the web portal after the experiment is done.
+
+# input('Press enter to quit')
+experiment.stop()
+
+# %%
+# :meth:`nni.experiment.Experiment.stop` is automatically invoked when Python exits,
+# so it can be omitted in your code.
+#
+# After the experiment is stopped, you can run :meth:`nni.experiment.Experiment.view` to restart web portal.
+#
+# .. tip::
+#
+#     This example uses :doc:`Python API </reference/experiment>` to create experiment.
+#
+#     You can also create and manage experiments with :doc:`command line tool <../hpo_nnictl/nnictl>`.
diff --git a/docs/source/tutorials/hpo_quickstart_pytorch/main.py.md5 b/docs/source/tutorials/hpo_quickstart_pytorch/main.py.md5
new file mode 100644
index 000000000..1dc17777f
--- /dev/null
+++ b/docs/source/tutorials/hpo_quickstart_pytorch/main.py.md5
@@ -0,0 +1 @@
+e732cee426a4629b71f5fa28ce16fad7
\ No newline at end of file
diff --git a/docs/source/tutorials/hpo_quickstart_pytorch/main.rst b/docs/source/tutorials/hpo_quickstart_pytorch/main.rst
new file mode 100644
index 000000000..00abe19c0
--- /dev/null
+++ b/docs/source/tutorials/hpo_quickstart_pytorch/main.rst
@@ -0,0 +1,335 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "tutorials/hpo_quickstart_pytorch/main.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        Click :ref:`here <sphx_glr_download_tutorials_hpo_quickstart_pytorch_main.py>`
+        to download the full example code
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorials_hpo_quickstart_pytorch_main.py:
+
+
+HPO Quickstart with PyTorch
+===========================
+This tutorial optimizes the model in `official PyTorch quickstart`_ with auto-tuning.
+
+The tutorial consists of 4 steps: 
+
+1. Modify the model for auto-tuning.
+2. Define hyperparameters' search space.
+3. Configure the experiment.
+4. Run the experiment.
+
+.. _official PyTorch quickstart: https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
+
+.. GENERATED FROM PYTHON SOURCE LINES 17-34
+
+Step 1: Prepare the model
+-------------------------
+In first step, we need to prepare the model to be tuned.
+
+The model should be put in a separate script.
+It will be evaluated many times concurrently,
+and possibly will be trained on distributed platforms.
+
+In this tutorial, the model is defined in :doc:`model.py <model>`.
+
+In short, it is a PyTorch model with 3 additional API calls:
+
+1. Use :func:`nni.get_next_parameter` to fetch the hyperparameters to be evalutated.
+2. Use :func:`nni.report_intermediate_result` to report per-epoch accuracy metrics.
+3. Use :func:`nni.report_final_result` to report final accuracy.
+
+Please understand the model code before continue to next step.
+
+.. GENERATED FROM PYTHON SOURCE LINES 36-57
+
+Step 2: Define search space
+---------------------------
+In model code, we have prepared 3 hyperparameters to be tuned:
+*features*, *lr*, and *momentum*.
+
+Here we need to define their *search space* so the tuning algorithm can sample them in desired range.
+
+Assuming we have following prior knowledge for these hyperparameters:
+
+1. *features* should be one of 128, 256, 512, 1024.
+2. *lr* should be a float between 0.0001 and 0.1, and it follows exponential distribution.
+3. *momentum* should be a float between 0 and 1.
+
+In NNI, the space of *features* is called ``choice``;
+the space of *lr* is called ``loguniform``;
+and the space of *momentum* is called ``uniform``.
+You may have noticed, these names are derived from ``numpy.random``.
+
+For full specification of search space, check :doc:`the reference </hpo/search_space>`.
+
+Now we can define the search space as follow:
+
+.. GENERATED FROM PYTHON SOURCE LINES 57-64
+
+.. code-block:: default
+
+
+    search_space = {
+        'features': {'_type': 'choice', '_value': [128, 256, 512, 1024]},
+        'lr': {'_type': 'loguniform', '_value': [0.0001, 0.1]},
+        'momentum': {'_type': 'uniform', '_value': [0, 1]},
+    }
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 65-72
+
+Step 3: Configure the experiment
+--------------------------------
+NNI uses an *experiment* to manage the HPO process.
+The *experiment config* defines how to train the models and how to explore the search space.
+
+In this tutorial we use a *local* mode experiment,
+which means models will be trained on local machine, without using any special training platform.
+
+.. GENERATED FROM PYTHON SOURCE LINES 72-75
+
+.. code-block:: default
+
+    from nni.experiment import Experiment
+    experiment = Experiment('local')
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 76-82
+
+Now we start to configure the experiment.
+
+Configure trial code
+^^^^^^^^^^^^^^^^^^^^
+In NNI evaluation of each hyperparameter set is called a *trial*.
+So the model script is called *trial code*.
+
+.. GENERATED FROM PYTHON SOURCE LINES 82-84
+
+.. code-block:: default
+
+    experiment.config.trial_command = 'python model.py'
+    experiment.config.trial_code_directory = '.'
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 85-94
+
+When ``trial_code_directory`` is a relative path, it relates to current working directory.
+To run ``main.py`` in a different path, you can set trial code directory to ``Path(__file__).parent``.
+(`__file__ <https://docs.python.org/3.10/reference/datamodel.html#index-43>`__
+is only available in standard Python, not in Jupyter Notebook.)
+
+.. attention::
+
+    If you are using Linux system without Conda,
+    you may need to change ``"python model.py"`` to ``"python3 model.py"``.
+
+.. GENERATED FROM PYTHON SOURCE LINES 96-98
+
+Configure search space
+^^^^^^^^^^^^^^^^^^^^^^
+
+.. GENERATED FROM PYTHON SOURCE LINES 98-100
+
+.. code-block:: default
+
+    experiment.config.search_space = search_space
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 101-104
+
+Configure tuning algorithm
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+Here we use :doc:`TPE tuner </hpo/tuners>`.
+
+.. GENERATED FROM PYTHON SOURCE LINES 104-107
+
+.. code-block:: default
+
+    experiment.config.tuner.name = 'TPE'
+    experiment.config.tuner.class_args['optimize_mode'] = 'maximize'
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 108-111
+
+Configure how many trials to run
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Here we evaluate 10 sets of hyperparameters in total, and concurrently evaluate 2 sets at a time.
+
+.. GENERATED FROM PYTHON SOURCE LINES 111-113
+
+.. code-block:: default
+
+    experiment.config.max_trial_number = 10
+    experiment.config.trial_concurrency = 2
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 114-124
+
+You may also set ``max_experiment_duration = '1h'`` to limit running time.
+
+If neither ``max_trial_number`` nor ``max_experiment_duration`` are set,
+the experiment will run forever until you press Ctrl-C.
+
+.. note::
+
+    ``max_trial_number`` is set to 10 here for a fast example.
+    In real world it should be set to a larger number.
+    With default config TPE tuner requires 20 trials to warm up.
+
+.. GENERATED FROM PYTHON SOURCE LINES 126-131
+
+Step 4: Run the experiment
+--------------------------
+Now the experiment is ready. Choose a port and launch it. (Here we use port 8080.)
+
+You can use the web portal to view experiment status: http://localhost:8080.
+
+.. GENERATED FROM PYTHON SOURCE LINES 131-133
+
+.. code-block:: default
+
+    experiment.run(8080)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    [2022-04-13 12:07:29] Creating experiment, Experiment ID: hgkju3iq
+    [2022-04-13 12:07:29] Starting web server...
+    [2022-04-13 12:07:30] Setting up...
+    [2022-04-13 12:07:30] Web portal URLs: http://127.0.0.1:8080 http://192.168.100.103:8080
+
+    True
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 134-141
+
+After the experiment is done
+----------------------------
+Everything is done and it is safe to exit now. The following are optional.
+
+If you are using standard Python instead of Jupyter Notebook,
+you can add ``input()`` or ``signal.pause()`` to prevent Python from exiting,
+allowing you to view the web portal after the experiment is done.
+
+.. GENERATED FROM PYTHON SOURCE LINES 141-145
+
+.. code-block:: default
+
+
+    # input('Press enter to quit')
+    experiment.stop()
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    [2022-04-13 12:08:50] Stopping experiment, please wait...
+    [2022-04-13 12:08:53] Experiment stopped
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 146-156
+
+:meth:`nni.experiment.Experiment.stop` is automatically invoked when Python exits,
+so it can be omitted in your code.
+
+After the experiment is stopped, you can run :meth:`nni.experiment.Experiment.view` to restart web portal.
+
+.. tip::
+
+    This example uses :doc:`Python API </reference/experiment>` to create experiment.
+
+    You can also create and manage experiments with :doc:`command line tool <../hpo_nnictl/nnictl>`.
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 1 minutes  24.367 seconds)
+
+
+.. _sphx_glr_download_tutorials_hpo_quickstart_pytorch_main.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+    :class: sphx-glr-footer-example
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-python
+
+     :download:`Download Python source code: main.py <main.py>`
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+     :download:`Download Jupyter notebook: main.ipynb <main.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/source/tutorials/hpo_quickstart_pytorch/main_codeobj.pickle b/docs/source/tutorials/hpo_quickstart_pytorch/main_codeobj.pickle
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literal 0
HcmV?d00001

diff --git a/docs/source/tutorials/hpo_quickstart_pytorch/main_zh.rst b/docs/source/tutorials/hpo_quickstart_pytorch/main_zh.rst
new file mode 100644
index 000000000..0ca5d9106
--- /dev/null
+++ b/docs/source/tutorials/hpo_quickstart_pytorch/main_zh.rst
@@ -0,0 +1,327 @@
+.. a395c59bf5359c3583b7a0a3ab66d705
+
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "tutorials/hpo_quickstart_pytorch/main.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        Click :ref:`here <sphx_glr_download_tutorials_hpo_quickstart_pytorch_main.py>`
+        to download the full example code
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorials_hpo_quickstart_pytorch_main.py:
+
+
+HPO 教程（PyTorch 版本）
+========================
+本教程对 `PyTorch 官方教程 <https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html>`__ 进行超参调优。
+
+教程分为四步：
+
+1. 修改调优的模型代码；
+2. 定义超参的搜索空间；
+3. 配置实验；
+4. 运行实验。
+
+.. GENERATED FROM PYTHON SOURCE LINES 17-34
+
+步骤一：准备模型
+----------------
+首先，我们需要准备待调优的模型。
+
+由于被调优的模型会被独立地运行多次，
+并且使用特定训练平台时还可能会被上传到云端执行，
+我们需要将代码写在另一个 py 文件中。
+
+本教程使用的模型的代码是 :doc:`model.py <model>`。
+
+模型代码在一个普通的 PyTorch 模型基础之上，增加了3个 API 调用：
+
+1. 使用 :func:`nni.get_next_parameter` 获取需要评估的超参；
+2. 使用 :func:`nni.report_intermediate_result` 报告每个 epoch 产生的中间训练结果；
+3. 使用 :func:`nni.report_final_result` 报告最终准确率。
+
+请先理解模型代码，然后再继续下一步。
+
+.. GENERATED FROM PYTHON SOURCE LINES 36-57
+
+步骤二：定义搜索空间
+--------------------
+在模型代码中，我们准备了三个需要调优的超参：features、lr、momentum。
+
+现在，我们需要定义的它们的“搜索空间”，指定它们的取值范围和分布规律。
+
+假设我们对三个超参有以下先验知识：
+
+1. features 的取值可以为128、256、512、1024；
+2. lr 的取值在0.0001到0.1之间，其取值符合指数分布；
+3. momentum 的取值在0到1之间。
+
+在 NNI 中，features 的取值范围称为 ``choice`` ，
+lr 的取值范围称为 ``loguniform`` ，
+momentum 的取值范围称为 ``uniform`` 。
+您可能已经注意到了，这些名称和 ``numpy.random`` 中的函数名一致。
+
+完整的搜索空间文档： :doc:`/hpo/search_space`.
+
+我们的搜索空间定义如下：
+
+.. GENERATED FROM PYTHON SOURCE LINES 57-64
+
+.. code-block:: default
+
+
+    search_space = {
+        'features': {'_type': 'choice', '_value': [128, 256, 512, 1024]},
+        'lr': {'_type': 'loguniform', '_value': [0.0001, 0.1]},
+        'momentum': {'_type': 'uniform', '_value': [0, 1]},
+    }
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 65-72
+
+步骤三：配置实验
+----------------
+NNI 使用“实验”来管理超参调优，“实验配置”定义了如何训练模型、如何遍历搜索空间。
+
+在本教程中我们使用 local 模式的实验，这意味着实验只在本机运行，不使用任何特别的训练平台。
+
+.. GENERATED FROM PYTHON SOURCE LINES 72-75
+
+.. code-block:: default
+
+    from nni.experiment import Experiment
+    experiment = Experiment('local')
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 76-82
+
+现在我们开始配置实验。
+
+配置 trial
+^^^^^^^^^^
+在 NNI 中评估一组超参的过程被称为一个“trial”（试验），上面的模型代码被称为“trial 代码”。
+
+.. GENERATED FROM PYTHON SOURCE LINES 82-84
+
+.. code-block:: default
+
+    experiment.config.trial_command = 'python model.py'
+    experiment.config.trial_code_directory = '.'
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 85-94
+
+如果 ``trial_code_directory`` 是一个相对路径，它被认为相对于当前的工作目录。
+如果您想在其他路径下运行本文件 ``main.py`` ，您可以将代码目录设置为 ``Path(__file__).parent`` 。
+(`__file__ <https://docs.python.org/3.10/reference/datamodel.html#index-43>`__
+只能在 py 文件中使用，不能在 Jupyter Notebook 中使用)
+
+.. attention::
+
+    如果您使用 Linux 系统，并且没有使用 Conda，
+    您可能需要将 ``"python model.py"`` 改为 ``"python3 model.py"`` 。
+
+.. GENERATED FROM PYTHON SOURCE LINES 96-98
+
+配置搜索空间
+^^^^^^^^^^^^
+
+.. GENERATED FROM PYTHON SOURCE LINES 98-100
+
+.. code-block:: default
+
+    experiment.config.search_space = search_space
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 101-104
+
+配置调优算法
+^^^^^^^^^^^^
+此处我们使用 :doc:`TPE 算法</hpo/tuners>` 。
+
+.. GENERATED FROM PYTHON SOURCE LINES 104-107
+
+.. code-block:: default
+
+    experiment.config.tuner.name = 'TPE'
+    experiment.config.tuner.class_args['optimize_mode'] = 'maximize'
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 108-111
+
+配置运行多少 trial
+^^^^^^^^^^^^^^^^^^
+本教程中我们总共尝试10组超参，并且每次并行地评估2组超参。
+
+.. GENERATED FROM PYTHON SOURCE LINES 111-113
+
+.. code-block:: default
+
+    experiment.config.max_trial_number = 10
+    experiment.config.trial_concurrency = 2
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 114-124
+
+您也可以设置 ``max_experiment_duration = '1h'`` 来限制运行时间。
+
+如果 ``max_trial_number`` 和 ``max_experiment_duration`` 都没有设置，实验将会一直运行，直到您按下 Ctrl-C。
+
+.. note::
+
+    此处将 ``max_trial_number`` 设置为10是为了让教程能够较快地运行结束，
+    在实际使用中应该设为更大的数值，TPE 算法在默认参数下需要评估20组超参才会完成初始化。
+
+.. GENERATED FROM PYTHON SOURCE LINES 126-131
+
+步骤四：运行实验
+----------------
+现在实验已经配置完成了，您可以指定一个端口来运行它，教程中我们使用8080端口。
+
+您可以通过网页控制台查看实验状态： http://localhost:8080.
+
+.. GENERATED FROM PYTHON SOURCE LINES 131-133
+
+.. code-block:: default
+
+    experiment.run(8080)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    [2022-04-13 12:07:29] Creating experiment, Experiment ID: hgkju3iq
+    [2022-04-13 12:07:29] Starting web server...
+    [2022-04-13 12:07:30] Setting up...
+    [2022-04-13 12:07:30] Web portal URLs: http://127.0.0.1:8080 http://192.168.100.103:8080
+
+    True
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 134-141
+
+实验结束之后
+------------
+您只需要等待函数返回就可以正常结束实验，以下内容为可选项。
+
+如果您使用的是普通 Python 而不是 Jupyter Notebook，
+您可以在代码末尾加上一行 ``input()`` 或者 ``signal.pause()`` 来避免 Python 解释器自动退出，
+这样您就能继续使用网页控制台。
+
+.. GENERATED FROM PYTHON SOURCE LINES 141-145
+
+.. code-block:: default
+
+
+    # input('Press enter to quit')
+    experiment.stop()
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    [2022-04-13 12:08:50] Stopping experiment, please wait...
+    [2022-04-13 12:08:53] Experiment stopped
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 146-156
+
+:meth:`nni.experiment.Experiment.stop` 会在 Python 退出前自动调用，所以您可以将其省略，不写在自己的代码中。
+
+实验完全停止之后，您可以使用 :meth:`nni.experiment.Experiment.view` 重新启动网页控制台。
+
+.. tip::
+
+    本教程使用 :doc:`Python API </reference/experiment>` 创建实验，
+    除此之外您也可以选择使用 :doc:`命令行工具 <../hpo_nnictl/nnictl>` 。
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 1 minutes  24.367 seconds)
+
+
+.. _sphx_glr_download_tutorials_hpo_quickstart_pytorch_main.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+    :class: sphx-glr-footer-example
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-python
+
+     :download:`Download Python source code: main.py <main.py>`
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+     :download:`Download Jupyter notebook: main.ipynb <main.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/source/tutorials/hpo_quickstart_pytorch/model.ipynb b/docs/source/tutorials/hpo_quickstart_pytorch/model.ipynb
new file mode 100644
index 000000000..f1acdfc84
--- /dev/null
+++ b/docs/source/tutorials/hpo_quickstart_pytorch/model.ipynb
@@ -0,0 +1,162 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Port PyTorch Quickstart to NNI\nThis is a modified version of `PyTorch quickstart`_.\n\nIt can be run directly and will have the exact same result as original version.\n\nFurthermore, it enables the ability of auto tuning with an NNI *experiment*, which will be detailed later.\n\nIt is recommended to run this script directly first to verify the environment.\n\nThere are 2 key differences from the original version:\n\n1. In `Get optimized hyperparameters`_ part, it receives generated hyperparameters.\n2. In `Train model and report accuracy`_ part, it reports accuracy metrics to NNI.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import nni\nimport torch\nfrom torch import nn\nfrom torch.utils.data import DataLoader\nfrom torchvision import datasets\nfrom torchvision.transforms import ToTensor"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Hyperparameters to be tuned\nThese are the hyperparameters that will be tuned.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "params = {\n    'features': 512,\n    'lr': 0.001,\n    'momentum': 0,\n}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Get optimized hyperparameters\nIf run directly, :func:`nni.get_next_parameter` is a no-op and returns an empty dict.\nBut with an NNI *experiment*, it will receive optimized hyperparameters from tuning algorithm.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "optimized_params = nni.get_next_parameter()\nparams.update(optimized_params)\nprint(params)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Load dataset\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "training_data = datasets.FashionMNIST(root=\"data\", train=True, download=True, transform=ToTensor())\ntest_data = datasets.FashionMNIST(root=\"data\", train=False, download=True, transform=ToTensor())\n\nbatch_size = 64\n\ntrain_dataloader = DataLoader(training_data, batch_size=batch_size)\ntest_dataloader = DataLoader(test_data, batch_size=batch_size)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Build model with hyperparameters\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\nprint(f\"Using {device} device\")\n\nclass NeuralNetwork(nn.Module):\n    def __init__(self):\n        super(NeuralNetwork, self).__init__()\n        self.flatten = nn.Flatten()\n        self.linear_relu_stack = nn.Sequential(\n            nn.Linear(28*28, params['features']),\n            nn.ReLU(),\n            nn.Linear(params['features'], params['features']),\n            nn.ReLU(),\n            nn.Linear(params['features'], 10)\n        )\n\n    def forward(self, x):\n        x = self.flatten(x)\n        logits = self.linear_relu_stack(x)\n        return logits\n\nmodel = NeuralNetwork().to(device)\n\nloss_fn = nn.CrossEntropyLoss()\noptimizer = torch.optim.SGD(model.parameters(), lr=params['lr'], momentum=params['momentum'])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Define train and test\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "def train(dataloader, model, loss_fn, optimizer):\n    size = len(dataloader.dataset)\n    model.train()\n    for batch, (X, y) in enumerate(dataloader):\n        X, y = X.to(device), y.to(device)\n        pred = model(X)\n        loss = loss_fn(pred, y)\n        optimizer.zero_grad()\n        loss.backward()\n        optimizer.step()\n\ndef test(dataloader, model, loss_fn):\n    size = len(dataloader.dataset)\n    num_batches = len(dataloader)\n    model.eval()\n    test_loss, correct = 0, 0\n    with torch.no_grad():\n        for X, y in dataloader:\n            X, y = X.to(device), y.to(device)\n            pred = model(X)\n            test_loss += loss_fn(pred, y).item()\n            correct += (pred.argmax(1) == y).type(torch.float).sum().item()\n    test_loss /= num_batches\n    correct /= size\n    return correct"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Train model and report accuracy\nReport accuracy metrics to NNI so the tuning algorithm can suggest better hyperparameters.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "epochs = 5\nfor t in range(epochs):\n    print(f\"Epoch {t+1}\\n-------------------------------\")\n    train(train_dataloader, model, loss_fn, optimizer)\n    accuracy = test(test_dataloader, model, loss_fn)\n    nni.report_intermediate_result(accuracy)\nnni.report_final_result(accuracy)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/source/tutorials/hpo_quickstart_pytorch/model.py b/docs/source/tutorials/hpo_quickstart_pytorch/model.py
new file mode 100644
index 000000000..1bb07bf14
--- /dev/null
+++ b/docs/source/tutorials/hpo_quickstart_pytorch/model.py
@@ -0,0 +1,125 @@
+"""
+Port PyTorch Quickstart to NNI
+==============================
+This is a modified version of `PyTorch quickstart`_.
+
+It can be run directly and will have the exact same result as original version.
+
+Furthermore, it enables the ability of auto tuning with an NNI *experiment*, which will be detailed later.
+
+It is recommended to run this script directly first to verify the environment.
+
+There are 2 key differences from the original version:
+
+1. In `Get optimized hyperparameters`_ part, it receives generated hyperparameters.
+2. In `Train model and report accuracy`_ part, it reports accuracy metrics to NNI.
+
+.. _PyTorch quickstart: https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
+"""
+
+# %%
+import nni
+import torch
+from torch import nn
+from torch.utils.data import DataLoader
+from torchvision import datasets
+from torchvision.transforms import ToTensor
+
+# %%
+# Hyperparameters to be tuned
+# ---------------------------
+# These are the hyperparameters that will be tuned.
+params = {
+    'features': 512,
+    'lr': 0.001,
+    'momentum': 0,
+}
+
+# %%
+# Get optimized hyperparameters
+# -----------------------------
+# If run directly, :func:`nni.get_next_parameter` is a no-op and returns an empty dict.
+# But with an NNI *experiment*, it will receive optimized hyperparameters from tuning algorithm.
+optimized_params = nni.get_next_parameter()
+params.update(optimized_params)
+print(params)
+
+# %%
+# Load dataset
+# ------------
+training_data = datasets.FashionMNIST(root="data", train=True, download=True, transform=ToTensor())
+test_data = datasets.FashionMNIST(root="data", train=False, download=True, transform=ToTensor())
+
+batch_size = 64
+
+train_dataloader = DataLoader(training_data, batch_size=batch_size)
+test_dataloader = DataLoader(test_data, batch_size=batch_size)
+
+# %%
+# Build model with hyperparameters
+# --------------------------------
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Using {device} device")
+
+class NeuralNetwork(nn.Module):
+    def __init__(self):
+        super(NeuralNetwork, self).__init__()
+        self.flatten = nn.Flatten()
+        self.linear_relu_stack = nn.Sequential(
+            nn.Linear(28*28, params['features']),
+            nn.ReLU(),
+            nn.Linear(params['features'], params['features']),
+            nn.ReLU(),
+            nn.Linear(params['features'], 10)
+        )
+
+    def forward(self, x):
+        x = self.flatten(x)
+        logits = self.linear_relu_stack(x)
+        return logits
+
+model = NeuralNetwork().to(device)
+
+loss_fn = nn.CrossEntropyLoss()
+optimizer = torch.optim.SGD(model.parameters(), lr=params['lr'], momentum=params['momentum'])
+
+# %%
+# Define train and test
+# ---------------------
+def train(dataloader, model, loss_fn, optimizer):
+    size = len(dataloader.dataset)
+    model.train()
+    for batch, (X, y) in enumerate(dataloader):
+        X, y = X.to(device), y.to(device)
+        pred = model(X)
+        loss = loss_fn(pred, y)
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+def test(dataloader, model, loss_fn):
+    size = len(dataloader.dataset)
+    num_batches = len(dataloader)
+    model.eval()
+    test_loss, correct = 0, 0
+    with torch.no_grad():
+        for X, y in dataloader:
+            X, y = X.to(device), y.to(device)
+            pred = model(X)
+            test_loss += loss_fn(pred, y).item()
+            correct += (pred.argmax(1) == y).type(torch.float).sum().item()
+    test_loss /= num_batches
+    correct /= size
+    return correct
+
+# %%
+# Train model and report accuracy
+# -------------------------------
+# Report accuracy metrics to NNI so the tuning algorithm can suggest better hyperparameters.
+epochs = 5
+for t in range(epochs):
+    print(f"Epoch {t+1}\n-------------------------------")
+    train(train_dataloader, model, loss_fn, optimizer)
+    accuracy = test(test_dataloader, model, loss_fn)
+    nni.report_intermediate_result(accuracy)
+nni.report_final_result(accuracy)
diff --git a/docs/source/tutorials/hpo_quickstart_pytorch/model.py.md5 b/docs/source/tutorials/hpo_quickstart_pytorch/model.py.md5
new file mode 100644
index 000000000..4559246a4
--- /dev/null
+++ b/docs/source/tutorials/hpo_quickstart_pytorch/model.py.md5
@@ -0,0 +1 @@
+ed8bfc27e3d555d842fc4eec2635e619
\ No newline at end of file
diff --git a/docs/source/tutorials/hpo_quickstart_pytorch/model.rst b/docs/source/tutorials/hpo_quickstart_pytorch/model.rst
new file mode 100644
index 000000000..fde392d32
--- /dev/null
+++ b/docs/source/tutorials/hpo_quickstart_pytorch/model.rst
@@ -0,0 +1,303 @@
+:orphan:
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "tutorials/hpo_quickstart_pytorch/model.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        Click :ref:`here <sphx_glr_download_tutorials_hpo_quickstart_pytorch_model.py>`
+        to download the full example code
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorials_hpo_quickstart_pytorch_model.py:
+
+
+Port PyTorch Quickstart to NNI
+==============================
+This is a modified version of `PyTorch quickstart`_.
+
+It can be run directly and will have the exact same result as original version.
+
+Furthermore, it enables the ability of auto tuning with an NNI *experiment*, which will be detailed later.
+
+It is recommended to run this script directly first to verify the environment.
+
+There are 2 key differences from the original version:
+
+1. In `Get optimized hyperparameters`_ part, it receives generated hyperparameters.
+2. In `Train model and report accuracy`_ part, it reports accuracy metrics to NNI.
+
+.. _PyTorch quickstart: https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
+
+.. GENERATED FROM PYTHON SOURCE LINES 21-28
+
+.. code-block:: default
+
+    import nni
+    import torch
+    from torch import nn
+    from torch.utils.data import DataLoader
+    from torchvision import datasets
+    from torchvision.transforms import ToTensor
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 29-32
+
+Hyperparameters to be tuned
+---------------------------
+These are the hyperparameters that will be tuned.
+
+.. GENERATED FROM PYTHON SOURCE LINES 32-38
+
+.. code-block:: default
+
+    params = {
+        'features': 512,
+        'lr': 0.001,
+        'momentum': 0,
+    }
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 39-43
+
+Get optimized hyperparameters
+-----------------------------
+If run directly, :func:`nni.get_next_parameter` is a no-op and returns an empty dict.
+But with an NNI *experiment*, it will receive optimized hyperparameters from tuning algorithm.
+
+.. GENERATED FROM PYTHON SOURCE LINES 43-47
+
+.. code-block:: default
+
+    optimized_params = nni.get_next_parameter()
+    params.update(optimized_params)
+    print(params)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    {'features': 512, 'lr': 0.001, 'momentum': 0}
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 48-50
+
+Load dataset
+------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 50-58
+
+.. code-block:: default
+
+    training_data = datasets.FashionMNIST(root="data", train=True, download=True, transform=ToTensor())
+    test_data = datasets.FashionMNIST(root="data", train=False, download=True, transform=ToTensor())
+
+    batch_size = 64
+
+    train_dataloader = DataLoader(training_data, batch_size=batch_size)
+    test_dataloader = DataLoader(test_data, batch_size=batch_size)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 59-61
+
+Build model with hyperparameters
+--------------------------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 61-86
+
+.. code-block:: default
+
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    print(f"Using {device} device")
+
+    class NeuralNetwork(nn.Module):
+        def __init__(self):
+            super(NeuralNetwork, self).__init__()
+            self.flatten = nn.Flatten()
+            self.linear_relu_stack = nn.Sequential(
+                nn.Linear(28*28, params['features']),
+                nn.ReLU(),
+                nn.Linear(params['features'], params['features']),
+                nn.ReLU(),
+                nn.Linear(params['features'], 10)
+            )
+
+        def forward(self, x):
+            x = self.flatten(x)
+            logits = self.linear_relu_stack(x)
+            return logits
+
+    model = NeuralNetwork().to(device)
+
+    loss_fn = nn.CrossEntropyLoss()
+    optimizer = torch.optim.SGD(model.parameters(), lr=params['lr'], momentum=params['momentum'])
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    Using cpu device
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 87-89
+
+Define train and test
+---------------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 89-115
+
+.. code-block:: default
+
+    def train(dataloader, model, loss_fn, optimizer):
+        size = len(dataloader.dataset)
+        model.train()
+        for batch, (X, y) in enumerate(dataloader):
+            X, y = X.to(device), y.to(device)
+            pred = model(X)
+            loss = loss_fn(pred, y)
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+    def test(dataloader, model, loss_fn):
+        size = len(dataloader.dataset)
+        num_batches = len(dataloader)
+        model.eval()
+        test_loss, correct = 0, 0
+        with torch.no_grad():
+            for X, y in dataloader:
+                X, y = X.to(device), y.to(device)
+                pred = model(X)
+                test_loss += loss_fn(pred, y).item()
+                correct += (pred.argmax(1) == y).type(torch.float).sum().item()
+        test_loss /= num_batches
+        correct /= size
+        return correct
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 116-119
+
+Train model and report accuracy
+-------------------------------
+Report accuracy metrics to NNI so the tuning algorithm can suggest better hyperparameters.
+
+.. GENERATED FROM PYTHON SOURCE LINES 119-126
+
+.. code-block:: default
+
+    epochs = 5
+    for t in range(epochs):
+        print(f"Epoch {t+1}\n-------------------------------")
+        train(train_dataloader, model, loss_fn, optimizer)
+        accuracy = test(test_dataloader, model, loss_fn)
+        nni.report_intermediate_result(accuracy)
+    nni.report_final_result(accuracy)
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    Epoch 1
+    -------------------------------
+    [2022-03-21 01:09:37] INFO (nni/MainThread) Intermediate result: 0.461  (Index 0)
+    Epoch 2
+    -------------------------------
+    [2022-03-21 01:09:42] INFO (nni/MainThread) Intermediate result: 0.5529  (Index 1)
+    Epoch 3
+    -------------------------------
+    [2022-03-21 01:09:47] INFO (nni/MainThread) Intermediate result: 0.6155  (Index 2)
+    Epoch 4
+    -------------------------------
+    [2022-03-21 01:09:52] INFO (nni/MainThread) Intermediate result: 0.6345  (Index 3)
+    Epoch 5
+    -------------------------------
+    [2022-03-21 01:09:56] INFO (nni/MainThread) Intermediate result: 0.6505  (Index 4)
+    [2022-03-21 01:09:56] INFO (nni/MainThread) Final result: 0.6505
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 0 minutes  24.441 seconds)
+
+
+.. _sphx_glr_download_tutorials_hpo_quickstart_pytorch_model.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+    :class: sphx-glr-footer-example
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-python
+
+     :download:`Download Python source code: model.py <model.py>`
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+     :download:`Download Jupyter notebook: model.ipynb <model.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/source/tutorials/hpo_quickstart_pytorch/model_codeobj.pickle b/docs/source/tutorials/hpo_quickstart_pytorch/model_codeobj.pickle
new file mode 100644
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diff --git a/docs/source/tutorials/hpo_quickstart_pytorch/model_zh.rst b/docs/source/tutorials/hpo_quickstart_pytorch/model_zh.rst
new file mode 100644
index 000000000..5ec045658
--- /dev/null
+++ b/docs/source/tutorials/hpo_quickstart_pytorch/model_zh.rst
@@ -0,0 +1,300 @@
+.. e083b4dc8e350428ddf680e97b47cc8e
+
+:orphan:
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "tutorials/hpo_quickstart_pytorch/model.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        Click :ref:`here <sphx_glr_download_tutorials_hpo_quickstart_pytorch_model.py>`
+        to download the full example code
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorials_hpo_quickstart_pytorch_model.py:
+
+将 PyTorch 官方教程移植到NNI
+============================
+本文件是 `PyTorch 官方教程 <https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.htlm>`__ 的修改版。
+
+您可以直接运行本文件，其结果和原版完全一致。同时，您也可以在 NNI 实验中使用本文件，进行超参调优。
+
+我们建议您先直接运行一次本文件，在熟悉代码的同时检查运行环境。
+
+和原版相比，我们做了两处修改：
+
+1. 在 `获取调优后的参数`_ 部分，我们使用调优算法生成的参数替换默认参数；
+2. 在 `训练模型并上传结果`_ 部分，我们将准确率数据报告给 NNI。
+
+.. GENERATED FROM PYTHON SOURCE LINES 21-28
+
+.. code-block:: default
+
+    import nni
+    import torch
+    from torch import nn
+    from torch.utils.data import DataLoader
+    from torchvision import datasets
+    from torchvision.transforms import ToTensor
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 29-32
+
+准备调优的超参
+--------------
+以下超参将被调优：
+
+.. GENERATED FROM PYTHON SOURCE LINES 32-38
+
+.. code-block:: default
+
+    params = {
+        'features': 512,
+        'lr': 0.001,
+        'momentum': 0,
+    }
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 39-43
+
+获取调优后的参数
+----------------
+直接运行时 :func:`nni.get_next_parameter` 会返回空 dict，
+而在 NNI 实验中使用时，它会返回调优算法生成的超参组合。
+
+.. GENERATED FROM PYTHON SOURCE LINES 43-47
+
+.. code-block:: default
+
+    optimized_params = nni.get_next_parameter()
+    params.update(optimized_params)
+    print(params)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    {'features': 512, 'lr': 0.001, 'momentum': 0}
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 48-50
+
+加载数据集
+----------
+
+.. GENERATED FROM PYTHON SOURCE LINES 50-58
+
+.. code-block:: default
+
+    training_data = datasets.FashionMNIST(root="data", train=True, download=True, transform=ToTensor())
+    test_data = datasets.FashionMNIST(root="data", train=False, download=True, transform=ToTensor())
+
+    batch_size = 64
+
+    train_dataloader = DataLoader(training_data, batch_size=batch_size)
+    test_dataloader = DataLoader(test_data, batch_size=batch_size)
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 59-61
+
+使用超参构建模型
+----------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 61-86
+
+.. code-block:: default
+
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    print(f"Using {device} device")
+
+    class NeuralNetwork(nn.Module):
+        def __init__(self):
+            super(NeuralNetwork, self).__init__()
+            self.flatten = nn.Flatten()
+            self.linear_relu_stack = nn.Sequential(
+                nn.Linear(28*28, params['features']),
+                nn.ReLU(),
+                nn.Linear(params['features'], params['features']),
+                nn.ReLU(),
+                nn.Linear(params['features'], 10)
+            )
+
+        def forward(self, x):
+            x = self.flatten(x)
+            logits = self.linear_relu_stack(x)
+            return logits
+
+    model = NeuralNetwork().to(device)
+
+    loss_fn = nn.CrossEntropyLoss()
+    optimizer = torch.optim.SGD(model.parameters(), lr=params['lr'], momentum=params['momentum'])
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    Using cpu device
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 87-89
+
+定义训练和测试函数
+------------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 89-115
+
+.. code-block:: default
+
+    def train(dataloader, model, loss_fn, optimizer):
+        size = len(dataloader.dataset)
+        model.train()
+        for batch, (X, y) in enumerate(dataloader):
+            X, y = X.to(device), y.to(device)
+            pred = model(X)
+            loss = loss_fn(pred, y)
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+    def test(dataloader, model, loss_fn):
+        size = len(dataloader.dataset)
+        num_batches = len(dataloader)
+        model.eval()
+        test_loss, correct = 0, 0
+        with torch.no_grad():
+            for X, y in dataloader:
+                X, y = X.to(device), y.to(device)
+                pred = model(X)
+                test_loss += loss_fn(pred, y).item()
+                correct += (pred.argmax(1) == y).type(torch.float).sum().item()
+        test_loss /= num_batches
+        correct /= size
+        return correct
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 116-119
+
+训练模型并上传结果
+------------------
+将准确率数据报告给 NNI 的调参算法，以使其能够预测更优的超参组合。
+
+.. GENERATED FROM PYTHON SOURCE LINES 119-126
+
+.. code-block:: default
+
+    epochs = 5
+    for t in range(epochs):
+        print(f"Epoch {t+1}\n-------------------------------")
+        train(train_dataloader, model, loss_fn, optimizer)
+        accuracy = test(test_dataloader, model, loss_fn)
+        nni.report_intermediate_result(accuracy)
+    nni.report_final_result(accuracy)
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    Epoch 1
+    -------------------------------
+    [2022-03-21 01:09:37] INFO (nni/MainThread) Intermediate result: 0.461  (Index 0)
+    Epoch 2
+    -------------------------------
+    [2022-03-21 01:09:42] INFO (nni/MainThread) Intermediate result: 0.5529  (Index 1)
+    Epoch 3
+    -------------------------------
+    [2022-03-21 01:09:47] INFO (nni/MainThread) Intermediate result: 0.6155  (Index 2)
+    Epoch 4
+    -------------------------------
+    [2022-03-21 01:09:52] INFO (nni/MainThread) Intermediate result: 0.6345  (Index 3)
+    Epoch 5
+    -------------------------------
+    [2022-03-21 01:09:56] INFO (nni/MainThread) Intermediate result: 0.6505  (Index 4)
+    [2022-03-21 01:09:56] INFO (nni/MainThread) Final result: 0.6505
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 0 minutes  24.441 seconds)
+
+
+.. _sphx_glr_download_tutorials_hpo_quickstart_pytorch_model.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+    :class: sphx-glr-footer-example
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-python
+
+     :download:`Download Python source code: model.py <model.py>`
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+     :download:`Download Jupyter notebook: model.ipynb <model.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/source/tutorials/hpo_quickstart_pytorch/sg_execution_times.rst b/docs/source/tutorials/hpo_quickstart_pytorch/sg_execution_times.rst
new file mode 100644
index 000000000..95fbbb046
--- /dev/null
+++ b/docs/source/tutorials/hpo_quickstart_pytorch/sg_execution_times.rst
@@ -0,0 +1,14 @@
+
+:orphan:
+
+.. _sphx_glr_tutorials_hpo_quickstart_pytorch_sg_execution_times:
+
+Computation times
+=================
+**01:24.367** total execution time for **tutorials_hpo_quickstart_pytorch** files:
+
++--------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_hpo_quickstart_pytorch_main.py` (``main.py``)   | 01:24.367 | 0.0 MB |
++--------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_hpo_quickstart_pytorch_model.py` (``model.py``) | 00:00.000 | 0.0 MB |
++--------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/source/tutorials/hpo_quickstart_tensorflow/images/thumb/sphx_glr_main_thumb.png b/docs/source/tutorials/hpo_quickstart_tensorflow/images/thumb/sphx_glr_main_thumb.png
new file mode 100644
index 0000000000000000000000000000000000000000..b06c4e6a17748efb9e7d009eb1e161759cfa2b74
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diff --git a/docs/source/tutorials/hpo_quickstart_tensorflow/main.ipynb b/docs/source/tutorials/hpo_quickstart_tensorflow/main.ipynb
new file mode 100644
index 000000000..98982659f
--- /dev/null
+++ b/docs/source/tutorials/hpo_quickstart_tensorflow/main.ipynb
@@ -0,0 +1,215 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# HPO Quickstart with TensorFlow\nThis tutorial optimizes the model in `official TensorFlow quickstart`_ with auto-tuning.\n\nThe tutorial consists of 4 steps: \n\n1. Modify the model for auto-tuning.\n2. Define hyperparameters' search space.\n3. Configure the experiment.\n4. Run the experiment.\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Step 1: Prepare the model\nIn first step, we need to prepare the model to be tuned.\n\nThe model should be put in a separate script.\nIt will be evaluated many times concurrently,\nand possibly will be trained on distributed platforms.\n\nIn this tutorial, the model is defined in :doc:`model.py <model>`.\n\nIn short, it is a TensorFlow model with 3 additional API calls:\n\n1. Use :func:`nni.get_next_parameter` to fetch the hyperparameters to be evalutated.\n2. Use :func:`nni.report_intermediate_result` to report per-epoch accuracy metrics.\n3. Use :func:`nni.report_final_result` to report final accuracy.\n\nPlease understand the model code before continue to next step.\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Step 2: Define search space\nIn model code, we have prepared 4 hyperparameters to be tuned:\n*dense_units*, *activation_type*, *dropout_rate*, and *learning_rate*.\n\nHere we need to define their *search space* so the tuning algorithm can sample them in desired range.\n\nAssuming we have following prior knowledge for these hyperparameters:\n\n1. *dense_units* should be one of 64, 128, 256.\n2. *activation_type* should be one of 'relu', 'tanh', 'swish', or None.\n3. *dropout_rate* should be a float between 0.5 and 0.9.\n4. *learning_rate* should be a float between 0.0001 and 0.1, and it follows exponential distribution.\n\nIn NNI, the space of *dense_units* and *activation_type* is called ``choice``;\nthe space of *dropout_rate* is called ``uniform``;\nand the space of *learning_rate* is called ``loguniform``.\nYou may have noticed, these names are derived from ``numpy.random``.\n\nFor full specification of search space, check :doc:`the reference </hpo/search_space>`.\n\nNow we can define the search space as follow:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "search_space = {\n    'dense_units': {'_type': 'choice', '_value': [64, 128, 256]},\n    'activation_type': {'_type': 'choice', '_value': ['relu', 'tanh', 'swish', None]},\n    'dropout_rate': {'_type': 'uniform', '_value': [0.5, 0.9]},\n    'learning_rate': {'_type': 'loguniform', '_value': [0.0001, 0.1]},\n}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Step 3: Configure the experiment\nNNI uses an *experiment* to manage the HPO process.\nThe *experiment config* defines how to train the models and how to explore the search space.\n\nIn this tutorial we use a *local* mode experiment,\nwhich means models will be trained on local machine, without using any special training platform.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from nni.experiment import Experiment\nexperiment = Experiment('local')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Now we start to configure the experiment.\n\n### Configure trial code\nIn NNI evaluation of each hyperparameter set is called a *trial*.\nSo the model script is called *trial code*.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "experiment.config.trial_command = 'python model.py'\nexperiment.config.trial_code_directory = '.'"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "When ``trial_code_directory`` is a relative path, it relates to current working directory.\nTo run ``main.py`` in a different path, you can set trial code directory to ``Path(__file__).parent``.\n(`__file__ <https://docs.python.org/3.10/reference/datamodel.html#index-43>`__\nis only available in standard Python, not in Jupyter Notebook.)\n\n.. attention::\n\n    If you are using Linux system without Conda,\n    you may need to change ``\"python model.py\"`` to ``\"python3 model.py\"``.\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Configure search space\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "experiment.config.search_space = search_space"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Configure tuning algorithm\nHere we use :doc:`TPE tuner </hpo/tuners>`.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "experiment.config.tuner.name = 'TPE'\nexperiment.config.tuner.class_args['optimize_mode'] = 'maximize'"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Configure how many trials to run\nHere we evaluate 10 sets of hyperparameters in total, and concurrently evaluate 2 sets at a time.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "experiment.config.max_trial_number = 10\nexperiment.config.trial_concurrency = 2"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "You may also set ``max_experiment_duration = '1h'`` to limit running time.\n\nIf neither ``max_trial_number`` nor ``max_experiment_duration`` are set,\nthe experiment will run forever until you press Ctrl-C.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>``max_trial_number`` is set to 10 here for a fast example.\n    In real world it should be set to a larger number.\n    With default config TPE tuner requires 20 trials to warm up.</p></div>\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Step 4: Run the experiment\nNow the experiment is ready. Choose a port and launch it. (Here we use port 8080.)\n\nYou can use the web portal to view experiment status: http://localhost:8080.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "experiment.run(8080)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## After the experiment is done\nEverything is done and it is safe to exit now. The following are optional.\n\nIf you are using standard Python instead of Jupyter Notebook,\nyou can add ``input()`` or ``signal.pause()`` to prevent Python from exiting,\nallowing you to view the web portal after the experiment is done.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# input('Press enter to quit')\nexperiment.stop()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        ":meth:`nni.experiment.Experiment.stop` is automatically invoked when Python exits,\nso it can be omitted in your code.\n\nAfter the experiment is stopped, you can run :meth:`nni.experiment.Experiment.view` to restart web portal.\n\n.. tip::\n\n    This example uses :doc:`Python API </reference/experiment>` to create experiment.\n\n    You can also create and manage experiments with :doc:`command line tool <../hpo_nnictl/nnictl>`.\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.4"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/source/tutorials/hpo_quickstart_tensorflow/main.py b/docs/source/tutorials/hpo_quickstart_tensorflow/main.py
new file mode 100644
index 000000000..9413ba5e3
--- /dev/null
+++ b/docs/source/tutorials/hpo_quickstart_tensorflow/main.py
@@ -0,0 +1,157 @@
+"""
+HPO Quickstart with TensorFlow
+==============================
+This tutorial optimizes the model in `official TensorFlow quickstart`_ with auto-tuning.
+
+The tutorial consists of 4 steps: 
+
+1. Modify the model for auto-tuning.
+2. Define hyperparameters' search space.
+3. Configure the experiment.
+4. Run the experiment.
+
+.. _official TensorFlow quickstart: https://www.tensorflow.org/tutorials/quickstart/beginner
+"""
+
+# %%
+# Step 1: Prepare the model
+# -------------------------
+# In first step, we need to prepare the model to be tuned.
+#
+# The model should be put in a separate script.
+# It will be evaluated many times concurrently,
+# and possibly will be trained on distributed platforms.
+#
+# In this tutorial, the model is defined in :doc:`model.py <model>`.
+#
+# In short, it is a TensorFlow model with 3 additional API calls:
+#
+# 1. Use :func:`nni.get_next_parameter` to fetch the hyperparameters to be evalutated.
+# 2. Use :func:`nni.report_intermediate_result` to report per-epoch accuracy metrics.
+# 3. Use :func:`nni.report_final_result` to report final accuracy.
+#
+# Please understand the model code before continue to next step.
+
+# %%
+# Step 2: Define search space
+# ---------------------------
+# In model code, we have prepared 4 hyperparameters to be tuned:
+# *dense_units*, *activation_type*, *dropout_rate*, and *learning_rate*.
+#
+# Here we need to define their *search space* so the tuning algorithm can sample them in desired range.
+#
+# Assuming we have following prior knowledge for these hyperparameters:
+#
+# 1. *dense_units* should be one of 64, 128, 256.
+# 2. *activation_type* should be one of 'relu', 'tanh', 'swish', or None.
+# 3. *dropout_rate* should be a float between 0.5 and 0.9.
+# 4. *learning_rate* should be a float between 0.0001 and 0.1, and it follows exponential distribution.
+#
+# In NNI, the space of *dense_units* and *activation_type* is called ``choice``;
+# the space of *dropout_rate* is called ``uniform``;
+# and the space of *learning_rate* is called ``loguniform``.
+# You may have noticed, these names are derived from ``numpy.random``.
+#
+# For full specification of search space, check :doc:`the reference </hpo/search_space>`.
+#
+# Now we can define the search space as follow:
+
+search_space = {
+    'dense_units': {'_type': 'choice', '_value': [64, 128, 256]},
+    'activation_type': {'_type': 'choice', '_value': ['relu', 'tanh', 'swish', None]},
+    'dropout_rate': {'_type': 'uniform', '_value': [0.5, 0.9]},
+    'learning_rate': {'_type': 'loguniform', '_value': [0.0001, 0.1]},
+}
+
+# %%
+# Step 3: Configure the experiment
+# --------------------------------
+# NNI uses an *experiment* to manage the HPO process.
+# The *experiment config* defines how to train the models and how to explore the search space.
+# 
+# In this tutorial we use a *local* mode experiment,
+# which means models will be trained on local machine, without using any special training platform.
+from nni.experiment import Experiment
+experiment = Experiment('local')
+
+# %%
+# Now we start to configure the experiment.
+#
+# Configure trial code
+# ^^^^^^^^^^^^^^^^^^^^
+# In NNI evaluation of each hyperparameter set is called a *trial*.
+# So the model script is called *trial code*.
+experiment.config.trial_command = 'python model.py'
+experiment.config.trial_code_directory = '.'
+# %%
+# When ``trial_code_directory`` is a relative path, it relates to current working directory.
+# To run ``main.py`` in a different path, you can set trial code directory to ``Path(__file__).parent``.
+# (`__file__ <https://docs.python.org/3.10/reference/datamodel.html#index-43>`__
+# is only available in standard Python, not in Jupyter Notebook.)
+#
+# .. attention::
+#
+#     If you are using Linux system without Conda,
+#     you may need to change ``"python model.py"`` to ``"python3 model.py"``.
+
+# %%
+# Configure search space
+# ^^^^^^^^^^^^^^^^^^^^^^
+experiment.config.search_space = search_space
+
+# %%
+# Configure tuning algorithm
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^
+# Here we use :doc:`TPE tuner </hpo/tuners>`.
+experiment.config.tuner.name = 'TPE'
+experiment.config.tuner.class_args['optimize_mode'] = 'maximize'
+
+# %%
+# Configure how many trials to run
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# Here we evaluate 10 sets of hyperparameters in total, and concurrently evaluate 2 sets at a time.
+experiment.config.max_trial_number = 10
+experiment.config.trial_concurrency = 2
+# %%
+# You may also set ``max_experiment_duration = '1h'`` to limit running time.
+#
+# If neither ``max_trial_number`` nor ``max_experiment_duration`` are set,
+# the experiment will run forever until you press Ctrl-C.
+#
+# .. note::
+#
+#     ``max_trial_number`` is set to 10 here for a fast example.
+#     In real world it should be set to a larger number.
+#     With default config TPE tuner requires 20 trials to warm up.
+
+# %%
+# Step 4: Run the experiment
+# --------------------------
+# Now the experiment is ready. Choose a port and launch it. (Here we use port 8080.)
+#
+# You can use the web portal to view experiment status: http://localhost:8080.
+experiment.run(8080)
+
+# %%
+# After the experiment is done
+# ----------------------------
+# Everything is done and it is safe to exit now. The following are optional.
+#
+# If you are using standard Python instead of Jupyter Notebook,
+# you can add ``input()`` or ``signal.pause()`` to prevent Python from exiting,
+# allowing you to view the web portal after the experiment is done.
+
+# input('Press enter to quit')
+experiment.stop()
+
+# %%
+# :meth:`nni.experiment.Experiment.stop` is automatically invoked when Python exits,
+# so it can be omitted in your code.
+#
+# After the experiment is stopped, you can run :meth:`nni.experiment.Experiment.view` to restart web portal.
+#
+# .. tip::
+#
+#     This example uses :doc:`Python API </reference/experiment>` to create experiment.
+#
+#     You can also create and manage experiments with :doc:`command line tool <../hpo_nnictl/nnictl>`.
diff --git a/docs/source/tutorials/hpo_quickstart_tensorflow/main.py.md5 b/docs/source/tutorials/hpo_quickstart_tensorflow/main.py.md5
new file mode 100644
index 000000000..eebd98254
--- /dev/null
+++ b/docs/source/tutorials/hpo_quickstart_tensorflow/main.py.md5
@@ -0,0 +1 @@
+b8a9880a36233005ade7a8dae6d428a8
\ No newline at end of file
diff --git a/docs/source/tutorials/hpo_quickstart_tensorflow/main.rst b/docs/source/tutorials/hpo_quickstart_tensorflow/main.rst
new file mode 100644
index 000000000..39dded2a1
--- /dev/null
+++ b/docs/source/tutorials/hpo_quickstart_tensorflow/main.rst
@@ -0,0 +1,337 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "tutorials/hpo_quickstart_tensorflow/main.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        Click :ref:`here <sphx_glr_download_tutorials_hpo_quickstart_tensorflow_main.py>`
+        to download the full example code
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorials_hpo_quickstart_tensorflow_main.py:
+
+
+HPO Quickstart with TensorFlow
+==============================
+This tutorial optimizes the model in `official TensorFlow quickstart`_ with auto-tuning.
+
+The tutorial consists of 4 steps: 
+
+1. Modify the model for auto-tuning.
+2. Define hyperparameters' search space.
+3. Configure the experiment.
+4. Run the experiment.
+
+.. _official TensorFlow quickstart: https://www.tensorflow.org/tutorials/quickstart/beginner
+
+.. GENERATED FROM PYTHON SOURCE LINES 17-34
+
+Step 1: Prepare the model
+-------------------------
+In first step, we need to prepare the model to be tuned.
+
+The model should be put in a separate script.
+It will be evaluated many times concurrently,
+and possibly will be trained on distributed platforms.
+
+In this tutorial, the model is defined in :doc:`model.py <model>`.
+
+In short, it is a TensorFlow model with 3 additional API calls:
+
+1. Use :func:`nni.get_next_parameter` to fetch the hyperparameters to be evalutated.
+2. Use :func:`nni.report_intermediate_result` to report per-epoch accuracy metrics.
+3. Use :func:`nni.report_final_result` to report final accuracy.
+
+Please understand the model code before continue to next step.
+
+.. GENERATED FROM PYTHON SOURCE LINES 36-58
+
+Step 2: Define search space
+---------------------------
+In model code, we have prepared 4 hyperparameters to be tuned:
+*dense_units*, *activation_type*, *dropout_rate*, and *learning_rate*.
+
+Here we need to define their *search space* so the tuning algorithm can sample them in desired range.
+
+Assuming we have following prior knowledge for these hyperparameters:
+
+1. *dense_units* should be one of 64, 128, 256.
+2. *activation_type* should be one of 'relu', 'tanh', 'swish', or None.
+3. *dropout_rate* should be a float between 0.5 and 0.9.
+4. *learning_rate* should be a float between 0.0001 and 0.1, and it follows exponential distribution.
+
+In NNI, the space of *dense_units* and *activation_type* is called ``choice``;
+the space of *dropout_rate* is called ``uniform``;
+and the space of *learning_rate* is called ``loguniform``.
+You may have noticed, these names are derived from ``numpy.random``.
+
+For full specification of search space, check :doc:`the reference </hpo/search_space>`.
+
+Now we can define the search space as follow:
+
+.. GENERATED FROM PYTHON SOURCE LINES 58-66
+
+.. code-block:: default
+
+
+    search_space = {
+        'dense_units': {'_type': 'choice', '_value': [64, 128, 256]},
+        'activation_type': {'_type': 'choice', '_value': ['relu', 'tanh', 'swish', None]},
+        'dropout_rate': {'_type': 'uniform', '_value': [0.5, 0.9]},
+        'learning_rate': {'_type': 'loguniform', '_value': [0.0001, 0.1]},
+    }
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 67-74
+
+Step 3: Configure the experiment
+--------------------------------
+NNI uses an *experiment* to manage the HPO process.
+The *experiment config* defines how to train the models and how to explore the search space.
+
+In this tutorial we use a *local* mode experiment,
+which means models will be trained on local machine, without using any special training platform.
+
+.. GENERATED FROM PYTHON SOURCE LINES 74-77
+
+.. code-block:: default
+
+    from nni.experiment import Experiment
+    experiment = Experiment('local')
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 78-84
+
+Now we start to configure the experiment.
+
+Configure trial code
+^^^^^^^^^^^^^^^^^^^^
+In NNI evaluation of each hyperparameter set is called a *trial*.
+So the model script is called *trial code*.
+
+.. GENERATED FROM PYTHON SOURCE LINES 84-86
+
+.. code-block:: default
+
+    experiment.config.trial_command = 'python model.py'
+    experiment.config.trial_code_directory = '.'
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 87-96
+
+When ``trial_code_directory`` is a relative path, it relates to current working directory.
+To run ``main.py`` in a different path, you can set trial code directory to ``Path(__file__).parent``.
+(`__file__ <https://docs.python.org/3.10/reference/datamodel.html#index-43>`__
+is only available in standard Python, not in Jupyter Notebook.)
+
+.. attention::
+
+    If you are using Linux system without Conda,
+    you may need to change ``"python model.py"`` to ``"python3 model.py"``.
+
+.. GENERATED FROM PYTHON SOURCE LINES 98-100
+
+Configure search space
+^^^^^^^^^^^^^^^^^^^^^^
+
+.. GENERATED FROM PYTHON SOURCE LINES 100-102
+
+.. code-block:: default
+
+    experiment.config.search_space = search_space
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 103-106
+
+Configure tuning algorithm
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+Here we use :doc:`TPE tuner </hpo/tuners>`.
+
+.. GENERATED FROM PYTHON SOURCE LINES 106-109
+
+.. code-block:: default
+
+    experiment.config.tuner.name = 'TPE'
+    experiment.config.tuner.class_args['optimize_mode'] = 'maximize'
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 110-113
+
+Configure how many trials to run
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Here we evaluate 10 sets of hyperparameters in total, and concurrently evaluate 2 sets at a time.
+
+.. GENERATED FROM PYTHON SOURCE LINES 113-115
+
+.. code-block:: default
+
+    experiment.config.max_trial_number = 10
+    experiment.config.trial_concurrency = 2
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 116-126
+
+You may also set ``max_experiment_duration = '1h'`` to limit running time.
+
+If neither ``max_trial_number`` nor ``max_experiment_duration`` are set,
+the experiment will run forever until you press Ctrl-C.
+
+.. note::
+
+    ``max_trial_number`` is set to 10 here for a fast example.
+    In real world it should be set to a larger number.
+    With default config TPE tuner requires 20 trials to warm up.
+
+.. GENERATED FROM PYTHON SOURCE LINES 128-133
+
+Step 4: Run the experiment
+--------------------------
+Now the experiment is ready. Choose a port and launch it. (Here we use port 8080.)
+
+You can use the web portal to view experiment status: http://localhost:8080.
+
+.. GENERATED FROM PYTHON SOURCE LINES 133-135
+
+.. code-block:: default
+
+    experiment.run(8080)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    [2022-04-13 12:11:34] Creating experiment, Experiment ID: enw27qxj
+    [2022-04-13 12:11:34] Starting web server...
+    [2022-04-13 12:11:35] Setting up...
+    [2022-04-13 12:11:35] Web portal URLs: http://127.0.0.1:8080 http://192.168.100.103:8080
+
+    True
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 136-143
+
+After the experiment is done
+----------------------------
+Everything is done and it is safe to exit now. The following are optional.
+
+If you are using standard Python instead of Jupyter Notebook,
+you can add ``input()`` or ``signal.pause()`` to prevent Python from exiting,
+allowing you to view the web portal after the experiment is done.
+
+.. GENERATED FROM PYTHON SOURCE LINES 143-147
+
+.. code-block:: default
+
+
+    # input('Press enter to quit')
+    experiment.stop()
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    [2022-04-13 12:12:55] Stopping experiment, please wait...
+    [2022-04-13 12:12:58] Experiment stopped
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 148-158
+
+:meth:`nni.experiment.Experiment.stop` is automatically invoked when Python exits,
+so it can be omitted in your code.
+
+After the experiment is stopped, you can run :meth:`nni.experiment.Experiment.view` to restart web portal.
+
+.. tip::
+
+    This example uses :doc:`Python API </reference/experiment>` to create experiment.
+
+    You can also create and manage experiments with :doc:`command line tool <../hpo_nnictl/nnictl>`.
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 1 minutes  24.384 seconds)
+
+
+.. _sphx_glr_download_tutorials_hpo_quickstart_tensorflow_main.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+    :class: sphx-glr-footer-example
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-python
+
+     :download:`Download Python source code: main.py <main.py>`
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+     :download:`Download Jupyter notebook: main.ipynb <main.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/source/tutorials/hpo_quickstart_tensorflow/main_codeobj.pickle b/docs/source/tutorials/hpo_quickstart_tensorflow/main_codeobj.pickle
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diff --git a/docs/source/tutorials/hpo_quickstart_tensorflow/model.ipynb b/docs/source/tutorials/hpo_quickstart_tensorflow/model.ipynb
new file mode 100644
index 000000000..16fb6e59d
--- /dev/null
+++ b/docs/source/tutorials/hpo_quickstart_tensorflow/model.ipynb
@@ -0,0 +1,180 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Port TensorFlow Quickstart to NNI\nThis is a modified version of `TensorFlow quickstart`_.\n\nIt can be run directly and will have the exact same result as original version.\n\nFurthermore, it enables the ability of auto tuning with an NNI *experiment*, which will be detailed later.\n\nIt is recommended to run this script directly first to verify the environment.\n\nThere are 3 key differences from the original version:\n\n1. In `Get optimized hyperparameters`_ part, it receives generated hyperparameters.\n2. In `(Optional) Report intermediate results`_ part, it reports per-epoch accuracy metrics.\n3. In `Report final result`_ part, it reports final accuracy.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import nni\nimport tensorflow as tf"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Hyperparameters to be tuned\nThese are the hyperparameters that will be tuned later.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "params = {\n    'dense_units': 128,\n    'activation_type': 'relu',\n    'dropout_rate': 0.2,\n    'learning_rate': 0.001,\n}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Get optimized hyperparameters\nIf run directly, :func:`nni.get_next_parameter` is a no-op and returns an empty dict.\nBut with an NNI *experiment*, it will receive optimized hyperparameters from tuning algorithm.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "optimized_params = nni.get_next_parameter()\nparams.update(optimized_params)\nprint(params)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Load dataset\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "mnist = tf.keras.datasets.mnist\n\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\nx_train, x_test = x_train / 255.0, x_test / 255.0"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Build model with hyperparameters\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "model = tf.keras.models.Sequential([\n    tf.keras.layers.Flatten(input_shape=(28, 28)),\n    tf.keras.layers.Dense(params['dense_units'], activation=params['activation_type']),\n    tf.keras.layers.Dropout(params['dropout_rate']),\n    tf.keras.layers.Dense(10)\n])\n\nadam = tf.keras.optimizers.Adam(learning_rate=params['learning_rate'])\nloss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\nmodel.compile(optimizer=adam, loss=loss_fn, metrics=['accuracy'])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## (Optional) Report intermediate results\nThe callback reports per-epoch accuracy to show learning curve in the web portal.\nYou can also leverage the metrics for early stopping with :doc:`NNI assessors </hpo/assessors>`.\n\nThis part can be safely skipped and the experiment will work fine.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "callback = tf.keras.callbacks.LambdaCallback(\n    on_epoch_end = lambda epoch, logs: nni.report_intermediate_result(logs['accuracy'])\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Train and evluate the model\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "model.fit(x_train, y_train, epochs=5, verbose=2, callbacks=[callback])\nloss, accuracy = model.evaluate(x_test, y_test, verbose=2)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Report final result\nReport final accuracy to NNI so the tuning algorithm can suggest better hyperparameters.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "nni.report_final_result(accuracy)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/source/tutorials/hpo_quickstart_tensorflow/model.py b/docs/source/tutorials/hpo_quickstart_tensorflow/model.py
new file mode 100644
index 000000000..c81c280dd
--- /dev/null
+++ b/docs/source/tutorials/hpo_quickstart_tensorflow/model.py
@@ -0,0 +1,88 @@
+"""
+Port TensorFlow Quickstart to NNI
+=================================
+This is a modified version of `TensorFlow quickstart`_.
+
+It can be run directly and will have the exact same result as original version.
+
+Furthermore, it enables the ability of auto tuning with an NNI *experiment*, which will be detailed later.
+
+It is recommended to run this script directly first to verify the environment.
+
+There are 3 key differences from the original version:
+
+1. In `Get optimized hyperparameters`_ part, it receives generated hyperparameters.
+2. In `(Optional) Report intermediate results`_ part, it reports per-epoch accuracy metrics.
+3. In `Report final result`_ part, it reports final accuracy.
+
+.. _TensorFlow quickstart: https://www.tensorflow.org/tutorials/quickstart/beginner
+"""
+
+# %%
+import nni
+import tensorflow as tf
+
+# %%
+# Hyperparameters to be tuned
+# ---------------------------
+# These are the hyperparameters that will be tuned later.
+params = {
+    'dense_units': 128,
+    'activation_type': 'relu',
+    'dropout_rate': 0.2,
+    'learning_rate': 0.001,
+}
+
+# %%
+# Get optimized hyperparameters
+# -----------------------------
+# If run directly, :func:`nni.get_next_parameter` is a no-op and returns an empty dict.
+# But with an NNI *experiment*, it will receive optimized hyperparameters from tuning algorithm.
+optimized_params = nni.get_next_parameter()
+params.update(optimized_params)
+print(params)
+
+# %%
+# Load dataset
+# ------------
+mnist = tf.keras.datasets.mnist
+
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
+x_train, x_test = x_train / 255.0, x_test / 255.0
+
+# %%
+# Build model with hyperparameters
+# --------------------------------
+model = tf.keras.models.Sequential([
+    tf.keras.layers.Flatten(input_shape=(28, 28)),
+    tf.keras.layers.Dense(params['dense_units'], activation=params['activation_type']),
+    tf.keras.layers.Dropout(params['dropout_rate']),
+    tf.keras.layers.Dense(10)
+])
+
+adam = tf.keras.optimizers.Adam(learning_rate=params['learning_rate'])
+loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
+model.compile(optimizer=adam, loss=loss_fn, metrics=['accuracy'])
+
+# %%
+# (Optional) Report intermediate results
+# --------------------------------------
+# The callback reports per-epoch accuracy to show learning curve in the web portal.
+# You can also leverage the metrics for early stopping with :doc:`NNI assessors </hpo/assessors>`.
+#
+# This part can be safely skipped and the experiment will work fine.
+callback = tf.keras.callbacks.LambdaCallback(
+    on_epoch_end = lambda epoch, logs: nni.report_intermediate_result(logs['accuracy'])
+)
+
+# %%
+# Train and evluate the model
+# ---------------------------
+model.fit(x_train, y_train, epochs=5, verbose=2, callbacks=[callback])
+loss, accuracy = model.evaluate(x_test, y_test, verbose=2)
+
+# %%
+# Report final result
+# -------------------
+# Report final accuracy to NNI so the tuning algorithm can suggest better hyperparameters.
+nni.report_final_result(accuracy)
diff --git a/docs/source/tutorials/hpo_quickstart_tensorflow/model.py.md5 b/docs/source/tutorials/hpo_quickstart_tensorflow/model.py.md5
new file mode 100644
index 000000000..29a82de30
--- /dev/null
+++ b/docs/source/tutorials/hpo_quickstart_tensorflow/model.py.md5
@@ -0,0 +1 @@
+d0c869d9d7c7f9208abc10357de52056
\ No newline at end of file
diff --git a/docs/source/tutorials/hpo_quickstart_tensorflow/model.rst b/docs/source/tutorials/hpo_quickstart_tensorflow/model.rst
new file mode 100644
index 000000000..e1cd53d1d
--- /dev/null
+++ b/docs/source/tutorials/hpo_quickstart_tensorflow/model.rst
@@ -0,0 +1,279 @@
+:orphan:
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "tutorials/hpo_quickstart_tensorflow/model.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        Click :ref:`here <sphx_glr_download_tutorials_hpo_quickstart_tensorflow_model.py>`
+        to download the full example code
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorials_hpo_quickstart_tensorflow_model.py:
+
+
+Port TensorFlow Quickstart to NNI
+=================================
+This is a modified version of `TensorFlow quickstart`_.
+
+It can be run directly and will have the exact same result as original version.
+
+Furthermore, it enables the ability of auto tuning with an NNI *experiment*, which will be detailed later.
+
+It is recommended to run this script directly first to verify the environment.
+
+There are 3 key differences from the original version:
+
+1. In `Get optimized hyperparameters`_ part, it receives generated hyperparameters.
+2. In `(Optional) Report intermediate results`_ part, it reports per-epoch accuracy metrics.
+3. In `Report final result`_ part, it reports final accuracy.
+
+.. _TensorFlow quickstart: https://www.tensorflow.org/tutorials/quickstart/beginner
+
+.. GENERATED FROM PYTHON SOURCE LINES 22-25
+
+.. code-block:: default
+
+    import nni
+    import tensorflow as tf
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 26-29
+
+Hyperparameters to be tuned
+---------------------------
+These are the hyperparameters that will be tuned later.
+
+.. GENERATED FROM PYTHON SOURCE LINES 29-36
+
+.. code-block:: default
+
+    params = {
+        'dense_units': 128,
+        'activation_type': 'relu',
+        'dropout_rate': 0.2,
+        'learning_rate': 0.001,
+    }
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 37-41
+
+Get optimized hyperparameters
+-----------------------------
+If run directly, :func:`nni.get_next_parameter` is a no-op and returns an empty dict.
+But with an NNI *experiment*, it will receive optimized hyperparameters from tuning algorithm.
+
+.. GENERATED FROM PYTHON SOURCE LINES 41-45
+
+.. code-block:: default
+
+    optimized_params = nni.get_next_parameter()
+    params.update(optimized_params)
+    print(params)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    {'dense_units': 128, 'activation_type': 'relu', 'dropout_rate': 0.2, 'learning_rate': 0.001}
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 46-48
+
+Load dataset
+------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 48-53
+
+.. code-block:: default
+
+    mnist = tf.keras.datasets.mnist
+
+    (x_train, y_train), (x_test, y_test) = mnist.load_data()
+    x_train, x_test = x_train / 255.0, x_test / 255.0
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 54-56
+
+Build model with hyperparameters
+--------------------------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 56-67
+
+.. code-block:: default
+
+    model = tf.keras.models.Sequential([
+        tf.keras.layers.Flatten(input_shape=(28, 28)),
+        tf.keras.layers.Dense(params['dense_units'], activation=params['activation_type']),
+        tf.keras.layers.Dropout(params['dropout_rate']),
+        tf.keras.layers.Dense(10)
+    ])
+
+    adam = tf.keras.optimizers.Adam(learning_rate=params['learning_rate'])
+    loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
+    model.compile(optimizer=adam, loss=loss_fn, metrics=['accuracy'])
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 68-74
+
+(Optional) Report intermediate results
+--------------------------------------
+The callback reports per-epoch accuracy to show learning curve in the web portal.
+You can also leverage the metrics for early stopping with :doc:`NNI assessors </hpo/assessors>`.
+
+This part can be safely skipped and the experiment will work fine.
+
+.. GENERATED FROM PYTHON SOURCE LINES 74-78
+
+.. code-block:: default
+
+    callback = tf.keras.callbacks.LambdaCallback(
+        on_epoch_end = lambda epoch, logs: nni.report_intermediate_result(logs['accuracy'])
+    )
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 79-81
+
+Train and evluate the model
+---------------------------
+
+.. GENERATED FROM PYTHON SOURCE LINES 81-84
+
+.. code-block:: default
+
+    model.fit(x_train, y_train, epochs=5, verbose=2, callbacks=[callback])
+    loss, accuracy = model.evaluate(x_test, y_test, verbose=2)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    Epoch 1/5
+    [2022-03-21 01:25:00] INFO (nni/MainThread) Intermediate result: 0.9153500199317932  (Index 0)
+    1875/1875 - 17s - loss: 0.2914 - accuracy: 0.9154 - 17s/epoch - 9ms/step
+    Epoch 2/5
+    [2022-03-21 01:25:18] INFO (nni/MainThread) Intermediate result: 0.9588666558265686  (Index 1)
+    1875/1875 - 18s - loss: 0.1387 - accuracy: 0.9589 - 18s/epoch - 10ms/step
+    Epoch 3/5
+    [2022-03-21 01:25:38] INFO (nni/MainThread) Intermediate result: 0.9677000045776367  (Index 2)
+    1875/1875 - 20s - loss: 0.1073 - accuracy: 0.9677 - 20s/epoch - 11ms/step
+    Epoch 4/5
+    [2022-03-21 01:25:56] INFO (nni/MainThread) Intermediate result: 0.9738666415214539  (Index 3)
+    1875/1875 - 18s - loss: 0.0866 - accuracy: 0.9739 - 18s/epoch - 10ms/step
+    Epoch 5/5
+    [2022-03-21 01:26:16] INFO (nni/MainThread) Intermediate result: 0.977483332157135  (Index 4)
+    1875/1875 - 21s - loss: 0.0728 - accuracy: 0.9775 - 21s/epoch - 11ms/step
+    313/313 - 2s - loss: 0.0702 - accuracy: 0.9776 - 2s/epoch - 6ms/step
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 85-88
+
+Report final result
+-------------------
+Report final accuracy to NNI so the tuning algorithm can suggest better hyperparameters.
+
+.. GENERATED FROM PYTHON SOURCE LINES 88-89
+
+.. code-block:: default
+
+    nni.report_final_result(accuracy)
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    [2022-03-21 01:27:08] INFO (nni/MainThread) Final result: 0.9775999784469604
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 2 minutes  27.156 seconds)
+
+
+.. _sphx_glr_download_tutorials_hpo_quickstart_tensorflow_model.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+    :class: sphx-glr-footer-example
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-python
+
+     :download:`Download Python source code: model.py <model.py>`
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+     :download:`Download Jupyter notebook: model.ipynb <model.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/source/tutorials/hpo_quickstart_tensorflow/model_codeobj.pickle b/docs/source/tutorials/hpo_quickstart_tensorflow/model_codeobj.pickle
new file mode 100644
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diff --git a/docs/source/tutorials/hpo_quickstart_tensorflow/sg_execution_times.rst b/docs/source/tutorials/hpo_quickstart_tensorflow/sg_execution_times.rst
new file mode 100644
index 000000000..fdbfe7c3d
--- /dev/null
+++ b/docs/source/tutorials/hpo_quickstart_tensorflow/sg_execution_times.rst
@@ -0,0 +1,14 @@
+
+:orphan:
+
+.. _sphx_glr_tutorials_hpo_quickstart_tensorflow_sg_execution_times:
+
+Computation times
+=================
+**01:24.384** total execution time for **tutorials_hpo_quickstart_tensorflow** files:
+
++-----------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_hpo_quickstart_tensorflow_main.py` (``main.py``)   | 01:24.384 | 0.0 MB |
++-----------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_hpo_quickstart_tensorflow_model.py` (``model.py``) | 00:00.000 | 0.0 MB |
++-----------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/source/tutorials/images/thumb/sphx_glr_hello_nas_thumb.png b/docs/source/tutorials/images/thumb/sphx_glr_hello_nas_thumb.png
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diff --git a/docs/source/tutorials/index.rst b/docs/source/tutorials/index.rst
index 883277d4d..16974c630 100644
--- a/docs/source/tutorials/index.rst
+++ b/docs/source/tutorials/index.rst
@@ -11,14 +11,14 @@ Tutorials
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="Get started with NAS on MNIST">
+    <div class="sphx-glr-thumbcontainer" tooltip="Introduction ------------">
 
 .. only:: html
 
- .. figure:: /tutorials/images/thumb/sphx_glr_nas_quick_start_mnist_thumb.png
-     :alt: Get started with NAS on MNIST
+ .. figure:: /tutorials/images/thumb/sphx_glr_pruning_speedup_thumb.png
+     :alt: Speedup Model with Mask
 
-     :ref:`sphx_glr_tutorials_nas_quick_start_mnist.py`
+     :ref:`sphx_glr_tutorials_pruning_speedup.py`
 
 .. raw:: html
 
@@ -28,18 +28,18 @@ Tutorials
 .. toctree::
    :hidden:
 
-   /tutorials/nas_quick_start_mnist
+   /tutorials/pruning_speedup
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="Start and Manage a New Experiment">
+    <div class="sphx-glr-thumbcontainer" tooltip=" Introduction ------------">
 
 .. only:: html
 
- .. figure:: /tutorials/images/thumb/sphx_glr_nni_experiment_thumb.png
-     :alt: Start and Manage a New Experiment
+ .. figure:: /tutorials/images/thumb/sphx_glr_quantization_speedup_thumb.png
+     :alt: SpeedUp Model with Calibration Config
 
-     :ref:`sphx_glr_tutorials_nni_experiment.py`
+     :ref:`sphx_glr_tutorials_quantization_speedup.py`
 
 .. raw:: html
 
@@ -49,7 +49,237 @@ Tutorials
 .. toctree::
    :hidden:
 
-   /tutorials/nni_experiment
+   /tutorials/quantization_speedup
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Quantization reduces model size and speeds up inference time by reducing the number of bits req...">
+
+.. only:: html
+
+ .. figure:: /tutorials/images/thumb/sphx_glr_quantization_quick_start_mnist_thumb.png
+     :alt: Quantization Quickstart
+
+     :ref:`sphx_glr_tutorials_quantization_quick_start_mnist.py`
+
+.. raw:: html
+
+    </div>
+
+
+.. toctree::
+   :hidden:
+
+   /tutorials/quantization_quick_start_mnist
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Model pruning is a technique to reduce the model size and computation by reducing model weight ...">
+
+.. only:: html
+
+ .. figure:: /tutorials/images/thumb/sphx_glr_pruning_quick_start_mnist_thumb.png
+     :alt: Pruning Quickstart
+
+     :ref:`sphx_glr_tutorials_pruning_quick_start_mnist.py`
+
+.. raw:: html
+
+    </div>
+
+
+.. toctree::
+   :hidden:
+
+   /tutorials/pruning_quick_start_mnist
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="To write a new quantization algorithm, you can write a class that inherits nni.compression.pyto...">
+
+.. only:: html
+
+ .. figure:: /tutorials/images/thumb/sphx_glr_quantization_customize_thumb.png
+     :alt: Customize a new quantization algorithm
+
+     :ref:`sphx_glr_tutorials_quantization_customize.py`
+
+.. raw:: html
+
+    </div>
+
+
+.. toctree::
+   :hidden:
+
+   /tutorials/quantization_customize
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="In this tutorial, we show how to use NAS Benchmarks as datasets. For research purposes we somet...">
+
+.. only:: html
+
+ .. figure:: /tutorials/images/thumb/sphx_glr_nasbench_as_dataset_thumb.png
+     :alt: Use NAS Benchmarks as Datasets
+
+     :ref:`sphx_glr_tutorials_nasbench_as_dataset.py`
+
+.. raw:: html
+
+    </div>
+
+
+.. toctree::
+   :hidden:
+
+   /tutorials/nasbench_as_dataset
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Users can easily customize a basic pruner in NNI. A large number of basic modules have been pro...">
+
+.. only:: html
+
+ .. figure:: /tutorials/images/thumb/sphx_glr_pruning_customize_thumb.png
+     :alt: Customize Basic Pruner
+
+     :ref:`sphx_glr_tutorials_pruning_customize.py`
+
+.. raw:: html
+
+    </div>
+
+
+.. toctree::
+   :hidden:
+
+   /tutorials/pruning_customize
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This is the 101 tutorial of Neural Architecture Search (NAS) on NNI. In this tutorial, we will ...">
+
+.. only:: html
+
+ .. figure:: /tutorials/images/thumb/sphx_glr_hello_nas_thumb.png
+     :alt: Hello, NAS!
+
+     :ref:`sphx_glr_tutorials_hello_nas.py`
+
+.. raw:: html
+
+    </div>
+
+
+.. toctree::
+   :hidden:
+
+   /tutorials/hello_nas
+.. raw:: html
+
+    <div class="sphx-glr-clear"></div>
+
+
+
+.. _sphx_glr_tutorials_hpo_quickstart_pytorch:
+
+
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="The tutorial consists of 4 steps: ">
+
+.. only:: html
+
+ .. figure:: /tutorials/hpo_quickstart_pytorch/images/thumb/sphx_glr_main_thumb.png
+     :alt: HPO Quickstart with PyTorch
+
+     :ref:`sphx_glr_tutorials_hpo_quickstart_pytorch_main.py`
+
+.. raw:: html
+
+    </div>
+
+
+.. toctree::
+   :hidden:
+
+   /tutorials/hpo_quickstart_pytorch/main
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="It can be run directly and will have the exact same result as original version.">
+
+.. only:: html
+
+ .. figure:: /tutorials/hpo_quickstart_pytorch/images/thumb/sphx_glr_model_thumb.png
+     :alt: Port PyTorch Quickstart to NNI
+
+     :ref:`sphx_glr_tutorials_hpo_quickstart_pytorch_model.py`
+
+.. raw:: html
+
+    </div>
+
+
+.. toctree::
+   :hidden:
+
+   /tutorials/hpo_quickstart_pytorch/model
+.. raw:: html
+
+    <div class="sphx-glr-clear"></div>
+
+
+
+.. _sphx_glr_tutorials_hpo_quickstart_tensorflow:
+
+
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="The tutorial consists of 4 steps: ">
+
+.. only:: html
+
+ .. figure:: /tutorials/hpo_quickstart_tensorflow/images/thumb/sphx_glr_main_thumb.png
+     :alt: HPO Quickstart with TensorFlow
+
+     :ref:`sphx_glr_tutorials_hpo_quickstart_tensorflow_main.py`
+
+.. raw:: html
+
+    </div>
+
+
+.. toctree::
+   :hidden:
+
+   /tutorials/hpo_quickstart_tensorflow/main
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="It can be run directly and will have the exact same result as original version.">
+
+.. only:: html
+
+ .. figure:: /tutorials/hpo_quickstart_tensorflow/images/thumb/sphx_glr_model_thumb.png
+     :alt: Port TensorFlow Quickstart to NNI
+
+     :ref:`sphx_glr_tutorials_hpo_quickstart_tensorflow_model.py`
+
+.. raw:: html
+
+    </div>
+
+
+.. toctree::
+   :hidden:
+
+   /tutorials/hpo_quickstart_tensorflow/model
 .. raw:: html
 
     <div class="sphx-glr-clear"></div>
diff --git a/docs/source/tutorials/nas_quick_start_mnist.ipynb b/docs/source/tutorials/nas_quick_start_mnist.ipynb
deleted file mode 100644
index 0ed38ebd4..000000000
--- a/docs/source/tutorials/nas_quick_start_mnist.ipynb
+++ /dev/null
@@ -1,65 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n# Get started with NAS on MNIST\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "a = (1, 2, 3)\na"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "print('hello')"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.8.8"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/docs/source/tutorials/nas_quick_start_mnist.py b/docs/source/tutorials/nas_quick_start_mnist.py
deleted file mode 100644
index 7e5827823..000000000
--- a/docs/source/tutorials/nas_quick_start_mnist.py
+++ /dev/null
@@ -1,11 +0,0 @@
-"""
-Get started with NAS on MNIST
-=============================
-"""
-
-# %%
-a = (1, 2, 3)
-a
-
-# %%
-print('hello')
diff --git a/docs/source/tutorials/nas_quick_start_mnist.py.md5 b/docs/source/tutorials/nas_quick_start_mnist.py.md5
deleted file mode 100644
index d5985319d..000000000
--- a/docs/source/tutorials/nas_quick_start_mnist.py.md5
+++ /dev/null
@@ -1 +0,0 @@
-f87a716bc3274d0f9a77db503198ac4a
\ No newline at end of file
diff --git a/docs/source/tutorials/nas_quick_start_mnist.rst b/docs/source/tutorials/nas_quick_start_mnist.rst
deleted file mode 100644
index 61f70b8d8..000000000
--- a/docs/source/tutorials/nas_quick_start_mnist.rst
+++ /dev/null
@@ -1,97 +0,0 @@
-
-.. DO NOT EDIT.
-.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
-.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
-.. "tutorials/nas_quick_start_mnist.py"
-.. LINE NUMBERS ARE GIVEN BELOW.
-
-.. only:: html
-
-    .. note::
-        :class: sphx-glr-download-link-note
-
-        Click :ref:`here <sphx_glr_download_tutorials_nas_quick_start_mnist.py>`
-        to download the full example code
-
-.. rst-class:: sphx-glr-example-title
-
-.. _sphx_glr_tutorials_nas_quick_start_mnist.py:
-
-
-Get started with NAS on MNIST
-=============================
-
-.. GENERATED FROM PYTHON SOURCE LINES 7-10
-
-.. code-block:: default
-
-    a = (1, 2, 3)
-    a
-
-
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
-
-    (1, 2, 3)
-
-
-
-.. GENERATED FROM PYTHON SOURCE LINES 11-12
-
-.. code-block:: default
-
-    print('hello')
-
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
-    hello
-
-
-
-
-
-.. rst-class:: sphx-glr-timing
-
-   **Total running time of the script:** ( 0 minutes  0.002 seconds)
-
-
-.. _sphx_glr_download_tutorials_nas_quick_start_mnist.py:
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-    :class: sphx-glr-footer-example
-
-
-
-  .. container:: sphx-glr-download sphx-glr-download-python
-
-     :download:`Download Python source code: nas_quick_start_mnist.py <nas_quick_start_mnist.py>`
-
-
-
-  .. container:: sphx-glr-download sphx-glr-download-jupyter
-
-     :download:`Download Jupyter notebook: nas_quick_start_mnist.ipynb <nas_quick_start_mnist.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
-    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/source/tutorials/nas_quick_start_mnist_codeobj.pickle b/docs/source/tutorials/nas_quick_start_mnist_codeobj.pickle
deleted file mode 100644
index a1aa43e911b0ff88de811fb305eac4b3febc9917..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 121
zcmZo*otno00X^Kw`8heM$t9WjdBszDxc!S#Qj1bkTr!hOrc9oq88oGbF>y-ll-em8
zJuG>Nxv5inSW8L^a)2~jZhlHBh{2Imnwe9Q3DV935sNR*$S*3HlEKx(ky#v{oRe5w
LJf*WVu~-iPU^p%(

diff --git a/docs/source/tutorials/nasbench_as_dataset.ipynb b/docs/source/tutorials/nasbench_as_dataset.ipynb
new file mode 100644
index 000000000..ce56dea80
--- /dev/null
+++ b/docs/source/tutorials/nasbench_as_dataset.ipynb
@@ -0,0 +1,238 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Use NAS Benchmarks as Datasets\n\nIn this tutorial, we show how to use NAS Benchmarks as datasets.\nFor research purposes we sometimes desire to query the benchmarks for architecture accuracies,\nrather than train them one by one from scratch.\nNNI has provided query tools so that users can easily get the retrieve the data in NAS benchmarks.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Prerequisites\nThis tutorial assumes that you have already prepared your NAS benchmarks under cache directory\n(by default, ``~/.cache/nni/nasbenchmark``).\nIf you haven't, please follow the data preparation guide in :doc:`/nas/benchmarks`.\n\nAs a result, the directory should look like:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import os\nos.listdir(os.path.expanduser('~/.cache/nni/nasbenchmark'))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import pprint\n\nfrom nni.nas.benchmarks.nasbench101 import query_nb101_trial_stats\nfrom nni.nas.benchmarks.nasbench201 import query_nb201_trial_stats\nfrom nni.nas.benchmarks.nds import query_nds_trial_stats"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## NAS-Bench-101\n\nUse the following architecture as an example:\n\n<img src=\"file://../../img/nas-bench-101-example.png\">\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "arch = {\n    'op1': 'conv3x3-bn-relu',\n    'op2': 'maxpool3x3',\n    'op3': 'conv3x3-bn-relu',\n    'op4': 'conv3x3-bn-relu',\n    'op5': 'conv1x1-bn-relu',\n    'input1': [0],\n    'input2': [1],\n    'input3': [2],\n    'input4': [0],\n    'input5': [0, 3, 4],\n    'input6': [2, 5]\n}\nfor t in query_nb101_trial_stats(arch, 108, include_intermediates=True):\n    pprint.pprint(t)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "An architecture of NAS-Bench-101 could be trained more than once.\nEach element of the returned generator is a dict which contains one of the training results of this trial config\n(architecture + hyper-parameters) including train/valid/test accuracy,\ntraining time, number of epochs, etc. The results of NAS-Bench-201 and NDS follow similar formats.\n\n## NAS-Bench-201\n\nUse the following architecture as an example:\n\n<img src=\"file://../../img/nas-bench-201-example.png\">\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "arch = {\n    '0_1': 'avg_pool_3x3',\n    '0_2': 'conv_1x1',\n    '1_2': 'skip_connect',\n    '0_3': 'conv_1x1',\n    '1_3': 'skip_connect',\n    '2_3': 'skip_connect'\n}\nfor t in query_nb201_trial_stats(arch, 200, 'cifar100'):\n    pprint.pprint(t)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Intermediate results are also available.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "for t in query_nb201_trial_stats(arch, None, 'imagenet16-120', include_intermediates=True):\n    print(t['config'])\n    print('Intermediates:', len(t['intermediates']))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## NDS\n\nUse the following architecture as an example:\n\n<img src=\"file://../../img/nas-bench-nds-example.png\">\n\nHere, ``bot_muls``, ``ds``, ``num_gs``, ``ss`` and ``ws`` stand for \"bottleneck multipliers\",\n\"depths\", \"number of groups\", \"strides\" and \"widths\" respectively.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "model_spec = {\n    'bot_muls': [0.0, 0.25, 0.25, 0.25],\n    'ds': [1, 16, 1, 4],\n    'num_gs': [1, 2, 1, 2],\n    'ss': [1, 1, 2, 2],\n    'ws': [16, 64, 128, 16]\n}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Use none as a wildcard.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "for t in query_nds_trial_stats('residual_bottleneck', None, None, model_spec, None, 'cifar10'):\n    pprint.pprint(t)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "model_spec = {\n    'bot_muls': [0.0, 0.25, 0.25, 0.25],\n    'ds': [1, 16, 1, 4],\n    'num_gs': [1, 2, 1, 2],\n    'ss': [1, 1, 2, 2],\n    'ws': [16, 64, 128, 16]\n}\nfor t in query_nds_trial_stats('residual_bottleneck', None, None, model_spec, None, 'cifar10', include_intermediates=True):\n    pprint.pprint(t['intermediates'][:10])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "model_spec = {'ds': [1, 12, 12, 12], 'ss': [1, 1, 2, 2], 'ws': [16, 24, 24, 40]}\nfor t in query_nds_trial_stats('residual_basic', 'resnet', 'random', model_spec, {}, 'cifar10'):\n    pprint.pprint(t)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Get the first one.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "pprint.pprint(next(query_nds_trial_stats('vanilla', None, None, None, None, None)))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Count number.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "model_spec = {'num_nodes_normal': 5, 'num_nodes_reduce': 5, 'depth': 12, 'width': 32, 'aux': False, 'drop_prob': 0.0}\ncell_spec = {\n    'normal_0_op_x': 'avg_pool_3x3',\n    'normal_0_input_x': 0,\n    'normal_0_op_y': 'conv_7x1_1x7',\n    'normal_0_input_y': 1,\n    'normal_1_op_x': 'sep_conv_3x3',\n    'normal_1_input_x': 2,\n    'normal_1_op_y': 'sep_conv_5x5',\n    'normal_1_input_y': 0,\n    'normal_2_op_x': 'dil_sep_conv_3x3',\n    'normal_2_input_x': 2,\n    'normal_2_op_y': 'dil_sep_conv_3x3',\n    'normal_2_input_y': 2,\n    'normal_3_op_x': 'skip_connect',\n    'normal_3_input_x': 4,\n    'normal_3_op_y': 'dil_sep_conv_3x3',\n    'normal_3_input_y': 4,\n    'normal_4_op_x': 'conv_7x1_1x7',\n    'normal_4_input_x': 2,\n    'normal_4_op_y': 'sep_conv_3x3',\n    'normal_4_input_y': 4,\n    'normal_concat': [3, 5, 6],\n    'reduce_0_op_x': 'avg_pool_3x3',\n    'reduce_0_input_x': 0,\n    'reduce_0_op_y': 'dil_sep_conv_3x3',\n    'reduce_0_input_y': 1,\n    'reduce_1_op_x': 'sep_conv_3x3',\n    'reduce_1_input_x': 0,\n    'reduce_1_op_y': 'sep_conv_3x3',\n    'reduce_1_input_y': 0,\n    'reduce_2_op_x': 'skip_connect',\n    'reduce_2_input_x': 2,\n    'reduce_2_op_y': 'sep_conv_7x7',\n    'reduce_2_input_y': 0,\n    'reduce_3_op_x': 'conv_7x1_1x7',\n    'reduce_3_input_x': 4,\n    'reduce_3_op_y': 'skip_connect',\n    'reduce_3_input_y': 4,\n    'reduce_4_op_x': 'conv_7x1_1x7',\n    'reduce_4_input_x': 0,\n    'reduce_4_op_y': 'conv_7x1_1x7',\n    'reduce_4_input_y': 5,\n    'reduce_concat': [3, 6]\n}\n\nfor t in query_nds_trial_stats('nas_cell', None, None, model_spec, cell_spec, 'cifar10'):\n    assert t['config']['model_spec'] == model_spec\n    assert t['config']['cell_spec'] == cell_spec\n    pprint.pprint(t)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Count number.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print('NDS (amoeba) count:', len(list(query_nds_trial_stats(None, 'amoeba', None, None, None, None, None))))"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.8"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/source/tutorials/nasbench_as_dataset.py b/docs/source/tutorials/nasbench_as_dataset.py
new file mode 100644
index 000000000..91cb37757
--- /dev/null
+++ b/docs/source/tutorials/nasbench_as_dataset.py
@@ -0,0 +1,185 @@
+"""
+Use NAS Benchmarks as Datasets
+==============================
+
+In this tutorial, we show how to use NAS Benchmarks as datasets.
+For research purposes we sometimes desire to query the benchmarks for architecture accuracies,
+rather than train them one by one from scratch.
+NNI has provided query tools so that users can easily get the retrieve the data in NAS benchmarks.
+"""
+
+# %%
+# Prerequisites
+# -------------
+# This tutorial assumes that you have already prepared your NAS benchmarks under cache directory
+# (by default, ``~/.cache/nni/nasbenchmark``).
+# If you haven't, please follow the data preparation guide in :doc:`/nas/benchmarks`.
+#
+# As a result, the directory should look like:
+
+import os
+os.listdir(os.path.expanduser('~/.cache/nni/nasbenchmark'))
+
+# %%
+import pprint
+
+from nni.nas.benchmarks.nasbench101 import query_nb101_trial_stats
+from nni.nas.benchmarks.nasbench201 import query_nb201_trial_stats
+from nni.nas.benchmarks.nds import query_nds_trial_stats
+
+# %%
+# NAS-Bench-101
+# -------------
+#
+# Use the following architecture as an example:
+#
+# .. image:: ../../img/nas-bench-101-example.png
+
+arch = {
+    'op1': 'conv3x3-bn-relu',
+    'op2': 'maxpool3x3',
+    'op3': 'conv3x3-bn-relu',
+    'op4': 'conv3x3-bn-relu',
+    'op5': 'conv1x1-bn-relu',
+    'input1': [0],
+    'input2': [1],
+    'input3': [2],
+    'input4': [0],
+    'input5': [0, 3, 4],
+    'input6': [2, 5]
+}
+for t in query_nb101_trial_stats(arch, 108, include_intermediates=True):
+    pprint.pprint(t)
+
+# %%
+# An architecture of NAS-Bench-101 could be trained more than once.
+# Each element of the returned generator is a dict which contains one of the training results of this trial config
+# (architecture + hyper-parameters) including train/valid/test accuracy,
+# training time, number of epochs, etc. The results of NAS-Bench-201 and NDS follow similar formats.
+#
+# NAS-Bench-201
+# -------------
+#
+# Use the following architecture as an example:
+#
+# .. image:: ../../img/nas-bench-201-example.png
+
+arch = {
+    '0_1': 'avg_pool_3x3',
+    '0_2': 'conv_1x1',
+    '1_2': 'skip_connect',
+    '0_3': 'conv_1x1',
+    '1_3': 'skip_connect',
+    '2_3': 'skip_connect'
+}
+for t in query_nb201_trial_stats(arch, 200, 'cifar100'):
+    pprint.pprint(t)
+
+# %%
+# Intermediate results are also available.
+
+for t in query_nb201_trial_stats(arch, None, 'imagenet16-120', include_intermediates=True):
+    print(t['config'])
+    print('Intermediates:', len(t['intermediates']))
+
+# %%
+# NDS
+# ---
+#
+# Use the following architecture as an example:
+#
+# .. image:: ../../img/nas-bench-nds-example.png
+#
+# Here, ``bot_muls``, ``ds``, ``num_gs``, ``ss`` and ``ws`` stand for "bottleneck multipliers",
+# "depths", "number of groups", "strides" and "widths" respectively.
+
+# %%
+model_spec = {
+    'bot_muls': [0.0, 0.25, 0.25, 0.25],
+    'ds': [1, 16, 1, 4],
+    'num_gs': [1, 2, 1, 2],
+    'ss': [1, 1, 2, 2],
+    'ws': [16, 64, 128, 16]
+}
+
+# %%
+# Use none as a wildcard.
+for t in query_nds_trial_stats('residual_bottleneck', None, None, model_spec, None, 'cifar10'):
+    pprint.pprint(t)
+
+# %%
+model_spec = {
+    'bot_muls': [0.0, 0.25, 0.25, 0.25],
+    'ds': [1, 16, 1, 4],
+    'num_gs': [1, 2, 1, 2],
+    'ss': [1, 1, 2, 2],
+    'ws': [16, 64, 128, 16]
+}
+for t in query_nds_trial_stats('residual_bottleneck', None, None, model_spec, None, 'cifar10', include_intermediates=True):
+    pprint.pprint(t['intermediates'][:10])
+
+# %%
+model_spec = {'ds': [1, 12, 12, 12], 'ss': [1, 1, 2, 2], 'ws': [16, 24, 24, 40]}
+for t in query_nds_trial_stats('residual_basic', 'resnet', 'random', model_spec, {}, 'cifar10'):
+    pprint.pprint(t)
+
+# %%
+# Get the first one.
+pprint.pprint(next(query_nds_trial_stats('vanilla', None, None, None, None, None)))
+
+# %%
+# Count number.
+model_spec = {'num_nodes_normal': 5, 'num_nodes_reduce': 5, 'depth': 12, 'width': 32, 'aux': False, 'drop_prob': 0.0}
+cell_spec = {
+    'normal_0_op_x': 'avg_pool_3x3',
+    'normal_0_input_x': 0,
+    'normal_0_op_y': 'conv_7x1_1x7',
+    'normal_0_input_y': 1,
+    'normal_1_op_x': 'sep_conv_3x3',
+    'normal_1_input_x': 2,
+    'normal_1_op_y': 'sep_conv_5x5',
+    'normal_1_input_y': 0,
+    'normal_2_op_x': 'dil_sep_conv_3x3',
+    'normal_2_input_x': 2,
+    'normal_2_op_y': 'dil_sep_conv_3x3',
+    'normal_2_input_y': 2,
+    'normal_3_op_x': 'skip_connect',
+    'normal_3_input_x': 4,
+    'normal_3_op_y': 'dil_sep_conv_3x3',
+    'normal_3_input_y': 4,
+    'normal_4_op_x': 'conv_7x1_1x7',
+    'normal_4_input_x': 2,
+    'normal_4_op_y': 'sep_conv_3x3',
+    'normal_4_input_y': 4,
+    'normal_concat': [3, 5, 6],
+    'reduce_0_op_x': 'avg_pool_3x3',
+    'reduce_0_input_x': 0,
+    'reduce_0_op_y': 'dil_sep_conv_3x3',
+    'reduce_0_input_y': 1,
+    'reduce_1_op_x': 'sep_conv_3x3',
+    'reduce_1_input_x': 0,
+    'reduce_1_op_y': 'sep_conv_3x3',
+    'reduce_1_input_y': 0,
+    'reduce_2_op_x': 'skip_connect',
+    'reduce_2_input_x': 2,
+    'reduce_2_op_y': 'sep_conv_7x7',
+    'reduce_2_input_y': 0,
+    'reduce_3_op_x': 'conv_7x1_1x7',
+    'reduce_3_input_x': 4,
+    'reduce_3_op_y': 'skip_connect',
+    'reduce_3_input_y': 4,
+    'reduce_4_op_x': 'conv_7x1_1x7',
+    'reduce_4_input_x': 0,
+    'reduce_4_op_y': 'conv_7x1_1x7',
+    'reduce_4_input_y': 5,
+    'reduce_concat': [3, 6]
+}
+
+for t in query_nds_trial_stats('nas_cell', None, None, model_spec, cell_spec, 'cifar10'):
+    assert t['config']['model_spec'] == model_spec
+    assert t['config']['cell_spec'] == cell_spec
+    pprint.pprint(t)
+
+# %%
+# Count number.
+print('NDS (amoeba) count:', len(list(query_nds_trial_stats(None, 'amoeba', None, None, None, None, None))))
diff --git a/docs/source/tutorials/nasbench_as_dataset.py.md5 b/docs/source/tutorials/nasbench_as_dataset.py.md5
new file mode 100644
index 000000000..73343d3ca
--- /dev/null
+++ b/docs/source/tutorials/nasbench_as_dataset.py.md5
@@ -0,0 +1 @@
+715de24d20c57f3639033f6f10376c21
\ No newline at end of file
diff --git a/docs/source/tutorials/nasbench_as_dataset.rst b/docs/source/tutorials/nasbench_as_dataset.rst
new file mode 100644
index 000000000..2fdfcc23d
--- /dev/null
+++ b/docs/source/tutorials/nasbench_as_dataset.rst
@@ -0,0 +1,834 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "tutorials/nasbench_as_dataset.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        Click :ref:`here <sphx_glr_download_tutorials_nasbench_as_dataset.py>`
+        to download the full example code
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorials_nasbench_as_dataset.py:
+
+
+Use NAS Benchmarks as Datasets
+==============================
+
+In this tutorial, we show how to use NAS Benchmarks as datasets.
+For research purposes we sometimes desire to query the benchmarks for architecture accuracies,
+rather than train them one by one from scratch.
+NNI has provided query tools so that users can easily get the retrieve the data in NAS benchmarks.
+
+.. GENERATED FROM PYTHON SOURCE LINES 12-19
+
+Prerequisites
+-------------
+This tutorial assumes that you have already prepared your NAS benchmarks under cache directory
+(by default, ``~/.cache/nni/nasbenchmark``).
+If you haven't, please follow the data preparation guide in :doc:`/nas/benchmarks`.
+
+As a result, the directory should look like:
+
+.. GENERATED FROM PYTHON SOURCE LINES 19-23
+
+.. code-block:: default
+
+
+    import os
+    os.listdir(os.path.expanduser('~/.cache/nni/nasbenchmark'))
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+
+    ['nasbench101-209f5694.db', 'nasbench201-b2b60732.db', 'nds-5745c235.db']
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 24-30
+
+.. code-block:: default
+
+    import pprint
+
+    from nni.nas.benchmarks.nasbench101 import query_nb101_trial_stats
+    from nni.nas.benchmarks.nasbench201 import query_nb201_trial_stats
+    from nni.nas.benchmarks.nds import query_nds_trial_stats
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 31-37
+
+NAS-Bench-101
+-------------
+
+Use the following architecture as an example:
+
+.. image:: ../../img/nas-bench-101-example.png
+
+.. GENERATED FROM PYTHON SOURCE LINES 37-54
+
+.. code-block:: default
+
+
+    arch = {
+        'op1': 'conv3x3-bn-relu',
+        'op2': 'maxpool3x3',
+        'op3': 'conv3x3-bn-relu',
+        'op4': 'conv3x3-bn-relu',
+        'op5': 'conv1x1-bn-relu',
+        'input1': [0],
+        'input2': [1],
+        'input3': [2],
+        'input4': [0],
+        'input5': [0, 3, 4],
+        'input6': [2, 5]
+    }
+    for t in query_nb101_trial_stats(arch, 108, include_intermediates=True):
+        pprint.pprint(t)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    [2022-02-28 13:48:51] INFO (nni.nas.benchmarks.utils/MainThread) "/home/yugzhan/.cache/nni/nasbenchmark/nasbench101-209f5694.db" already exists. Checking hash.
+    {'config': {'arch': {'input1': [0],
+                         'input2': [1],
+                         'input3': [2],
+                         'input4': [0],
+                         'input5': [0, 3, 4],
+                         'input6': [2, 5],
+                         'op1': 'conv3x3-bn-relu',
+                         'op2': 'maxpool3x3',
+                         'op3': 'conv3x3-bn-relu',
+                         'op4': 'conv3x3-bn-relu',
+                         'op5': 'conv1x1-bn-relu'},
+                'hash': '00005c142e6f48ac74fdcf73e3439874',
+                'id': 4,
+                'num_epochs': 108,
+                'num_vertices': 7},
+     'id': 10,
+     'intermediates': [{'current_epoch': 54,
+                        'id': 19,
+                        'test_acc': 77.40384340286255,
+                        'train_acc': 82.82251358032227,
+                        'training_time': 883.4580078125,
+                        'valid_acc': 77.76442170143127},
+                       {'current_epoch': 108,
+                        'id': 20,
+                        'test_acc': 92.11738705635071,
+                        'train_acc': 100.0,
+                        'training_time': 1769.1279296875,
+                        'valid_acc': 92.41786599159241}],
+     'parameters': 8.55553,
+     'test_acc': 92.11738705635071,
+     'train_acc': 100.0,
+     'training_time': 106147.67578125,
+     'valid_acc': 92.41786599159241}
+    {'config': {'arch': {'input1': [0],
+                         'input2': [1],
+                         'input3': [2],
+                         'input4': [0],
+                         'input5': [0, 3, 4],
+                         'input6': [2, 5],
+                         'op1': 'conv3x3-bn-relu',
+                         'op2': 'maxpool3x3',
+                         'op3': 'conv3x3-bn-relu',
+                         'op4': 'conv3x3-bn-relu',
+                         'op5': 'conv1x1-bn-relu'},
+                'hash': '00005c142e6f48ac74fdcf73e3439874',
+                'id': 4,
+                'num_epochs': 108,
+                'num_vertices': 7},
+     'id': 11,
+     'intermediates': [{'current_epoch': 54,
+                        'id': 21,
+                        'test_acc': 82.04126358032227,
+                        'train_acc': 87.96073794364929,
+                        'training_time': 883.6810302734375,
+                        'valid_acc': 82.91265964508057},
+                       {'current_epoch': 108,
+                        'id': 22,
+                        'test_acc': 91.90705418586731,
+                        'train_acc': 100.0,
+                        'training_time': 1768.2509765625,
+                        'valid_acc': 92.45793223381042}],
+     'parameters': 8.55553,
+     'test_acc': 91.90705418586731,
+     'train_acc': 100.0,
+     'training_time': 106095.05859375,
+     'valid_acc': 92.45793223381042}
+    {'config': {'arch': {'input1': [0],
+                         'input2': [1],
+                         'input3': [2],
+                         'input4': [0],
+                         'input5': [0, 3, 4],
+                         'input6': [2, 5],
+                         'op1': 'conv3x3-bn-relu',
+                         'op2': 'maxpool3x3',
+                         'op3': 'conv3x3-bn-relu',
+                         'op4': 'conv3x3-bn-relu',
+                         'op5': 'conv1x1-bn-relu'},
+                'hash': '00005c142e6f48ac74fdcf73e3439874',
+                'id': 4,
+                'num_epochs': 108,
+                'num_vertices': 7},
+     'id': 12,
+     'intermediates': [{'current_epoch': 54,
+                        'id': 23,
+                        'test_acc': 80.58894276618958,
+                        'train_acc': 86.34815812110901,
+                        'training_time': 883.4569702148438,
+                        'valid_acc': 81.1598539352417},
+                       {'current_epoch': 108,
+                        'id': 24,
+                        'test_acc': 92.15745329856873,
+                        'train_acc': 100.0,
+                        'training_time': 1768.9759521484375,
+                        'valid_acc': 93.04887652397156}],
+     'parameters': 8.55553,
+     'test_acc': 92.15745329856873,
+     'train_acc': 100.0,
+     'training_time': 106138.55712890625,
+     'valid_acc': 93.04887652397156}
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 55-66
+
+An architecture of NAS-Bench-101 could be trained more than once.
+Each element of the returned generator is a dict which contains one of the training results of this trial config
+(architecture + hyper-parameters) including train/valid/test accuracy,
+training time, number of epochs, etc. The results of NAS-Bench-201 and NDS follow similar formats.
+
+NAS-Bench-201
+-------------
+
+Use the following architecture as an example:
+
+.. image:: ../../img/nas-bench-201-example.png
+
+.. GENERATED FROM PYTHON SOURCE LINES 66-78
+
+.. code-block:: default
+
+
+    arch = {
+        '0_1': 'avg_pool_3x3',
+        '0_2': 'conv_1x1',
+        '1_2': 'skip_connect',
+        '0_3': 'conv_1x1',
+        '1_3': 'skip_connect',
+        '2_3': 'skip_connect'
+    }
+    for t in query_nb201_trial_stats(arch, 200, 'cifar100'):
+        pprint.pprint(t)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    [2022-02-28 13:49:09] INFO (nni.nas.benchmarks.utils/MainThread) "/home/yugzhan/.cache/nni/nasbenchmark/nasbench201-b2b60732.db" already exists. Checking hash.
+    {'config': {'arch': {'0_1': 'avg_pool_3x3',
+                         '0_2': 'conv_1x1',
+                         '0_3': 'conv_1x1',
+                         '1_2': 'skip_connect',
+                         '1_3': 'skip_connect',
+                         '2_3': 'skip_connect'},
+                'dataset': 'cifar100',
+                'id': 7,
+                'num_cells': 5,
+                'num_channels': 16,
+                'num_epochs': 200},
+     'flops': 15.65322,
+     'id': 3,
+     'latency': 0.013182918230692545,
+     'ori_test_acc': 53.11,
+     'ori_test_evaluation_time': 1.0195916947864352,
+     'ori_test_loss': 1.7307863704681397,
+     'parameters': 0.135156,
+     'seed': 999,
+     'test_acc': 53.07999995727539,
+     'test_evaluation_time': 0.5097958473932176,
+     'test_loss': 1.731276072692871,
+     'train_acc': 57.82,
+     'train_loss': 1.5116578379058838,
+     'training_time': 2888.4371995925903,
+     'valid_acc': 53.14000000610351,
+     'valid_evaluation_time': 0.5097958473932176,
+     'valid_loss': 1.7302966793060304}
+    {'config': {'arch': {'0_1': 'avg_pool_3x3',
+                         '0_2': 'conv_1x1',
+                         '0_3': 'conv_1x1',
+                         '1_2': 'skip_connect',
+                         '1_3': 'skip_connect',
+                         '2_3': 'skip_connect'},
+                'dataset': 'cifar100',
+                'id': 7,
+                'num_cells': 5,
+                'num_channels': 16,
+                'num_epochs': 200},
+     'flops': 15.65322,
+     'id': 7,
+     'latency': 0.013182918230692545,
+     'ori_test_acc': 51.93,
+     'ori_test_evaluation_time': 1.0195916947864352,
+     'ori_test_loss': 1.7572312774658203,
+     'parameters': 0.135156,
+     'seed': 777,
+     'test_acc': 51.979999938964845,
+     'test_evaluation_time': 0.5097958473932176,
+     'test_loss': 1.7429540189743042,
+     'train_acc': 57.578,
+     'train_loss': 1.5114233912658692,
+     'training_time': 2888.4371995925903,
+     'valid_acc': 51.88,
+     'valid_evaluation_time': 0.5097958473932176,
+     'valid_loss': 1.7715086591720581}
+    {'config': {'arch': {'0_1': 'avg_pool_3x3',
+                         '0_2': 'conv_1x1',
+                         '0_3': 'conv_1x1',
+                         '1_2': 'skip_connect',
+                         '1_3': 'skip_connect',
+                         '2_3': 'skip_connect'},
+                'dataset': 'cifar100',
+                'id': 7,
+                'num_cells': 5,
+                'num_channels': 16,
+                'num_epochs': 200},
+     'flops': 15.65322,
+     'id': 11,
+     'latency': 0.013182918230692545,
+     'ori_test_acc': 53.38,
+     'ori_test_evaluation_time': 1.0195916947864352,
+     'ori_test_loss': 1.7281623031616211,
+     'parameters': 0.135156,
+     'seed': 888,
+     'test_acc': 53.67999998779297,
+     'test_evaluation_time': 0.5097958473932176,
+     'test_loss': 1.7327697801589965,
+     'train_acc': 57.792,
+     'train_loss': 1.5091403088760376,
+     'training_time': 2888.4371995925903,
+     'valid_acc': 53.08000000610352,
+     'valid_evaluation_time': 0.5097958473932176,
+     'valid_loss': 1.7235548280715942}
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 79-80
+
+Intermediate results are also available.
+
+.. GENERATED FROM PYTHON SOURCE LINES 80-85
+
+.. code-block:: default
+
+
+    for t in query_nb201_trial_stats(arch, None, 'imagenet16-120', include_intermediates=True):
+        print(t['config'])
+        print('Intermediates:', len(t['intermediates']))
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    {'id': 4, 'arch': {'0_1': 'avg_pool_3x3', '0_2': 'conv_1x1', '0_3': 'conv_1x1', '1_2': 'skip_connect', '1_3': 'skip_connect', '2_3': 'skip_connect'}, 'num_epochs': 12, 'num_channels': 16, 'num_cells': 5, 'dataset': 'imagenet16-120'}
+    Intermediates: 12
+    {'id': 8, 'arch': {'0_1': 'avg_pool_3x3', '0_2': 'conv_1x1', '0_3': 'conv_1x1', '1_2': 'skip_connect', '1_3': 'skip_connect', '2_3': 'skip_connect'}, 'num_epochs': 200, 'num_channels': 16, 'num_cells': 5, 'dataset': 'imagenet16-120'}
+    Intermediates: 200
+    {'id': 8, 'arch': {'0_1': 'avg_pool_3x3', '0_2': 'conv_1x1', '0_3': 'conv_1x1', '1_2': 'skip_connect', '1_3': 'skip_connect', '2_3': 'skip_connect'}, 'num_epochs': 200, 'num_channels': 16, 'num_cells': 5, 'dataset': 'imagenet16-120'}
+    Intermediates: 200
+    {'id': 8, 'arch': {'0_1': 'avg_pool_3x3', '0_2': 'conv_1x1', '0_3': 'conv_1x1', '1_2': 'skip_connect', '1_3': 'skip_connect', '2_3': 'skip_connect'}, 'num_epochs': 200, 'num_channels': 16, 'num_cells': 5, 'dataset': 'imagenet16-120'}
+    Intermediates: 200
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 86-95
+
+NDS
+---
+
+Use the following architecture as an example:
+
+.. image:: ../../img/nas-bench-nds-example.png
+
+Here, ``bot_muls``, ``ds``, ``num_gs``, ``ss`` and ``ws`` stand for "bottleneck multipliers",
+"depths", "number of groups", "strides" and "widths" respectively.
+
+.. GENERATED FROM PYTHON SOURCE LINES 97-105
+
+.. code-block:: default
+
+    model_spec = {
+        'bot_muls': [0.0, 0.25, 0.25, 0.25],
+        'ds': [1, 16, 1, 4],
+        'num_gs': [1, 2, 1, 2],
+        'ss': [1, 1, 2, 2],
+        'ws': [16, 64, 128, 16]
+    }
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 106-107
+
+Use none as a wildcard.
+
+.. GENERATED FROM PYTHON SOURCE LINES 107-110
+
+.. code-block:: default
+
+    for t in query_nds_trial_stats('residual_bottleneck', None, None, model_spec, None, 'cifar10'):
+        pprint.pprint(t)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    [2022-02-28 13:49:36] INFO (nni.nas.benchmarks.utils/MainThread) "/home/yugzhan/.cache/nni/nasbenchmark/nds-5745c235.db" already exists. Checking hash.
+    {'best_test_acc': 90.48,
+     'best_train_acc': 96.356,
+     'best_train_loss': 0.116,
+     'config': {'base_lr': 0.1,
+                'cell_spec': {},
+                'dataset': 'cifar10',
+                'generator': 'random',
+                'id': 45505,
+                'model_family': 'residual_bottleneck',
+                'model_spec': {'bot_muls': [0.0, 0.25, 0.25, 0.25],
+                               'ds': [1, 16, 1, 4],
+                               'num_gs': [1, 2, 1, 2],
+                               'ss': [1, 1, 2, 2],
+                               'ws': [16, 64, 128, 16]},
+                'num_epochs': 100,
+                'proposer': 'resnext-a',
+                'weight_decay': 0.0005},
+     'final_test_acc': 90.39,
+     'final_train_acc': 96.298,
+     'final_train_loss': 0.116,
+     'flops': 69.890986,
+     'id': 45505,
+     'iter_time': 0.065,
+     'parameters': 0.083002,
+     'seed': 1}
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 111-121
+
+.. code-block:: default
+
+    model_spec = {
+        'bot_muls': [0.0, 0.25, 0.25, 0.25],
+        'ds': [1, 16, 1, 4],
+        'num_gs': [1, 2, 1, 2],
+        'ss': [1, 1, 2, 2],
+        'ws': [16, 64, 128, 16]
+    }
+    for t in query_nds_trial_stats('residual_bottleneck', None, None, model_spec, None, 'cifar10', include_intermediates=True):
+        pprint.pprint(t['intermediates'][:10])
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    [{'current_epoch': 1,
+      'id': 4494501,
+      'test_acc': 41.76,
+      'train_acc': 30.421000000000006,
+      'train_loss': 1.793},
+     {'current_epoch': 2,
+      'id': 4494502,
+      'test_acc': 54.66,
+      'train_acc': 47.24,
+      'train_loss': 1.415},
+     {'current_epoch': 3,
+      'id': 4494503,
+      'test_acc': 59.97,
+      'train_acc': 56.983,
+      'train_loss': 1.179},
+     {'current_epoch': 4,
+      'id': 4494504,
+      'test_acc': 62.91,
+      'train_acc': 61.955,
+      'train_loss': 1.048},
+     {'current_epoch': 5,
+      'id': 4494505,
+      'test_acc': 66.16,
+      'train_acc': 64.493,
+      'train_loss': 0.983},
+     {'current_epoch': 6,
+      'id': 4494506,
+      'test_acc': 66.5,
+      'train_acc': 66.274,
+      'train_loss': 0.937},
+     {'current_epoch': 7,
+      'id': 4494507,
+      'test_acc': 67.55,
+      'train_acc': 67.426,
+      'train_loss': 0.907},
+     {'current_epoch': 8,
+      'id': 4494508,
+      'test_acc': 69.45,
+      'train_acc': 68.45400000000001,
+      'train_loss': 0.878},
+     {'current_epoch': 9,
+      'id': 4494509,
+      'test_acc': 70.14,
+      'train_acc': 69.295,
+      'train_loss': 0.857},
+     {'current_epoch': 10,
+      'id': 4494510,
+      'test_acc': 69.47,
+      'train_acc': 70.304,
+      'train_loss': 0.832}]
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 122-126
+
+.. code-block:: default
+
+    model_spec = {'ds': [1, 12, 12, 12], 'ss': [1, 1, 2, 2], 'ws': [16, 24, 24, 40]}
+    for t in query_nds_trial_stats('residual_basic', 'resnet', 'random', model_spec, {}, 'cifar10'):
+        pprint.pprint(t)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    {'best_test_acc': 93.58,
+     'best_train_acc': 99.772,
+     'best_train_loss': 0.011,
+     'config': {'base_lr': 0.1,
+                'cell_spec': {},
+                'dataset': 'cifar10',
+                'generator': 'random',
+                'id': 108998,
+                'model_family': 'residual_basic',
+                'model_spec': {'ds': [1, 12, 12, 12],
+                               'ss': [1, 1, 2, 2],
+                               'ws': [16, 24, 24, 40]},
+                'num_epochs': 100,
+                'proposer': 'resnet',
+                'weight_decay': 0.0005},
+     'final_test_acc': 93.49,
+     'final_train_acc': 99.772,
+     'final_train_loss': 0.011,
+     'flops': 184.519578,
+     'id': 108998,
+     'iter_time': 0.059,
+     'parameters': 0.594138,
+     'seed': 1}
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 127-128
+
+Get the first one.
+
+.. GENERATED FROM PYTHON SOURCE LINES 128-130
+
+.. code-block:: default
+
+    pprint.pprint(next(query_nds_trial_stats('vanilla', None, None, None, None, None)))
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    {'best_test_acc': 84.5,
+     'best_train_acc': 89.66499999999999,
+     'best_train_loss': 0.302,
+     'config': {'base_lr': 0.1,
+                'cell_spec': {},
+                'dataset': 'cifar10',
+                'generator': 'random',
+                'id': 139492,
+                'model_family': 'vanilla',
+                'model_spec': {'ds': [1, 12, 12, 12],
+                               'ss': [1, 1, 2, 2],
+                               'ws': [16, 24, 32, 40]},
+                'num_epochs': 100,
+                'proposer': 'vanilla',
+                'weight_decay': 0.0005},
+     'final_test_acc': 84.35,
+     'final_train_acc': 89.633,
+     'final_train_loss': 0.303,
+     'flops': 208.36393,
+     'id': 154692,
+     'iter_time': 0.058,
+     'parameters': 0.68977,
+     'seed': 1}
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 131-132
+
+Count number.
+
+.. GENERATED FROM PYTHON SOURCE LINES 132-183
+
+.. code-block:: default
+
+    model_spec = {'num_nodes_normal': 5, 'num_nodes_reduce': 5, 'depth': 12, 'width': 32, 'aux': False, 'drop_prob': 0.0}
+    cell_spec = {
+        'normal_0_op_x': 'avg_pool_3x3',
+        'normal_0_input_x': 0,
+        'normal_0_op_y': 'conv_7x1_1x7',
+        'normal_0_input_y': 1,
+        'normal_1_op_x': 'sep_conv_3x3',
+        'normal_1_input_x': 2,
+        'normal_1_op_y': 'sep_conv_5x5',
+        'normal_1_input_y': 0,
+        'normal_2_op_x': 'dil_sep_conv_3x3',
+        'normal_2_input_x': 2,
+        'normal_2_op_y': 'dil_sep_conv_3x3',
+        'normal_2_input_y': 2,
+        'normal_3_op_x': 'skip_connect',
+        'normal_3_input_x': 4,
+        'normal_3_op_y': 'dil_sep_conv_3x3',
+        'normal_3_input_y': 4,
+        'normal_4_op_x': 'conv_7x1_1x7',
+        'normal_4_input_x': 2,
+        'normal_4_op_y': 'sep_conv_3x3',
+        'normal_4_input_y': 4,
+        'normal_concat': [3, 5, 6],
+        'reduce_0_op_x': 'avg_pool_3x3',
+        'reduce_0_input_x': 0,
+        'reduce_0_op_y': 'dil_sep_conv_3x3',
+        'reduce_0_input_y': 1,
+        'reduce_1_op_x': 'sep_conv_3x3',
+        'reduce_1_input_x': 0,
+        'reduce_1_op_y': 'sep_conv_3x3',
+        'reduce_1_input_y': 0,
+        'reduce_2_op_x': 'skip_connect',
+        'reduce_2_input_x': 2,
+        'reduce_2_op_y': 'sep_conv_7x7',
+        'reduce_2_input_y': 0,
+        'reduce_3_op_x': 'conv_7x1_1x7',
+        'reduce_3_input_x': 4,
+        'reduce_3_op_y': 'skip_connect',
+        'reduce_3_input_y': 4,
+        'reduce_4_op_x': 'conv_7x1_1x7',
+        'reduce_4_input_x': 0,
+        'reduce_4_op_y': 'conv_7x1_1x7',
+        'reduce_4_input_y': 5,
+        'reduce_concat': [3, 6]
+    }
+
+    for t in query_nds_trial_stats('nas_cell', None, None, model_spec, cell_spec, 'cifar10'):
+        assert t['config']['model_spec'] == model_spec
+        assert t['config']['cell_spec'] == cell_spec
+        pprint.pprint(t)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    {'best_test_acc': 93.37,
+     'best_train_acc': 99.91,
+     'best_train_loss': 0.006,
+     'config': {'base_lr': 0.1,
+                'cell_spec': {'normal_0_input_x': 0,
+                              'normal_0_input_y': 1,
+                              'normal_0_op_x': 'avg_pool_3x3',
+                              'normal_0_op_y': 'conv_7x1_1x7',
+                              'normal_1_input_x': 2,
+                              'normal_1_input_y': 0,
+                              'normal_1_op_x': 'sep_conv_3x3',
+                              'normal_1_op_y': 'sep_conv_5x5',
+                              'normal_2_input_x': 2,
+                              'normal_2_input_y': 2,
+                              'normal_2_op_x': 'dil_sep_conv_3x3',
+                              'normal_2_op_y': 'dil_sep_conv_3x3',
+                              'normal_3_input_x': 4,
+                              'normal_3_input_y': 4,
+                              'normal_3_op_x': 'skip_connect',
+                              'normal_3_op_y': 'dil_sep_conv_3x3',
+                              'normal_4_input_x': 2,
+                              'normal_4_input_y': 4,
+                              'normal_4_op_x': 'conv_7x1_1x7',
+                              'normal_4_op_y': 'sep_conv_3x3',
+                              'normal_concat': [3, 5, 6],
+                              'reduce_0_input_x': 0,
+                              'reduce_0_input_y': 1,
+                              'reduce_0_op_x': 'avg_pool_3x3',
+                              'reduce_0_op_y': 'dil_sep_conv_3x3',
+                              'reduce_1_input_x': 0,
+                              'reduce_1_input_y': 0,
+                              'reduce_1_op_x': 'sep_conv_3x3',
+                              'reduce_1_op_y': 'sep_conv_3x3',
+                              'reduce_2_input_x': 2,
+                              'reduce_2_input_y': 0,
+                              'reduce_2_op_x': 'skip_connect',
+                              'reduce_2_op_y': 'sep_conv_7x7',
+                              'reduce_3_input_x': 4,
+                              'reduce_3_input_y': 4,
+                              'reduce_3_op_x': 'conv_7x1_1x7',
+                              'reduce_3_op_y': 'skip_connect',
+                              'reduce_4_input_x': 0,
+                              'reduce_4_input_y': 5,
+                              'reduce_4_op_x': 'conv_7x1_1x7',
+                              'reduce_4_op_y': 'conv_7x1_1x7',
+                              'reduce_concat': [3, 6]},
+                'dataset': 'cifar10',
+                'generator': 'random',
+                'id': 1,
+                'model_family': 'nas_cell',
+                'model_spec': {'aux': False,
+                               'depth': 12,
+                               'drop_prob': 0.0,
+                               'num_nodes_normal': 5,
+                               'num_nodes_reduce': 5,
+                               'width': 32},
+                'num_epochs': 100,
+                'proposer': 'amoeba',
+                'weight_decay': 0.0005},
+     'final_test_acc': 93.27,
+     'final_train_acc': 99.91,
+     'final_train_loss': 0.006,
+     'flops': 664.400586,
+     'id': 1,
+     'iter_time': 0.281,
+     'parameters': 4.190314,
+     'seed': 1}
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 184-185
+
+Count number.
+
+.. GENERATED FROM PYTHON SOURCE LINES 185-186
+
+.. code-block:: default
+
+    print('NDS (amoeba) count:', len(list(query_nds_trial_stats(None, 'amoeba', None, None, None, None, None))))
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    NDS (amoeba) count: 5107
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 1 minutes  2.214 seconds)
+
+
+.. _sphx_glr_download_tutorials_nasbench_as_dataset.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+    :class: sphx-glr-footer-example
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-python
+
+     :download:`Download Python source code: nasbench_as_dataset.py <nasbench_as_dataset.py>`
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+     :download:`Download Jupyter notebook: nasbench_as_dataset.ipynb <nasbench_as_dataset.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/source/tutorials/nasbench_as_dataset_codeobj.pickle b/docs/source/tutorials/nasbench_as_dataset_codeobj.pickle
new file mode 100644
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diff --git a/docs/source/tutorials/nni_experiment.ipynb b/docs/source/tutorials/nni_experiment.ipynb
deleted file mode 100644
index bfd30a452..000000000
--- a/docs/source/tutorials/nni_experiment.ipynb
+++ /dev/null
@@ -1,187 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "%matplotlib inline"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n# Start and Manage a New Experiment\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Configure Search Space\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "search_space = {\n    \"C\": {\"_type\": \"quniform\", \"_value\": [0.1, 1, 0.1]},\n    \"kernel\": {\"_type\": \"choice\", \"_value\": [\"linear\", \"rbf\", \"poly\", \"sigmoid\"]},\n    \"degree\": {\"_type\": \"choice\", \"_value\": [1, 2, 3, 4]},\n    \"gamma\": {\"_type\": \"quniform\", \"_value\": [0.01, 0.1, 0.01]},\n    \"coef0\": {\"_type\": \"quniform\", \"_value\": [0.01, 0.1, 0.01]}\n}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Configure Experiment\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "from nni.experiment import Experiment\nexperiment = Experiment('local')\nexperiment.config.experiment_name = 'Example'\nexperiment.config.trial_concurrency = 2\nexperiment.config.max_trial_number = 10\nexperiment.config.search_space = search_space\nexperiment.config.trial_command = 'python scripts/trial_sklearn.py'\nexperiment.config.trial_code_directory = './'\nexperiment.config.tuner.name = 'TPE'\nexperiment.config.tuner.class_args['optimize_mode'] = 'maximize'\nexperiment.config.training_service.use_active_gpu = True"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Start Experiment\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "experiment.start(8080)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Experiment View & Control\n\nView the status of experiment.\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "experiment.get_status()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Wait until at least one trial finishes.\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "import time\n\nfor _ in range(10):\n    stats = experiment.get_job_statistics()\n    if any(stat['trialJobStatus'] == 'SUCCEEDED' for stat in stats):\n        break\n    time.sleep(10)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Export the experiment data.\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "experiment.export_data()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Get metric of jobs\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "experiment.get_job_metrics()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Stop Experiment\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "experiment.stop()"
-      ]
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.8.8"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/docs/source/tutorials/nni_experiment.py b/docs/source/tutorials/nni_experiment.py
deleted file mode 100644
index f6619dd31..000000000
--- a/docs/source/tutorials/nni_experiment.py
+++ /dev/null
@@ -1,67 +0,0 @@
-"""
-Start and Manage a New Experiment
-=================================
-"""
-
-# %%
-# Configure Search Space
-# ----------------------
-
-search_space = {
-    "C": {"_type": "quniform", "_value": [0.1, 1, 0.1]},
-    "kernel": {"_type": "choice", "_value": ["linear", "rbf", "poly", "sigmoid"]},
-    "degree": {"_type": "choice", "_value": [1, 2, 3, 4]},
-    "gamma": {"_type": "quniform", "_value": [0.01, 0.1, 0.01]},
-    "coef0": {"_type": "quniform", "_value": [0.01, 0.1, 0.01]}
-}
-
-# %%
-# Configure Experiment
-# --------------------
-
-from nni.experiment import Experiment
-experiment = Experiment('local')
-experiment.config.experiment_name = 'Example'
-experiment.config.trial_concurrency = 2
-experiment.config.max_trial_number = 10
-experiment.config.search_space = search_space
-experiment.config.trial_command = 'python scripts/trial_sklearn.py'
-experiment.config.trial_code_directory = './'
-experiment.config.tuner.name = 'TPE'
-experiment.config.tuner.class_args['optimize_mode'] = 'maximize'
-experiment.config.training_service.use_active_gpu = True
-
-# %%
-# Start Experiment
-# ----------------
-experiment.start(8080)
-
-# %%
-# Experiment View & Control
-# -------------------------
-#
-# View the status of experiment.
-experiment.get_status()
-
-# %%
-# Wait until at least one trial finishes.
-import time
-
-for _ in range(10):
-    stats = experiment.get_job_statistics()
-    if any(stat['trialJobStatus'] == 'SUCCEEDED' for stat in stats):
-        break
-    time.sleep(10)
-
-# %%
-# Export the experiment data.
-experiment.export_data()
-
-# %%
-# Get metric of jobs
-experiment.get_job_metrics()
-
-# %%
-# Stop Experiment
-# ---------------
-experiment.stop()
diff --git a/docs/source/tutorials/nni_experiment.py.md5 b/docs/source/tutorials/nni_experiment.py.md5
deleted file mode 100644
index 6ca09d538..000000000
--- a/docs/source/tutorials/nni_experiment.py.md5
+++ /dev/null
@@ -1 +0,0 @@
-9f822647d89f05264b70d1ae1c473be1
\ No newline at end of file
diff --git a/docs/source/tutorials/nni_experiment.rst b/docs/source/tutorials/nni_experiment.rst
deleted file mode 100644
index 13133bda7..000000000
--- a/docs/source/tutorials/nni_experiment.rst
+++ /dev/null
@@ -1,265 +0,0 @@
-
-.. DO NOT EDIT.
-.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
-.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
-.. "tutorials/nni_experiment.py"
-.. LINE NUMBERS ARE GIVEN BELOW.
-
-.. only:: html
-
-    .. note::
-        :class: sphx-glr-download-link-note
-
-        Click :ref:`here <sphx_glr_download_tutorials_nni_experiment.py>`
-        to download the full example code
-
-.. rst-class:: sphx-glr-example-title
-
-.. _sphx_glr_tutorials_nni_experiment.py:
-
-
-Start and Manage a New Experiment
-=================================
-
-.. GENERATED FROM PYTHON SOURCE LINES 7-9
-
-Configure Search Space
-----------------------
-
-.. GENERATED FROM PYTHON SOURCE LINES 9-18
-
-.. code-block:: default
-
-
-    search_space = {
-        "C": {"_type": "quniform", "_value": [0.1, 1, 0.1]},
-        "kernel": {"_type": "choice", "_value": ["linear", "rbf", "poly", "sigmoid"]},
-        "degree": {"_type": "choice", "_value": [1, 2, 3, 4]},
-        "gamma": {"_type": "quniform", "_value": [0.01, 0.1, 0.01]},
-        "coef0": {"_type": "quniform", "_value": [0.01, 0.1, 0.01]}
-    }
-
-
-
-
-
-
-
-
-.. GENERATED FROM PYTHON SOURCE LINES 19-21
-
-Configure Experiment
---------------------
-
-.. GENERATED FROM PYTHON SOURCE LINES 21-34
-
-.. code-block:: default
-
-
-    from nni.experiment import Experiment
-    experiment = Experiment('local')
-    experiment.config.experiment_name = 'Example'
-    experiment.config.trial_concurrency = 2
-    experiment.config.max_trial_number = 10
-    experiment.config.search_space = search_space
-    experiment.config.trial_command = 'python scripts/trial_sklearn.py'
-    experiment.config.trial_code_directory = './'
-    experiment.config.tuner.name = 'TPE'
-    experiment.config.tuner.class_args['optimize_mode'] = 'maximize'
-    experiment.config.training_service.use_active_gpu = True
-
-
-
-
-
-
-
-
-.. GENERATED FROM PYTHON SOURCE LINES 35-37
-
-Start Experiment
-----------------
-
-.. GENERATED FROM PYTHON SOURCE LINES 37-39
-
-.. code-block:: default
-
-    experiment.start(8080)
-
-
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
-    [2022-02-07 18:56:04] Creating experiment, Experiment ID: fl9vu67z
-    [2022-02-07 18:56:04] Starting web server...
-    [2022-02-07 18:56:05] Setting up...
-    [2022-02-07 18:56:05] Web UI URLs: http://127.0.0.1:8080 http://10.190.173.211:8080 http://172.17.0.1:8080 http://192.168.49.1:8080
-
-
-
-
-.. GENERATED FROM PYTHON SOURCE LINES 40-44
-
-Experiment View & Control
--------------------------
-
-View the status of experiment.
-
-.. GENERATED FROM PYTHON SOURCE LINES 44-46
-
-.. code-block:: default
-
-    experiment.get_status()
-
-
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
-
-    'RUNNING'
-
-
-
-.. GENERATED FROM PYTHON SOURCE LINES 47-48
-
-Wait until at least one trial finishes.
-
-.. GENERATED FROM PYTHON SOURCE LINES 48-56
-
-.. code-block:: default
-
-    import time
-
-    for _ in range(10):
-        stats = experiment.get_job_statistics()
-        if any(stat['trialJobStatus'] == 'SUCCEEDED' for stat in stats):
-            break
-        time.sleep(10)
-
-
-
-
-
-
-
-
-.. GENERATED FROM PYTHON SOURCE LINES 57-58
-
-Export the experiment data.
-
-.. GENERATED FROM PYTHON SOURCE LINES 58-60
-
-.. code-block:: default
-
-    experiment.export_data()
-
-
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
-
-    [TrialResult(parameter={'C': 0.9, 'kernel': 'rbf', 'degree': 4, 'gamma': 0.07, 'coef0': 0.03}, value=0.9733333333333334, trialJobId='dNOZt'), TrialResult(parameter={'C': 0.8, 'kernel': 'sigmoid', 'degree': 2, 'gamma': 0.01, 'coef0': 0.01}, value=0.9733333333333334, trialJobId='okYSD')]
-
-
-
-.. GENERATED FROM PYTHON SOURCE LINES 61-62
-
-Get metric of jobs
-
-.. GENERATED FROM PYTHON SOURCE LINES 62-64
-
-.. code-block:: default
-
-    experiment.get_job_metrics()
-
-
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
-
-    {'okYSD': [TrialMetricData(timestamp=1644227777089, trialJobId='okYSD', parameterId='1', type='FINAL', sequence=0, data=0.9733333333333334)], 'dNOZt': [TrialMetricData(timestamp=1644227777357, trialJobId='dNOZt', parameterId='0', type='FINAL', sequence=0, data=0.9733333333333334)]}
-
-
-
-.. GENERATED FROM PYTHON SOURCE LINES 65-67
-
-Stop Experiment
----------------
-
-.. GENERATED FROM PYTHON SOURCE LINES 67-68
-
-.. code-block:: default
-
-    experiment.stop()
-
-
-
-
-.. rst-class:: sphx-glr-script-out
-
- Out:
-
- .. code-block:: none
-
-    [2022-02-07 18:56:25] Stopping experiment, please wait...
-    [2022-02-07 18:56:28] Experiment stopped
-
-
-
-
-
-.. rst-class:: sphx-glr-timing
-
-   **Total running time of the script:** ( 0 minutes  24.662 seconds)
-
-
-.. _sphx_glr_download_tutorials_nni_experiment.py:
-
-
-.. only :: html
-
- .. container:: sphx-glr-footer
-    :class: sphx-glr-footer-example
-
-
-
-  .. container:: sphx-glr-download sphx-glr-download-python
-
-     :download:`Download Python source code: nni_experiment.py <nni_experiment.py>`
-
-
-
-  .. container:: sphx-glr-download sphx-glr-download-jupyter
-
-     :download:`Download Jupyter notebook: nni_experiment.ipynb <nni_experiment.ipynb>`
-
-
-.. only:: html
-
- .. rst-class:: sphx-glr-signature
-
-    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/source/tutorials/nni_experiment_codeobj.pickle b/docs/source/tutorials/nni_experiment_codeobj.pickle
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diff --git a/docs/source/tutorials/pruning_customize.ipynb b/docs/source/tutorials/pruning_customize.ipynb
new file mode 100644
index 000000000..394274eb3
--- /dev/null
+++ b/docs/source/tutorials/pruning_customize.ipynb
@@ -0,0 +1,97 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Customize Basic Pruner\n\nUsers can easily customize a basic pruner in NNI. A large number of basic modules have been provided and can be reused.\nFollow the NNI pruning interface, users only need to focus on their creative parts without worrying about other regular modules.\n\nIn this tutorial, we show how to customize a basic pruner.\n\n## Concepts\n\nNNI abstracts the basic pruning process into three steps, collecting data, calculating metrics, allocating sparsity.\nMost pruning algorithms rely on a metric to decide where should be pruned. Using L1 norm pruner as an example,\nthe first step is collecting model weights, the second step is calculating L1 norm for weight per output channel,\nthe third step is ranking L1 norm metric and masking the output channels that have small L1 norm.\n\nIn NNI basic pruner, these three step is implement as ``DataCollector``, ``MetricsCalculator`` and ``SparsityAllocator``.\n\n-   ``DataCollector``: This module take pruner as initialize parameter.\n    It will get the relevant information of the model from the pruner,\n    and sometimes it will also hook the model to get input, output or gradient of a layer or a tensor.\n    It can also patch optimizer if some special steps need to be executed before or after ``optimizer.step()``.\n\n-   ``MetricsCalculator``: This module will take the data collected from the ``DataCollector``,\n    then calculate the metrics. The metric shape is usually reduced from the data shape.\n    The ``dim`` taken by ``MetricsCalculator`` means which dimension will be kept after calculate metrics.\n    i.e., the collected data shape is (10, 20, 30), and the ``dim`` is 1, then the dimension-1 will be kept,\n    the output metrics shape should be (20,).\n\n-   ``SparsityAllocator``: This module take the metrics and generate the masks.\n    Different ``SparsityAllocator`` has different masks generation strategies.\n    A common and simple strategy is sorting the metrics' values and calculating a threshold according to the configured sparsity,\n    mask the positions which metric value smaller than the threshold.\n    The ``dim`` taken by ``SparsityAllocator`` means the metrics are for which dimension, the mask will be expanded to weight shape.\n    i.e., the metric shape is (20,), the corresponding layer weight shape is (20, 40), and the ``dim`` is 0.\n    ``SparsityAllocator`` will first generate a mask with shape (20,), then expand this mask to shape (20, 40).\n\n## Simple Example: Customize a Block-L1NormPruner\n\nNNI already have L1NormPruner, but for the reason of reproducing the paper and reducing user configuration items,\nit only support pruning layer output channels. In this example, we will customize a pruner that supports block granularity for Linear.\n\nNote that you don't need to implement all these three kinds of tools for each time,\nNNI supports many predefined tools, and you can directly use these to customize your own pruner.\nThis is a tutorial so we show how to define all these three kinds of pruning tools.\n\nCustomize the pruning tools used by the pruner at first.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import torch\nfrom nni.algorithms.compression.v2.pytorch.pruning.basic_pruner import BasicPruner\nfrom nni.algorithms.compression.v2.pytorch.pruning.tools import (\n    DataCollector,\n    MetricsCalculator,\n    SparsityAllocator\n)\n\n\n# This data collector collects weight in wrapped module as data.\n# The wrapped module is the module configured in pruner's config_list.\n# This implementation is similar as nni.algorithms.compression.v2.pytorch.pruning.tools.WeightDataCollector\nclass WeightDataCollector(DataCollector):\n    def collect(self):\n        data = {}\n        # get_modules_wrapper will get all the wrapper in the compressor (pruner),\n        # it returns a dict with format {wrapper_name: wrapper},\n        # use wrapper.module to get the wrapped module.\n        for _, wrapper in self.compressor.get_modules_wrapper().items():\n            data[wrapper.name] = wrapper.module.weight.data\n        # return {wrapper_name: weight_data}\n        return data\n\n\nclass BlockNormMetricsCalculator(MetricsCalculator):\n    def __init__(self, block_sparse_size):\n        # Because we will keep all dimension with block granularity, so fix ``dim=None``,\n        # means all dimensions will be kept.\n        super().__init__(dim=None, block_sparse_size=block_sparse_size)\n\n    def calculate_metrics(self, data):\n        data_length = len(self.block_sparse_size)\n        reduce_unfold_dims = list(range(data_length, 2 * data_length))\n\n        metrics = {}\n        for name, t in data.items():\n            # Unfold t as block size, and calculate L1 Norm for each block.\n            for dim, size in enumerate(self.block_sparse_size):\n                t = t.unfold(dim, size, size)\n            metrics[name] = t.norm(dim=reduce_unfold_dims, p=1)\n        # return {wrapper_name: block_metric}\n        return metrics\n\n\n# This implementation is similar as nni.algorithms.compression.v2.pytorch.pruning.tools.NormalSparsityAllocator\nclass BlockSparsityAllocator(SparsityAllocator):\n    def __init__(self, pruner, block_sparse_size):\n        super().__init__(pruner, dim=None, block_sparse_size=block_sparse_size, continuous_mask=True)\n\n    def generate_sparsity(self, metrics):\n        masks = {}\n        for name, wrapper in self.pruner.get_modules_wrapper().items():\n            # wrapper.config['total_sparsity'] can get the configured sparsity ratio for this wrapped module\n            sparsity_rate = wrapper.config['total_sparsity']\n            # get metric for this wrapped module\n            metric = metrics[name]\n            # mask the metric with old mask, if the masked position need never recover,\n            # just keep this is ok if you are new in NNI pruning\n            if self.continuous_mask:\n                metric *= self._compress_mask(wrapper.weight_mask)\n            # convert sparsity ratio to prune number\n            prune_num = int(sparsity_rate * metric.numel())\n            # calculate the metric threshold\n            threshold = torch.topk(metric.view(-1), prune_num, largest=False)[0].max()\n            # generate mask, keep the metric positions that metric values greater than the threshold\n            mask = torch.gt(metric, threshold).type_as(metric)\n            # expand the mask to weight size, if the block is masked, this block will be filled with zeros,\n            # otherwise filled with ones\n            masks[name] = self._expand_mask(name, mask)\n            # merge the new mask with old mask, if the masked position need never recover,\n            # just keep this is ok if you are new in NNI pruning\n            if self.continuous_mask:\n                masks[name]['weight'] *= wrapper.weight_mask\n        return masks"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Customize the pruner.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "class BlockL1NormPruner(BasicPruner):\n    def __init__(self, model, config_list, block_sparse_size):\n        self.block_sparse_size = block_sparse_size\n        super().__init__(model, config_list)\n\n    # Implement reset_tools is enough for this pruner.\n    def reset_tools(self):\n        if self.data_collector is None:\n            self.data_collector = WeightDataCollector(self)\n        else:\n            self.data_collector.reset()\n        if self.metrics_calculator is None:\n            self.metrics_calculator = BlockNormMetricsCalculator(self.block_sparse_size)\n        if self.sparsity_allocator is None:\n            self.sparsity_allocator = BlockSparsityAllocator(self, self.block_sparse_size)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Try this pruner.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Define a simple model.\nclass TestModel(torch.nn.Module):\n    def __init__(self) -> None:\n        super().__init__()\n        self.fc1 = torch.nn.Linear(4, 8)\n        self.fc2 = torch.nn.Linear(8, 4)\n\n    def forward(self, x):\n        return self.fc2(self.fc1(x))\n\nmodel = TestModel()\nconfig_list = [{'op_types': ['Linear'], 'total_sparsity': 0.5}]\n# use 2x2 block\n_, masks = BlockL1NormPruner(model, config_list, [2, 2]).compress()\n\n# show the generated masks\nprint('fc1 masks:\\n', masks['fc1']['weight'])\nprint('fc2 masks:\\n', masks['fc2']['weight'])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "This time we successfully define a new pruner with pruning block granularity!\nNote that we don't put validation logic in this example, like ``_validate_config_before_canonical``,\nbut for a robust implementation, we suggest you involve the validation logic.\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.8"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/source/tutorials/pruning_customize.py b/docs/source/tutorials/pruning_customize.py
new file mode 100644
index 000000000..278b6fce6
--- /dev/null
+++ b/docs/source/tutorials/pruning_customize.py
@@ -0,0 +1,174 @@
+"""
+Customize Basic Pruner
+======================
+
+Users can easily customize a basic pruner in NNI. A large number of basic modules have been provided and can be reused.
+Follow the NNI pruning interface, users only need to focus on their creative parts without worrying about other regular modules.
+
+In this tutorial, we show how to customize a basic pruner.
+
+Concepts
+--------
+
+NNI abstracts the basic pruning process into three steps, collecting data, calculating metrics, allocating sparsity.
+Most pruning algorithms rely on a metric to decide where should be pruned. Using L1 norm pruner as an example,
+the first step is collecting model weights, the second step is calculating L1 norm for weight per output channel,
+the third step is ranking L1 norm metric and masking the output channels that have small L1 norm.
+
+In NNI basic pruner, these three step is implement as ``DataCollector``, ``MetricsCalculator`` and ``SparsityAllocator``.
+
+-   ``DataCollector``: This module take pruner as initialize parameter.
+    It will get the relevant information of the model from the pruner,
+    and sometimes it will also hook the model to get input, output or gradient of a layer or a tensor.
+    It can also patch optimizer if some special steps need to be executed before or after ``optimizer.step()``.
+
+-   ``MetricsCalculator``: This module will take the data collected from the ``DataCollector``,
+    then calculate the metrics. The metric shape is usually reduced from the data shape.
+    The ``dim`` taken by ``MetricsCalculator`` means which dimension will be kept after calculate metrics.
+    i.e., the collected data shape is (10, 20, 30), and the ``dim`` is 1, then the dimension-1 will be kept,
+    the output metrics shape should be (20,).
+
+-   ``SparsityAllocator``: This module take the metrics and generate the masks.
+    Different ``SparsityAllocator`` has different masks generation strategies.
+    A common and simple strategy is sorting the metrics' values and calculating a threshold according to the configured sparsity,
+    mask the positions which metric value smaller than the threshold.
+    The ``dim`` taken by ``SparsityAllocator`` means the metrics are for which dimension, the mask will be expanded to weight shape.
+    i.e., the metric shape is (20,), the corresponding layer weight shape is (20, 40), and the ``dim`` is 0.
+    ``SparsityAllocator`` will first generate a mask with shape (20,), then expand this mask to shape (20, 40).
+
+Simple Example: Customize a Block-L1NormPruner
+----------------------------------------------
+
+NNI already have L1NormPruner, but for the reason of reproducing the paper and reducing user configuration items,
+it only support pruning layer output channels. In this example, we will customize a pruner that supports block granularity for Linear.
+
+Note that you don't need to implement all these three kinds of tools for each time,
+NNI supports many predefined tools, and you can directly use these to customize your own pruner.
+This is a tutorial so we show how to define all these three kinds of pruning tools.
+
+Customize the pruning tools used by the pruner at first.
+"""
+
+import torch
+from nni.algorithms.compression.v2.pytorch.pruning.basic_pruner import BasicPruner
+from nni.algorithms.compression.v2.pytorch.pruning.tools import (
+    DataCollector,
+    MetricsCalculator,
+    SparsityAllocator
+)
+
+
+# This data collector collects weight in wrapped module as data.
+# The wrapped module is the module configured in pruner's config_list.
+# This implementation is similar as nni.algorithms.compression.v2.pytorch.pruning.tools.WeightDataCollector
+class WeightDataCollector(DataCollector):
+    def collect(self):
+        data = {}
+        # get_modules_wrapper will get all the wrapper in the compressor (pruner),
+        # it returns a dict with format {wrapper_name: wrapper},
+        # use wrapper.module to get the wrapped module.
+        for _, wrapper in self.compressor.get_modules_wrapper().items():
+            data[wrapper.name] = wrapper.module.weight.data
+        # return {wrapper_name: weight_data}
+        return data
+
+
+class BlockNormMetricsCalculator(MetricsCalculator):
+    def __init__(self, block_sparse_size):
+        # Because we will keep all dimension with block granularity, so fix ``dim=None``,
+        # means all dimensions will be kept.
+        super().__init__(dim=None, block_sparse_size=block_sparse_size)
+
+    def calculate_metrics(self, data):
+        data_length = len(self.block_sparse_size)
+        reduce_unfold_dims = list(range(data_length, 2 * data_length))
+
+        metrics = {}
+        for name, t in data.items():
+            # Unfold t as block size, and calculate L1 Norm for each block.
+            for dim, size in enumerate(self.block_sparse_size):
+                t = t.unfold(dim, size, size)
+            metrics[name] = t.norm(dim=reduce_unfold_dims, p=1)
+        # return {wrapper_name: block_metric}
+        return metrics
+
+
+# This implementation is similar as nni.algorithms.compression.v2.pytorch.pruning.tools.NormalSparsityAllocator
+class BlockSparsityAllocator(SparsityAllocator):
+    def __init__(self, pruner, block_sparse_size):
+        super().__init__(pruner, dim=None, block_sparse_size=block_sparse_size, continuous_mask=True)
+
+    def generate_sparsity(self, metrics):
+        masks = {}
+        for name, wrapper in self.pruner.get_modules_wrapper().items():
+            # wrapper.config['total_sparsity'] can get the configured sparsity ratio for this wrapped module
+            sparsity_rate = wrapper.config['total_sparsity']
+            # get metric for this wrapped module
+            metric = metrics[name]
+            # mask the metric with old mask, if the masked position need never recover,
+            # just keep this is ok if you are new in NNI pruning
+            if self.continuous_mask:
+                metric *= self._compress_mask(wrapper.weight_mask)
+            # convert sparsity ratio to prune number
+            prune_num = int(sparsity_rate * metric.numel())
+            # calculate the metric threshold
+            threshold = torch.topk(metric.view(-1), prune_num, largest=False)[0].max()
+            # generate mask, keep the metric positions that metric values greater than the threshold
+            mask = torch.gt(metric, threshold).type_as(metric)
+            # expand the mask to weight size, if the block is masked, this block will be filled with zeros,
+            # otherwise filled with ones
+            masks[name] = self._expand_mask(name, mask)
+            # merge the new mask with old mask, if the masked position need never recover,
+            # just keep this is ok if you are new in NNI pruning
+            if self.continuous_mask:
+                masks[name]['weight'] *= wrapper.weight_mask
+        return masks
+
+
+# %%
+# Customize the pruner.
+
+class BlockL1NormPruner(BasicPruner):
+    def __init__(self, model, config_list, block_sparse_size):
+        self.block_sparse_size = block_sparse_size
+        super().__init__(model, config_list)
+
+    # Implement reset_tools is enough for this pruner.
+    def reset_tools(self):
+        if self.data_collector is None:
+            self.data_collector = WeightDataCollector(self)
+        else:
+            self.data_collector.reset()
+        if self.metrics_calculator is None:
+            self.metrics_calculator = BlockNormMetricsCalculator(self.block_sparse_size)
+        if self.sparsity_allocator is None:
+            self.sparsity_allocator = BlockSparsityAllocator(self, self.block_sparse_size)
+
+
+# %%
+# Try this pruner.
+
+# Define a simple model.
+class TestModel(torch.nn.Module):
+    def __init__(self) -> None:
+        super().__init__()
+        self.fc1 = torch.nn.Linear(4, 8)
+        self.fc2 = torch.nn.Linear(8, 4)
+
+    def forward(self, x):
+        return self.fc2(self.fc1(x))
+
+model = TestModel()
+config_list = [{'op_types': ['Linear'], 'total_sparsity': 0.5}]
+# use 2x2 block
+_, masks = BlockL1NormPruner(model, config_list, [2, 2]).compress()
+
+# show the generated masks
+print('fc1 masks:\n', masks['fc1']['weight'])
+print('fc2 masks:\n', masks['fc2']['weight'])
+
+
+# %%
+# This time we successfully define a new pruner with pruning block granularity!
+# Note that we don't put validation logic in this example, like ``_validate_config_before_canonical``,
+# but for a robust implementation, we suggest you involve the validation logic.
diff --git a/docs/source/tutorials/pruning_customize.py.md5 b/docs/source/tutorials/pruning_customize.py.md5
new file mode 100644
index 000000000..3ae92c751
--- /dev/null
+++ b/docs/source/tutorials/pruning_customize.py.md5
@@ -0,0 +1 @@
+5b92fe6666938105b07998c198077299
\ No newline at end of file
diff --git a/docs/source/tutorials/pruning_customize.rst b/docs/source/tutorials/pruning_customize.rst
new file mode 100644
index 000000000..4f32862cf
--- /dev/null
+++ b/docs/source/tutorials/pruning_customize.rst
@@ -0,0 +1,285 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "tutorials/pruning_customize.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        Click :ref:`here <sphx_glr_download_tutorials_pruning_customize.py>`
+        to download the full example code
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorials_pruning_customize.py:
+
+
+Customize Basic Pruner
+======================
+
+Users can easily customize a basic pruner in NNI. A large number of basic modules have been provided and can be reused.
+Follow the NNI pruning interface, users only need to focus on their creative parts without worrying about other regular modules.
+
+In this tutorial, we show how to customize a basic pruner.
+
+Concepts
+--------
+
+NNI abstracts the basic pruning process into three steps, collecting data, calculating metrics, allocating sparsity.
+Most pruning algorithms rely on a metric to decide where should be pruned. Using L1 norm pruner as an example,
+the first step is collecting model weights, the second step is calculating L1 norm for weight per output channel,
+the third step is ranking L1 norm metric and masking the output channels that have small L1 norm.
+
+In NNI basic pruner, these three step is implement as ``DataCollector``, ``MetricsCalculator`` and ``SparsityAllocator``.
+
+-   ``DataCollector``: This module take pruner as initialize parameter.
+    It will get the relevant information of the model from the pruner,
+    and sometimes it will also hook the model to get input, output or gradient of a layer or a tensor.
+    It can also patch optimizer if some special steps need to be executed before or after ``optimizer.step()``.
+
+-   ``MetricsCalculator``: This module will take the data collected from the ``DataCollector``,
+    then calculate the metrics. The metric shape is usually reduced from the data shape.
+    The ``dim`` taken by ``MetricsCalculator`` means which dimension will be kept after calculate metrics.
+    i.e., the collected data shape is (10, 20, 30), and the ``dim`` is 1, then the dimension-1 will be kept,
+    the output metrics shape should be (20,).
+
+-   ``SparsityAllocator``: This module take the metrics and generate the masks.
+    Different ``SparsityAllocator`` has different masks generation strategies.
+    A common and simple strategy is sorting the metrics' values and calculating a threshold according to the configured sparsity,
+    mask the positions which metric value smaller than the threshold.
+    The ``dim`` taken by ``SparsityAllocator`` means the metrics are for which dimension, the mask will be expanded to weight shape.
+    i.e., the metric shape is (20,), the corresponding layer weight shape is (20, 40), and the ``dim`` is 0.
+    ``SparsityAllocator`` will first generate a mask with shape (20,), then expand this mask to shape (20, 40).
+
+Simple Example: Customize a Block-L1NormPruner
+----------------------------------------------
+
+NNI already have L1NormPruner, but for the reason of reproducing the paper and reducing user configuration items,
+it only support pruning layer output channels. In this example, we will customize a pruner that supports block granularity for Linear.
+
+Note that you don't need to implement all these three kinds of tools for each time,
+NNI supports many predefined tools, and you can directly use these to customize your own pruner.
+This is a tutorial so we show how to define all these three kinds of pruning tools.
+
+Customize the pruning tools used by the pruner at first.
+
+.. GENERATED FROM PYTHON SOURCE LINES 51-128
+
+.. code-block:: default
+
+
+    import torch
+    from nni.algorithms.compression.v2.pytorch.pruning.basic_pruner import BasicPruner
+    from nni.algorithms.compression.v2.pytorch.pruning.tools import (
+        DataCollector,
+        MetricsCalculator,
+        SparsityAllocator
+    )
+
+
+    # This data collector collects weight in wrapped module as data.
+    # The wrapped module is the module configured in pruner's config_list.
+    # This implementation is similar as nni.algorithms.compression.v2.pytorch.pruning.tools.WeightDataCollector
+    class WeightDataCollector(DataCollector):
+        def collect(self):
+            data = {}
+            # get_modules_wrapper will get all the wrapper in the compressor (pruner),
+            # it returns a dict with format {wrapper_name: wrapper},
+            # use wrapper.module to get the wrapped module.
+            for _, wrapper in self.compressor.get_modules_wrapper().items():
+                data[wrapper.name] = wrapper.module.weight.data
+            # return {wrapper_name: weight_data}
+            return data
+
+
+    class BlockNormMetricsCalculator(MetricsCalculator):
+        def __init__(self, block_sparse_size):
+            # Because we will keep all dimension with block granularity, so fix ``dim=None``,
+            # means all dimensions will be kept.
+            super().__init__(dim=None, block_sparse_size=block_sparse_size)
+
+        def calculate_metrics(self, data):
+            data_length = len(self.block_sparse_size)
+            reduce_unfold_dims = list(range(data_length, 2 * data_length))
+
+            metrics = {}
+            for name, t in data.items():
+                # Unfold t as block size, and calculate L1 Norm for each block.
+                for dim, size in enumerate(self.block_sparse_size):
+                    t = t.unfold(dim, size, size)
+                metrics[name] = t.norm(dim=reduce_unfold_dims, p=1)
+            # return {wrapper_name: block_metric}
+            return metrics
+
+
+    # This implementation is similar as nni.algorithms.compression.v2.pytorch.pruning.tools.NormalSparsityAllocator
+    class BlockSparsityAllocator(SparsityAllocator):
+        def __init__(self, pruner, block_sparse_size):
+            super().__init__(pruner, dim=None, block_sparse_size=block_sparse_size, continuous_mask=True)
+
+        def generate_sparsity(self, metrics):
+            masks = {}
+            for name, wrapper in self.pruner.get_modules_wrapper().items():
+                # wrapper.config['total_sparsity'] can get the configured sparsity ratio for this wrapped module
+                sparsity_rate = wrapper.config['total_sparsity']
+                # get metric for this wrapped module
+                metric = metrics[name]
+                # mask the metric with old mask, if the masked position need never recover,
+                # just keep this is ok if you are new in NNI pruning
+                if self.continuous_mask:
+                    metric *= self._compress_mask(wrapper.weight_mask)
+                # convert sparsity ratio to prune number
+                prune_num = int(sparsity_rate * metric.numel())
+                # calculate the metric threshold
+                threshold = torch.topk(metric.view(-1), prune_num, largest=False)[0].max()
+                # generate mask, keep the metric positions that metric values greater than the threshold
+                mask = torch.gt(metric, threshold).type_as(metric)
+                # expand the mask to weight size, if the block is masked, this block will be filled with zeros,
+                # otherwise filled with ones
+                masks[name] = self._expand_mask(name, mask)
+                # merge the new mask with old mask, if the masked position need never recover,
+                # just keep this is ok if you are new in NNI pruning
+                if self.continuous_mask:
+                    masks[name]['weight'] *= wrapper.weight_mask
+            return masks
+
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 129-130
+
+Customize the pruner.
+
+.. GENERATED FROM PYTHON SOURCE LINES 130-148
+
+.. code-block:: default
+
+
+    class BlockL1NormPruner(BasicPruner):
+        def __init__(self, model, config_list, block_sparse_size):
+            self.block_sparse_size = block_sparse_size
+            super().__init__(model, config_list)
+
+        # Implement reset_tools is enough for this pruner.
+        def reset_tools(self):
+            if self.data_collector is None:
+                self.data_collector = WeightDataCollector(self)
+            else:
+                self.data_collector.reset()
+            if self.metrics_calculator is None:
+                self.metrics_calculator = BlockNormMetricsCalculator(self.block_sparse_size)
+            if self.sparsity_allocator is None:
+                self.sparsity_allocator = BlockSparsityAllocator(self, self.block_sparse_size)
+
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 149-150
+
+Try this pruner.
+
+.. GENERATED FROM PYTHON SOURCE LINES 150-171
+
+.. code-block:: default
+
+
+    # Define a simple model.
+    class TestModel(torch.nn.Module):
+        def __init__(self) -> None:
+            super().__init__()
+            self.fc1 = torch.nn.Linear(4, 8)
+            self.fc2 = torch.nn.Linear(8, 4)
+
+        def forward(self, x):
+            return self.fc2(self.fc1(x))
+
+    model = TestModel()
+    config_list = [{'op_types': ['Linear'], 'total_sparsity': 0.5}]
+    # use 2x2 block
+    _, masks = BlockL1NormPruner(model, config_list, [2, 2]).compress()
+
+    # show the generated masks
+    print('fc1 masks:\n', masks['fc1']['weight'])
+    print('fc2 masks:\n', masks['fc2']['weight'])
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    fc1 masks:
+     tensor([[0., 0., 0., 0.],
+            [0., 0., 0., 0.],
+            [0., 0., 0., 0.],
+            [0., 0., 0., 0.],
+            [1., 1., 1., 1.],
+            [1., 1., 1., 1.],
+            [1., 1., 1., 1.],
+            [1., 1., 1., 1.]])
+    fc2 masks:
+     tensor([[0., 0., 0., 0., 1., 1., 1., 1.],
+            [0., 0., 0., 0., 1., 1., 1., 1.],
+            [0., 0., 0., 0., 1., 1., 1., 1.],
+            [0., 0., 0., 0., 1., 1., 1., 1.]])
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 172-175
+
+This time we successfully define a new pruner with pruning block granularity!
+Note that we don't put validation logic in this example, like ``_validate_config_before_canonical``,
+but for a robust implementation, we suggest you involve the validation logic.
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 0 minutes  1.175 seconds)
+
+
+.. _sphx_glr_download_tutorials_pruning_customize.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+    :class: sphx-glr-footer-example
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-python
+
+     :download:`Download Python source code: pruning_customize.py <pruning_customize.py>`
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+     :download:`Download Jupyter notebook: pruning_customize.ipynb <pruning_customize.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/source/tutorials/pruning_customize_codeobj.pickle b/docs/source/tutorials/pruning_customize_codeobj.pickle
new file mode 100644
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diff --git a/docs/source/tutorials/pruning_quick_start_mnist.ipynb b/docs/source/tutorials/pruning_quick_start_mnist.ipynb
new file mode 100644
index 000000000..65389f50a
--- /dev/null
+++ b/docs/source/tutorials/pruning_quick_start_mnist.ipynb
@@ -0,0 +1,173 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Pruning Quickstart\n\nModel pruning is a technique to reduce the model size and computation by reducing model weight size or intermediate state size.\nThere are three common practices for pruning a DNN model:\n\n#. Pre-training a model -> Pruning the model -> Fine-tuning the pruned model\n#. Pruning a model during training (i.e., pruning aware training) -> Fine-tuning the pruned model\n#. Pruning a model -> Training the pruned model from scratch\n\nNNI supports all of the above pruning practices by working on the key pruning stage.\nFollowing this tutorial for a quick look at how to use NNI to prune a model in a common practice.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Preparation\n\nIn this tutorial, we use a simple model and pre-trained on MNIST dataset.\nIf you are familiar with defining a model and training in pytorch, you can skip directly to `Pruning Model`_.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import torch\nimport torch.nn.functional as F\nfrom torch.optim import SGD\n\nfrom scripts.compression_mnist_model import TorchModel, trainer, evaluator, device\n\n# define the model\nmodel = TorchModel().to(device)\n\n# show the model structure, note that pruner will wrap the model layer.\nprint(model)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# define the optimizer and criterion for pre-training\n\noptimizer = SGD(model.parameters(), 1e-2)\ncriterion = F.nll_loss\n\n# pre-train and evaluate the model on MNIST dataset\nfor epoch in range(3):\n    trainer(model, optimizer, criterion)\n    evaluator(model)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Pruning Model\n\nUsing L1NormPruner to prune the model and generate the masks.\nUsually, a pruner requires original model and ``config_list`` as its inputs.\nDetailed about how to write ``config_list`` please refer :doc:`compression config specification <../compression/compression_config_list>`.\n\nThe following `config_list` means all layers whose type is `Linear` or `Conv2d` will be pruned,\nexcept the layer named `fc3`, because `fc3` is `exclude`.\nThe final sparsity ratio for each layer is 50%. The layer named `fc3` will not be pruned.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "config_list = [{\n    'sparsity_per_layer': 0.5,\n    'op_types': ['Linear', 'Conv2d']\n}, {\n    'exclude': True,\n    'op_names': ['fc3']\n}]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Pruners usually require `model` and `config_list` as input arguments.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from nni.compression.pytorch.pruning import L1NormPruner\npruner = L1NormPruner(model, config_list)\n\n# show the wrapped model structure, `PrunerModuleWrapper` have wrapped the layers that configured in the config_list.\nprint(model)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# compress the model and generate the masks\n_, masks = pruner.compress()\n# show the masks sparsity\nfor name, mask in masks.items():\n    print(name, ' sparsity : ', '{:.2}'.format(mask['weight'].sum() / mask['weight'].numel()))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Speedup the original model with masks, note that `ModelSpeedup` requires an unwrapped model.\nThe model becomes smaller after speedup,\nand reaches a higher sparsity ratio because `ModelSpeedup` will propagate the masks across layers.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# need to unwrap the model, if the model is wrapped before speedup\npruner._unwrap_model()\n\n# speedup the model, for more information about speedup, please refer :doc:`pruning_speedup`.\nfrom nni.compression.pytorch.speedup import ModelSpeedup\n\nModelSpeedup(model, torch.rand(3, 1, 28, 28).to(device), masks).speedup_model()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "the model will become real smaller after speedup\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(model)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Fine-tuning Compacted Model\nNote that if the model has been sped up, you need to re-initialize a new optimizer for fine-tuning.\nBecause speedup will replace the masked big layers with dense small ones.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "optimizer = SGD(model.parameters(), 1e-2)\nfor epoch in range(3):\n    trainer(model, optimizer, criterion)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/source/tutorials/pruning_quick_start_mnist.py b/docs/source/tutorials/pruning_quick_start_mnist.py
new file mode 100644
index 000000000..a2cee7d99
--- /dev/null
+++ b/docs/source/tutorials/pruning_quick_start_mnist.py
@@ -0,0 +1,109 @@
+"""
+Pruning Quickstart
+==================
+
+Model pruning is a technique to reduce the model size and computation by reducing model weight size or intermediate state size.
+There are three common practices for pruning a DNN model:
+
+#. Pre-training a model -> Pruning the model -> Fine-tuning the pruned model
+#. Pruning a model during training (i.e., pruning aware training) -> Fine-tuning the pruned model
+#. Pruning a model -> Training the pruned model from scratch
+
+NNI supports all of the above pruning practices by working on the key pruning stage.
+Following this tutorial for a quick look at how to use NNI to prune a model in a common practice.
+"""
+
+# %%
+# Preparation
+# -----------
+#
+# In this tutorial, we use a simple model and pre-trained on MNIST dataset.
+# If you are familiar with defining a model and training in pytorch, you can skip directly to `Pruning Model`_.
+
+import torch
+import torch.nn.functional as F
+from torch.optim import SGD
+
+from scripts.compression_mnist_model import TorchModel, trainer, evaluator, device
+
+# define the model
+model = TorchModel().to(device)
+
+# show the model structure, note that pruner will wrap the model layer.
+print(model)
+
+# %%
+
+# define the optimizer and criterion for pre-training
+
+optimizer = SGD(model.parameters(), 1e-2)
+criterion = F.nll_loss
+
+# pre-train and evaluate the model on MNIST dataset
+for epoch in range(3):
+    trainer(model, optimizer, criterion)
+    evaluator(model)
+
+# %%
+# Pruning Model
+# -------------
+#
+# Using L1NormPruner to prune the model and generate the masks.
+# Usually, a pruner requires original model and ``config_list`` as its inputs.
+# Detailed about how to write ``config_list`` please refer :doc:`compression config specification <../compression/compression_config_list>`.
+#
+# The following `config_list` means all layers whose type is `Linear` or `Conv2d` will be pruned,
+# except the layer named `fc3`, because `fc3` is `exclude`.
+# The final sparsity ratio for each layer is 50%. The layer named `fc3` will not be pruned.
+
+config_list = [{
+    'sparsity_per_layer': 0.5,
+    'op_types': ['Linear', 'Conv2d']
+}, {
+    'exclude': True,
+    'op_names': ['fc3']
+}]
+
+# %%
+# Pruners usually require `model` and `config_list` as input arguments.
+
+from nni.compression.pytorch.pruning import L1NormPruner
+pruner = L1NormPruner(model, config_list)
+
+# show the wrapped model structure, `PrunerModuleWrapper` have wrapped the layers that configured in the config_list.
+print(model)
+
+# %%
+
+# compress the model and generate the masks
+_, masks = pruner.compress()
+# show the masks sparsity
+for name, mask in masks.items():
+    print(name, ' sparsity : ', '{:.2}'.format(mask['weight'].sum() / mask['weight'].numel()))
+
+# %%
+# Speedup the original model with masks, note that `ModelSpeedup` requires an unwrapped model.
+# The model becomes smaller after speedup,
+# and reaches a higher sparsity ratio because `ModelSpeedup` will propagate the masks across layers.
+
+# need to unwrap the model, if the model is wrapped before speedup
+pruner._unwrap_model()
+
+# speedup the model, for more information about speedup, please refer :doc:`pruning_speedup`.
+from nni.compression.pytorch.speedup import ModelSpeedup
+
+ModelSpeedup(model, torch.rand(3, 1, 28, 28).to(device), masks).speedup_model()
+
+# %%
+# the model will become real smaller after speedup
+print(model)
+
+# %%
+# Fine-tuning Compacted Model
+# ---------------------------
+# Note that if the model has been sped up, you need to re-initialize a new optimizer for fine-tuning.
+# Because speedup will replace the masked big layers with dense small ones.
+
+optimizer = SGD(model.parameters(), 1e-2)
+for epoch in range(3):
+    trainer(model, optimizer, criterion)
diff --git a/docs/source/tutorials/pruning_quick_start_mnist.py.md5 b/docs/source/tutorials/pruning_quick_start_mnist.py.md5
new file mode 100644
index 000000000..8952d6490
--- /dev/null
+++ b/docs/source/tutorials/pruning_quick_start_mnist.py.md5
@@ -0,0 +1 @@
+930f8ee2f57b70037e3231152a72606c
\ No newline at end of file
diff --git a/docs/source/tutorials/pruning_quick_start_mnist.rst b/docs/source/tutorials/pruning_quick_start_mnist.rst
new file mode 100644
index 000000000..9c685293a
--- /dev/null
+++ b/docs/source/tutorials/pruning_quick_start_mnist.rst
@@ -0,0 +1,357 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "tutorials/pruning_quick_start_mnist.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        Click :ref:`here <sphx_glr_download_tutorials_pruning_quick_start_mnist.py>`
+        to download the full example code
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorials_pruning_quick_start_mnist.py:
+
+
+Pruning Quickstart
+==================
+
+Model pruning is a technique to reduce the model size and computation by reducing model weight size or intermediate state size.
+There are three common practices for pruning a DNN model:
+
+#. Pre-training a model -> Pruning the model -> Fine-tuning the pruned model
+#. Pruning a model during training (i.e., pruning aware training) -> Fine-tuning the pruned model
+#. Pruning a model -> Training the pruned model from scratch
+
+NNI supports all of the above pruning practices by working on the key pruning stage.
+Following this tutorial for a quick look at how to use NNI to prune a model in a common practice.
+
+.. GENERATED FROM PYTHON SOURCE LINES 17-22
+
+Preparation
+-----------
+
+In this tutorial, we use a simple model and pre-trained on MNIST dataset.
+If you are familiar with defining a model and training in pytorch, you can skip directly to `Pruning Model`_.
+
+.. GENERATED FROM PYTHON SOURCE LINES 22-35
+
+.. code-block:: default
+
+
+    import torch
+    import torch.nn.functional as F
+    from torch.optim import SGD
+
+    from scripts.compression_mnist_model import TorchModel, trainer, evaluator, device
+
+    # define the model
+    model = TorchModel().to(device)
+
+    # show the model structure, note that pruner will wrap the model layer.
+    print(model)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    TorchModel(
+      (conv1): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))
+      (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))
+      (fc1): Linear(in_features=256, out_features=120, bias=True)
+      (fc2): Linear(in_features=120, out_features=84, bias=True)
+      (fc3): Linear(in_features=84, out_features=10, bias=True)
+      (relu1): ReLU()
+      (relu2): ReLU()
+      (relu3): ReLU()
+      (relu4): ReLU()
+      (pool1): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
+      (pool2): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
+    )
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 36-47
+
+.. code-block:: default
+
+
+    # define the optimizer and criterion for pre-training
+
+    optimizer = SGD(model.parameters(), 1e-2)
+    criterion = F.nll_loss
+
+    # pre-train and evaluate the model on MNIST dataset
+    for epoch in range(3):
+        trainer(model, optimizer, criterion)
+        evaluator(model)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    Average test loss: 0.5368, Accuracy: 8321/10000 (83%)
+    Average test loss: 0.3092, Accuracy: 9104/10000 (91%)
+    Average test loss: 0.2070, Accuracy: 9380/10000 (94%)
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 48-58
+
+Pruning Model
+-------------
+
+Using L1NormPruner to prune the model and generate the masks.
+Usually, a pruner requires original model and ``config_list`` as its inputs.
+Detailed about how to write ``config_list`` please refer :doc:`compression config specification <../compression/compression_config_list>`.
+
+The following `config_list` means all layers whose type is `Linear` or `Conv2d` will be pruned,
+except the layer named `fc3`, because `fc3` is `exclude`.
+The final sparsity ratio for each layer is 50%. The layer named `fc3` will not be pruned.
+
+.. GENERATED FROM PYTHON SOURCE LINES 58-67
+
+.. code-block:: default
+
+
+    config_list = [{
+        'sparsity_per_layer': 0.5,
+        'op_types': ['Linear', 'Conv2d']
+    }, {
+        'exclude': True,
+        'op_names': ['fc3']
+    }]
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 68-69
+
+Pruners usually require `model` and `config_list` as input arguments.
+
+.. GENERATED FROM PYTHON SOURCE LINES 69-76
+
+.. code-block:: default
+
+
+    from nni.compression.pytorch.pruning import L1NormPruner
+    pruner = L1NormPruner(model, config_list)
+
+    # show the wrapped model structure, `PrunerModuleWrapper` have wrapped the layers that configured in the config_list.
+    print(model)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    TorchModel(
+      (conv1): PrunerModuleWrapper(
+        (module): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))
+      )
+      (conv2): PrunerModuleWrapper(
+        (module): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))
+      )
+      (fc1): PrunerModuleWrapper(
+        (module): Linear(in_features=256, out_features=120, bias=True)
+      )
+      (fc2): PrunerModuleWrapper(
+        (module): Linear(in_features=120, out_features=84, bias=True)
+      )
+      (fc3): Linear(in_features=84, out_features=10, bias=True)
+      (relu1): ReLU()
+      (relu2): ReLU()
+      (relu3): ReLU()
+      (relu4): ReLU()
+      (pool1): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
+      (pool2): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
+    )
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 77-84
+
+.. code-block:: default
+
+
+    # compress the model and generate the masks
+    _, masks = pruner.compress()
+    # show the masks sparsity
+    for name, mask in masks.items():
+        print(name, ' sparsity : ', '{:.2}'.format(mask['weight'].sum() / mask['weight'].numel()))
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    conv1  sparsity :  0.5
+    conv2  sparsity :  0.5
+    fc1  sparsity :  0.5
+    fc2  sparsity :  0.5
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 85-88
+
+Speedup the original model with masks, note that `ModelSpeedup` requires an unwrapped model.
+The model becomes smaller after speedup,
+and reaches a higher sparsity ratio because `ModelSpeedup` will propagate the masks across layers.
+
+.. GENERATED FROM PYTHON SOURCE LINES 88-97
+
+.. code-block:: default
+
+
+    # need to unwrap the model, if the model is wrapped before speedup
+    pruner._unwrap_model()
+
+    # speedup the model, for more information about speedup, please refer :doc:`pruning_speedup`.
+    from nni.compression.pytorch.speedup import ModelSpeedup
+
+    ModelSpeedup(model, torch.rand(3, 1, 28, 28).to(device), masks).speedup_model()
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    aten::log_softmax is not Supported! Please report an issue at https://github.com/microsoft/nni. Thanks~
+    Note: .aten::log_softmax.12 does not have corresponding mask inference object
+    /home/nishang/anaconda3/envs/MCM/lib/python3.9/site-packages/torch/_tensor.py:1013: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at  /opt/conda/conda-bld/pytorch_1640811803361/work/build/aten/src/ATen/core/TensorBody.h:417.)
+      return self._grad
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 98-99
+
+the model will become real smaller after speedup
+
+.. GENERATED FROM PYTHON SOURCE LINES 99-101
+
+.. code-block:: default
+
+    print(model)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    TorchModel(
+      (conv1): Conv2d(1, 3, kernel_size=(5, 5), stride=(1, 1))
+      (conv2): Conv2d(3, 8, kernel_size=(5, 5), stride=(1, 1))
+      (fc1): Linear(in_features=128, out_features=60, bias=True)
+      (fc2): Linear(in_features=60, out_features=42, bias=True)
+      (fc3): Linear(in_features=42, out_features=10, bias=True)
+      (relu1): ReLU()
+      (relu2): ReLU()
+      (relu3): ReLU()
+      (relu4): ReLU()
+      (pool1): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
+      (pool2): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
+    )
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 102-106
+
+Fine-tuning Compacted Model
+---------------------------
+Note that if the model has been sped up, you need to re-initialize a new optimizer for fine-tuning.
+Because speedup will replace the masked big layers with dense small ones.
+
+.. GENERATED FROM PYTHON SOURCE LINES 106-110
+
+.. code-block:: default
+
+
+    optimizer = SGD(model.parameters(), 1e-2)
+    for epoch in range(3):
+        trainer(model, optimizer, criterion)
+
+
+
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 0 minutes  58.337 seconds)
+
+
+.. _sphx_glr_download_tutorials_pruning_quick_start_mnist.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+    :class: sphx-glr-footer-example
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-python
+
+     :download:`Download Python source code: pruning_quick_start_mnist.py <pruning_quick_start_mnist.py>`
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+     :download:`Download Jupyter notebook: pruning_quick_start_mnist.ipynb <pruning_quick_start_mnist.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/source/tutorials/pruning_quick_start_mnist_codeobj.pickle b/docs/source/tutorials/pruning_quick_start_mnist_codeobj.pickle
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diff --git a/docs/source/tutorials/pruning_quick_start_mnist_zh.rst b/docs/source/tutorials/pruning_quick_start_mnist_zh.rst
new file mode 100644
index 000000000..b529bbb14
--- /dev/null
+++ b/docs/source/tutorials/pruning_quick_start_mnist_zh.rst
@@ -0,0 +1,332 @@
+.. 5f266ace988c9ca9e44555fdc497e9ba
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        Click :ref:`here <sphx_glr_download_tutorials_pruning_quick_start_mnist.py>`
+        to download the full example code
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorials_pruning_quick_start_mnist.py:
+
+
+模型剪枝入门
+============
+
+模型剪枝是一种通过减小模型权重规模或中间状态规模来减小模型大小和计算量的技术。
+修剪 DNN 模型有三种常见做法：
+
+#. 训练一个模型 -> 对模型进行剪枝 -> 对剪枝后模型进行微调
+#. 在模型训练过程中进行剪枝 -> 对剪枝后模型进行微调
+#. 对模型进行剪枝 -> 从头训练剪枝后模型
+
+NNI 主要通过在剪枝阶段进行工作来支持上述所有剪枝过程。
+通过本教程可以快速了解如何在常见实践中使用 NNI 修剪模型。
+
+.. GENERATED FROM PYTHON SOURCE LINES 17-22
+
+准备工作
+--------
+
+在本教程中，我们使用一个简单的模型在 MNIST 数据集上进行了预训练。
+如果你熟悉在 pytorch 中定义模型和训练模型，可以直接跳到 `模型剪枝`_。
+
+.. GENERATED FROM PYTHON SOURCE LINES 22-35
+
+.. code-block:: default
+
+
+    import torch
+    import torch.nn.functional as F
+    from torch.optim import SGD
+
+    from scripts.compression_mnist_model import TorchModel, trainer, evaluator, device
+
+    # define the model
+    model = TorchModel().to(device)
+
+    # show the model structure, note that pruner will wrap the model layer.
+    print(model)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    TorchModel(
+      (conv1): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))
+      (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))
+      (fc1): Linear(in_features=256, out_features=120, bias=True)
+      (fc2): Linear(in_features=120, out_features=84, bias=True)
+      (fc3): Linear(in_features=84, out_features=10, bias=True)
+    )
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 36-47
+
+.. code-block:: default
+
+
+    # define the optimizer and criterion for pre-training
+
+    optimizer = SGD(model.parameters(), 1e-2)
+    criterion = F.nll_loss
+
+    # pre-train and evaluate the model on MNIST dataset
+    for epoch in range(3):
+        trainer(model, optimizer, criterion)
+        evaluator(model)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    Average test loss: 0.5266, Accuracy: 8345/10000 (83%)
+    Average test loss: 0.2713, Accuracy: 9209/10000 (92%)
+    Average test loss: 0.1919, Accuracy: 9356/10000 (94%)
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 48-58
+
+模型剪枝
+--------
+
+使用 L1NormPruner 对模型进行剪枝并生成掩码。
+通常情况下，pruner 需要原始模型和一个 ``config_list`` 作为输入参数。
+具体关于如何写 ``config_list`` 请参考 :doc:`compression config specification <../compression/compression_config_list>`。
+
+以下 `config_list` 表示 pruner 将修剪类型为 `Linear` 或 `Conv2d` 的所有层除了名为 `fc3` 的层，因为 `fc3` 被设置为 `exclude`。
+每层的最终稀疏率是 50%。而名为 `fc3` 的层将不会被修剪。
+
+.. GENERATED FROM PYTHON SOURCE LINES 58-67
+
+.. code-block:: default
+
+
+    config_list = [{
+        'sparsity_per_layer': 0.5,
+        'op_types': ['Linear', 'Conv2d']
+    }, {
+        'exclude': True,
+        'op_names': ['fc3']
+    }]
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 68-69
+
+Pruners usually require `model` and `config_list` as input arguments.
+
+.. GENERATED FROM PYTHON SOURCE LINES 69-76
+
+.. code-block:: default
+
+
+    from nni.compression.pytorch.pruning import L1NormPruner
+    pruner = L1NormPruner(model, config_list)
+
+    # show the wrapped model structure, `PrunerModuleWrapper` have wrapped the layers that configured in the config_list.
+    print(model)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    TorchModel(
+      (conv1): PrunerModuleWrapper(
+        (module): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))
+      )
+      (conv2): PrunerModuleWrapper(
+        (module): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))
+      )
+      (fc1): PrunerModuleWrapper(
+        (module): Linear(in_features=256, out_features=120, bias=True)
+      )
+      (fc2): PrunerModuleWrapper(
+        (module): Linear(in_features=120, out_features=84, bias=True)
+      )
+      (fc3): Linear(in_features=84, out_features=10, bias=True)
+    )
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 77-84
+
+.. code-block:: default
+
+
+    # compress the model and generate the masks
+    _, masks = pruner.compress()
+    # show the masks sparsity
+    for name, mask in masks.items():
+        print(name, ' sparsity : ', '{:.2}'.format(mask['weight'].sum() / mask['weight'].numel()))
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    conv1  sparsity :  0.5
+    conv2  sparsity :  0.5
+    fc1  sparsity :  0.5
+    fc2  sparsity :  0.5
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 85-88
+
+使用 NNI 的模型加速功能和 pruner 生成好的 masks 对原始模型进行加速，注意 `ModelSpeedup` 需要 unwrapped 的模型。
+模型会在加速之后真正的在规模上变小，并且可能会达到相比于 masks 更大的稀疏率，这是因为 `ModelSpeedup` 会自动在模型中传播稀疏，
+识别由于掩码带来的冗余权重。
+
+.. GENERATED FROM PYTHON SOURCE LINES 88-97
+
+.. code-block:: default
+
+
+    # need to unwrap the model, if the model is wrapped before speedup
+    pruner._unwrap_model()
+
+    # speedup the model
+    from nni.compression.pytorch.speedup import ModelSpeedup
+
+    ModelSpeedup(model, torch.rand(3, 1, 28, 28).to(device), masks).speedup_model()
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    aten::log_softmax is not Supported! Please report an issue at https://github.com/microsoft/nni. Thanks~
+    Note: .aten::log_softmax.12 does not have corresponding mask inference object
+    /home/ningshang/anaconda3/envs/nni-dev/lib/python3.8/site-packages/torch/_tensor.py:1013: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at  aten/src/ATen/core/TensorBody.h:417.)
+      return self._grad
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 98-99
+
+模型在加速之后变小了。
+
+.. GENERATED FROM PYTHON SOURCE LINES 99-101
+
+.. code-block:: default
+
+    print(model)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    TorchModel(
+      (conv1): Conv2d(1, 3, kernel_size=(5, 5), stride=(1, 1))
+      (conv2): Conv2d(3, 8, kernel_size=(5, 5), stride=(1, 1))
+      (fc1): Linear(in_features=128, out_features=60, bias=True)
+      (fc2): Linear(in_features=60, out_features=42, bias=True)
+      (fc3): Linear(in_features=42, out_features=10, bias=True)
+    )
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 102-106
+
+微调压缩好的紧凑模型
+--------------------
+
+注意当前的模型已经经过了加速，如果你需要微调模型，请重新生成 optimizer。
+这是因为在加速过程中进行了层替换，原来的 optimizer 已经不适用于现在的新模型了。
+
+.. GENERATED FROM PYTHON SOURCE LINES 106-110
+
+.. code-block:: default
+
+
+    optimizer = SGD(model.parameters(), 1e-2)
+    for epoch in range(3):
+        trainer(model, optimizer, criterion)
+
+
+
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 1 minutes  24.976 seconds)
+
+
+.. _sphx_glr_download_tutorials_pruning_quick_start_mnist.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+    :class: sphx-glr-footer-example
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-python
+
+     :download:`Download Python source code: pruning_quick_start_mnist.py <pruning_quick_start_mnist.py>`
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+     :download:`Download Jupyter notebook: pruning_quick_start_mnist.ipynb <pruning_quick_start_mnist.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/source/tutorials/pruning_speedup.ipynb b/docs/source/tutorials/pruning_speedup.ipynb
new file mode 100644
index 000000000..04090cba1
--- /dev/null
+++ b/docs/source/tutorials/pruning_speedup.ipynb
@@ -0,0 +1,140 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Speedup Model with Mask\n\n## Introduction\n\nPruning algorithms usually use weight masks to simulate the real pruning. Masks can be used\nto check model performance of a specific pruning (or sparsity), but there is no real speedup.\nSince model speedup is the ultimate goal of model pruning, we try to provide a tool to users\nto convert a model to a smaller one based on user provided masks (the masks come from the\npruning algorithms).\n\nThere are two types of pruning. One is fine-grained pruning, it does not change the shape of weights,\nand input/output tensors. Sparse kernel is required to speedup a fine-grained pruned layer.\nThe other is coarse-grained pruning (e.g., channels), shape of weights and input/output tensors usually change due to such pruning.\nTo speedup this kind of pruning, there is no need to use sparse kernel, just replace the pruned layer with smaller one.\nSince the support of sparse kernels in community is limited,\nwe only support the speedup of coarse-grained pruning and leave the support of fine-grained pruning in future.\n\n## Design and Implementation\n\nTo speedup a model, the pruned layers should be replaced, either replaced with smaller layer for coarse-grained mask,\nor replaced with sparse kernel for fine-grained mask. Coarse-grained mask usually changes the shape of weights or input/output tensors,\nthus, we should do shape inference to check are there other unpruned layers should be replaced as well due to shape change.\nTherefore, in our design, there are two main steps: first, do shape inference to find out all the modules that should be replaced;\nsecond, replace the modules.\n\nThe first step requires topology (i.e., connections) of the model, we use ``jit.trace`` to obtain the model graph for PyTorch.\nThe new shape of module is auto-inference by NNI, the unchanged parts of outputs during forward and inputs during backward are prepared for reduct.\nFor each type of module, we should prepare a function for module replacement.\nThe module replacement function returns a newly created module which is smaller.\n\n## Usage\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Generate a mask for the model at first.\nWe usually use a NNI pruner to generate the masks then use ``ModelSpeedup`` to compact the model.\nBut in fact ``ModelSpeedup`` is a relatively independent tool, so you can use it independently.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import torch\nfrom scripts.compression_mnist_model import TorchModel, device\n\nmodel = TorchModel().to(device)\n# masks = {layer_name: {'weight': weight_mask, 'bias': bias_mask}}\nconv1_mask = torch.ones_like(model.conv1.weight.data)\n# mask the first three output channels in conv1\nconv1_mask[0: 3] = 0\nmasks = {'conv1': {'weight': conv1_mask}}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Show the original model structure.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(model)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Roughly test the original model inference speed.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import time\nstart = time.time()\nmodel(torch.rand(128, 1, 28, 28).to(device))\nprint('Original Model - Elapsed Time : ', time.time() - start)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Speedup the model and show the model structure after speedup.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from nni.compression.pytorch import ModelSpeedup\nModelSpeedup(model, torch.rand(10, 1, 28, 28).to(device), masks).speedup_model()\nprint(model)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Roughly test the model after speedup inference speed.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "start = time.time()\nmodel(torch.rand(128, 1, 28, 28).to(device))\nprint('Speedup Model - Elapsed Time : ', time.time() - start)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "For combining usage of ``Pruner`` masks generation with ``ModelSpeedup``,\nplease refer to :doc:`Pruning Quick Start <pruning_quick_start_mnist>`.\n\nNOTE: The current implementation supports PyTorch 1.3.1 or newer.\n\n## Limitations\n\nFor PyTorch we can only replace modules, if functions in ``forward`` should be replaced,\nour current implementation does not work. One workaround is make the function a PyTorch module.\n\nIf you want to speedup your own model which cannot supported by the current implementation,\nyou need implement the replace function for module replacement, welcome to contribute.\n\n## Speedup Results of Examples\n\nThe code of these experiments can be found :githublink:`here <examples/model_compress/pruning/legacy/speedup/model_speedup.py>`.\n\nThese result are tested on the `legacy pruning framework <https://nni.readthedocs.io/en/v2.6/Compression/pruning.html>`_, new results will coming soon.\n\n### slim pruner example\n\non one V100 GPU,\ninput tensor: ``torch.randn(64, 3, 32, 32)``\n\n.. list-table::\n   :header-rows: 1\n   :widths: auto\n\n   * - Times\n     - Mask Latency\n     - Speedup Latency\n   * - 1\n     - 0.01197\n     - 0.005107\n   * - 2\n     - 0.02019\n     - 0.008769\n   * - 4\n     - 0.02733\n     - 0.014809\n   * - 8\n     - 0.04310\n     - 0.027441\n   * - 16\n     - 0.07731\n     - 0.05008\n   * - 32\n     - 0.14464\n     - 0.10027\n\n### fpgm pruner example\n\non cpu,\ninput tensor: ``torch.randn(64, 1, 28, 28)``\\ ,\ntoo large variance\n\n.. list-table::\n   :header-rows: 1\n   :widths: auto\n\n   * - Times\n     - Mask Latency\n     - Speedup Latency\n   * - 1\n     - 0.01383\n     - 0.01839\n   * - 2\n     - 0.01167\n     - 0.003558\n   * - 4\n     - 0.01636\n     - 0.01088\n   * - 40\n     - 0.14412\n     - 0.08268\n   * - 40\n     - 1.29385\n     - 0.14408\n   * - 40\n     - 0.41035\n     - 0.46162\n   * - 400\n     - 6.29020\n     - 5.82143\n\n### l1filter pruner example\n\non one V100 GPU,\ninput tensor: ``torch.randn(64, 3, 32, 32)``\n\n.. list-table::\n   :header-rows: 1\n   :widths: auto\n\n   * - Times\n     - Mask Latency\n     - Speedup Latency\n   * - 1\n     - 0.01026\n     - 0.003677\n   * - 2\n     - 0.01657\n     - 0.008161\n   * - 4\n     - 0.02458\n     - 0.020018\n   * - 8\n     - 0.03498\n     - 0.025504\n   * - 16\n     - 0.06757\n     - 0.047523\n   * - 32\n     - 0.10487\n     - 0.086442\n\n### APoZ pruner example\n\non one V100 GPU,\ninput tensor: ``torch.randn(64, 3, 32, 32)``\n\n.. list-table::\n   :header-rows: 1\n   :widths: auto\n\n   * - Times\n     - Mask Latency\n     - Speedup Latency\n   * - 1\n     - 0.01389\n     - 0.004208\n   * - 2\n     - 0.01628\n     - 0.008310\n   * - 4\n     - 0.02521\n     - 0.014008\n   * - 8\n     - 0.03386\n     - 0.023923\n   * - 16\n     - 0.06042\n     - 0.046183\n   * - 32\n     - 0.12421\n     - 0.087113\n\n### SimulatedAnnealing pruner example\n\nIn this experiment, we use SimulatedAnnealing pruner to prune the resnet18 on the cifar10 dataset.\nWe measure the latencies and accuracies of the pruned model under different sparsity ratios, as shown in the following figure.\nThe latency is measured on one V100 GPU and the input tensor is  ``torch.randn(128, 3, 32, 32)``.\n\n<img src=\"file://../../img/SA_latency_accuracy.png\">\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/source/tutorials/pruning_speedup.py b/docs/source/tutorials/pruning_speedup.py
new file mode 100644
index 000000000..7cb4fd456
--- /dev/null
+++ b/docs/source/tutorials/pruning_speedup.py
@@ -0,0 +1,239 @@
+"""
+Speedup Model with Mask
+========================
+
+Introduction
+------------
+
+Pruning algorithms usually use weight masks to simulate the real pruning. Masks can be used
+to check model performance of a specific pruning (or sparsity), but there is no real speedup.
+Since model speedup is the ultimate goal of model pruning, we try to provide a tool to users
+to convert a model to a smaller one based on user provided masks (the masks come from the
+pruning algorithms).
+
+There are two types of pruning. One is fine-grained pruning, it does not change the shape of weights,
+and input/output tensors. Sparse kernel is required to speedup a fine-grained pruned layer.
+The other is coarse-grained pruning (e.g., channels), shape of weights and input/output tensors usually change due to such pruning.
+To speedup this kind of pruning, there is no need to use sparse kernel, just replace the pruned layer with smaller one.
+Since the support of sparse kernels in community is limited,
+we only support the speedup of coarse-grained pruning and leave the support of fine-grained pruning in future.
+
+Design and Implementation
+-------------------------
+
+To speedup a model, the pruned layers should be replaced, either replaced with smaller layer for coarse-grained mask,
+or replaced with sparse kernel for fine-grained mask. Coarse-grained mask usually changes the shape of weights or input/output tensors,
+thus, we should do shape inference to check are there other unpruned layers should be replaced as well due to shape change.
+Therefore, in our design, there are two main steps: first, do shape inference to find out all the modules that should be replaced;
+second, replace the modules.
+
+The first step requires topology (i.e., connections) of the model, we use ``jit.trace`` to obtain the model graph for PyTorch.
+The new shape of module is auto-inference by NNI, the unchanged parts of outputs during forward and inputs during backward are prepared for reduct.
+For each type of module, we should prepare a function for module replacement.
+The module replacement function returns a newly created module which is smaller.
+
+Usage
+-----
+
+"""
+
+# %%
+# Generate a mask for the model at first.
+# We usually use a NNI pruner to generate the masks then use ``ModelSpeedup`` to compact the model.
+# But in fact ``ModelSpeedup`` is a relatively independent tool, so you can use it independently.
+
+import torch
+from scripts.compression_mnist_model import TorchModel, device
+
+model = TorchModel().to(device)
+# masks = {layer_name: {'weight': weight_mask, 'bias': bias_mask}}
+conv1_mask = torch.ones_like(model.conv1.weight.data)
+# mask the first three output channels in conv1
+conv1_mask[0: 3] = 0
+masks = {'conv1': {'weight': conv1_mask}}
+
+# %%
+# Show the original model structure.
+print(model)
+
+# %%
+# Roughly test the original model inference speed.
+import time
+start = time.time()
+model(torch.rand(128, 1, 28, 28).to(device))
+print('Original Model - Elapsed Time : ', time.time() - start)
+
+# %%
+# Speedup the model and show the model structure after speedup.
+from nni.compression.pytorch import ModelSpeedup
+ModelSpeedup(model, torch.rand(10, 1, 28, 28).to(device), masks).speedup_model()
+print(model)
+
+# %%
+# Roughly test the model after speedup inference speed.
+start = time.time()
+model(torch.rand(128, 1, 28, 28).to(device))
+print('Speedup Model - Elapsed Time : ', time.time() - start)
+
+# %%
+# For combining usage of ``Pruner`` masks generation with ``ModelSpeedup``,
+# please refer to :doc:`Pruning Quick Start <pruning_quick_start_mnist>`.
+#
+# NOTE: The current implementation supports PyTorch 1.3.1 or newer.
+#
+# Limitations
+# -----------
+#
+# For PyTorch we can only replace modules, if functions in ``forward`` should be replaced,
+# our current implementation does not work. One workaround is make the function a PyTorch module.
+#
+# If you want to speedup your own model which cannot supported by the current implementation,
+# you need implement the replace function for module replacement, welcome to contribute.
+#
+# Speedup Results of Examples
+# ---------------------------
+#
+# The code of these experiments can be found :githublink:`here <examples/model_compress/pruning/legacy/speedup/model_speedup.py>`.
+#
+# These result are tested on the `legacy pruning framework <https://nni.readthedocs.io/en/v2.6/Compression/pruning.html>`_, new results will coming soon.
+#
+# slim pruner example
+# ^^^^^^^^^^^^^^^^^^^
+#
+# on one V100 GPU,
+# input tensor: ``torch.randn(64, 3, 32, 32)``
+#
+# .. list-table::
+#    :header-rows: 1
+#    :widths: auto
+#
+#    * - Times
+#      - Mask Latency
+#      - Speedup Latency
+#    * - 1
+#      - 0.01197
+#      - 0.005107
+#    * - 2
+#      - 0.02019
+#      - 0.008769
+#    * - 4
+#      - 0.02733
+#      - 0.014809
+#    * - 8
+#      - 0.04310
+#      - 0.027441
+#    * - 16
+#      - 0.07731
+#      - 0.05008
+#    * - 32
+#      - 0.14464
+#      - 0.10027
+#
+# fpgm pruner example
+# ^^^^^^^^^^^^^^^^^^^
+#
+# on cpu,
+# input tensor: ``torch.randn(64, 1, 28, 28)``\ ,
+# too large variance
+#
+# .. list-table::
+#    :header-rows: 1
+#    :widths: auto
+#
+#    * - Times
+#      - Mask Latency
+#      - Speedup Latency
+#    * - 1
+#      - 0.01383
+#      - 0.01839
+#    * - 2
+#      - 0.01167
+#      - 0.003558
+#    * - 4
+#      - 0.01636
+#      - 0.01088
+#    * - 40
+#      - 0.14412
+#      - 0.08268
+#    * - 40
+#      - 1.29385
+#      - 0.14408
+#    * - 40
+#      - 0.41035
+#      - 0.46162
+#    * - 400
+#      - 6.29020
+#      - 5.82143
+#
+# l1filter pruner example
+# ^^^^^^^^^^^^^^^^^^^^^^^
+#
+# on one V100 GPU,
+# input tensor: ``torch.randn(64, 3, 32, 32)``
+#
+# .. list-table::
+#    :header-rows: 1
+#    :widths: auto
+#
+#    * - Times
+#      - Mask Latency
+#      - Speedup Latency
+#    * - 1
+#      - 0.01026
+#      - 0.003677
+#    * - 2
+#      - 0.01657
+#      - 0.008161
+#    * - 4
+#      - 0.02458
+#      - 0.020018
+#    * - 8
+#      - 0.03498
+#      - 0.025504
+#    * - 16
+#      - 0.06757
+#      - 0.047523
+#    * - 32
+#      - 0.10487
+#      - 0.086442
+#
+# APoZ pruner example
+# ^^^^^^^^^^^^^^^^^^^
+#
+# on one V100 GPU,
+# input tensor: ``torch.randn(64, 3, 32, 32)``
+#
+# .. list-table::
+#    :header-rows: 1
+#    :widths: auto
+#
+#    * - Times
+#      - Mask Latency
+#      - Speedup Latency
+#    * - 1
+#      - 0.01389
+#      - 0.004208
+#    * - 2
+#      - 0.01628
+#      - 0.008310
+#    * - 4
+#      - 0.02521
+#      - 0.014008
+#    * - 8
+#      - 0.03386
+#      - 0.023923
+#    * - 16
+#      - 0.06042
+#      - 0.046183
+#    * - 32
+#      - 0.12421
+#      - 0.087113
+#
+# SimulatedAnnealing pruner example
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# In this experiment, we use SimulatedAnnealing pruner to prune the resnet18 on the cifar10 dataset.
+# We measure the latencies and accuracies of the pruned model under different sparsity ratios, as shown in the following figure.
+# The latency is measured on one V100 GPU and the input tensor is  ``torch.randn(128, 3, 32, 32)``.
+#
+# .. image:: ../../img/SA_latency_accuracy.png
diff --git a/docs/source/tutorials/pruning_speedup.py.md5 b/docs/source/tutorials/pruning_speedup.py.md5
new file mode 100644
index 000000000..9f9728e3d
--- /dev/null
+++ b/docs/source/tutorials/pruning_speedup.py.md5
@@ -0,0 +1 @@
+dc5c2369666206591238118f0f746e46
\ No newline at end of file
diff --git a/docs/source/tutorials/pruning_speedup.rst b/docs/source/tutorials/pruning_speedup.rst
new file mode 100644
index 000000000..068d7a9eb
--- /dev/null
+++ b/docs/source/tutorials/pruning_speedup.rst
@@ -0,0 +1,415 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "tutorials/pruning_speedup.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        Click :ref:`here <sphx_glr_download_tutorials_pruning_speedup.py>`
+        to download the full example code
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorials_pruning_speedup.py:
+
+
+Speedup Model with Mask
+========================
+
+Introduction
+------------
+
+Pruning algorithms usually use weight masks to simulate the real pruning. Masks can be used
+to check model performance of a specific pruning (or sparsity), but there is no real speedup.
+Since model speedup is the ultimate goal of model pruning, we try to provide a tool to users
+to convert a model to a smaller one based on user provided masks (the masks come from the
+pruning algorithms).
+
+There are two types of pruning. One is fine-grained pruning, it does not change the shape of weights,
+and input/output tensors. Sparse kernel is required to speedup a fine-grained pruned layer.
+The other is coarse-grained pruning (e.g., channels), shape of weights and input/output tensors usually change due to such pruning.
+To speedup this kind of pruning, there is no need to use sparse kernel, just replace the pruned layer with smaller one.
+Since the support of sparse kernels in community is limited,
+we only support the speedup of coarse-grained pruning and leave the support of fine-grained pruning in future.
+
+Design and Implementation
+-------------------------
+
+To speedup a model, the pruned layers should be replaced, either replaced with smaller layer for coarse-grained mask,
+or replaced with sparse kernel for fine-grained mask. Coarse-grained mask usually changes the shape of weights or input/output tensors,
+thus, we should do shape inference to check are there other unpruned layers should be replaced as well due to shape change.
+Therefore, in our design, there are two main steps: first, do shape inference to find out all the modules that should be replaced;
+second, replace the modules.
+
+The first step requires topology (i.e., connections) of the model, we use ``jit.trace`` to obtain the model graph for PyTorch.
+The new shape of module is auto-inference by NNI, the unchanged parts of outputs during forward and inputs during backward are prepared for reduct.
+For each type of module, we should prepare a function for module replacement.
+The module replacement function returns a newly created module which is smaller.
+
+Usage
+-----
+
+.. GENERATED FROM PYTHON SOURCE LINES 41-44
+
+Generate a mask for the model at first.
+We usually use a NNI pruner to generate the masks then use ``ModelSpeedup`` to compact the model.
+But in fact ``ModelSpeedup`` is a relatively independent tool, so you can use it independently.
+
+.. GENERATED FROM PYTHON SOURCE LINES 44-55
+
+.. code-block:: default
+
+
+    import torch
+    from scripts.compression_mnist_model import TorchModel, device
+
+    model = TorchModel().to(device)
+    # masks = {layer_name: {'weight': weight_mask, 'bias': bias_mask}}
+    conv1_mask = torch.ones_like(model.conv1.weight.data)
+    # mask the first three output channels in conv1
+    conv1_mask[0: 3] = 0
+    masks = {'conv1': {'weight': conv1_mask}}
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 56-57
+
+Show the original model structure.
+
+.. GENERATED FROM PYTHON SOURCE LINES 57-59
+
+.. code-block:: default
+
+    print(model)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    TorchModel(
+      (conv1): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))
+      (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))
+      (fc1): Linear(in_features=256, out_features=120, bias=True)
+      (fc2): Linear(in_features=120, out_features=84, bias=True)
+      (fc3): Linear(in_features=84, out_features=10, bias=True)
+      (relu1): ReLU()
+      (relu2): ReLU()
+      (relu3): ReLU()
+      (relu4): ReLU()
+      (pool1): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
+      (pool2): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
+    )
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 60-61
+
+Roughly test the original model inference speed.
+
+.. GENERATED FROM PYTHON SOURCE LINES 61-66
+
+.. code-block:: default
+
+    import time
+    start = time.time()
+    model(torch.rand(128, 1, 28, 28).to(device))
+    print('Original Model - Elapsed Time : ', time.time() - start)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    Original Model - Elapsed Time :  0.5094916820526123
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 67-68
+
+Speedup the model and show the model structure after speedup.
+
+.. GENERATED FROM PYTHON SOURCE LINES 68-72
+
+.. code-block:: default
+
+    from nni.compression.pytorch import ModelSpeedup
+    ModelSpeedup(model, torch.rand(10, 1, 28, 28).to(device), masks).speedup_model()
+    print(model)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    aten::log_softmax is not Supported! Please report an issue at https://github.com/microsoft/nni. Thanks~
+    Note: .aten::log_softmax.12 does not have corresponding mask inference object
+    /home/nishang/anaconda3/envs/MCM/lib/python3.9/site-packages/torch/_tensor.py:1013: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at  /opt/conda/conda-bld/pytorch_1640811803361/work/build/aten/src/ATen/core/TensorBody.h:417.)
+      return self._grad
+    TorchModel(
+      (conv1): Conv2d(1, 3, kernel_size=(5, 5), stride=(1, 1))
+      (conv2): Conv2d(3, 16, kernel_size=(5, 5), stride=(1, 1))
+      (fc1): Linear(in_features=256, out_features=120, bias=True)
+      (fc2): Linear(in_features=120, out_features=84, bias=True)
+      (fc3): Linear(in_features=84, out_features=10, bias=True)
+      (relu1): ReLU()
+      (relu2): ReLU()
+      (relu3): ReLU()
+      (relu4): ReLU()
+      (pool1): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
+      (pool2): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
+    )
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 73-74
+
+Roughly test the model after speedup inference speed.
+
+.. GENERATED FROM PYTHON SOURCE LINES 74-78
+
+.. code-block:: default
+
+    start = time.time()
+    model(torch.rand(128, 1, 28, 28).to(device))
+    print('Speedup Model - Elapsed Time : ', time.time() - start)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    Speedup Model - Elapsed Time :  0.006000041961669922
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 79-240
+
+For combining usage of ``Pruner`` masks generation with ``ModelSpeedup``,
+please refer to :doc:`Pruning Quick Start <pruning_quick_start_mnist>`.
+
+NOTE: The current implementation supports PyTorch 1.3.1 or newer.
+
+Limitations
+-----------
+
+For PyTorch we can only replace modules, if functions in ``forward`` should be replaced,
+our current implementation does not work. One workaround is make the function a PyTorch module.
+
+If you want to speedup your own model which cannot supported by the current implementation,
+you need implement the replace function for module replacement, welcome to contribute.
+
+Speedup Results of Examples
+---------------------------
+
+The code of these experiments can be found :githublink:`here <examples/model_compress/pruning/legacy/speedup/model_speedup.py>`.
+
+These result are tested on the `legacy pruning framework <https://nni.readthedocs.io/en/v2.6/Compression/pruning.html>`_, new results will coming soon.
+
+slim pruner example
+^^^^^^^^^^^^^^^^^^^
+
+on one V100 GPU,
+input tensor: ``torch.randn(64, 3, 32, 32)``
+
+.. list-table::
+   :header-rows: 1
+   :widths: auto
+
+   * - Times
+     - Mask Latency
+     - Speedup Latency
+   * - 1
+     - 0.01197
+     - 0.005107
+   * - 2
+     - 0.02019
+     - 0.008769
+   * - 4
+     - 0.02733
+     - 0.014809
+   * - 8
+     - 0.04310
+     - 0.027441
+   * - 16
+     - 0.07731
+     - 0.05008
+   * - 32
+     - 0.14464
+     - 0.10027
+
+fpgm pruner example
+^^^^^^^^^^^^^^^^^^^
+
+on cpu,
+input tensor: ``torch.randn(64, 1, 28, 28)``\ ,
+too large variance
+
+.. list-table::
+   :header-rows: 1
+   :widths: auto
+
+   * - Times
+     - Mask Latency
+     - Speedup Latency
+   * - 1
+     - 0.01383
+     - 0.01839
+   * - 2
+     - 0.01167
+     - 0.003558
+   * - 4
+     - 0.01636
+     - 0.01088
+   * - 40
+     - 0.14412
+     - 0.08268
+   * - 40
+     - 1.29385
+     - 0.14408
+   * - 40
+     - 0.41035
+     - 0.46162
+   * - 400
+     - 6.29020
+     - 5.82143
+
+l1filter pruner example
+^^^^^^^^^^^^^^^^^^^^^^^
+
+on one V100 GPU,
+input tensor: ``torch.randn(64, 3, 32, 32)``
+
+.. list-table::
+   :header-rows: 1
+   :widths: auto
+
+   * - Times
+     - Mask Latency
+     - Speedup Latency
+   * - 1
+     - 0.01026
+     - 0.003677
+   * - 2
+     - 0.01657
+     - 0.008161
+   * - 4
+     - 0.02458
+     - 0.020018
+   * - 8
+     - 0.03498
+     - 0.025504
+   * - 16
+     - 0.06757
+     - 0.047523
+   * - 32
+     - 0.10487
+     - 0.086442
+
+APoZ pruner example
+^^^^^^^^^^^^^^^^^^^
+
+on one V100 GPU,
+input tensor: ``torch.randn(64, 3, 32, 32)``
+
+.. list-table::
+   :header-rows: 1
+   :widths: auto
+
+   * - Times
+     - Mask Latency
+     - Speedup Latency
+   * - 1
+     - 0.01389
+     - 0.004208
+   * - 2
+     - 0.01628
+     - 0.008310
+   * - 4
+     - 0.02521
+     - 0.014008
+   * - 8
+     - 0.03386
+     - 0.023923
+   * - 16
+     - 0.06042
+     - 0.046183
+   * - 32
+     - 0.12421
+     - 0.087113
+
+SimulatedAnnealing pruner example
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+In this experiment, we use SimulatedAnnealing pruner to prune the resnet18 on the cifar10 dataset.
+We measure the latencies and accuracies of the pruned model under different sparsity ratios, as shown in the following figure.
+The latency is measured on one V100 GPU and the input tensor is  ``torch.randn(128, 3, 32, 32)``.
+
+.. image:: ../../img/SA_latency_accuracy.png
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 0 minutes  4.528 seconds)
+
+
+.. _sphx_glr_download_tutorials_pruning_speedup.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+    :class: sphx-glr-footer-example
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-python
+
+     :download:`Download Python source code: pruning_speedup.py <pruning_speedup.py>`
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+     :download:`Download Jupyter notebook: pruning_speedup.ipynb <pruning_speedup.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/source/tutorials/pruning_speedup_codeobj.pickle b/docs/source/tutorials/pruning_speedup_codeobj.pickle
new file mode 100644
index 0000000000000000000000000000000000000000..84feb0712669541b7c126fdade9bfca2a53651af
GIT binary patch
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diff --git a/docs/source/tutorials/quantization_customize.ipynb b/docs/source/tutorials/quantization_customize.ipynb
new file mode 100644
index 000000000..2d07a5d9d
--- /dev/null
+++ b/docs/source/tutorials/quantization_customize.ipynb
@@ -0,0 +1,79 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Customize a new quantization algorithm\n\nTo write a new quantization algorithm, you can write a class that inherits ``nni.compression.pytorch.Quantizer``.\nThen, override the member functions with the logic of your algorithm. The member function to override is ``quantize_weight``.\n``quantize_weight`` directly returns the quantized weights rather than mask, because for quantization the quantized weights cannot be obtained by applying mask.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from nni.compression.pytorch import Quantizer\n\nclass YourQuantizer(Quantizer):\n    def __init__(self, model, config_list):\n        \"\"\"\n        Suggest you to use the NNI defined spec for config\n        \"\"\"\n        super().__init__(model, config_list)\n\n    def quantize_weight(self, weight, config, **kwargs):\n        \"\"\"\n        quantize should overload this method to quantize weight tensors.\n        This method is effectively hooked to :meth:`forward` of the model.\n\n        Parameters\n        ----------\n        weight : Tensor\n            weight that needs to be quantized\n        config : dict\n            the configuration for weight quantization\n        \"\"\"\n\n        # Put your code to generate `new_weight` here\n        new_weight = ...\n        return new_weight\n\n    def quantize_output(self, output, config, **kwargs):\n        \"\"\"\n        quantize should overload this method to quantize output.\n        This method is effectively hooked to `:meth:`forward` of the model.\n\n        Parameters\n        ----------\n        output : Tensor\n            output that needs to be quantized\n        config : dict\n            the configuration for output quantization\n        \"\"\"\n\n        # Put your code to generate `new_output` here\n        new_output = ...\n        return new_output\n\n    def quantize_input(self, *inputs, config, **kwargs):\n        \"\"\"\n        quantize should overload this method to quantize input.\n        This method is effectively hooked to :meth:`forward` of the model.\n\n        Parameters\n        ----------\n        inputs : Tensor\n            inputs that needs to be quantized\n        config : dict\n            the configuration for inputs quantization\n        \"\"\"\n\n        # Put your code to generate `new_input` here\n        new_input = ...\n        return new_input\n\n    def update_epoch(self, epoch_num):\n        pass\n\n    def step(self):\n        \"\"\"\n        Can do some processing based on the model or weights binded\n        in the func bind_model\n        \"\"\"\n        pass"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Customize backward function\n\nSometimes it's necessary for a quantization operation to have a customized backward function,\nsuch as `Straight-Through Estimator <https://stackoverflow.com/questions/38361314/the-concept-of-straight-through-estimator-ste>`__\\ ,\nuser can customize a backward function as follow:\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from nni.compression.pytorch.compressor import Quantizer, QuantGrad, QuantType\n\nclass ClipGrad(QuantGrad):\n    @staticmethod\n    def quant_backward(tensor, grad_output, quant_type):\n        \"\"\"\n        This method should be overrided by subclass to provide customized backward function,\n        default implementation is Straight-Through Estimator\n        Parameters\n        ----------\n        tensor : Tensor\n            input of quantization operation\n        grad_output : Tensor\n            gradient of the output of quantization operation\n        quant_type : QuantType\n            the type of quantization, it can be `QuantType.INPUT`, `QuantType.WEIGHT`, `QuantType.OUTPUT`,\n            you can define different behavior for different types.\n        Returns\n        -------\n        tensor\n            gradient of the input of quantization operation\n        \"\"\"\n\n        # for quant_output function, set grad to zero if the absolute value of tensor is larger than 1\n        if quant_type == QuantType.OUTPUT:\n            grad_output[tensor.abs() > 1] = 0\n        return grad_output\n\nclass _YourQuantizer(Quantizer):\n    def __init__(self, model, config_list):\n        super().__init__(model, config_list)\n        # set your customized backward function to overwrite default backward function\n        self.quant_grad = ClipGrad"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "If you do not customize ``QuantGrad``, the default backward is Straight-Through Estimator. \n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.8"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/source/tutorials/quantization_customize.py b/docs/source/tutorials/quantization_customize.py
new file mode 100644
index 000000000..9305e1978
--- /dev/null
+++ b/docs/source/tutorials/quantization_customize.py
@@ -0,0 +1,123 @@
+"""
+Customize a new quantization algorithm
+======================================
+
+To write a new quantization algorithm, you can write a class that inherits ``nni.compression.pytorch.Quantizer``.
+Then, override the member functions with the logic of your algorithm. The member function to override is ``quantize_weight``.
+``quantize_weight`` directly returns the quantized weights rather than mask, because for quantization the quantized weights cannot be obtained by applying mask.
+"""
+
+from nni.compression.pytorch import Quantizer
+
+class YourQuantizer(Quantizer):
+    def __init__(self, model, config_list):
+        """
+        Suggest you to use the NNI defined spec for config
+        """
+        super().__init__(model, config_list)
+
+    def quantize_weight(self, weight, config, **kwargs):
+        """
+        quantize should overload this method to quantize weight tensors.
+        This method is effectively hooked to :meth:`forward` of the model.
+
+        Parameters
+        ----------
+        weight : Tensor
+            weight that needs to be quantized
+        config : dict
+            the configuration for weight quantization
+        """
+
+        # Put your code to generate `new_weight` here
+        new_weight = ...
+        return new_weight
+
+    def quantize_output(self, output, config, **kwargs):
+        """
+        quantize should overload this method to quantize output.
+        This method is effectively hooked to `:meth:`forward` of the model.
+
+        Parameters
+        ----------
+        output : Tensor
+            output that needs to be quantized
+        config : dict
+            the configuration for output quantization
+        """
+
+        # Put your code to generate `new_output` here
+        new_output = ...
+        return new_output
+
+    def quantize_input(self, *inputs, config, **kwargs):
+        """
+        quantize should overload this method to quantize input.
+        This method is effectively hooked to :meth:`forward` of the model.
+
+        Parameters
+        ----------
+        inputs : Tensor
+            inputs that needs to be quantized
+        config : dict
+            the configuration for inputs quantization
+        """
+
+        # Put your code to generate `new_input` here
+        new_input = ...
+        return new_input
+
+    def update_epoch(self, epoch_num):
+        pass
+
+    def step(self):
+        """
+        Can do some processing based on the model or weights binded
+        in the func bind_model
+        """
+        pass
+
+# %%
+# Customize backward function
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# Sometimes it's necessary for a quantization operation to have a customized backward function,
+# such as `Straight-Through Estimator <https://stackoverflow.com/questions/38361314/the-concept-of-straight-through-estimator-ste>`__\ ,
+# user can customize a backward function as follow:
+
+from nni.compression.pytorch.compressor import Quantizer, QuantGrad, QuantType
+
+class ClipGrad(QuantGrad):
+    @staticmethod
+    def quant_backward(tensor, grad_output, quant_type):
+        """
+        This method should be overrided by subclass to provide customized backward function,
+        default implementation is Straight-Through Estimator
+        Parameters
+        ----------
+        tensor : Tensor
+            input of quantization operation
+        grad_output : Tensor
+            gradient of the output of quantization operation
+        quant_type : QuantType
+            the type of quantization, it can be `QuantType.INPUT`, `QuantType.WEIGHT`, `QuantType.OUTPUT`,
+            you can define different behavior for different types.
+        Returns
+        -------
+        tensor
+            gradient of the input of quantization operation
+        """
+
+        # for quant_output function, set grad to zero if the absolute value of tensor is larger than 1
+        if quant_type == QuantType.OUTPUT:
+            grad_output[tensor.abs() > 1] = 0
+        return grad_output
+
+class _YourQuantizer(Quantizer):
+    def __init__(self, model, config_list):
+        super().__init__(model, config_list)
+        # set your customized backward function to overwrite default backward function
+        self.quant_grad = ClipGrad
+
+# %%
+# If you do not customize ``QuantGrad``, the default backward is Straight-Through Estimator. 
diff --git a/docs/source/tutorials/quantization_customize.py.md5 b/docs/source/tutorials/quantization_customize.py.md5
new file mode 100644
index 000000000..c71bddacd
--- /dev/null
+++ b/docs/source/tutorials/quantization_customize.py.md5
@@ -0,0 +1 @@
+387ac974594fa239c25479453b808ec8
\ No newline at end of file
diff --git a/docs/source/tutorials/quantization_customize.rst b/docs/source/tutorials/quantization_customize.rst
new file mode 100644
index 000000000..e156df77b
--- /dev/null
+++ b/docs/source/tutorials/quantization_customize.rst
@@ -0,0 +1,200 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "tutorials/quantization_customize.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        Click :ref:`here <sphx_glr_download_tutorials_quantization_customize.py>`
+        to download the full example code
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorials_quantization_customize.py:
+
+
+Customize a new quantization algorithm
+======================================
+
+To write a new quantization algorithm, you can write a class that inherits ``nni.compression.pytorch.Quantizer``.
+Then, override the member functions with the logic of your algorithm. The member function to override is ``quantize_weight``.
+``quantize_weight`` directly returns the quantized weights rather than mask, because for quantization the quantized weights cannot be obtained by applying mask.
+
+.. GENERATED FROM PYTHON SOURCE LINES 9-80
+
+.. code-block:: default
+
+
+    from nni.compression.pytorch import Quantizer
+
+    class YourQuantizer(Quantizer):
+        def __init__(self, model, config_list):
+            """
+            Suggest you to use the NNI defined spec for config
+            """
+            super().__init__(model, config_list)
+
+        def quantize_weight(self, weight, config, **kwargs):
+            """
+            quantize should overload this method to quantize weight tensors.
+            This method is effectively hooked to :meth:`forward` of the model.
+
+            Parameters
+            ----------
+            weight : Tensor
+                weight that needs to be quantized
+            config : dict
+                the configuration for weight quantization
+            """
+
+            # Put your code to generate `new_weight` here
+            new_weight = ...
+            return new_weight
+
+        def quantize_output(self, output, config, **kwargs):
+            """
+            quantize should overload this method to quantize output.
+            This method is effectively hooked to `:meth:`forward` of the model.
+
+            Parameters
+            ----------
+            output : Tensor
+                output that needs to be quantized
+            config : dict
+                the configuration for output quantization
+            """
+
+            # Put your code to generate `new_output` here
+            new_output = ...
+            return new_output
+
+        def quantize_input(self, *inputs, config, **kwargs):
+            """
+            quantize should overload this method to quantize input.
+            This method is effectively hooked to :meth:`forward` of the model.
+
+            Parameters
+            ----------
+            inputs : Tensor
+                inputs that needs to be quantized
+            config : dict
+                the configuration for inputs quantization
+            """
+
+            # Put your code to generate `new_input` here
+            new_input = ...
+            return new_input
+
+        def update_epoch(self, epoch_num):
+            pass
+
+        def step(self):
+            """
+            Can do some processing based on the model or weights binded
+            in the func bind_model
+            """
+            pass
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 81-87
+
+Customize backward function
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Sometimes it's necessary for a quantization operation to have a customized backward function,
+such as `Straight-Through Estimator <https://stackoverflow.com/questions/38361314/the-concept-of-straight-through-estimator-ste>`__\ ,
+user can customize a backward function as follow:
+
+.. GENERATED FROM PYTHON SOURCE LINES 87-122
+
+.. code-block:: default
+
+
+    from nni.compression.pytorch.compressor import Quantizer, QuantGrad, QuantType
+
+    class ClipGrad(QuantGrad):
+        @staticmethod
+        def quant_backward(tensor, grad_output, quant_type):
+            """
+            This method should be overrided by subclass to provide customized backward function,
+            default implementation is Straight-Through Estimator
+            Parameters
+            ----------
+            tensor : Tensor
+                input of quantization operation
+            grad_output : Tensor
+                gradient of the output of quantization operation
+            quant_type : QuantType
+                the type of quantization, it can be `QuantType.INPUT`, `QuantType.WEIGHT`, `QuantType.OUTPUT`,
+                you can define different behavior for different types.
+            Returns
+            -------
+            tensor
+                gradient of the input of quantization operation
+            """
+
+            # for quant_output function, set grad to zero if the absolute value of tensor is larger than 1
+            if quant_type == QuantType.OUTPUT:
+                grad_output[tensor.abs() > 1] = 0
+            return grad_output
+
+    class _YourQuantizer(Quantizer):
+        def __init__(self, model, config_list):
+            super().__init__(model, config_list)
+            # set your customized backward function to overwrite default backward function
+            self.quant_grad = ClipGrad
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 123-124
+
+If you do not customize ``QuantGrad``, the default backward is Straight-Through Estimator. 
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 0 minutes  1.269 seconds)
+
+
+.. _sphx_glr_download_tutorials_quantization_customize.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+    :class: sphx-glr-footer-example
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-python
+
+     :download:`Download Python source code: quantization_customize.py <quantization_customize.py>`
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+     :download:`Download Jupyter notebook: quantization_customize.ipynb <quantization_customize.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/source/tutorials/quantization_customize_codeobj.pickle b/docs/source/tutorials/quantization_customize_codeobj.pickle
new file mode 100644
index 0000000000000000000000000000000000000000..af4d95a375b38dfd033718612ae73f9810f52258
GIT binary patch
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diff --git a/docs/source/tutorials/quantization_quick_start_mnist.ipynb b/docs/source/tutorials/quantization_quick_start_mnist.ipynb
new file mode 100644
index 000000000..1d9d3e40e
--- /dev/null
+++ b/docs/source/tutorials/quantization_quick_start_mnist.ipynb
@@ -0,0 +1,151 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Quantization Quickstart\n\nQuantization reduces model size and speeds up inference time by reducing the number of bits required to represent weights or activations.\n\nIn NNI, both post-training quantization algorithms and quantization-aware training algorithms are supported.\nHere we use `QAT_Quantizer` as an example to show the usage of quantization in NNI.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Preparation\n\nIn this tutorial, we use a simple model and pre-train on MNIST dataset.\nIf you are familiar with defining a model and training in pytorch, you can skip directly to `Quantizing Model`_.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import torch\nimport torch.nn.functional as F\nfrom torch.optim import SGD\n\nfrom scripts.compression_mnist_model import TorchModel, trainer, evaluator, device, test_trt\n\n# define the model\nmodel = TorchModel().to(device)\n\n# define the optimizer and criterion for pre-training\n\noptimizer = SGD(model.parameters(), 1e-2)\ncriterion = F.nll_loss\n\n# pre-train and evaluate the model on MNIST dataset\nfor epoch in range(3):\n    trainer(model, optimizer, criterion)\n    evaluator(model)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Quantizing Model\n\nInitialize a `config_list`.\nDetailed about how to write ``config_list`` please refer :doc:`compression config specification <../compression/compression_config_list>`.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "config_list = [{\n    'quant_types': ['input', 'weight'],\n    'quant_bits': {'input': 8, 'weight': 8},\n    'op_types': ['Conv2d']\n}, {\n    'quant_types': ['output'],\n    'quant_bits': {'output': 8},\n    'op_types': ['ReLU']\n}, {\n    'quant_types': ['input', 'weight'],\n    'quant_bits': {'input': 8, 'weight': 8},\n    'op_names': ['fc1', 'fc2']\n}]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "finetuning the model by using QAT\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from nni.algorithms.compression.pytorch.quantization import QAT_Quantizer\ndummy_input = torch.rand(32, 1, 28, 28).to(device)\nquantizer = QAT_Quantizer(model, config_list, optimizer, dummy_input)\nquantizer.compress()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "The model has now been wrapped, and quantization targets ('quant_types' setting in `config_list`)\nwill be quantized & dequantized for simulated quantization in the wrapped layers.\nQAT is a training-aware quantizer, it will update scale and zero point during training.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "for epoch in range(3):\n    trainer(model, optimizer, criterion)\n    evaluator(model)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "export model and get calibration_config\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "model_path = \"./log/mnist_model.pth\"\ncalibration_path = \"./log/mnist_calibration.pth\"\ncalibration_config = quantizer.export_model(model_path, calibration_path)\n\nprint(\"calibration_config: \", calibration_config)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "build tensorRT engine to make a real speedup, for more information about speedup, please refer :doc:`quantization_speedup`.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from nni.compression.pytorch.quantization_speedup import ModelSpeedupTensorRT\ninput_shape = (32, 1, 28, 28)\nengine = ModelSpeedupTensorRT(model, input_shape, config=calibration_config, batchsize=32)\nengine.compress()\ntest_trt(engine)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/source/tutorials/quantization_quick_start_mnist.py b/docs/source/tutorials/quantization_quick_start_mnist.py
new file mode 100644
index 000000000..c126e6fb4
--- /dev/null
+++ b/docs/source/tutorials/quantization_quick_start_mnist.py
@@ -0,0 +1,89 @@
+"""
+Quantization Quickstart
+=======================
+
+Quantization reduces model size and speeds up inference time by reducing the number of bits required to represent weights or activations.
+
+In NNI, both post-training quantization algorithms and quantization-aware training algorithms are supported.
+Here we use `QAT_Quantizer` as an example to show the usage of quantization in NNI.
+"""
+
+# %%
+# Preparation
+# -----------
+#
+# In this tutorial, we use a simple model and pre-train on MNIST dataset.
+# If you are familiar with defining a model and training in pytorch, you can skip directly to `Quantizing Model`_.
+
+import torch
+import torch.nn.functional as F
+from torch.optim import SGD
+
+from scripts.compression_mnist_model import TorchModel, trainer, evaluator, device, test_trt
+
+# define the model
+model = TorchModel().to(device)
+
+# define the optimizer and criterion for pre-training
+
+optimizer = SGD(model.parameters(), 1e-2)
+criterion = F.nll_loss
+
+# pre-train and evaluate the model on MNIST dataset
+for epoch in range(3):
+    trainer(model, optimizer, criterion)
+    evaluator(model)
+
+# %%
+# Quantizing Model
+# ----------------
+#
+# Initialize a `config_list`.
+# Detailed about how to write ``config_list`` please refer :doc:`compression config specification <../compression/compression_config_list>`.
+
+config_list = [{
+    'quant_types': ['input', 'weight'],
+    'quant_bits': {'input': 8, 'weight': 8},
+    'op_types': ['Conv2d']
+}, {
+    'quant_types': ['output'],
+    'quant_bits': {'output': 8},
+    'op_types': ['ReLU']
+}, {
+    'quant_types': ['input', 'weight'],
+    'quant_bits': {'input': 8, 'weight': 8},
+    'op_names': ['fc1', 'fc2']
+}]
+
+# %%
+# finetuning the model by using QAT
+from nni.algorithms.compression.pytorch.quantization import QAT_Quantizer
+dummy_input = torch.rand(32, 1, 28, 28).to(device)
+quantizer = QAT_Quantizer(model, config_list, optimizer, dummy_input)
+quantizer.compress()
+
+# %%
+# The model has now been wrapped, and quantization targets ('quant_types' setting in `config_list`)
+# will be quantized & dequantized for simulated quantization in the wrapped layers.
+# QAT is a training-aware quantizer, it will update scale and zero point during training.
+
+for epoch in range(3):
+    trainer(model, optimizer, criterion)
+    evaluator(model)
+
+# %%
+# export model and get calibration_config
+model_path = "./log/mnist_model.pth"
+calibration_path = "./log/mnist_calibration.pth"
+calibration_config = quantizer.export_model(model_path, calibration_path)
+
+print("calibration_config: ", calibration_config)
+
+# %%
+# build tensorRT engine to make a real speedup, for more information about speedup, please refer :doc:`quantization_speedup`.
+
+from nni.compression.pytorch.quantization_speedup import ModelSpeedupTensorRT
+input_shape = (32, 1, 28, 28)
+engine = ModelSpeedupTensorRT(model, input_shape, config=calibration_config, batchsize=32)
+engine.compress()
+test_trt(engine)
diff --git a/docs/source/tutorials/quantization_quick_start_mnist.py.md5 b/docs/source/tutorials/quantization_quick_start_mnist.py.md5
new file mode 100644
index 000000000..fac5ad2ad
--- /dev/null
+++ b/docs/source/tutorials/quantization_quick_start_mnist.py.md5
@@ -0,0 +1 @@
+bceaf8235b437428267b614af06634a0
\ No newline at end of file
diff --git a/docs/source/tutorials/quantization_quick_start_mnist.rst b/docs/source/tutorials/quantization_quick_start_mnist.rst
new file mode 100644
index 000000000..10eaddb63
--- /dev/null
+++ b/docs/source/tutorials/quantization_quick_start_mnist.rst
@@ -0,0 +1,290 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "tutorials/quantization_quick_start_mnist.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        Click :ref:`here <sphx_glr_download_tutorials_quantization_quick_start_mnist.py>`
+        to download the full example code
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorials_quantization_quick_start_mnist.py:
+
+
+Quantization Quickstart
+=======================
+
+Quantization reduces model size and speeds up inference time by reducing the number of bits required to represent weights or activations.
+
+In NNI, both post-training quantization algorithms and quantization-aware training algorithms are supported.
+Here we use `QAT_Quantizer` as an example to show the usage of quantization in NNI.
+
+.. GENERATED FROM PYTHON SOURCE LINES 12-17
+
+Preparation
+-----------
+
+In this tutorial, we use a simple model and pre-train on MNIST dataset.
+If you are familiar with defining a model and training in pytorch, you can skip directly to `Quantizing Model`_.
+
+.. GENERATED FROM PYTHON SOURCE LINES 17-37
+
+.. code-block:: default
+
+
+    import torch
+    import torch.nn.functional as F
+    from torch.optim import SGD
+
+    from scripts.compression_mnist_model import TorchModel, trainer, evaluator, device, test_trt
+
+    # define the model
+    model = TorchModel().to(device)
+
+    # define the optimizer and criterion for pre-training
+
+    optimizer = SGD(model.parameters(), 1e-2)
+    criterion = F.nll_loss
+
+    # pre-train and evaluate the model on MNIST dataset
+    for epoch in range(3):
+        trainer(model, optimizer, criterion)
+        evaluator(model)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    Average test loss: 0.7073, Accuracy: 7624/10000 (76%)
+    Average test loss: 0.2776, Accuracy: 9122/10000 (91%)
+    Average test loss: 0.1907, Accuracy: 9412/10000 (94%)
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 38-43
+
+Quantizing Model
+----------------
+
+Initialize a `config_list`.
+Detailed about how to write ``config_list`` please refer :doc:`compression config specification <../compression/compression_config_list>`.
+
+.. GENERATED FROM PYTHON SOURCE LINES 43-58
+
+.. code-block:: default
+
+
+    config_list = [{
+        'quant_types': ['input', 'weight'],
+        'quant_bits': {'input': 8, 'weight': 8},
+        'op_types': ['Conv2d']
+    }, {
+        'quant_types': ['output'],
+        'quant_bits': {'output': 8},
+        'op_types': ['ReLU']
+    }, {
+        'quant_types': ['input', 'weight'],
+        'quant_bits': {'input': 8, 'weight': 8},
+        'op_names': ['fc1', 'fc2']
+    }]
+
+
+
+
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 59-60
+
+finetuning the model by using QAT
+
+.. GENERATED FROM PYTHON SOURCE LINES 60-65
+
+.. code-block:: default
+
+    from nni.algorithms.compression.pytorch.quantization import QAT_Quantizer
+    dummy_input = torch.rand(32, 1, 28, 28).to(device)
+    quantizer = QAT_Quantizer(model, config_list, optimizer, dummy_input)
+    quantizer.compress()
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+
+    TorchModel(
+      (conv1): QuantizerModuleWrapper(
+        (module): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))
+      )
+      (conv2): QuantizerModuleWrapper(
+        (module): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))
+      )
+      (fc1): QuantizerModuleWrapper(
+        (module): Linear(in_features=256, out_features=120, bias=True)
+      )
+      (fc2): QuantizerModuleWrapper(
+        (module): Linear(in_features=120, out_features=84, bias=True)
+      )
+      (fc3): Linear(in_features=84, out_features=10, bias=True)
+      (relu1): QuantizerModuleWrapper(
+        (module): ReLU()
+      )
+      (relu2): QuantizerModuleWrapper(
+        (module): ReLU()
+      )
+      (relu3): QuantizerModuleWrapper(
+        (module): ReLU()
+      )
+      (relu4): QuantizerModuleWrapper(
+        (module): ReLU()
+      )
+      (pool1): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
+      (pool2): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
+    )
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 66-69
+
+The model has now been wrapped, and quantization targets ('quant_types' setting in `config_list`)
+will be quantized & dequantized for simulated quantization in the wrapped layers.
+QAT is a training-aware quantizer, it will update scale and zero point during training.
+
+.. GENERATED FROM PYTHON SOURCE LINES 69-74
+
+.. code-block:: default
+
+
+    for epoch in range(3):
+        trainer(model, optimizer, criterion)
+        evaluator(model)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    Average test loss: 0.1542, Accuracy: 9529/10000 (95%)
+    Average test loss: 0.1133, Accuracy: 9664/10000 (97%)
+    Average test loss: 0.0919, Accuracy: 9726/10000 (97%)
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 75-76
+
+export model and get calibration_config
+
+.. GENERATED FROM PYTHON SOURCE LINES 76-82
+
+.. code-block:: default
+
+    model_path = "./log/mnist_model.pth"
+    calibration_path = "./log/mnist_calibration.pth"
+    calibration_config = quantizer.export_model(model_path, calibration_path)
+
+    print("calibration_config: ", calibration_config)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    calibration_config:  {'conv1': {'weight_bits': 8, 'weight_scale': tensor([0.0031], device='cuda:0'), 'weight_zero_point': tensor([76.], device='cuda:0'), 'input_bits': 8, 'tracked_min_input': -0.4242129623889923, 'tracked_max_input': 2.821486711502075}, 'conv2': {'weight_bits': 8, 'weight_scale': tensor([0.0018], device='cuda:0'), 'weight_zero_point': tensor([113.], device='cuda:0'), 'input_bits': 8, 'tracked_min_input': 0.0, 'tracked_max_input': 12.42452621459961}, 'fc1': {'weight_bits': 8, 'weight_scale': tensor([0.0011], device='cuda:0'), 'weight_zero_point': tensor([124.], device='cuda:0'), 'input_bits': 8, 'tracked_min_input': 0.0, 'tracked_max_input': 31.650196075439453}, 'fc2': {'weight_bits': 8, 'weight_scale': tensor([0.0013], device='cuda:0'), 'weight_zero_point': tensor([122.], device='cuda:0'), 'input_bits': 8, 'tracked_min_input': 0.0, 'tracked_max_input': 25.805370330810547}, 'relu1': {'output_bits': 8, 'tracked_min_output': 0.0, 'tracked_max_output': 12.499907493591309}, 'relu2': {'output_bits': 8, 'tracked_min_output': 0.0, 'tracked_max_output': 32.0243034362793}, 'relu3': {'output_bits': 8, 'tracked_min_output': 0.0, 'tracked_max_output': 26.491384506225586}, 'relu4': {'output_bits': 8, 'tracked_min_output': 0.0, 'tracked_max_output': 17.662996292114258}}
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 83-84
+
+build tensorRT engine to make a real speedup, for more information about speedup, please refer :doc:`quantization_speedup`.
+
+.. GENERATED FROM PYTHON SOURCE LINES 84-90
+
+.. code-block:: default
+
+
+    from nni.compression.pytorch.quantization_speedup import ModelSpeedupTensorRT
+    input_shape = (32, 1, 28, 28)
+    engine = ModelSpeedupTensorRT(model, input_shape, config=calibration_config, batchsize=32)
+    engine.compress()
+    test_trt(engine)
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    Loss: 0.09358334274291992  Accuracy: 97.21%
+    Inference elapsed_time (whole dataset): 0.04445981979370117s
+
+
+
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 1 minutes  36.499 seconds)
+
+
+.. _sphx_glr_download_tutorials_quantization_quick_start_mnist.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+    :class: sphx-glr-footer-example
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-python
+
+     :download:`Download Python source code: quantization_quick_start_mnist.py <quantization_quick_start_mnist.py>`
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+     :download:`Download Jupyter notebook: quantization_quick_start_mnist.ipynb <quantization_quick_start_mnist.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/source/tutorials/quantization_quick_start_mnist_codeobj.pickle b/docs/source/tutorials/quantization_quick_start_mnist_codeobj.pickle
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diff --git a/docs/source/tutorials/quantization_speedup.ipynb b/docs/source/tutorials/quantization_speedup.ipynb
new file mode 100644
index 000000000..cfbda8bc9
--- /dev/null
+++ b/docs/source/tutorials/quantization_speedup.ipynb
@@ -0,0 +1,115 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# SpeedUp Model with Calibration Config\n\n\n## Introduction\n\nDeep learning network has been computational intensive and memory intensive \nwhich increases the difficulty of deploying deep neural network model. Quantization is a \nfundamental technology which is widely used to reduce memory footprint and speedup inference \nprocess. Many frameworks begin to support quantization, but few of them support mixed precision \nquantization and get real speedup. Frameworks like `HAQ: Hardware-Aware Automated Quantization with Mixed Precision <https://arxiv.org/pdf/1811.08886.pdf>`__\\, only support simulated mixed precision quantization which will \nnot speedup the inference process. To get real speedup of mixed precision quantization and \nhelp people get the real feedback from hardware, we design a general framework with simple interface to allow NNI quantization algorithms to connect different \nDL model optimization backends (e.g., TensorRT, NNFusion), which gives users an end-to-end experience that after quantizing their model \nwith quantization algorithms, the quantized model can be directly speeded up with the connected optimization backend. NNI connects \nTensorRT at this stage, and will support more backends in the future.\n\n\n## Design and Implementation\n\nTo support speeding up mixed precision quantization, we divide framework into two part, frontend and backend.  \nFrontend could be popular training frameworks such as PyTorch, TensorFlow etc. Backend could be inference \nframework for different hardwares, such as TensorRT. At present, we support PyTorch as frontend and \nTensorRT as backend. To convert PyTorch model to TensorRT engine, we leverage onnx as intermediate graph \nrepresentation. In this way, we convert PyTorch model to onnx model, then TensorRT parse onnx \nmodel to generate inference engine. \n\n\nQuantization aware training combines NNI quantization algorithm 'QAT' and NNI quantization speedup tool.\nUsers should set config to train quantized model using QAT algorithm(please refer to :doc:`NNI Quantization Algorithms <../compression/quantizer>`  ).\nAfter quantization aware training, users can get new config with calibration parameters and model with quantized weight. By passing new config and model to quantization speedup tool, users can get real mixed precision speedup engine to do inference.\n\n\nAfter getting mixed precision engine, users can do inference with input data.\n\n\nNote\n\n\n* Recommend using \"cpu\"(host) as data device(for both inference data and calibration data) since data should be on host initially and it will be transposed to device before inference. If data type is not \"cpu\"(host), this tool will transpose it to \"cpu\" which may increases unnecessary overhead.\n* User can also do post-training quantization leveraging TensorRT directly(need to provide calibration dataset).\n* Not all op types are supported right now. At present, NNI supports Conv, Linear, Relu and MaxPool. More op types will be supported in the following release.\n\n\n## Prerequisite\nCUDA version >= 11.0\n\nTensorRT version >= 7.2\n\nNote\n\n* If you haven't installed TensorRT before or use the old version, please refer to `TensorRT Installation Guide <https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html>`__\\  \n\n## Usage\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import torch\nimport torch.nn.functional as F\nfrom torch.optim import SGD\nfrom scripts.compression_mnist_model import TorchModel, device, trainer, evaluator, test_trt\n\nconfig_list = [{\n    'quant_types': ['input', 'weight'],\n    'quant_bits': {'input': 8, 'weight': 8},\n    'op_types': ['Conv2d']\n}, {\n    'quant_types': ['output'],\n    'quant_bits': {'output': 8},\n    'op_types': ['ReLU']\n}, {\n    'quant_types': ['input', 'weight'],\n    'quant_bits': {'input': 8, 'weight': 8},\n    'op_names': ['fc1', 'fc2']\n}]\n\nmodel = TorchModel().to(device)\noptimizer = SGD(model.parameters(), lr=0.01, momentum=0.5)\ncriterion = F.nll_loss\ndummy_input = torch.rand(32, 1, 28, 28).to(device)\n\nfrom nni.algorithms.compression.pytorch.quantization import QAT_Quantizer\nquantizer = QAT_Quantizer(model, config_list, optimizer, dummy_input)\nquantizer.compress()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "finetuning the model by using QAT\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "for epoch in range(3):\n    trainer(model, optimizer, criterion)\n    evaluator(model)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "export model and get calibration_config\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import os\nos.makedirs('log', exist_ok=True)\nmodel_path = \"./log/mnist_model.pth\"\ncalibration_path = \"./log/mnist_calibration.pth\"\ncalibration_config = quantizer.export_model(model_path, calibration_path)\n\nprint(\"calibration_config: \", calibration_config)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "build tensorRT engine to make a real speedup\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from nni.compression.pytorch.quantization_speedup import ModelSpeedupTensorRT\ninput_shape = (32, 1, 28, 28)\nengine = ModelSpeedupTensorRT(model, input_shape, config=calibration_config, batchsize=32)\nengine.compress()\ntest_trt(engine)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Note that NNI also supports post-training quantization directly, please refer to complete examples for detail.\n\nFor complete examples please refer to :githublink:`the code <examples/model_compress/quantization/mixed_precision_speedup_mnist.py>`.\n\nFor more parameters about the class 'TensorRTModelSpeedUp', you can refer to :doc:`Model Compression API Reference <../reference/compression/quantization_speedup>`.\n\n### Mnist test\n\non one GTX2080 GPU,\ninput tensor: ``torch.randn(128, 1, 28, 28)``\n\n.. list-table::\n   :header-rows: 1\n   :widths: auto\n\n   * - quantization strategy\n     - Latency\n     - accuracy\n   * - all in 32bit\n     - 0.001199961\n     - 96%\n   * - mixed precision(average bit 20.4)\n     - 0.000753688\n     - 96%\n   * - all in 8bit\n     - 0.000229869\n     - 93.7%\n\n### Cifar10 resnet18 test (train one epoch)\n\non one GTX2080 GPU,\ninput tensor: ``torch.randn(128, 3, 32, 32)``\n\n.. list-table::\n   :header-rows: 1\n   :widths: auto\n\n   * - quantization strategy\n     - Latency\n     - accuracy\n   * - all in 32bit\n     - 0.003286268\n     - 54.21%\n   * - mixed precision(average bit 11.55)\n     - 0.001358022\n     - 54.78%\n   * - all in 8bit\n     - 0.000859139\n     - 52.81%\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/source/tutorials/quantization_speedup.py b/docs/source/tutorials/quantization_speedup.py
new file mode 100644
index 000000000..132fc6281
--- /dev/null
+++ b/docs/source/tutorials/quantization_speedup.py
@@ -0,0 +1,168 @@
+"""
+SpeedUp Model with Calibration Config
+======================================
+
+
+Introduction
+------------
+
+Deep learning network has been computational intensive and memory intensive 
+which increases the difficulty of deploying deep neural network model. Quantization is a 
+fundamental technology which is widely used to reduce memory footprint and speedup inference 
+process. Many frameworks begin to support quantization, but few of them support mixed precision 
+quantization and get real speedup. Frameworks like `HAQ: Hardware-Aware Automated Quantization with Mixed Precision <https://arxiv.org/pdf/1811.08886.pdf>`__\, only support simulated mixed precision quantization which will 
+not speedup the inference process. To get real speedup of mixed precision quantization and 
+help people get the real feedback from hardware, we design a general framework with simple interface to allow NNI quantization algorithms to connect different 
+DL model optimization backends (e.g., TensorRT, NNFusion), which gives users an end-to-end experience that after quantizing their model 
+with quantization algorithms, the quantized model can be directly speeded up with the connected optimization backend. NNI connects 
+TensorRT at this stage, and will support more backends in the future.
+
+
+Design and Implementation
+-------------------------
+
+To support speeding up mixed precision quantization, we divide framework into two part, frontend and backend.  
+Frontend could be popular training frameworks such as PyTorch, TensorFlow etc. Backend could be inference 
+framework for different hardwares, such as TensorRT. At present, we support PyTorch as frontend and 
+TensorRT as backend. To convert PyTorch model to TensorRT engine, we leverage onnx as intermediate graph 
+representation. In this way, we convert PyTorch model to onnx model, then TensorRT parse onnx 
+model to generate inference engine. 
+
+
+Quantization aware training combines NNI quantization algorithm 'QAT' and NNI quantization speedup tool.
+Users should set config to train quantized model using QAT algorithm(please refer to :doc:`NNI Quantization Algorithms <../compression/quantizer>`  ).
+After quantization aware training, users can get new config with calibration parameters and model with quantized weight. By passing new config and model to quantization speedup tool, users can get real mixed precision speedup engine to do inference.
+
+
+After getting mixed precision engine, users can do inference with input data.
+
+
+Note
+
+
+* Recommend using "cpu"(host) as data device(for both inference data and calibration data) since data should be on host initially and it will be transposed to device before inference. If data type is not "cpu"(host), this tool will transpose it to "cpu" which may increases unnecessary overhead.
+* User can also do post-training quantization leveraging TensorRT directly(need to provide calibration dataset).
+* Not all op types are supported right now. At present, NNI supports Conv, Linear, Relu and MaxPool. More op types will be supported in the following release.
+
+
+Prerequisite
+------------
+CUDA version >= 11.0
+
+TensorRT version >= 7.2
+
+Note
+
+* If you haven't installed TensorRT before or use the old version, please refer to `TensorRT Installation Guide <https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html>`__\  
+
+Usage
+-----
+
+"""
+
+# %%
+import torch
+import torch.nn.functional as F
+from torch.optim import SGD
+from scripts.compression_mnist_model import TorchModel, device, trainer, evaluator, test_trt
+
+config_list = [{
+    'quant_types': ['input', 'weight'],
+    'quant_bits': {'input': 8, 'weight': 8},
+    'op_types': ['Conv2d']
+}, {
+    'quant_types': ['output'],
+    'quant_bits': {'output': 8},
+    'op_types': ['ReLU']
+}, {
+    'quant_types': ['input', 'weight'],
+    'quant_bits': {'input': 8, 'weight': 8},
+    'op_names': ['fc1', 'fc2']
+}]
+
+model = TorchModel().to(device)
+optimizer = SGD(model.parameters(), lr=0.01, momentum=0.5)
+criterion = F.nll_loss
+dummy_input = torch.rand(32, 1, 28, 28).to(device)
+
+from nni.algorithms.compression.pytorch.quantization import QAT_Quantizer
+quantizer = QAT_Quantizer(model, config_list, optimizer, dummy_input)
+quantizer.compress()
+
+# %%
+# finetuning the model by using QAT
+for epoch in range(3):
+    trainer(model, optimizer, criterion)
+    evaluator(model)
+
+# %%
+# export model and get calibration_config
+import os
+os.makedirs('log', exist_ok=True)
+model_path = "./log/mnist_model.pth"
+calibration_path = "./log/mnist_calibration.pth"
+calibration_config = quantizer.export_model(model_path, calibration_path)
+
+print("calibration_config: ", calibration_config)
+
+# %%
+# build tensorRT engine to make a real speedup
+
+from nni.compression.pytorch.quantization_speedup import ModelSpeedupTensorRT
+input_shape = (32, 1, 28, 28)
+engine = ModelSpeedupTensorRT(model, input_shape, config=calibration_config, batchsize=32)
+engine.compress()
+test_trt(engine)
+
+# %%
+# Note that NNI also supports post-training quantization directly, please refer to complete examples for detail.
+#
+# For complete examples please refer to :githublink:`the code <examples/model_compress/quantization/mixed_precision_speedup_mnist.py>`.
+#
+# For more parameters about the class 'TensorRTModelSpeedUp', you can refer to :doc:`Model Compression API Reference <../reference/compression/quantization_speedup>`.
+#
+# Mnist test
+# ^^^^^^^^^^
+#
+# on one GTX2080 GPU,
+# input tensor: ``torch.randn(128, 1, 28, 28)``
+#
+# .. list-table::
+#    :header-rows: 1
+#    :widths: auto
+#
+#    * - quantization strategy
+#      - Latency
+#      - accuracy
+#    * - all in 32bit
+#      - 0.001199961
+#      - 96%
+#    * - mixed precision(average bit 20.4)
+#      - 0.000753688
+#      - 96%
+#    * - all in 8bit
+#      - 0.000229869
+#      - 93.7%
+#
+# Cifar10 resnet18 test (train one epoch)
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# on one GTX2080 GPU,
+# input tensor: ``torch.randn(128, 3, 32, 32)``
+#
+# .. list-table::
+#    :header-rows: 1
+#    :widths: auto
+#
+#    * - quantization strategy
+#      - Latency
+#      - accuracy
+#    * - all in 32bit
+#      - 0.003286268
+#      - 54.21%
+#    * - mixed precision(average bit 11.55)
+#      - 0.001358022
+#      - 54.78%
+#    * - all in 8bit
+#      - 0.000859139
+#      - 52.81%
diff --git a/docs/source/tutorials/quantization_speedup.py.md5 b/docs/source/tutorials/quantization_speedup.py.md5
new file mode 100644
index 000000000..76e0ef222
--- /dev/null
+++ b/docs/source/tutorials/quantization_speedup.py.md5
@@ -0,0 +1 @@
+2404b8d0c3958a0191b77bbe882456e4
\ No newline at end of file
diff --git a/docs/source/tutorials/quantization_speedup.rst b/docs/source/tutorials/quantization_speedup.rst
new file mode 100644
index 000000000..c408e39ba
--- /dev/null
+++ b/docs/source/tutorials/quantization_speedup.rst
@@ -0,0 +1,331 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "tutorials/quantization_speedup.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        Click :ref:`here <sphx_glr_download_tutorials_quantization_speedup.py>`
+        to download the full example code
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorials_quantization_speedup.py:
+
+
+SpeedUp Model with Calibration Config
+======================================
+
+
+Introduction
+------------
+
+Deep learning network has been computational intensive and memory intensive 
+which increases the difficulty of deploying deep neural network model. Quantization is a 
+fundamental technology which is widely used to reduce memory footprint and speedup inference 
+process. Many frameworks begin to support quantization, but few of them support mixed precision 
+quantization and get real speedup. Frameworks like `HAQ: Hardware-Aware Automated Quantization with Mixed Precision <https://arxiv.org/pdf/1811.08886.pdf>`__\, only support simulated mixed precision quantization which will 
+not speedup the inference process. To get real speedup of mixed precision quantization and 
+help people get the real feedback from hardware, we design a general framework with simple interface to allow NNI quantization algorithms to connect different 
+DL model optimization backends (e.g., TensorRT, NNFusion), which gives users an end-to-end experience that after quantizing their model 
+with quantization algorithms, the quantized model can be directly speeded up with the connected optimization backend. NNI connects 
+TensorRT at this stage, and will support more backends in the future.
+
+
+Design and Implementation
+-------------------------
+
+To support speeding up mixed precision quantization, we divide framework into two part, frontend and backend.  
+Frontend could be popular training frameworks such as PyTorch, TensorFlow etc. Backend could be inference 
+framework for different hardwares, such as TensorRT. At present, we support PyTorch as frontend and 
+TensorRT as backend. To convert PyTorch model to TensorRT engine, we leverage onnx as intermediate graph 
+representation. In this way, we convert PyTorch model to onnx model, then TensorRT parse onnx 
+model to generate inference engine. 
+
+
+Quantization aware training combines NNI quantization algorithm 'QAT' and NNI quantization speedup tool.
+Users should set config to train quantized model using QAT algorithm(please refer to :doc:`NNI Quantization Algorithms <../compression/quantizer>`  ).
+After quantization aware training, users can get new config with calibration parameters and model with quantized weight. By passing new config and model to quantization speedup tool, users can get real mixed precision speedup engine to do inference.
+
+
+After getting mixed precision engine, users can do inference with input data.
+
+
+Note
+
+
+* Recommend using "cpu"(host) as data device(for both inference data and calibration data) since data should be on host initially and it will be transposed to device before inference. If data type is not "cpu"(host), this tool will transpose it to "cpu" which may increases unnecessary overhead.
+* User can also do post-training quantization leveraging TensorRT directly(need to provide calibration dataset).
+* Not all op types are supported right now. At present, NNI supports Conv, Linear, Relu and MaxPool. More op types will be supported in the following release.
+
+
+Prerequisite
+------------
+CUDA version >= 11.0
+
+TensorRT version >= 7.2
+
+Note
+
+* If you haven't installed TensorRT before or use the old version, please refer to `TensorRT Installation Guide <https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html>`__\  
+
+Usage
+-----
+
+.. GENERATED FROM PYTHON SOURCE LINES 64-92
+
+.. code-block:: default
+
+    import torch
+    import torch.nn.functional as F
+    from torch.optim import SGD
+    from scripts.compression_mnist_model import TorchModel, device, trainer, evaluator, test_trt
+
+    config_list = [{
+        'quant_types': ['input', 'weight'],
+        'quant_bits': {'input': 8, 'weight': 8},
+        'op_types': ['Conv2d']
+    }, {
+        'quant_types': ['output'],
+        'quant_bits': {'output': 8},
+        'op_types': ['ReLU']
+    }, {
+        'quant_types': ['input', 'weight'],
+        'quant_bits': {'input': 8, 'weight': 8},
+        'op_names': ['fc1', 'fc2']
+    }]
+
+    model = TorchModel().to(device)
+    optimizer = SGD(model.parameters(), lr=0.01, momentum=0.5)
+    criterion = F.nll_loss
+    dummy_input = torch.rand(32, 1, 28, 28).to(device)
+
+    from nni.algorithms.compression.pytorch.quantization import QAT_Quantizer
+    quantizer = QAT_Quantizer(model, config_list, optimizer, dummy_input)
+    quantizer.compress()
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+
+    TorchModel(
+      (conv1): QuantizerModuleWrapper(
+        (module): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))
+      )
+      (conv2): QuantizerModuleWrapper(
+        (module): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))
+      )
+      (fc1): QuantizerModuleWrapper(
+        (module): Linear(in_features=256, out_features=120, bias=True)
+      )
+      (fc2): QuantizerModuleWrapper(
+        (module): Linear(in_features=120, out_features=84, bias=True)
+      )
+      (fc3): Linear(in_features=84, out_features=10, bias=True)
+      (relu1): QuantizerModuleWrapper(
+        (module): ReLU()
+      )
+      (relu2): QuantizerModuleWrapper(
+        (module): ReLU()
+      )
+      (relu3): QuantizerModuleWrapper(
+        (module): ReLU()
+      )
+      (relu4): QuantizerModuleWrapper(
+        (module): ReLU()
+      )
+      (pool1): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
+      (pool2): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
+    )
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 93-94
+
+finetuning the model by using QAT
+
+.. GENERATED FROM PYTHON SOURCE LINES 94-98
+
+.. code-block:: default
+
+    for epoch in range(3):
+        trainer(model, optimizer, criterion)
+        evaluator(model)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    Average test loss: 0.5386, Accuracy: 8619/10000 (86%)
+    Average test loss: 0.1553, Accuracy: 9521/10000 (95%)
+    Average test loss: 0.1001, Accuracy: 9686/10000 (97%)
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 99-100
+
+export model and get calibration_config
+
+.. GENERATED FROM PYTHON SOURCE LINES 100-108
+
+.. code-block:: default
+
+    import os
+    os.makedirs('log', exist_ok=True)
+    model_path = "./log/mnist_model.pth"
+    calibration_path = "./log/mnist_calibration.pth"
+    calibration_config = quantizer.export_model(model_path, calibration_path)
+
+    print("calibration_config: ", calibration_config)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    calibration_config:  {'conv1': {'weight_bits': 8, 'weight_scale': tensor([0.0029], device='cuda:0'), 'weight_zero_point': tensor([98.], device='cuda:0'), 'input_bits': 8, 'tracked_min_input': -0.4242129623889923, 'tracked_max_input': 2.821486711502075}, 'conv2': {'weight_bits': 8, 'weight_scale': tensor([0.0017], device='cuda:0'), 'weight_zero_point': tensor([124.], device='cuda:0'), 'input_bits': 8, 'tracked_min_input': 0.0, 'tracked_max_input': 8.848002433776855}, 'fc1': {'weight_bits': 8, 'weight_scale': tensor([0.0010], device='cuda:0'), 'weight_zero_point': tensor([134.], device='cuda:0'), 'input_bits': 8, 'tracked_min_input': 0.0, 'tracked_max_input': 14.64758586883545}, 'fc2': {'weight_bits': 8, 'weight_scale': tensor([0.0013], device='cuda:0'), 'weight_zero_point': tensor([121.], device='cuda:0'), 'input_bits': 8, 'tracked_min_input': 0.0, 'tracked_max_input': 15.807988166809082}, 'relu1': {'output_bits': 8, 'tracked_min_output': 0.0, 'tracked_max_output': 9.041301727294922}, 'relu2': {'output_bits': 8, 'tracked_min_output': 0.0, 'tracked_max_output': 15.143928527832031}, 'relu3': {'output_bits': 8, 'tracked_min_output': 0.0, 'tracked_max_output': 16.151935577392578}, 'relu4': {'output_bits': 8, 'tracked_min_output': 0.0, 'tracked_max_output': 11.749024391174316}}
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 109-110
+
+build tensorRT engine to make a real speedup
+
+.. GENERATED FROM PYTHON SOURCE LINES 110-117
+
+.. code-block:: default
+
+
+    from nni.compression.pytorch.quantization_speedup import ModelSpeedupTensorRT
+    input_shape = (32, 1, 28, 28)
+    engine = ModelSpeedupTensorRT(model, input_shape, config=calibration_config, batchsize=32)
+    engine.compress()
+    test_trt(engine)
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    Loss: 0.10061546401977539  Accuracy: 96.83%
+    Inference elapsed_time (whole dataset): 0.04322671890258789s
+
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 118-169
+
+Note that NNI also supports post-training quantization directly, please refer to complete examples for detail.
+
+For complete examples please refer to :githublink:`the code <examples/model_compress/quantization/mixed_precision_speedup_mnist.py>`.
+
+For more parameters about the class 'TensorRTModelSpeedUp', you can refer to :doc:`Model Compression API Reference <../reference/compression/quantization_speedup>`.
+
+Mnist test
+^^^^^^^^^^
+
+on one GTX2080 GPU,
+input tensor: ``torch.randn(128, 1, 28, 28)``
+
+.. list-table::
+   :header-rows: 1
+   :widths: auto
+
+   * - quantization strategy
+     - Latency
+     - accuracy
+   * - all in 32bit
+     - 0.001199961
+     - 96%
+   * - mixed precision(average bit 20.4)
+     - 0.000753688
+     - 96%
+   * - all in 8bit
+     - 0.000229869
+     - 93.7%
+
+Cifar10 resnet18 test (train one epoch)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+on one GTX2080 GPU,
+input tensor: ``torch.randn(128, 3, 32, 32)``
+
+.. list-table::
+   :header-rows: 1
+   :widths: auto
+
+   * - quantization strategy
+     - Latency
+     - accuracy
+   * - all in 32bit
+     - 0.003286268
+     - 54.21%
+   * - mixed precision(average bit 11.55)
+     - 0.001358022
+     - 54.78%
+   * - all in 8bit
+     - 0.000859139
+     - 52.81%
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 1 minutes  4.509 seconds)
+
+
+.. _sphx_glr_download_tutorials_quantization_speedup.py:
+
+
+.. only :: html
+
+ .. container:: sphx-glr-footer
+    :class: sphx-glr-footer-example
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-python
+
+     :download:`Download Python source code: quantization_speedup.py <quantization_speedup.py>`
+
+
+
+  .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+     :download:`Download Jupyter notebook: quantization_speedup.ipynb <quantization_speedup.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/source/tutorials/quantization_speedup_codeobj.pickle b/docs/source/tutorials/quantization_speedup_codeobj.pickle
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diff --git a/docs/source/tutorials/sg_execution_times.rst b/docs/source/tutorials/sg_execution_times.rst
index c7785ec1d..59fe26c9c 100644
--- a/docs/source/tutorials/sg_execution_times.rst
+++ b/docs/source/tutorials/sg_execution_times.rst
@@ -5,10 +5,22 @@
 
 Computation times
 =================
-**00:24.663** total execution time for **tutorials** files:
+**01:04.509** total execution time for **tutorials** files:
 
-+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorials_nni_experiment.py` (``nni_experiment.py``)               | 00:24.662 | 0.0 MB |
-+-----------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_tutorials_nas_quick_start_mnist.py` (``nas_quick_start_mnist.py``) | 00:00.002 | 0.0 MB |
-+-----------------------------------------------------------------------------------+-----------+--------+
++-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_quantization_speedup.py` (``quantization_speedup.py``)                     | 01:04.509 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_hello_nas.py` (``hello_nas.py``)                                           | 00:00.000 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_nasbench_as_dataset.py` (``nasbench_as_dataset.py``)                       | 00:00.000 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_pruning_customize.py` (``pruning_customize.py``)                           | 00:00.000 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_pruning_quick_start_mnist.py` (``pruning_quick_start_mnist.py``)           | 00:00.000 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_pruning_speedup.py` (``pruning_speedup.py``)                               | 00:00.000 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_quantization_customize.py` (``quantization_customize.py``)                 | 00:00.000 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_tutorials_quantization_quick_start_mnist.py` (``quantization_quick_start_mnist.py``) | 00:00.000 | 0.0 MB |
++-----------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/docs/static/css/material_custom.css b/docs/static/css/material_custom.css
index 749a56cfa..ff08341e6 100644
--- a/docs/static/css/material_custom.css
+++ b/docs/static/css/material_custom.css
@@ -1,3 +1,27 @@
+/* Global font */
+body, input {
+    font-family: "Roboto", "Noto Sans", "Helvetica Neue", Helvetica, Arial, sans-serif;
+}
+
+code, kbd, pre {
+    font-family: "Roboto Mono", "Consolas", "Courier New", Courier, monospace;
+}
+
+h1, h2, h3, h4, .md-header, .md-tabs, .md-hero {
+    font-family: "Google Sans", "Noto Sans", "Helvetica Neue", Helvetica, Arial, sans-serif;
+}
+
+/* Title font */
+@media only screen and (min-width: 45em) {
+    .md-header-nav__title {
+        font-weight: 500;
+    }
+}
+
+.md-typeset h4, .md-typeset h5, .md-typeset h6 {
+    font-weight: 600;
+}
+
 /* viewcode link should have left padding */
 span.viewcode-link {
     padding-left: 0.6rem;
@@ -8,24 +32,55 @@ dt.sig-object {
     position: relative;
     background: #f4f5f7;
     padding: 0.5rem;
-    border-left: 0.2rem solid #ec407a;  /* this should be matched with theme color. */
+    border-left: 0.2rem solid var(--custom-color-accent);
     word-wrap: break-word;
 }
 
+.class > dt.sig-object {
+    border-left: none;                    /* remove left border */
+    border-top: 0.18rem solid var(--custom-color-accent);
+}
+
+.function > dt.sig-object {
+    border-left: none;                    /* remove left border */
+    border-top: 0.18rem solid var(--custom-color-accent);
+}
+
+.exception > dt.sig-object {
+    border-left: none;                    /* remove left border */
+    border-top: 0.18rem solid var(--custom-color-accent);
+}
+
+/* Padding on parameter list is not needed */
+dl.field-list > dt {
+    padding-left: 0 !important;
+}
+
+dl.field-list > dd {
+    margin-left: 1.5em;
+}
+
+/* show headerlink when hover/focus */
+dt.sig-object:focus .headerlink, dt.sig-object:hover .headerlink {
+    -webkit-transform: translate(0);
+    transform: translate(0);
+    opacity: 1;
+}
+
 /* logo is too large */
 a.md-logo img {
     padding: 3px;
 }
 
-/* Add split for navigation */
+/* Split for navigation */
 nav.md-tabs .md-tabs__item:not(:last-child) {
     padding-right: 0;
 }
 
-nav.md-tabs .md-tabs__item:not(:last-child) .md-tabs__link:after {
-    content: "»";
-    font-family: "Material Icons";
-    padding-left: 0.6rem;    
+nav.md-tabs .md-tabs__arrow {
+    padding-left: .3rem;
+    font-size: inherit;
+    vertical-align: -10%;
 }
 
 /* hide the floating button generated by readthedocs */
@@ -44,11 +99,388 @@ nav.md-tabs .md-tabs__item:not(:last-child) .md-tabs__link:after {
 }
 
 /* toc style */
-.md-nav span.caption {
+li.md-nav__item:not(:first-child) span.caption {
     margin-top: 1.25em;
 }
 
+@media only screen and (min-width: 76.2em) {
+    .md-nav--primary .md-nav__title--site {
+        display: none;
+    }
+}
+
+.md-nav__overview {
+    font-weight: 500;
+}
+
+@media only screen and (max-width: 76.1875em) {
+    .md-nav__overview {
+        display: none;
+    }
+}
+
+/* hide nav bar in some cases */
+.md-tabs.hidden {
+    display: none;
+}
+
 /* citation style */
 .citation dt {
     padding-right: 1em;
 }
+
+/* inline code style */
+.md-typeset code {
+    padding-left: 0.07em;
+    padding-right: 0.07em;
+}
+
+/* for release icon, on home page */
+.release-icon {
+    margin-left: 8px;
+    width: 40px;
+}
+
+/* Similar to cardlink, but used in codesnippet in index page. see sphinx_gallery.css */
+.codesnippet-card-container {
+    display: flex;
+    flex-flow: wrap row;
+}
+
+.codesnippet-card.admonition {
+    border-left: 0;
+    padding: 0;
+    margin: .5rem 1rem 1rem 0rem;
+    width: 100%;
+}
+
+/* Controlling the cards in containers only */
+.codesnippet-card-container .codesnippet-card.admonition {
+    width: 47%;
+}
+
+@media only screen and (max-width:59.9375em) {
+    .codesnippet-card-container .codesnippet-card.admonition {
+        width: 100%;
+    }
+}
+
+.codesnippet-card .codesnippet-card-body {
+    min-height: 4rem;
+    position: relative;
+    padding: 0.9rem 0.9rem 3rem 0.9rem;
+}
+
+.codesnippet-card .codesnippet-card-footer {
+    padding: 0.8rem 0.9rem;
+    border-top: 1px solid #ddd;
+    margin: 0 !important;
+    position: absolute;
+    bottom: 0;
+    width: 100%;
+}
+
+.codesnippet-card a:not(:hover) {
+    color: rgba(0, 0, 0, .68);
+}
+
+.codesnippet-card-title-container {
+    margin-top: 0.3rem;
+    position: relative;
+}
+
+.codesnippet-card-title-container h4 {
+    padding-left: 2.3rem;
+    line-height: 1.6rem;
+    height: 1.6rem;
+    margin-top: 0;
+}
+
+.codesnippet-card-icon {
+    position: absolute;
+    top: 0;
+    left: 0;
+}
+
+.codesnippet-card-icon img {
+    max-width: 100%;
+    max-height: 100%;
+    /* horizontal and vertical center */
+    /* https://stackoverflow.com/questions/7273338/how-to-vertically-align-an-image-inside-a-div */
+    text-align: center;
+    vertical-align: middle;
+    position: absolute;
+    left: 0;
+    right: 0;
+    top: 0;
+    bottom: 0;
+    margin: auto;
+}
+
+.codesnippet-card-icon {
+    width: 1.6rem;
+    height: 1.6rem;
+    padding: 0;
+}
+
+.codesnippet-card-link {
+    position: relative;
+}
+
+.codesnippet-card-link .material-icons {
+    position: absolute;
+    right: 0;
+}
+
+/* fixes reference overlapping issue */
+/* This is originally defined to be negative in application_fixes.css */
+/* They did that to ensure the header doesn't disappear in jump links */
+/* We did this by using scroll-margin-top instead */
+dt:target {
+    margin-top: 0.15rem !important;
+    padding-top: 0.5rem !important;
+}
+
+:target {
+    /* header height */
+    scroll-margin-top: 3.5rem;
+}
+
+/* fix code block style on mobile */
+@media only screen and (max-width: 44.9375em) {
+    .md-typeset pre {
+        margin: 1em -0.3em;
+    }
+}
+
+/* Responsive nav bar */
+.md-source__fact {
+    padding: 0 !important;
+}
+
+/* collapsible toctree */
+.md-nav--primary ul li {
+    padding-left: .8rem;
+}
+
+.md-nav__item {
+    position: relative;
+}
+
+.md-nav__expand > a > .md-nav__tocarrow {
+    transform: rotate(-90deg);
+    font-size: inherit;
+    transition: all 0.1s ease;
+    position: absolute;
+    left: .1rem;
+    top: .05rem;
+}
+
+.md-nav__expand .md-nav__list {
+    display: none;
+}
+
+.md-nav__expand--active > .md-nav__list {
+    display: block;
+}
+
+.md-nav__expand--active > a > .md-nav__tocarrow {
+    transform: rotate(0);
+}
+
+@media only screen and (max-width:76.1875em) {
+    .md-nav--primary .md-nav__link {
+        padding: .15rem .2rem .15rem .6rem;
+    }
+    .md-nav__expand > a > .md-nav__tocarrow {
+        left: 0;
+        top: .25rem;
+    }
+    .md-nav--primary span.md-nav__link.caption {
+        margin-top: 0.75em;
+    }
+    .md-nav--primary .md-nav__item .md-nav__list .md-nav__item {
+        padding-left: .3rem;
+    }
+    html .md-nav--primary .md-nav__title--site .md-nav__button {
+        height: auto;
+        font-size: inherit;
+        left: 0;
+    }
+    html .md-nav--primary .md-nav__title {
+        padding-top: 2rem;
+        padding-left: .6rem;
+        height: 4.6rem;
+    }
+    .md-nav--primary .md-nav__item, .md-nav--primary .md-nav__title {
+        font-size: .7rem;
+        line-height: 1.3;
+    }
+}
+
+/* Increase TOC padding */
+.md-nav--primary ul li ul li {
+    padding-left: 0.8rem;
+}
+
+/* Nav bar and heroes */
+@media only screen and (min-width:60em) {
+    .md-search__form, .md-search__input {
+        border-radius: .3rem;   /* even rounder */
+    }
+}
+
+.md-header-nav__source, .md-source {
+    padding-right: 0 !important;
+}
+
+.md-hero {
+    position: relative;
+}
+
+.md-hero__background {
+    max-width: 73rem;
+    position: absolute;
+    bottom: -46px;
+    left: 0;
+    right: 0;
+    margin-left: auto;
+    margin-right: auto;
+    width: 100%;
+    z-index: 0;
+}
+
+.md-hero__background img {
+    width: 100%;
+}
+
+@media only screen and (max-width:59.9375em) {
+    .md-hero__background {
+        display: none;
+    }
+}
+
+@media only screen and (max-width:76.1875em) {
+    .md-hero__background {
+        bottom: -5%;
+        top: auto;
+    }
+}
+
+.md-hero__inner {
+    z-index: 1;
+    position: relative;
+    padding-right: 35%;
+}
+
+@media only screen and (min-width:76.2em) {
+    .md-hero__inner {
+        padding-top: 2.4rem;
+        padding-bottom: 1.2rem;
+    }
+}
+
+/* make title look larger */
+.md-typeset h1 {
+    margin: 0 0 1.5rem;
+    color: rgba(0,0,0,.85);
+    font-size: 1.5625rem;
+    line-height: 1.3;
+}
+
+.md-typeset h1, .md-typeset h2, .md-typeset h3 {
+    font-weight: 400;
+    letter-spacing: 0;
+}
+
+/* Enlarge table */
+.md-typeset table:not([class]) {
+    font-size: 0.7rem;
+    box-shadow: 0 2px 2px 0 rgb(0 0 0 / 8%), 0 1px 5px 0 rgb(0 0 0 / 7%), 0 3px 1px -2px rgb(0 0 0 / 14%);
+}
+
+.md-typeset table:not([class]) th {
+    padding: .5rem .7rem;
+    background-color: #e6e7e8;
+    color: inherit;
+    font-weight: 500;
+}
+
+.md-typeset table:not([class]) td {
+    padding: .4rem .7rem;
+}
+
+/* On this page TOC */
+.md-sidebar--secondary .md-nav--secondary {
+    border-inline-start: 4px solid var(--custom-color-primary);
+}
+
+.md-nav__link {
+    margin-top: 0.45em;
+}
+
+/* Override style for copy button */
+button.copybtn {
+    opacity: 1;
+}
+
+.o-tooltip--left:after {
+    transform: translateX(-5%) translateY(-125%);
+    padding: .4em;
+    font-size: .5rem;
+    font-weight: 600;
+    background: #5f6368;
+}
+
+.o-tooltip--left:hover:after {
+    transform: translateX(-5%) translateY(-120%);
+}
+
+/* Sphinx tabs */
+/* Copied from https://github.com/executablebooks/sphinx-tabs/blob/master/sphinx_tabs/static/tabs.css with modifications */
+.sphinx-tabs.container {
+    margin-bottom: 1rem;
+    border: 1px solid rgb(232, 234, 237);
+    border-radius: 8px;
+}
+
+[role="tablist"] {
+    padding: .3rem 0 0 0;
+    border-bottom: 1px solid #a0b3bf;
+}
+
+.sphinx-tabs-tab {
+    position: relative;
+    line-height: 2rem;
+    font-weight: 600;
+    padding: 0 1rem;
+    color: #80868b;
+}
+
+.sphinx-tabs-tab[aria-selected="true"] {
+    color: #3f51b5;                     /* primary color */
+    border-bottom: 2px solid #3f51b5;
+}
+
+.sphinx-tabs-tab:focus {
+    z-index: 1;
+    outline-offset: 1px;
+}
+
+.sphinx-tabs-panel {
+    position: relative;
+    padding: 1rem;
+}
+
+.sphinx-tabs-panel.code-tab {
+    padding: 0;
+}
+
+.sphinx-tabs-panel.code-tab .highlight {
+    margin: 0;
+    padding: .5rem;
+}
+
+.sphinx-tab img {
+    margin-bottom: 2rem;
+}
diff --git a/docs/static/css/material_dropdown.css b/docs/static/css/material_dropdown.css
index d329e0a3e..52c9a6c5d 100644
--- a/docs/static/css/material_dropdown.css
+++ b/docs/static/css/material_dropdown.css
@@ -1,10 +1,26 @@
 /* https://codepen.io/mildrenben/pen/RPwQEY */
 
+@media only screen and (max-width:44.9375em) {
+    .drop {
+        display: none;
+    }
+}
+
 .drop {
-    width: 125px;
+    width: 5.3rem;
     vertical-align: middle;
 }
 
+@media only screen and (min-width:60em) and (max-width:70em) {
+    .drop {
+        width: 4.7rem;
+    }
+    /* also narrow nav source width */
+    .md-header-nav__source {
+        width: 10rem;
+    }
+}
+
 .drop button {
     color: inherit;
     font-weight: 700;
diff --git a/docs/static/css/material_theme.css b/docs/static/css/material_theme.css
new file mode 100644
index 000000000..c8ee9b58a
--- /dev/null
+++ b/docs/static/css/material_theme.css
@@ -0,0 +1,97 @@
+/* Theme related customization */
+/* https://github.com/bashtage/sphinx-material/pull/122 */
+
+/* first part */
+button[data-md-color-primary=custom] {
+    background-color: var(--custom-color-primary)
+}
+
+[data-md-color-primary=custom] .md-typeset a {
+    color: var(--custom-color-primary)
+}
+
+[data-md-color-primary=custom] .md-header,
+[data-md-color-primary=custom] .md-hero {
+    background-color: var(--custom-color-primary)
+}
+
+[data-md-color-primary=custom] .md-nav__link--active,
+[data-md-color-primary=custom] .md-nav__link:active {
+    color: var(--custom-color-primary)
+}
+
+[data-md-color-primary=custom] .md-nav__item--nested>.md-nav__link {
+    color: inherit
+}
+
+[data-md-color-primary=custom] .md-nav__extra_link:active {
+    color: var(--custom-color-primary)
+}
+
+[data-md-color-primary=custom] .md-nav__item--nested > .md-nav__extra_link {
+    color: inherit
+}
+
+/* second part */
+button[data-md-color-accent=custom] {
+    background-color: var(--custom-color-accent)
+}
+
+[data-md-color-accent=custom] .md-typeset a:active,
+[data-md-color-accent=custom] .md-typeset a:hover {
+    color: var(--custom-color-accent)
+}
+
+[data-md-color-accent=custom] .md-typeset .codehilite pre::-webkit-scrollbar-thumb:hover,
+[data-md-color-accent=custom] .md-typeset pre code::-webkit-scrollbar-thumb:hover {
+    background-color: var(--custom-color-accent)
+}
+
+[data-md-color-accent=custom] .md-nav__link:focus,
+[data-md-color-accent=custom] .md-nav__link:hover,
+[data-md-color-accent=custom] .md-typeset .footnote li:hover .footnote-backref:hover,
+[data-md-color-accent=custom] .md-typeset .footnote li:target .footnote-backref,
+[data-md-color-accent=custom] .md-typeset .md-clipboard:active:before,
+[data-md-color-accent=custom] .md-typeset .md-clipboard:hover:before,
+[data-md-color-accent=custom] .md-typeset [id] .headerlink:focus,
+[data-md-color-accent=custom] .md-typeset [id]:hover .headerlink:hover,
+[data-md-color-accent=custom] .md-typeset [id]:target .headerlink {
+    color: var(--custom-color-accent)
+}
+
+[data-md-color-accent=custom] .md-search__scrollwrap::-webkit-scrollbar-thumb:hover {
+    background-color: var(--custom-color-accent)
+}
+
+[data-md-color-accent=custom] .md-search-result__link:hover,
+[data-md-color-accent=custom] .md-search-result__link[data-md-state=active] {
+    background-color: rgba(83, 109, 254, .1)
+}
+
+[data-md-color-accent=custom] .md-sidebar__scrollwrap::-webkit-scrollbar-thumb:hover {
+    background-color: var(--custom-color-accent)
+}
+
+[data-md-color-accent=custom] .md-source-file:hover:before {
+    background-color: var(--custom-color-accent)
+}
+
+/* third part */
+@media only screen and (max-width:59.9375em) {
+    [data-md-color-primary=custom] .md-nav__source {
+        background-color: var(--custom-color-primary);
+        opacity: 0.9675;
+    }
+}
+
+@media only screen and (max-width:76.1875em) {
+    html [data-md-color-primary=custom] .md-nav--primary .md-nav__title--site {
+        background-color: var(--custom-color-primary)
+    }
+}
+
+@media only screen and (min-width:76.25em) {
+    [data-md-color-primary=custom] .md-tabs {
+        background-color: var(--custom-color-primary)
+    }
+}
diff --git a/docs/static/css/sphinx_gallery.css b/docs/static/css/sphinx_gallery.css
index 5879dc9f2..fad00758f 100644
--- a/docs/static/css/sphinx_gallery.css
+++ b/docs/static/css/sphinx_gallery.css
@@ -11,7 +11,7 @@ p.sphx-glr-script-out {
 }
 
 .sphx-glr-script-out .highlight pre {
-    background-color: #f2f3fa !important;
+    background-color: #f0f6fa !important;
     padding: .6rem !important;
     margin-bottom: 1.5rem;
 }
@@ -26,6 +26,7 @@ div.sphx-glr-footer {
 
 .sphx-glr-download-link-note {
     margin-bottom: 1.5rem;
+    line-height: 2.5rem;
 }
 
 .notebook-action-div {
@@ -37,7 +38,7 @@ div.sphx-glr-footer {
 
 .notebook-action-link:hover .notebook-action-div {
     /* match theme */
-    border-bottom-color: #f50057;
+    border-bottom-color: var(--custom-color-accent);
 }
 
 .notebook-action-link img {
@@ -83,6 +84,11 @@ div.sphx-glr-footer {
     font-size: 0.75rem;
 }
 
+/* hide link */
+.card-link-anchor {
+    display: none;
+}
+
 .card-link-tag {
     margin-right: 0.4rem;
     background: #eeeff2;
@@ -103,8 +109,8 @@ div.sphx-glr-footer {
 }
 
 .card-link-icon img {
-    max-width: 80%;
-    max-height: 80%;
+    max-width: 75%;
+    max-height: 75%;
     /* horizontal and vertical center */
     /* https://stackoverflow.com/questions/7273338/how-to-vertically-align-an-image-inside-a-div */
     text-align: center;
@@ -119,7 +125,7 @@ div.sphx-glr-footer {
 
 /* link icon background color */
 .card-link-icon.circle {
-    background-color: #283593;
+    background-color: #E8DCEE;
     border-radius: 50%;
     width: 4rem;
     height: 4rem;
@@ -127,50 +133,18 @@ div.sphx-glr-footer {
 }
 
 /* pallette */
-.card-link-icon.red {
-    background-color: #C62828;
-}
-
-.card-link-icon.pink {
-    background-color: #AD1457;
+.card-link-icon.cyan {
+    background-color: #DDEFF2;
 }
 
 .card-link-icon.purple {
-    background-color: #8E24AA;
-}
-
-.card-link-icon.deep-purple {
-    background-color: #512DA8;
+    background-color: #E8DCEE;
 }
 
 .card-link-icon.blue {
-    background-color: #1565C0;
-}
-
-.card-link-icon.light-blue {
-    background-color: #0277BD;
-}
-
-.card-link-icon.cyan {
-    background-color: #006064;
-}
-
-.card-link-icon.teal {
-    background-color: #00796B;
-}
-
-.card-link-icon.green {
-    background-color: #2E7D32;
-}
-
-.card-link-icon.deep-orange {
-    background-color: #BF360C;
-}
-
-.card-link-icon.brown {
-    background-color: #6D4C41;
+    background-color: #DBE8FC;
 }
 
 .card-link-icon.indigo {
-    background-color: #3949AB;
+    background-color: #E1E4F3;
 }
diff --git a/docs/static/img/Comfort.png b/docs/static/img/Comfort.png
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diff --git a/docs/static/img/home.svg b/docs/static/img/home.svg
deleted file mode 100644
index 5360482f3..000000000
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iiUMHIJa7IT9JEXklEel5KVaKMhAokUlK5xWLJml04geWl5XRlrp5SMSSPHz20drb3dG8mFlpVnF6ooVZyAJEQjKebSN016VgMtZKHUs5BFAoUPA4mY+zvbSEKA8wvnEC5UpMnllepklIdHOyTrc1N7O9u4eZHH+GtN/8BG48fII5iOK77aagtpXohUFIiFQlKlYpybAej4H51v71bCoPgdL5QHIVpfLS9u7O3v7/3YDQMblPX2+BE3FIEtwilh4wSMQnJNRgMBsPvwIhNg8FgMBgMnw+VDaysSCEvRpT+TKXiNaVQ9nKFoee614MgqPU63el8sWCfvXQleul73yerCy2Q0Q7dvPMhiYYdBKMAtUYRJ6ZLmG2UwWwGTFJnJ0pQG5jwHAuuZUEohSiVCEYRuodHGAz6ui+TcM4V51yH+pDW7DymGtOIonOYnZ2H53t49603sbn+SA0HQwKisnAhgGXuphAJOp0u9vZ2leu4qtM5RK/b5Wkcc6KUu7m+XlUKJ+IguMgY7SdCHJVyuE+FdS0OrdvdgLQLvt12LLbDGW0TQlJzBBkMBsNvYsSmwWAwGAyGfxYtAAm3l0oUf4ZE/kASsqqUmgZIUiwUDyv12nBnc2tKycPciaUV/PD1Px1dufIsJcERu/H2h/zR48dIwhFmp3ycXShjoVkA92wgleMM2OwBFNGfhnGKg94Ig1GMREhEw5hEQmG610UUjI5XlUzSY7X2JLZtQy9nLjyDSq0uV0+dVm+98UvywXvvkPbeLoQQIFRlAULa6RTBCFvrj6nWt8N+n4hUgFKOKI5xsLeLnGuxnGfnpBS5cBhNxzRd6bbFlbDf2et1/aN+UNudqZPbTUU/qOXkTduxNjm3epRSEzBkMBgME4zYNBgMBoPB8DvRMbEps07Bs/4yJ8RfpxBrUirdZWlbnAnHcWYY4dLL5Spr585Hr73+p/K5F16iNkn49XevszeuvUfu3H6IcsHGC2encHm1gUrZA4QcL4Q8MXWEZG7mIEgxitJstEmS6LLXAJ1OB/1uF2magnNO8ZlM/RTbdtTCiWXVaEyrxaVVMr+4hDd+8fe4d/dm5mr6+TxUFhwUjUWmFCBKwrEtUEqRCglCJDyLoZp3MYoSxEmCcDi0t0ej6ThV057vYnP3MKpV9rozU6XHa8uLN1bozMe+at9MpHpk2/4Wt+wRpTSklBrX02Aw/NFixKbBYDAYDIbfBYeyLihYfyNY+ipSUQVDBAVdl+oWiqW4Od1yypWas3zypH363IWj5158JSkUi9WP3vw7+81f/px89MkddPtDNGp5zE+V0KwVQbVW1OWz6gm9KMd9lb5tZf2ats0ysal/TglBGIwywRnHkRab+HWh+QTU9X116uw5UqnVUCwV8e/+H4Lu4SFK1ZoUSUy7nQ5Ggy6iKIVjWch5TqZ7tcDVLmgqCRLFIHU2kU4HCmMkUiKKBXoDvR4955G11fB9v/Fwo3P+bFv8sNLaWXfylTtT063bruPuuV5uv1ytPvI8T1uoPZiQIYPB8EeGEZsGg8FgMBh+k0zGkRKkfYEQ9i+Zkv8JZKLjYjcpqARRc5wxXms2hxeffW54+uyFtFqv89n5BWHZTvzowb34rTffcO/cvoPhaITpWgHnlxqYb5TGQlMrO3XsZqpPH55QIJ+zMVXyUM456A0iREmahfrEQYB+9whxGMD3c//UTiNPfpxqTuN7r/0wy879+P331HAwQJKE0qKE9rnAoKfg2QylvIs4lUhS7XpK9EQMyaJMAUexhMgcTwLGOaRIs/mfQaDQ7YfoD2N3+7A7WygWZz2veHlqZvqoWCj2GzOz3cWl1VvTrdZ7lUrlru242wD00tbjYcxRZzAYvusYsWkwGAwGg+FXUBK+EmRJSfoiIfR1ReWrlIoZovg+Y6wM0JpU0s8XC+mJ5RV5/tKl3MXLzzPtKAIoHh4c2O9c+0dcv/6xOuj0UMy5uLjSxJWT05iq5sYyMP31CljyaTmtzanumcRMLZ993O8MsbnXRbfTw0F7H4PBAOVq/XPvtKmpaXz/T34Mz/Hx3ltvYn9/C4wCnLKsfJYxmglJzgkciyC2aOZmRmGYrY9ScrKKBBYjYJaTfV8IBSFVNtdzc30dTEcbEebYH9vTfqEwPd2aRWt24dLy6smXT50+u9uaX3hUKJY+4dz6EMA9AIcA+gB0I2pijkKDwfBdw4hNg8FgMBgMx+hq1YYU9DmRsp9QSl6lNF6jlLi+l0cun68qoByGEaeck9WTa6Pnnv+emD+xXFTjqFfR7Xb49Q/es+7fuqHiYU/pstiF6SKeWW2gVc+P62SFmJiZZBLzo7Kk2ExsqhQUFKWci8VmEf0gRipSbO0rDIYD7O/vZ6W0cwu/0a75O6nVp3Dlhe8R1/Nw/YO3cefGRxgGEZQYb/QwTMGoQinvoFb0slVK0jRzOYcRxTBKsn5OLSh9m2fiNIhTKB08pG+c3V4gSWOEwwFGoyGODg7w6ME9vre9dWJ3Z/tEa2HhuZWTa6/ML5y473n+bUrpPcb4YwCPJosWn8NxQbHBYDB8+zFi02AwGAwGg+6KZFTKeUWsHxOL/TWDuKqkqiZCMs9y1MzMrJxpzclOv0N6vX48OzenXn7tR+L5l76fFkpl3YvohFEk79y8kV7/4B2bU8GWF6bRO6Tq9GKVLM+W4Hg8s00hnyifzYSmHoCpxhpLpxExoJS3MxG43R6g3QtAKAHVwUG9LjpHB0iSBJZlf6EdV5uawsuv/ZA0GjWIJMCgc5j1f1qcZTM5pRSolVzMTxVQL/rwHI5BkODhdheP9nvoDMJsfRXRm6CQivF2WBYHAYFMkkxM550cGGfoDYPM9Tw6OkDnnWv44N232PLJtZnzly5Pz7TmLlUrtaNSuXJgu+4D23beZ4xpx/MOgL2J4xkZ4WkwGL7NGLFpMBgMBsMfOUrBsihflVA/VSr+KyhcJaC+hIqVEhGllOcLRcy05mVxVAWz+Ojis8+lz77wopXPFd2sKFYpcXjYtu7euUn293ZJteAQu5AjBbtGFpol5D137FzKJ7XTseiUn32fUi174ToWuqMYd7cO0RnEmSbVabJBEKDX6WE0HKJU/mJiU8MYx+rpcwgGXSAc4N7dWxgOg+y+s7ErUYreKM56S88s1mEzihPNUrYeD7Z7GAQxlHZCgwijIM4CjLhDwRnJLqsiIZB5nVIRxhhsbmWzRQejPoLBQG1tbODjj94ns/OLubVTZ3P16eZcuVJZW1xYvlprNh8zxm8xxq5TSj4GyCcT4akTbcWvNLcaDAbDtwAjNg0Gg8Fg+KNG2aDWSSGSnwLqh1BY0hNEtDNnc440oZwSyhIhpJvzxdqFi+nc/ELcnJ1LfD9naUdTX090u11y+5OP6cO7d8mw21NU2KrAAnp6poCl6Qo8x0LWCCn/Ob2kZ2FS+I4FXzuGimQaVa9PIhJEUYgwGmbjT35fuOXg3PlnkA6PkMQh7ty6lYX+aPdUj1yJdrvglGblsjO1IpZmyjg1V8bWUYD9TgAhJfaOBnjv7i4ebXezeaB530a14GVBQu1eRIIkAWUEQTjKBLLu+ySEkmA4QL/XxdFBGwd7O+CWrcOO3LWzF1on10636lONi5VK7bVcsfjQtq1rnFtvcG7dBrADoDcRnkZ0GgyGbwVGbBoMBoPB8MeCGpuLlkUhiQWRJLUoGr2qKH0RlJxWCrNKSk/3I1oWF57nKcpypFptiLMXn4nPXbw0XFpdSwqlog6itSeiRwWjoX37kw/Je9feINubm5QzQEYpmZqiODNfRLXsjrsr5eeoCFVZ9yfyHkej4qFSdLKyWv39AyERBqGej5mVvP7+ENj5Ks49+yKIEsi7HDdv3cb+QRdJkmb9o0eDEA/3eoiFBGclLE4XcbHsZyNR9ITRdneIRjmHd+/s4OF2J3M7pyo+OCVZue1QpODgY6GcrT/JHpdYPJvnmQQhNh8/zspxOWPYfPSYvP/2NcwvLvqLSyvz9anmjJ/z18rl6iszrfmb5Vr1YwAfT4KFdiZltgaDwfBUY8SmwWAwGAx/ZGTCj1KHUvxQKPrfKIJnKCGBzXmimGJJktAoDKnrOnJm9kT47PMvJS+8/Apm5xfgeTlFCCGTawit+NJ2u21//OEH9MGDuxAiITmbolW0yPn5IpplLxNg2aiTz0PWuqnFLkOjlkOzmscoFAjCGEKkCIIRet0u4jD6cjuNEPiVGVy4+iqatSoWFz/Ahx9ex8bGBoiKUfQ4RkGEh9sJdg8G2Dkc4eJSA61GXvu+yBUsVIsOFht53No4wubBIHM4h2GCQs4GsyiiSGS9nFBP5CFlLaokc0dFGIJxS9u26uiojYODXXLQ3iPrjx8x13WZ7+dmpqdnpxaXl9cWV1avzi0sPiyVKg8BaKfz+iRUqD1xPA0Gg+Gpw4hNg8FgMBj+aMjGi1iAWhRJ/D0i1d+C4GUFlScKI8eyR6lIneGoTx3Hwez8El75wU/F1Re/l8zMthjjlu7PPG6U1CW0tHN0yD58/y1y784tMgqCbKRIjtl4ZqmC1YWach1G8IVdyHGVaNG1kXetLAm2N4ogFYFIYvQ6HQyHA3yxPNrfAqFwSk3MXcijsrCKuZMX8d4713D3xvvoHR1i73CY9W/qKS3r+330hjHOL9VwYrYCv+CiXLNwzncx1yxlgvTBXg87BwMsz9YRRgk2djs47Ec46AUYhFGW9MMZBdNxTDpQiCgI7eRKkW1GmgoctvfR6/V066rK54s4PDjg9+/frub+MZ87ffZC69nnX7g8PTt/VMgX7hFK7wJ4OHE7709meBrhaTAYnhqM2DQYDAaD4bsOIXoqCaQIdIXqSQHyt1DypyDkBCGEMqlSKOUEwchOZcoKxTLOnr8Y/+D1Pxtcfen7qR4bQiiRE23nH4fVjIYD/tY//sL6h//w77G/twtLz8dkEgs1TpZnCsj5DvlszMkXhcB1GHyHQyqZtXv6nq1NQLT3drDx+DEWl1bguO6X23lEl7bmka/nsVachltugtg+PnrvLRz2NzAI06yUdvtwCH5/F/u9EU4dhTizWEWrlofl26i7HMWcjWY1l4UagTAMRxE222VsHwxwa/0AD7Y7aHdDSApUiz5cTtHRabWJgEUkFKcQkkKkKXSwEIiF4WCgP1fOyCVbG+v21uam3T7Yq1y4dKUxP784b9n2xWKpvJfL5fe5ZWnReWuSZvsAwDqAwJzbBoPhm8SITYPBYDAY/hhQqqSQrkpFfyoV+TGhaCgg4JRJ13crUkpnNBqp2lQzeeHlV8Urr/04WDt7oV8oFnXHYW7yDKUTR5MIIcWtm5+oN37xd7h940Y2v3JpvonT0y6ePZFDOeeMezT/2UCg38Kk1DTnWijmXPi2hf4oyUQYhUKvc4idrY3M3fzSYvMJuO1i4eRZUEu7lk3cuv4eHt+/g73dXURxjEe7R9hoD/B4r4/7W4e4vDKFc8t15Eo+bN9B3bJQK8vMwUwiD/ONfOaMrs1X8GC7i9sbhzjsh1nSbpzIbIaobxPkPJ6J2kj31Oo5pCDgnBHXcVXOdRVhlgoJSLu9S97+5T/gcH+fNluzecaYWyqWqvXG9Fprfv7ZxvRMu1gsbTLGteD8YNLjqUXnvl4lc54bDIavGyM2DQaDwWD4jqKUOm4UnAbBFaLI61LhZQk1xYAjAAUlZcHinFq2jebMjHjuhe93fvSzPw+WVk9q0/O4/vW4RzOYBNPY+/u77P233qQPH9yHUBK+72NlvonvXWhirqzAdenslwnxISQTZVpw2pxlI0Z0PyQjBAUhkMQxwjAYBwqRL1VM+ytYnOPEyilUqjXMzc/j7V/8f3jn2hvY3tnBQOqk3ATDMEa7M0C7M0QUp3juTAte2c/WhQgCpjOOXA7Xs1Apepip5nHmxBQu697Pdh8H3SEe7fYRxynCOM36N3XZbZKKLCwoTRJwTuGV8mCckMFwoOIoVrqOeH93Rw2HIzx4cA+cc06k9EuVKpuenS+35uaardn5k625hTwXX/kAACAASURBVCvNmdYPisWSLrN9D8DbE9HZBdABoSZcyGAwfC0YsWkwGAwGw3cUzllOELUmpLwKoi4TRa5Sxk7qICBCCUnStJKmaS5JEkzPzuLqi99X3//B6+LEysl4co3AJ0JTZBGt48+jOI7x8QfvFq6//w4bjUZkqjmt5qencGKmTBamK2BkBIy0EJQTIUjGovBz19NmQzWz8SM534bn8uzrOEn1NkE/fr/fxaDXg2gKMP7VXs7oVS5Xa/DOP5sFER12BghTBT22RAgd+iMRJSnu6xRaORb0l0/PIJ93xtGzOmJWf1/qfCAF2+OoejpQyMPq/BQOBwLLO0dY2NzH/e1DPN7toR9wxKnIRqc4nMOiVI9MIb3BCFEQEamUDmZSSZqQXveIRvEInFmIwhFr7+1h4+EjEM5YtVrzVtZOe+cuXGqcXDt7aqrZPOO6zllG6QZhNIAS9yGid0DINiiNoBABajzI1GAwGL5ijNg0GAwGg+G7hyulnOG29ZKS6U9FIk8DKk+BmuPYdqFQYFGS5ORwYOeLBTI3v6iee+H7yfdfez1eXF51J6WyclI2O1GL0PWdnpSS3rt9i1375S+s7a0NatsepupTWGpVMFuEYmJAIGJAJpNZK2zyq7p383M6nWr8H6MUlZybleQ6Nst6QhklGAUDbG9v4OjgAOly8pWLzWMcP4e1C1cgQFFrNLHx8E42rqRzdJiF/EghcXfzCKMoRWcY48raDGqVHBzXUroMGElCMsGdPYsqW5xiSc1M1dCcGZHl1gbuPPZxe7OLrYMRNtsDHHQGcB0X+XwevSDA8LCLKEy0qUkI1dZp5lQTkaYqiROSJilEOkIURUiFUDo8qdfrEl1mfP/uHef0mTNzzamqyykJiwXPKeesjgX5PoW8gTQ9BOgOmH0PPNkB1NCU2xoMhq8SIzYNBoPBYPgOoQipUuAlIZKfKIrvA1iFUp5S4wmWlFFYlkVSIZjveuLshYvipVd/QJ959vlwenZ+SAhxAJQmoqM/EZrHipHt7+zwN//h7+ntmx9zbjtoTLdUo+xjvgxM5xVBPARE+llOrBZZWnB9EeeMjO1FPV/T1uvLKHI6HIjpESoCKSGQaYokiSfO4h+OYqWCS1dfxExrDrdufIh//Pl/xOHRuxiMRsjnXESJxMeP2jgaxdg9GuLsYj1zL+sVH5Tr9SXjvlU1WaIEYDGoTVCt5nCRN7HQLKMbSGxrwbl7BO2d2q6Pg94IjzY57m8f4KATghNGHM6yRFvtmcZZ2S3NjFRm26hXKymnVI2GQ3rv1ifkcHdXtTcfOMW8N5MmAeqVonX+1NLS1FR9BanocDkcFYvunpdzblKHfgCQj0HIA4AcjkumyZcZZmowGAxGbBoMBoPB8B2BQamplLOf2Ur8F0LJq1JmolExxmQWuaPA0iRVSRKLSqWSzMzOqT/50c/Sy8+/RErlSjpxM8XE0aQTh1McC80oGtHrH71H3n3rDdbe28VUs6VmZ6fVuYUiVisxzTtkLCwz6Pju5CSN9ou2VU5+R39Ihcpmdbo2R6q1GizYlgVK2ZcdfvK5cD0fi6unUChXkEqg3xvg4b1b6I90Fao2Gyk6gwjXbm3j0W4Hlw+G9PLJKcxO5WHbPAs7wnGWbxoSDHbHnysJL+dkyww4lmYkhks1RGmCRBIcdkPUcwSESHh2HwQMQo2HdSqd8isVmGUhjLOZo6pWLgrftdTGekD6nYC0kx0y7B8xKQWJoxD1ah69ozaqlWqx3zsqUhHgxOzM2trK/DPNRvFV7hXvUaS3iJIfQakPQcRjEDKEIslxArF5sTAYDF8EIzYNBoPBYPj2o9/PF6SIX49B/pZK+ZLKHEoVUUJDzhizbNtyHTf1XFsVSuX4/MVL0eUXXrROn73MC6XSsYhwJ4JiNHEzj9ECldy9dYte//BdostItZiUQpCqT3B2rohWLgLSUeY8ZqWzhI4dvax8lH7ah/m5mJTRapGmy2cdiyGKRSY6db9knAboHHUxGg4hpfzadl61PoWrL3wvG0/ieB7u3rqB0aCHUtFDpZxHEifZqJM43sBRr49nTk3j9HwVubwLons5M4GYTkqM6ViEZmWxWohKOA6B43iAcrMk32rOgmcT1Ioe2r0gE5/rewN0gzCT8qWcxChRSEWqXIvAEgFPhiMEYUDiNKWxEBiGISFKQf+8Pxjhxq17SuI+6Q90T22KT+7tkIcbW6UzJ2ZKrZnmSrkxfMkqRRteLr3BbHKDOM59gNwF1GMA7Ynj/UUacA0Gwx8xRmwaDAaDwfDthipgLkzin1Ih/gqEXclKYZVGe2eSSyq553lqbnY+bs1Mi+n5heSZKy+MVtfOuLbrlyY2JH0iDCicuJza2WRCCPL4wQP5xi/+nt698Ym+HerNGcw3q5grElKyQoDK8W9k6bB4QouQsfjMhOYXNccICr6NmXoeD3a7mXuYJCmGQQR7fxd7e9uIwhCe74N8hYm0v4up6Rl877Ufodpo4r233sDdmzcw7B1loUWWDhYq5REmKd64uYPOSEBI4OyJOgp6FIwW3loca/FNJrbtpwJcjJ8rerzpBHnPwnKrgrmZGoYxweZ+D7fvb+DRXje7re5f1fM7B0OKgmshiQPeHYTQgU/aBdZiM00VCr6DatFBmkps7B2RNBFZ0JK+j+5wH3uHXTxY38X8TNWqVipVv1CtzE5Pn2o1Sq8WK8VDr1C5S5zCR7D4hyDkNoCNLNV2vNIGg8HwT2LEpsFgMBgM31LGylBNj9L0p4zgrylll9RkJiallHBuMdf3lee6qlavxisnV4fnLjxDlk6dYY3pWWK7npg4Vc5EWNLJ1+x42qX+/tHRId59+5r86N23VXtvH5IQtJozeObCaZyec+BrIzQSk5mak+RZ9cTUlKy09guaYZO78l0LSzMVdIYR7mweYn03QZQIjEYj9Ho9hFH4Ne883WtZx3PPv4TFxSXcuXkD77/7Nq6/9zb63T2USw14toPHux189HAfijKMwhRnFutoNKdA8zYQBUAcfLaRWUntxP097kGdxDJZDoNlefCoh1zOQ9UFFps5jGKZBRPphN4oSUkqJLb2u5nQLXgOvKKLTj/CIEqyYCUteoNYZOuSmcaMZHo3SiV2OyN0hxEebB/Bd2zYtkWa1ZI7P9uYXlqcbS626qea1fwLuZz7iDi5W/BrH8P2boHybRB6CKiDifg0bqfBYPgVjNg0GAwGg+FbCAFcRdR8SsVrVOE/JZK+CIIclKJ6bmOW5FqtYPXUqaBeq6WFXF4unz4dnr7wLKlOTRcoZe6kNzOaiEv+hFNlTX4mw1GAOzc+YXdufCSEFKLRmmVBGJFapYjl+QaaDQkSDIA4BRh/YszJhOzz9Iv3Vk4CdXQATt7jKHo2dDiOtmu1sNLzLfUYkm9C32gX1XFctOYWUJ9qYqo5o/N68PYbf4/DoUChUAB3XBx0A7x7Zxf7h13s9VK84LWwOD8Nwg+zaSNI0880+PHTo37to95GFYFyIOcQuDMlNKoOwlii0xtl5bHVgoPb64e43x9BpAp2nmRjY3TlLqe651WgOxiPaxlnL9Gs/Dh7WH07RbL7C6IAlIaZY/p45xC3NvbJ4sY+WZkp0dmq15yuFeut1vS5+szoVS6iHaTDfdjFDVD3BsBugtiPQMguVBYs9fXVNxsMhqcWIzYNBoPBYPjWoWZTJV5JhfieArnCKDkLpQpasSglkSZJJvxKxaI6d3otWlk9FXn5kt2YnbfKUzOSUl3zCnuy1cehQMdC83i+pkjTVNy+eR3/+PN/TzfXH8lytS6aMwuCU8lP1F3MVxgYiXUELsBsZIpI/bYRJ79niSsh4Jxmbt1ed4RhkGbloVNlD7VaHq5Ns/7JbwpKKVzPw9oZ/fQLUAqsP7wPxThcN4deb4j2UR+9fh/9COiiiMu9GCs1haotAV3Kmj378omZpL9GptXjcdAS59kfETzfgesCBd9CpehifqqE6Uoe07VDdAdxNquzH8QQUmWLvpPMBZcqezj9sBZjYz2r54YykjmaWt8HSToW81IiaPcRRQn2dvd03yypFBy+OtfIn1s7zJ9YnJ2rVIrC9v0QXnkbbukRrPIjKPcWmHsDhB33eHYnZdkGg+GPECM2DQaDwWD4tjAubD0pifgbmcq/VFKdJITmFGATqExk6FEgjHFVKBbjZqMxmmm15PzyMs+Xm7bj521CaXrsWk62Oh2PucjcTRufdVzS7c11du2XP7c+ePdtNhwOQbkt52YFfebUHE42KCpOBMSD8T1poamXT93GL9lDOelntLQyUsAwiLOQIF0iOjVdwPKJFuoFG2EQIk0FLOubu6SxbBtrZ8/Dth188O7buH/vDrqHB/A8Lyv3DWOBB1ttHA3exoP7d3FxoYJnlqtYapUz4Ti2H3/HeJhJcm3mhI6/yNo+dd9luZRD3ncwVc7hwspUVg57cDTC4/0e7m53sXs4hB6Rop1MMZRIonHQUqL/3kCQCUud9Kv7Q22L47AXYhTF+hjKnOPRKEQYxkiy1ZO4u97B3c0uzq9u89W5Mm/V8k61Vim5xakl4tUj+OUO8Wr3wfl9sPwWCL0D4GMA6xPhGX3d+8dgMHxzGLFpMBgMBsNTji4dhYSnLHJBAf9SKfnXUGqFja2wQAGxkNJNEgHbsjE900xOra31Lz97ZTS3tMqLtRnH9vL8CUEZTpZ04mgmkyRab9KnmQ57XfbRe+84H3/4Ae/3+rqVkPeP2spND8lSuYGpnG740+Wz0UQYinH4jZJf3TgSpbIyz6Jvw7F55nDqAFfGXPgWyxy/0XCAJE2/UbGp0UJzde0MSuUqSuVSNpqE2xY2N9Yx7PczwbYbtdHrHmFnu42NvTpeOtfCMytNFEreWHBmDufk6dM2qcSvlSTLT8Xmpz40o+AWQ7XqZ3M7hRQIoxRnjoY4s9fH+n4fe0dDHPRCbB70sX0wxHCUYJDG4BaFw1hWciuyQ0xlTrKjeCZmo1hl6VOMMkiiMIoENg+H6IcbOOz28XizgGY1h2algLlm1Zlq1B2/UC56lalZu9R8Fn4jRG5qG1bxQ4B+BOARgF0AWwB2AAxMn6fB8N3GiE2DwWAwGJ5mFCHctWekSF6EVH+hlHodCq2sZFa7RFIK3dqoW/Ac15GtViu6cuXZ+Mpzz6vlU2d4tdFitpfjx1MeJx+dicAUT6TQThJqIJVUZGtjg39y/UO+s70Fxjk8x0Wj7JGVOkfVGQJhotXIZ/Mjj4Ntvuq5l5SgUvBQLXjQG9obxdjc1yu+g6kkD7exgdnFJbiOA0q/nkTafwrGGKZbLTjOK8jlCvjw/XcQBAHCIIBSydgtBMX+IMQHD/YRJAJhInH19DRKldy4jPbY4VSYlNWqX5Njv6bNhPxMnFJkj5HLc+R8G416AWdGMY66AbYOh3i408Gt9SPs7PfRGUWZSC3nXQRRisNukJXe6rvhbDySRZfajqt3GWxdestJdts4S7XtoTMI4ThHKPsOTi928PzpADO1DkZH24y7uaLlV4p2fbHh1k6s0ELzJVj5XYDuj8eokJsA7gHYGwtPcjA5Jg0Gw3cIIzYNBoPBYHh68ZSIzlOL/BCK/kil8iooKYMg0SEsQkpLSeUzSkmpUBTzC7PRxWcu9Z+9elWunDqbK1abLuF6LkkmKu0nBKY/cTLJpJzWORajSZKo9v6e8/jRfd7vdZGkSaZnZisFPHe2hdOzJTj6buJ47GQex6b+ASnlXTQqPoo5OyuZ1amq9zcPsD8Q8KtzWDt7AeVK+ddGg35zVGp1PPv8i/A8H3EYZiK0c3iIMBhlYT2+58J2XTzcGyIIHyNJBZ47PYNq0QPTDqckE/GuPt9TqyaiM3M7J+KTEViOhbLnoFwpYHY6xupsGacXatg/GuGgHyKME1BCsNkeIAgSHA7CzNGkUJmbyRiBECoT+doB1QFNrsWzsmAdNnQ0jGDHKZJUotYNMhHKmcLjzV08bg+yES+t1h3ML57Il1unVt3G8jJzczGkCEFZG9TeAHcegvAbUPQDEPYQBEeTOa+BGa1iMHz7MWLTYDAYDIanEzch9Fkp4v9MSPITAjpHCPEzn0upSEkllFSu7VisWqmKleWV4NIzz0RnLz6DuaUV5Eo1QXSd5BgxEZXHo03CiUJkk+RZflxSe9hu808+ep89enCfUs6QL5Yh4hCn5sq4utZAvcS1JB0LIkKfeOL+AIJzkkjruwwzFR+taj77WifR9oYBgv02dre3MBoNnrpiTD+Xx7mLl2E7Dqaa07h98xPcvX0LB+092JaDaqWGTqeXpciGcZQ5i+eXprA8W0Gh4E7+DCB/j+2aOKHaISUSoFZWomv7NpoORbOe1zXXCMIU/X6I7iDA+v4gK1XWJbd6DEq7M4IuyebMQs7X7qb6tLxXBxRZloJU433vWRyePRag2wcjcMbx4YNDXLu1jTCKsTKzg9OP1tFsPUB9doVUqjUHlLiUkbJj+6tesTZ08tVN4pVvwi7dBmP742Ah8gCA7vfcN32eBsO3FyM2DQaDwWB4+rAhxZmYs78givyEKLVKVNbftiOl8iFFnlLml4oFOTvbik6unRLnzpyLVk+dVvXWfD5XqgrC+GgiIK2J2Iwn7qb+ejjxw3KT7wUTN8kJRkNrZ2uTba4/xnA4QrlaxUzRxtXTTSxM6VJV7Wp+BQFAn4dMaCkwztCs5DBTzWX9h8Pj0SeSZAFBelFZP+PT4Wwe4/oezj3zLKpTU5ienUM+X8Qn199HGidIBNGBSxAguLPZwU4nwJ2tI/zJhUVcWmuiUnbHpbHiidmbvwfZ85LIcdG1XjjJ+npt10Ep76AR+Wg1Sji7VMfuwSArs/3g3i429gfwbY5qycPRIMx6PqNQTHpmaRbcJKVCqiSGYYrHu310+3GWkKsF66O9buZ4dgYxNvb7qNzeUdXybVGtlpXt2MzllJZzDqZnpnMzsydWKq3lllWdfxFOLgG1+iDWPVD2FpR6C4TcBcix4zkyfZ4Gw7cHIzYNBoPBYHg60FaRSwCPQdWVTJ+jFFcATAEkAdQuIXSXKjGlKFkulov22trp+NKFi6PVtTXMzM2zcr3J3HzJIpRbT4w0ObYf48nXx+/96eRnWSCQdktTISxKCbUsB/1BHwf7bcy26nj29BxOz/tgTAsX9akI/FrQYsuimKrmUS/6n+bluLaVldMOBj30OodIkgQ82+ynC91HOju3kIUGNZpNlGtVfPDuO+j2erBsC43WLLqdLtqHB+j0ttAfJoiSBFdOT6Na8cG0e0zwTyfV/pOQSRXuJHT4+PfjJ8pzKWC7NmzPRqWax8JMCcutMuYaedx8eIhEKFBOQPd6iBKJOAkQxik4IdnYF6EkRpEEoQmIAnaOhmBEZbtMf21xin6YINjLXE/C+SF3LAbPsZFzxrNTF+e2sbK/R5e7+/nm/H7erzZBnDJg5ZfBnasg7BYY/wDMvgvC9TiVmxO3M5wc07F5/TIYnl6M2DQYDAaD4ZtHC74mgAsMcsEjqSdAXQUcgaCvshAgOcr5bsdyHeJYTn35xFLx+eevhqfWzgwrU9O0UKmV3ELJJ4Qeq5L8r4UA0cnnWmQWjsXt5GMpiiJ/89FDvv74Puv3e0hTBZsqrNYtXJz3UPDouHxWfM2mUvZwBI7DkPMtEEpACEW54MGNU6gkQPfoIAvh0T2STyv5fAnnL11BvlgCszjev/Ym8vk8mq1ZbG1uZv2cWnRef7iHRIgsCOm5U9OYbRZgOxxEly0/KTjZRFiLfyZT57eK1MkfDMR4zuY4IkploUC1ah4v+i5W5hrYOxxho92Bwy0sTpWwvtfDva2jLBhI6t8jBPqfHrejez8di8K2WNaDqltIdc+n0mNWhEQsxn+n0LM+tYBVEXA0GAKUIU1Sst8+wOLWOhZnZ9NifXpoF2qEecU6cQp14uTPUrvQgVvaA8iHAHSy7fZknIpeepNj3QQMGQxPGUZsGgwGg8HwzaIv9ZckxY8B+bKdiGIK0s8SOxUOoTAQQjqE0HKpVGYLiwt2ozmdLs4vDFZPrqE+02K5Uj1x/FxMCD1WW8duj5pcgJNJ+Ww6+dqalNAe18I6R51D54P338WND9/HMApRKOVxcjqH83Mu6t7kGv44tOa3Qp5IT/2ygvTJMt3x/Wkxk3MtFH0HgyBGmsrMNXRdB0rPkEzTbEQMId9sIu3vQguzpeWTwI8IfM9He38PSRQjHI0rQ7nFs+16uNuDUhvojmJcWWtidbaUzdKkn/bI6lrWSaZTNhYn/XIrJvHZ2NXsObUx7/molXOYrjoYhklWEru+20et4GWCU5fWalGcJDILC5JZta/KhKbu6/RdnrmXcaJLaSNEaQqmSLYfbR2CRBSGSYqdzghhmmb3+eG9XcxNrdOzK3P2wvxsaufKKbF9u1is1ApTrSrh9iK4dxKgVwF0Jg7nnYn4/GgiPIPJxphSW4PhKcCITYPBYDAYviH0Bbpr0UUL5C+DNP0zRsgJKESZyIQqSmBKSulRRkf1+pR76tRa4+zZs6o1O0NqtYaqNmbsQq3h2K6bAPQ45EdNxKaaCNnjktroie/LyZI1OaZJjEf37+HmJx/h1s0biEWK2WZN99NhrqHLZ7VEjX9H76B2xRjA+Pg26ktUNpJJ36WaBJGScTmoTkXlnKOYc7HfGeJgMIICQTWOMRgOMOgPshRYxp6uvs3fxsKJJXi+h+vvv4tf/N2/w+PHDzAcDjMhZrlulgS7343w/t0djMIIveE0zpxooFkvZWIQiQ5oSiZ7Uv72BzkW3V9U+GdHRgpQCd8G3Ho+ewzdnzlXK2Bpupj1mN7dPMLGfhe7RyN0BkrHGCONFAKpkMu5yFGazUhVukIb4z8MOBaHxTmCKMlmgWqRupdKdIdxNlYlTfq4t35EH2x33JX5PVUq+Cj4DhZadcwvrqAQdIlVmimiOL0E6ncBnATw2mR+5z9MBOcmgAeTWZ7Dr3znGQyGL4QRmwaDwWAwfAMQbWQxcj6V8s8TIbTQPA0oP1NuBGUlUVJKVbVrV61Vowvnz7HLly6T+YWFpFyt2YVKw85XKoRbrraJXHxWGMkn5bH/P3vv3STXkV8Lnsxry/uq9t4ADUs3Q46Go9Eq9EbSSm9X0nu7G7t/7zfciA1tSHoaTw4tCE94096VN/fezNzIvFlAEQMQ3SBoAOZhFJrdXb5uVefJc37nDPTPXD3fVtdUIqerThQra7aa2Lh/F7euX8X6vbvY2t5Ss3bp8SRmChZKWU/3P36deiYJIYcqZXykSh5XWIptnIrZChJXeQxDbWRIkFLECKRLOOJC2TV914JnURAWIgwH4Iy/EmRT3sdabRzeu++phyzVwMuffYpOpwnbdZBMppVd+LDbw+c3d3DQDtBnNn6WyiIrg4Pk8xwFz5idHVV2X2DOUyYJySNQvZbxbCYsCmoJZGSHZ8rFZDmN1ek8bqwf4Pr9QxUA1Oj00A244r5yplMSyUY7AJObBlzoMVGCUk5asl2sH7TQq0fo9UMEIYPnUghOEEQhevd3yfZBi8hjb7KUQadVx+7OLsnnr9LqxCTNTZ1OpMdWBlamFABWAsAKgCyAoeIpyecVrXpu6dOeUTsNDL57GLJpYGBgYGDwHYMCqYDQ9yNu/Qvh/OeCsEnZlqHm3whkqUkuYkw4lk0LpTyWl5btkydO0qmZaatYGePZUpUks3lYtsO1LZaOFF5a+u8704Sz/QTxlETTkZbTfr+PG1ev4NM//QGXL17Ag/WH4JxjulrEW8tVLE+kYdtD4vc8iJhwEjtW3x4Rz6OCaKWOjRAmXf0hCBzHQjHtQSaYShWMOTY820YY9DEYBIq0SYL2qkCqfvl8Ce+89z5yuZKqQvnkT3/A/sGeCjoqlSsQPIlOfQ+3Nw5B6X24lODNE1XkU67q0cSwkuQr0D94IVol4q0OBfJIVX5kZZYHmkWRzvqYTzoo5xKYr+Wx0RK8GTry7pCo18bdBxvk4q0t1Ft9BDxWNeVmQRRF6A4CuA5Rh4gt/5EVKkKARULdnGMR1aW6c9BGu9tHGDA0OwMQaweeTUmpcAvJ/HW3Or2QX1xcak3MLLRSpXEPbrYqK071cX8ewHsAbuvZTnmSs56facXTwMDgO4IhmwYGBgYGBt8RSByuWmUs+lsuxK8A/ASETAkBhwB9QtBnkYg4Z34ylaBzM7NsYWnRWphfYHPzC+1KbTKZLVVtP5OjlmWLkf7McETVJJpU2lrRbGrqkdOhQY6q/CcEW+sP8NlHH+DalUvotJoquKZcTOPcUgXnlyrI5fyYaB41FEiIkXG5FxSR+Ih9lnhxl6eIQMAwVkrhjcUK6q0eLt3dw2G7h04gsLOzi06nDSHYK7W0ka9BNpfHmTfeRCafQ7FSxkd/+C02NzbQajaQSqdhe0m0G3VcubOtrKos7OHETAm1UlqlyCqCz16kj/Opd0g/31yfhr8YuXJl2yWwLQuFbAKZlIcpmiVhssaJ4/GwsUevXfYlMSYPdpvYa/Rw2B6g0x+g1Y8QHgg02gN1U3IGV3Lb7iBEyLnaJbEo0eIqMAiFUnWbvUCl4Eqbse8dgon7NJO+7M2O1+js/Gx3anaezswvW5WpeSuRr3JieQmtdJb0RstAk88PAVzSSueuPrVewjNnYGDwDBiyaWBgYGBg8B1AGkCJ4DMc+C+ci3+gECdBSH6YFMs5Z2EUdjgTPJVOeyvLK+5bb78t5uam7XKpJgq1iV6uWAm8ZDpNqOVqVkdG/pazEf8qG1E7bU0+c/qrgiQz1+WM5pVLahG/euIEOq0GfHaIM7N51Ao6gIYd57kRihh+7e+P1M8pYiut7am0UkRx5ks65WOinEEu5SGS4TQRAxd97G5vYHPjIWYXl5HPea/c4ez5PpZXT6p6lMmpafzhN/+JOze/BAsjEGIhYELZVKXqzEWI9b0WVmdKOLVUQzbtBjCqXAAAIABJREFUq+7MuI/zKAr010DVrNjx3sXTiOYQw6Ao+RLZFjIeCJKcIOkDXgFuMElqaYLdRgeb+x3c2WrizlYd63tttDoBuv2umt0sZX1lhWZS2RyE4IJDyP5RKsksVYS01QtUVyjX1ulBxJWSvbN7SNY3dtxLN25ZlUJezM/NYHXtlDWzuMzLtXGk8zXbT+WKxEkw/X6QyucSgIeabO4AuK+ttncBbANyVtrAwOBlwpBNAwMDAwODbxmyIWIgwvOEiv8Kwf6KAtMCSBHAFyCWEEwwzqSsZBcKeSwuL5E3z7+RWD15gpYrNWTzVSddLPmOnwiI6txU6qQ90qFp6f8XI3ZajMxmuqNEM+j3ceWLC/j4wz9i/eE9ZHNFTE1P4+TCJKaTBaxWHbWwV3Oa4rgEZkhOniSVx7G46vPKoBqpVgrVoyEnN9HqC+w2+6pOQ84+SgK2u7ODzYcP0G21kc8VXsnDWc5Gjo1PIfPLPAqlCj796ANsPLiPjYcPEUWhqhoZcII7O33sNbZxd7eF7UYPp+eqmKnl4CZtQB5C0bF2B56AVqZlSJOc1eTR128QCK1mDzqS6FIEXfWTfMpCbraEuSiHTjfEXqOralNurjdwa6OOrXobzU6g7ypBwrMRhQydILZeO7BgW1A9nnGzi1DWaSBOvJVvqEiQuMMzaFh7+w083NzFnfvr1uz0F2KsVuHl2iSbmluyJ+dWgnRpvA/LdwjBPIBxPdc50Mr/tg4U+lLbbK9oxfMbMncDAwMYsmlgYGBgYPCtwwtDsRpi8M82If8EIeZBSE/SAiFgMcGobB3M5XLB2Ni4Nzc7Yy+vLGN+dl4Ua+M0XxoXqWzBdjzP0bUOPZ0662oiyUYIZviE4qnLGB+RUoWdrU386fe/xeWLF1E/PMBhvalmIc+/t4IzCzWkfR1Aw9gx1MjhvbD1HF40clf0dcjAXDFMmuUjF3oGWF//XACOB84tFUIj1S4p4lkqt4ij3qhjf28PvW5HkU8VavOKIpVK4/xbP0FtfAK3vryGT//0AXqdNvb2dmA5LoSVxF6rg73mHtZ3WrjxsI73Tk3h9EIZ+awPuHZMONlj9fHZz/ETGwPSwixrWokbk00csVZFWa07QNiTQ8dEjXtaFJ7jwvM9FAspzE4UcGqhh429tjrd32kqhVZWo0iLbBgytPoBwkhAyMNExMK85JaWfjkZ47GAK2JyrvKkKEXIIuw3u+j075OtrW2STfukkM/QpfkZcfLUObs4vWLbyTxy+RKKxZJvJzwZ6xvF8clYBPCWnuuUVttPAFzQqbYdHSzUeGUPKAOD7xmGbBoYGBgYGHwLiJfcqDGGtxgjf2UR/ktwMqfURiIC6QyMwlAwznmukGcn105Gb55/k05Ojtu5QikqlMqDdKEiUtkisRxn2CUynMcUmnDSx3LUo99haM19Gsvodjq4fOkCrl7+Av1eV9k0wQbIoImJVIR0yortmMPwmSP3VmpCKa9P2jElcfmKA5PHd0+FBx1lxlD82awgFRESHkXad+BIMkS4Ss7lUYRWo4FmqwXO2StNNiUcx8HM7DwKxSJy+YKy2X70we9xuL+HyA4RMY5BP0Sz1cdOo4vDzgDtdg9vrNRQKadBdY/lI8J5HCgle3T/gj7/Ooju+5SbE0LERFXNfurfy9qThIsxeSplcHIuwkGzi62DFnYPetg8kFbbBPztOvbrXZVOK6tu5GNQV02IstHGX/kjsinDq6gK1bJVqFA/YIiiHnpBRNq9PpFK9+bWDvGzn2fT2ayYn50mU5MzyOWziWwx38uWJkKSzNuAJ2eZJwBIW/s5nWZ7R2/sSMXzDzpsqI1jGssNDH7sMGTTwMDAwMDg5SMhBFbA+V+D41eU0jVAFETMx+pywSy48KhNSTqRZotzC/b5c+e902dOhZlsnrmJDEkXysxPZSJqKV1nGHKS0NZYSTS7w/oSzQacEdsseRrRHAwGKgzoT3/4Lba3t5BIJJBOJTFV8vDe2hgmC5ZUp4BIB8QcN9xVTYsyRQK/qoiSmPxItVOM/Oy5GDmPYCCcI2EL5JIuUgkntlS6QH8wQKN+iANJxsJIpbm+Dshkcjhz/k31OsmX46MPfoP6wQEGwUBxOhmjU2/3cXP9QL3o7V6AcwtlTE/k4acTMu41tkKzZ9hhqRMTRWVXFiMVNmGscD6XqI5U3DzalNBEXwyTcsXjrRBJQm2KhONhMuVivJJBrxfgoNnH5kEP97abuLd5gAc7dWzud9HoDhAxocj1sBmHqIRiAtul8F1b1d3Iw1V2k3L5O+kTEBRBBKzvdXB3u045brnFbBIbt0tcKseOl6DTk2Pk9Npqb2pphSUrsz07Ue2BqtAtSTqnNOmUD+YQwM8A/CeAq1rpbOpgoY6x2xoYfD0M2TQwMDAwMHh5ICqIh4l3GWP/i+Di55YlQ0mIL3UfEDQh0ICAn0wm/Wq1humZqXBpcQkLC/NWJlckSXnKFqiXTHnkMTmwniCWYiSBdvjzUWvtn0HWTty6cR2//vd/xdVLF8GYrAvxMFHO4udvzOH8iSqSSQGEwWPicSwM+e2TiubwXhN9F5/Kg58PdZ1cVWNkUx7y6QT6QagULxYF6HcbaDUOEYYh/ETitTmkHcfF8soahCBIppP49MM/YH39AQb9AUQkFCeU45K7HYaPb+1ht9HFG+0+Ti7IWd8MiGNrlXn0RRGx1dnyYmuz3GDgg5GAIF1j8xWb7VP6PIcEU4gnfvaUA0AM7bb8EfGUNSqpjI9UxsNkNYeTcyXsHVZw6+EBvri9i2sPDrDb6GEgFUvGEXIGzgQoEXBBVbAVH7k5y1IyKAYhUycZIhXIzQ/OEAQhGs22KseRKcC5dNq9du2qtbayxOdXzvLq4hlWHJvreslU07KsBKE0qzdvcjrV9oQOE9rTM5/X9YznplZAB0b1NDD4cxiyaWBgYGBg8I2hVrsEhIwJzn7BGPtHwcTfqARMtR4XUopS1faUEJpMpdjMzOzg3Jmz1sLCXFAsV3ixWKWpfDmVKpQs1/W5Jo1cn+T/+4/ZnLrBYZ+mP8IGnko0ZafmzvY2Pvv4T7jy+WcY9HvIZrLIJH0sTeWxNldBIZcAWE+TjBfpqySaqLAjhAq9QE+HtvRmkz7Gi2nc326iNxgoDpvwHXgOQTjooj/oIaNaL14f2K6Dk6fPIpvPo1gs47NPPsSdWzexs7kBwZh6amw/jTZnuHD3UKXANtsDnF+dQKmYhCUVTq5TZB9ZWxEr0MP5WXX02ICTjMlj2H76sfAVLjn6u2NsUDwinloRtYiy/mZyPjJZD7VCGmOFjEoevrPTRLszQKsb4qDVV0qunPGMuEAY8ZjbEqKOcUk+iZrjjcmp/MaxbFDbVmFD9Q5X1So2lXO/h2T7oG5/eXsTk5fuYHbhij23ctKZnF1qFUq1w2KlKnzfDy3bbhBCEppsTo94jGWS7QeagG7or3d06BAziqeBQQxDNg0MDAwMDL4RZCsgI8LCGAT9l4jx/5UJsUYoKZN4Uc65lN9AZN2Dn0wlrcmp6ej8ubPRqbWTvFSpkGQmLzKFKktm89yyHXtkOT8MAGIjIT/DhSzV1ln/eYN13XYb6/fv4ECGzLgOkpmc6jhcmszj7EIZtbQAkWE8jL0g0Rx2NA4VTM1IyHDmj7+gWjoCEc/+pZIO8mlPXZ0kIFIdyyUdZBMeBAsx6HYV8SBHnjV9NSAfjqxFcX/518iXykj87tfo93o42N/VI7AWCLHR6DWwX99DvdnDXqOD80tjmB7PIZ32QCxLz1Xq5yYaaJtrPB8ZEz8nJpnhk0Ry9LUe2mTFN3pJ46sWseAt/2Eq8QfJtIuTc0XUSgkctCTRHKDZDbBb70F2d95+2MDGYUcFBkmTuaUaemIirZpWHSoK2YSghJJ2b0CCiKnZTtumsoAovsucoRcxbOw3sd+6gbsPHtoXLnyeKlXHUBmfGczOLzvzC4tkcnaGFUuVpmU7ISEyOUl11UryWdCW2wOdaCstthf1V1mvsq+t7vzFdlcMDF4PGLJpYGBgYGDwzSDZVIUL/g+Ci/9TCLxNQRylZAqwONBEOMlkErVKlU1PT2NxadlaWpoPS5UyT2ULTqZYcVPZUkAoDR6zM4WRKNdHX+2RAKDRv+NPXfYHwQAP7t3BvTu31BmmZ+extbmBqHeIyZKLhaqHhDc6Y3fU5FnyeCYPOljmkZ1yZG39DFflsTEUjylRo38R5+iHHJac2XMJ+r0+Os0G+v2estYqS+VrBkmgK9Uazr31tuqhlJ7QKxcvoNNuo35YV6E5snu03WO4ev8Qh+0+Huy1cX55DGcXqhgrpUBkpQ0bBkCNiG/D2c1B64nMqSfwZOjTV75/0RdaX06SXmn5JQSWZ6PiZ1AuZSCkhTZiGAQR9ho9XL27j8/v7OLhTgOdXoj+IALvh+iHEQQTKOeT7MRsmUEI59q9fSKDg+TzZUkFVM5Lq4dL4cifSVWVCNQbLezu1a07d+9n3MTFtKwDmpmZxekz5ypn336nPjO30EllckzOAxNCAm1ZrwEoAigDmAXwCwAPAFzSFSrSantLE08Dgx8lDNk0MDAwMDB4QQghsozx05RYv+AMf8c5P0cAhxAiCKVCLp6pZZFUIoHpmRmsnVwLlxYWupVyyc7kck46X2GZQpUmMlmHUDpaXeLor6G+Z7YeeISeI/NHmMAzo1cZi3D31i18/MEfcfPLy5L5wnWT8B0HKcvBTNFFNmGBUJlAqxNMj9qrKef7VLIsezrJxHCW7xkznMdGPKoquzXTCQ/ppKsIBOMMrVYfET9Aq93W44CvH9EcQhLOQr6IN9/5KYqlCgqlEj74/W+xvbGuHrdlOciksyrBNiDAp3cbWN9rY++wg5+emMDUeB6+Zz82aX9llJMDUf9xouz3BaV2CnVky8eralRcC27KQzqbQKWQwsJkTs10Ptxu47DZxcZBBzv1DjqDSHiOJR8e6UYMIWPgMvpZaptixOyr/4n3QuJaHmpJl4Ig3U6HNOsN7G9v4eH9O/7t2zeyJ86cJ+MTk7RYLIvKWC3M5wvCdVzp/430ezOlg4Wk3fZNTTqvAfgTgM8BbOlgoba22hoY/ChgyKaBgYGBgcHxYQkeTRbSuZ+EEftVELGfAmISEI4AEZRSbtu2cGybZHNZMTc7x5ZXV8ni/LwoFYvC9X2RzOZJtjju+Kk00UTTGQn/GdaWRNpKK0bCR55Za/Ikup0ubl6/is8+/RDr9+/DcV1kUinUsg7eOzGOtbkSXNeNrZV/FiLzNSDaKnsUg+BRieYjC+7XXECmjjoWaoUkxospfOnbaHQZWoMQgg5AOVOWyteXasaQBCyVzmJ17bR6Tfv9AB/1f4v9vW1Fn0rjk5iZX8BgEOLLa1dwZ3Mr7iNtBTizUMHaXBHlQlom6sTJw19Roskx6m6ee09HQoReYIRRXUS/JThRj03ucWQyPlaSLsaKWRzI6pdWDw93W2qOd6veJa1eYD3ca9F2L6Q9qXYKqDoVqpVxixJ9qHNVSRoXg8owJksRdvmrgFhqs2ZrY4PsHx4mb375pVMul1m1WuPTs7N8amoShXKFl8tVFMo1z09nGSFU2maTOlAoNZJqe0crnA81Cb2p61W6IxtKBgavJQzZNDAwMDAwOCL0sjctuFgOw8571LLeBiFrAqJGQFzfd/uW7TosilwiBCkWC9Gp06eiU2tneLVWpcmEb3vJVCqVK4WZYoV5iZRH4uHCYTfFcEZzoMmnO6JcDr9aI6FBz4QQHJ1OG4NBD2EQoN/vq9qIRNbCqckM3liqIJtJxp2X/MmqkudA8BHL7UuQLYnuZQR/DuEVSp3K+B6myhmMFZLoDAJ0OUc3CLC7u4v9vV1Mzi0pEva6w7ZtLCyt4G9+9fdIeB4+/eiP2N/fU8+TDIEa9ANFvi3bxU6jj+bVDTzcb2G/3cW7JyYwXssDLo1VxOEM5suEmgMdVqF8zbFylI0God99LD6vJIyFfALZrI9pxrEyXcJuvYOtgzYe7nXIw90mOWz1sdd0sXPYQbcXqdAgOefJeRwkJK9j6AanOmSI6+t2XVt9H4Uc3VaHbPTuufX9bfHw3m0hO2pzuawolQpsenrWmppbtKuTc/2xqblOuVLt27ZMWlIkMtBW2yHp3NeVKbK78yMdLCTTbO8CaLz2B6zBjxKGbBoYGBgYGBwRDkGVWng3CvELIXCSMV4AoXI6s2XbtpNJZ4jjuKLVbjtxMf/sYG3tdDA7O2O5juNTz7fT+TLNFivM9ZN8pLpkyPZsvfQe1igkR/5Wfy25HIXsX9zcWMeXVy+j3WqhWK7isF6Hjz7WprN4Z6WMct7TnYqj46HHwYum1n71nqrrkJZNYuln4WtCinTIkOvZStmsFVJ4sNsGIRbanS5u3b6D27duYXnt7I+CbEITzuWTa8hkcxibmsKFTz/C/du3cP3qZZlMpZ5Jx/XQH/TRaHdQb/fQDUIQQfBz10K5nAVsrXCK6Ai3+Az8WQ3KkEA+5zhRSqoW78URm0O4DjWSM5fKOmzBdRNIZ2WFShar3QD19gAHrR4e7DTVqdUN1LynVD9lf6e0zDq2rW4+JqCa6jJpuKWwbUul3cqHIDO7XJXoy0mj2SQH9ToO97axt+nRB3fviMTnnyOTL9O5xVV2+twbvZm5+TCXy0V+MtVyHKetlU5ZoZLVm0gn9GkYLnRBz3lu6u7OjrbbGhi88jBk08DAwMDA4HmQ42MkmiM8+lsA/8g4ToBSJx7bFF0u5RAIKhjziIOoUq32ZRDQmVOnovHx8dB1Peomkla6WEE6V4oc1xtWmgz032L7iRnMp6XLHpnZNQ7r+OTDD/DB7/9TiUteKoNsLo+sqOPMfEXN7Uk7oQqK+TOMdis+DUOB9WU0O+jb4Lp78XnERGhVy6ZIJ1z1vQyOURZLQtBtN7G3u6UU3Wy+8BLu36sBGVozNTuHXLGI6Zk5/P7X/4FPPvwD9vZ3kfATSKSzoJaNoB+i2w9xd6uBD71NmdqK8ysC46WsIm3fSKh+atrwMHTq6xTLkXTb44KNKPIqOIrAT7rqVC6mEQ4iLIzn0eoFYEwo1fPj61u48fAAPV2f0ukH6HRZTMwV8dUOdaVycpU1TS0L1LYgo4W4+iyIyagQghwcHpLBzj4ovePeu30zd/vGlcTM/BKfmJji1fFJuzo+3iyXy51MNm/J/k69aSSJ56p2NMjTT7TNVtapHGoC+qUOGNp5XY5Tgx8nDNk0MDAwMDD4GhCChO+Tk5Th70LG/okLekoQeAQk0EtrRoQaSPMs13XGJia6S4uLncXFRatWqTheMsP8dIani2WezhWoZTmRUi3jxXUEQthIZ2ag7XdEBwEdO6UlCkPcufklLn7+CW5euwrXc1Gq1FQQ0JmZBawszcPyZOro4BnXMEIYhvZWMVSdxMjPopdTaYKhLffotSuEUnWzykIbhOpiuVQCmYQDFvTR7Xa+2f15RZHJZHHm/FvqazqTxSd/+iM6rSYS6QxC30On1UIYBYqg399pApcI2j2Ot1YizI2l4bj2SJPrd9jWwdmLs9z4eBTKG6uSbKmeaKZwPBtlP4UyMoBNMDuZx2Q5g5vrBypQaHO/jXtbTayHHCEPFLmUkPsXMtlXqaYOUQFCTKVKxym2MtWWcYp+KBAyoZwErgUSdRvOjUsXnBvXr4pcviSTg5PjU1PJ2fmF3uLSqqw8Iqlstu/7yVArnMN1eAXAslYzm/okyefvAHymFdCWJp7fQH42MPjuYcimgYGBgYHBMyA5DATeg43/6tjkb6LIWlIhJUROuQlHcE6ljdFP+yiVSnxldSVaWzspZiankMlmue361E/lUpliNUpksozGCZ/kCVbF9AKSjigd3hPzmkdCGEXYeHAfGw/vq5mzYqmE/f1dtHYfYnVtFu+9sYp8tQQ017Wa+JSrl8m0KvxH/57oMdKvJM5+j7WBsej0aPFvUQrbpfAdqmot5KxiFPx4M1ekpXRx5QT8ZBLlcgVXL1/E/t4eDvr92KUsFTsm0OpIhbONkO0glPUxrIL5sZxSBdWgp5rlPe7r/CLHhb7McFPjuNdBXSk9MiCwFOEUeq4TLH6XUW3lZQSO52B+uoixShoHzR7Wd1r48v4Brj84wMZhWwUNtXuhuljSj5fIsqNThitFw70WQtTz2A2iYY6tqlCRtnlptR20+5BW2739fTy8f9e5fuVirlQpJ+ZmF8Ti6qqYX1oli0srUbFSdWx5h2J3Q6j7O+X7PiMbigAs6dPPAdR1mq0knjf07GdPn47oPTYw+H5gyKaBgYGBgcEIhstdDhSpEL+EEP8cROQXFGJap1ZyKWxIh508XyqdxuzsLFZXVqLVEyeCWrXKHMexLMdjyXwhyuQrKS+dlRNfgfaJxjZZQvhIAm2oSSbRtSbuceVCSSL2d3Zw5eIX6muxUMZheR/txj7GMhbemMtgrmQDYRuIoqdbHoc8mIzUoHyFAJBYgRT8CHbb4+Lo10MUqaJIeC5yCS9ODx30EQ1i5dV1nJd0n15dTE7NIJ8vYGxyChc//QR3brsIwhBb6w9VYJQQEQiNsN3s44u7+wiiEO1ugOlqFumUh6TvqNCcx1bYo74yOD5hfGRfPcbc5iOSqg5++lSFXWU4jwQLSROBPG6SHiaTPsYKaczVsjg1V1J9pNJevL7bQm8QqpnOQcggQ4Y4C0GIgODxcyHIV29L/rzbC9ClFBETsG1XbYaEYYjDgwOrcXiQ2Lh/X1y/flksLa3Qc+ffxNKJNZErj9vpdMZNJJOBl0g04udbfT7IA1habk9pu+1AK5uyx/MLba/d1wFDMul21yieBj9UGLJpYGBgYGAwApvAcSgmIMSvOMG/EOAsIcjJgS3ZwSdTZkGJ49guyeYyYm52Fm+cPy+Wl5aRysi2AxLAdsN0oYxsuUZdLxFom+ww4Gd0JnNYaxJohcLX6sax2VIwGODB3Zu4cvEzbG1tKpWv06pjqpjEz9aqWJvJg4R1oMeHEs0T1zAMeGFPkEzxFDH2e1Q2hVB3PZ10UM76yKW9eO6uEyGfz2FyvIp8PvP93b8fEFLpDE6feQO12jh2d3dUYNSv//1f8eW1S8pOS7mN3iDCw9022u0udg7bmCxnMVnJYHWqiLFiSs11akn0OQ9sGPTzvEThpyBO5jnmEzc0sYdEDfIe5TblWYZVLxaB5VJUq1lUSyms9iMcNnvYr/ex1+pic6+DnWYH67ttbO63UG/3Ne8W6qaG461q1JUSRTJjEh/3vNpqztNW52dRhF5/QLY3t0jQ6yUaB7v2lYsXBol8iY5NTPnj41OYnp1rVcfGSDKZytqOI3QXp6dPgd6AOgtgTgcJyQ0qGT38sT7d0fOeXX1+A4MfBAzZNDAwMDAw0BCEZB1LnE8R8neDkPxjJLAWV5MQZllEeeaiKCQWtUltrMZOnTopTqyu8snJKZZMJh0I7jm+38lWap18qebbjkdHVEt3hERSvbruj9ScZEfCgo4Fxhi2tzdx/cplfHn9Ku7euYMwGGBurIA3fzqPn56eRDab0omjzwvhedLK+KzzvqTak+MiXs0jm/Lj+buNQ1XqH4QCSc9HwqHgUaCUXvLS+iJfXXgJX4UHydPM7DyajTrWH95HfX8PQtpnowiDgKPdjrDb6OLKvQPUimnsHXbw7slJTFTTcBz7sTr4rNwqoqtOHgUCHee5P456+uRFX0RlF7HiyR7PJSeTHpJpD5Njkr8y1Ft97Na7uLlxgM9v7uD2xiHa3VAl1PYCpuy1XG4/WUS9YaUSakmBVYYIcaHVdws2JQhkxYplK3dyp93B7Vt3nJs3b9vEskQuX6CFYtleWFzxF0+cILPzi874+KQM9OK26/bldejPkMEI6ZzTyqckoj/VYUIyzfYygGt63vNAX+5lJHkZGLwwDNk0MDAwMDCAWrRa1LPfbgf9/5sI/IpSUohVDAGpYqaSSTXBNhgMrHK1xN9683x09uw5VEqlASjhIWM0mcw4+eqYnS2UYTvecAU8lGwsTSqHMiHT1jgZClLQ9QjHZkcyRfPgYA8XPv4TLl74VJbQo9vtIpfycGq2iLeWayiXMnENxaNuzJeB70nd1H2QsoC/lEsg47uwHQvEotjc28enn19AZfYE3iyMwU8kvp/7+ANFKp3Cyok1rJw8hRtXLoNFDASSIDEEYYRuP0CTErS7AzX3yiKOn5ycwOxYDrZrg1C5WUHxqJxSjPAYMapoftck/xvc3rAzlg/tvFBpx6ViAoWMh0LGRTGXwPJEHtuHXWwddrC518ZBu4deyJWFNhIClAsdiktBqbTcxpsxTNeXDrdmQi4Q9Adqa8uihERdwhthx/psfzd/+fIXKJer1uzCIllaXeULyyfqYxNTwnPdLCFEdvJuE0IkgcxrIik3rSYBjAM4rVNsbwP4HMAner5zS29qMTPfafB9wJBNAwMDA4MfO+Q6MBVFwQkShn9B5ZwUJVJllOtQLbkIuXwUiUSCjY+P8VOn1nDq1ClSLBZVSojggqYyORSqEzydr/i2bee0milGgn6GquZgxOpGdTCI8wJykEK71cD1Sxdx984t9PsDxSk918ZsLaeI5lQtG58x+p5Ccwh5nOrz0ggqUd2aSddBynPg2TZsm6LV6eHug02sr2/gVL9nyOYTkH2bJ0+fk1HIKOQLuHzhMzSbTWX79DxPHT8ybTWVyoG7aVx52EQ/4ngriLBQyynrMpH9pZYPsAiIenrG0tIE9MnXlz6utPkenddHhtrI0IFChKhE2mo+hWImibXpklJ+H+y1sLHbxu5BB/utPtr9EE3Z6dnuoy/rVBiH71rIpFxVrdLqDdTP5DwmF3rmUzZ5WgQJjyKT8CRfJQfNQ7u9vUXu3bmJ61cv4vrlBffE6XOJxeVVUanW3HyxaBeLlWQqnQ4JJZwQMrTfB1rhrAIo61ChNwD8JYArWvWKjcLkAAAgAElEQVTc0iT0ht7gehVeDYPXBIZsGhgYGBj8mCFXlrMREb+kQfg3nGBJgGSJUCmPKRKDRxGTAicdq9X4uXPnwqWlRSedSsf6jkVpKl2gucpEL50rCNu2fZ0o2X3CPjtUM4dkE7pvzx+Z4zwWZADJ/Xv3cPmLz/Dg3l01t5nxfYznHLxzYhwrs0W4vhNbBsVwTPQ7Xmd+JcX2uHiKVVcranKxns+4KBUSyO/7qj+y0x2g1Wnj4OAA3U4buULxu32sP3BIS2a5WsW7P3sf+WwWru/h0qefKhup7TjY3d5StTJLJ9eQTvi4ee0ydq9t4bA7wDsrNaxNF1Eq++o88TElXyIHoHZ8aLNwZL9E1+RYTlyTo373EmpyvnXo400ft/KxOhaQsxOq23WimEZ3JkJ7EKInj7lBgI39Dm6sH+L2+qEKGJIXtWVCrR2Pkg6CCP0wUi4J33Jg2UQRUWkHb/UCK1LJz5Kk2uj0BmjUD7Hx4J4tWJi5d+OasH3fnpicweLKarVSHe/kC6V+qVqmmUzGVjPiMTz95FKtdMo6lXnd4Sk/hx4C+ADAR5p07hjSafBdwJBNAwMDA4MfJ+I17wo4+2dGyD+A4CQRwhcgoRDC4pCF7gSu47JUOsUnJias02fO0LW1NVsWb3LBhWU7VqpYRr40JpLpnKCWNRwgs/Sib7jCHhLJ4e+S+m9w8pv8LW4cHOLGtcu4d/c2djbX0Wq3kHUJ1maK+OnJCdTKhTi0RapPdKjcfAuv9uhs5FODWl6QaNJhwf6wh1M/jTyekcskPORTnlLmuE4j5apCNEAQmIyUp0GqdclkCmtn34DlxB2sW+vr2N/bRf3wQHu8KRrtDnbrbfQ6LaXOyflOhxK4roNslsfzmQqjqcWjRFIrhIJ9j/balwD9npHzv9KuLU/JlIeSbIZR/wEr3QAnp4u4vn6Iizd3cXP9EI1uoO3e8jmzEIRxj6wlZzwpBeORmv+UG0RywlMSU+gNAck9+4M+trc27DAKyWAQ4faNG7h543qiUCyKam2MzC8uY2pm3pmcmkYmlxsagdnIXGdC2/NTevPrjA4Y+isAV7XV9jMdLPSs0l0Dg28MQzYNDAwMDH6cEOIc5/y/E+BXAE5CiJRMcIVl+VLNkDNYju2Kcrks5hbm+qsrq1hYWvQTyaQtuOg7flLkilUnW6rZfioVEqLkHTKy4OMjabNsJJHWH1EzrRd97mWf5PXrl3Dh049x/85dNFt19bPyWA4LkwVMTeRhJRwgYLEKRV6ujfUr13Xc9NEjXbe2NMrez0ezgaO3I+D7tqro4JwhCEKUsilMz09jbGwM1LLVPKvuNjV4Ar6fwIm1MyiVKrj55VVc+OQjNOuH6PU6sicy7iuNIsg6yWYnwK3NpnouZQ/lmfkyxqpZwLPi0CkWPf3Ykq8bD7+dDY7h66pneL91DG+DxGRc7uFQbR92sxZSCQe1chpL4wVcu7ePGxt1bOxKoh6oQCHXZRgEQimclGliSUicVstk2BCNO1BFPDcq02uDQaAsuPJ16PV6fO9gP8pms/zkiRXLtyndfHDHvZIp0JUTpzAxMy2o3IERwnY9L0im0v0nPoNSanMNmAXwtiad0mb7oVY7h2m2JlDI4KXCkE0DAwMDgx8VCFAQQpyD4P/dct2/sRx7kgWh77gucoW8SKUzjMnkFM5IqVDG3Px8NDs/x2tjVZ7wfS4JqZdIimxlPMwWyrbnJyni+akheRyWWA5nNIUuX3+h/synQnDcv3cHX3z+KTYfPkCv10LQ78FzLMxOVLG0OI90Ng+wfmxffOmC0pAMisczmX+GF0mrffJ6+AiZGFVPVb6KUoFSCQ+2bcGiBAnfUQpRq9XA/v4eKrWaIlUGT4ec05ycnkE6k0EuX0C5WsP1Kxex+XAdzVZDkfVhEu3OYQet9gB79S66AcPbsDBRSYO4FKACCJ+RPfO89OMXhRhW9TzrGHte5+eIyn/U1OJH+zV85CHF5NC2beQyNtJJD+PlDE7Nd3B7o4472w1s7Lexvt9Us57Nbh8W5Iysq6yzkSSaXNJXrhRP2bvEBUfIOIJIqJtz1PENIqI+7za52N/eIr5Dcdho0k6n71z8/DPMLi6yfL5IbMdxHcdhtfGJaG5hkeYLxWEadqTnwxN6/Z/X851S7XxHd3fe1Krn3RGrv4HBN4IhmwYGBgYGPwpYFs0LIVYjJn5CCJHhGX9hWVaN2hYREYPjOJidnQ2WllcOqGWFgrNEpVh2xmpVJJJJKRpQGcfp+clBvjyOTKlKHdcL9N/S4dJzqCR4j32ESuV8WlnlC+Phgwf4429/jWsXvwCLBvA8B4gczI/n8NNT05ieqMXJoYM2wJnuP3yZICML9NGZzJGHR/T42FeqKZ6HJ0nl1wXLCNVvmPEd5DM+EgkXgyjC/fsPEXAb5doE5hYWDdl8DqQ9NF8o4tTZNzA+OYVypYrf/Nv/h73dLQz6XVBqKfFSKsf1SKAXhCpEqN2PcGKygMXpAvL5hBxSBNjTOja/Jevss2aBCYnLL+WxJ4998cTvMHIxghEGeYzaFIyQ3SFpJULbZCnyhSRyWR9j5TRW60Vs7ndwc/MQl+/u4fZmA0EQwbEpBiFTZFPObwomYHsUnkPV08i4ALfkh4uAY1nwXIs4NnXCkFnb29u8026IKAzoYbNDbt++jWtXL9JCoYRkKkUKpbI3OTVjbaw/IAtLJ6yJyckomUyFtmP39OdTqD+3CtpiOwOgrtNsr2ilU1apPNBdnr0Xfp0MfvQwZNPAwMDA4DUHIRHn04SIn0GIv+KCvOG4/jyltCxjILkUMeW2P2OwqY2JsYlwbGJsQCmljm1xm1KLMe4Rall+Ksty5VqQzhcd23GtkfoBR1tlI/23dTizOTqv+VL8nEEwwKUvPsOHv/sNdrc2kM0kVSrreKaA989M4I2FPNJOAAQh1PDXkRbRZKQj8XnQPQ7UBagT2yR58IzLvoi98YiXEXGZvkxIlXObruOg0Rmg3W5hEAlsbW5g0O+/wO3/OOG6LsbGJ+G966pDQVqTL128gHarqTi/ZdvghKDdC9RM4kGrh7ubdZw/bOPsYhUT5Qws1wZk8A37Lho2Rq3VTxzj4lnH/MjPLTe24rJAq/9HIptC1778+ZmH/aKRiMN5KUE26yMruzsrGSxM5rE6XcatzRZanS6CKMLOQQd7zR52Gz20O6Ga5xTqsyh+DyRcW1ltZW8njy22lrz2Zrsjut02XNtGFEaiPxiQQa9L93e2RTKTJsvklJNIJJ3bt26I61cu2XMLS6xQKLJMLs+rtTFRrlRJIpkcboRZunZJEs8JrXYOlc4vtcp5U6fZGuJpcGwYsmlgYGBg8HqCqI5M6a5cZoLJ+aRfCYg3LFgl23GpbVthr91xZJAMtS2ZIiuEsmzybDqZ7MvvB4M+GYQBtW2PpvMlK1uqIZXNQwcB0ZE5TDrCkp5smX9pf2ulhffB3Tu4eukCdrbWIZjsQnSQTVg4O1/CO6vjKGbdWNFkejFOjspxh+c7ysiWfnhysS6sZ6TGfgejXyS2zqY9Ry3M5eJbCI7uYICOJJ0DQzaPi0K5gnff/0sk0mm4iQQufvqJsiU7jgdZHclYiEanj0HE0Is4mBCKe0mCOlbJwPbsWEl/qsr5sjFSYokRW+0zj73h+Wm8USLngaX6iaPUAon4/CpwCyOhVU8B4497O2WoUsLBWMJFOZ/G6mwF/X5fzW7u7newcdBRNtvDZhfNboCtgy4291tqVlMe09LG3JPJt4MIQajtu4IQxgQCeR+IpSpt5OMKw4D0uh3U6/tSsMbW1oa4cW1gXbrwqZ1IJEUylY7Gxidkz6p18vRZMT45HSSTyaHKOUzKllaAU3q28x1dlXJnpLfzof7+wBBPg6PAkE0DAwMDg9cSgtlJYttvccp/ZVvWu5RYa1EU1RQN5SwKQ07CKFKKZjGfFXNzi/2FhYVI1gmwiDk8ioKIhcRyHDdTqFiFyjhJpDMOUf5UpWByPYNpj3xva9b28qckuVCps7/5H/+O61evwLFteL4LhwqM5T2cni+hVs7EBDDUoSxHnUWLb+F4d0jGvsrFrwqBeXJ287tpVJC35jk2MkkPmaSDRtuG7dhKlZOKXLvZ/E7ux+uGbL6Ad372cySSCRQKRdy8fhV7OztqjjPkHIRaEERNGaIdWLiz3UEwCDF7mMX8RB6FfArEpnF40LdKOElM/siTIUHPuM3R80hFk2ur7XPfJ1r1lwRVKqLSMSCPf2XVjZ4+lyr07Q3fG5Qom2w+IYmhpy4zXcliqTNQtT3dQYCdgy4++3IbHwuBnUZHXSXjDGHElOWWEqbnNyk4o+q1ECLunJWap02JChmSc9w7W5uCsUiEQYidzU0qhOwKJtRxHfH5Jx/RU+fO8zPn36ILyytscnJGJW5TaoV6VpPpGfNxfZrVs51S2dzUFluZZnsNwD1Z92uChQyeBUM2DQwMDAxeN8hYxzwL0u9S1/m/COc/dyw3a8toWbX7L6sEBg6Vc1AJH7l8ji8sLARvvvV2sLy0xFLJhCQrARci8hIpJ1Os2rliTfip9NAiO2qZ9fUqcxjA4Y10ar5UtNtNfPbxn/CH3/4nttcfIpNJw/UcVDMEp+dKmJPps45WlHRoydFxnEoUos8bxQtpBaoV1GFC6HHmNF8UQs0bupaFZMJV19ELojhsJWJoHh6ivr+viKecOzQ4HnzPxxtv/QSFYhkXPvkTfvfr/0Dj4ucI+z1QN6GsnfJQG0TAvZ0ObtzbQuG2j3NLVby9Oo7xalYRo7iP81vgIZLISfLn+DFhjPrx7Rxlg0Wpn4FWH8XzL6MOZRJ3iqr5Z5VaDRA7ts3yp1W/PHF7Su3UxFadKCzXQsFLoqB/NjseoJBJIJ/xcHOzjnp7gGanr+Y5JbFkkQCR10P1zdHhzpaInw75HhRAv9slkqTKaiaLUiE7P0PGwKKQ9Js9+Xt2sLvLvrx8yZpbXrHOnH2DLK2sIl8o8Ww+30um0nu2bWf1ZtqwSmVen6T6+RaAXwD4FMDH2m77UM99Bvqz0HR4GigYsmlgYGBg8DrBFRDzVhT9TMD7n0H4X9qWVZakJAqjIIoiIWsGXNflpVJJVKpVOjY23pufm2svLC5auWzaDcNQcAh4iZSXL495uXINjuezkZ37YfAP1wsvS5NPjPz+pZJNeZ/XH9zD5oN7GHRaam6z3SEopjJYm6ngzeUaitnE44X9N1nmkRE74teCxgtuRT7Z48t+FzUUI5BqDhFCVXK0u4M4vdOJQ3/DKIAMFqauIZsvAst2sLi8ikQyhXanjWbjAOv37iEMIgx6AzTVscmlqxODXh8PD/s47EVodAK8d3ICCzMluKoehRyh4/UF0ovJUNnEMd9y4vHs8XFui0cjSbTkMeM7zs3y4W0/VjyHnxiub2N1toRyPonz9Y4KFtrYbeLBbgsP9trYPeigMwjU/KacCZXptXLOU3Jd2dkp7458S8qwLCEECZjqCiaO6wrKOezIge9zUEH6waAX3r19y93YeODduHbFml9YwvTsHB8fn/SmZueDyZmZMJcvWrZth4SQcKS7c3TGUwYLvQlgXRPO61rplMFCuwD6RvE0MGTTwMDAwOA1AUkC5AwD+2sq+H8hlJ21LDsnU2alotnv99zeYAA/keBTk5PB2tqamJ2b8yq1sUEul23Ytp3o9Xuya9P1U1lRqEwgW6rKeahIPz9DxjLs0JSrzI4mmgl9eumqZhiGeHDvLq5c+ByNgx34vgtiWbBEgIVqAm+v1jA5louVxeglB7MMF/OKKDxx3fL27KS27XaBKHhKQu23DC0mUULg2RSppAuPA4lUBulsFrLORnwXs6OvOcYnJvEXv/grJPwEvvjsY9y5cQP7e7vodjtqbyNbLCGfyqLdbuDhXlyR0u1H6rVZnCnCkcFBbKgAjh5D+i2lEomOedwQraAPFc3jksfjqP9EE2E+IBBSTtQB1KpflOlAoiNszgxVTR49dhMw8ZWzSLWzWs2gKjs7pwI0Zd1Mo4cH201cv7+Pm1t17Df76nMhiGIbraxQkV+ZrKqhFK60kguOSAxdCPqRxh2hcj5dfobwKGKi3+vi7q0bONzbwfr9e3Y6k82mM1l/9fRpcersebc2NulmsrleMplqWRaVxJOOpG5Xtc1W6BnOe5p0XgDwmf7+QM99Rs96ZgxebxiyaWBgYGDwikMQAZLl1H9HCPsfhBW+T4S9BIisiPvvIotSKi1khFJMTk6yc+fPi7Nnz6JUKkaW68kVmRMFIRGCMD+ZJYXqBM2XapHluEM72OgcJtV/P4UmnsGIffal/l2VJPne7dv4/W/+DVe/+BzNg100Gw04lOPEdBHvn5nC0lQFsoTvmT2Hx7/Rxwtn1VzvxERT1UiIx7UnowroMIlT8e5vqeriafdTKTxALu1ivJhCKICAW4hgIwhlMm1bVXa4nv/d3KfXFNIZsLh8AqVSRSmdFz77WFm679y8qYheKuEjlU6j322h0Y/Q74X46PqWNHciCCPMT5TURoC0rsfzjvo4ofrtIiJFmL6aIvs1/ZnKLsr0vHAwcsw+59gbkts/s3kfcZNEjPa+YmQD5jlmBvk4Le+xC0Dd/+jPXQCSyDI8Uj5934Gf9BT5XJkuYHW6iOv3D3B/t4WdwzY299tKRe6HXGZuq/lzeVFpuaWSVsYGW2V5lnyextlFJBCRTzn35P2xLZvKGVAKIetUyN7+TrLX7SVv37zKL1/4HHNLy2RufonMzi/UJyYneTqTy8ikbkLI8AkfVj9JtTM5MuN5Ss93SsL5he7vbD8RoGbwI4AhmwYGBgYGrzQ4UAso3ucQ/2QJ/r5lu9WIRQ5nHJypHjuSTCWjfDGPbDYXLS0vszOnT9vlSlka2AZRMOBRFBUsatN0thDmymOdbKlkW5Y70DGVrj4N1UxP/7ylF1kpvch66V7Ng4N9XPj0Q3z0h99hZ3MdNmWqVmKqnMY7Jydxcq4C4tvAIHx59lUy5NUjabaUxkSSaXFimPgpFU3o/z9y6u3LhUUt5FIeCmkPrZCDER8HrT4O9/ews72N/qCHdCbzvdy31w35Ygnn3voJJmdnMTU9i9/9x7/h5pfXVOpvxCL0uj1wFkoCg+16Fx9e3kS3F+L0Qhers0VM13JwPffxRN8wSfYRgSPP531UK4tME7ZH+DqiOdw8IfptGo1cln71PF+L4QYLPxq5fSS9u4CbjH/Atdc96FNEXfpINX1EvqmuE4oeVxJZBLbvYHaygGohgZ1GF+s7bWzstbF1KOtT+kpJbvcH2JZW234Ix5JXHW8E8RGOrK5RCEvOdAq9keC7rgraCvpd9LsDFUq0s71Fd3f3cPfOLUhr7eTsbGb15BpbWFoVhUJR5PIFmkjKUCEaatWyOeLykPUpYyM9w7K789+04rmlz9sypPPHAUM2DQwMDAxeWQiQqQj4+wj4PyjE25ZtpRzHprJYvasWvkzIUzqdDlZPnIjm5ub5WG2M53IZJriwIs5CzljfonY6mcu5hcpEP50rDahlM71I6ukF09AeO1wcsRFFs/Bt2GejMMSN61dw5eIFHBwcIOJcCZhSxVubLeHkrCxw9+JOw5c5J0l02A/XfYVEL/Aj+sT6WsSL4uFlvmsM1VSbqmRealEkPRn65KmS/IMOQ7PZQK/b/e7v22sMy5I2z3H87P1fIl8o4OMPP8Dd2zew8fCBeq4FZ2qmUFCKZj/C5fuH2K73cG+7oTZIVufGVLiVUjIDTaoEe/z2+tpDWTyuL1EqqU5FPhKGM5YvKyH3iG93NZcp50odAccRasiVhRxWRMDsx+5SMkJih7Ogw0+ciGtBlyKZ8THjuyhnk1iZLSnL8m6ji3q7j516F5fv7uP2el1Vq6hQIcHVU8S13VzusFF93/kjrk8QhhGRGwZSHaUy7Va6BMJANA73Sa/TcbY21rP3bt1kpVKFFUpFOre0QpZXToRjE5NRJpOXKbd6R0p9Fib0I5Lfy52eaR0udEnPcz7QabY3TZLt6w9DNg0MDAwMXkXIosuTAtbfO9T5J2KzNznjrqrCI0TNNMoQIALOisV8ML8w31k7cTIYn5xIOI4jywKaEQudKAp9x3btdL7cy1XGSCqT96hM3IgXSbZWNCOtZCb08yS0PJLXv3/pRFPaZ5uNOm5eu4KdrQ1EUaRCWaIgwlypiLPzY5gbK6iQkLhe4mXc6qhqo9d+VM+ZKQvtUxb135OaGd92PI8mv8jQU9exkE0kkEwlUG8F6Pea2N/ZwcHeHiYmpxVJMnhZTz1BKpPF+bd+iompWXz28Qf4H//2r9ja3MAgCMAdG4lkWs1yyuPp3m4L63tNrO+28G4zwk/Pr6FSTAC8DgTBcW45lunk29M+VrpP/EWRWvbVn31r4tqI3VZWxbhpQtyEnDElCDoU0ejxqGcrRfj4e9UDqudChbYYM67rPgnSaR8pQlAqpDA1nkM4CLHf7GGsmEY1n8LdzUPstXoIwzg4qC9DncJ4I0B9Ro5kgcmkWi44UTUqNH5Py88WjziSG3M5mzvo92izcWjfuHqZWrZNK2NjYnp2ni0ur7LVk6fI3NJyolyuUsdxxUgVlLY+IAdgDcCk3qRraOL5obbYXtOBQgavIQzZNDAwMDB4haDq49OAeIcS/DOxnb91bWeWhqHT530IMCFHL+XKLJfLhZVqhc3PzbLV1RNifHyCurZtMxYp4YULQRzb99P5klWsTdQT6YzsDyxotTLQ/jpXJyrqJJBHVtlh5clLT56NQdBqt1E/2Ee33UK304JgEWZrOby5OqFUTT/pPO7xe5kYLcWXC9woeKwGHau389sEeayEEYF0ykE26eP+7iHu7bSxc9hCt8fQVQmqdaUSG7L58iEDmCanZyAzY5r1BlqNQ9y7c0dtjriui3Q6gygKUW80cdjuotePEAhbWZ3PzpcwlgESjohJ1SNt62mzlE+CaeL1Agm23xmG9z1WK9XGELGIsH2LUFuADQSCJ5VcbV9/NBP9lMc/DFKSKczg6hwyEMh1LSSSrqpOma5kcGsjj/s7DQSBFFQpDho93N1uKvVT2mzlHKdMcrbt2DbP9dwr1QKwJWuFbBpHbkecMOUSiUQYRUT0e+jf7pGNBw/cL69e4hc//4Qsrpywl5ZX3enZhX51bLybyeaZ6zoJzTWYHjeo6RdMKp3LAH6i61P+qL9e112eBq8RDNk0MDAwMHglIMBBqVOjtvcrDvHfwNh7LIrKA2kVk/OZnMtFLpEjRJlMViwuLpCTaycxMz0d5fIFx7EdP2IRZYzJmaWs5yfcbKHiSEXTT2V8EhOpSBNKPTylFkbDxdLQTkuP13dwfLTbLezvbCPo9xBEAcLBQM1qvXNqCj89M41SOaW7+17mQvspi3w+VIKOMp/2XUKMEA2CTDKBQtZHs9PDtXu7qm/TdROIwoGqieHcuPS+TdTGxvH+L/8neL6Hj/74O9y8fl3NcDYODxHJ+hkWwfM8OIkEmr0In16+iZ2thzg7X8aZ+TKSaS+eTWQjc8IqRIc/21o7GmT1g8DovOnojKj8VOkB3V3A6YBQD6qvhEUkfnxPvq+0XX00sfappHPkmCZcpzJTZDI+lpMuJsoZHMjUWs7hWhSt3gB3Nhu4dGcPV+7tY/OgjYhxOESGBFGEcoaTx5ZaEZd2Sns6kXtZEQPhggv5Gek4DlEOesYQDAZka3PDOtjfw+2bN8Wn5Q/d2viYmJqZC+bnl+nk1IxfrtWsYqkceb4/0J+lw77iNIA5/X1Fd3fKmc5PdKjQjh5jMHjFYcimgYGBgcGrAeEvCsH+OeLh/w6B04xxLwgiTUJJ7G21bRQLBb60vMTePP8GmZ2dFalMesAFrCiSoUExO3P9hJcrVa1cZTzwk3LNoyyyga4ysUcWRX29ok1pldP6NlmX7NPc2ljHhU8/xo0rl3D/3h20mm1lEV2YKOH0XFUtIuUIGMLoO1hr/5DzO/ij3kLq2KjkksgmHDAu0B9E4Lyvknsb9UOVTJtQGU4G3wYs28L03DxS2TxqE5P48Le/xuVLF7AtbbW9vgoFcj0Pvu+pecDdwxZ2Dw7Q6vaVsnZqvgY/5QCyzpFJX3RCV/n0H88Ff+VtN9xo+CFtgnyNLVe6A1gdGDTl7KaeiQ7+/OKjFz1yCNFIIrRWRKWKn80kkM1lVAcq4fL9IFOBC1idKmJhfBtf3N7Bwz3pAIgQSjb5qAZU6ARbgkHEtHdADItlYgMuJcqK61q23FSQFSqoHx7w+sE+uX3jupPJfJatjo3z2ti4PTY5TZdPnBycPH02mJicjuxYSg31A+vrz9RJHSokOzvf17baS3qmc0un2obPfyIMfogwZNPAwMDA4AcLvfZKEcLPQ9j/jTH2DxDREtVl8oxHan1lWZ5IpVKiOlYTy0vL0crKcjgxPkGTiURsmeW8IxgLCOB5yZSdrdS6uVKNe15yqFSykRMdmTdqacvsMAToW0W9vocP//g7/Pv/+/9g/f5dhFEEh3CszlbwzqlZzI/lYFPZLfi8IJWXhW9rIf/k9X6DB6Mb/woyOKWaxc3NQ9U/GIYRDg8PVHCNJJ25XOGb3mmDrwOhKJZKePvdv0ClXMXE9Cw++uD3uP3lNcgux0F/oEgRCx3YMqjGsXBnuwvx+QP0AoG1xRqKuZT0hOoKkEBXm2j7NsEPUGEfxfNqU5hM/QLEQPtURwwSZDQR9yg24qdAqcF2fN2yb1el2Mbztapd07KQyjhYSbio5pM4NVvC7c06bm40cH+7iYNWD51egO4gQhAJFSzUD7iml/Hz/uieyQ/V2FYvOzsDSojUWS0WRRZnzOq1W+7Dez3x4N4dYjsu+eLzT7xzb7wtTp97I6qNj/uFQpHn8sWu43qBZdG0ftC+Vjgl8VzRSueettb+HsBFrXYavGIwZNPAwMDA4IcJIYgtxDgT5P0gCh8CeMYAACAASURBVP43xtkvLJuWHduV0fyCRIzIsvikn+TZXI5PT02z1RMn+PzCHCvkC4xaVp9xTjnnNiGkbdlO5/9n70242zrSa9FddWbMAElwJiWKmmVZdruHpJPOcG9yk3RWJ7k3K2+9+97vfFPndqe70223PMm2ZA0UNXCeQMzAGave+uockBAtWZKtgbLPXks2SQAHhYMz1K79fXtbTqZYGBm3C6PVjmFYUWJcMVhhN5MIE3OIcLJE9bRe9j4KAh/LS3fw4fu/Uy60/W4XTNNx8cQY/uLtefzowiwqBROQ/ovv03wuDKkoxwYSGUtHpWijkLFgmR4o+qbf62BnexP1Wg2zcyeO0Xi/u7AtG2cvvoXq5DTmTy7gd7/6JT6/9hF2d3bQ6XYhZBalUhlO1kK/5+Lj21vYrvdQ63h47+ICJsYy0KjIIHLjY4wiQ1TJuD9ExERMrMjMRkSHPcavFU9bAGKHpPIrp84znM+DSCL5dXm64vAfbTLwDzfPksc5Q6lC30EWp6YruLDXxsOtFtZ229iotbG601Z9nX0/QBgmmuZBR0FCXqVUlyBJDtkajyRDCPpR07R8zkbONlThRb3ViRqdNut2O6y2s23f+OxTMT41pS+cOkURKt1CsYxsPl+wbUdatiNy+by0LIsnDrbU03kFwA8BXALwAYAPE7VzL1U63xykZDNFihQpUhw7MI2ZzNDP8SD4hQzFPwZe8E4YCdO2bSrDi2gCZNkWd5yMrI6ORbOzs3Lx1KKYm5sXTtZWYXoiinwhZSTCiGmGYWfyRVkanZC5UgWabgzKYQf/H2RpOkPusmZCRvnLllOop3D14QN8/MEHWLm/DMvUYbAs/CjCxGgOF+ZKqJbMeML3UqdYA4Xl6ybvSfbft8KLNjUCTFOHberKrCbOFZTwXQ+N/TqajX01aWbHVhX77qFcqeAnP/2ZUpTLI6P45Or72FhfVb2b5cooMtkMRCFCd1Xgi+UdtHt9dNwAf3JhDieqNshDB9wBtGzcv6giUuRhvamK5NETMfBxJafH0TyIPf5K8rVRLIMYFC0JExGP/1yq4fLIeXvQ18pitZjO62jQ78wV4V90DGU81uzFGZ23V/Zxd6OBzVobu80+vIDiUKRys6VKCyY5NM6QsXUYOmdeEFl+KDQhhUamQrahI2ubrO8FMHQtsGw7YmD2/t6uvrezrd29cxPLS7f49PTnjuNkRGlkxCiVRpDN5VGdnMTcyQUxOjJqZ7I56hHNJK7fE0mJ7cDB9iMANxPS6X7bbyXFy0VKNlOkSJEixfHAwIBRynIUiR8IXf/vfdf7h0jKeZrEUPeQ5/alJyVZ76NYLIrJyalgYWEhOHv6tJwYnzQ0QzPIACgSsdMIAwJNN4JMvpgpjU1k86VRxmNbUiuZhQ3KaDND5HOAo7+/FBAp2t7awPu//TWuXX1fRR2cOTGlHDwD38fsWAEFx4x7vF5knuZXwA7Vyie6fA4iGXAYyfC6kewTXddQytooZUnZ5Oi75MEiVPZjp9VW/bC6lk57XiWodPP8pbeRLxYxPjGBj6++j7WHD+F7LqZnZzG3cArZYhEffvA+ltf21WIAo1LTc+OYqhZgZ+w45Gh48eOgXTM6dEo+eABHfn7RhFMMqfqvauFiyKyLfRNTJHZ4bg8Ip6qMiJIcXQbDNjDq6BjNO5ioZHFufhQPtuq4u95Ap+8rorlZ62C70YXvR0QyMTmSQylvY2O3pe+1+urEMnVNDbHnhvD8kLZODuARY0wKyu4kM7cwwO7WJt/f3c0KIWQml8dIpSJ13YDlZDA9N2ecO38+c/7S22Jyep6Zlqlpmm4mamc5MRX6YUI8P0miU7YT0vl1sm+K14T0qpsiRYoUKV471DwoYmYUsukw8P+2UW/8S7vT/ZFlm2XHskWPc0T9LnP7HvN9H47jIJvPsbn52WBxcbE9PjkpDNPI+YGfEUJZqAbKxl83kC+N9kuj40amUCpwTRuolHoyawsSIyDjZeRlPgu67TY+++gqfv+bX2N9dRUT42VUR/LImQyjeR3vnZ1EoWB/tZ3rEbyASfXw/PlJ7p9UykfzPlJZRG+opO81K4ZCZTVgvJzBwmQZq7UuWj0fmmEim8vCpD5A1VX2eof5fQRlQs6dOIlSuYyZ+ZP47a9+iRuffoLA85HL5jA6Nop8IY9up4XN/T4+XNpFs+fj8vwIzp+IUBqNlKkQuEyMkQdKHb6GW8hDQijxjOfGoFT0cQ6xQ8c/ex2KqRzK7Rwe25N6PR+3icc8RybbpI9MmSc65XZmMFLKYGYsi8WZCpFGkEr5YKuJ2yt1bNe7KjalWnJgGDr2KHaFrs+M1E5TmbURQfWDkIMxS2PUosuYxjUwpiESunpLz3MRBBHzvABur8Poqk3Oxffu3MDyl5/J2198zi+8/S5OLJyS49WqzBVLkW5aEeMaWRdfBPAWgL8E8GmidH4J4AGAVlpie7yQks0UKVKkSPG6wRgTheY++2Hoe38P3v3rXt+94HuuWcjnpWEYQd91NSFA/ZdM0zRZqVTkqRML4dkzZzA1NW7quiaCIGAiUnkAZJ8T6Lqu5QoVo1ydtDL5omBcC5LSWDE0S42G8jNfi/vI+vpDfP7JR1h9cA9u4KHZ6uDBSoTpkRzeOzOBU7MVWKYWE6rHio1awtbp8fCbfwT1+q8RBliSATjopXvW91LPPTqBH/75BUzck01Q+bFjaZCRBDlk5gsORseqqmyT1PAUrw+FYglX3nkP1HMdBiF2Ntdw7ZOraLdaKgfVMg24QYi7azXUGi52WwGafoR3whDj40VwWieKhgnTU0jf80SjMHbYE0mXEDY41wZ5swl5tYqAZgJ+Gwj6h699ZTjyXgcRMdFTejmfYV8M+sB5/JkKeRv5jBXTbyEU8bw4P4b1vRZ2my6aHQ87zZ5qm9WoYiASKkrFCyT6Xqh+Vo+x+Noa72KuOC0pqlTuTi9jLC53F0l3KJHQ9ZVVtr21zW58+ZlcXDiJS+fPRicXTrGxyTmtUB6TZq4Ycc0sM86nAJwF8FeJkdB/JmW2y+S3liwkHmdL7e8F0itvihQpUqR4rdA0faTd2vr5xsbW//R870dChHnP8zTKfnPdHut0I6PT7XEqvTMt05+Zme299dZb/vnz5zAxXiVPCiMMQ11KSQEoNC+iHk0rWyrrpbFJ4eQLDuMalVh5BwnrcX6bTLLe7CFToFcKys/cXF/Dg4f30Ok0qZcUvh8AwsTJ8TxOTZWU8c3j54oJN47D4pOetpc5+mRyHwXPSWip985MXh4l+YDDvP5FKUUMfhih1XPh+n7snSmkKtns93vKndY0zRfwPim+KTTDwNnzF5V5029/8//h5ufXsL29Tee5KrklxcwLItR7LpY2Guh5AeoNF++dC7AwU1ZKmnJaPehjHCySPK3HeFAeLh79e/zHZHNJiThRIzrG1WLK4CkyJplOCSBhLfKAoHdkO68D8utL6weLUI/FMGkfgkj2CWdgGo9bYzWGcsFBMWvhxGQRzY6P3UYX2/UONvcLeLjdUqZC5GZb73rwo1CZCxkaJwMhIp0sIlOhQCTu4Rw5x4BtGfC8UL0u/jtT7099ol3qt263WLu+z/a3N8TyzRsoj4xkSyNjoxNzJ/rzpy9G1akTEdeMUiREiXM+xYBZAJeTEtuPEwK6+5Qm9BQvGSnZTJEiRYoUrwUszoNb6PWC/9Fstv+3vtu95LqeGYahynUjgtJstam0ipmmLSYnJjA/f8I7e+G8e/LkyahcLGm0jSgSTAhpSkSc4uEMw2K5yqherk7pdjbvca56NPUjpVVBomjmkv7N11JgWW/UsLGxjl6noyZ9RLDLeQvvnZvATy5MolJ0ksSHJ/RPIunleq7MwW9K7r6h8Ksm8gMnTXHkgReF2DDG0DWYhq72I5Ve9nt9rNy/j6VbX+L0ufOoTky9wPdM8U1gOTbOXboE3dChQUP06VXUanvwvXgtSDMttSBRb7TRanWxtdvE2m4Df/b2Cbx7bgJOVgeCKFHi+DMcR88SEyQTRVMkp8cRE56BOu81k/xPb8jshx3hMs8TV8KecXxPGrb4eh71JKJJn4EnMSmDHNOjlwUhHzECY0mealbX4NgGRkoOTk6VVKbtVq2DnWYf240O7m82sLzRwG7DVeqmpXPoGkPPj+D5gcrnpPOzks9gejSH7UYPrh+AIpCJ2kZJCbSu60pRdV0PO9s72v5ejUVqvUIvjVcns5euLEWnz1/iplOMwHVtdHQ8U52cXtQMvQrgbQA/A3ADwB+Svs61b7iXU3xLpGQzRYoUKVK8cui6VthvNC52Op2/r9Xr/9LqtM+FYaQrcYFKrXQuDMOQvU6Xc87l7NxsePHCBXKcRXV83HIcW5cSPIqiUEoZMSYCKaSmmxYKlTFeqk5zO5OjbcnDJMaDXs1B32aUqJqvhWiuPLyH3//2N/jo/T+g224ik8nAsQzMV3P40ZkJzE+VlOlN7D6Z4KAPbehvUj6HL8bAKATfgnB+A4hEJXpZhkIy3g9Zx8TsaA6To1nstbpotlzs7Gzj4f1l7O3spGTzmMB2HJy5cBF2xsFotYoP//ifuH3zhsrjNC0LnGuq1LYXRWh3++i5PrhuIpcxcHamBDtrHBLEZzLNemwT8pFfo/hcY0cXRZLzjhZ1+s3Dlw6XzzLtcMHnWU8RllQkHPSevqrzUR7GsAziUA42FX31qcOIRFJMwVRpv2VpyOdtlIsOFsiMq+9hp9bBve0W7m82sbHXRrPro9Xz0O4H8MIIBtcghQS11nddUjADhJS/Ong/KZRvtBRqMRKlnIVy3mCtboC9/Q5VLRhb2/v69u6uXLp1UxSLJZh2lo1NTLGTp8/k5+cXMsXRiYpuOSeT6JTLSWTKtcTB9n5qJPRqkZLNFClSpEjxykA1r4yzM/v15t94W3t/WW82r9R2d2Y77aZOq+AiitQkLGM55EIoTMOIpiam+OXLl/ni2dNyZKSiG1zjkRC6iKQWm84Kj2ncdbIFs1AZtQuVcViZXBJmrhAkM6pBmewg4kS8LqK5t7eD//yPX+Hf/+//CzvrqzAMoJCzMTWSww/PzWBxdgw6lXxGj5v8fRtFUL6AbXyTt33Zc7u4ZNa0NIyP5FRovalpqizP4BK9dhM729s4K5Is/RSvHVTSfGJhEfl8AblCHoZhYvnOLXQ7VM0QUJauEtao9DZiJlZrPfz2s1Vs7zZxebGqIoGgFmOeh3Q+7TBK3GYfp06yob7Gg4dlrNpTRItavnIBOSgz/xqFc0A08Zy9pd8EX1lcGu7vHv5wzziGg83Ea3ZUrm4YGgxLR84xUS5kMTdZxtsLfWzX21jf7WF1t4UHWw1s1rtodQJV7l5vu2i6Prr9EH4g1blqmxpMbqDnh3C9SH23dImnx+k1VIpL+mff89nK2gbb29vj+awNy7KRyeb47ZufyYVTp7X5hdNs/uQCr1SnCnauQtUri0lf5y0Av0/6OlPS+YqQks0UKVKkSPHSQcSPMVbc39v9mfCDX2zs7/95r9ub7XY6GXKXpfgPmmCGYSRN05S2bcliuYzR0ZHo/Nnz4fz8CZ4v5jXOOAsjEUmpSmd1ynajaZLjZFEem0KhUuW6aTHlOhFPi/TEZXY4EZ4nJPO1EE3PdXHj2jV8+uEf0dhZRcaMkCWFp5TBj89P4ofnJ+HkMzHRFEcngC9iUvod9MtIlE2mc+QzBnKWEZfr6RqKeRsW91HfWUOruYdyefQYDDgFVIWDjvHJKfzJn/8VRscm8OUXn+LWl1/g3t07qO3uKiqTyThKAW12A3x8exv312rYaXTx4wtTWJiqQLOpj5MBoXgBZj1PIYhaomAOuzAPqsuJdEb86dvBwHBo0G/5ktsJHxtjlORtAoelwM+dqKLFnzlM1vJEXDVBEcZ5w0E+Z2GymsfpuQD1Zg979R5Wdlu4td7E8kYL+40uXMripFYI2nUi3g+mqalSWnVPiASaXS9WPyMhBRgMXWf0GC1GtEj17rvQqSmU77K19U12b/m+nBz/3Dh3dkHOnjiB6sxicWx6oVQarS5qXCOl8z0AvwXwvwB8BmAr7el8uUjJZooUKVKkeKmgsljG2Mxebe+/ra8//N9bzc57nV6n2Gq3EQUhdMOIuK6zqO9yIqX5XC6YmJoK5+fnjbmZGczMzLpONgMpo1wUhpIx7gkyPqTqWQbmZAuZ0tgkiiMTXKNk/8MmqsEEwhgqndVfhxHQISTWVh7i9pfX0djfU+6pGctEKZ/Bhbkx/OT8FMbHsrFSo3oxnxDD8Cpx0Jf2tPD514yktDFrm8hnLWi6WuBQv+eMCFF7C/29DZTLI69/n6Z4BJWRUbzzwx/jxKlFnDpzDu//7j+UQ3OjUYeWlF32fB/tdhtbe5FSxNquwJ/2IyxMF9X3DTIPEuLIAo18RNB7+vf+NQqfahEVj/6O5JwgwyDJEhL6jJcX+U3U2KHxPVfr9YDcPsYFWj6yg559ewMV+OD18jBKBbHhkmHqKFk6SkUbs1NlnOmO4PSJLm6tNLC0uod6s6uU0U4/xH63rzI8W11PfY2WocHUOXpeqHI7KVJF4/EY6bxWhlIRRyik6vek9UXX76HV6bPd7T1td3tdlr+4JgqVCX7i9EV25vx5Nj41W86Xqu8USqVKYib0cWImRLEpG8miZIoXjJRspkiRIkWKlwJVSMa4HgbR+b1a41+Wl+/989r62luaxih3DSKIpIiEDBFIzyM3Qi6KxWK0sHjKv3Tpkj8/N8+KhYJhWaaIIAMRCdWnybmgyWfIGIsyuYJVqk7xYrkKzTQHsyWelEcNp72byT3vVaaxfwX7tRpu3biO/d0t2CZHh2tqMjU/aePSwhimJ0rx6H3/eAiQB+V+T3P8PC5gKlOTCCaR+I4exiajkQfdbwC9bUAsAjzzBnyW7xeorHZ8YhKFYhHFUkVF1ly/9ik2N9ZUNUAcjUGKlsROK8DHd+to90O82wlwaXES1YoDrofxuaMOVXZEheTPoHx+3UknH1NpMPh7kBCv57i0PDfRHGSHJooilcE+0zYSsyOmH/ajvohzmRbDHinFHQwzGVOU5Hey2DGbFM9iKYOLOQez1RwuzhWxW+/B90PlQLzb6uPueh13VmsqVkXXTOVaK9RulyA3uHgVUR7sCs44BJPJOgLp4NSKQdErPtY2d9nq+ramGQ9w9+5dsXT9YzkyPo3pk2dx7uKVyanZuZ85jnPBMM3VxEjow4R8riXO5SleEFKymSJFihQpXjjUyjPnZc/3f7S1WfvXW7dv//3Kyup0p91TbVamZctBT2Wv29OEFLJcLvfPnD7rvfX2W+L04iJy2Sy16OiRCEwJFjLGXSGlFYahSWpHNl/SSuNTLF8e83Td8JKyWDuZMYZDSiZLCOhr7dajMuFbN27g2sdXsb+7DluXyJi6MsA8MV7A4kwJzNCoQWkol/J1K3BsiLu/GaB5OO3XYsZCo+Oh2/fQaHUR9ttg/T3AawFOSjaPKxwng4uX38HI6Jj699v/9UssLy8pQxnTMsBgqYzG3WYfnX4PDU9iP7RxZdHAyRFd9Q+qpF1VJqoBRnJJUNFAL3HB5Jm547eM+qFSXiKO0eAyd2TbB3EnQ4pj/MJk4ShMCDgbIqDR4d+eGQMV82teM1A+aax0oSN1khy3SzbKOUv1ZQbkLMwA1w+xvF7H1S8zuL22rx6jhTgGP3Eu5yC7cdXbH8bxKYZG9JLFJbgs7iDVVUUDEAgg9CV46GN7fZPXtrbBjc/F2ORneHB3yTh9/tLYxNT06Oj4xGJlZOy9TDb7Z5qm/Q7A7xL32s1U6XwxSMlmihQpUqR4oeCcG2EUzLfanb/d3dv756Wl5Z/cvbuc7/U6ajIQRTQ5kIwy86gLx7RMVi6X5enTi8Gli5e6J06eYLl8PhuFgpHbLDlRMMZ0CRlKGUVc061soayVqlNaoTwiuaZT01B3qD8zTEyBzISAHgNLmLh89tMPP8AXn38Cv9/GRCmDqREH89U8fnB6XDk6qj7Np03gXikSp9tvVPL3OoYrlYNlIWehkDVU3xcZkVBUKZVkdve3INs1MKd6PA6LFI8F9drOzM1D1ymH0YPne1hffagIpyISvocoFHD7wJLcRyey0ehG6JzI4cwEudbqcdkoM4BMlTI7gH4tXmgYZHQqvKhjmj3h5yPbP3CT/hbvO1Amj5r+YLgSIXrMeyQq7+BcVj2oRvyPolyi8NFtfeWzfU380tMweJqSKaM401TjsLM67CS2KM+5inqarGRVdMp2vYf1vTZWt1tK6SQnW8pd9UOh7hsqz0qPv0cyEBLJ51W+Rckbaoam+jnp87Z7HgLR491+nzf2G1i6cxPjk9OoTkzqE5Mz9uyJhbGp2bm5Yrn0lmVav9c0/YMkp3MnVTq/HVKymSJFihQpXggSE6CMlOIH+/XGP2/ubP18a2vr7Nr6Gvr9LiTlqBkcnGmSMUjK08xms3Jmejo6c+YMFhZP8YnqOCzHosdoMkkzhoDKZWlyIaJI1zgXuVIlLI9N82xxBFzjAxMgZ0iGE6/TAOgoiPBQ7MYnf3wfN69/hv29bfiuB5sJXDpRwU8vz2JxdiQx23hMWdrrHfybo2oqrxWhjrGRgoNy3lZ9Xj1PoN0LsVNrobazhbnWJszKSUBP1c3jjompKfz5X/2NikP5w29+jXt3bqHTbSPwg5gvaRoo7ojpDm6t1rGztYndUyX88MwYKmU7dqvVDUC3Ad86NMIhS2Ilcj7Lsf11ZOub4JtuJxm7IoXhE0qCH2c4NOi3HsQPycOqCZn0hj/JgRcH7O3FOkqLxEF4uAeWU8aqhvmZCqarBXTcALVGFxu7XazuNnFvo4GH2y1QBEpE9wMRmwgRDJ0hCA8/uUxKb+N8K6mqm5lhwoSuSrFrtX3s7dfx8MEDWJaFTCaHqdk5nDl/cWr+1GJlenrmZHVi8kqxVP5c142rnLNPGee1x0jJKZ4BKdlMkSJFihTfGowxTQhR9N3un+3ubv2fyw8e/M3W5nap2Wyg1+0SwYTkTM1tdMuQuXwmoHK52Zm56K1LF6OFhQWezReIr5oUnRnFvVERiRhgCIWQlsZ1LVsoe5XxmX62VHE449mEXFqJoimHnGbt46JoNut1/PH93+IPv/01NtZW1ATIsW2QZf/MWAHz4yUYjgl4wevxREwiDL4SZH/4EV71gL55fmDyskLWwtRIXkWg+EGEfiCw3/HQarcQdnZgenVAd46RgpziSZiamcV/+W//iHyugF8yhqWlm2i32vBdV4mXk5OTOHvhHJaXlvHxzbvYqe0jBMdPL06iWBBgbg3gGhC4h32PjA/laT7tWGNDh+Q3zfQcPPQ8x/XjSO6wGQ++evwO3G0f21oqHlVA6UmRH6uaX2uexJ6wr74tCZePXu/omh/GCwE6maY5JkqVLE7NhNitj+DL+7uortRVZufApXan0UO758d9E0SahVA9vXQjGYi3MsnwVEbCVIibGD1RzFa700azWQelNa+uPGRLt29iamrGnp47cerUmbPV85cuvzs9M/enhmX8xveD/6CcTsZYJyWdz4eUbKZIkSJFim8Dsm0wpZRn93e3/7pZb/58bX3jT7a3d7LddguBssWHNAwqafTJ2IfZlsXm5+bE4uLp4OTCYjQ5MQHLMslRkMpmTTIBYoMANwguBbN13dBzhTIq1akwU6z0WRxtYiWlssORJvI4qZpBEODu0i28/7tf486tOLRe0znGijm8dWocF+ZHkMmYQPi4mJNXAJp086SdVchjUir7bUoM40lr1jEwP15UuaUbtQ66no+uH6Lv+oj6XcBvAZmJw6zDFMcaZBr0oz/5c3W4lj8YxZ1bX2JjdRVRGEIIAYpPoosGcZd7m2386vNtCM3GD04yjBV9ZU6jqBEfqGoDJsKewfFZDi2APK7E/UUrn8+y2a/rk3yecld5SFyfaJ6UkFcmj4zlJV0rosRRWHkhMYCul5Us3tE1TI3mFdnseyH2Wy7Wdtt4uNvC+m4XYbuHMBSqPVSZgsU3EDV8NuDLg9QZFYukx8oo/d8Eo2Npb3cb+7U93L19m9+8/llu+c7t0qkzZ+eL5cpbhWLpByMj5f83CMJPpZRrUspGUl77HcySerFIyWaKFClSpPimoJl60bbMi5srq/+2vrbyD9s7uydbrTajPiu6eeuaLm3HkrZtgginZdnRiRNz0ZW3r7DF02dkvlCg1WjuuZ6iOozBots/U4vRjP6oc123c8WyrFSno0yh7CREkx1xnB02ADo2zXi9Tgf3bt/EztoqQr+v/pZ3LFw4WcWPzs9iplqK5yrh65ivJArPgWLzHVD5kt1IJjFEOCk+gWaXUSiV4Uij48J32zHZpLLClGy+MSiUS/jhn/4MpfIIxquTKo+TnGpX7t/D7s6mMhYqFApo1Ju4fW9LxWg02i7eOTWK2YkKslkTuiAlz01iheJ4DpUP+bVIFmGemEd5VHkcKlP9NniRCz8DpvU4XvQ0h97X4UIt5cHCkdqPGlDIWyripu9SFIoPNwhxeqaC1Z02rt/fw521GnabPUVE49VKib4fqgUFLW7xUJse/rhs6Bfq9ZakjEYRXK/LN9dXWafTxs0bn/GRserUhYtv/dPciZOXdnf2r7XbzY+4zj+WUt4EUD+S45ziCFKymSJFihQpnhPqBm1FwluIguZ/MYT4s3a79dOV1fXJvb1dUjqlqZPYKTn11li2ExXyOWEYppiYnHAvXrwYnJw74diODSEjLxIRE1IYyQTNiztxJOOMR7phIVuoyHJ1gmXyRXK/zyRqpj+UpWkdR7cX13WxtHQb9+8tQUeAkWIB7b6LiZE83j0zhQunxmBnqLxv2JiDxQoCfZwnlfgNz5a+1YQ0Met4omLzBmIQ8Zdk8lHfFu0iQ9XQMXTdEP1+H9JtgRHpMM20lPYNQqFQxKUr72JyegYXCkUO2QAAIABJREFUrryNT67+Eb/71S/RajaQyWRh2w4Yq6s8ztvLPTRbLaxuT+D86XlcXBjH/IgFy5RA4MVmXMCQe+vXgQiPGT9OpadPej5L2sblCzqnBtuDOGIc9pzn/YAsv5F0SMYLAok6mbF12KamSl8wIjFfpVaEPBZW8niw1USr66PvBai1XOw0e0lqk1QGQ1EkEpWTHWz6kV1Crre6DoOb0A2dhV5P7nSa0u932dTEhJ7LOGduXv98YW1j/U/zmdznksnfMyauAlgCUEuUzrTE9ghSspkiRYoUKZ4DqsY1J2X43n7z7j/t1ff/rt1uz26sbziddpuTmpnL5WWhUIykFMILfK7puiwWy8HM9FS4cGohnJmb9U3d1EMR6UJIXUpJul7IIGkGQS8LmBRcs0xeGKmiNDIRWJmcRpmdyX1LO7RJVTh2bIFcM9ce3scHv/sP3Lr+OYTXhW5wlA0HJyeKODmeQdbmQyHoPFbZWKIkfK1rpTzCrb8l4VT4rhCupA9M05C1DWQsXRHPKBFJyFimq8poO0DQA4z8M2QvpjhOsCwbE1Mz5CKKSmVMZXBSdq1pmSpeSNM16BpTP2/uNtDzBdZqfWzu1vGXl6dxejoLQ0/OtYFRzdPyNcEOz88oiQph7FHyR49rVux8S9kcRGifZNYzeL8D850nqYds6H2GzvvnNus5yqreMAyUzmT30Tkd7x6GTN7EolPB5EgWjZar1Mz9jos7a3Vs1jpodgNs17vYaXThB4JuMNA1DZyxpKxWqgU+Iq8DEqqWO6MImqmznG6q/f3w/pLc21rH/ZWHRqvZnmlnM+Nc09+xDXYzo2e+6EfsRhj6dyDlHTBsp0rnIVKymSJFihQpnhESkW9X+y3zr8Jo/X88XF35iztLd0fbrRbv93sIghBc06hPkfd7XUGks1wpy0K+yGdmpv0zZ053x8erDmd6MYwiQTd9KaXNAJ/FSqUuImFLKSPDykSFSlUrj01w08nJpHQWyarxQNU0EpXz2LGFrY1N/PEPv8NnH11FbW8bpsaQsS0sTpfx4wsTmBnLxR1F0RG1QmIoZuQJc5VBI1KKJyCePFJ8hqnrsQICCcfQUMoZ0GUESZmbXhugCJSUa75x4JyrfwuLZ/B3v/gXVEZHcfv6F1hbfaj6xKkfj3HK+tUhIoGdWhOf+h6kCOB5k1gcd5Cz4+fEMbID8ofHXE6S30nRHCwAsaHFnoHiSETTzAGKnNCCES1odOPqgWFTIsJBGW9Sxi2eQDi/ci2Qx+h4fQ3VEPLID0kTJpXJFnI2clkrXoUMIpyaqqDRC+XD7Tburu+yhxsNFaVCUUh9yveUkYpFscy4n3cQqYIkescyOPQkr7Pf6aLZWlYflsyJyHvA63dNQ9On3EAfZYz9yA+j3UiyL3TO/p0x/AYMKxJwIylCuqd9ny80KdlMkSJFihRPBQMzLEu75An/b/dqe391f/nee3eXlkdWHj4ExZSwZPJFIgGVj/q+pxumEc5XTvinFxfF9PQMxqpjuqHrLAhCU5FGRmqmqnSkxjoLAoaE1EwnK8tj41FxdFKzbIcnE7SBqklyQS+ZZhSOixHQMJqNOq5/9jGuX/sE+7UdtVOkbqKcd3BlcQwXT1bh5Ox4giuGJpEDJ8nB7ym+FUi9IlfaSt6Jg+TzNsbLeZQyFjiV0IbdRCFK+zbfVFDJ4+KZ86q8dmR0DFf/8J/oX3dR77nU7I18LgfLcVRMRseL8MntXTTbHt5bHMHlkxVUK3bC94Z6lx976slYrXwiBoY2BmAVExImDskmT4oxFOEcegO6YLKnlcMPP/YEp9lHnvuySU3yHpI+y9PMlV4BDlx/qAWXJ7yfo+qYGBUaxitZLE7lsLLdxMo2mQm18XCridXdDnwRIWMZ6hYT9f14PYGqd5LvyqNebz9UfZxSRErxpIUMjXMytFMVLH4oTMZhSsnyHHxSMHHCl9EVEck7Dsf9bLZwNwr1NcCrgb2OBtjXj5RspkiRIkWKJ4LUA03Ts17o/0mv1f7XzZ3t/7p0587EzZu37PW1dTJVkLZtIQgiZQrEmYSTy4pSuRzOzc/7Z86d9RdOzEeFfEHXNM0JyamFCZ+Bypi4lAx9IYQuRJhhjHE7k/NKo+OiODrJTNsZ1IoN7lXDv7PjyBJozkOGJdc+vortrfWk5Yoj5xi4dGIUby9UUczbQ+YXw0jVyheCpOTOtnSMFB2UizZ6fqBMRaiUrt0PMMKTQ4gIxKAXL8UbCZr8T83MIV8oYqw6CV038PHV9+F7vsrhJLdaKqkNggjb7S7a7Ra8IEIADWfcEJMVE7lcokhSzi0ZealFiKP9kU/KoUz+Iyi6yD98Tegn5bb8MGrkQKkccoIW0RFH3KeVtg///WgUyZMe+xZQQz/ixkvnDDcB4QHkOM6GSC47MoYXtW72tJ7TQVUIDsfMOVgxa6CQKWKkYOPMTAW9vq/I5rV7u3i43USn56PZ9WHrGvK2oXo7qbc7FH5iLCSV4ZihGQijCCGRTFXJTIQ0gkFRLUwD0xj1hDqRkG8xiZNSyjZn8oHG9M+ZiTtSal8wz7wmpaiROspVZc8L2jfHHCnZTJEiRYoUjwURTbffPyF892/rvfY/bqys/2jp9p3xO0t3sL2zI2m1d2x0DOQ2W6vVmWWZ0fh4FbOzc+Hs7Kw3MzsrRsdGtYzjqCDHKIq0OPGMC+rPVJnbjGkyXiLX7GwO5epkVKiMc8OyzaRkViQkMxwimLkj7rPHBpTZtr6+ilajjsDvqx5BDo6FySJ+eHYck9VCPNRQvKaYETZkiPIdnekkAXumztUEktSInhtgW0RYWq9hbqKC6RNM1WArgjAI5EvxRoPI5tvvvofA96AbBh7cW0azXkO9tquMYTSuI5ISLd/Fzftb6PY9PJgs4NJ8EZdPaSiPZQFahCDyFA1XGAxcm59wzijSGLsew+sc9hdSX7BEEi002NxguyTDGfFjMon6OCiR5UmZrnz0+Y++6ZHSWh5vj47jgVqqDJCib6k8DnpV2aNvSZ+XVFwZPMFgjA/9/du9vbrkq/0fPfuC3EDtpMUkboDa/QtZoJCx1NCmxwtYmC7h/mYLN1f2sLTeUCMuZixs1LtYWqsrNTyfNVUBCrV8kLN13wP8IIBDveCMwQ/DxIyMFmWpYF+RUR5FsiAEChJygjFxlnG2LyN+IwzY/2NzfGiXCy0NWjcKw7oQ0uPf8eKKlGymSJEiRYqvgHOmcWhnG73mv+3u7P73tc2tsyurq+b66orcq5HpHphlWTISEXq9Pvd9D2NjY+7bb1+JLl64ICujY9KxLcY1rotQsFBEEdnT0lozY8wQUioWIFVvE6STLchSdUovjlYd3VBKkzYUbzKYxQzI5rG8d9Vre/jkwz/i9pdfAJEHHQKB72OslFGK5unZMkzLeI1EcxhvrDXls4HEF85UP5Zh6BCMoRdK7LU87NQ78NwODFXiGDxmopziTYVl27jy3o9RLI/gs4+v4qMPfo+lW0216CMNqRbQyASm3/dwb31fHQt7jQ78ALgccZRyNixiHXqSO6tCGzVAN2LyRuTqK1mTPCap9PyQlL5+rASyAXlMVEvJvnqYyWT76rImYnLEBkROxEqper8j5PJJLtWMP2Nu6MGLnuGbHjyHH5ofUb0pkVo6fw4cczHUDvAiFrOGlNIDsv+81R98aN9zOVChbYezEzMVjI8UMDdexLm5hurFJCxvNOCYGnzlXiuVo61SOkOhVE06fhxTVwZkbdeHFwj1XJ3eQwpEQqrnJWsAtJg6ygRGhZTTAsFJk7F3Ldu6Bs43XY+5AtgBsMOAOotbRL5zSMlmihQpUqQ4hJof8XIQyPc6nd7PH66v/8PtW7dOrz1cQ7Pdgh94Qtd1lslkFVGkGAlDN+T4eFUuLp6JFhdPu9NT00y3TCMIAhb4QZQQK5nMPegurIFu+xRqxpnm5PIoj05xUjR1CuM8NAGSySzMPEI4jx1EFOHOzRv43X/8EivLS+RQgV7PRc4xcenkKC6dHEM2az+D8+XLxjGJOTlQSl6CSyZN+kIBrnGMlrKYHS9jve6j3aEweIFGp49ur4ts6CZGnynR/C4hm8vhwuXLyOcLcBxH9WyuPriHdqulFn8y2QwK5QqCMEKj0cBNz4eAjoYrcWo8h+myjpG8Cd0aKI/DWbRPgBgiWXJA9h5X0jqI3EiIGqmRug2YVvy7n/R4iqSXmEpVBwrdsAL/yFpRkqEZBQkpGzYVGh4De1Q9/crYhsY3/DeZFJUMophobHKo2EQpt4myKwd9rUdIIRva7vOc7wMztCfFQD3TNpL9MRgnXYNp4YADjmPg1HQZs+N5dTx0XR+z1TzeWayi0aUFiYbK8FyPpFLHHZMjklyV2pqRpoyEiFh6fogwOjQ4PvjItF6hym5VBFNW5/wK42ysH0bjUoT7jLMy57wnpbgTSnkDUt5gDOuSsc4b7h/8CFKymSJFihQpEjCN6eyk6wV/t71T+/ny8t13b3xxfezhygO0Wx0hyAiIM8a5zohpBoHHNE0TCycX+ucvnhezs7Pa6MiIRY5+QeBrIhJcSZ9ku6ochGS81i9iSZNpmsgWS7I0Nol8aZTrukEzlSBRM4NkUNk3wcFlZ3sLt299ifXVFezt7cB1PdUz+NaJKn56cQZzE4V49qHUkdc82KdNnF8JBpPxl0G+4zgL0uYrxSxmxooY2ejA80JEIkC91cN+s48xek5qxPSdBBnFzJ44CSfrYHxyEh9/+AFufvEFdrY2YZg6LNME10Klfre7PdxZ2VPq1fpuFpM5htMTBZycKiBXKsLQWEzkxOPiRpLjeEDABkolKZ3U18iS0lZSRIN+QgjZIROxcoCViUmiSMpeqRQ38hIiasWPqbgVfyiLd4jVPOLQKoZ45FBkimSHzyd1kgiuek8vMT5ih0Rw4LZ74Jw7rKhSWSqLP99wfIsc5AIPX6rloxx7EPGEIfL5FVI8GMPwnwYlxix+78FQBs9h7FBJPiD9w2NIvjcqb2BDqmvceKm2aVo6zEStrJZyart9L8TiVAlz1QLubjbQ7Hrw/ACb+z1s73fR7vnxLhQyFrVVhmec7zs89PirisekaToMyxwPwvCngecLKUQWkD2AXQyE/GGAaMkM2XURhTd1hmUAHQD9N70MJSWbKVKkSJECMamLzsGI/m1lfeUXN2/dXrzxxXV9b3+fSs+kk3Gk2+tLSKFJGbFez6M2FTk9PSOuXLnSuXDpYpjLZQsiirLk0CdD6shkEXRGd2IOKQezDNpGxDiHUyiySnXaz5dHfc61wewlSu5NAzXz2N+nuu02PvvkI9y5eR2e21eGJRlHYK5awo8uTOLc/ChMW49X1I/FlOF1l9AOJrYveQyMwdIYHF3CZCEMLYJt6PBDiW6nA+k2AbcRZ21SbEVaSvudAvXSVccnUSqVUR4ZU5mc1z/7BOsrDxTppJgm0zSU0ygpnDL00O5kscyBLx/uY3Eyhx+cm8Uilb8bA1L1mIpWRR4HfZdJySe1AtgVwBkBjAxZnQKdHaC/d6i00essB7CykFEQZzwadkwAqaSTRfE/2pZpKrIqPFf1D8a0TSr1XhEwIVVvMpGaA/+hZDGHLr1MSkRBCErgYJGljJMY0yCJ1IYhhGSqOiMKY9Mag3IoDS1plZTKJIcnvaf0XEV+D4ibiHMvlagrD6NBYzUPIhSJm6uEQU6uhn7QJBE/7dChl4E/5jQcuN+yIf48dO2I1zHjbXD5lUzO+Hd2SJYH6u7gT+LItnhMah1Dx6JtYGIkh7eao6i1XKzttnFndV+1RtDi1X7bQ73djwmnurvJeBhJhueggOMwLpXT96LpXBuLyLwqXnykhdUiGCswKc8GXvgzKYMlR9M+kxJ3wbAEYCOp9iE3dvdNO09TspkiRYoU33MwoCgh/qLntv61vt/4yy+//HLmk48+Zusb67DtjCyWSuBUPhSQo2OgbpDk3zM7O4d3rrwdnj6zGOULuVCl1QkWSsbIlHawHM1lPKMQTCpRU2imGeSKJaNUndSy+RHJuSaHzIDoZytRNHHcGQDli3726Ye4+vvfYGvtAUTgq2zH0UoGP7kwjXdPj6NcepL7LIZmIS+IfD2iGDwJx0BafdlDSLZPh5Za7xABRBBCsyxYJldqjuzXgM4mYOYBZxRg6ZTouwjTsnHuwmXkiiWMTYzj2kcf4uaNL5R5kKoC1TiEF6Ldc8FNB5blYHOtgftbDXR9CUMD5ifzShEdvhqxo9mZPGEwiXWZpNW4XBUozMUut+Y6ZGcdorMN0W1Q9Qf87h483lFE0UQEm/kw4IExEXMor4+IERsx4bk++p2eWtCKIJB1LJSylnIAdwMBN4hUTAeVdGoqY1RTapumG6rH0PVdtDsuAiKThg2dMpF9T+WSBqFQDr103aC/00qioTPltErb0DUG29TVZ6bn0r/4M1NMCEPG0SEFB3VNENsiMx2mDHQEum6AvhfA0DnKOco3NSCSy57iyqqrQqoqV1oMOnyfEEEUwTC4uqZSmavnC0V8TUNTBJd2a0QVCgzQGVdlrbqlqe90wElpYZPJAZkdqMKH7baPXINxpLxf05ArZJAr2JhyI8yM5nF6qoyOF6DTC1Scys21fTRbfbT6ARpdX7le00sNgyWHBO1LU2W6el6Afs9lmiLyDEwwKYXMAILEeLJqpxGNMcZmhWRXpBB7DOw+JB5A9XbKZSbZDTC2nVT/vBEW5umVNUWKFCm+v6A78qiU4r/2u/X/ubHx8C9W11ZzS3eWxNr6Onq9HgvJ3Ec57nEtCHwqf0U+l48WTp2Sb799GacWFmQun9ciITgko7oinzFpSSFstWRNswapltupcyXSdYPniyNGeXyaZ/IFzjg5Yah70ZDDhFI1j53T7OOwuvIA1z65ivX1FTVxy1oMZsbG/FgOZ2fKmKjkVDmnUiqewDUPnCzl1+X4Pe617AipZC/OBfKF4DH9X68KyWSRzIFKeRuFjIm1xASYStnUwdXvAG49jrqwj1NYfopvA3lALGIMiMzc7BQqxRxmZuYxNT2Hqx/8Jx7eW4bv9uPyR92AZWVgWiZ4z4A0bdzb88GvreNSvYSTkwVFosgAhkgExWEw3QQVYzMRgdO8n8x9OINkEfxeE73+DoJcBpqZgfQsdHcZGutNNLY30O910Or56HrkP8RQsHRkqH2Tqj4MA7bFEQqBTj9CywP6bhgTVM9TJK9azqGUtRVZ7LihKvts932E1E9o6CA7Nj8IkcvmMFK00HF9bO110O314wITMLheqEimyqHN2ZgoZ2BqGrabXdQaPbXvqAOCK3dnBsvS1O+trqcMc0gBJRMuXY8v9UEglLpK5FRXRFXCI0WV3IIzJvKOpQgmxdBQ8QtFixBBZOpc5ShkTfU8qIW8QI0tmzHU63w/RKvrq+3btqHIpudFipDS+xExzlgmKgUbpZwTezapPktDLTARgbYNA6YZO1TTfiLCR2qrGpRI+lF5cl1N1GdG49M5jIyGqm2iOpaHCIT6DBdPjMq3FsdZu+tir9HD5/f2cP1hDe2epz4/6Zt0fOiGrvab5/nq+yJ1WtN1odZlWWRCMoOeSt3mMq7ucSQTRUg2CynOAdp+ROW2MtzSQvNTTdM+ABM3IbE1lDt9bIlnSjZTpEiR4nsJyTVNX2Qa/nGvufd3y8t3frB8dym3v19HfX9fBkHIaGWcVo27vZ66ITuOjWKxGCwsLPhvX77C5ufnNcu2yHfPgBDkMhswpnk045LUnymZzoRkQvWyILAc28sVR8zi6ETGyRUY49oglM44YgL0RsDt91W8woNlilhoQIQusnkD89UiLi+MYX6iGKshX+fYL4cmNs+Lr7xmOCrhGGBQP/a6nHcFTeg4KoUMxss5FXNA8QSkBpkGhyDnUOqjozxEmmi+EcsbKWKaRKpWbJqjllj02HPMd11lAqSMU6kXMegjcNtKBLJJIWMGpkYL4D+4gsjvoF3fxcrDOk381fFCBLDfE0oFt/J5bO13sbG5i+WNPZwl99JyBgZn6IcB+i6RFQ2GaSiiGQaBWsyIyHNbM8CtDBrhA7RDB06+BI1J7G2sora9hm67oa4fLpGPQCjVr5C3FNGLe/8oJzYhVH4I1xMIVW+ggIgkHMvA9LgLnTHsUP+g56syVSJAkjNFunK5PDIZG7udFpbXPfR8iTaR0q6LIPAQRqFSRAMhUczayJ+0MDlaQDFnoRNE+OTODvaaXehK4dTUPifSRoS70/fh+hFMXUPGMhBENL4obl2VUo17sM5IXxC9xiLCpVol4x5HGis9h4gsp3J3U1PPiy8ZUrW3076wDI4MqaFSKsJN9yS6N9E+osephFVZ/1DUkaUjZxko5Cyl7pKpT86xUMwZsM34H/Vl0uJTIRPfcjzyImDxc2lfEwElvknqLWcaClkLhZwBxzZg0T1Rklkxi5XagsWIDNNnIOI+MZrHaMnBg+0mGi0XnX6Avh8qIz3KexVCStM0hMYTSVX9P/YyoJxpGUX84G6hhFZVY8wlRMgYSpDytBT8nUiyn4QQ70vIa4xhBQApnZtJme2xQ0o2U6RIkeJ7BimRM3TtPVMzft5otf72zp2lxTt37tg7O9uy026h0+5oahLAOUQU97hki4XwzJmz/ukzZ8K52ZlgfHwChmGalJ1JZa9KZFPZmaEhhTSkuv8LApORgGk7yJeqvFidZLaTk9QHOlTMJIeLmt4U3L1zE59e/SPWV1bR7rTVJDdrZDBVyeCtk6MYKTmJk+RTSlofazzyTXGMfCRee7xL/P55x8DsWAE7E12lFJFKQiqImvkS0QzdF/wdpHjub0qIhGBESr3zPE/9TOQqJGVMREqRpgl7r9dFGPjodtrw/QDUI041iCY5upKjSruFbqcOeD2EXlsROw0hSjkLQuoIuINcoYScIZDPOuqyE4ZStQi0mnU1DkU+BI2FiJ6HeqeLrXoP05U8chkdjY6L7f2OytAt5m1FgJtE4vxYsdN1E9mMo/obe14AwTR1PSUy3Ov14HmuckclZc9XrQkSjZ6vSB2RKkVMGEtqFeRBea5IXFHzWVu1MtBz13fbaHe9RF2MDWscK0SuVEWpUsXGxjrW1mvwqZLXspSKGTENniqfjVTRhesFqLV7SpGbHi9jfnocH9/eQbDXVL2dVN5Kii7l1VKpLJXHEsGkvxH5ozGRiikPTHtikybFNaVUr2nDj8kokwkRPKwmYEmPJI3N8yNF9FhSzkraID2fKkxFYgAU359YfAMRSZslZwc3EdoXMiGFRFRJhdaT0lp6j/FSBguTJfWa9b22IoRE1vtBfB3QaOy0WMW5UlYpnzPvmMjZOkwrIc7gShAlpbSSt5G1LUyUs/jppWl5cqLoPtxp9zf3+9pOy7Vqzb7uui7nGmOGrguuaZIzZtAeoPtGFEaxhxNp1nTPZExy4phSaMnySiwyg5kSsiplVBDAHGPsx5zxFRHJO5EQn3PO7yfEs5n4HxwLpGQzRYoUKb4viJ3yRnSGX3ie98+Ndv2d+/fuT3zx5XVjZ2cHXt+V7XaH+UEgNU2jyZ5qdSmXy+G5c+e7P3jvB/78iXk962Tpzu9HkSKWFuM8AOOesmcQUVI+S3NAakiJYDqZsDA2wcuj046VydIKrj9EMN3EwlB7E1xnB9iv7eKjq3/AzS8+RatRU+VSeUfH7HgBZ2ZHUB3JqdIrBE+7378OQnYkhuGVvNerfM8EIj7mKYB9rpqHR2Si46tJcdfzFYkxB7mIlGcI59WN7XsAyt4l0mSbtiIj9cY+er2OmsAToSNSSf2URCBajQa8fi8uCe20UN+rodNpK8JHJIAhNqghYtPr9uD2e2i3m4psRqRwEsHKZmBnMnB7XTRq2/D6MSn1fA8GA3IZK1bCwFEpl5WaScZAZBQkVURFqMYbt+0x+KSQKqMXpsxgqEySyKRpctWD2O76isz0PE+RL1XCGsSRGlQyaZkdWJapetxpnLpJ6lhWqZHS0OBksup5ge8q4uVTeaUqN7WV+tfquEp1MzhXAhgT7CBy0/UD7NS7iqTRAoqWlK0qs+8oVKWrnV5fZct6UoMbSLiuq8ipkzHVc3Xq6WR0fSbFz0GtHeAPNzdQJ2WX6wijSPVGZi0LjmWi73kIfKm+DxpH3NvJ1HeiCgO0oYiYZLEyNoOViiQPsq8GXq2kBjJ2+BIirtSPOfDv0dXSI4dPiqNSGQ+dmRT3HOrDNDWuynmp7LbvRer/tD1SRWn/BGFCimU8/vWdtjL7IdV2v+Gi54WKBEeJsyxtjxakYtOlmKCqflJLQ9YxYOlcEXX6/qhkeaRoY6SQRTlnIpcxJdOMXqGQ3+qHLPSh57iVz/b8MON2u07ge5y2S2XbUAblUWzsRCZKaqFCV0xa0GCIdUJS5U+WDV9EGRwOnAQwIyLRllKuM4Y/Y5BboQiXpMGvS8lus9hY6LVnd6ZkM0WKFCm+B1Cr6n4w43veLzzX/z821rfeuXXrlr10d4lRbAf19igGGU8QJa0cG4aJ0dFRXLh4Ibj81mV/fm42tDO2IaTURRhqQgiDUYlP3B+XRBZyKWQk1KRNSG45GVGqTshSdYrbdtaMZxNqxXVYxXxjZCUafG1vGx+9/3t89vGHaDf2kXM0pZrMjRfwk/MzODM3CjKAQHRMk9IOTIRe5XsO1hFegQvtACqGginloZgzVYnddqOHVt9HrWXC9yM4GMSfpA2bRyGT/FgtITHDIDWx1+uj3+ko4xlVQuoHqj/Zdmyl4O3v7cELfDIZg9vrY3NjVRFE+l5ct68UPjLjoWjdbreDTqulCCqRw06rqXIxqSpCN4zk+uWr0lnqdyPF0+311IRfRKRuUomnAcux1eTd7Xfj+BsWk6GBNagqUYWEtb6r+nmp7JUncRosKftOahoTN9W4r1BBEJHx0XVlXGLJOXSDSEecpUgloFqSbiTI1Id63cn3eshwAAAgAElEQVS4RpntaMqlmkhepGsq+5M+N5HGfCajiFK93UEYEHHUlJpJfYhUkkv/p/HQZ6QxxY9TD6eXJJVIVc5KpFmRTREXjezV9iAZV8qpKg2lfRhQ86dQY6J9SCQ1n81hbGwCQRRidXsTO/v3lALa7PSgUb8i56o/k9RfMdwTS32rCWGiklja73R5Z4lpEmOHEaBCHLqz0j6iY4rpUvWVisQ8TRE9Eap9qZ6nxaWyQmqxGy7jSWOiUOa/A5NZ2sdEiukcj0KJHdmHr/Zj/P70nccDlolrL9Dqeeh5vlr4UOosfc9JZauKQE18n+i1XTdKxh6Xb1NvqZFEriifJKapEuCsrSNj6shkTJ7JZC0wnmt1er2OG4BZmYCB9aMo1IMgcJS+S6o9qcPqOGHq+7M0TRqWzUjJd12XcWaoPOtICEe5mTM2vCDLpVRmehZjrMQgz8pIUCH1XqTx2xzsKpfiOgNuALj9Ou+zKdlMkSJFiu8yVP8Jy3LGztdq+3+/t1v7l3areenevXvGp59dw9bmppoN6JpyWpSxRb2QmWyWzc7OyouXLgUXLlxwx6tVpuuaE0WCCdWbI+iGSambPTUlk5HJKOpEIpBkSwipkaJZIkVzfFK37KweWzeq+7Z9ZI+/MSW0pMB8/Mf38dtf/Tu21tfVRLCYMTE1mse7ixN4+1QVhbyTBIe/7P7JbxBhQnMVNV8Rz29I9G1ATq9KxggPc+9eOpJsO11TbpZUnre+21KT26nxEkKKO6EsQ5WJ+MaI6i8VRC7b7RaajX006g2l9mWyWZimrRQzmmwTKanVdrC+soLd7W3VexhP5GPlKJPJqCG26vvo9WN1k57T73VVryD9TESTjG7ou7EpZ5JDlccGnqdIEalgqveSZtOWowhPp9NR5bW2ZcLOOCrKgwgdxZfQgoLv+ej1e/HFhMV9ixlbh0+qXhCq05EjAl2kiOQRUeVa7JoqD8hTTHBigsoPzzCprLYTFSomdOQYS75oqmeQ0k40HZ7aVoTYGkaqUkx6D2VCQ52jXj92U9VNpTQSEXQ0G5zpSml0IzLB6R+MwdBiUxvalhfEpJk+G5HEuNx2ULrK1H6m72fgG0bb39rajAlVFCqCp0qVwwgh+dkKIliaKk/WbQMs0lT/Zr3WVsSdI+6HpVJiT5lrxaWtPDHQEYmBjm1bYCxEr9+P+zUHCbpCHBJCHpPMg8hMatLXNVVlQItydKUMVfRKHMtM+0uIwzJbnhjGquAQyZQKGce8MLVdphynh4xlVSksO8jAzKm+ythkyYuEet2gBJjUWSKKthGPh4gqqaq0j6UkA6S49zTuDxWqTzefz9G7s/1mWznM0vHU6CSxJ6SKGs2MoXMzCMMgopul7UrqOgmopzgI1HeY7J+4Wpoxpr4H3VBieihjD3dN0yIp1YKu8cjdRB78V8n+DJIcoXRIbjOGvCbkJJg4JSXuiyC6xqT8NYAvhZRNIaQb94Kq/M5XgpRspkiRIsV3FXTjdYPJiPG/rtUaf3375u2frqysnOp22/r29o7cr9WYiCJpGKaklW/qrzRNU2YyGXnixHz0zjvvyNNnzrjlUoUcVMwgCC0p4THJ4prDmCIqQwIphZFk5IdUJ+tk86wwMs5LYxPMsjPaEJk8SirfmNpFUmPu3r6JT//4ATbXVsGEgKlzRThH8w7mx/Mo56144v1SiOagwWlIZZJ4PsKp4hq011C+OxTW/sreUsaB9Ywr5YEiF2heRxERQcQgiGgeCO1vdGb6YxE7boaKtIVKdUKsQAZxpmOkTG5cdHtdiDA2a6ES1tUHD7C++kBlkTLVs5ZTaqLr+6p0kogLVUNsra+hReqjIPVRU8oTTeDJeZMWr+h55L5J/YlEVC3bVtUSMdnsxpmMRCwMSxlpEXEkAknlrbppqvdWs3FxSEAYuY7qhpqU03sYUYSx0RGYtom9vRoa9ZY6TVSZqKEp9VKpVkIgY2uqXJS+fxVPEediJORHqh48Uq2IfLi+jHsKcUhgVHA/fT6ekL1IwJcxcZAJOYwGOZNyUBqK+P0R96MSwaAeQHKvpX0fG8t4ypiG/tH3NdgGT4x1VKmqTMpAyatUjzeuqiyFTMiyVKW3LKmxHBSYUhST2o/KVEeHZRnqc5Oqp44JUBlwH9tbW+rzxMdGXNIaq6ZCkfJBgz0RTWWrQwsCYaz0klkPVB+mVBm2cYt6rD7LRCVWhDCJhgmJrIexa61SgQ9Is4RlmokZEF1vA2U6FBfO0M0lUt8rT1KkIhkbokmuqbGRGy8tKoRJv6dMao6JsNN1erCiwBJfHvW9ETnksSptq3iYSJkmUdluvKDAY9Ip4rZuUu4LuRymp6fQ7vTQbPegsVhBV+SXxQUtke9zzxcmic60Jw0tEJVyKRodqbBGo8FazRapx6JcqVDcCWvs7+ue56kruzoGaMeo/lvBpRTD0vsjSNZJ6D+BcsFikswQLCZpQVcuAmxSkqmQlFd0jS/phvlASrHKwJoSck8KuSmF3H/ZxDMlmylSpEjxHQTXdId7bFHWGn/X6LT/6c7tWxc++fjT4t7ujgodc11PrRHbdpZuppImdLad4ZOTk3J+fl6ePr0YLS4u+rlcPgyjSA/8QCfjAopAkbEvQxRP0ZS66UGKvohC5VCRyRZQrk7yfKVqGpYSMQfM63H3nDemfrFRr+PLL67h/vIdNXkihYWC3kYLNs7OljE5VgIzjLiO7sAp4wWDDS3dfyMH2+hQXniVEOFhjt0TMazUvoD8UTYIgqfIBh3VckZF0ey3XViaiAPyQx8s8mMl+hhBJt+RKkkcAsU1RFGg1D8iblCLID3VEydU6V0f3U5X/a3f66FVb6DdbSf5uORE2oHneor4maaJRrOO7c1NRQCJLBIJ3N7cQL1eUxNo27bV30klo5JYUt+Q5MsGnq8m36TkyERWIiJo2pZ6DZW9knpJpEOVQfqacvckMkm9mnKwzxP3UaWymVwRmbjfj6s+Nj8hx5lMFvlCgdrY0G421eSeafH2iEBoqn9OO1D6qNSVokVIrSLFLJ8xMFbOqP48IiRElAY5kYYqdeWJK+nA1yshb6rXncfJGEIiTC5n6hOT2nVQKhqTIhKlktYCFYFCwZZKFVMluUlRARTLUn3d/z9778EsS3JeB57Msu27r3fPvzeDmcGAA5AUSYkiRcWKq9UyxKBipbX8g/sDNhSxod0VlxTFhSGA8e+Nee56196Uydz4vsysqr5jAA4BEDPsRFzMfff27XJZ1d/Jc75zCMwRW6ytxNf1MUorBMmseZJweMOCNMC4vkqLOAzwBPeE0jmkSJFO7CEQAlMywOH+5ZBBKe3EwspZKd9TzEcg/rRXE5h6IRaZ5j5Ujpqk6A4LsIUQhTkPkWPUT5mnC17IqTVCztRMciN59aXPoI5dcm3PJgF6JYzxXGL7MGkBgVjIehzhYHeTWEMcn13h8PgMmVZswsQ9oSyl1cZEic5UZhxw2fRHGrA3oSiUzEiQWULjS9Riz5o96cJgCDbj0xiBa24joY0sONIk4+uYaQnpG8kxX2/aPs0W6rPNlJhO59yLzNeAvrQxaArCEJ70+X7MjFKIJNSy0+3I/f0DHB8fE0Ov47ime2sbmhYgxoMBhvM5SayFsopfvi9ULr/88acdAa3ss9UonYWbLroJIZpa4JYQ+D1PCGpSfgGtDrNcjKDEYVCL3oOUH0LhmVaYas2Sl1+o7GUFNldjNVZjNb5JQ7AUbm12dfn7SNI/vjo9+YMnH330+o9++hPvoycfE9ug4jgU9CGpePVUCsKQVHjevnVLv/XWd/WjR49kt9ejQjPLslylWUp9moKJC5VTYBhpDxX5S5ADrYUPY+nJKKw3/O7WbtjZ2I68IPItbfS1p46oIDk/O8EnH32Is/NTXt0mJmCtFuI3HmzjOw+20em1bZPQ4pfYHkNL7LYZSnzVuBRXR/wqcf6XgLnPi+N0oPorAU1lvKaiNhA2Wbrr45r7ag82myx73mgGkAQytWN6fzXnIsvSAnMTi0SgjYAcs3xkUJJlyJjlWjCrRKCs3mwy2JmMRri8vOR+xEajia3dfc6IvDw/w3wxZ8B4cX6Ky8sLTCYTjPp9XF9dsmSV+8PSlE14iDVptTpod3ssmT09fonZbM5gT+kcaZLaUHofCbOSioEjgUZpG/EIcFAeZRBFNoA/5aKa92t7F2EtZuazf3XFoIOzZhXYFIiYMAKlykb+KMteuaxLltHavkxtgSjLUaVEs2XusVF/gAUxg57A5dUV/71h5WTRx8j9cKk1ptHAIlFsnOOihhiw0bbpWIjZinwGKuNFYoyJLEsYWEBHkIMcWBPLCAa+sD2hZmFA2f5DNjNiwGfuUQZDDJIMaPVpoSokh9c6gxTKiZwu8iL3EdoAJwJQATO5HgNhpq9yc06IvqKf0Zd2fYbKsGEBqy1MVudWJ2LwOVrkmKUZA+Iky9Grefw+HAUS+qgFAg1fgK786WCBqzHlf2Z8qujcUC9iMzb5lPRzc5yCzX9IZkr3FAFXyvskEElMYi30ef9pe+bndE6lYWnpOKD5dXRO6Hyt9bq4d/sWpB9CUyzLfGKuAM+fgCXXPoNEbRZOZgmDdDLzIdMexW6y1H+ZWMbS9FfSNUoyEx3Dcl5t2E4y+6G5PV5kmMxSzD3F+1sPjZMtAc+M5g8tqAhrFgSF0WjM4JTu2zw3CynuMcWLAnzOfOq6RaCs9BYCw+EIrdaQF37oYi6Shbw4P/eVygS7E9v+Yprnws57VfTHfsHzSRcPLt8a7C21pJRqW/hK65bK8haAWxp6KCGVEGIu4+h5mpPUVv0wjuTLOAouZip7prU+17+g7M4V2FyN1ViN1fjmjNDzvPtSen94fXT4Z5Np+t13H3/Q+/FPfuw9f/6CWYsoCgWvzmeZMIWfQLPVwcH+Qf7md95Ub3z7Db25sUm9mzpNU89KkYRp99HGq1HrUFJ/phCUTu3lKo8pyqTe7IrO+nbYXtuKLdCEc6L/usWa3ByXF+d4/N47OHzxDNPxCCrLuFDbO9jAt+9vYmOtaRBEXmHmvvL4IgbQGQ7pyvdfl/F3PR8V2a20ges/1wGb3kH4IVDfBjoHHG0i8w/RqfVxd6eF4STFVreGuBZBRB0g7pi+zV/SoGKT7rWrywv0Ly+MZFAAg+srnJ0csxRV2BKRTG/mM9PXRYCHjGQ6nQ4X4/T3l+fnLLPb2NhCb3MTo+EA/cvLIuz/8vIcw2EfySLlf8/mE5YsGpk8TN8lb7uP+vUlM36UAUj/VdJIaVmi6vsWWILZwzCOTQyHlEV+IhfDbCBEfW0RA0R6UNDzZGN7k1nUQf/aAkWPC3UDqBQDW2P4YoAbAUvaMVr0oi8Cuay+IJdRMv3h4n6Bq+srBF7AEl06D1ILZnJZwmjZQPri47BPHHYT9QUDhOPLCYM4Ig1rkWcBiGTmm/Ii81BhTqA8sQCUJcGiMI9JEiPDpucmSbPD0DcRHLkxDaK9YEmmL4veU49jPQzrSsCqToZV9RD1yDB/lH3ZHy+YlWO2UpgpLBn4BGjXfH6tkYBnhm2UYABI78mgT9seSRhAFZEJEWVgxj6zksQ2DufkUJuymU8QEJMY8hfFeNR88BcxkOejBYbTlBlgOndBKDm7shH5DOCHkwUSZeYCcetRJBhcSZZma+6JNbJUj1lj2meKFkmswRADTWV7ZAPJvaEUTdJqtdFsxWzIk+/WsNPegwwaUH4Dfr0NUsmQ9DtfTJFO+5iNR+Z8RxHnmhJg6zWucHo9ZKdgI2oQbGxEcyIKLIMeSbRqMdqtBu/P1WiBMWVJBwHWOnW04pCdqy/7EwzGczZVontR2Hk1syw/9SZzDupiwXNRWFmu1olZLCDW3TORN3Q/Hx0e4uLighd76POX9r3f73t0Muh+Y4BqwaXWqljUccDzc551rq/C/d8XPmRlyXjallrRhnX+zXO9o/L8kSfUb0W14Kn08LFMvB/HfvhDX3rPtHGM/3uNFdhcjdVYjdX4ZoxIQ31X5/m/h9L/7fFl/8GTDz+M33nnHX10dKQWaSpJVlWr1fiDjXPctE+FbHbv3v309ddeV49eeSja3bbKVDbP0kzkSpHLXWBW2+nzk5pTmBajZXZf5yrWbDTgRXGjFfc299Fa2wg8tmIthvi6f9b0+1f40d/8Nf7mL/8zzo6PuLjzobDTCfHmvTXsrDfMUSbzgjX56kN8Jd+fv9v4NVMu6898U3ESEZVzcuOkfB7zqW3wHjup+EDYMK/zfTYJ2d9oIY4SI/trdSA6e0BjB/Cjv9MuE9ghMEfxKbS1IAoQBiFLS6eTkWE8OKc2x2DQZ1b80ydPcHF2YvrdtOYFDJKwErvpc76iwmwyQZImbNpipJABM1UkfSW2korQuNbAceclO76OxmNjqmKNW6g/0zAnxn2U5HxeaOS4ppcusJETkplQkqDW6k3DitkeO6WctbRbH3L0iTD7xVJT01emmbUBA0Qyy6FzMh710eqY806vz5AZxkepgnVUDEDN5Ytrdd7ePDF59PR80jrEghlWA34JLBAoJhY3JmArgHq9VmQn5ipjd+HcOHYyOCPWjHrvqKbmPkloBlsEvqjHmn5PfbthXGcwGCOBFDk2myEDLQJ63VbAzqfEXhL4I7CV2P5WAmnNWsjgkUArxaP4HhjgxcUjUDDQJfAVkWSVGMdQohGF7EBLj1ZiCkeztGD/PHst6foRO9hu+IjJTIdiTDJzvikdg9g5Yl2FPQda6ULKy4Da9pkayavAPFfck0g/DOl4I5/PNXcxanv9ITg3lK5VZvspPWZXrcQ4y7HIasjpsS/IldW4uKqKcREZ6AibEWqcVHPbkymK/lJ32wp7a3v2eFM1QUT5p3sxtNeGCDvI4g3Iepd7gclgKZkMkY5DZLOYwVxYa0FLs0AxuY5weBrgxcUM/UmC0Ty1eZuSzY/oH91mjM21Djq9NXhRHQeZ4IiZWhRgc72HZq2OaZrg9LyPo9MLXJyfs/GUtBLijPuPI6yvrbNU/fLympUFfE9kxkRqPs+Luex5hhGm6JgBLSyxrD9i5QDLsxXsOcnZbNa9nu8vLex9oqzLMCqLkT/zg8L5Xi09Hat/yNkq9IHuez1o2Z3nalOlswMf3qNGGN9RWvzHXKl3YHpCv/JYgc3VWI3VWI2v96B6pZNDfWc6n/yPp2cn/8Pp6enms6fP8P777+fn5+ce5WUGnq9r9RrW1ta0ZSVEJ+rmr73+evIbb765uHX7QNTr9UhlWlK5arVnZG5QE9qj5fS5tS+VwlSuFD4dyCAQtWYH3c1tv9Xb5OLymzRG4yF++qMf4P/9v/9PfPzh+1jMZ6hFEe7sdvHP3jjA9x7tUK4abEPX3zNPkjRigZF1ssTza5MI8xVGteb5knNlzUY+F8B/3s8YdfnGzWN+AQwkkCVAOoEkwNkIILyIv/fqPaCxBcTdCrCyDJEySfH0X5JnKsviEZiivsjRsM9yVZJz0t9ubG6ht76Gy/MLllqTxJMkoQTiCGAevXyBoxfPmX0hZ1faz9IoR3HfI0lsSa5qJKwharU6Gg2BYX+Gq6srfi9yhvWjlFlO6vPyiN0KY44dybkvVhQOq8QmesI1b3HwrQEP1FcZRjy/uPhXYKde+l3ghRAhgYqEgRXHT1QK2yw3DA6dewIHyvYLkhGQY9ao35PuhPFohDiuMaNJkSZZYuYz7ReBRO7lhEB3bR21WoyLszOOQCF32TpnUAqOVyGwR8YxQWgY11a9ScUxavQzn1TrU6SzCe/DlCM4NIM9YuII5NHCwkY3YlA0mhhDn24rZPloVG+i3l5nmWs+ueD+3VyZxQBi3RqRx0CTWEtiOKVdKOBbFSZjkVhRlkCmOYNNNqMR2oJ2wfJSloo6ea1VOgrPNGcSQ9prRiZeRJm57lkGmSSahh0TfFxNVfYIOvDB/a3C5l8ynjIyZM661I7/Epw5Gdt4FtcXynJjJ1kmMOgFDGA9mcAPbHoQjCSYczCJpY0McNeVdkFqLHQLHgxKWaFM+2TMnTybVyr4vUWRXwknEdXGCZmeobHnoUlMLn2y0UdPnELLEUCLD/kMmZwD9QyyHnIPqPCJUVdAw0dea2K/pXF3K8XLyykOr8bsLksy2stxgiSX7J7bbRojIq/ZxlZnE+02saoNdFothGGNpb5kAHR+cY6zs1MMBgOTu2o9pSiOp16rYzad4fr6GsPRkBdD6J4mlQEtNjmjHz4nHJMTQjdzXjih39ECU63R4NeQQoGzWH0wEHVMtXs+ljJa8zPuaxa+BaFf/jlhT7O0X9pSosq6Akv7b/pvIiBCT3oHlN+5SJO2zrJrKeU5gMNi1ekrfMStwOZqrMZqrMbXd0SeJ+8JgX82nA7/+OXR89998uHjjadPn+L8/Iz6u6QLjKaCZHtrS21sbmA8nkoChbdu31K/+Zvf0/fv3eUiLkkSoXNFlU2NWqCsyUWqpaLMg7mER6ubkULuaSgp/VDXW23R29pHs7fO0rhv0iBQ8d5Pf4K/+ov/i02BaIWeWBZiLl49WMPvv7mP7Y2mOeLCYOYrsobOGdMLzRfh+jxbfj+zBm1f/zUHosIZFCvnmFL9ZaWi+TnkwtVTzoWrZ/5mdgm9uLb1Wc69X+RaGtZDJIgwUTVk1xNAX8HzIyMDzHN2YSXgR0zKZDrG1fkZSw+pR+vi5BhX19cYjwa4ujhj0OcJD/u372D34BbOTk/xzk9+xCwL9+eFJAFNMRr1MWVmkiSd1gDHOOMYuxaSYCrNvYxFdEccmx5FTUzI1ERqUFbibM5z0QsCtNsdBnST6ZRlq8yGwATeU/HOTqS2UGXZo+8jqtURUcYjOcXOZ5aFEuxW2ui0mTm8ujxFksxNQVxr8PtQbAmBxtxKBhnQ2t5aBrLKmOAQyGSQCGB9Y4MXoIjdnXlzjvxo1BuI6zGztQRq6ZlELFOtXsf15TkX22u9Hr9fvz/g4r1Rr6Pb7WJtYwMbG5u2mzJHqCaQk3NkkytjbtSf4/iaQvpT1GOJR7sd3NtqYq0VMah3pjeRJ0xwPznkxub8ZI06hIqMxFei6KGzXj8MuqQFS4VjK5v8EHsVWIegzF5T1ytqynjD8kmOTzE9qDlEblxd3fSl9/WpMd512lmXVqWMmMTsk2YWVSgrZRZGc8vg8jPEv/OkNYsmLCu1Nj3mzjIMs7Z9peAooBoyTTJPKx2Fmzs+P564hzC3mIPnlgeolFlRk0tJ71p6ywi7IMSiUW36zTNr/MUGTG4OWSkw/5cMeMhsh+ZZOoZOFwYwU8aqMn25wkXWEChLZkbNQNcGQK9ZQ7seYbMTY3+tjv5kgdPrKUuJBwtyr9VG1n5xjtnVFL3NHNIP0O71IMOIW+J9P8TWdgOb25t4+PA+JhPTW03nmMC/iVQxn62z8QSj0YgXkogBnTHgzIxBls2GTbNM0b3ZaNRFHMXi7OyMnyM0r5UyWZt0z9D8otfTNozENTeqAAG70FHey57na833XS60PZdf8mS88XBdapSXHPopxIJcbDV14mpcc6uMp7e1Su/oVA+1kkNaQHALcyL/WQ/mcqzA5mqsxmqsxtdz1KSU3/Wl/LPpZPzfHJ28vP/uO+823nn3XUUrrSrPhMGYHoMWKYUgCW2j0co7nS5a7Za4d/eu2tvbp6SSIE0SpbiyIkWVCiHJ3JHEV2JuyhNelVZMi+ZKSC+QjU5Xdjf30OxuFMHo36Tx7OOP8Nf/+f/BT3/4I/SvL3i1mliL7W6DweZGt2YKebav/IoHXjBqxEQo69oK53VpeGvt6gPy6w+tVaaygPNLW3V+jYejV6SVvVbHz3kynT1nxbTW9DZJdqskSasBR5oXU6iNmExGUl/gOgnw9PACR1dH0MFPbCxExH1bJFclKWq9VsPJyRE+fvIBZzhSEXh1dWGcUG2MxZzZDoXjk0P0PtpEupjj8vyUdy7NEoRRjRcouI008IuIB2MsYpgy32Z85o5R1ZpjPaiQHQ6MEQ79rRBW/q5s3iKxTVTUysT+27Nsl6slyz5Xun9J5ttZW0ej1cJkOMDs+or7zei4iamhr5D73zzeFjEwXhCi1e3wfhKLQ6CK+uYIjOdWTkogsdVq8ZYmwzFvm2S19UYN27t7HJ0ynZiMTTIQ2tjcZpBMkSvj0ZCPlQBk7bVv8fkjVjqOIp77E9vDSv9eX98gZQbqrSa72qbpHHpyjnAQwJuRgVKEDw4HGM4yzFmOavojySSn24hMX6el05zjp0YKNV/wPUZOuwRitO3zlG4NSLvYDtNvSLe7tBpgnm/W+IajRjInh3R9rbY50diy8vala7Er9kdbt1kHwj6PPFL2OWDAJT+oicGX5vmjbyx2lf2DGm5dg7afa8OWCuhS8a+tPoZzQRcFYDbrPNqy4i7D08asiAA6aJhNphk/ugxgLO9lId3rUbCu5q9hSbIyr9P1QzomlZ51DKRI4ZEn/L5kRmTWkoIy4EXb+8Eev2LGz2y/FXlobTcwS2vYbNXQrEU4G835WGmh43I0xzQboD+YUO8kL9bsb/VYvp3LEBvr69jeWkenWUOjVud+VuGAsgWBdK9Q/z4x/rxsRhmmWWoAZJ470Kkp1oQURu1Om3o99eHRkX78+DHGo7Gg+6nX62l67XDYF/3+wC7/COiFiS9yTCdnpcJku2qkQt9s29C6FM5aIO/6q5efnHppZdiaL8eCH8iiLwQuNHCohZoJX7bTvLY+Pvdm4+kg9Rs9fsZqNUXNRuX8rLECm6uxGquxGl+zoSFaAvIP8lz+h8vrq3/+ydNPt588eRx8+ukn4vT0VCqltW9DxKkgpXgTKqIXSUIGQbj/4L7a3d1Fu9kioCmz1JCYxScpVEqL2EL6VJrQp2qgtWpoZHUTa+ZnjU7PW9vYlfXO+jcSaF5fX+H9d9/GB+/8FBdnpywz4zzNTh2v3d3E/f0uSx9tw81XHLqSazgAACAASURBVNYJldmJzICurOrFoO3vfft7WJdbt82vq+eStgWtuAGWKyeyKJi+SGlrfkBFF7ECi9T0hRFzNUs0xonCcDzDZDrHLM25tK3VAtTDGkaJwMsR8OnRNV4enWJBzInSzOjRggxFF5DJDbnAHr18jk8/eszFKfU2Ut8zyWo5mJ3iMljiaAxrLs9P2Mk1isl8KMZo6CJH0uI43b3iij96D4oXIYZr4QLfrbEMSUpdjWiY0OWJxs6itD2S2zZjBhEE6vK8NBdxERxKapYGCmvCM/P9QurnhwHHTYRBxEwu9X02m02QIzUV0fR6kvTGNq6E9iXLTG8osbfEOO7s7SOu1TDo93lWEvikc9nudFj6y26uZNISh2g0m0b6m6Yca5InCWqNOhuuCOOmzdshFiVTmSmyiZUNQwbUfFxsZFSH9jaBvI9Q9ZHNZ8zckvKAjpVcWE+HM/QuQwOKQ79kykWp0OZIfCLOKtm4vA0XjQELCh2Gt5marp2YQRxJtWFQqrbg0bngskmodkahaZHfyGDMLQm47RSzu9LU6JxOoZ2niwFq1LJAKghQdM/coGJaXCR5axE1ZGGdMIhTWOlyeWOVLKogp2SKULaLOKZX3+2RMfUpPiZoQYKdjNKl9oGSXRPlLVtxVK3mXzp23DkRl9LeHFJZ1tV1FhK4kcLmZBpJstt/PleydEnWtldUWMBL/bu3Nn2stWP0pwuc96c4vp4hjQPUhYdFnnD/dCBzLPp1XPZHbKa0u7WJV+/fxt7eLurtHsvviXWlLwe+TU+uWcTiqxbafRLm2c3uyEoJt0hEr6P0sHq9oTbWN2gxSWZZpsMwILApjo8PxfHxiclCnc8w7A/4PplMZ9ah2vRysjt0Znuh7UnVDmhWGGN6XhEbajJev1QN4wvTI0/rGDUI3DWXTIyh8TRJVTjPM19IkRbP6pWMdjVWYzVW45s36LNkXgvXgtD7U2T5/3x1fv6bj588br3z7jve8xfPBTEP9AkQhoEJ3FKaZDp6bb2nG41Gvr9/oB89ehW3bh2IOI4E96PluSTZjJSS9V0aypPGcUJppTztbB5U7muVh54fqkZnTfe2970GfQDLr7XJ7OcOkkN9/PhDPOYezRE2e7FhSeIQ33m4iT/6zgH2t1o21ezv6eRTyN+q71OphrVlN1nDllGq/Nc7RYZPmjNErFI4lcrFsbmaE/HZr5QKTeqr07avjdL3prMMJ5dDXA1nnCNIQG4yS3ByNWWHyXliDFfSXDCjo6UJ4p9ME8xSYiVTZteyJOGizLApioEVyTopA3E6HLKskn7PnWjCtwxgyEYo1MMpbI8lFXQkPWSn1CRhFkIpz2RMShOnQGwgMyJCFpc4JcDnm/cFM5y5dXpVlpnyTPEsSqDqvkjqqlQDcdRAntVsz2fJaBgTn5yZUTo/8+kE58mCaYxOp80gk8AxsYbUxzkYhFBZip3dPaytk+R+zE6h9WaDwR8V1gSOw8AYIgW03+R62mhw35uwZk7M1mrFr6eFLo+2r21RbI+jxmxtnedCbl1Y+bxIj88hAWQfnp0eooBgHJWijAGT8ELo+haS2RB+NMZuN8R80cD1LDVB//MUHxz2OfD/7lYbrbpfkOEoO+CWldiF95RjHVEclwPvDjQtu4WaXjqOLrFMm3DXWWu7vwEvPhgwalkypStb1tb11ytAqwNlxf65eUBgTCrjwCvLrFNYsymhcxs1a3orhbaLHS7PopCwwgJjbSXA0sIoUcLVAhiaOQmVQGYGGOpKHuxnGVlt/2Z536t5nW5OF2eAJaqKgbHdutlPkpgz06zKbYhybgConHfHqNK5NPcbufq2YoluzUenHmGwyEBtxCf9BU4HM/TPDtE/1RiMp2zENLo4weXxc+zv7eD+3QPsbm8jqHfYdEzKkPuVVS7t9pVd5CnnCp0gx97CLvxqY5IlozCU29ubemNjjZ+CFH5NZmObm+v64aNHNCfEdDpV9FkkhBT9qyvdHww5ImUymQiS7Y5GIz2ajKlPVBg3XGnMhZSJKjLnQxY9v2lqWWBzdd0SxjJk5JBY1Czg7EGgrQXZNFChkNCBPQW70/7dFphXYHM1VmM1VuPXfDhyo1XzHzWE98fzJPkPxyen33vn7bfjDz78UJ2cHIvJdMqfcJ6UWvBHQ06rqGprZ0s9evBQ7e7sptu7OwQ8/SgIpMrzLOeVTkHgkjwEyUePbW5YZiWk1Fp7WivPROEpTcYize66v7ZzSzdaHfFNBJo0ri4u8N67P8GLp58gkBq9jS4XKbe3u/jd1/dw73aP++6Q/n0jyPQX9F6WMig2utG6dFj9OgwBfKYYkTbLkvR23JcasHGPTuamryuwkkCK5VlkuBzOcDWmHigyBzHFUuSBozMG04TjES4GM5xcDDGczDGdZ8w0zxL6+ZQD+11+O7k+Uv8jsV2D4QSLJGPXTJJiUj8cFWOGVTOGNOQOS8WbiaGQJt9PB5wlScCQ2chmk0EU7Tsbg8znRQFNjF9mX0cOplqVzK0y4UEM2Oj3pDbgvj+KSbDoM/RjBhvT8Zi3SUCR9pEAGGyvnpseVMxSjyQxr1T0U89nu9lmt0xh+780yb/JFKXb5f5OOimNRgPdbgdGAaHhBxEzRgReCVyurW0wK0n7S4winUNzGc2+cAYkHb/win49eg+SxHIgPm/XSMKNO6nkua5sriVPbe5DFPx6Ogf09Ml1XjgPK+XYqbLvVNtMTlimTtF/gzbyuAc5u0CvueC8S3k1w9lggcGMYkXmmC1yNgpqxk0mBK1aucxHtPedtGyjqgQY3rwlix/Z7w3wty8qyHpRuJfSuykL4mjuM0dNbLdlokrgasBpyQg6IrYKOHWxPU1zlBYTpIOF7LLD55vvr9yyhLztL3h+FLssjGNMFSZqB3argM5mQPIJTCqy2OKPls4XHOC2+630MhAtX2wluFoXO+XOheublRb4K44h0SVQNk3Khcy3AKAunocf06b/lB4xxHzv+R62cs2qB4p8CUSOyTwxMSgz44qcpwKXFwnOLy5xfHyEV+/uYm1jG3F3G93eBmqNJvfrmkihvFh3lG6xLDeg2XnCOtm8kUwbYGdjegwP6vu62+2pXm9NaaVllueEMzMppZjP53I+n6k8y/VsNpf9/jVOz8708ckJGWuJ+XwmoijmBaury0tSODHg9HlxKLJSaJt9Iktw/jmRKqKy9hJCiweCbaQVNW4fSC/8AYT4W5lOprQY9HNpaFdgczVWYzVW49d72Ed5R2v9R0LIP1FK/c7J0fG973//+/X33ntPj0Yjzs2kIpNWRslltjAYqdX03u5u/uDhI29/f4/cZrXNhCPOJCcxGhkfaq18eNx1kwmIRGteaKWF8BnlaGqV1/0gCJu9DbG2vY9vMtAk8PH0k484U/P0+BCCZWIB9nodvPVgC68crIOTXbJfSNb1zzG0YTQ/D8D9It77q7xnVYEnrI9htejwTFFaWGHSij9V+Nw/TGy65LB0YhynU43ZdG77FAWDxMOLAT560cfR1YiNeRSM/X8oJQMSioggBnM0S5DQ77XCfGH6ZolJyyj8PjXOqVQB+uRQmhqn1MQaLRMA1EyieTZ2hIr3vIgFIpCnc+McqRMjY201W2x2M51OsZjNucDMbe9mVjHNcWwUnR5iN+nUEEgloEx/Q/LSujUG4WB4bf+eWM40QShCBJ5vYxMkA0NyYlU2E5KdTaVnHUIVy+moPiS36d7aGrYog3OtiyAKWWZL0l4Ci8Q80raJpSfZaq3e4EvFbrtKFwCLmE7qwxQW05FZiq4wRi66xcWemL47c34pHYkYWddDpy0DJ7QuWTE3iYQumChpnyeFE3CFnSp0pvYeMO6eRpZKDq3cuxh1oGtbPEfWvSGG0wSfnhhjGIoW6TRCznpMsxwhSQvtewubD2IAXUW9rYX9vpzsDlBKKSrT3fYZSstJ2u+N06wuQCwvqKi0aC7WxYEt33/FssRn++w+AxaNAZAqWre1a19Wpl/VEskMNAUqwGzJdMv+3G7YxeJo20vpNqt1iRIN5hVLjKVekogre4yilCtXjqvcf1H0GBf91hYYSwS0SmRkvyqzCxJZ2b+ujUGTk3PS9VIoAejStLHgkxY0UrswQrExsU85phKRX0c79nnOvIimGJMiItWcU0rngEDo8ekFxxLFzVP01jbYfZrY/83t2yy358UgXixRlsW+ebnK8+8EK2QKpqzrtTlRSnCOrST7J6FDj2LH2DsYzWYgmo0mr1zkuVLb21vY29/D7ctLfXVx5ZHElhaF6Bnz4sUL/v7y6gpZmvBGc/tMcwZHBGBLSfPnmgu5sxhAi/vQsq6FuKdy9UDk05aU+TtKyL5QqtT6f8lYgc3VWI3VWI1f46G13hIQf5rB+/Ozi6s3j46POu+99554++13KCBahGEo6/VaTh8dtPpJzEez2VSb6+ty/+AAr776qt7d3dG1WuyzQEm5yomIFGltIbhgCXmBVehMK/bRV5r6N5SWfhB6rd6WXNvZQ63ZLiRT37RBRcLLZ8/wwbtv4/TwEIvpmAuU9VaIh/tr+NbtHoKaBZp/X/nsl44bRWjp8FFWT5+R3t4c1eLh8xgN8YW/+uzfVvonZeU1DmjqysuUwmKWsmyRCnsyLEyTHMIPaekeg6nCeX+B4WyBlOVeGYajKabTOVkiYjyb4uRyhPPrKWcick6fNkWkZ4vczNr9m6gGUxinmQE0DT9Eq1ljcDceT7jYomtIQf1wWYCVhRJhZZ2cTamc7DOE0gvukTKmHD52dnewt7fPPZHj6RjTyQz+3Eg8szwrzoljnyT3dVG/ppN/agaOdTbpanD/IwFV6o2km5CMeWhKJYGN92i1wH3V7RZa7TYijv0IENVi3j9PmOgQOk5wH6XHLq+tdgvtdpONeHwvsM6iKQsjqReVjoUZV2miKJgpzFXRo8fsj+9xPIq2smA49kq7ee9ZEJnDPVBgwVnOfWTG+ZTfPzdIqBDrVaTAwA1Gj1hk5Hy9C1YTZc9kwexZgae2Z5YXvupdBiMqjBHGQ2zpGLujDMc0j2aKme9n5xMEUuLedgP1WsCHkltzmtLYppoBKQtzHCeTrY5lVrQKFA0Q1DfBFSOkrOzVpE3KkskrTJ20NdS50QdZBQYOzKhcV35nWT2trAusva6oAmf3GFFL+66LNxFFJIssMKkF1IX+WFfYdXcldIX9FMuut1XTIPsYq/ZrugUHN0dMP6vkeaZIFaAzc50LhtBCF5UXs8Ddy8WinL5x7mz/prT9uU4gTPdkux6gHgRIOjHazZCzUyn3lBawTgZTdtilBb/TxRCCzYRGODo+xvHxMXZ3T7G1t4+tnT20Oj1E0qPWFCuFh1EiOFdgy9S63knlmGM7D3iuaS1p0aucSIpXVYT0lGmh1MR25qRQiMLAa7ea3t7enlJKKzprSbKQBEIfPLiP8/NzDIdDdtIdj0fsCD0eTzUtQJGDrbL3thCyWOyxCzzVpQg6mdQQfltAb2qt9zX0VqbEE53pl4tO87+oyH+fxAz4krECm6uxGquxGr+mQ0p5X0r5p/3h8H87v7h868WL5/Ljjz/mD7nFYq496Yksy8V8vhDc4yLIRCPQ6+vr6o03v4NvvfqKIPfGKI5yrZTkjykhra2pLVdEAT89CG2axngVWEliPIMo9Nq9Tdnd2ke92f65ZTNft0Hn7/nTT/H9//qXePzBu8jmY6w1QjTiAK/f2cCbD7bQ6DZNHMkvFWiidKjVzpE2MNmRLL1LLGvoWaOgL3HCLXV+n/NL3yaP5KXzbfmHy2xlUfXbPkrO7zA/XMwSXA4X3AdJ53CxSHFyPcHp9QQTClQnWSxJQ8MapBdgMFf8u+F4ykUZA8U0w3yRWsOYhGWurjfOBcD7vpHSEjCi7dCCf8jyUsFh/nzGiLWnHjJdAhVpJZjayjF95yrq2DZyjbSmPGz8wmyl4u1F3a7tsVRY662h3Wpz8UaRIRwLIZzmzBbcwt1VpkQLvZDZxiiOCzaTQCTl83EfIzEMnimsiV0ldpPcYel3JGElBqXX7XAGJbvpkkw1YIMRs69pRq7TfHzc82nNc0j2y+xjGWRoilzLHAkb8ZJbiXZVekhSWnpP47Zp4zdERept82SLbERrhEOZfxAVtqukTSy6EaVk1Bm/CMcAm15aKXUBeLA8G+17awtWNLNC3N7JmY0eENQg6mvIA2o320G7tonXlMdSwp98esms1QdHAwynCzaRerDbQqse8jRWDr5qFG7BS/2xBVBQZSt1Yf5qoYtl2aQnS7Po0tnnxihl0JUDdLD6C16vC3CGJdnr8m3uJJrKGec4wGdBhagcE9wySGUNy7j0KhuFI4v4lWI3ROkHpO06mLYUKr/OxjMJF/eidPHocFt0WZyFHx0qeFtKC/Jpv1LrvKwY6CoLtnmesGGQtM2n5WpXsQBh56e7L9094C6gm/MOEJN3FJlLhUETm60GxosELy8nmJE5zyzlrM6U2P+EIpAyxLHJx+1f9/HRs+e4c/8R7j94BRv0eRtGrExQ5nPUyN9hZPtmYdc9s4XNy6ziY6M2KueNdhNxoVmOkZN2XUhjPiTDMBLkeM0LxUpr2la728HB/j4/S6gtYDwZs9S+f32Ns/NzfXR0gpOTE4zHQ4EK6HUUvXAU/9LnBgfkNITAPQ2sKY3fR6YHWRB+R2j1v4tcfR8C05sz140V2FyN1ViN1fg1GuZzkXIH8KaQ4s8Ws8W//fijj3/j6PAQR8dHODk55SgAZjminKMBJtPEazQa+u7dO9jc3MTO9o5+9dVXs/1b+4QuqahXJt6bP1N8zV0+BjUos16qpU82hFRnaM80F2nfD0PRXtvE2tY+om8w0KQxHo7wzo9/gHf+9kc4Pz3hmIZ27OHRrTX89rd2cWu7bT78f9nyWa6grWtmnpQ/c6Y65Qt/hgRW33jpjb8lJ11p+oq0LdyNERHKyoeNeMBAIM0JSGYMIPNMGRaS2KLTEZ6dj9AfLxgsUSFGbo+Xoymb87ApiUdgKOIsO+Ii5vOECyFjqFHas2S5KoCitGkRdNzEPgTWPCeDAfuucJa6UqZrgYSk5OOJjZUoTXUEjFEGgVMKzy+Kcnqdy6asyBe7zR7u3LnDBeLJ2RH3ZVIf1GwyRZvdVEMTleIHNl/QADLpS+5bJHlus97AxsYGs5f0RYwmgU76O9MHGTJI5B5GbRjSLF2YntB6g2KKbF+oYtMXzrG1LJuWVoZnmTI2lFG2J9LKURmIKmM2w0DcSlQ5T9TGcKCCCzmAXjimLDPxH0JWPJzMRTEgvSzxDepwb+SK1CrfpwujHFfe33yFMf0p2SgHdOzOVKa1ZcSELgCPqZMFZFADvBiZkAj8CJtbQ3x7eo35YoH3Dsdmvl5OkSnBwOFbtzro1kNeeMg/R7JaMJpGD81mRAZhq6UFDQi7/uTkmrpkRx2Q4N/bPBUGZQQymPgqZbVCowIgBYxL2w1AWb21bSSLA5kl51sCSllkfDpgXPW8RYnydJmfaIC/rIBbGzliGeaqFJoNrGDcgY18VpjoHHucvBBhzbGUc9EWVi5t73vpjqGyX+xGrO1CGINgCyTtvGVpsDU+soiTt/mZq+gMlm4+EytiDPYhs+cuolzZyEMz9hBKI7fd7DRwNc4wnCXoj6YYjhOQYKGxSNmAqn98gfOrIU7PLrG7s8uRKb1eB61Oh12p6RmhrKzYLFJIk72ql3sezUKNKlQjxTXnnWSAqoWZQx4EBZWRAxHz0LKyYqB96YswiDQpnHo9JfI8E/SspYWXwWCAly8PxZMnTziLm8Z0NlcERGezGbGdIic5RqGa5nYDOty5EJzRRKm0a3Y+7CPP18mhLINsKOCvIMQAnzNWYHM1VmM1VuPXaORKNzzkv52k2f86Hk7/8JNPPt1//733cH19rZ0RyWw+E9JjrZMipiMMavL+/Qf6u999S+3t7Qlyiez11hLP8/M0TU2VLkVuqyAhuK/TdPqQrwf5CUGKhCoGpbNYgBjNWLaIWdncQ0zB8r/wfsFfo3Oe58xqfvTBe7g4OeJQ/9ksQbdRx929Nbxyq4eQVMhWivnLHW5VGeU5516v3FIKjl3MPlM8fe6Q1UY06wDLZa4BIlTUUV8k9Rtltogm5ockrNN5ykUSgUACk+eDKUaThAtiYhMvhnMcXYxwPV6wZDblgiZnSSv9T1SZBz1nyStbUTl5YRFtIJgRCn0qrqWVu2o2evE8w9pJy8bR/rlCmECjCYunnmXPcRrGyVQXZbutKtnlimV5Bhhqy5aZcyQZf3tsxkMAj5jMne0dZikWixnOTs94m/u39tHmWI+6YStrNS6u87zsGQ3DgNnFet04tEYMLI081oaxG/axzHow50MKW9Arjk0wvaSa91cQE+mYGNsXpt3fZq7wrsb167J3jmWA5XnPCzapkvUoXY+hLmSMMrc9cJUeTbpOJs9TMbgtWEe7vVLKCOvKafbLIKtSNyssI2hYFGkkkZxJWcr5lgvx5bleGMAoVdwFxCRx2D8frg8/bGBrvYs3bk8wTzU+PZ+yC/HJgBbrBogjiVf22qgFAUxypd23JURnv6dT6YcGJKSJle+WrzIuulWwbK+PaeYs+l9hewddNS/g4mmUcel10SaiPE5zSiwgsw47paNuCS6XbHYhSsNR4TByufhSMIlLLrelUv+z47PPf+fCW/RE2uOUju11rHDRAoDKf40EmKCSA4PsQgs7hyogtxrnUTQaVqdw9XI5MFu8xgJNXYJsx2JXr3Exh2yGJp3VtWaIdiPCvV0Pg7nGYDTB8XkfT44HOBkmGM8SxJHP739xSpLVCV6+fMEOz+tr67h95w7u3rmL9bU1Nu6hRTq6Z4r2g6V1y3Lx0M394vrS57VWocm69qSBliw3SATnYiEkMz92kzdD8Jdi1yRFciY/ilVcq6tGo+V3u11JPZ+D/oDffjgc6cdPnuD07BTj0UBQZNNikfB++LzIInJeXlLFSWNjfPu43dRa/usMuqlU1hXQ/wkCpzfnygpsrsZqrMZq/BoMeoL7nreVL7I/6E9m//7k9PRfPP306fqTjz7Kzs/PkizL/TzLyA7d2JoLwaYfjUZD3btzL/+t3/4n+euvv45aLfKo/0yADGfzDEaS4zHFxNmZghGTzm29w4vSVInDU+wZpHw/imV7fcsAzUbrGz09CCQcvXyBd378Qxy+eI7B8BrX/T6bQ9zd28Jr93bQ6dQM0Ptly2fhltlvgFrOzLvpOFEJca8yl67ARCmFms1SlrfC9isSeCHTi/Es5eOk/LnRNMOTwyvulZwlKfqTBYYTG+vhewwyjy7HmC4opkOyjf4izfjLYRUKPaf3JmDVINms73FmY5pmRQHHTZzSRnPYYHzYHkJhd5++c4YbsOArcbJPKtSlx8eRC9OzGIU+us3YVEBJjkVaGvyYYzZh/Y5ViyOP8zFJ6ubMPHxPml5Jkq+ub6PR7DDgGo2HXHRtbG7g4YOHePjqQ3Z79QgMBh4DSQYUUhgZq/SN7I9AMjmCGvrUMlRl8Z8rZ6KiCqbWsHY2OkPnSPK0ZJLovzZH0WjfrPjTstHMYtreXo3SAVMWP9OG3ZWOBbZslQWAQlfQBrNGHke8wkWuMEjKzOsDA5SFdcY17JpCScqUoKkMT8SyTNQCB5b7VmZ1FUDdHFVpqwNqZa9gye4ZbOYjC1sQrQ3s7+aYZR5m6TkOL6e8MHJ4NS16Dx/udDiLkQkiYQytlHWJNjJfu4/K3XPKMpblYoGw8mwD6GxPrDbVOE0wAlVG/uzx4pGmXmKU/YvVe9l1FLtLYqdQFXyUu1LpiSydbO3+6AoItefKKlkgq8Y97iW6slxxo0fVvU1xXJUcUVVebMtaln9Xlcou9U86wFxhNIWVIxvWskrrlXL1cpKIwmF32fxoWYntXAmq0mhUgDqq0971ojojKwFEgUQtDtBtekjaEnttH71WiPcPh5guyOU5RD2KcXY9oQgSXui6OD/Hi+cvcH5+gel4itt3bqO31uMFKmnvSV7aEMtsPkuIK+exyMsEL7x5fEWsigGG/TWuU4p6Y4rVSaG1s4WSUltqnKwYqO+TLjsZBd6+fVuoA/OcmC/mgqKPTk4OxdnpKU5PTnE9GFAfu3ZLRGmahNPJ1M8VmxhRxtpzcAY37kJgW0P8K6HTlsx1qIX4jwADzmIirMDmaqzGaqzGP/Qg3azWvcD3/vViuPjzTz99+jtvv/t268njj3B5ecmf7NIUtIJWSCneZJEkQkpP7G7vqbe++93F62+8nrUaLT/NFlxC5jqnT7TQ5LWpnFY6hVkazjicDXlk6hyWzxLrE1IoYMBAcwfdzR0DNL+5hCYPCqH/2+//DZsCXV9dYjAcsoT2/s4GvvdgHXd2m2A91exL/Q9+geNzqAVX1FpZKSq1aSEiLXqXzOuZEZulOO9P8PR0iMF4zq+lYnM0neNqNMflaI6dtSb+xVu32d31r989xLufXmJB0R0UHJ4qlrA2yUwFAoPRnLMrXaRDFJkYDHbGp1UNqpE8D3EQsN2+2S/PAs2KKY8o4zuM5M65XxKrIVmKSj+jXE1aDCBAmbseTjbUsAYbIHALRBTYb2W2igFQxu+jTA8z4loIPzAOr/XYQ+xRP6VAxH2TBtjkbLajKNoHdx48RHd9GyenZ/j44wt+33v37uHN77yJg1sHLJOlXk5VADFYh1Sf4+ap4HSGLLmVs2qXteHYSWnyNx1LI510UGWGJXMluGPuhO17dIV0YfwinNTtM1JB46jq2XnjpMPCmBe5KB0J48yrS3lrdWrp8s0YKLFhU54VvbCoskTOYKbIEFXlVHU9ci6Bw7VxLjFM7tW6MBwq5v8SWNEF+OHXleih7Bf1IuS1DT7WyK/hnggwWaSYUi/e1RyDWcIROjTHA1/i1f0uL5KwkS5dobw818V5IVl7xQF16f4sWEEH1mVlFuU7iwAAIABJREFUf3QRKcsxMM5d2rF09mQb06ZKP6jbji4DRoRjoCv9sA5cFbuhS/MdBx/dfHRAUBZgFGXvrHPlLYBbhX2uxK8Ie++5XSh7KUWpFkAJwKvSW/coE3bhS+mK+RNNG1UaGmmbFV3MHStFrUbAAKVxUUXdW8yJm62Hjo0tn0P2trIbFJV7lN1rUwVfmcXdUBqzuCjsoNeM0R8lSOHheq6QZwn605QXyGhhbzwac5sAtQu8PHyJne1tHNy+ha2tLTbyEja6RxSSYqsEgSyl2/b6Vs+HvR70r1ALcornoFOaMXMttDQtMpKt04iL504DrZ3XgNSsTBC553m0/uFppUQrbIlHDx/ovb0d3b/u6+v+tbi8usb52Zm+urrMR6OxHo76nolCs+BVMLNKbTcOt7eE1L/p5zpVSmTCx/9BKWLuKFZgczVWYzVW4x92+FrrO81k8UeTfv9Pnnz80Vs//tEPW48fP8H1dZ+Keel5nqLPl976Wt7t9PR0PJKT8VR01tbUt9/8dvrolUdpo9HIyT0zy1JpG3eItpFGb8laQ2PlIKTJ84YJPqFWMZVr+kDyo6gm2xtbWNvaRVRrfuOBJjHEzz6lmJO3cX56yCYKVG3e3mzjd17dwWsHHUShMHmaxcLxr4DddJtyFb+soEvLKsAacxBAyZKU+9GIsZHWYZOYy6PLCT58cYlnZ0MsEiN5HE6JtaTcwZT7K7d7zSLn8PHhFV5eDLhgCjxjFiM8wyp6lobwrQEKXIC6kLy9auFHvYez+czI4lQl7sIG25tsRwPEeA3FkzYGQBZAmfs5lZHm0mvZcMMy0e4aBFKwvJkjClSOMBDoNHz4gvqtYs6abDRbCGs1w+t5AVpxgJpU8CgMX4qin4yMQMbTBRT1XOoE7VoAbG7g6uKcpW8kmaU+S2m1kwyOc9NP5sx4tF6YglUasOikfJ+bcbhcGZfMNMUnsKrdMB1MRaoKO+WwCW+zpHFIvsssiTbdbaoooi3Y0KLI+IQoQZH+zNqGZa0KF1rjCMOsCwfGC9tPV94ODBaKTNG8ZJEqvYvuvaWV1XIJ7VxRbWOjk2M6JrYww6lEylTPnbsltSj/4XoL+QYJ29BBhDyI0IDGK4sZ9adhmuQ4us4xnmV4eTXDp6cTbLZr2Op6BRAuTJ8KMtrutfudYwILzKcLhtCZ6jjQ5bADLcqA3YNzC86IUpdseuWOnY1/hFq+FhZZGyMmFHM2z0sA5/bBaRxht19EcLj3F2VO51Ifd/XcFkyxM/IpzYdKqa7pKeWPFrfYpS3TXpn7ovIhUrCcxdS3agahl17j5L1lHI2RErMqXJULDeXSRAWQ6fI8VBljx5Uvtf9WpLeV28uaI5XnXlUalOk/jcjHvU0fSY9crzWupjnqnsLz6zmuJzkrUehZtkjmOD8/xcXlBZ4/f46zi0s8fPgA29vb3M8dRoExPrMmWW6/3Yb00gKOXe9xwJ/vGj4rJKWYaSEW1qopMCZL5M1AyFVJu1TEPQ2S/L7gKTM/ycNWsyc9tQ9QDm+r3dH7t26REZp48fw5yJBwOv1EzGYzLzWROsLULNjjWBQhYruzpC1vQIvf8YExpDqEwl/QxwNWYHM1VmM1VuMfblDevBDie2I8+e8Hw+Effvrxpw9+9KO/rb//3vt6NBiIKK6ptbV1zsZMkkTu7uxmt27dUqPh0E8XKQ5u384evfKInCsjDpLPM1NeKlPBUqlKLgKWPcg5UV8pArdkEmSSJHIVUnxfHNdkZ2MLna09hPXmN35WUOFNpks/+K9/jaefPEH/6hJ5tsDd7Rb+yau7+P0397G13jDVRfazAKYuCrplEdzPM6qxB3ZUKinut3PFIa20Zzn3UlJvJGVMjhcpLkcz9Edz/jf1MRLwvBxOcXI1xsX1lG38nQMkmVxQliWBBhqUZfmffviUCx7KrSSmz7NSL98zvZHM+lEECLERnixMS6jYpcLYmbW4jDsCipw/WWEQYM1OSABGLKMX+FhQ/Ema0vJ6YRLCmZWJAUGe1Mw+tps1tOoRl1KZFlyg1USKyFMMMENBclYPcUjZefRfjwP82606wkYbmfKY1Zpn2vRLCs0RGw4F2OQPfn963eXpIUvXaq01bK6vM3C+phD101N2pKVeTGO3qYo+xcIgBSVoNP1ZpbujC+d3zqqF0STD0byYN1zbskultNSfKtxcq+y1yUywzKoQ1gTI7Itn94fTjKp9kNYAxvRHWvbHiSdFaUKlVQUgS2f8Ig27aYt+oFRtc+4nLUTQPmS2PVxVGCb3vi7cXojiPVh9YXtqTQSLMUIy4f2i2G/HDlcwgulVde9TPbfcg0vvVUOmexD1BJ2NGd5IU1Zya3WF475xpj3tz0wsiufxIgPje2lYcgc0ZOWeLNYFVKWXVKFynqvsmWPkjemSAxGuV5UWQHKO9shLMys+JlVxwrXnyxkJyRJYqYoBTtlSVzKCRedfBbA7CW7ZD+kAVwXYFEyieyahAL8FAJKlE3NJxBUHa/Zt6cHmgKKBftyLXH1fVAAtSolx0dO5tGhT6ekt+jErMl1Urs1N5rvymHUbcjJyXShq3dwsXl24tkobmdKsmUWzRpyjFbfQa0Y4ul7goi7Qn3hIFYmLfIxnC3aC/VR/gvl8xvLaXq/Lz5J2p2mfKXUrQS6dn8vra2XE0kSUmP0zi0qmB0bTwjLVEloLGRgzbG06hBkcKqPMNwsGQinlaXsxeEHQAHRhejR9ikgS3W5XsDQeykvSRE/GY5EmGT3XszzPEylFCCEDVAQQytwm6xD6ezpVv+8r/+Oonb6AivQKbK7GaqzGavzDjOYoy/6gGUT/Lsmz7z05Ot354MMPO4cvX8TT6ZRN5ygXrxbHvFxJH8zUe7axvqFu37qtqD9sbW1Ntbsd+hgJsjzzIYQSQqbaUR5SZkJpApe0IMkf/downmQ2EGhbNUZhLLobW+hu7yOqNf5RTIfB9RV+8Fd/ge//l7/E2fFL/pBf78Z4894Gfu+NPdzZ73HPIbvPVpfAP3fcYDyLJXdRSuHc0vkXgMqbjpt5mnOvIwFLYiOpf5ICu+dpjuvxHJfDGf9sPE+5j3I2Txj8Ecs4mCzQH8/ZPVY7CR4VhlaGWY8Cw3SR1FZpnF3PeLMELkNirzy7Rs7DAEF2WBWSwa7K80pjlK6suFvpmy00i3w9IUuHUgKiSjErGceBCXAghhNA5FtTRUm9UuTmGmF3o4m9tRaHrhOYEUGNTStiPUMsM44y8Tj6w8SaULVDDpJRJBFGIUtpR3PFBjsBNFJags9NbIRwfYvWdZXBNTTGwwGbJrXXE+RUT2mF87Nzlr9tb20xy2nYJY9NjMo8RK8Ak6o6Z/RyAev6swz7VDbfOXDhuT5MpSpSW2OY5Pq4DHWpC/mgixNRqpxvwp73gpkRFshahlNWpJblvK3KKCv7rYxjsbDOyNpet0KKK7QFreYcOlfc0kTHLZqUMsWSxSyNZgAUQOsmMjDGRGVcC4HSpdcVYNCZB+Wm31L6SOMNhD1gQ/h4JdUYjBemb3mR4aQ/R3hoGP2H2010m+FyLEcBYAxYUqhIfN2GLfZRJf4vjV6cVFLf+Du9PDeKpE/rxVLtZSy2YxlNYa/Vco7nTeKwIruuAjmn6S0ie8tUzJJBLplZ9+5VYlIUbKIFuo7RtYso7po71hEVdtTJhQ0gRiHLX84zra7A6SJuxeVBVqWm5rxW5o+T2lqG2UjcXTDpDbbdyYu1Lo5JF144LqblxuMZKGJeWEJE9qwNn/s7Nxo+hrMQ0zTHJJUYzDQuhjPO6yQvgEW64LzLGmXiNpoMOg8ODnDr9gE7VgdeaC9Nzp89LoMUtrfcaphty34xn4jdbNg+aMFO1AQe6cya+4ReTfocaqfxoRRlanO+jBa0OK3sKdUiVYnwFC8caWI6ozASnXZH97qd7L33P/BOjo+J5aT7cAxqwVGqCyFodTqyS1BjIkuRq7cE1GMS0wDor8DmaqzGaqzGr3KYD7etZDb9l8+nk/+lHoa/G9VieXp2ql+8eOGNBkNJhS/1wiWzuTw7OxXS91W9VlNxHIt2pyP3dvfItMUaehpTfdNDIbQ1cbB1q6AUgFxo4dvOL+PDIqTU5NipcuGHsTCM5i6iuP6PYiqQac2H772DH/x/f4WXz5/yanOnWcOtjSbeuLuJ/a0OZOCDGwJv9k8uDVvw+DHgR8bYJ1uYv/HcyrtcdoRlQxhROhJmOff4TKcpA8kFAcokw3iSIskyTBcpnh4P8fxyhCzJmXWZJhmuBnMGm3RdSUZKIJN6DqnoTxKNeZLyJnybfcDB/bSAEUjEYcB/Q9LaCWVaWnRhGBTjGMvutLkxr/F8yTJWKvTo56Sh4tQUKokI4No+NleeOykbAcBa4DGTlOWmvzHwfbTrHnqtGLXI51xNikLxWZ4mEXlGjtmph+i1athZb2KtFSEUmn/O8RaUBJIb5tPJ7QprFFtQ8/4pMguaIUlNtUjdy7k1w1G28KfuJropAquWlhYAkdlHen6CXISYzaZFBuZsPufvSbZKb1QwTlSwsQGMNEY6VjIMFxfhpo2uFPXCMVUmQ9RJhA2DKJFnGRfBzn23iMTVrvC2YEZr+7eVIlxYJ2IXi8F9g6VDaMHUOcAjHFtigUIldN8UuvY4yBNEiOLLHY9hIlVlu9Y1172jY1CW0JAFwLnL6rTg00pzHQgFu+B65ftyP6Bv2XDyL5WVnrYSophgfeMSLPwaMm8HgRdgS0s8nGmcDDNcH/WZ2SRjKZLVzhYZHu02sd6KeK4aBhZWGrpMLEuvlLWXkk3nQisK8IeKk2p1rhogkZhz56JUivcpFyFML7NlhKUBRkpVrl0x11DsoJkzlhmj+aGWIGsRY1JpjK3i0spjr2iONSCueM1yj4XLVy0ibrSzpCrfprBXUs4krPJLN0S5DXf9tdaf2Z5A6bZ78xEtl19aYXWX9ngpKqY45fKzC4tLax7SyYeFZYjLiKUGmQm1Q2y1KeJJYp4Dg4XGed+H9yLDh6MJhv0E2WLOCgvyXyBG8/r6CpPJGDs7O6yaoFzeOA75NVIahr+QMbuFJ7PkYRlPIYuHgr3euoDoUpsebZqoOVnMziBEXQgRmWliZO3KrkQ7ZQA9Wn0/UJ1OlDebjbzTaYtutyefPH4sDg8Pg/OLCyTzBQNYd63saZ+wS65QD4VM/7lSwbsSGK3A5mqsxmqsxq9o2A+825Pp9b+5OD35nz755JPfTlVei+Na8uLZM312duqPJxPeGXrgU2GbZKnY3NyQ9+7dyx89eiQ31tZEHEdcjmVZxnWHlF7CEhqtfE3ZEUJkUgqluV9DZEKIVCgdaChfcInOPZrCDyI2QulSjib3aIpv/FSg80oxJz/8/l+z+6xjlpp1H68c9PBgr8eGMia8/guA5lKvkwKCCIjaQDo1xaMrjqyUMFvkbLqTMLDMGFRycacUS1cvBzM26yEDnsEsZZkrSWKp0F1kGV6ejXE+nHGRFgemx2eWGOMccorlOItcM1DVFjlwMk6FSXVOh6aP0rJftCS/oGVxwwRKKyU1fYgGBJhED4UFmRhDMLtKxh6BL9jtdZEbF1Jhey+ZHSXWMvSw1a5huxuzS+2c8i812LCn26zx7+LYR5Yq6jNG7CuWvgbMogo0ooAdQqMoNMeijdiUzJSdT44uZIK6uFSGwMuRU+ZmpvmLbgMnsWQ5aMGscfy8AeWeQKg8hL6HJCCgrTGfjTGaZUiViV+hmy1JUmpyhi/8Qr5ZADpmUdRn+8XkzYB0V0kvzylp+zBdUe/0aQUhJSREVbKIsgB1kRgl26PKnjtpKEarkqz0+KFg3lxGYpG1ahCTYZ5tXI4Dk8Zt1WQcuoJ7GQyUxjam37S6b8vsvpNUun2RVkKqKiyaLoDZZ+9l7QQCouyxq7LIpeZWcXRJ7m+iHkS4rWt4OAauJh/g6HyAyyH1Jmte6CHA+e3bXeyt+8ZAyu5LXkh47bmtHIgDRGWuawmW4OJkqsDF6V5ZQq6tJLeczxXhcamKsIPdTJ37r65uDaWRVOVSVBeTSjksClVC8Ryz/aKOaXSAv5Ayq5JrLXpqLcqvOhELy6Cr5fZMy3lbFpUAmucVctfiPLkJWqwe6OLv3VxnQGtRbPEsK7FSceAaVSZUFY9lUZ0abt7bC0PSaSdJd6+RKI+tfKBWMmoMmOPfkCohoHtDgGX97VigFwGezuDpFCfXcwznc4ymil+7mI2RJwuMhn183O6g3e5iY32N87I3t7ZJ2cSZ2gXw5kUdXdy3VWkyLCtrs3BpJBwwo0UI0qsIas8UM0jq2aQVHl0z10uSfYNR7ZPpraA4T7pGpMrVqR+E2ebGZhCFcdptd9Fuf+AlP/1p82x+Giuta56drXbaURjthmZ/N7Gr535bhblYgc3VWI3VWI1fwZDS86WUbw5HF//26OTlv3nv/fff+uTJR+FgNKTiNZrPpphNZ1zMC1t4NpoNvbe3R5Em+tGjV8TOzraoxbHIsyx33RZCeja0jT+wHZeQ0wJ4nmuq+8kZJuM6iNzokEtFPuphiNbaFtZ29hHVW0t5a9/kMR6P8PiDd/Hy2TO0GjHi7TVcD4c42Gjh1YMe1tYaBhzk1cYmVL6vUBvCGrgIMv2YmzxMpTCn8O/xguWt5HZ5OZri9HrCTOR0nmOappwxl2cak0WCyTzlAnc8TVgWm5JkmnMnJYPO6SxFZpkC+jveJDmwxiGDI8GurMYt16gUtenZq1xHl3NDIGxBvZ25WR2nXkyqmyI/YMBFskIjv9W8TVMPC47l9Bi4aBjfGkm6KfiRRC2keAAPjThEux6aIivycbDeZOMVYjbZUZaLbpND2YgMm0iFkydriEPzno4pI+AqbBA8OdoSsFQZ7XMG4QUsjSTmjwl9k+pTcKvCVj1Ki0LOK7j6MX2Qnqi8WhvPVE8b1pXWcRbWHRKpRrqYY7rIoaSP5nhMMQH8HlKSGj217p6OOcl5m5JzKm2/3xcMbfqtDPfjwKITH/JdLAtwCctkSZfQb0GfIzGZWbEFX2le44Ca5gx47osk40oL2oqeQo5rMQsQyjLWpUFMKVEtGFNt2R9RSjydNJbZR7MMVmHILAqhAlhWz4UDEXZxo8jgBMulWairqna4jsnT1rEzZ88z956Fm67TsmobqVMVZbLiIIKqbaC9G+Dbb8yQzkbQ6QKngwXmixSnA7r1h2g1Qmx1YtT8kAEY80jauQQ745hyEUBZnaeUbtFGVUAUil6/5bxQd9VK8xeD6W66j1p5NufEWoafQLlURR+hKiNK7dktFwCWmchS8ls9t072WshkqxFK2jLQxSXQxb+hSmm0M+kpme0SKEprAqUq86ggup3pmd1OscdLwLjy/qKMWNEl1izeb8lMSpQLFdXokwoRWMqMyyelfc/qgkL5W/Nuyt6THs973oY2NF9ugBo/J30h0IklXt1rY60Z4cXFCJ+cDPHyaoJZopCnCSbjAdL5BOcnx4hqNfTW1tFb38T+/gG6nQ46nQ7anTbiuGaUEzorrrG229WeZxlXp6Zw9zk/EKkGCCiTU0rPs75cGQs9JC9I07nRwjThFmoGsgZWhlnnd202m+ntu7eR5ql3dHJcI6f8bLGwIFO4h4IN5RaJggiRq7oMhsEKbK7GaqzGavxSBzlFyiBPJr93etr/88eP3/vvfvr221vPn78IODx5PtdxLdbkMluvNeT19TXJ9QSFwb/yyivpW2+9NX344KFutzt1qnFVnivF2jBoIX2qYJR2pj9kZCkEfdZ5itPxWU7r86omFJkC+WQO4gWBaPU2sbZ1gLje+kfBaMIa11ycn3HECWWUbm9vQixq2N+o4bv31/Fgfw1hYB0pSRLpciCFLlfbqecwNTl89Mnshx6wmGExmnDf5GV/ihfnQxxfTTCZpZgkKY4vxji+mmI6T9jAJyt60srCx+AIAwSJoaS+w5zBpuYPc1otd5Ivq9JjiWwY+NbEh1LP0qVOp8IQxq3qw/WVCZuPafqZPG5Npf0SLK1dWOaV2MtW3WeWsRH7qAWykE4So8oZdIFkYwySvbbJlKcRslQ3Cnw0I9+CYYbDvN/cOUkgQSdcA0nhMbBkeWBu2EuO11COdLC9Sto42/KlIMMaMqrhSnKZCBFLlaPtsTM+PQym81xavyfTc5g7LCRMzyoxsrPU44UAkrLTz0ltsEg16vUm5iypJcOlhunZpMzEyrllNkiaLFDkahlcWHZpuS/NSUXzzxCEnDfK3GIlLwTVorqS/ej6QGGCD/jUCGVBrZXummyJ8m9s1IM5v6KAKJ50BjUlS+sMjQrDGxfJUh3WyCZX1YwTt51KkY/y8A10lDAJDraHlZkracAhSjK04k3Dc0HaKKCCySykqpXz51WAoD2ORAN+GHGvnJdOsEgyzD94zos+ufLQnyZ4fj7BbreGvZ7keY4Kk7es/iyBUZWFdmxjaW7kQIoFqbJirePAmmXvhHM0doCqAD9Y2sgyh2qfI0IsP89F+RwoZbA3okiqfZ/2HHFErX3+mfmJ5cmJ5X+7826UqNX3K0123E89aRhx43gsy1ldHOQNgOeAZIXNw5JTbuUcLv3tsqJAV0/h0nMSRb5ocZ3LK3jjLxzIdi93izYukdJIlx0Qp3uQ9qkVk2kZLb6F2OnEeHo+wvH1DJfjlNUXgaAFmhTpNMFZmuD66gpHhy8RRzVsbm6QGSD292+h1zURPbD3Sm7nFkckCekuKGzUmcePXKPVTyXJQgQ/gH2boJJTXIrxEJOajamghFP7KJ3Tt6R/8dm21pN+HEdia3Mz397ZyZ49ex4uFgtqCE3torbPLvjmnFI9sq5Etg+gtQKbq7Eaq7Eav6RhJWwtpZLfvTg//PNnT5/9yQfvvd/78PETDMcj/pAglue111+fv/Haa5RvFf3kxz/xLq+ucOfOHfzWb/2Wfv3119JGrUmS2ZBWFHlJXwjjfOHETWxowMvuOQcKKBWSkaKUIiEvFq1UzI2CmkihEK31TaxtH6DWbH+2fviGDmLBTo6P8ezTTwjgs8nLeDBBLBS+9+oO/unrB1gj91lTLlFJxNlkihg5lXNBQOY4BPMXeY6L/oTBUbMRcs/l1WiGq9ECz05GeHpyzQwhfcgTU3k9mGG8MAHexkHQFPkEsELftxEcgotaku0RSep8YdhR04W1V2Rp9E7UB0nMJwE/HoYKLFR0rkDn1kuboefZXin6HWVNElCkLzIKms6N4Y4QAb9ntxFipxvzinwz8ljCq2zR6vkeGpHHuZUEKqk3k96P2EEq8KnwySjrkivQlFf6jYmHlUjmgLPkICaHiNkij8/KXRnwQ6NwB3GFH6NHE7VflpKGUTJ1qOkjdbJHPr++KcRSrSqFZSkrdSUuyXgjz8MCpTEUO+sqzUDzirJYB0MGnssSQJRh+doV0sujAEG6Urg6h1EbCSLYRdWzrI2uMHaVtyoLyuI9XV9nGZpRKaIZ5eeFRLZg22xMiLagqczHdCCxBLdu7qIAJK7Itw6xVOySeRStXFRMZ2DLc6FL6k1X+CNt2WtVuKEqcws65YA1N9KonA/hWF5ZgC/H6DOgtMZBhWzVMVR2fw2tIxDUN7B9z8Mbsxz9yQIfPjvme53SSZ5fTHgef/tA4/ZmA7WgKm9dFjNXJb7aMl4FiWblmELpZVV+YQBUjVWyxLUz63GzhBhNGEZZFFmZyjKClYmsy/mMpX0s0jPLPN7KpFo6Gne+lkCq6y02G3C9taV7rjXjsUy2I0XLv0cxN90WjUw3X0J81SlesKVL+7AsFYeT7KJUagtrznXT1EdWnGaX3sIdh7svVPkbdwHMopZVSFSOCTZLVufufJRv7oB3sTKUKxBG7DUCNKMOdns1nA/mOBqkuBhRX3nKix5ngwTjyQiZHOPi7Bh5LvC0XsezZ89w7/59PHr4ELu7e6g3GrwYRs9pWkzkLF9tP7vMopahJt3dLbAQJKEFR5VEQtqVKOOWLTxPKqucsca8LBkhrZSnjdOQ5jYBAd1sNae7O3uzdrvbGPT7DW6b9ql/pNKiq7UvoHakxEE+CRsrsLkaq7Eaq/FLGHaF+Pbw6vxfHZ6e/ruPH3/wT58+fdYdXPe5eCX4QLmYvu+RzThe/da38jRZ5FJ48ur6Cvfu3tUPHzwUtVotTrMkz0zPm2JCB+x1YkPviKqgL87TlFZ7ZisxEj8yvclfYRChub6J3vYB6q32P6rLfn5+hp/+7Q/w4vlTzCZjzOdzDt5eXw/xrTtb2Njukf4J2XzGqmOyPrgezjEcz1jqejGYswtsHPrMkL08HWCyyPjf7P46mTMrSK+7Gk65UCV2j3o1yXyEQZwvbUGklx0ULYPB0SDShJ0TC5spUzwb/wdnaKEtE+jx6xdJyiH7zgmScim5pzIw5hxU+IYE2pio1cyYkjEPubtSzMN2t4799ToDUuofJQAbEsMX++jWI6w1AwaUvhAlQJCBBcqS94U5Ri6IDZCmY8mZy5Qm1p98KXJXrKXGPKVwNRWFQ6SL43CB56by1ia1x8lKhbJgLedC3qyxlHJMx0y4Yo8BJO2nb5xjjQ+tkWjSefIrHIeyESx0XJ4sWQzqmYLIMZtOcHx8goODC6ytryMKQ1SrWmEBHzMaKrcRFbIsOKvuoMXPs5KVdI6Y1giGjIgY5NH+YxlIlvW2WCrITY0p7WsNMBCWbTHZBBJK6iKuxekpXUyGKPSIJUjTRZkOcx2YdSQ2PS/PvTaRKsJlI/7/7L37jyVJdh52IjLzvuv96urumenuee/uLJfUA4ZEWxYN0jYs0IJWoEARBuz/zvrBhk3DsGwTMCWIBmjZ0O7szqu7Z/pZXe+qW/d9MzMijHPinIjI6iW1ImhD5N4Aerqn7q18REZmnu983/kO94JUbPBj+W8BfS7pK+mDZK4ZJIWwjU6ufDzCzkJgyzQ/7kw0kHHAZkJ56BVqxVwIgFuqWBIqAAAgAElEQVSpcKsVp2GhWpD3W/DeoxJm4wlMZjN4/uYK5iifthbmJT0+qdXOO9tt6BSa11U0PrI2XnstTsyBbW/WHZIoOulb6SdN8LJj8xlRHwhjnfRqtbF9SWTHBWFqfpZE4EvZRzbD8v1sPVANmxaNMSZqNNeAKs0ucs3aYFl5lLhJZMPisOtBrL9uTrI+YvYU2F52TA61nTqwlA2kqWzsJ3wrGRpwPT+PFM+rtIORg1bccigynw2iHXSqr053nZYfSJIj/FbzF/z1ZAk8XpPMrwEF6S3vd0SJr8rXu+MfTPCtd1twuI0mQiWMp3M4vpxCXTvIdUX38XxeU9uq0WQCo+EQhlfncHN5DA8efgDbe3dgbW0dtjY3oL82gAxb6NQ+kWJREqs1tSsWW110n8dHITg3cQDXAK5nwW1TOpwNhKyYfDFFzhXb/iEd0Loy7U4Xdnd31Obmuj16rV2NDyrQM56qIuRJtCuU04WbuFXrk9VYjdVYjb/UwYbjkKmPJsvZj58+ffx7n//six88+/a7DOsFsccgSh87nQ4GPfSmvby4aF9dX6n9vYP60+99z9V1pXe2t+1gbWDLssqtqQvKUpKFuVXaEcLEtKPGcgoFqtQoXsIyOuMI0jjcuHNd4Fc/Mpr97V3YPrgH3bWNX6lrju9CZDS/+PzfkIQWzV4mkxlsbW3Bb/zwPtw/3ITRaAnHZzcwmUxBqxpGsxKen47h9GoKk3kFlzcz+hkCNXRyvZkuqEcfBo+T2ZJaZbQI0AAZjWDvSMuupNRSQ+sY/XFdk2ITwVoCt6UjkCrhjuVm+sDBqJhQ6NBOwQe37QzZuJwCqImtiGHsdfxxtrXvOYky2BY60RaaJK/Y5mWr24J+K4fNgW8ujvXCVBOGbUAKTSyf79Im9VEMFrRIjf1xYSsTchT1yXQO0ow34mW2JzBnIFQHa4c9bRbrqbRvVYGtSrA5OgHRsF2hi9RbQaI03G9yFr5uypuTuBj3h/6IbHTjvOmGtn67WNPZauXQcT3odQ102ktyB57NZnB9PYSbG2yNUkKn06Y6vih0jNRGAMwQ5dLiAOv4GlrlAoAABmmiqgRmK10Aqa4hmYVbfSWD4QsEOsznnLIIwoH9QZERsSb2sJSG8mEubWRrIQ2vta8f9D/0zDzI+UvNaWBJoxFLYys20T0DNJDzrcvn/0cnP3v7iHg64vaE4Yw4L0xmIrX0A9lvbBXY3z6E9z/+HlyOpjCcfgkX1xM6THSC/u50RE3817tb0Gm1QwIHEiBE88iAlmjRoI5gA5sAwOO5+MNJ14wNIFzAp6wq3yXnz5CgJEtPbovQRoaSAoZl+t7lGbifqYBabxp8ewPJGmIg56WafM/fYpmFnQaA0HvTJvW+Mg9pfS1uBdeh3HsuUW3IXHnFtw2nGSY8sNqmcf4gzwGVAtw4TbFFETOa2q8Bz9r7RIzKpBZaalRtcq1T2bCsRMV5IxdapqhbK1QMwsgsDqWvxh8c1sD3O4paPC37OT2Lse4d5dw31PM4gyyfUzITk83Xl+fweDmFs/NzWNvYgc2NLbh//y48ePgAtrb3KLZA+X6WISsJpXUW80HYVhgnqmWtxR5XPwVwT51zvwHO/Qd8UxtjDIJNHeYMy3A0glZ5AXA2MNMu03mr3+3mvV5XZ3lm6rqeg4Mbr1eBASXDlcYK+wtj7JVudWYrsLkaq7Eaq/GXNHym3vXMcvHpdHLzj1+fvPlH3zx+/NGzp0/g8vKaXsA5MpSdNgwGa+7gYA2Bpet0Ono+n+e1NfOd3Z0qQzehonBEHgC5Q2RkYaKpMZYPDxSVntFb3VHCHCv9nbVE/ahco07GGUzx00uov7kNWwd3oTf41QKaGNi8evUCvv755/D65TNqY6Gxd6LK4MNH78PD99+HBUzhp9+8gJ89fg1Xo7GXv84rOL6ewXC8IBBW2yS44ngZQaTlJvlUN8mtM3Lt3QkNA7eIqWJAjqASJaeG+05a8qOx1L5Eauh8gJgFp1M8LmRt8qxFjGKROWi3FOyttWCrV9BxTpeGajC7rQw2+i3Y7nVIDovyLQTKeeZr0PrYvF4YCmRUC2/0I2DBMbuCx2MiWcIYwDvLOpPCK2kbIoEh2udXPqtPrQ4yVsXG3pKxblWxfFQRAxCLn2oGQtGZUwxZvF1/UmmVFvSFEluuI0PGkiLijNk+NpIJvyuxZOxjSSZGeY8KkUbTGdyMJiRzK8sFtUCha4vnFIJuBWlvDM+eeSbHNWrJBBAIe5dFvbOKiQVg9g7rP1O5bNhG7DYP4uIK4GKQL9rCVI4YyEkdCyCljlQodiX7s3EetQ71oIqP1Ql7pP36Dy6mVO/pglLT+yfp0FvRCePLfUZBSc9Mzf0/dcLUMlACrlfWYq0qyMKDZ8p9yPW0YkrkguNwWLds4iTOPgT4kYHPujA4eACffGZhWiv46utvYDqd0b7x/n92NqJaO1QFrHVzUimIQRCtpyxL7oK0B2a8GyL8kLtDJaY5IueE8JkHRJKlADKwcklvSpEy2/jeYSYxSXAkx0L/NVHSKuApYqh0zlWjf0jskxnrpp3Unab5HxXPSRufTFHxtmokBOJ9Qu0ePeAM7W5USMyoRh9YF1urAMRzbTx3+PkVmF/dOD7+NEjp/TxKbXJ6S/EcGUlS+edxijnpWvhOYl6+a5hlVzFhKMXKwhUrrkPHa47P+8xYeibjtvc2erAz6JBr+dW0hP3NHpxcz0nSPZyU1Gt7uSzh9asjyE/OodfpwtHr5/D66AgePHwEewd3YH2wTu1UWp1WqcWG2lnskLYAgKcA8M8VwJED9Z7yDKjn153zl47vX5ahew9r/JFJMiPO5XlR2MFgYLudLjp041OjUEq1kiLXC2XVzy2Yb/O13nwFNldjNVZjNf4SBr4EjXMb0+noN4/fPPvxi+fPfvvxkyf3v3v6HQxvRhSQaOpLWEFVaXwhuPv379vNrU373rvvucM7h9Bpt3WWZTUFaabOfICel06rzBmb+/y2r6ngd5fAgNyBRatxxao5dJ6loAr3S0Dzzn3or2++rRv6azrwZVmVSzg/P4V//Sf/Av7vP/0TODt5411WjYUu9Tfbhp989QJmkxv44usX8OzFMQGLyvpWIlgPSW6pyrujSpIXpBUEM0oIkpAt5QLawDAJm+DrrCwF5cA/z7hvJSmQWF7oQRRwgGnJmXDQ69ExY10ogqZOtwPrgwGstxX0cgsbHQW7awVs9wto5T4oEpfM9W4Ga502mSEhwEQG1BKjY6NTK/g2HzU7mgZGQAuZFtmqRrycMlccpaWBcBxCU3DoIrWQoW5Q8+94dTh9Ts66t8BCyiIFha2Lx5U0x4ekeMhwMK8ZfGVcHxsa87tku1rFYJyMWmoodAaddofuo7pewHK+gPlsBlW1ZDme9ixhCMhNAhoZsGWae1zaWyGxBOhicgPh9zyD7INYZ0wAhRAC4YBMAiCkz4w3LKK2Ekqx0Q94IyYG8wF4MSJU0qeRHx6+XYRO2CtmQ0nqXadHHwCo9j3i+dgiABc2T+lA6XJtqyLbsrjg4joR6aEo+kBF9lfOX651lNsyl61sgwBWAhBYeu3C+vAgAe+p2mrIO+tw+PBT+JtFh/rQfvPNE7i6uYF5VcH1ZAlfHw1hsqjh0cGAjIMKr5mN846KRSPXKKlp5Zul2YeTF2um2bTIxvs+abWR3kPCjiUCAClBZbAJ8Vrr6NYqINbXbpsw3UIwC/Mv95Gly+tZcbnXwpy7xK1Wc9Nm6cvIa1VzogrXPBlk8e9K796UcZS15ffDLXaYmQ4EeNpbVSUJLb6XxEgJQn1zrH0Ox5z8Xnh+JS1DVJjwaA4kANG7v9rQ0iU+/iIA93gy56RNzRBXBWM53nKiFohyaUo2GC584XYp/W4OG4MW3N/pwdW4hMOtCZwMF1RTf3Y9haPrGdVo2noO58dTeHN8Bs+ePYf7774Dd/YP4eAO/tnPer2+a7Vamc9/qjfOqT921v3EgvueUuoBHwK+7TLfqpYzE/5eY38uniXKQxJtnVlLJoVlr9tDsNkZjccbzrkNLBQJj10HFwrU161269vM1SuwuRqrsRqr8UuPW0FPMlRdl+uXF6d//9Xzp//1zz//yX/8zZdfbhwfHcNkOvGuljpzxtRK+hEis7WxsV5//NHH5p137ldF0dK1qTLqlektJDNuoVBx7Qg2x7LCn+DnDpThuAv7axbYDlCxVpECgCyD7tombO4dwmDw1w9oYu1rhf0rl0t2CNWQ64z6k04nY7i8OIcvfvYT+L/+1R/D0yeP6bsICvPCs04/+cnP4fN/8zlljOfLJX1OdV5c54QZ56yI+8PXbs6sUlrBI1gMkuBGMUsALidzGsp8S00MxpnEWHpYis29sf6vaLX8zxEA1oZkvtjoGz+7QefiqoLN9QHc3+7CnZ6BzWwJ/cISY4lAM2UZSdAUkIh34p0ZHyCSeQ5m4QMZExBX6NPpKRPXZGgYsCmd1gnGuyF1uAxGJtwGRJhKaRFA39QgXXv8fiiuZA+sJDBLz8wSJLUhsA2AM+7Y78t4gJkJOOP8TJak3n0wruO1M4EYBWNKWM69U6Svs8ppXczmcxheX8HN9TXJ2JABVSw5TOWsWIsZgA31SMhiO4hETqiTdi9WxH6BcZRJaoJ3CdglWHXpauR9RP7P90u1ua/zJXtKU8fkhpOm9Jww4wb1+F2P9kxybYXJkeQCN/IPSQExqFJBBRCZZyGasjDHwkAplVw7MQkKJHFwfoG4YD149vVyRooA/eoQmXrYM4NKSvY4kSSILJAPzILB/rFFB+7cf0RnWRkN5VdfwWw5JIn8i9MxnGF7lLKm+uXdjS4pGAgoGkLzDaMoOYS0tlLYSJkQ64RATOq3IdZUxiuOp2Ub4E/uB4a2jfsvElDpfmOiwyYmONKLVaCGl5W75LtNcChDwGW6jtPtgIIEIPPP5XmiIkgNqoCgBW72SRVAy91+wvs39JpMGNhY4+u4/VBGaz0F+w0zJ77+lBTkNSLHEx+JPjkl7WVAJLrJ9fGA0zDzys+CpP7ZKUjOn08zPHtjbTIlSElJ4o+hyAvY28J2TNhGqqYa4qPLDvSOc7ieVuRQju+Ei+EcXr16CdfXl/By4yUcHBzoBw8etA8O79Y7Ozt6fW3NFUX+xDn447o2M1Du7+hcf4hFlWSnDa4Vn/ZWLpMoJbg1CidOFcB0NoGjoyN1dnaWVXWJVyBLH8PgfSJaVqtOXxeQq3pVs7kaq7Eaq/HLDmQwkInKch1lcUrlxpg7L799/Nvz6egfP3/27W9++eUX6ycnp24+nTtDrTCJcSTLcp21od/vu4M7B+bdd951+/t7LstzW2O20Fh+L3IuGm3unGpz0ZklV3LHANMiL+DywBiA/z3FDo4IcLr9Ddg8uAuD9e2GLOqv0sA59w3MXYM1xL6N11fXcH58BFfXlyRtzJgRQTYTe5YNr67g5YvncHL8Bqqy5GDaQOYyCrDH4zGBB99GgdlJ8AxNdouhk0y5SkEVxIASwWESiyWZc4spAQJ3QGYzOWf6Mxj0+wQ0W60W9jAjh1z8OfVotJZ6q73zzgPo9bpw8uaIeoQerBXw3rqFzWwKLXR5ZWYMqFzMS3JjsMlusMwmNhgxuIUUk0w7Zd41JMyibQSyvjckNKSsWmJFpkWdGP5ge5PMI2FnY6ArYLFxKMi8sHFOlPupEBQGOWlqigPe7CaawPD5Yy9C56CFNVLKM32e4fM9SMU11gqLCBEwYpET1r0uaF4NJSMMf1ZVNYzRsONmREkOdINUqaRSe9MaK/0lmUkO5Xr4c8PGIcw8CkMu4DINqL35tBxeBC6OTVXk2I0EyVxfJzWZQRKLx4TJEwKRntrytXh+/9RkP/T7jEvDl9da7rfKtZm55to877As7so+USOyQjF/iXVvAmIpcKfeGiz/dRGcCCMYrrMYCHGdbzBB0kmygaP/IP/jBEezp6U/Fg8sMqopRsY4+J6Ymq4xfrZ/9134wXIJtpqBeVzD9WhCrOZoXgN2OsL2OCrPYX+9492V8fo6HWobXWCPJQHQBGrSJ4Na74gs1MXa23DvJIkS6dka3zvClMXtqwTECZtn+ZoKu5kCScdlwDoqU+N9pRLAYeVO42vIteZKyf2sotFXs1A3rkEj8nff61VLHXuyrn19IIPooBJXccqCGkPWRjoHvP5T5QBL0a2NDKtkmprPdmaUtciQm+cbeuHKGgqHlSg68DmXtHgJ+DlOd3hOS4ImPL9Y2SHnJm+Xyvka6UHXUs09KlC2BgUcbnfh9cUMXl7M4HICsDZwMF5UlCxdTEfw+tVCDUcjvXt83Do8uOPu37876nR732gNL9fX1vf6vcG71rou1TkoacEsagG+IHICXurOjyvllstlfXT0Rv38Zz9rP336VOM7lIMWxyIS4EwbLtlMGYcWFmoFNldjNVZjNX6JgVnG05M3cHV5BffefQcyNPY2SrWL1v3ZzcXvv3j+7T95+eLF95+/eJ5fXl3RWxBBqautQslkv99TKJ1tt9vu8O6h++EPfwgPHzzMWq1WXlcV9rIyWqkFvnWssy18a1ufcs/BYeNlqCh2UE5TPKxVAQ4KKSNUvkCDXTA1dPprsLl3B/ob217S9FdsYICArSaGw2uYjkcwm85gNpsS2MfPppMRHB8dwfHRaxjfDLHRNNjawLKcw3g8gdlkAmVZUssTcv8VA4+EpWm3WxT0IVCMMqqkN55k15V3LvW2+rGJPiRBYJox12ySgZek3ekQgMSBTbn7/S593un04PDwELPQJOlFw6hupxNcGvE7WA8z2NggIL0cX0G3uob9Vg0b2kJuFgScqeZPAIIV+RkzDeiiilmKuvSOsByMBu8aPmQK/G6DaB4+Dk5AoeJzjkn6yNIkZkYNFOssd8iIAXE6v/LvACQkEGswqhKgRVrBCaWsIhOiQjDnzYG0mAEx25FTo3UHldNgOEyW4JFFzuQ23EZMlVuAZUWBJLoOd9otKNGo42YE0+mUAAGuK2KJGbwJECZ5Y8aAMnyuA3iWQDZwktqL6yJYykLT9tCgnU9OxUkL+7M29moMwAL8MZC00fr2C9gWRHGtogu/44/Xq+SA+5wK+xkBv7VezptREJzTMVLbEmbsfa2mDe16Arups8AGhSCfg9kGQEh69ugkmE+XUwDe3MpFs1wYPIy5dY9DMKCh3204lqpQtxfatDgquoaiaMHuwQE8+vAjWC5r+Prb5zC5HkFlDZyPlvD8bALrnTask3FQ7udAEi0q7cf5i9erv00yTgrYwN7JCrfKm8mohEETR2qZgyABVSqAJw+kFSVHCBSRfBsaxmK+36fj9cZA7hfck1IDDEGGG1vkxDm2XKPJDHmDxoZwTs7FtiGxVlKHHqhe3ttMbGlep8bK/2teg6lyJMp608QLfU8p7vfJ+7dRReGSZxQZnBkbakRlAYpk13FLl+AKnD6z5H3hBORH8C3PvbhFWWcsAAhqgphgCWZIdC96g3lrozEYbmO7V1CN/t5Gh3oavzifwvloAe1pCRYySmbOFqU6Oz3Jbq6u1PXpKbx+8Z2aLxZb/X7vb//G3/ib9z/86NODLMuwJ3dBKBGvAT6j2K8bQhLAgs8d0bWy6CJ0eXmxfPLkaf7ixcvOcrl0mc6U9XXlI3CwVF5MUisF55lyz2qlT3Jw9QpsrsZqrMZq/AUGsU+VeXh+dvxPnj1/+k+//vKrj49ev8pHozEGpIq/4lksrWF9fd2+++675uDgoD48PLTv3H9Hd3tdBEgaXeCwU70uMizqX0Jt+tbZQmkosXOFta6HsXCmdMVvu5bW1H9CW1/PFVK2+IJs93qwsXsAa9u7nlX6KzZQAntxdgavX72AV8+/gzevX8J0NiMmCRlKPFX8e3h1Se6yZclGNGTQYMkSfnNjAyaTCdwMhzRlyCrHwAIBRc7ZcM9eBRmhMQEAIEhE5+CMAQXuJ7pL+oDCf6cFeZHT3KNroIDbfr8H+/sH1Caj225Db9BHVhvyLAO0j9/e3iapbKsoKNAQVlOCcGShsLfn1eU53JwdQbu8gvagA1rlbKjhe++xEjAESTSIsuCAxrmwTYBgMimqtRhosbmKT26LrJFZwIBNOYgXoZiKDJIVrVswEBE5rRi2CCvZzOyH0suk115QON6StYlUVAJGkRY6l9RgqShFDTyBs4kgVwXmSJSn4ecMQ1AiiYYwuO1WXkC/24bZbEF1vFVZwQJNgpitVikjIiY3OtKDChKUlAJ3YfsShEhAD9co5Lwe2DHTuVAnJgce6hGVShwvXQLevCEJOkZh6xf8nqkrcAg6nQnzJSw8Abjwu4nMF1xoMUIAQBI44nxlfOsSkSN6h1PLBkm+ri4AHWJcNbva2qbjMu4jOPn6pAZJVBsglacZ71PjGW1qAySgXEBmAqJE1Ejnj8dqyvhAVMKk+fnD88LjaffX4c6DD8HqNtROwfKbJ3B5M6G+uafXM/iuk8FarwXdzga1ygF2nk7NnFRYcWnTmsgYyhAwqCDWomqVmPQ0ycLmfADE76m4V5sAvJS1TMG2yHYDOZyKHrjtEiTfUZFXTe7FhBYVnJWyZL4TJZvjCLsNcU0lTxRhUEE6BEUinxMjjmufE9b4trFxmCvnDdt0BHQ2uf1C3SxTuFbmPM4WKw+A3bL5nS9OzQKO+d4TqW0wEQo15beuVeNaNkG+MKdh7qgWnK8lb7VmcLvRb8On9zI42OjC6XABxzdLOL6au+PrqboZL/B+V4PCQcdNYXh23R/ejH99bW1t49labx1A7x/cvWv7vZ7K8jzHdyFBeQ+a/YxYh/b3yjtOO7eYL9xoNHJnp6duNBo6LB/Y2tpSyGxW4zF+pfJ9PAEdCHOl9DgrspdOwXiBpSJvL+HVWI3VWI3V+PNGluV5XS3+1tNvnvz45ctn/+Dk6Oj9k5OTHGWOhkBDlBGSLK/I3ObWpnv/gw/cB48eKWSy8ryo+fNCK11Tk2XrWk5ZbFnSUlSHaRWHEsa/8K0WsoBebByAWkYb+GJo9/qwuXMIg639f6+BZl2VvoTLm+XBdD6Fm+E1XF9cEGP5/LvHcHz0htjk87Mz2NzcgFanA8PhEMrZghibui6J/cQAMss0swY+u4tArtftkpkLSmVRqtput0l+i0AV2UInbTsYqLTbRfgOZtsRRHq2EQHwFJaLBR0zSl8LMkzxxjDra2uws7sL7XaHto0/7/UHcOfOAdy/f5+OBfePRj3YszFjNgYDZS/djeYewKyogLjRzRCef/cUnj39FnbaFez2d2C73Wc/e5WwUxAy9cDGOFBXHI9Z1n4l2fOk/i4EXhwY2cR5V4kUr1GPySASEhDkuObQpeFa0kohAL2MmTIbUK9mkyAPdPnIdHAoaqwbXxsmDIMEas16ucCC0PYsd2yI5xMC8GgAG4EfqABS0dSE2tZkQPLZxbIKNY2YVFguF1wTFg2JKElBgMuDMp/IyBLWnAGZyFcZaPr6UQ60xfWT54UMjZgNCbVsbMoDSRN7kDpKkezx+saPMMGhyNSqYoZSjrfZouI2y9VwvQ14Jq2345pc59cyglpTVlB7pxl/XfC48xy4YJT3o33rHuv7wpLc0ZmwngFEFsv1rgyolLC5WERgPPNbC/sjDr5+IhLmz68qze2HQs9PBsCQAAKaeyyVyDLY2NwldYGrazi7uoLRZAZlaeBstCQOCFuhrPU7cLjVpuQEJgVsI2GS1vCqRs2lAEFvvMT3QaiFTYyqQm1s7L8arwFEya6Ac041iHxUzs1xf0vfJ1Pqx+UYm/tPt6mC6ZkOIDGwe+zuKyZF/vp491zl4nFDAHkqrBn8sWmwui64NwO3UnnbMVo1jJTDHZfIUiOa8/eyuNnK9wPDLs9ArYKiIKoSWNpuVGRChQtv1E8zAHdRvh8l74npU5DRcmKM5yky1BDlybcrHXjywl3oFFTY1TJz5Dh+Z6MNm/023NkF2NmcQue1dkcYMDijtnoZ7HQd9hIuFkV2bzGfbz/+5qvecDwdPLj8wBadtmp1e3Bn/0Btb206nzDNtGPvQe0UZHnmynJpT09P7ZPHj/XLV686k8lIDQZ912q33KJcKjeiPt5rSqk+AHSd1cNaqaem5b4DoEzwCmyuxmqsxmr8soMYslYrd8b+1psXr//p11988VvfPP3m3mI61XVd27o2FE9kee4QKFa1QyBiP/rww/IHP/w18+jRI725sU7NNY11Y+vrGWqdZyUym87YjrEGe2NmHEPia7tWoDBriHrJgjAmPruN1Q1jDvxh0YH17X3Y2N2HrCj+vbyu+HI9PzmFk+NXUFZLAnDj0RiG15dkcHB1fg5nJ8dwfnkGi+kMFvM5yWEx8B/gS7QqYTy5oe10ex0YrK2R8QcG0AjocMaW5RKur68bNYoole12M2i3M2J3lotlrJGRAD7Tvv8pgtHFEkxloFRL7+bablOQpvOcait7/T4FGtifcn9vD+7euwf9tTWqG8Wguz/ow+bGJtVittotDvBcAoZsqDdzihv2cz0fRdLas7eL2Qg6rqLtGpTeoeOlrelYHbdYEWYmEHmJnFVxDzkb40O+EBDcH5v1Xmmtm3r7+iWfiIRPtitLMbQtkIBYfpHdGUHnPsBFU6fbQDkJtnyZlmLgJYGw5XYosp4kvE64UpbR4prRJmn5wZ/7oDG77bsTz1CBcGEE0jBoxeQDzvl6ZwCoSMA1icz51vY2seR+yhgYgTCywLV4NoANOWZg51eqc2MPIAE+WrqTOstSSteIsBWzXkHyzSY/gTlOmJsUsGgdHYRdUlMq31MswwwgRq5Lymc7r9RAPbIwzyJjdCKTlFpLuCVJV8IqC0/OIby1jd8FF8GBCiAzubbcLF+FcL5pFqNExspyXuVc6BML4n4M6TrXCSnn908u1FT+sAZ333sI77x8BeeXN3B5dQ2VMTCaV/Dt6RA210udfs8AACAASURBVArY7O7C9qAAgygguR9cmD0BCy6AnRT8NDIyiZNto/xRlAhh0TLzphhos1wWlG3OebK4UxVBCsDinR1viLA6QklsZIwh3O8qqALS41ThvBMQGtqfxN2Ex4eDpOeo7CHhh4NbdWxjIt9xifRaJRtUnIzx9crQ+L0whwmjq29JfMO1Cr1ouYWSGCElPT7luSussbCojbWZJHHo2ZDFOtdYzyzrWFNmwBLwNV6VEdyy4zbk93otTSZCa93C3V3v2Nd7I/3y4kbdTGuS2E7LWk/Lem1Z2rVZWeuqfqaG10O9rC20Oz31/gfvw3vvvoflHA6TH91uR7GHALrOVtfDoXny9En+089/mp+enuH7jUqCslyr+XSmmIlvcTu2U6XVn2oL/4ceL46WO116B63A5mqsxmqsxr9liAxzsLa+/uL58995/u23f3B2dvx3n794todMHPU6zjKsbaBkLGbGi6JwB3fu2A8+/LD+7LPPzLvvvms7rZYy1moGGaj/woJO4xto0ou7ppoHfo/w294nmqnbFT2z8U8mjKZUfWWtNgx29mB95wCyov3WCaW1iP+/DIetQ0rqC4by17JcEnN59OolPH/6BIbDKwqAx6MhXFycw3w6JSZoNpvDbDohkzwCkVxzNR2PPYhxvhl4jZJWkgMiGKkpCMD6x93dPTIMOjs9g8ViQcHAoD+g7YzHI2/mw0ZPIoPE48CaXPyDoLLVbpMENsta6BgMW5sbBC4RRCLwRPam2+0Rk0nroteF9fUtAqE5un5mObmUCmvhuIdcatufhlRaWkFQgOMlmPh9BM3ofGqqJWxv9mCrAzBoF8D2IiDt8CQ4lYjHVyH6gMixm6gfEWk2zGPgdnAq5hoQe9klP4cQmvIxM9jNnPY8ohYwwPvidgQSPftt+sbsLpgPRZZUh2A4MekIzB34ULgRwEWjkLAtvn20x+18PCjn8uZPnvJotndIQuR45FjDh8mDbpvV6jmUywWcnJ3Cvev7cP/+u5B3WmDMwoMKrM9lhikjEOp8ba0v4gzRaQT3plHPGJk9R6Y1NpG6RmZWzJBMABpRFqyD9BUEzOAx1DWZL0mwHKTLAAmQgSRijjWzLmGmJJmR5QVJWUmWy19GsOWkxi7TwdgLWEodZKVSn6Z9Swz8GR5fBAiR/RFWUsCqgFNgZYCvybTJjSDToBIkwOyw8T1wfP9S5VvsBKDBJke0ZjLfJ9d4Vnp7dx8++fQHcHE5hGVZQid3sNlr0X7fnE/gfLsH/VY/gBZvOqUD6BK2Pmok4zNZrpsNCMVx3SIkrVQY3N3+XZ4pqaGWGkngBlkBdAbQ77yRVFSQ387DxLXgVCID5XmXfpSgwtqQEdsexfsnfd/I9yVxIteYapmFDeUnW3PcAsGN3rrwi4e02hEWOySxuF/rrX2E2kkbe2qGn7P8QZ4v0iYIn9OOBEfqraOFW9cqvWa3vxzYUhX/n4yvvG09S4hjYib4/DmfoPSJOCwNqWGtUKq/3YGtXqZ219vuzeVUXU0X0F5orMvMbkjVgO/cCVyN5goNh/B+w/ftyZtjUly12m21u7sNjx49cnv7++XoZrT86quv9NdffVUcHx+rOaqEijZUVUkiA1QIyUV3DoZO6z/RGfxha7n81zBZzlTlpfkrsLkaq7Eaq/FvGciYvXz1Yk2rN//wy59//t+8OXr1t+bzeX8+X3Ag7QOsVqugyDXLMnf/3r3qs88+qz/+5BO3t7dXtPKWNqbG5/sCFApUXAFYg4l5P2NbPhhXc62zylnbA/wcmN10LnPoPKtUoTnSDFJB490+17Z2YGv3AFrt7lsn83ZL+P9vBgK45XION9dDcoId3gxhfHMNo5sbqp08OX4Nr1++gJvry8AcLKlNyYQMSRDsEcNh6sC4oewUQR6+cFESS21C2h5MV1QfOaWgG4Fet9uHd995hwDi8HpIzNPGxgZkKiMAOpl6mTO2TUCJLcpf8XrNZjOSy909vAtb21vUFBv7WWJ95cbmBmwM1snoh2RG0k8u09yqxPfXzLX/TGqJNBu8gEjgEs1mKnsUkKGy3BucOOle4Qh4X1xew/nlNQyUgf21Ngy6GdfEcSDIdXMhUFIQ5KmBjVDx35AwCsKwCOBKWyJE0tI1wtIQ80NyAsRweJddzfLYECJyACyMkadgqrAyITFZku1LvWVayxlWsTAqAl1UM/B1CcCSDeqwHR+EIh7MjGqcb5DLJY3oRUGKgGh90KW1iPVQN8MRgS1cj2L0kdbLRa2xBP8M+K1nNIABlUpo38T2J5rbhDYoKpgPpbJMYMlf44ok7I136o3mN9YJa5O0u1DJ/kJgGy90wzQqao59yxFppyEsKTOswpyl7DNI3ZlSQQoLof3KLeQQ5tMlV/UWI8U9Fj07bON6SZerzK9K6Deu/YMwF35EQ5yYCALu2YnPpfcePICL6ytyIraza+prO69KOL9Zwk+fX1FLlHvbPRh0C2KlxGVUC8SSljRpV6HGCSdzzey05bQRye6db38ioM46aa6R3KWNliKSF9AxESFzl3SHSaswwzULJONttjUeruwnJmscO9vqBshs3pnpudrmdoJKh2sfRVrPwFueSbKOYt/MOJFpzWyo2XbpXuV7t5NuwC1XmmUA4RelZjM8m+ytpBvEtR2WGkvlk8rdKD1X4Gp+B4iMWPk6ec/A1wTPorKA50zqsWljkeWlhJYzfAQ6G7S1em+3T0ZC6FI7X9ZwPq7gdLSEk+spnFxNYVlWZBZmqwW8OXoD5+dn0O8P1N7+PrHIyGpeX1/b169e6y+/+qp1fnKcl+SXoGgdmqXx9c9x/vHwbsBUT1udzk87ef9kdnkNuvTHuQKbq7Eaq7Eaf87ALHeWdd/94vMv/vPpePxfXV6e/+3hcFjggxfrz/JWAYXOXZZphXV6a2tr9uDgoPrk44/Ljz76yG1tbRXO2rwqqQm8UVqXSomfOqWlFz5Iq7sKTX+yvPQdrilCMJzqRbmtxhFZDra01xk5zm7tHkKnN3jrRHydl2W24y+P2Vws5jCbTNEulySq4+sbGI9vYDQawunJMbx59YpMfmazMZTLEkbDIVxenpGrLPbryih49gyNN1bwATUCS1Q+InDFdhU9dmrFjCoCU3IuRAUmt0QplAej+JK+vLyAV697JHFcVks6ZwShOE/ITK5trBMTiWN7axvee+89amODpk4YUO7v7cPu7i5eQ3KI7XYZYKLkEm4F8onxieUgmxgsHsSKgK9nI8mr1IZKAGGTMIIDYhV6TPrzw/MoWh0o8hYUbg793ELbtyn05hfioCE9Bq1pgjsGPEF2qJJ6RxvbdUT2xDOr0ujdihQzTVc4kSdGMBNie2TOFNfSYWCspG7KO3X4+j9LyQTHLTKi6E4FxsRKD0S2iU3DSnH6TcHMn8fYa/4T+nFqf34+L6CYGY/1fYKb8LoWmT8fkle327AsjXc9Bt97E1sNzOczktVm2jN5loELSuK8IU44SGZQuGZVDHdAN5FHqCNUnNQIgtQQ+AZZbpLcUMKC0EZskEP7NiRZAs9lYqKWUa5DDIgTABvYeF5O3CcD1Rv+emTe8Vg2m0kSTOScihkgvoLM3PrEjA6gKTDCLsomyYPbpseh+Hf5mIzhpICOLVsSN2NiKgVEWgitSeL11kFOKqyuYpDgHaT9edSmhrW1AXz88adwM7yBJ1/+FC6xPt8YmC5qGM8ruJmV8P13tuCjw3WUM9IaIeCQuNI24RYE91iRzEazK4/rM4jsvSgfjNR2Jyyir5+We/mWQgEic+uYmXMsfwWWy6QtZ+TZlNKeLrhEx+Ean6UmXdHgScARbR8g9GCVuzk40IqcP9m+GAmhEbtJWgOpW9+BW8eSbsNyvbLjOm9at0lyJsi9Q81l3K6S2ljnAa8AQ+8SbNn9THvH2+QaNeZN2vokShKA5LkqJQfSJcippM62ZsDN7Ywg/q6vkW0AvTAPWDVMJoEFQLfVgu21NtU0360ArmcGXl+M4XHrAl5ezmC8tCSlQgXSbDGjdbFHt5W1r169dleXF9mrV6+Ly8vLHFt8K2G8jWnWC/vkUp1rfVZbeGKcft3a2DH2XjS5WoHN1ViN1ViNP2OgNLacT34wGU/+4PXL1787GQ0/tOByDLAKchDFYKQmuWV/bWDvHt4177//vnn06P3y8OBO3e/3UDLr6ro2xhqjfGRRUHIQgSRWRKBiBtRSed1X7pxr+/jY8vvIv+boeR0oKC/xQrDWW9+ErYO70F1bb4JJkc5hrVlisPEXHcjiINhbLGZwdXkBNzdDdlDN4friHF589x2cnR0Tm4iynKuLC5KAYvDZ7/VRdkN1kD5YzNgMwnBQ4eeTGEwBnyJtUzHYJGlbVQFicvougs08o1YF+PLDOs3ZdAoiOdzZ2yUnWGxxgmzo7s4e1VDi2xplsrt7B5C3PLuKB4bfwWPFvxFkKu6RGAxyjA2spATmJrBCIvVTYf4RcCgoOGtuPRukEtZQ6jWtZTgRpW+eJS6h3WnB5sYadOsltFoqcTv0wEZRvZsHkk4nkVdDfhbnUGs2D/0FgYoHLYl0j2vZQBuf9kgkZ0oluwIOwCSkvq3Pc1xXlukkz58G4ZGl9AxrZBijGDfZF7eGSKdbzhnZgWZigIyevXRT6rO0gyzyDc07InHSxcJpbIOSawULBzBbeBdaPCq8PqPRiNrsbG5ueRBmvCOq4+Ddy7xtbHXBc6GiqJLlg0n9IFMoAqJ831zH/VM92BNZnUsmQTFbCmKyEvgkx0kPHdpniMNrmFidzIVNtpkyx7K+Mz52lrJiQoiAjzFR6upSg5uEXU33KayMGKuQ5Fg1rJL9tbW3r1A4NjRnCoZPqQQ5yIMZNDiIfU9VA3I3jgNBieZeqX6buWeZlD/Xg/0D+MH3P6Ok2jdffgHT8ZSeB1MwUNYWytqze5++s0GAkzpXSf5JxbWV7hdfCaahKFCBzdPhPvOJm3Au9tbjnJMjcIsdlYkWWbUJaoiI/eN+o8ohrqvm7aGSWtwmoyjJLL8uI5/H32F/MguqsV4xEYNzm96vAraoPjIkyGyTWU1GetvEcwnVl8yGyjPX3brnVfhvALM6/syzwlECHAH+W/7CUcgAnoEGJ884usHCvRSukfT5FZddiA9UFcojbKwtDyQ9b0NFFlYnTLdMZqhkx/7emYKNXEG/XcBmR8N2V8HO8Q28upzDeJbBdGmgdJnr9QdYs+OG10N3czNUJ8cnxWg00pZbPAU2Oc45PhRQR19qDUdZrv7VotR/6mp1pVodaN+5G768ApursRqrsRq3Br90u4vZ7Eevn337Bydvjn88uhneQYkovvyQaet02naxmKvZbKHa7Y7a398vP/n0k+n3P/2+2dtDJ9jMGKq5suicX+hMo3tNG8BVGLcinrDgCgXQUVoZpfIZP75z50wbVaao4PVmQdQTOQtyNGZFuv0BbO0dwmB9qyEJcwzKjKm4fcK/O6uJx26wb+ViAaenx+SIisY9CDKHVxf00rv/3gPIdAHPnj6Gp998CWdnJ+TSSTWVBJaWBNyywRoMsMVHqwU3NzfE2PnA0DODoWWC9fVtiuux6qqifoZVXXFftoxq5pB5xG3hseFnvW6PJK+Zzum40bTn4OAQ9g/2YX1zHdYGa7A2GMD6xgb1utQS9eksuptyYK59wSGBeUvgUiRTCTvhYoRiExBA58BZX5luMW4J5g6BxYwhj4PIJiE4R2CB0t4rdMC8voB6PoeNfuHBptIchPjfF3v/rGiB0QVYnKt6wcDLNbL/HtRKsBLhnhyFOJimTrQ+CBUGQihMMelxwfTISyNdCIi0OGAy26AYrDuVgVU+EAduIxMa96i4X4qIrQtRdXDMVY5dTDnTb5uOozHYbnSqD+eIoDij1iYZ9hFIzHsCM8Bf9YEmGjNlxOQvyY10OsOWJ/64Ly4uiOEXlsYy40AKU8P1sLRrzfJABKN1YIZBxfYIAbwl96APQG0wgfK1ePFCOBsZQ/n9X/Qwo6QO9y2k76QAKAl+ow7VT78m5lHqQV0EoxgA64yXArNH7LxtjeiV5RrZGKS7uFOR29J9YRyz8sBiDw9QIX1qCeBVzBYmhkASpAugFYaJZKgCbnRkjGltqmRNhX3YsO30HsBrW9eWZNTvvnMflotfp8TZ4yePyaUaU2TTRQWPT0YEOluFhu/d34R24Z8HRmSiLj5nIovcdKyVZ4Rz8Xwie6jZzdglpy2AMmH50mSRiiAx0xaMk4RHuCPeuk1CskknYE7+m9QjBvDDc5xpBkZWnHHj8y0m6ASYNRAUI6bkWlgpzfZ10/g8ptaqoqpoMKsQ5sfJeuV3S8r0pq2DZPeSPBTZLkAixYX4zGsCXXnexKSY/5JOITivNa5XT0pfXGAv+QqkLZCkF6wkoBKU7a+9PySbnL+T52gAnH7fVlrG8Dmig/lmN4P+4Rpsr7fh4XABZyNsm1K7sWnXpQEzmUyy84tzPR6N9Ww2p+xkK8sbzC+oeP5KwdAp9Y1S6l9UFv6wNubrzGdWuZWSHyuwuRqrsRqr0Rwqy9WWVe43j1++/H2zXP4n8/lsD01oKE6zJN90ed6xrZbNiqKNckz32Q8+Mx9/8nG5u7NbYw/M2tTKOV+9r/ybFXtP5eibo5QrvfpRZfgz7o9Qa6WnzroMe1yBhTZo3dJKFaHmhgNqfPC3On1Y39mHwea2l9DxwAc8GvLUXAOJtWXp579oIAM6nU7IUAdf6gjiyuWc3FBfvXgGX/78c3j27RO4PD+D+WxB9SHowrmcL0nuid85PTmC+WxKL3l05yRJbOFfMWW5JIC4ublJABS3mxdt37eMX1oILC0HLXnmm8AbakNQcf3kmmeJlKI6zP5gQIAW6zbX19fIERZBJTKeyGRu7+zC1tYm1VVin0RiKjMO1pzv01dhMsBaam/hQh7asfV9dDHkMNAbo7BMFBKWL7Yl8NuxJlIPlvrvAWR5TiDYJr0Z09YESkVTD9we1oadnp6SO287M3C326JgTgK0YNzjfLDiCxHbvpYH+wg681Y7gOB+KNJLFT+LEjUxKoolemIiK8Fl6AeXBDwRKDWZFQl+FYgZURbla8yiqTwyJi6pZ23wJ8E3J21zIvu3IaBu6PE48pV6KwFTAsKl/QZXP/FOVHA+RYYE1wY1n69rkoPXFbXwp3WMPeZmXLeZsUGNkoASj4mUdhkHgyYiJ6zpC4ymbQDpVLoYDKwk4eGi5FaJEZQSQJmyK83rHtZIYkzkz5V5YwloVULdhClUAazxgg6MJvbrRGVHwKkQ5zhJzTTAflgLHHST6oISCHVwN/WS81inCiGJ4BjvWpZD554FrG1gNOM/YmKOKcS4ooJjL3CyI4LW9DlgTRWSAXjf1r4uHx49fEgSahxPnzyBejGjc5gtavj2ZEQmXuvdAh7uDYgVl968kNRTAoMgBLGQHIuYA4nwNcUbuB4193Q0/IwSUH6b5felCRnND66jTGdBUSK1dvE+S2BhuGRs13NLuhpgW5KciC7SQsklay85Hog4hZNbbPilbn9HJUA3abXS3KKs0DCnaULDUQuieF1tujJknZORk08USVsSa134ZqN2vHFPJEiUTb8ctS1h/hZrtNNnbFIKCwnojawrt6PB6eMJD8qAsH8F0rcb79qMv09JUZesWpeoQqSHKE8XPrfwd1t5Dnd32u5gqw+XoyX0T2b22bUxR1czc3V9k83mM21QiJVpUdI4ToyExca3Wu2sepll+n+urPpDY+1j5188b40V2FyN1ViN1YgDtZP3O73id6bl8veW49HfcdZ18ZmfF7nnPapa4QtJ6wxrM+3h3cPse9/7vnr44KFaW1vrOnBVWZVkRunAFp6pdOIiiy+YlgK14Uhsh2E2lPSuqE3fZB7LOEAmVCETWoSXFb9QMF+cFy1Y39qlNicIJmVgQIx9J6tqCe1Ol+r91C+Uzzqqs1zO5zCdjEka++boNXz39Fu4vDiD5RLr0AbUZ/Lxlz+Dr7/8OX3P92ZD6WwBpqxhiSyPrckpFmWy+HPNffey3LOGyHQicMJAZ7A2IKZTrW/AxtYmgdyLqysK3BWzhPQiRQavyKHX6lE93M7OLklg20XBhkga1tfXqe1J0W5Du9Uisx+UyPb6PTpuz4RKM3kO2LiZvTWmMRfYooY7WfuXuk7gh9QiifE+G/gkqWXZTMxAcz8/kJYUTAxJPZvPvMeebsrTSLx/vxk0B0JZ8PV4DLtrHQKqufaBiAkushLbUZErKDsDZZLATxqgg2TOdXD3RcaOyD0TQYhNoio0xbEs8woGR06F424Ee0lLhcBKqvip1EX57dgwv8RSCYvF9Zmpe0qQUUqMk8w3uZiGa5sC3AhSNddPqswH1qZWUFsDNZq41hgUhoYUIWGAQZwhmbpngRBs4rprtXKq4SS34dyvc1y3KBvHgA/XILHy5HKqQgsKke9R+CpJHyvnlfBKmQ4sR6w9S2CAc1L0FuoXM1lPxjQDdiXtU3RSMyeVn5aXagSWAciFSebaRkpiQAyaQ4seR/JtEVSKPBPveVz7liW1Sqnw7AomMOw66pyNzzWV9MYU0yBm1P1S0U05LRujZTmDAt94uMEIRRGjMLLxGsvKTIiuBBppXlc2AZ/+P1QvWVnq9/vBRx/xtx2cH78ihtu5BUznJTw5voGNfk7M5p0NqeeN/SBdcGWO92aob70lg3XyO9D8Ln1ukzpAa1khkvlkV8reWZGr5h7U6CiraABUkT2Hmm92rm2gzRSMQWjNQ/WZYo5G29HhGSV9eFOQDYGpiwmgmJyIiQ+nYnKIynpTYJkkzEIZg1OcJOMSB0FaPL+Kn5dSA00qoQTQyiNQ+pnaUMPMkt6M2xXRAz0Hh+9fKlNp+zd6vQRVzsG6OiYO+FiDQiGwvNFQzKSMsWs+69JEAuJYK4kLeQzL85LTZpZVA4ntmlxgUQa4TGlXO4OgErSt1Wwyzq4ux/l4MscWayrjkgfyInBJYX7colGgvzPO/XG/hP8tB/h6noOr4BePFdhcjdVYjdXw0UwbHHzkKvNf6Ez/eLFc/jDLsrxo51hzSSwawYIsg3an7e7cOTCffPxJ9ej9h+2Dwzu61+0ra/DhbTMp1HDWtZ2zLRAmwoMZtBMdWH5JY1zrWzC6tjJuAFobn7IniW14SQIzmghYeutbsI7Os51OuHTIDCzmE2IR2x10Ux3wi90Gp1IMoFEKjAzk+ekpfPfkMVxdnhHLeHN9BT/76f8DL59/S8e6t38IvV4fjl+/pM8wiMyJKfWBSF2WJKfVua9fwxcxZv3F/VDqzYwyFJSjMy2yrej6ur2zRewkBmhXuG3qEZr7bRRYo7hBrrBoyoGMKLKo+/sHsNYbUG9O3B+CSpTF+tpZS8F10W7R/2Owg/JayvqKpT3KiJBxFc4l1brGSeRAv6B5RgqUAKqEehhEx9R8kHKGvpk2upoKGFWcwiaGwSbNvpOg0pv7hG7fUNcVzQ2aSiGoabcK6BQZsPEsS1g5lNY+gNDIZtZs2MMAIe7HB3/KnxI1K6egP4AzrpHlQC5m0LnFCq8fkaUB/xZwaxBhg4lrU1GWrBh4k7zSCoMrgEcKjlzcFoN7UWwFCZxL5cCRlZRjV9IjLwTQMbgihp3ZSqMsVMZBZfzfPobKwr2FoACv76KuYb7wBjitXFOQ22m3oNNpQb4sfa22iw3rhekLzqpci0f/Ni4x4BG+z4bG8bJ+GvVcyQzLuG2GAtzKwW/PBOZdpjW2/vDHJ3V7wXwJhKWEJhuc1I41PXLF/ZKb3rN+W64pQJR5qhQYco2p1GYH1kZH2T9JMFUWyCJ/rDoYkaT9MsMciYRd2CdJLLxVu5jFpIXc5rKGGHSRFJz3ExInSX9YkZxDUlOIrY8evf8+sVhPv27By5cvSLFhag2TRQ1fvBpSYuJH7+1Q431k2UhOq30ioCmlbF5fF5740UNaAKYNrXE05AhibR177cp9m+nImKeJGUliMDBrSG/h7cehgGErpk2SpLjFWnpKkMG8vkWxBjQfoX96PvF6cqubZNspoAxS/vDs1gkgj890UWeAMORcZqBDXbLy9dOUzagadZzx1ovy+iBlBX4wZgXVgKuq8mAv64ErujznBpwpWUnhwlqSpImcp850kMIqriWXfVhJzvCzNJW2k+lXcOdN5kdmNtYCyJT7RyxLkendoMj0Sl1MpvD8fALPz8cwm5d6NF4oLAvCMhTNzxErbYVAEiU053iWlXLuO63df6/z4n/Qc/PVZmkRw8LsF+W2V2BzNVZjNVYDENQdOlA/qi38jlqa366V+xBlsBnJOZ2rq1qVZamQFcFmxthe40c//DX18aefus2tzQXWVJKsypHSCp/xiGoKarxp04CforvaOV05tNmjSJcaK7appIKktjTwIa+cdTFgNz6N3B2sw+buXegM1sOVQ6npYnIF9XwMnW4POr22r/ubTmA6HcN8MiOQNry+hrPjN3B5eQ5vjl6RNHY0uoH19U2SWR0fv6AWJfhvg8Al01AvS2i1O1EmKc576NKrY40KvtC2qC6zTf0s0aXT15R5ZgUdHRF0dgggKzJXQYdZ3Aa2KtlY97WV27u7cOfOAbnCIlPZ7njmElnMdqtNbCgyKdTzsuVZXWRpyeGUjXVUI2CGeOwiocO6QcetGzgTTi3iuW40ggiOW9Lm3olkNAIDCQaYmQwGPB6EZMz0OTECkiBComsXQRQGjthzdDIe0/8f3tmHu+s59Fs1aJRwUyGTg4wDFpNExo7dPGlJpYxS0p5AaqC8s2UMotP4kYKf2kbtrAzpfBGkfjxNLmEz2QzG9+yJLJJgC8XyNd9fT1hPR42/3645VLHFS2BKk0pTx203JDhKAnilhO1goxtx2LUWprMKZhXKd7uQF95JVbEhVVkbamMxW9Y0t+08J7CJRkHIbuL3cB275JphUkOCvdgTD5rMFDMkgZ0TTQAAIABJREFUlFiQ+JGBgwtMFdf3yvpQUXcY159tBNsuwY4Zy/nY5tJfC2HoxA04NQcK08xNEyCy+MCAVfP2AgizNqwzZRk86cScKAWzfI20gN9bOEUAMa0DDNbxvrRsGBW80JDKEWBxi4108fkoNbB+7rjHLAhQ9a6mJCR0nuWkOmc0JaIfVwkXznXInASwkhzi/eLpibx5MOjD+x9+RDW8lzcjuB4OodvJYVE5OLtZwE+eXeFLANqPtmB3vRMBJzPEVEeXafZkYkCeSZJI7Mql1Yd3riUXYN/XmZ2MdVBkuJDQFAxumRH0QN7wfefEzZyTQGEJSE05M4OxDNiK6CJZczYY6KS3rmOQEks/PMxX4d6P0mxvJJQkTZT0fYLkfpZaTE46CZBMEksiM1UhASNpnVhvT88XDQ3rIv95ZJXTxelr0SXJlUjjORsn7Kl/8NYA2GO2LsFVc1C2pmuNSQUxmBMhjJJJYjms5XWsObEmSazAwNP6TOpJIR6quAQrBpK3kwCKEw0yZ7WxMJtXcDVdwrcnE/XV6yEcXc+h29JMa7uQ4DOp+kdpp5WulVIlKDfLnX5hrf1fF2b+z7TKvqSlpW7vvTlWYHM1VmM1fpVHTyn3QwXmt7XTv2Uh+0FZVTvyzkIjm4UxFDO32i0YtNvu3r179Y9+7dfM977/PbO5s431mIu6NvgOQqCIr0i0H+1onRESolcqtcSwvtzSuUopu1A+zdjWgEBT57rInEOYydJL/45jRooDHeyhubFzBwabWyxVqsAu5zC7PoZyeAqtzIG2a3A9uYajswm8evUaTk6Oqe0ISmsvzy/I5Gc0HpGEdjafEot2fX5ONYWOXFr7dNQoi7VLy0FOBhm2AMkyD/Yo4HFUrxlMR1C2mmf0BxlUNFRB9nFvc5sOHoElspdYt9ludaCuS2Ih0UwJJbG7Ozuwv78P2OdrfWPTtx3hIAkYoCluJq9xfijATZ0yo+SJ5GTUm86G2iUItYaWzU8yci6FpMee4qwzXbWkh1jGvUBEsSQBubQ+CTVLXIPn/63DNRTzIGqCH0paVDTGsbH+c75cwOnZKRwdHZGUdn9nE3Y2WtBvO7LDt+zAqXPvp+qlwXKkOkjSgOWbOCeZSoIycWZkh1OBblIvKwdkkuAy1IpK8BuApuN6wgxU1gWrNZh6AcpWXrAZGCRmIV0E5Lx13keUsvpgSrTK6fdU4xiEeaLrkEZyoZ5V2AQb3HcxnqqwGdxsCZOlg1ZHwyDLKTAUAFDVxtdl8qRiYoNa9RQZSZkFEGKiA+cFazZRSot1xRCC8ZS1lvZDzLhKskKp0JZDOelNGTiWAJz8Om8COFlPEAAFBCZbJN6BQeeJx2dQxvWycUpjEibt0yqBrtwLkeqL5iaimhDUJNfbMYCgYW0E8l7HTnVk4Uo6SXxIc3pmfFCOjDWu1spjkxh8/yFLZskdEwIwjKeaJJlctG6V54Ryca6s3POp/JqvuxNZNwJZGwGAuGT7c9XQH2zA4Tvvwb2TE5hORnCNZQH1koyCEHB+ezaG/Y02dNs59Nu5r7VzEXilzJ1jQyytIkDw+8o88OdnCbV/ojY7wsvpUC4h6zbKQpkBDomKuO/YZxbinIW5A+m6FbYTnsXhfoSwLiBJYokhED1H5d6TNRMAl+T+2BQnyH3Dkg1HJAkycq+FuAGp79bMUDupfbSpAiKKx7Xz7aKcLROzJj/XYmgmTtA2bTnaQNJVfDYhIKsm4EpFgBPNv8T4ygbDoThNdJyaW8LwvqIVkU6Sm/Jc5CuCH2U+qSBtrADSZ2lUqAAz21rcrPFdXhsYzko4Gc7g+HIGR1dzeHE+gYvRgtZpu8gpmYYtUkKbHAH35FOglcr12IH7WV3brzKnfgJZ9S+hdk8g2o39uWMFNldjNVbjV28gG1GVLVcvPwUF/9DlxT+yDh5iKYNzloR+OYERLyPZ3N6C+/fuucM7h+7e/Xv1gwcPlmsb647rGTDzaBT1V6DAL9e+KEmIQK4bVJyIdzl66FCIjE0N/ZsOgyeS6DqU65rKZ6/RO4hqsirotFqwsbcLW9uboNwYYHgJs5tLuLo4h/HFG7DlmADWrFTw4mwMX393DK/fnMJweENmP8gszucLMg/CgKRVFFCgaQ0CSDTLqUpqIdLp9CjY8zVIzBZoRX0G0WwHQWNdlVBVloJtlLni5+gai4EWbgOZHwSSCB4//vgTcoLFgBxfXNvb257d5AC21erQNvAPAl38zGfiObC1EIJYy8Ejvqw113pJzEOBhbMh2An2/QHgCAhQzIT4l73moN4mAaVIzqQfnzSedBK4MhKjlzkqnq2XMYaWLonxj/9/3icCYMPJBM3MBQNN+l1k3SYTePniBTx79ozAZjer4dHmHSgGbe4B6M9XpMGh5UXISse0v4a0D38aHMaAUnpGhriIJXoexDhmJD1LZvkaaGEbGLzTpkiuyKA+MRQisB9cN2PwKEyU3CMi20pBlQt9IhMQABDklAKO5bqK2yMEg5XIwqjAzHughrXGlbWU7UeGi66l83OB4LNVZFRGiTWbeK2KKqOaTdwesuoHe3uwf7BH+8drtr25RQkbqVKzgRZK2pwIPSbXJ1OxH6aLoEzmSXrPBidTZhTJJCbzwJeeLVnOVzatXYtrkAb3Z3QMMnU4Jk6SNOR5OgDlKFOVejhuXyMSTBsDZg8WdWCQPYsrmj5OCCUOoQoka6GCoyZNkXGxrjp1WIHoOuxPAwGniYw2zbsRTUGUi0K8ETTXR9NW0CAsMKMs01UCirifoYZgZBaPx5+7YSMwVGZ8/MnHAGYJTx4/huXyDKUBlMwZTkv47mwC7ZaGhwdrBDhLE1UMxtgISjhRwM0/kv0ZeoYYYa/4eKxx8T40ljvYiDzZJdJpE+4PMnkW9cbbZJifWxelBJZra5MvRDflBAxKAi3u3jWeg/FmF2mp9CFiDjIx4hFwGTFeUsPpJHET+2A2clHSo9WmzxpZ4xDeO5zG5ZV72wRINYCiS5QFkBrH4XOlWoZ7gR+d/lnGSiTlWMIu6hK5HdP7PJyoZ0OlpjfVNXuVEz/vlHdNx4SpsS7W1WvuI0yfW6pNR5fkm+nCfXs6hi9fXas3V3OYLLG0xa99fNahmqMyt8okHPcJ9Y7GlbLu68y5/85a+N8rV73JM5j+skATVmBzNVZjNX71BmZIyz1jqh9opX9TK/UfgbUPsKmar53KidWiej9ryaDm/YcP4Uc/+lF99+69ur82cO1WC13EDZrfOHADqgrLFJrUOqrRVNRXWdgCptsUd1qnTobYxkQrpDL5CU+mQ/jsrpcA5ZSAk2LDgXoxBdsdgF3vw/h0DvOrl3Bz/B0cHb2G49NrmM6mFJwtjYXRtIbj8xG8OrmA2aL0mXIJjNCdqPB1lYr7ZgXTn7wg91phLMRdVQCX1GPWla+FxK0i24OmP9IAHT8nVhJbw7TacOfuITx6/xHs7OzQ6x3Bbafd4ZqVKHtCEFvkLYwyiTWt6ppWJR2HsJccUEVlofVOr2nApLUoPaPrq2KWMgQdOjj2SfAuffSIRVLA/S4hACInfdNYNhi8NqnHpfGBgBamSNP2gpyQgkpv208tI4RBSvVQodOHoz6h52fncHl5CQZ79827gC5TCHQwCBcmlc5P+5rDEI+n8t8GMwhJu4Uo4/K2vP5QMiUyPAiul8656KQpTCXEQFSoNNpvvWBTDUMGJEE5HoJFydrLryfB5W0pb7J9pdIP5HOdBLvSjxVY+qiC/C8Y9LjoNpwRU6t9Fr+2iaTOHx/Oc7dTQMt5z56CWtEAlC0HnXYB7VYOdlGR4zKue0ys+PY8NeRFEdmGWIwX28Ak9b/itKogTpSfTgGXLiQ3pJVHYkod6hRj4sAlgFFqWi0DuljfSsygliSIi9FxY4r99QoBu8iRRfIR5tYnIERHGXp5ioJA6VCA+1ZdYGBQIoCm73HSJ8yfDtmSBBvxPZwACid6zoA45BpYro2zgf0L+FfOVuS4gha8C4t39XTMbkIETtFN1zvU4nPt8PCuX1t5Cyr3BUxfvqK68ZuZB5s4WkUOD/b6FOBbrZkFjlLrGOnbUE4rrD+oqIQIrKhywUgJONFGzGlQDCgGyja0EZGa22BNpeSmDJc9GbyGxKQp9KlVIZEDnDRR3KpFM4yzCYsqhmBW2uSEfcW628Ya4DVMSSFUg3BS5S1ZfwCQ8XkSWq0mYBici8ZWiQpA5teK4zN+RcfP6Blo4/PLLydOPIRnpKxRdDm3zNInsgs5BmHPk+RMWnzpOHkQ7hEVQS44CO9hpRLZujDOLiZb6AlsHEznNbwZLuHN9cxNFyW8upwqBJw3swoKrTnRKknXeE9Kcgl9HDrttjHWnS/K+U8ra/6n3GX/iwJ47lLvoV9yrMDmaqzGavwKDYUSzLuZhX9glPpPtdYfaaUPHLgMuJ4tyzN612AQicHkvXv34bPPqK3JrNcb1NaYrjF1yzq74EimA9og2vTCI+uU0wQi2UmPcudoF2mQOcX6BxLL0rPe5aQgwhcdZqnLBajyCtRy4q32yyUsxkO4vh7CHAqov/6G5LCL4Qmcn57A0ekQxrOSJDuY4SzLCpaV8c3nK+OlNMq7wiL5gSYneE5okFLXvo8m/u1fPBkFjuWiop53/ncy3zid7fuljqPd7RFoxRpKdJftdruwt7cLe7u7sH9wSK1I8DOUFq6RQ2wfcuy3CRHcemDv5XuWQrmagJgH+Y4ztGxPr1QzsOagWdpLSKCh2I2RGOfQ3y++5InRUF7+JNlswzb1yFbR5yw/pDpLqqfy7rVSRxYBENc9MjCmIASqJABKnESpXsZQtll6+UXBJDN2xv+NjDDWqmL9KzLRyJoMen2Swvqsut8qrarg3Hq7hlT+HR0eKd8BIr2MQbrI0SQYEhbCb88kIEEEZ3H7gfmlREDpzYW0N3pyCcOUAiwtLKZN5kcOOkSizAimxydxmEt6LCZOtJKQ8C7ESYuJhHnBFa6sgfF8CRc3Mxj0HPS6XZLHEpvAtbBdrRrMD26mlWnod9qwPuhBVY/h4vICxtMJ1Rav9Qfw4MF7oLM+AKkSpM6apdiZjg6WCZMGSewspyg1koGt4w+o56tcS3KaqhvfC/Pn0wJB6hjmRdh5UQewdNa7ZjKAUHEjzrogGYYEvMh6DfLUpMaZ/taxb6TUFDqXAjW5ZnI83Kszk/o/F5JMkATVClJ5Mt99zkSZNteSCosVUZNuyALDWlLRpMix3NfXQbpQN6obIDaCgrAdTgpgOWir04N77z6Eot2B0WwBx6fnMJ3NYQRL9O2iecoz//37OwNotTI/NyyJDcxxeg0YtNN082dB8ptIm1OwJc6p4TtOJNsJttG+LYZL8Y5MrDxn5fyE7QqsYTiyIGON4C5dhPJBXJ6iyFZa2gtBSGZ5yWg0oYrmZc3lHZ4nDsA1PpX7yHHSS4W1q1QKOCHNYoVr2Fxn8i+pA1bhfrLBRZqTi1rM2uyfwRT7fWuXzlkT0vOBx/Nx0Ni3PAOdJHD4myJR1qw+QqC/rGo4H83h+dkYHh+N4Oh6RhOB8cCysgQ0i1wHIyKZ12QZ0dpvd9pmsLb+k2pZ/vNlufij2sDPCg3XfxGgCSuwuRqrsRq/OsMpZ6sHxtjfBWd/Xyv1GT5TMRbR/CrwdYi1QhOgzc1NhfWFv/6jH9UffvDRst9fM84YZa2nLx26h4PKtbhTOOYkQj2FvLxRf4h1n5owlVJGK+sy7RTJbZWplDZLks+a2TXY6Sm4egbzpYGL4QReH1/Am6sRLEoHo3lFgYytS5jMS5iXFnqdDrGvKIuZTUsCT+i0iYETsjIodbUcfCJgsQw0kZWlbG6mSTKIYItaPtSGACYymWjMk2WdwID0Bz2SDqLctdVq0Yup3+/Bzu4O7B8cwO7OLoHLTrfrpbCWjWA4GKw5MPX+HXVkgWi/AuaSHm7BZVSCdHYOvdUnLtJoMdvuf57GPuqtbDd/4LcpwEm2pf184SEaqbcTm2Hp0YhgjHv+OWZQxdUy1WEplbEDqQkBnEjH5FuG2ZRerwfvf/gBCufg+M0b2N4/gKI3ANDTkIV3wgqzOyGEGp/k9Hg9S82cSoIzH1TYmOGn+k2pv5VenknbgpSdlK275N+yVacZcGpySE14Jq8Wl7okYIkZ7yHUgTamLWECgkETBBbT96VrAkKJvWPAzi6lAqaslw1T4LWs6LrivYPGPxKgapWybo7bzHgpJtY2DfoeTNyMJnAzGtFxzRYLqAX0sSmP0lHyGZgkZoSo+b327A8IcwiusV4hkSaS3JI0nb4+FsRUKRhASbDJfSchrjORpyqWwNJ1CdQvgPD0EGrMEpaZnWVpZrKYaqBkjvVrPbJc4pYtDJTfPqoYhLHRqQNtAzwYYp/DXCR0i5PWOAJUeVUFpjUBN9I/Ufli3VhnyQxZNJlhKCHfk/lmVtbxvggAG19zj0msaPSUBOkBhTlodzuwf+cQ3n3nPXj8zWMYYq08Gk4ta7hUAM/OJsQ84ry+s9uDIs/EIiep0+V9Kxsfay6uBZ+ckxYxKoJOkCROyEJFCb9ICkIj/thTMk3kyHMyNT7T6T54qsNSTxnxAILjmhK5tOX1KMdIiZ9MJ4mSCKhEhp2qKlLwGdQAjQd9kgyR30sSG7xV3/NUklAJAR7mJNmcajCTLrhdq/ATORfHZlQuiARUc0qTeWkedmrwlSa34qmqt35HDtAnWaRNjU/STBYVHF1N4asXQ3h6NoHjqxmMF2WUJYOCbpFDq1BQWgtlGStG5dnLSamlc/bzZbn8b52p/0et1AvUrtw+in+XsQKbq7Eaq/ErMNya1vn3rXP/mXXmd5WCHyruIMcP+fA4z7MCDg/uwEcffeSQ1Xznnfvlxsbm1FQmr+uyg7WZSmc1gG1plRXaF175wJFaIGiWUHKXO7NEiWHmA/ncKYVor2yBqTKkP6tqDnp5A4vpBK4vz+Hm5goWy5LA5vnNAr47vobzSUkvSnS8ncy91X3tfJ1fD80AMs1Oe6g09TUboo/BbDrGelhjuVxUQVaKoBIDyH6vS1JYDMYm4ykzHDm02x1qQYLmPcheYhuU3b0d6nmJ7UsQnOJxICDd3Nog+WzBtZYUmHFNkhVJq2OHUg60HdcraTHPsVIHmQe30hAUsxJQq5hdVkoMTCIrCKE+yYUXeXQo1bH2RlwwbWy0btmUKUptmVVwUsPGCyQFBFL/GRr5i+SWDVxSwMlukYxiYnAWAh0Hs+UMrq9vSKrc6/bITGlrc4Pkd0okxFnhf51YXRsy/EqC5cT0Q4CkUmlgHKLAUJsVZIF+MvmXXagXDPdJGn9RvU+WsC+8Bwp0My89TAPZZP6kY4Vgm5Td84fngqttKr+UesMgh4RmoBaDTYB4U8cLppn1bOcZ9Ds5VJjxLysCBCiFBO7Nl4SdtObwHipy7COpQ3CI17wgZr9D9wwlb8SJlp2HIe2fKDK4ZCJCMJwwbg3TFk7SeFDKtWr6Vt9SlRoGKZY7JkyjihfNr58ssOs+YcHnFGA5X3te25yTCO1bgIFzXNvskso1mg6yhstmAHUNM5qk5yXV+WbRNZcRlgvOvhDm0cm8Wl4jOuM1HiYsMEnR2ZevPTs3x3phFRIuiteqMPyU1ELG19lU9y0pHD4kFag6xQAc71tSw9y/Bx9++CEZsV1dXdJze5EpuJwg6z3xEm5wcH+3H+qlU+Vwei/I/IJ1b4MOldwEcoV909GkDtBFBpdbbfgdaZDUVXpdIh2ZOLmqaPwjZdWhzCCsLHGsjc6zUrNuw8+FGVaBJg19giO686dk4/7TVtGSv3W3jzsBo4EFDPcAm+aQyVC8L3QKMAXZyxTIknWS3vPP9bhbFf4bGUkVnmnxfRalqqGLiDDwDdCfZgtjXbPcD/FBqdiJHPtDW1IyLSpL8tg3V1N4ejKCJ0djuBwvqSbdJ6v8aeRZfA5n/CyVVkh5js+5zOZ5/tQ6+386U/5RtYR/qZV+BX8JYwU2V2M1VuOv7/Av4R3n1N8DyP9Lp+DvAcA7PlZUdYaN0DB88z6w0O/14ODgAH7w/e/DZz/8zO3u7lqts8paZ4ypWtZaVKHUGoqKGrtrrRW7etKLSLHRB7FUKAtdgi7HAMuRMtZkGDdiPxOznOpyMVEIAC9Gc6imQ5hMx3B2NYLT4RxGs4pc4pAtGU5KmJUWWoXvq4aMIzbiV+zeWC7L4A7LpRgh0DYM8PAlaDgYRAmNr/v34CSnhvUdMg7Cl2Z/0CeGEucBpbEbm1ski91Y34C1NQ88QdilPINce5daehcatnm3dawr5CEhTGqGotmmHg8IjVhceHG7+OINfeUgGP1I7ZgwN9ZrY6OnhgTKDhh8pmxXwhrpCKKErZM6zdSsxrv6uWgSknbtS6R2aW0TpCyB1N5REBudbIOBCTO248kEnjx5TLW4CMR7KEPutqANJclUrdVsBqNYrmkDRAiBXwKEtYp1bRiYN5gwBtQqAeYyDxLUQPJXTOfHIX0KvatvlIdF91UVzleuXwCHcdbDXDUZ0wRMMeOkgnNwDPTS9ZXWmUpSQaSZPhBUdA+1c0V1c6iArsqalABSuyYN5JUAW2mVXoCXn6G8vfJMF7XOAKCaTWwxhNJnnXGyxLCcD9krnA9jwnUXttO36ZAazGQtST0V+HrfOF+pnSc7vYI0X/f3nSRngnRVWp7wbFMyJ5c5sYEBh2QNpfdITC4kCwEzdTpvrBXpNQss7b5tdgSQ9JgFYa6Apf6a5LzU5iTe7OGYIiHqGldeJzJYYJbVBtm147XozVIgkm/hH6RESKSQlhlpxcDA+d5A7GadtNhIwE1QKZADcEXPwt29Pfj13/gbdA//9POfwMXFJZRlDROlYVkaWJY1BfvYJmWPWqIAg6BoxBwZRmFrxaCM+3SGqb9Fo0X01LhDUsYyfY7Fey3Ba4JDk56kAqxTPBqAvoIgaQ/PT2bTow1PBMd0TbTjdaOCAiOV8QoAj8tO2GyXKDkiIyojgNeU/XRJm5eU1kwMgmRNAj9R+ao2ab9E6hofs4kSB1zTQyCAbrmPmvMu91bKlCudHjYDTzkE8O/JHN3dS0PM5ZvhHIazCi5GSzi+msLVZAk308o7xaOy6ZZkF0toSIVPiTVvZIS9hAfdou51Wn+aq/yfjRflHy2r5XMAN28+Zf/iYwU2V2M1VuOv1UhkNH1wcMcZ8x86q3/POfd3lYJ1fmkukVXUWDXpPOhBFunBe++6Dz/60H3wwYdqd2/P5Hk2q6p6bo3J0YQS+2YqrVtZlumsaGkSt5W1slyPhfVgaOiT1TNQJI2dQz0bw2w0hNFsAvNlhagvXy5majQew3i6hNdXc6rtWZYVZSjHixqmSw+QMLg1HPyWlQWTeaMYT5p5sIVBjDf4yTwDk2lmaHwAg4BWEt5kfoD1RXlOoNXbOXjTn5qksy24e/cumiHBw4cPCVxiy5d20aJaQsWZULHex8AajVKQ1cFtIFMaAFUzUvCvcJEY44ElTdWB2KOc6uaohhQ8Q2xrwyBDJc3EXajlioYrMVgQpjSa5TQDLpDgNP3/JJhH4OR0rPcUc5dYbwcxTc0sQsIrMFPjGkGQODz+v+y9Wa9lS3IeFplrrT3vfeY6p+bhDn3nyyatJiWRJi2YliBTMCTCsOAH/Q/7zf/Az34znwwYhgzIhg3TgAHbkgmTzWZ33x7uXPfWeKrOfPa4hkwjIiMic526TcLi1N3a2ai+VefsYQ25MiPi++L71GhfjbgDQhnk5i3MF0t49uwpPH76lBKXne1tGPYKyP2cZPUbRIBsEel8iE5jgJ9FupUE+IouWMv5ulEEShLAULhPvDevopB/ztD+PWhbHQSxyMRLEjigutL/B8mvDaM3KSVQPtMKoubT8FCCTat+gfH84yenqKzQnfF7sizMD0Q03UoCM/a9o7nZnjfAAkqIiBaoUosBnw+oLioGIxp9fn4B+/sVMQCCqEmY46rcSwlVQvulm+I0CdB5xXYNkojqHPYxCTKm3TYl11x7HSHSDo1eJw6jjWfEMqrPBtFSG3sduUgRCyoJeiZopfhvioozBH9X6n+UHmsD7b5eTZ6s2sDIfTH8cKXPpEnWDEV2VYFaGA/xu4BptYJcEt2ekDVGV41VQSxB3dN1QotGcria7LJqMEQ6vWRnci3lZpHSd7cL9x/cJ5QT5+9HP/oIjo+OYLFaQZPncAYePn8xha1Jl4oem/0OJZyNFKKS+yeiQIK2QaK63LhYDCO2h0/YH96p0nZc//gZUXoqaJHgFZqnFAv4QQ7Sa0Ep1aaer1fyXaViei7cQPIsmVjgw2dEn21I0GkWSwuIe5iDIrhjRAwJfFKASNcTtrJhxWR8rtMEN0VDDUBC4016wvlBcdo3LL9mKyUXL5IIGimKK32zaYsHJPMFItoZ70OCvtO1scnzL/uUByYk0Jp1OSvJH/NHj0/h4YspJZ6r2pEokMQLSNEGc4Ud4knXmIpr+CNMMoejHHbGXbc57P/p7v7ovzl6sfhX08Xq9NVyxF9urJPN9ViP9filGbioUv9hr9j23n/HG/Nbvm7+AYD90GTQN4Hjh9tWgZtdXdeWKtG7u/D222+5d9991926dQupo5iEuqpspg5g4Z2fGIP5ZccU3S6+J2vqCtVCPW342NODtKtmBc3sBOr5EdjVDKpyCaeXU3jy4hSeHp3DxWxlMQgtqwoupxXMVyWczkqqNgbqpZh+AwxQ/TW3sFxWIZDgja1uxKib/dhMBmUVfo4bYidDtDFDvRqojaMEFLdehGT7nRwmvRw63R40UMCq9iSn35BnWwOTjQk8ePAA3nrrLbh+cD0kKowakF9h02gw78Xc3Lc3M0g231eQEu2LAkUvRbXVG6OelET9MqzqSgI+VhOHVPBHVBpDMMjy8lmg8Ho12o8oHmjAxYiiMUmQZBhJYpqetYl6pQQ7UTQCWNG21SfvUL2DAAAgAElEQVSp9DKb2D4YFUkJqrghsA4Je6RsovXMYj6HVVlCnheEJCNCMhoMwNTnITjEIMVy1MGJGYcpfBaS5PnWeVlGwiT98gm9zOiNs5pwks+hSawBBNGJfMXA8OTsLu0tiuy9MC+sSdQnlX0ZixBtaunVYVrJo6CUNvPR6F1zIRNsJIDhjSt+lzJn8Erhc4CKs8gYQBXZihSWHfvTCcIOyWeHgBefuX6vA91uwTTUEBhOkS55fAKz6Yz6mT3TXqm1mJMputapT6VesJSlGenvWsAQmqVw4fSoYk4svqqxJ7L93Gndx4dk2nAiEN7nrx4OJ+dcIMpYyIgTC6FKNhAEyOTeOX7eUtGdV84roYxCQp1U9evUX5ITP322Is6q9iuIhBrH58zBueHeWAGkUsqvVSq7KN6C4NY8F6NPbWrvEvwNRdDpSp+jS55z/jcyTbDN4Pad2yQ+hb3uf/zHfwJPHj8GZxqyS5oua/j4yQV9/1s3N2BrlFOrpVBO5T7Gs45rRbsmxEIyqbWRotuGbUDSN6T3W1gi8VtUgZgTWyNiTPowSP7kuW/6yrqfFHOaVGAsGa3z8L6Vaca/ctrtIEkS28+z/iRtT9DpwP6ojVc7Fj0UUfhNjju0f0SEvmli8cJB7CUO+1KTAPBc2LPKgYDGNmG+pAl8UrdJ7wH9O5PzDj/PWIEa7aRQPwFvSV06uFwhmrmAx8cLeHQ0g0fHU7LWCVZNUXGavIF5z6cCrtge+Yhqj7oF3Nrpw529vtvfGP5o3B/8Qb49+J+Ojx6fzpc1OOuhePXW/VuPdbK5HuuxHr8Ug02hzbDf2eyC+87MNf+8cua3rTG3Wcxnxf1puXPO1nVDm+Lu9o7/8L334Fe+/W1/49ZNUxSFbxrnMHjKMmyrtMT5ybIiGwzHpjMcUmJQLVfgqtogZdbUC/DlFKrFOVwcHcLZ0TNYXJ7DcrWC42kJX7+8hGfHMzNdloBmKZaDitojVTYoXWIlEqmuSGtBRLPXsXR85SoEE/gzGVQxTyhOWca0WpQ9J9VDgHE3g96oC71eDl1EDgsDexsD2B4PYdFk8PS8hLNFA5UzsFzOiA57cO0A7t65BxujjaB+WdfqP2kpiQuJoGPUBqhCXTPaIPLtjvs+fUQuFW3wgdqD3pQ+COY0TLmt64p7u4CR2RpY3oaDCB/FlzjAM0lySsMkga+I70hAZaP4T4viZ4zSWMOHy1X2Kr4jAi7KIuMo34B4OibBMClrZtT36n0dXkPnlfSBJmhqVuRUvT87O4Xnz5/D5eWlJiTbGwMYFAagDBRog59HrWRI0a5Dh5qJKKaYmWtIk/RW+TSINsFDrZ3iCfIZVWAT/lekgyW0ukAtTnu3FJdR8RsRntGeUCO0X4hwlNgWOJ8ElMDJZgwwjbn6by+5mX4GiG9d8lqrqKsHfIxG3YyeJ/LZRGGNRQmjZQWjXocCt0iR45ngQk829td1kD7PiFav1wfX1HB6fARPnjyBe/fuU+FKBIEo4cSo1VmlF8rcJeq5zBzub6Z+WW9Z6CbQbC3Pb2eiQqr0XSvKZoQ2nMXnU4JougYVRsABdXegz7TMd72GxmvM7iWA1kIC91GroX1NhQ9MRoPgVyzgpAi0SXovhfVA58lovlzj8BxD0i8a1LydRP1JH1vGyTuuGYpY8w2TZ1KRx5QCCgklVG6xiHlpjyc5FIZ5Kutdwz2zQhv3yXfI92pfpCOxtxL3jzyDO3fuUMJ5eXEB56ensFzMoa49LEsHX72YUUFi2M+h15lAv0AklIsUSaFIqMFx/RN6uTwzyfVJzp+SpuR9uihcSdjiepFkRT564cqPtbihn2GDwjikYkbJPWyJDclniMrq1WNoq7FGhNCQMrj01TOcHddpQcXlUbjKZDHSHuFB02Yv6LFWk3SPkHUnrpugazZm1tprmaxNSgm3MfHXegp/vgoTGZP0OnCRSNZAiD6luN5gTyailtNFRT2Yj07n8NnhJTw9XsBiUVI7De76yHgSRNPJXsUCapbtnhx/AzKfNgYdd/9gXL57a7O+vdv/cjzo/be20/sfn87Lk93NHrxxexOen13CrMTXw1/JWCeb67Ee6/ELPwgpymzmG7i1quvfLl39D6w3fwcAtjjlKMnaw7kc92+ki45GHdjb23Pvv/e+//DDD83BjX1LJsmNa3yIcCrnXdeA9Z3+IO/2hlnR6xpEQMrFApqqBIfBzvIClhcv4eLkBZyevITnz9CS5AROLudQNrhheJitSpivGkosHQcqGVOIcjYNL3IDBQQKJqKUF/OSbot4jFkjdDkJ5j1RZ/Dnk0EBgyInafNV08Cgk8ONrR4JUOyOOiSG0kXKzKAPc5fDx0+npKg5X0lAE/qM3nr7Lbhz9y7KnrP6JTDvKQQTVqxDoJ2bha01oSRJp1tasU7iCsuUNvQDM41VmiOxrFBkCC9RVWkCJMFd2MyDCI43Yvgf7AMiby+18jAqeKOBuCaY0rNotI8Ob3sQC2nTURXJVJ5mO1FTpNZH5COYwWdsbcCCHfL9HOHn3Ku6LEt49uwZfP7553B+fgbDwRiG/R5sdBx0q0sw1Zw5VCWAX4XgKLMx0fa+rb+ix5X0oYofZhosCZ2OAqcGXM1oDgeYKlwBsccqKii2ETFBBZwoEGsCIzRAocuZK31NXhEzCXZNSxFEUC7Mwxh9USVWSWskaZNU9+p9ib6L2KbdxaDeG1iUNSxKD/3uCjYnNQy6Rav/Vc5NUDfL78d7S4rOdUWJyKIs4ejoCM4vzoMHrbF8VBkH4o65gUYpl5G+GS+FID0t3EavM3vj4X0ndeP6FcXgFg9akTtQCqgVeh4+C0IllzDX6Iv5IxL1ZqdXMum/s0mtwLG1Bh9vqsacPkc+PS8WNsrCMxJExJJ5otTLSFmVo20aUY7mj3QieJTYDrWQJC4dea9+mfrZct000Yr9eSB2SfLc1qmtRrvAdQULp3teV6H3dmtrG9595x24vDiHTz/5lNSLce3GI0GxoH4nFNnuXxvBuI+tCZ4M+aH1iZLIcP9hwhSQ9oFYcPF6ztC+dG3UW85XZtHPIhjIpyU+m5pcmjQBD0inF7G8FhR69btNnBfJdRQhLRGIwnPIWXBO1mQvarMmtCHQZ105Pym6kX5fgha3jqnl1wst4S1oFchkAxB2iIm+lzZOHVm/0nsAInYlhbFWUYw9SrU8E0R/pmUF54sKTqclKdCfXJJPZtBzWFRQ4Roj95+KiIaK2JK8CjuhrMK+UyAjo0BmU+avbfXL1w/GJ29cnxxe3+h/WZjeHxY75l+dz/3T/VEHbv3dO7Az7sEf/OGP4H//00PYGP7V4JvrZHM91mM9ftGHsY3vn0/6r68u639SzFa/76x9CwP9PDOWEhq0zywyaGpvOp2uOTi47u/cvevu3rlj7t+7b3b3dsm6uyahHcqGKuP9wmJrZrcYjza2it5gbGbTKZy+QNTyDOq6hHI+h/OXj+HFky/hyfNDeHF8DqeXc6pEBoGfaNiNSMqoa6mqvXIp/dTTzxYQNgXLKoHYv4m0vXGvQ++drWqybMAkNSdlTAPjogMb/Qxu7QywWgmLVU3fubvRh+sbXbg26cGon+MmQ8HI8bSGh0/P4KePz+DofEV9nbhxo+Ls3bt34I3X3yD6JonrU9CcccU+JIONbqYxMCNkw7uk8p0I6fDPQVRNRUQIkRaSvg89ObTp5kXS2wXcT9oQGiMBSUgyGs6B2adOKrmiGAjRJkGDoTTWSir7IWkFDSDVV1B84K5UyzV482zpoMFSm7ZqGL2DLKhlOlF4NUEJhOrRJtJ0l4sF2SRMZ1MoOh3Y2dmCa9sTGNglwOqcxKak0i/UYK/XO1Jf9bwSYRbxUzMxOuH3ungf5TNESRLk3zGZ4bhbk3RJ1L32G0nSHvtANUlPglopHKTCQPra9N+MjipCAlJwCKgG9faqz1ykQxOd1gniGz6Hrgz2NbMoFQb3G+MO7E57cDarKEHEXigNkBktIsTdR/oqvr9XZOS3OV+UdN/Q2xZZEnj/TkgIpoR+fwCuWjHilQW0S/qLE66xESsbJ/RnE5SkWRFaTe9B0HfBPzwn9lcFeJKii/SCJs+A9BimiZUVxVChikOc84JKOlXWtSqOYzgBwMRPaLCECrLwUVC9jbThMGeSdU/qGoTAmGilgs80enU26VMFbC+R9Aoqoi5JoQ293ELnRRSXRX+kEJbS5yGZx3rfRVUcBI1qlBopSbl8r/bEmiQ5NTb2d5JfsCOKNlJqH7z2ekhAmxp+8tOPqUcf1/LpfAU/+hpbLII38nu3N2DUy+l5Jap9qzjktDgG+oxzwgNRlEs9FIV9AQmjwafrlUk+RyydQBN3unYJcpomUj6h6aqikI9rU0rll9xL1qDY4yv1BS6m0ppj9P1a0Lyifqvnws+7JFeRRu4j4uuN3h55HrSQobWcRKhIe4WlYGK4UJH4a0JkBgh92UMiTGagrSrNn+chIqWWkX7a5ahwjGrkKPSzhMcvp/D4eA6HFwsqSuB3zZDa2kCgxvrQNgNx2WyNgP4bapHB7xz0O3BnZ1je3x+f3Nkff3F9q/eTzZ79NAf4Mzfr/7GryxN8vqiAzUgqJr1/fvHh/99YJ5vrsR7r8Qs9jDXb5aX7nY6tfjcD/+vOmjeNMf2iyOdFntdV5Tp1XZqqqry1xu3u7br333vPY1/izs6O63W6GBjYuuUZgV2ZttvtDzvDyWZh8yJDmuPhk6/g8OGncHZ0CIslUlnmcH70Ao6Oj+HkYkYBw6IOCUsmPYCMwiEiMhl2yG7jeLaiTQQX85wRN+q5dBIUhj9oJbI57kG/m4M/W4J3JWyNu5RMokrpRj+Ha5MCtkcd6OTBB85SgprBsBvofuSs54BU63741Sn84OEJnC8a6A1GZKOBAQ2qzt66fQc2NjbCRlpzndQ7kkRHqieJnjS1BjQgQQIlVDb0IqVbnzQKJX2PIipSNxVY7FHFgJCtUDJMNrHSX5XBloVVB50KSbCFCvdW0ZmJPYpGyRFXDdYTMaER6qD83nFPGpmy13XsbdMsrp0Ytfo1Vb01qWKLQqL0xpBglNffRZVdp8qoGLBjoDGdzeD8/JxQsuFwBINBFzYGOfSyBkxVspWNDWizA/WrdFfsNFKkIFVqTCvpkkwIGimzPsvEYN8nKGFCv7tKe3PAFjSsfOx8RMtMRKMBYoArrNykTTAGmxauGI0Lht9Gj1QwxaWInEki2gRMSVpZQwQZfoR9zVvDLtzdG8Gws4Ln0xLOZ3PYHPWg6Hc0IJbEThIMtD5BBdFBL6fne1E19NziMzSbz+Hs/IxQ6uF4jA3TIejLc7A+WA2Bj/1jwP3WGc8VEl6xsf8w+le28keiq2J/dShWWFYM8pwPxz7IECxnLWSZqPBgEoEs0ESrZf0hSSlf2/g7PrakINCKRxNEWumqjN4IfO35/AT9c1x4U/ot91uGQkNEVIEdqowkii4WQsRSyIuXpkknQSJSo2q/mn3EeyHiXUIxNgnk3DqnpE9PfID5mcm44BKEiZhIzSh/t9eHG7fuwv0Hh/D46TNYHR2FPNkAsVg+LRsqKk4GOby2Pw5JBcTnwSYCUlbokuJSJA8xI/tEvTXJa1WIJp466DOZsBXio64Jl2J+vHC4xKsTRGTHBB9WXeLMVZQ0+tZ+8zX1CYtC1oyUuu/096HQKIkkv1bXfhdYJ0YKZj4K/ejnSv1TjtG3vjOcj+zfibiSrrE6Zfi4ZTqLirnXz+KHgg86zDeyusmCx+6qbogmiwjms9MFPD9fwIvTBby4CAjmchUKLMh6stzLia01BRdZa7mvyfEDC0hRX3o3h9GgC3f2x9N3bm9///Vbo/9ndzL5s2Yx/HhuZs+G293T5cPVEqCEv+6xTjbXYz3W4xd2eG9uDYrq91zjfr92/gNjTR+ZqRDYPGwjyDugA7O7dw3ee/+D8r0P3vf71/axl8a6hsJKtK/LOCFyRafIO71hp9Mf22XpzOMvPoYvPv0pvHjyNSGbJ8cvYTpbQG487I0yGHcMXFrsAYtOHaq9acPmTN6WRIkNQUhuwyY06ua0qaJ/5qKqY68IemPWNVU7R/0u3NufQCczcG3SgYPtPvWXjTsZjHoZbUYkcACZ9rJlCYpxOlvBTx6fw/cfHsPXhxfkDXj95ibs7R+gtxYcHFyHWzdvEKrW6O7JqqW4yWfhs6yzsSYeLi6L3kQPNd3WdbPlajaLPxCdNeCZXJWPCrUSkHgRwLFX+wohIpcsquDUo9Do56UohGXEA8Tg3Ug1HziISozABTU0yXfxOURKLQetjKho8sPXxYkgD5uZp/6B4QhtFLFgKhcKRpE68XRKgkDWNdA1FXQgUZ7lnssAxaaVeq+iQ9L1ZDQZNhyg+1YiABCFQ2IPpqXcBOFuEtLJBN2QYNeodyihWaZtyaGBjpH76/R9SsnVADYiEPG/CQKR0DYjtVOSAc8IrPnGhNqYBA8TEShDpxWePw3iQ6KM4lynF3PwPof9zTFMBh16qwZy1kTExxsqABWEwgeUD5P0cOEC2toQtbYJKqiNky5CTVzlPPAcGyzQGBN7AeWwXUQLQUVbEiSDEMdG+5BjYJ4oCmehmBNQQBcTSqSskuJzsAzSvjzwmsBTfMz9vXoPxOrTBC9S0IQrsSCRZ5G8QSPF0+gzIyq0THqgKeKSRDZNeNr9fnrecl9FcTpkpvSs8Q1T31gQqq7asnjeCvyVZAgiS4HnG65TiDw557gP3TJq6LQnl/F3tWRyLno7iniZYf9TvFfj8Qju3XtA/b1oWbVcLCnZxlNAUZYvX8zgYPMSdsdd2J/06DOrCLTyXHDc05vkzCn2mPQf8gNBe46idiY+Z/Fd6bModkGupeytAjlXVkb5m0lyd9BEPHmFqHE7aKHHwHmaByl8QEtES/si06P0Ru2oIG3gcLLfGhGRlUuQtBPEX8RPTnrBBXF1Rns4jVqayFpsdFky0uOdPAdioUN+w1kHTIYFpxIyX9FnYJJ5eLaCJyczOLxYwYuzJaGYmHSuVg1fe15/aV1huyGHoj2CoFtSv5c6muFrh+/bGHXhxs4Irm8P/cHO5Os3Dnb/zb2D/v+6s9n7o6bqPnuxqFeOuv9TTvlf71gnm+uxHuvxCzc8QK8H8J5z7ve89/+R8eYNMKZnjK2NNaXByNH7blVXUK9K0+/3/c2bt+Cdd9+x777/Hhwc3GhyVJRtGlSWdVjxx9ppuVrC9PLS1E2TZd2pPZ9+BY+/+pL+PH38NZyfnQK+ZrFYEuVpUFjomCEMex3ajFD5tUmYcrLBWUZ+0A8r+OE56Bdh+UWhCEQPx4MM+s4SXaaqPPVgbm104cbuGB4cbMCNzS70cwPdjHovSFQot9LzkfSLcNCIIUzB6rbH0xU8OZ6Rcp2jXo4VTKeXcPPGDbh77x7cuHkLxuOxolpaKLfSZ8ZCBnidmB6Gtie44ZF/ZxM9A50X9dqYpBDlDowabaPaKsWujKCSsFFTawRDFNUm+lzGz0oU/ly0JzFCFFPmptD+IpIm/Z6ElvqmTdEjgREhkUa6baSISZARaH6Gg9e4U8dKvrkSeKfCNjEYCwkTiTlZA6PRgIRlHj96BBcXUzjtFlCXOwA+g8wW4LKSkhihe/EX0H8aoRYr6hIpZxqEMiqSUsnApDRVTr5cSEK9ES+8BFRMkgGXoGBpoBYTdVYG1eDOqFKuWAgAU+D0ingWn2nrErGNEcR7K88UIylt9U4JrhPxDqYZWsfJtQloB1LOX5wv4OXlEpYlo/aUPGbhGnFiElxBTBBUckLh89GjFfuvUSiFfTczG3ucDSk217EAwsmHVfpiw5TjLNIW9XqEGxd6u13sg1N1WlHejYUUIwG4oJyCuNSRZg3a3xbvuxZlJDnUXliZv5btgOLPtRdZijnJvcEkVnrvrJj/JsJSXhJnUf/kNZHLchEZUxnReH9baL5Qy43TKSDiQqDFmSjM4k1MQiURNWqtw0i2Zxqs80El1HKlgp9723gVWwEfWwZs2vctatqC3nJ1KM8KODg4gPc/+IDSocdPHsPx8TFRsW3u4XxZwk+fnsP2uEvMlHEvpznTuPA9FmICT7OIWxGMa6+VwlqQ512QXqHiWyky8BPik/VaCjiybmk6KuuJMdrTbtiCyzHVWvo5Za3hOxY+X+6ZZXKHFglF5M5owq6JZUp7TTJrtcNJin5RXMzo/ZYOSzk/WfPlXmlBwsS5YxMmTij4xD3Qu+hTrMyWxrf2AWtjGwVtNzao8mISidT780UJZ7MVFRY+fz6Fk+kSyqqBsvZQ1qHwkHEhlT5HFnEfpqGvHCXUIFRfXhe6RQ6bww5sTrqzg+3hy3v7G82tvcnhtbH/X24cwP827GafGJ9dNsYk3cB/c2OdbK7HeqzHL84IQfJk4NxvelP9fuPhtwzAHu62xmRVp0ALzMyicEdZlhYRI1SXvXX7jv873/kOvPn6a7C9s511shyqpjJNVRufZ0Xd1P7y8tIfHh7Cs2fPzcnxiUUvquOTEzh6+RRW8xnM50soyxVtUsiIw72ttADH8xXMUewBVWU7GaoKhX4K5fOFTQM3inJVkygODS7tr5yHbfS33OpTMnd8WZIi7e1rE3jj9hZc3x7AFtJiC6yTpjL2HOxosJ0kGyg4hL5tjYOjaQkPX8zg6HJFG2cfExkULmoaUtO8fv06bO9uUxWfAk8JDJVExDRTyzQ8HxVVKYn0TOHLOShwrEQa+ZwxYHAcJHc6QPqvmGxSXxInop57vRChZdGR9jBg80xtRDTYSfqOBNmSXlO8lij/G1CVFPGT3i9WBiWhkkh/44OP9zBFLHz8vaKdPokKJN5P+av8fUpf4wQL0bDCFrC/twcH+/vk24jnUBgHtllS9RpMh5OKRjlwQt2KqFki6MJUKp0XUvjQ/laROOJETH1JmRZoYo+bNdH6gvIxDnAiemK0d1KoYmnPaMqaizFjikRevdZe+a8aximV2epnRJTZtx1B2JJG/56IfAirAK9bWTbw4mwB59MlJx4hib6qrivKkp4T5pyKPR3q3bzERKCqw5oQ1hxKHEQdFmwkgkbaqdGEz6f06hQWkuG5p5iKL7GgFO9p0hfntSMsonj4lLFNiAirYDLtSGCo0eqB3BO5hAaEciooU+xfk/kdE0cp/ASPWmvj58TiV7sYAMkxa1Df+l2CMnGSrrRXEchJGN5a7InLYIJehQTWmtYbYoKZMjDUf1bWVoFf2U9UvWWvmpAkH+GaJJETyjgqewemRn/Qgwf3H0C304Xt3R348Q+/D0+ePYe6Dmjw89Ml/PjxOWwOOvDmjTF0Ozklm5ToWOEqyDEmyLZeh6iafeXQkuMyrZ/7V18ZC5hX6fPKZU2TspioqrWVdjZY3lMAfPpNRopQcl9NLEAwmi/nLFYgoDPKt+5vWvbT4iW8ehEUl1WENjIjICnKyaslgTVWKLb+yiRLihRs4xoor5nGCdP5El6cL+HZyRyeny7gZLaE6bJShVkUDjQmWCUry4BYD2niTCs6rd+1C+s4Wp3gWrRiBuxk0Ll8/cbmw3dubX1y58bmp9v7mxfjTufJZHv4b7r5+UM/retvWmL+psY62VyP9ViPX4jhA1vzRrOqvpMb+EeNd38PPNwOC7FBNY7CWptneWbImwr99Pp9f+fuHf9rv/ZrpAa4s7PtjTFZXdVmtVqhzUR2dnpqjo5P0HbCHR4emtOzU3Nxfkn2JuAqyKAmE2RUlUWpcdwTEW3osg/jcuXI/oD63xoXAyiAWOHmeAsDRkRkenlG6GS/U0C3k8H1zQFc3+rTJnhru4GNQQ/u39iC6zsjKLAyWpUqDgESIPgYFEmfFf2TY6r5qoaTWQlfHc3g88MLOJ2uqO8S0dfRZAL37z+A1954E3b39jEhJ5ETEvBIqHOC8GmMWTcafOBrm4ZpmrlVCDHNHeTvoeodg9CmqmK1GlhQhns3afNukkRT6Fdy0nJM3jPVSUMGDhCzGOiz3yGelqPt2mmiQ4mLBpJMHUxD5IQiGmIQE0MdTvrEZzHS+/gQpZ+MqXcm1cD0LISSBysZpM4+evQIvv76azi/mEKv24ODvS3YHOZg6zk4vPcg10bETiINWIIwo6iA14QrZ1GZRnpdrZHYmV6XKdWOhVmcnD1oUC3XwTHlUWBH72OvqGkFn06TUYArgWqCfl2lMbZFO3iu+JiA+CtBb7R/aKtkaA/YFXsDwzYz4ZpVRIfF70IhLgzmg80ACnvV0EEFLkhUP0nNw9Hz3CPqege6nYIQiNIGivxyuYTT01M4OTmBrZ3tkLSh562JKIqVawkR0TFGwjCXIOrJ9ZRkT/5fUCKb04IiPXPAFh2avPFjS8honoNh5JbQW2Yk0DHmscgS+jojkh/miyCctn1vWKCLCk9MG6bDozXSKnoOVws0Rp8wpr9yaeEKzTtNYmR9S4gBWsSKn8v2J1cSZ2E2hJ7bjNYscCmiL/PD6H2KwkMN+0ayQrb2jKeooG2xGjJCzRowDa/J1qvwmGdRGBRhwx7Oze0tNFAkpszL4xM6XlQwf/hyBgPcH7o5vHZtROb7SzrO2BNN/dKN13VR1mxBI+XEwj1M9o4E4Ws/f+kzrJkcsxWix6ciyPqcxUTT6+enGWxU7o0ECKPzPAGa4cqSkMwFSllbCGz7FYnAGIQWC2EWSJuD+DIT2o6/y0Iy6Rg1zhLRJ5+wKYK4V1IUiuUjZunYgMpya0NVNbSmXC5rOJ9X8OJiCU9eTuHrl1N4cbGiHs1QEBZVZdD9RZ9vZaBE2jKyn/B8UHEey0c5WaHZZWbd817WPD3YND9+57Wd733nvbuf7U16z7PV8rzudC87g+EZ1DPnffUzru/fzFgnm+uxHuvxcz1C0GO7+bD7Tu2b3/JV9WvG+vscQ07B2AF4P/DOZYvlwhRN4Xrdrr927Zq7desmvP32O+Zbb77htxtGMdwAACAASURBVLa2DS7eZ+fnMJvNLSaHGCA+/Oor/4Mf/MA8e/bMYgKKGw+KeWBisjvpwcZgACfzFZmYC60LAwG0F0HrhBCs1iGJchLgBXSPEhwf7AowOe32C9gc5XB9sw+b/Q6MBzmJBo17Bdkx4CaD8fD2eACb4z5k1hNS4uom6bdpKxGmVWb8Fe57KCzwxYsZfPbsAg5PF/D0dEnHWpcldLsdQErxex+8D3fv38eEHJqqiSIRXqLr4NdniB4YAtKgUBul35umhLwoKICiXk+xaWEqogURTRCqHtMFWTEy/DsL6nyUvGdEF0UfSYngVGgk/CNSDdXXk2mV3GcqSYlPvQQtW6IYp9GNZ1qckQSIz5vCiUY89ySJjwmqUL9AArTUiB6u/k7AtzQq5p+zWuf08pJ6uL748iG8fHkEG5MhXNscwbiL/polVORj2pCNhsAFPuYGUamy9RCHQN7oWxjBylgwhJMSDFio/9Ix8pcZRVHkusVEIQbg0n0qyamgKdDulApH0o48FZmMVLuIZIKRqZd40ul9gNZneO7XJTqhFaXitB9N+gnjZ1Ai5YOS82jYIQXnpycBSSa2ZOMJYSpsao8haF8IjskDt5uRIBcm82ijJHMKE83nz5/Bzdu3qPfWMc2U8vumZqQUohIw9gVyMcoz5TwieBy4i2enJoISQBtF/BRFdqKYGXuhwzwIPV743KGYEVHBOaETZVZVOhHxHscsAR+vfTtJEREieWbqmPQkaqcplkaMB9dmCwj9Vws8DpKey6igLNMaZJpxl3pMOCP/mveM8Gy6+Cwr8qfnYxUnjKcl1jusQsv2GA0rCss5h3O1vO6Ez8B1L7Qa1GE+2vRKCNsC2RsOhoMBdDsdKN//EKbzBSy+/wPy4sQl6nS6hJ889rQvoAjc9e0hva/EOcT0dqHmJnizJig4j5qWrRGoinBE89JLG+WtNc9ktrgqdovKKmdwqlfkfZyPrSJoTMviVyQNpvocR2VZfbkg59ZrEdUl6V5M9dLPb6PNVrO45Dvb0zckksn700MQb97Qa+6UvRH22PAsGgj9y0SBbTycz0t4eb4kxsTRJfpYV3CJKOaspGLvdFUTGokCZU3DdGeh/fNxYGGhQ57bwSpNWCcZFUsM9LtUpCwzk31ZN+7/Nd38u7dv7X72679y5+m7D6493dwbX/QyKGF22dSF0OL/FiFNHutkcz3WYz1+3sctm8FvFJ3O77iqftN6P/LgR9YY3+l1l0XRHZTLZYHKsNVqhRu4vX/vXv3222/DnXt3s2t712DQ78NsNoOXR0f20ePHbjGfm+2dLdMpOh4TzvOLc396emJwYS8oecIeiA4M0HYE+7Cggk4GMB4VRJ9DoR4M1I8uAGarVdjYuacCq5yh/clCz2aEYqL9CAa2Oyj8sNGF23tDmHTxswx0unlUMsVFGTejIiMPT1SsbdKkCdrV/FQdESSY8QBn0wo+fnYOP3x4BgvsAXWB+oqv3tregzfffBvu33sAg8GQUC/p3XM+ZDJIN3QgXFBHiRkkYjoq5KCqo0LlYvEC6cviY7bGqnQ82YrYxGak1f+W9h5BG0VIelMjGZQDHSN+m2lQxUAcBstZ8M5Me+I0qDQmqjoxRZSQIUQpsiKcP1tKGIg0zlR4RW+eDFGp1DgnUYi0MThdlSuYzi4poez1euSDioqo2+MedMVTEkIl2zKiFb8jZXMF8Zcg9muV/qaMtysITwsh5KArgMoinhPeF8Jty4UBQZvEUkNgAwMCrkVLgDQEdlcvTgx4Ve3VMILoY3L/jalr+jEBmU6DeT0fPXbHyFP4rSha4nzEZ3Jr3INeZwqrGqh3lgpNhNJl9AlICbWsoEzFCr7bGBDiOiDKkLhmVGWJTAlkSMBsNoXJaBREwZrQIiX2Jp6vm9JpfQyflSrIcz8m+goxBYFZD7EnNny4XgFKI4kbLefMCRJEdWxn5TmTPmq2DZE+TVlzmnj1o4Kt9Gzia2pF72mN8GKXFFCb0OTKIjMuUsiBLTkyfC3bA4FSFLlnXKx1QmWGj+xKuxkrfQMLLjnjdJ2S9YnUrmXdECq5HgeoQjaIAA61CsizlvQm0+/ykDTLffFiOwL6vcB3AfVXUoRQkumMszTsG0cU9Pa9ezCdzeHJs0OaN2GNsnAxK+Hjp+cwGRVQ5BlsDruUcDSCkhuraJo+8wmyqGtAKraV7COQFGL0NSoOB1zE4eKeUEVNMhUTpWZ5+GzqPxqXpvj9Mpd4MQqKysmLW/xfE9WZmW6un32VkisFJU26WXOrlWAaLeZQIUAFqlgBWdovVIlZntP4vXjvbG7DtTFAbKfn50t4frKgRBOZRM9JVXYOi2WjisDIGkIrNLFOwvVjVQWVeLVc4UIGKl1vT/qwWlVwOiv5HtCtbgbd3snN3cnh9kbnJ8tV9X9/+fjsX5+X7ouN7Y3L67eu15ONPu0XzplvWjH/Vsc62VyP9ViPn9eB6rDvOWj+qQf/D6GBO9aYuXf+EsBvQpb1Op1uNugPClRULTod3+124c7tW/DhBx+a23fuQNEtyFLiydOncHx0hCgmPHv+nPCarc1NGA6GZDtRZAXRF6nqzxscBpJobYA9FagUi7YH18ZdasLHCiX6XmLCiUErelMhPaYhtAhtRzKY9LuwO+5R7w0aI28PO7A97gREc1RQcCtBjcQ+loMfVMgMogvRpFlGEJy4sqFrnuSJuvPF4SV8/WIKJxdLsqPs5jnktgPjvX1478Nvw1tvvwsb400SL8E/XtG3MLyLYiBCazXio0j/boJYBolXNNF+ISLRoRrtnAajVgJWDOCLIogYUb9bQ2EKJbyNBLRJ8MQhvqjIyhnT4YlabRKk+BaSESlyIcENSaPVPrY0AGNF3CSoDoGAjUqXVpRm+Ss52DVaCjd6xPQZnlGQK+1ilhUqF/MlHJ+cwmKxgMl4AsN+D3ZGaMuRQeZW4cqw1QQfIEdQlsV7It2Qfxp0fi1bZ5hA8ZTAOvjAcVKKr9V6AHvJXelZbaGnPDeDEqkof8KVdNApUilgGd+tb6TuCY0yyFlRpy4rbdp4keWeJghSEBQJKBxRh8XXk8VIIDFbj7RIYE/IgGJ2M0PPJf55ed7Achl6Lts0PUYdKMhvmI7ooZMXxBAwJnxmUYTAuFyu4PjomOi0qHaNP8NeTkja3AIAbNlPFy9BrQm6c/FYU2wwUE4dazhnOqdlThpnWwWEmKDyZWZRHXptI2JBLUOIGJw69tyEqF76jUN9KKU/FYjDrxYYIG0EnPRKcpcgjpTY4ZwixM5xgpD0QRvxHeZEmj0s5d4ExCkWlwIdnym+ntWjUMhMrY9CwU0EpKQ4JguoZRQzenUygskqxli08tLHqaquTD/mhJr8NdmXUdYlZR7gtdXX0hUmq6P7D16DN978Gi4vz+Hs9JTOGPcH7N///sMzWkfeuTWBnUmP2iDqxmtbgswFFcWipJt/l9gTxec1/Peqzndb1TSyNSyfs1xO7WgAvQwx4bQimmzaSeQ3IKcy6xLRXL5c4isb+xd1DU9U3tPRXl5TfPNVQBMsaMLZvihyTFGdOwjKWpK3N6z4vqoamCFauarI6uzzwyl8/vySRH+Q4orCgRgb4HvHwy6pvSOjANtm0J8Xfz5flXRM2I+LLCj0XK35ULAAMeoXJAA4Leu6dmbe79nLySD/5Mb26Ht39rZ/ePfG8CcvTi8/e3x4cVzNsZfeQVWiX3AHuLH3526sk831WI/1+DkcfuSdf8+D+889uH8G3t9k2fEz703fWrthDPTRMLu0S9vv9d2DBw/c/Qf34eBg33TyPEM6GyKW0+kMjo6OzIsXL8yLl0dQlSvIixyePHqCG6nJiNLiAv1JNlQb+twuF442/bwIwTNakxRLC8ezEo4vF0SH2Rh2YJ6Fvs5+buFgs0tiP7d2hkSXRSuFbmGhm2fQLQxVN3GjqSle5+DfSODJJurAXozaq5QkU1c2a0FDcWBl9cePTuF7D09ITh37PEaDAka9AfSGE3jtrXfhV779q3D94CBUZquKEzM+FkrIYqInwWEa/pKyblKuJgEVDXyUNxrRT58mzCEIs2wtIEiYUesU16LdMfTAtFjQREOCAWCElNRlo5ZM4DOTdYGN/bOsAuoaQT9sCLo5gvJsT+NZ9TH0fwbqtCpLyn0QVcLE8JtY1twHKBRD6ScMqG6TeKoEk34ULzo9PoFHXz+iHq6b+7twsJXByK7AuxUjjqE3TjwJRWNWk2zXtjSIOIttpX9CYwzxt6BqYk8in++Zepoq6kYEBFT8NL1LEYlWpEJRM0HcQenKyv72MTlXOp/Ow+BNST1zqoMifaDxusagOKXtxbnTRnCTB8cElAJJBVgYeuEBzucr2FtVSr3Fe6P3NsGH8b4WRQ6dgkJQ6rfDeYP0WqQhL8oFnJ2d0bPV6XT5+ZHAWRDFpKFMjzpRNxUVUD5PK56yKNLUMCJEipVBxiu0TkZRLqeJHp+sCyIqguBooyQLjwjFUcskBnT9Sdecb0o8o2AOHpPXf9vkvMW7MCo5h3NEaqrlBF5QcylqOYiIGrSEWbxeU2UcyzUNMzQiYZ5RWi54JT5FeuRisyRWE6K8i2rEYuwfClDigywqwIwgN01EomVtAE5I5bplLLfmpH/TskJ3EDrb2t6Gb3/7V2A2u4Qff/QRzGdTKhKi/c6T4zklE8hw+fb9HdgZdwKKm3gGA9Nk0yKbSTwfgWmwUuwwXtZiFjNTH05h5cQCKL0ts+yjG1kEUtSxBlrCOT+rNmG4uOpMotLdfkUoWKYkk6TYKDRoy0rGSTeFztG43himG4t9SKoEHtcLyXClN1x6SanN2VqmkQchBNzfT2cVPD+dwaOjGRyeLwnNxIIAxgPLVU3fhcWAnP0wcxufY0wgr2+PYLZESu0caoeUWBtaenmNxeSzoB5QuxiPisfzsvp6VcOjm7vDL65vDX+4vQkfTXrm6aRfzE8uMl4Lvvl6/7yNdbK5HuuxHj9PAzlVd8C7v+988w8NuP8ADNx0YRPzmc02ik53hOt3VZXZcrGAuqygPxj4Gzdu+Lfefsvg5vDRD74Pn3z8iZktFyhYYc5Oz+Di8gLqqiGEEvsgkdriXE39iGhZkOzL0QXQezJ/x4oy2oYgZeblxYoqy9NlDeOe4YplDlujLuwMOnDv2ghuXxvA3qhDPyfBCOk54WCyEWBIK87BGUz60GTEnkGICZ3+UoL4EGQgAvvF4RT+9IsT+PT5JQV7G6MBbE3GsLW1Bbs3bsO7731A6rOEwlSl9mdG1UFJGMWrLlT00dahdjUjnFYtUECsIqygAW1allpfOKbPZkXof6obCArsMdGWQC/0c0a4Nga8jd4fTWhdagaeJLzc4WM5SVP0IRPOm6dA13GgK1+YCpJ4VqO07DGKUqy+UUc3jX0pEba2lUlFb8vwWs/IDZ4/aPIbVF9n8yk8f/YUBqMx3Lq2ARu9HAq/Ao+9q5KU2zz0sXLqaMTrkL0TJdBNqXKBjpfF45VLY0Lw5JOfxVTqSuRyFRZIko6WMqoijmwT4KWI0A4YFRPRSx6VTSnh5UQo7ePSJJoRjoytKqhAZEELD5IUKtKLryXkTCjPkR7MoDgFa8giqBk1RJGg4CHqtc/XKZrMAXtmgt9dkVHSia9ZrZZovQ79/oBes5jNqR8cLX5I6VjmYUoVZXRRhZaETs6iL+KTqAm0jSibT2i9Wsa5kkyDl0QyVS/lIoRVVZJ4a68gPa0+TS92EbHvThgL9FxD9Hkl6q0VLWuvTAHyaRWlUn5KcP1tvIU8z5UpYLQyIiwF137udW3Ez8zpGqDgWIuh4CPdltYl9B3OUC47JyaGoGc+getCztnQMxlYyGEtIgVuQiIbog1LIh7WZMl4ZM4FWrBpQOeg4aSUzt2wII1rSKwKMbO6KqHT7cL9Bw+IhYPI5pdffk7zh/psKw+PjhuajqgT8N6dTRj0Mn5O2qqsgTUshaRWVnilUBBF7FrPN7/e8QU2yXqo9OmEmh8ZD15rKPEjr1Z/4lolP5JiZKvnXJ/9tMD6KsKeEl0k0ZQ1yfiIhreKC94wMitJIIDsoJkyWcK1Q7Qd1wYU2rtc1HAyreDJ6RweH0/h6cmcPDExDgjFoVCYsOLJy8suvhe4/xkLyWVVk8cmFhFw7VmuKr1Fw17hdsaDam+z/3J/d/Anmc3/z2pRfWJ9+eTO3uhwazw8rvysROquWOH8Io11srke67EePyfDd7yBb0Ft/ql37p+A8W+BgZ73MCPJfpt1R6NRPhyN7GpVwvl5Rdt97ZxHYaCjly/yzz79FC7OL/0PfvBnJLqCikBFp0MUExJEKTLd2CjJzANJi+L+LEVLPFU28V+dTg7bwy5tGGgfguHPzqgDBxt9EgrBPsy9MQoJ5bAz6lLSORrkkJvQA1arD1dbkp4cPlp+ZC0A5c8fQVuEKLsYHGMCjNTZH399Bs/PFlQF76JP23gMmzv7cP3WTbh7/zXY39+nzbCsK6YwCfLGX54kEyZAmOEHuIm64NnpNfBstAcTAznnE7pegtBwhEobPAbfJEtfl5S8imBQQFEsB6noJVZrgKOoqiRReHyc6AIL/Qh0GpFg21aJkWSFe26Iolw3sXTPdz3L82CyLkqmkELJkcYpQ4Ig8Wb0CV1R/e8k8JfeNh8Cc0TYEQG7uLig+9FvashNDaOuhS7GkrUkZgHhA5dQFnWOeEUtJEgSep1QZiWhAabXghF0N9xH7aXVnly5ypIQh3vu2C82vazQQgz0oiTBt6CmSWCYvjTFsVOREVe3rrVYcQilV+iCqlCrKGk76QrJQyJ8pZ2SoEUgpLzh8aLoF77+Yr6Efq8DnU5BvYKEdmtiHoPiXieHzVEfZvMFzBcrKl6hjQ0imtg/vlouSXwrozAr9P16Vp+M9F7PfcBGQW/DSW7M43nu871Df1sq2LC6rJr3A/eY2djXLYG8xt0JcijXShSo431IGAnyU+lj87G3MVCVQ98qEVKD5LP2QUfhFYiqwK8sbQlDgpKxhlFsSV5MVEBOZk3wwJRzYnVmec5k3Ulfr4gXG+ErXdmraFN4SU3nht62wpoQpWxWy9FrRPMvqJOpWrYwwI1SSa0mNMDCadgqUDeVJs/IoKlWS+gNhnD/3j04PT6m3k1Up8ZCKu5ZZeUI4fzhVyfQ71i4vz+m3j7rv5lWKg+o4fkRRda8PqKajCaUbO3bB6/0VqoVNvxvXR+8ro9e9g71p2xfe/lCr390S0ie5yQptiYyXrzsxp5ZGMn3J4VIl/YTS7FJbYfSapqn87N8uCIiJD3CWGyalw6mixLmywrO5jUxhLAHE5PNy0VFrTXYNlMz8u3ZtzTP+DLwd+L+h37ZhIQjc2K6hOWqDGKBRQErQ/tMMxx0qs1Rb7Ez7p7sb42+vHOw9Ud7k84fPj2++OjQmouhdXVDVmXfZAX2izPWyeZ6rMd6/BwMMwJb/Xu18//YePd7BuBt3iBPAfxJZk03L7LtTrdriqJwq1WZUSJZFLiom8uLKXz00Y/gs88/w8TSXFxcQlXXYTPgnp4sZHe8OYSgCHEtpLpMuKcSN7aT2QrOZpyMgYfpvILj7pKoqq/vT2AyyOFgE3sxu0SZQSsERD8n/TzIkXOiVarR9NVhWpusMbGHKIILkvipMUAQ/rDi/RWOu2MtHM9r8mX77udHpISHv0fKbkZ0vw5sb+/C7Tv34OaNW9DrdTm5cC3FSgNRMTWkhRxUMeUN1WrFypwEdJyJgTChQg2IuL8GH0zLBL7qGYsPhcCNkQHidFoN/AgBICTPJdTPJFORAFkuk9alQVUjpbcQfDQalx4uolYxkk30MTZrl6QbuEfSiXgFH2cIZnySjCW3CKJiYOMl+bOEeijyYC37mLJaJKoin53Al19+CU+fPiM0a9DrwrDTgXG/gKwJ3qHUc0XoSaOJpFxfRQolKVdqmSBTJgnQYuKpwbKgBZ4TY0lSwGswLiFoA5LcxSTCtFDEhC6ZjpRK3Dju7QVK4ki4xbOnCvmmsoWMF8jCRyogq6YS+s8WBiq2SvRSQXOc3ht9b5MqV8olsOiTREgR/nfYyWEy6EJT13ByPoNhtwc7nSIoTnJCQGI/hChaCi57eQ6jYQ/GowGdAhbAEI2aXkxhNp/BcrWkfs2I8tvkeJNELouHJUFv+E9QwNS57rgXt8jCC+tIo9Q5IWrVEMRyAlLI9kBKx28C4u1DMu5cpJXL85U+u2lyyj/4xh45x9RyJd1fob5S8cQ0MZnlHkn8OkzSQw+41XWGCiP43LpUoiV+H0gfJtsq0RragCbB2lvJ7wk98E04riThiWuKF5IuGefjumRZhVuQUmvjHqIMaKZZyhxztWPacMT0gXtRhZ5OPozcLoHfjYUKLE5MxmN45513qFiB1lsvDp/T56PoXOUcPLtcwKfPLqkP8M7egOYtsRySFoK03d5amRdynnJ/20WI+FfpZVWyh14f7d/lgkvssYyFGEi8XuV45GPSdVs/0wkbAmKvOK9J7d5p0LkUacJXiksmzn/Ptj7hOzNsDDHG1CZF5cnuCJM456CqHMyWNZwtSjiZIjV2CUcXK/pzPF3CbFHTfp6RGnVGhao84zTYpdtTXO+oIOyDd6onMSEqzvqNQd6MxwWebbk96j19cGPj4a3d8fONYfHVxqTzZzvj0XfLOTz86sWpkoJ+GcY62VyP9ViPv61hOJMa5L3i79dN/Z85gL9nwdzmvQdxtToj/0VjjbF2Pl/Yqixt3dSG6I2UdIXeO6TJnp7V0EV6ksHNoAhUOhNSJ9mU8HeYd2J4QL6Z3hMV9rXrYxh1CvjJk3OSLMfkcnNQkMDP7qgLexuoEhoEgib9AgZIoWMzOwsS7Afq5c/aICQIBkHjwCebKB+jBg5eFSrFdgEgml7jecxXFXz1Ygo/fnQGX7+c0Ud2eBNEFHFzawdu3r0D16/fhOFwyL1oHKC6YIVtBF2EqOQYUBixKoFAbU38/sgChShDgU7rmybZ/F2CIMUAgwLDqoIsDz1XNi+IKhr6K7lqztRCUZcRwRoQewSNEBOkwohdh4siPdzfpwEQJ4Eh4fGQFYXaswAnoyoOJLaiVpAQn1TXuTCQoJoihmRE1IL5zYSUMyXYKqrp9D6fnp2Rv+bRyyMqAPS62FubQw8/rk5FjhK/wcR8VJHSJCGXQFMSxwACxN498coMpg2cHHKfplcxJ76E0kMnLU5scZDSLb3QP5M2KEUopcXLtX9PM4MKCgXT6kJCxoAoP0/CBmDFEUFwElVMm2c8F10S1gJTFsM1EpVoK3RPE9MfXDe2RgVsjzrUj4XXcrYowcECdjZWsFn3wHYs20gI4gqkUKtPKCNfknzgsSyWC5heTmExn1PySv6cJsx37H2DxmtSTIk+UTXzgOjU6b3mwoK1mqQYtkqhAoQxOu/Cdfd6L4SSGk62UeRXkgPTCG3fa1Jo5BlMkNxwO63OWc+FLklO8PxrcLqe0DHTMeolkoc/ip3xMxMLAJHOHopAIrEqhQGja0D63aEI1IT5KxZGTM33rYSHhWXEukkW4iiJ0zpYkh1C5NFbWicIfazLkOjnweZGqP54P2hdQ0p3kfH1q7WY56Vfwlp9Rg3fqrBPOE3gsDCB37G3twe/+u1v4yIA3/vun8Dzwxc0h9DXFd/39HQRegNzA/fRgzMLnqm8W/A8dTxnmMqcoILx2ni9BIJ8uiQ5l3ktL2VMVBNYn1qH4OPVyEtTFfJkveKJaRLSiRdP2MRH1TeSvIL23caCWcyklToLsXhgMuuNUHKQCRL8Xo1hOzIIjx8sUNwHqbGXK7hY1oRYXsxXxBDCQvMK9RrmNUwXFV1bfG+eJSiqTq90noXr2cj6yXt1wcXFIs/rbpG/6OfZ061hfn7/9ubl2/d3P7l/bfN7W6P+o+5g9BLy1dPVxcX06eUvMIT5M8Y62VyP9ViPv42BxcatpoZ93/jXFrX/ZxmY37UA+xBssEOcbGCATFYEMeumKcrViuxJur0u2Q1gcDca9Mg+5PxiBrPZEjpFSOQceUeyeI4HKJHm5gF66FVVBDl8VJBdVI7UZqvSw+ZWF25uD8hyBKmyt7eHcLDVh91JF0ZdFAVBgZ9o4h0sG7j/7y+oQEryo5XP8FPhu+nrWoGeRCZKEQw/w1Mqq4YSzR9+fQLPzuZQoGVKJ2PkrAM3bt0iMaDX33wDBv0RvZ8Cc66wB1VRUX4E9fKLaB4HVD4J+pJ8R4Kk0DvEBvGMJHuRKbxKyeNAzeahIOCsWKdAewOXQMOJYmSCxKnXJQdQBoWAHAd5PhEBCckY3R/H4ijgKagjkSIM/q1Vmp4Hm1grWO7XqxlRhiTRtdrDZdK+xBSZVvUK9vHjoAo/E5MkpFpeTqcwnU7DvKFzaiCHEqzLiBpo2IYhJIRsvdKiB4qUPwqJiA8rKEplWRU0WG+EBI/yjwYR/yCgJEhkxn93yq1jz1ItHJgkARVKZ8bnFhAkzxPWcvIdHmAXwSXJ4jmB4SpGSBgS5qMRCwp9BqJAlvTI0XPXMBoLbFvgkyCak4hoxe41QaEk2IakH03zR4MOHF1i8FnCCtkQeYcQD5wzee3Y0J4LD4wQk4BIZoMVRdMQMgUs8oG90Fj4ms/nHEjnkbaqSUEcAeGptcdRnxmQHssElcbX0jEFNWjp2wvqzVHIBkyKAMXigV4XH2nLYksUWAA23ipdGBi95vnf8NyU+YWFESf2Q3LkXhKUxBZJe0R9FDMSNkFmdW1QNC0N6k2cH3EdYRRbjiN5PpwYHvO6gZ9FiWwOqqyq1ybCd/HzbVjT0PPXcGFQ6KFi96RzwgQKsWFBJU/qwCZBnKVeEq6xN0kSxsrdJIjkgk1O0cng4PoBfPj++zC7OIXZdAZTFAxCkm8N8PJiQf35KCSDPpwoRkcKtXVkA7R2k/S5UE/ehFqse4C/cr2NovJSYEsvlbIaZA6D6X0DAgAAIABJREFUa313nD6xv1C5EgmiqnUXnl8O4jrW6r82VnUPDKTrsRw79sYbKkKLd2xoba4N2o7gMoMoJj7niFi+vETkcgnPThfkgYnIJSKc+OxndD98EPthNWO9ZkIHTqcjr1NoWYZ/X5YV/RvtaobdAk9x3uvkn417xb8u8uJP7l7f+fq3Prjm3ry9sewWg0fzeXm4apo6z17Fc39ZxjrZXI/1WI+/+VFkk3ow/pZbTH/dgP+NzPhfAzAHqAHE/maY6SCXNW+c6zvnRJE9JA1eDNhDJXtvYwDbvRy+PnSkECcG1J7pXNS4T7L/vHmyToxjYQAcs1Xo63z71gbcvzaEjUGg1vZ7OdGWgNECx4igv7I/wys1cvlZjJK8/B/DOCGQsa1afHvDNmpUT4MVWVEk4NnJHP7s4Qn89MkZUX0zfuNwOIC9a9fh7XffgzfefBNGoxH1dTaNSxJdr2IdXqimrBQI3D9mrASOQTnVZxwMOs/RWAjISIRFzNF9VDKNdMGAgBqVsg+JYVAuzehWOw5mM5Pxa2ICoq2fGuDFxJuSRkFjmTrrtY8tJn+exXIMhN6rQK+Unk0I6rimfQ+AlWsbqdQLjVOUQkGSqzygpnWt9LI0yBP8mlAM/gJMNtH3FQ8QPWAbTiisq8C4YOgt978dfKRiSPj3jIFgSwFupLh6VRmVhPtKTYNPKV5LYHQKkysryaCcR2RNg9BgPdFgG/25T1Rr0/lrbVQNjc8Aq8yCIKs+CepfPdDgKdi+pjJn6Lp5Zi8kAW94qSUQFRSVSxIQnBF5RrRl7IMrG+IDQLeTAbHhUcTDhbsv1Gd5lDEIRZXnybgL/dMCzqdBuZKms/MwvZxRIQHfgEIwlAg2kghCK3Oi2cR0UNAeZqZ326ToYkNSQyq5xrNKsiSbQjkOz6SlAkwWr6WqlSbKnsz4oLjcW2URWFnrwKn4CXCS7A1osO2cZQpkxoiX5Z4/p6rOupbZaLQhaJRntVb0rkRqomtCz6soDLcomgIVacAfinuhNUKSkkYRunTCKW00N8R2CZToOilwJUUs/vxwDYQtISrTwYPV8AMTFaBx1wrK0kg4D2q2hmnhggD7kIDmoqLrErTRtbiSaAeVFx3Y27sG77/1JizPj+Gnnz2EZd2EPc97WJRL+PgpwASZEB304CxIf8Dx/mBtZNrILINETio0Sgi66xObYaPXI/aiR0aL5sj8Jz4TSRtI0qMbEk2Iqsut9VwqsPE+v3q/k5qET5LV1v8J5T9cS1wLsHaxwgyPLUrOZxWJ/AT12BWcTUuovIdlVcPh6QIu5hUVTzIu3jlGpLMstK6QIE8TPFJxrpKlkUM9hoDYI0OK2mnGPTqik3PfdPJsdW9/c7E56ry8mK1+uDHo/NEbbxx8t7e399Fz9Eqx3udllhvbc4PesM595Rf+BH5ZxzrZXI/1WI+/sYF7S1NmB9XUvm3z2fsA/rfBmF8FAyMEnSAy6CrOXnLgNiBLYhQZLfDL1UoRnfnMwMiOYLLThctZB44vFipBjhsdEuNoo0chGxtEAGZozF5gZbhDSeWtnT7c3h3B3qQLKPxjMx83TDxmVkn0kBjrX6lu0jBxM4yiFGmymVaaY09NpJilVMk4JMEQUaGTixV89PgcPvr6BA7PlvSWglGeTt6Fm7fuwOvfegvG4wl9Nqq/AldnTRJ/yHFGU++AWmnvl6CXhFo4TZxCYhaDCwzixJpBrg32qzTcG0RJiLVM17MxKOG+SEEpQBO5qAwIaVAHCUBmErVLE1Es4GMPQR4wApjFoATFjMBrQBGk8Wto5zhRXdCxF6RlRU0QddM0WdO+He5p0gCfAyj1Dgyvw94+EgaqKugPBxTcToYDGPUyyAAR+BDgkaVLI/1isdIPMr9YOVNllNQqwrRfRw9VRT8HReGd9ppKYUAVII28N14PCQzpYrAlyRVIWgVpXim6aEDJF0CVdY0+T1aR9TZKbPS6m9hHqH+k90y+k1kH8uxJ0SCx5jFsZYG/QorboAhq0vgd02VI5pZlQwiHKHvK3BF0i2wKigI2+n0YD3pwUsxg4Wpmf6IADJq4h+IDKTfXUVWVHlYr5xbhPccovvFGE1yhHrYnZ+hD9YzOJmxIRYFb5a2EJimoseNn2OjdilRGRSC9pSKMoPZSSPKWhbBYbEgXBa0ORWRM1aBF1EcVcuUtsh5ZthTh/4ngjsxrqc/B1XU3sY9pLQ5x7iiSh9c3S/pavcx50HmnM9eENaPxdfinl0S+EcZ/6O10UjTk1acOSa0RurLeXykyWC2U+ETsS1BZKUrVdUnib3duHkD1zgMoFzP45NEhzFZNoHWDh8OzOfzwcQbDfgHv3NwgwSBlIF9hQOi6mtJatXLE4romXi+52DIngvppQEGtzp14zLJ3Rep2+BaXFgi9FBDiWpEuK3T9ZC1LbEz0+fCJFJIJDCMq7ojtEa5jFcAMC3nLmuxIXpwt4GSKyWXFWgwleWECFZpCbyeinplY0/jkmCHSehtBeK0hYbBOnkPZNLCqako0kVmD7TU7k96syO302lbv9NrG6OSNW3tPM+u/+/DR0/9jY9L54jtv3qiqVW/+2mu9stddInuiykU8LkmwfxnHOtlcj/VYj7/2IXVN5+o3XFX+h+Dtr1rX3PbWvA3e7wvRiEUvVwBm5b3vRu+GVIUuKBCKaAkG7KeXM6iqPHhdQZCxryV4Zgn3grwuc9pMhj1OMLeHcG2jB9cmXdgcdcg3M2MaTqBf+rh3pmVdgG/cGLxvBzuxSZMDK4joiMrIJ/HaVSpua0PmjRir26eokne+hItFw/13ISjo9nqwtYuCQHdgb3c32AK4Wg9FEyGnGRsH/L715WHzi+bQvmk04BZqlJw/9TpaQcKikqYXwRL5fPyJk58nn0HWKnJBuQeO+wU1UPMqA3SlJygijel/43xxARWTflDDfpuu0c5FCa68qAvGjJqod9Sz2VTxfgK0kmqk2iURN8dpV83iJPkJ1ffziwt48fJl8GOsG5hsbMLO5gQ2Rn0wfqWIsto+2EjdhiRIxQQldUFVsR6IPpFKd268Pj+KIOqz5ZLkJimUXAlaQ9AoQlGNBnpGqMaKtEX0Qntbte9K3h6PR56L9rGltD/DvYNNVJ60VuoXan8T0bQ0+Y75cztNCX2rSKXFN6GVEQamDlZE098aDwjllHOP6KDcg7CmoFF7r9eBZVWRtRIhHwmi5epGfSY1IWK0x5tknnCw7nXugKr6giRLlANyskTUaWj1SxpjVMRGRa0gJLeRIst2QU1kDNDrsiyKSvkoPBW9QSN1MvzL6XxRn0zXpmrqHKKE2CWoarwc+H1o/4EiWPo+zmBCa2Wk5Htwrbkpz7gUOcQeingWRqdQ6Mmma1jzzxgJl9wyETbzbCcFXvoeI53fa5KsR5/UAKKwk7QkaMFHbrvQ6/kRcvIdgrZrYh7eMBhPSKF2en4Gl7MpPD6acf8wopsOnp4EwaBJr4C714bQLTJoXtmXUp9KYYQkRU9JRAU51vke9z5eUYJlkomt8bpWmLRsEfssqa/fhzXZSX+nZKcQixIpcyeupMkW60HVcHEvz/jZbwidbKilBFtisAj7HNVjz5bUj3k2XcGqCf2uqOiLFFp8T5FlsFwGWnPop2TBJu0DNdQyU6AwU+2UWYCLHO6EpegyoLhYYfzmoL+4dW10urvV/3Rj2Pv0xk7/6M7B5tm18eaXh4fn33v46MnDIod60M3NcgnQ6/egMatf5tzylbFONtdjPdbjr3UQSwvMbp3l7zbO/S549++bDK57sJsAfgPbMXxQsZBtH6OlniSaEhAhhQlpLsNuDt28IFrM0je0eTw9npEy67RsOJAIGyBSWwbdArrWwKCbwWTQoURzb9yB27sDuL7Zp5/T5kUUOEO9Ws2VCyLKnK5Vcf0ZQxEZo5VhUf2JFXrfCqqF9qkJnKJ4XkNtzJWmVQXTeU09IdujAm7sDOFs3qGNNCs6cPfePfjg279KyaYlYYtKK9AhyGPaqshcCuVJCtKsPBkoeJFmFVjNJvjIWRd7EjFExI23LEOQayxXtx1/tlAjQ9CoyTUFWKGyjCeWWcNIQcMBdwyCU0AMA1KtXOD0sNHQXhMtsSARBMVARHOlp1Oq/Z4FfugFjQZO8lovZu+QqTDEVeA5JldW35NEYq3XYmCJFFr00zs5OYHTs1Pq6dvb2YZrkx6MSH+EAX5G9wyKklDQH4oGopAJ8i0OVPlSEAJB5eEb56vR5El7XFMkzQALhfjkUkRxJEI6+N5I75kobqqjY0L9FqQlFAISlNe3UThFr5mO62JNQRERI/6GnCgqMsP9pd5HRd6YuKaoeWLOxysTehYiOvH0dA6XyxIGvR4Fr3XjGBWPxQsj3+VCLyre8qJjYYDJ5rKCeb1iCxoH0+klTKcX5CGJjAwrCY+P6rothC+mL9yDGYsarXlt8yA0xM9w6B9mT1djo/CVb3geiIIqP3c0j3m5tZG2bLmgEdo+PfVRyrHxTYnK0FLQYWq+qGSTMI34UjY1IfMyR7Sf84qZf/jOhgtDOSt8SrKbFPlMuP+SDNFapNcrFtPCM2qVfQEkCCfJZjt9Cq19jLhJwp60AQTmRejllvPU+hcn5QZibyNdLscTQxkjsYeW1jc9PxsRc4gU6NBeYCk1d2YAg9E2vH77GizPX0LHGnh+UcLFrKT7hD2GyGz57PkF9DoWbu0MaS11nDBFsNcrs0WY0Xq8sqgltdSrhaJMClhX0MbY96l7d1z6SPXdfgOqmqyZmpTHYpk+mnI8Suk1VPjDqYLMg1lZwemsgpPLJdFgzxclHJ4t4Pi8hItVQ0koKtLj3n5vvweTYRcqFBKcLaFSlDSxwOGvzpnNYLm4i1oIlteScB18ZQCW3Y51tjZnO5P+p99+4+CLd+5uzfZ3xoejbv7jjTx/POw3l9AtTy9PihOHMrShKBaeaPczDWt+acc62VyP9ViPv/QwCS3HSwWbdnzYcr56o6rcb3pjfgcy+4H1ZsL10xFJoFCriTHOey4jQyc5nhCX8gaFVdKcK5GdPAh1IPqIynGG5eV7PUxGLSWZ17f7cLDRg1E3g3EvJ5Gfcb9Dqp/9riWRBeAAVfqMrg5REqXxFySaaXXdGAlU4waaBr822eQoyfPSVdWm50qP6uW8hC+P53B8sYRuEeh/W8MOQNYBl3VgsrkD777/Ibzz7vswHPSgqupgTg4ZJ1SgpeM0EZGAX8QuTBKAhso791xJ8gdpgBwtLzQg5kRFEUVJFIwY5Psg/p9llNR7EbAxNlIJbRDMoeDVJD6ELiYLcqWCgbfkpEYRJZ85tVYQhBtYIdKBV5qwGLyLSAew7QdRNrGHki0Zgupsw3m0ibRTnRYu9jQKwpryxnhO4HcsFwtYLRaUgIwnEzjYGcHuAKDTLChAd4rQWrYIichWarRu2YxekkO5EPIMynUS8ZJ4HNFj06UHbeJnSOqow/s2TS5JVOOEii93iZplqxdLzq31XenXtJN1QdFoZtB9cEnfYEyvIzrE1j2SgaRuLDp9wpzHQLTfLWgdIFEgrmWQLULdcI5ltefVJcqzWMxAKh+q2oZk0irNej6bk8/v3Tt3YDSaQJEXyVWQ0/ba6+yUbm5ZgEZQsIRCTucixExP18Kz0rK1oIlRQDB57qtqsSTJDaN08RnW6yIWMj6uQjG3MPqeiJIlPc6QqGt7p8Jj1N9ubVI8YgGo9N9akLMAyXJJz79PaelhvpFtSJKIGrHQYEEYub/x2RSBIsvXwCuqjmu1E2EmWVecT+aSIx9Rk9Kepdf4FUo7J/Ot6gjEZ4v7VBX1A7FCEXq8FA/Cc914RNVQbR17AYfw7p1tyFH79+tzuJiXNAdq5+Dl+YLaQ/rdjJTYd8ddyHLDLkNhT6H55ZLn0JiYzGn+bZRaHZNJLkraTMis/NwBpDdLElPg5B24V9OnH8/ppbI1AOL5a81AvHkhrm1AalyurJxfLCp7Nl2aw/MFoZeoHns6K8krF5NL/NM0YR2uk7oCUl6hDsky+Yg6eWZiscwRYmqIloyCabNlRRRb/Pd4UFSdwq46WfaiyO1PrWseTYYZMqG+yobDP37nW3tffXBnd7gxKEZQ9J7DefkIlocryHNoPD7767FONtdjPdbjLzUwrF3ZLvjCUyBfZEOomlV23slfb7z/u97Vv+md+Q1j4U3ME8H6pSGdg0BYyvLc+6YxjGSZNnUviYc4bsSNZdEEA3FELpEuOGtqqJyHzVEX7u6NYH+jR8IfqCy7Oymgh36Y1Ngf+5eoN6ZJlRNfHWmiGY7P/YWXynJwBUmVmHp1fEQw9Nw4IiExCRstPyRYIWEjY4gi9Px8CV88v4TTaQnjfk59Yc9OprB0Ody6uw/vvvc+fOtb34LxeEwomIplgABZPqESpmQlRkSkJ1CtD8L/Y0AfaZuNhiJaAMDzzeRso9iGVrrTmEta9jA5dQ1X9dnnMQ+qqBRgpB59bOlAXaPGsk+gAB6CvlmSxfdJpTpCUVEp1UgABQHVMUlfUaDkSrISaHYgHns2BGKiZGlssL3xmphGJBDRGU2+kiQMkSJEubDafnZxTvfv2v4B3LpxAPf3B7DRbcA2FQlPNHxP6AybUmeLXBeTBv0IKDem5ekqz0ySK7RmHR8oAcWeBUUAoOWLCq2ZasnyQxJ3yx8aabteEyTbsl2BK58jHx2Tz6R88cp5JukpU0ixjzDT2xvupQ0CLphIEWLnlYpohKaX6NKmZ0fU805BitbDXgfqcKuJmr8oQz9WpxPksTGvdRY44QsiJEUWzhWFyvC+AqMz88USzk7OyAJFFVZb9HVoJcWqHGulhzmhrcscZYqsUhHZt9ZDRJwDCaFJkLSQxJCFkTcBrU2UYJ2ooZqgvuy9KPxC7IVOkj2lmYOgT5wQiFdnFlglhm0nbMYoW+LZqbfBy3qQiBIFb5mQsDZ8DwVZlmeMrFXEJsnqwqJCNibOO6n3oNBTbgPqi32Yzje6JnkW7jIJmySu81IIsCxI5eCV/Sk5GcuskOD/WTPFPSZuyiRg8SdV1k4KCyAJLyegjc0h609ge3cHHtQlnC8bOJtXRBNFhgEK2x2e4b04p/n7wZ1NONgesNiNJw/kFCWn78T1i8S5anm6+HkInsPYqkH3G6Se1F4PWnm07s88h1XpGLRgYVr7Hl8vo3urF9Vuy9TisvGwWmJPZBDfQaXY08sSnp7M4PnpDE6mS+q/vFw2JJYH6tEcLE6oSNQEIT8sLCObCZ9nZCwAayVEthK3bOBSmlmizvfyAsOaedM0s8mwc7G/PXiytzl6uL8x+XhQFH96tlg+3N7sZddvb5z+6JNnzweDAno3tjM4fzoBu1PB3rdKuED54JewHmGsk81f0vE//19//Oee2B/89//Dv+uXaD3+CoYhgpGFpe0CdBvwVQNZZ4hBwu1zb/5xXjX/sfXmHW/NNmA66mkzINdtpMWyLLrFABXRBedBUQQZcZsLgcocq5elJ8VIpLhsDDqwNeqS79iNvRG8e2sLbmz26Pf9Tg65lYp62Pxw84p+Yj97tGg/4F81rP+mIbRU9vjDGC5XefxwHEIRlD4uxWac+CMGQQbDr0XLlhcXS3h6tqAN9hz/LEtS2kMZ/OFoA3b39uH+/fuwvbNFSot0nVF50bvoC9ZKADjZkvjMgoZN8f98O9Fu9fEZ9WsUeqLjfihCeVAUhQJGpymDZ4sG6QtDD0KbFax4GJKEgHZ68uMETdwZbZQgmE3ljaJAbOdCAWETMD1GYltoCnkAhoATRHAi6b1MBT28KJ2KZYX2ksrLXZJMXp2p/DM5Zjaqz2xQzjw5PYMXhy+gLCvY2NqCjfEYRv0CuvkKvK2JLu41DWuUMmtFqTfpQ/ROhF2gNcc0uVHUKR0mJvwopJWEgbEPLnxDQMdCwi/U3JTeqqRGl9LExX8040g1eotCfOsryGm7Q8wnr/RKPTeaUOT8bvGGtSQCxKmOnksUlvomNd7wOlRAHqFQ2LBH780Z4V6sSqgo6csZCZK0V/ojg8gIJVjsI4n0VstCWUiXxgSULIQUoVWYX5MbkxaojFF6cJPOKc+oo43nBSYG8PQ38VD1iciXjdY7SKG3iOTJehgeJrrHlCBhswN71VJhh3ucPbT790Bo0rJwSIFGDPV9o0uIiKOJdYtJkmC5514PJpYWgFHsmIhHFW1hYniZdzYirVLY44pQSEpTL1G2QzJstWIgsdukQkZC3xeBNJ2gfA34mbbsN+whZX5IL7GhvVB7/XTtzHiK21Z/dUjcrfYne/YdpeuZj8APD8D6BnaaBt4qHYncfPZ8StcVPaKxjeLocgWLr05IRG7ULWBz3A3H5oIPpyxX9JlqH6U7q95Xn6z3wUtZ9iruy3ZSGJF7KYmnikPhGz3NtaaxukZZXtPkutpQYHBcFMTnBxPCS7QmuVzByWUJs7KBVVmjD67FfupHJ3M4u1wpnZcBavIaxf5pvB5oAePl+fdAz/b+1pCEA4/OFmEdt+GZy5ndQWJgWDPNzCLL7GW/l73Y2+x/Nh4NvxiOisNhDl+OeoNPbmzvPJ90eyePFqt6c3MIN9/ahx9+/AwapN2Pe033dHYKrgOw9zaAPQF48RQAUHpiPdbJ5i/Y+K/+y/8iHPC/+OcA8F//Wx/8v/hPf1///t/9y3/579ZFXI+/8qEBDW7ItbuR5/YfuVX1nwDAh87ABGTbo/XfZAxfeue9qavGTEYdGPQKmJPB8orUU1Nkzot1IVPGunlGqObGsIB714ZwZ2cEW+MO/Xt72IVBJydabEAvY6B7Fa+RIUG2+YZfpgqgf+4wCeVL38zti2mTTCKQYyBaKiQs1dCjYgO69uxsAT99fA5Pz1AQqIKLVUUUv8Zb2N3dhze+9Sa88+67sL29Te9vuOIvyZkkyZRk2eBtmCKeirQokiiJDger/x97b9ZsW3Kch2XWWmtPZ77z0N23bw/oBhqQBEqQNYRDloJkSGE5grTksOwn/wSHH8Xwk8MvfrQfHP4H9outcNgPlu0Ihxy2ZZImIBAkCKDR453PPcM+e15rVaUjszKrap8LUKJIopvsU2DzTvvsvYZatevL78vvsw3b1oYy66MswDz2SMZqfBZx5s2LbdQYNGn/imxoJTpF4xNCsWEzJgOocJsp+nzs8OkyCA5l/5+CYdFbx00/NrXIJ/t2I+wipvuWcwZT/mQVgUbPTqPWR1ZKHFUGJmSMynkD+czZUb7nfN6bTQvnp6dwcc45jCs5pYvdEfQHR5rtZ6ZKOXOxcDFJYDjYbj63xyZJc2LTbVNettnZddJ/EJaISUHOobP4irLAUMgnjSjGYq6WsSaRYDaZYdxYcwYqUqdh7LA9hwqZLSrL5pRlJTX5Sk6fFI2onGNTojrG2DDbbSCWCx3mDHoJbBZPXZa42/9UThjZzaFsXn0fBGTyJjdKTp3ke5qBVEh76hgNwa60Eo2k15vZa5bytW0Lm81GWa5LLqnOJRMySuxrsWl3TplaSk6VVMz1tLgU5ipkWZZoOaeUg/3RWMPchi3Ay0WXb1QgavcI7B5ZYaUweEpXz+ZQ6VxtLrUJ5EHuFyzmYWLBLMhf3YFTocF6pZnJ1c8p40hMHiwsbnJfden3WMUiSSrwVVbgUibYxUiPZNiT+ohzkWOrjGQ9hnKt8noGxXnY2p+KkgZced3Bcj7aukzRfExzNtONzDVNPukArvFUVS6Qr4b9Gl/r1tIiwXiZZbO3D8ciq/3ex6fw6cs5fO8TgGvjAXxjcE1YutwbacfAv+0V5NnxQ7pH5ryavpOKe5IKTyqX5dUwuIhQnS1VqC4AsdBEzhG6yiUX8j4AbDwXURH6rof1psOeHeJ5bZxFY5+Xs7X8t1pHZ2428eJrOV9zFq6XwiJLhSswxh+V0TRJNYismFlPjoxh597Vuhdmn6XHLH9v2MG9lnmyQaTTyag5vntz7+n967vPbuxNPrx9fe+3j472ftI7uFiczi/Wi37DUSmjqpeIHs5rbtdtniTePMv5JDecYfNzdhtfzXEFNv8MjP/6v/yv/lQP8h/9+q/Lr//dP/knfy6u19X4woZz6O71bffv+G7zHzqH3wbCUbmPUqkdb2+rkrBkmcv+zgj6fgWQNrZa8QVIbgENOskUu3dtArePRnA4GYib7P3rE9ib1JKxxV8obEluG65ypM0c5I2FBcM7t/XXWyDzXwY20wa+6HMyJsTrplrCx8ugbN2oW4U49bhxX2pdyRfmyXQNv/f5OXz/k1O4WPbCzPGP166Bo4NDeP+9r8Mvfec78MYbr8vnMZsSd0NeZWaZ+7DNBqlJUOqH29q/pt2gXhunIf2x0yZdExcZoaD5diCxLHXa4InLn2ZSWmYmGBNRGfMY8zY7ogTq0gZbmRvS/s60wTO5G+VNY2TQQjbXqHL8QmlmYjeYLFidgZYzEOwKyW8RQI+YN96u4N1eYXm1HzP1pJqsMYadOw6Ud3x/NtKvydEnDDalMOBXsNvswdjpZl0lyFuyWGPWt44BtuaczeN0nQqAtcUkJsYTZGcbyBx9lc3UyJn4mRb14LaejWLmR3mxauo4VkamGsu4jTXW47bXA+ZoC9hSC7jtzbvLv3fKxiX2rtibozxjnZ6zSdiL5zb17EKSmpastLlRDhp2qm6l2CXSWE86v42JyvPYYHINDoZVLX3kLKUVgFfH3mPekK4Wy/hMgoFWKBxZSx7PGMRcAErARNyaq9TXJxx+ZT3DPrNgmtGZ80rz8QZ9JiOpVmVZq61bGKWkpIAxFDJ8tHubqxjp+KFoNYj5w8qGcRQSF/tYZQGmaIjANRBtgc0IIH2UVabiRQT2UnBDVc6XSgN9D9LPTtm+Aj6DmG4JO8zxM0SynpZsfpbzFu65lHv2E9NMsfc1y5hVFSBZnT4VDbaeP++31gAq14OCyRY2PvhCQUDq2JtYVH74Fg5hTcE1oR6adTNDAAAgAElEQVTv0uCoqnamcO9G65oKcLrUHnJ0UnzlIsenLxbwW6NTqAcVvHP7AIY15PaCZBJE6dkAVYtogYtnChrrbdeDiyrB+n11Pvhe1go9XLuven+Q5F9c7ZCzgLn9oSPHstdwdrGGk8UGFxuC1aaH+WKN8/UGZsuNGB/1XIGuADabHtebmMHMRSB22uX+1UEdkgbFlAB8ThtxfY4mZDXGfurgPJzOVjBdrOMz6gMMmkoUT6Om6mqHJ3VT/Wgy2vkXb94//Mlffu/u6Tu3xqvBYPgp7u78mGA5//DzC1isenCvrH1X448yrsDml3j8aYPMy+Pf/7VfuwKcV+NfaxDA2BE+HOLwl1vc/ANE/A4QDn9mYY+Sy3rauM9XnTT386IeW44iqOIvkrpxMJYviAqu7w3h4c096XG7vj+GnSGzCtqnESBKMMvohD9sFPmXee90WYp4GVhAYd+eX1aKkdIvxlhafEpAtc7PVvQqI07spma1Sy/YsvXw+clCrO0fny6FoR0OahgPBnBwsA8P3nwA7339fXjt/n0YDofCpJiNPlkgOWaDIWNmHGr/GVmsCl46n3x8l8GUFbrN/CfKJ13u07RLR6RywzJywvr6FPglgxgNIdF7njaSYBmbZNQXFAeQAKRsnvu4YZUNt4vZfsGMUIRV8pmlDbG6zdeqGjTCgKMCKjFQKbMAg7nwauXeqz1+CWYwFxbkugS7ilAwd6i9fCuYXkxhtV7K5drZ3YXDg104nNQwrj30ndHvWoAo5MvyXto/65JlDmbVYOqZMtZFDWt0k73V/KznG/8+qLup4j+dF8HiScTEmAoAXIBg+y3FSI5kJCIFBi9i0HgvDDhhAh2U8Up6M4kfwLj5NQCcCkYmuVM5rcT6eGNgtp9lKp7bYt15ZU0wA5qx9GlVsGk7WG06mAwHwnrP1msx2+LNKVNJ0lttUS967UUwUGlPaTAgSgIyL2YXsN6sM7tO2y665UGbU242ntL5b26i8uBWWXUgcyRnRWZRcgaIll+b5iHfHy4KoY+RPdyjalLPdJkKw5rU50yJ8YzDpTUAtBBSxq8ker3S59ZYWGs3LQBneYO2VCSUrzWZZJ9KMx2n62a6IqkQFYtd5TkVzKL1zlJe48gyVDGj/RSHoksNqZNurhBgKhQmQyCX72GSAKt0Nb0OVOacPoNSZBCQRfjo8sY8MSJLA3qqR40fXx+5fhX2uk3loK9PF5y1PIXTeQsXy42ArXUf4MdPLsS1/XDSwP2jSbpO+ZrbueZiT37WKKpP0OKgtDdXAKkvHqEoq4hqWAbZKi8Wd5/4DK99TbOW4CL2mdKzsyU9PZ7CcrXBVc+GP62odOarDXJ2diwM1DAe1RJPMho5eR7ZbdZhr4WV3IsblQROACT3mS51veZ/7NQoyEn2dBUmTdU3VR32dga0vzecEsCH5MP/d2Nv9L0HN/d/9I13bj555/UbYbLT1Ksez6a+WaxXmwJgX40/zrgCm1+i8Y//8X/8hR8MA04ev/lP/9sv/Fiuxp+JwcvwERL8DdeFX+0I/k1E+BoCSqPCz4N7DgwExc3KxXwT+wsJVZ4Tv0mYbbi2M4Br+0O4ezCGt+/swus3d2F3XEml0pgcr/sHZ2SU25alAmQwmRnNKlfWt6rPYatv1H4sMYHleZROtZCr4gZa5Viqwo2xYFoQc4ZbINoyLWC50IvzjeSGcb+NMBts9eA9dJ2H/f1r8M7X3oMHDx5C09TQblqwHtiUB1dY3tvxBSrC22Nw55ZgzFixJKpN8rZLgFsNJ8R8RDcy3qtxjzrrxj627JJqlegQA1fjsVYRnKDtRxOLl4ERRWl2As6QQJZyc8GrcY9R0yFLcI1RMWme9k8mmSxGsxH7c9wv+yRlNXbDXCjTvEBjP7b7PMnmhNOtm88GJpv1Cl6+fAmnJ6dy3Q4OD+H27Ztw5/o12OOgH2q1nymCbgbZFUbHVbJoDCtOmENuoTAOVtSIWvV0jVDD2NOcLTbJIbjExiZgaq7MGeMVQCZsAdssT4/GUrwhFFBVQVFoKGSIZFEtoM+b0/d7NWYlZXaaKUv6M27fD5WzQjJBsUIA5HleuH0mSGbuyOhgMkCY1MbqRZfPxXoD0/kajnY6kSvGEzAGPXtr1pWDwWAQ+41Dn9YSfh5m8wUsFkth2OphrWbK+SIYQ83XLhW/vD1rNv9DNGwpr7kygdZLR4X0GxFSjIkVu/Kzq9Jj7ZMu5b22JvG88hazktZM2JoTwI7Aun4yAOcsUVBWUw7Vx8gJXrdCmdVpvy+GSWKBCjltpaoJ73WuG9MeEiC1f7MCUorl4PdhRlPXOWeSfHUNzusSJKlwyo7VexrP220xdglEpz9bcaRY/wWYVJkJL74/LJ5JnqK+T88z5nvH06NXGM43iPsdd1TPEKAaOBjuBfDXAdopjPo1XN/rYHe0gE9fzuBkthHZKON7loFzPz+byXGBlpn7UpZwuTUkNmy45BgLSYINGdxfqrBygbe24iZiYDi4ZmURY0KsQhsqeDEL8Onx0h1PVzidz+Hl2Tks1xs3HAyJ1SyL5QZ5/e69Mbwy35HzsqmJkWSo/fUWt2oyifhdBjAaVnD9YELBk395sfbey1MmutumquhoZ9Qe7A1f1I378ObB3un7d/fqvaPB0+ed/2euw+9/687h6sHhZD152Z/Xp20Lb7ztRrc+oPHONTr+7v8I4dPncDX++OMKbH7B4zd+4z/5Uh7Xf/Zf/Dfwb33nv/8SHMnV+FIPpPtV1f+9inruz/wOIFzjdeXngUyrRjZSjXTQ9iQGOPxF03AfXTQhlJ7MazvRTfa16+woO4abe0O4ecCukXUETuaeWTAWlu/nFNS8Cg8huXy6ImPNlE+pnyiUX6zbYHL7vYoYAMsqK+VxOZYtvkPYfsssJ4u/r8UQKMDnL1fw48fncDLvRDrEJgfswtv7HiZ7u/D6g4fw8OFb4jzLm9ooxwJlOmAr5xDT55glfdjaeGRUnfvYULMWWSrGvVyZ+VDwFjRGgTcibIoiTEeWHUaZrFPDlvJyWbSK2d9bL1ovcs7Yn+aiI66C82SkomYk5RvyMYicjSCF2efNbGFKlDbMQavvtomPr0zMX3Lj3C5l5+u7dVFz1IqxAmR5h1oIwZCYOJbOnp+dQtu3cHBwJBX9ncbBTtXCUM102CVSPoOdTdWZMxoUl9Ey8fMZ5PCv3huTYhI9O1dI7K5LU7pgY9I9cZmZgQzwY/+jZswRZFOtYs5DKjzEecSy6MgYWQRESEzDq0/QqwypPQcIQYG/9XrquRUMjUlj7b1Q2alcMNEZq32MPrGSmbviv6gwwKRhV9rIVPLL2q6H1boTqWAE2L2iXyssRMkpH8+oqSVmqalrWLe9ZpBCAjXemxsypNDXuGcu5lko5NeuSowXpLUlKCGW5aJoztpFvyGZJJXfSfI9Oa+xB5PjCjjyvfaqVlIUyGDC3m/baMcYzqzfwPTsgRZVwFlxw0kfIik4cKk3oWTUcRvsYDkVtisSJvlEzfCV3tzC3E3iKuy9fQa0Bo6lVxC32UaRtEOnhTInc0aebzMtskdcCzXy7FW6ZvB6E3JESSpomgmQmSKZ9LdgtK0wgkqBB7JYJxFo8mLaBUDWcPNbDAGwIdGg+h1Ax9WFHqu6w+GBC/uvIVSD7g401V+C4AYNwEfPFvBy1sn36aAC+R7heBA2zzk6GMLhzjBu+O37oXwG9fmN66JGxGg/uyuetUp9ADjzkuf/YsOMZQ8XSw9rz/67FS7aHuabgPMN0Ol8gycXK9y0HW42G1osFuw6j77zWDUVbLpWnZdB+p6xUOVs2pD+nNhMPdgKHVVVxWUO1N7gs53R4JOd0eC48zQ4m61u+RCWw6Y+fe+No/UHr994PJxUv3m0N/z0rTfuwG5P5z/+vU8+eTSoFrfevzfcZ9T88oXXkFc5E/T9FiC/Gn+8cQU2/5jjN37jP/ozffx/2Pg/fusY/v4vf/vLe4BX4wsbsmfy4bW6C/+AiP4BVvgXCWAvqvj+kAUaMzujzg8S9cAsJfdejoaNMHlsV/7mzV1469au5IaNhjUMGKBqP1SgsjJduITCNjP3yscnV02n8q+k+4w/rz+LuI3HXjmn1P+l1vjm0qkyLJf6k3Abq6aNVbGR0s2i7EmQYLbu4POTJfz42VzkQzzWbSuh8XsHu/C1996D97/xPhwdXdfNvBeJH5qpCgeQJ1CCaQNmUk/wQUO/t2WVifHFykRpKqqKSCVKXSllkqUqvcS2XGIwVf6aJXXFJZDPixEmmWGIG3nLcUw+nVuytfwe8ZbliJhYOYDMLuo1lX5ZDpavGjWeCbL5442NgPS+zxLfEAozlkJOBpSiCGTjWEjngsqFkymKAlNnrCrkDSfLKpnlWi1XsFqvxanUjwgaP4a62gfACYS2BfBtKhpY5qcxmIjbmY9gktRiHhkDH5QFtMvvzIxFL2AgyxTNzLsYbRirS3FTaQ6PkFQHsYRgmYywxfCrNE/lqUFloOAw9STKvEiPU0izJqjkTZgoivtvKMBJCV6CZY5avEeIbquF6jjNlczM2saVMiAIVhuIfWUMTgZ1BHve59B+sAxcgq35jAr6ByrlY+aMAZ5kubJcm2W5qxX03Lc5Gsm8EMdtjAH1pIWbjLZj0cYAptXRDBgEvV+utjgScxktWPZK8h/y8+0yKyfXsQ/2UYm9sw190GsOaU3TE07mPllRwCZuEZhakQgS82hMpRdQnIFuKhpBZihjTzgmxrhcJ5wW/gTkJfWAubdicqOmS+DP1XXRZ0sCvCVKxYdUfIpKXCoyju2TC4Csv4lmQpAAa3JhdgbISYlfKSyhXntBQkEjvvjGmhGw6EHENUvekw9iw0EZEHysmKjMhctP8fZ4/ttKmOTBjsfBiHCw7wZY4T3f426D+OaNXXh8uoEX8xWcXbCbawc/fDSFxycLuH+DczqP4Pb+UJ7paPCtZxEoKY1Qpw8rHsDOgudFH6BlBjLECCp2/ZkuNvDxszl88nKBp9POtRBC3TTQ9QFny42wk20f36WqMKiihicCdG1L3sucyHZbymD7kJUVer1b3TcM7Isgcqk0w7r6/eHu5NHeweTxXl3/uKnwuO3Cztn5+pCITm7fmDz+m+/d3/3W194IIwc/nnezp6Pdoa8XfaBAXHUhHDbrJGmatlvxRFfjT25cgc1/jfHnGWBejavxLx0II/DhA+rhV5DC33MOv0kB9n8eyExsoX5NmFy01Yo/V165+f/O0Q48vLMDk4GTXswb+yM4mgxhWGP6/jeQkyqzBZNY9mkWsCcxPmploDtvSBscOy7rlcom/4XMqHCZLc8lb4TtfeKmyqd/L4FqdtZMn6mOmiYpZlfBz48X8PJiJTbw0yX3tMQq+O7+Lrz//jfg29/+NrzOhkDctSOVdtKNgQIicAks2Q0g66GELE+0cfm+kUoLzd6fN2wG7n20r0xxFtmZNuQermITKcyKGIeE1JMn8SOulp6eoOZBTqvaIcnhcuh/saGzA4wbEUXovPEur3PqH5VpE1IfKFmsQHr/YqZYU2buxtT/r3BbTXAMuKDNRZP9mWxb54btVwSEMQvgCebzOZyensLpyQmcTC/ktff2KxhVJDK3gA1A2wrzxPc2XIptSJt9NTPxwSfQBAaItK+UtuZ8BsZ2/ajo0DIH2IzMMnC1/uRyzhtVb9cA1CTIZjUlRtOla4j5EciLAsQNr/HbyeG0RHPW55t+prwWVSzqOFKJHb3yzEIC3YayKct6zRVUdvnxPdlYhMEmP1cs6/dkZlIRDHsIiXVLZjPS88lu2kOYr7skl+R7NF/OYT6fQde3heS4KOwUlwWL+ZaukWkHNVpDcm4ZEDS1SHb52a8wO32KZLqOkt8o6c39xMFYOf202KtcmB6p/B+LIh66oggUAVOSnVJM3AestXfSlAG6vIaitxLVcIesET9NW80OtVghp07MpmgwY7DOpzlt5j3WBmCqBGEsMeaMRideltPqNbV7IvdMC0iY2WDQdofEwOvFVGUEI0bsqY8973UM6ac+ZlTKvQkCMIN0sQfJjHYqieWKF2NZF09fZ3JVdc7VLQXvAoVGVghx5gkDDvGN7KoctNx27RuvYk9/HVzNMT0VUbf0wz7AjfG+2z+c450bc3zycgbf/fAFfHo8hxXLt9HB+aqDUTMIo+EArk0qdBgwUG5HsDtvRYlKgScz9Zxp+XLawsW6kyxrLuAtNz2ezNbw5HQNJ7OVeC1wqE7VVOTQIbd6BC0uSPQXNK4ZNNQwm7mOKcIhkD4SxpTLd0OQfoKoxp4DwSkCNUiwC0BDQOyQaB1cNW/r6neHRP/7Tee+/87NvRNf4cUffPR08/nZyRCprgc0WjL5+rU3H06uPfRj/PH5xaaFJVWXo6C2/H+vxp/SuAKbf4TxVQSZ/9P/9l349W+++yU4kqvxxQ/+YqqPgPq/E3z4NaDw19DhbQAYbcWw/YxhZidcVT3aG8HueAjLVSu9e5NRBTf3R/DuvX14cGMH9oaVyGwZqFiPnVX4E0sHRQ9X3lUXcjzrjcybLdurJglesYkO4ZI5xda3jhmuQBaRFWdaAkj9m/RL2pgBJDBj7+j0PJxu4GbrHj47XsBHz+cwXfTyhS8bXkC4ffsOvP/+e/DNb34L3nz4EEaDYQRm3jZRfdwwuXwdDICnM8KcuJikqWDExTb4jIo/ZSYDKrjX62Pa0yoyxGj9myaRjfV6+TdxgZRs02juwEwi93dhUylLRcnEJG72Qvz34jom/FFIas0sBFK/nko4S3AKkFxvQUG5zaeu3STASsay67+BMUTWA2jgtGCDreiBxoRgBsGyeXaRQSZx1Kyg9T3MZjM4OTmB8/OpWOYfHuzAzb0B7I4QqG/5qMTF1d5fqvyX+hAzyDdmy/oSUwUjOYrGexQ3+pIHGLLEt4yzEAxRuWwqlUg2TOwp2DylyFoiZKW5AcT8DNgcNEljVhvYo0UGoEJ0Rk6mUISvvBelZzZngCYWtQCvWQqa57H86jPTDsagU1Ggka0wwP64FkfPkHo72aSrF1XB/s4ANFkxxjLadeC+tQphPGpgMqzlWWDG2iJZmNnjQkqtbsvp2bpcENMiWIwGcplBqzBJWiPzbswff3YDTTXICk51hjYG0dh40uKQyCKVcbfzS/3ItoYVfdTCAiZ1g2UCm3usFhnqou8dcnZvuZaigr38NOf1OLGRRcHvcj9wYhYTGKVcHDRTomK+WTHBoo6EMRUpbBXZ26hvJZu3wXtVNSsDm517g8nA0aTTkKpVfVTDUhV9iiTlFyNxzYY+pAawWAOlmSgOO5z25VjHKxIUhxh7zGu942zpXZM+NI6dzZxO2rgK8wHWsQjhgIaHiEeDgH4Dw/VZNVqfQajP4OB4A6NnC/LBy3LYdh5mKza/8kDjBgbOQVMV0UY8b9lgR4367PvibLaBT17M4acv5lL4ZEDPjP9suZbCSqftJ6TqCO8J+9BpYUOjlJjtF7WAIPFUGKQkdYDEYnof5oT0Uwe4jCRxOCek64juogf3DAJ+5ir3qPL+M7fqPsSm/mnj8HRYo28rpLbvYdFucNK4NIWaZjjF4Ys5hHWAcJV5+UWNK7D5h4wrBjOO/+EHP4FfeffOl+FQrsYXNHgL0lP/gUP62yHg3yeC7yDAgW5y6OcJT4xFMvkd/9/eeAAP7+5BFQAGjuBgp4ZbB0Ppy+Qw6rrIy/LBK9lS6NYgG58moADF5gYLtsd62HSgupgy05RMeqjcHBWM5tZGGbdArX1Jpj4gYxaUITO73ST3A0hxErFiHz+vViMMrh4z2Dxn+3f+wu+DGh4h7O3twHvvvgd/9Tv/Brz2xmvQVLXEKqRzknJ/zLRLWZCF1X46HaLUr2r/5oooljxKVhfV1VTNjMgn6aUxqPZZUrX3GmzPxiC9h4vlAjbtSt5uNJ4ISI5A0YtUi3+2quPXUAjmOBk3s8yYoAac2wRLG9GQN/2kvXSleVFiWaoqRrBAn1jVKImzLNKiiLFVQbDN7zbzDeqwS2hsqjGwyiYroJOtp34mnydLDjlzUe4b2/cPh+KmfPdoDHsDzvxcCWOAKuUMW3O50BGjsvpo7qd8jpklSnpHMDBBct+RLt/jojKjcmMxzJFzKD4ypZNQ+pFsVrT9VvFaxd62LedN6zu0+AwwgBAlyRaNY6dKFLNKLysDKkzixewiqqZIkHFtWeXRq6CQ0s5Jn08y+TqgqCn2J9x3GT+T+zCbysF8uYbT6VKKY5PBMEk3pUyUWF0nRiagGatBncr4vVuWTs9nMJ8vYTzZ0YifXLxIwFknYpThDlIhKZpNac+wyL+HyW1ZnkAzvBFXFh/Bps/XBLAw3SnWxIj1K1Eqp8+3PNBK73WSaW8/d2AFKRfP2yI8IhhmN2iS6JSt+2RiggpT9iYWRmrlems95VHYaD+rjKu6aqNK4sl6mS1iSaXrtj6UImrr943nKYteb3U/IAaNAjcLAQXLLHmJoQYQvUPXsbWS95wdiavoLw5jEHmnWiizGNvVS8KKqexdBOJyQRe/nqhGJA6dXAroDKFmaS1W9ZpYPhuo0vYOYVLjVHMkBnPMjkZbVMm+EYabz7AZu9BMAlLnsRmCa8bVHu7i3dcD3Hgxg8VyKU7Lw0bk2ThbbOCFCzjmYq72cQd9/vkinS9aeHLGa7aHjgBOZy0cX2zoydkCF63n9ZuPCZarHnlu8/1nc6ymafiasBEWtuu19DzzWsey2tFoBMPhiELw1C9XjtRkLJdbsoQ5gH8JgD8igAWhG7rRcD4EeuZ7/8KF8D0f8KddXT93IZxh71dYV972AZZR7LZDrU2S73/2DuVq/KLGFdi8NP7zH/zwS3U8V+NqfLFDtoTsY/JX++D/USD4W0D0Juegw6tqsK2RNqQu9jVxMHM0/hnAa0cjuLM3hHHD+WAxI6xRZ7ugbIQPmXnYkpslJu7nfHu4yBhS2XtZyLYokzfbvSH/kpFksxopUh5PjNWIwNQiGu0L1b4EIQGTLLHlcbbYwA8/P4d1G2L8i4DOTkKombm4d/cOvPO1t+H+/XswHo+hb/toLuEiqAM147AgMGMqCsojASr7H+ZDSYYatgmWnliv/YsuAlRhvvhDQukKa7EuMVMzusDG8HVmZy6WU3j82edwdjGFyXgHbt65B7dvT2A8bKBdr8Shk10nWWYlG+bOK+BgSWmvtDTF+I1ys2hDcjo105OvQ8puLLLzUk9olUB+YrQpR4noD6a3Ro0jMDfbrZ9JU36bo7L5wPemsn9Xo6XlcgEX0wu5Z0dHh9C2HezvDCWagI2yJEvOs41/zLSDwp0xFBv+1BdWPHa2+Y5GQpCcnBNrEVRWa0UXM9Aqigyh2IuZwQ5izs3bYvo1c7V07H1VEbBd6DDpY9DQf7B7Y+kZRV8qWMZuUfSwdQQL1jPJ3n/Gs3v5b5KzJkS3TtDnBkWbGFe5QV3H3FZmOusIpNrWi1P2cs2OtIMs4YRiYaGYBRggP3b2mavlAj788EM4PLwBk50dODg4SJrrdIx876JWMkmh0yOsPcLke+1LjBL0EFoBV1zECJpnGbQok3uvwQixyCSruZfS7zrHI3gu10Duz0M0oEqpQJaAWjFPQHMNLULHgcvmUKYyMLFywthB2c64WG6t8Vb8ouLv7Ma5qGePU5PE8DousYTKfFNhHqSex65SJYChZKfzK1DKRJJokYoImhBRnpeFT5CnhBLrZXIdIXXiEkuycnD4oo8uYOTsjjqHnl8bZHGSxb+XRzP+nAgN+HOJQsPnVVXVkj3zYk8iK20lJ4izRxy56JSl6ob8+JMa40gjaF1VbCAUdrj6gLujPXh94+D+k2N88vS5yKm553q63OCHTz3g85hf6vQWtMpg7w5rWHsPHz2/gOmik5xZZvZ5NrQ9yWGxWIOjyAZNg5iyOmMxg58XLubt7O7Bnfuvs6qFphcXYb1ekUiLA1UBfJZkx0JESwSfOaAFBb+qnPt9RPdDIphBCBtou4sVwNwBPB0CfcrSWgLcljD8K40rpPlFjyuwqeM//eijL8VxfFnH//qTZ/CrX7v7Vb8MX6mhTnhvLNllNvh/D4j+NhFc/7nLdlK1xQ0hf6FxPt1kMhCJGRv93DocwZ39Edw/GMKt/WFkMXVb4TVsv8SvWJisZBmebkicyiJTYTw7ckI6Dsz9Z2ryAMqMmrFpyu671PdX/r4kNbOEUHvbUjakfmbIOXe2X04urBBzGJ1uSjni5CdPLuC7H50J0GQ2heVK3A/De4q7d+/Ce++9D2+88QAGnP3X9XKdLKUtMoqWf5klvuVmO+5Q1MQGSmdJ6zW1jX7+e7luuut11heIak1vu2kyIk1ZRhdlmyDmIT3MLubw+OkzeHl6BgdH1yDUI9nk3r5+JJE2qCHmUJjXRKbTxc/WTVbimzFDEEhS6GjCJOdOLqH/CMIZs3VyAaq6SSxQ3K5WskcsNz7pYhXD8k/lKISxrPLxFHx+dgWNrEwEwNHxs+87ePnyBJ4+fQqbTSuZqJxEsDNEGNeRFY/FAmVqKpWpJsOpPJ+NBQhJwou5nlC6sGo/Wz4lC3HVPlKA1HvoVC5rjKVViUwFG2tATgsRQU19SNUKKr816af1U+qzYc8WaPnHji1e7lBIriMQZaAtRRRjW2wuluRzSUAbmaufUZkc2MLxLYOFYMsEDFPMgq1Y8dkbDxqR+m+6HjqKvbQszet7r8+QsyckgWMXUAyCJiOW4Tbg+3hd+f26toXHj5/Anbufw4M3X4f9g/24tnivETHVlnxUAGO30QcYI0NI0dCFL1YvWbp9lPMKa24uqqmxVc16bK28VApBlwozcgxpzlgBzSTGTuXoJkXVtU5ZVZuH0jdsUUrmBq3usQhW4NLiQnSlUQaLMWqIlRTUHl951KrIfpJG9iQCWD6jQ4ctxH6/RsJC4wsYqOkHROSJzoFuDJMAACAASURBVLHpC9/CCYIwj10I8gXQUAyMJGsqds514FyrUvMGnesdYE8hjHlis5FxXPL55uNalMKBBiQ9hOL0A/L5enIU/AiQozfCIAQv+mlkMOhwg1EaWjOY5fOhEIQSdOhaqqQndxTPlR+OFJAsFxVjP0htyWEkrrWR3a5chU4MiMYeqW/3Dtr+2s2b+OzlWXV6umoghKrznhE2tT2hT4UmlD5MvnN741re+eSiFZM61Gghfib5ORjUEdLzUfE8Z9TODCbLatu2RYBGXn94eOC/89f+5vO7d+59/rvf/27127/1/7w5nZ7fGNRDhugzh27JLfvg4awH/LED+E0gek5QzRrXPHcIZ73vV87TvAvdagW4Hjb1ZnCFF/9MjyuweQU0r8bVeGU4xHo0qD+gvv3VDcKvVIH+ugPY/cPWe/v6CprpzmDz6GAMD27uwv6ohmu7DbxxYwLXdofCcFZiYlH0FMZSsv6+EOb+rApmApi5Xy3nLEIywyl/fKvHzRlA0U92bstSvxyI1leZkOM2pWsgU6W+gUICgiLhRGOTVNSnjritJ/j05RJ+//OpuM8uNq1Uh3kTyV/iBweH8M4778AHX/8Art+4KW/CQeykjp1gADrlSip5aqyLM8OL8lqY8UshQ06Zn1HKF2XHTqXKCmADpSa9CPBDyuw0doM3itzTw/d907awWq1hOpvDyekpLNYtzOZzePTZx/DWa/fh7Xffhp3JrkHmyIZWRWFBN+SeGRu7v+kkXGIxkTJITiZN2hsb0lzCeKwU++GC5W7CNnDZmloKeBJwt2xI3AZqEYDrxrzIPCRljaTv0vewXC4l3J+ltJu2h3Hj4XC0I/mOsZ80vr0HdfBU5ouCfU6R2wrF/FMADMk5NDJlZopjAH3rAhooKQBpKq5ktKkvDjlj0Jh9VQ7E+ZaZRQN6woxDZvjBYlPS5xqZTonwAgWBfA+5vzE+b14daO1WhQRc0jTRrFOvrr3OZNLeHHJd6p3EYu5KMazKUTkkwe8opmTPzkZwMl1C37diXNYqq5hg5qUiFL/NzqiBa3sTeDlewnLdx3WMz6GP90Xu+6aNhj5VPH4B1VUj0lcfOcEYldL3qR9UwCTm683FkzjNUvd1LmhZ/Ihmi5JJYdNNt+OuUsRLZmdBixzZPdpaAoRe0yKD+gXr8ef2A2GE9RmQQovKhWOsRUjHmYs7WeobVx30KkqvdOHk20/6OGOoTESLfUy1ouiXGvNIhDU07Yg+qB5dtYbgnbQ+soYTJV6Ej7qCZI0jKwnvg4N0TyJVUVLLtyrEbwluQ5QPDwNHrhUmFGhCRENhNzElx8ZL7sOYgJuRhX1kxpQ9nNq6rubRbChMKNLqGwbKIYSdylUz9tUhD2P+GUTsVVVSu6oW2InR9pdXSb4GvZRbibgAuV6uNisffO/7rpvPppvjZy/wYtE2842vL5btDoWwO1tjHUIQoOlDLMcYg8/PxOlyrc9hXD8cqUIoyDONAynE1HLdWC7OV5LN2cC7lHOqPfVdXVU/HE3G/6wZDCa+6355s15V9ch9BOB+Gxx8xnfOAzyn0P/IufqnnnAWsG4Rmx6h9xYyFOff5SCqq/FncXylweYVyLwav+hhDAQkq/Uvz8jyNBh6Ch8MBtU/arvuV5uA7xLC5GcdqMqY4h+0wt3or7uTGt64sQPfeG0fru8MhMk5mDQwrOvoAJnYlMw+Wn/Vz744SWMHUG2JrPLx6A8bK5NMIML2BtE+whjLkk2FAogkaGYAqIhuKOVkpQSQIIOZ9I6xLi4Am3dJ3BPzZLqB3388g8enSwnh5kNkBoW//K8dXYN333sXvvWtb8G9e3clw48ZsmS6krY22QHSrlvqRU2XzTbn2T3XyK58CbF4uTKmggT4uCrd7F6SNSawmmNSQJwyB4C9F3B1fnoCTx8/0g1p/Ixn9+/BcrWE+/dfg/FkIvLECP5Je8Aq6fusVYJnP2dg0gARs0yuUuDN1y5EtwqXciN9kvFZTiZLwETH1veQTDZKI5LyaqT+zsyol/MjAcw0NVVyWMXoGN6C9qEXw5idnQncu3cf5qsVnD95BgeDCm7scWZsJRty6e2VkPwYJUOFGRICpKgeu52o8TMRqLsiniU/CInMK+dKAa6yOZax25cdl3WjKYCT9DoWx5ZyHY0lLIFkOf1w67qmCJGtxzfHkYQQklB46xVYFoxMuo5x01vWpLZ9gyC3KmcoQpQlogYVWM5/43AC18/WMFu2sFluoBMGkaJZWTL4yl1mvYKruq7hcHcCh3sTOJ2thNFknMVvXYs81yWzGsE6yfmVtP93u5eaNKvSAF9QNtjWo9IJt4STpuCI8UXb16HMzRSGVrShJhNPzcsmV3h1CU4LTLlWFN9pIeTuOzLDI5XX28Kc1CGYXI8ja1d1DLAAmLGkARCFtJRZ6y4IcBsBR1bEugzfEab0PEVraMafLvo/UeMi6GPc79MJKiGb0kktUsP3NZFauAU/4HZ6fXyCAEaIQlpCGsYbKOUAr5PKAj7BSH3KoazqRRWlOCwBVYuBmnWpgWjgiAaEXB7B1olOmuScqqbqHVYSdEw+9F3XVcx0O3QXgHThex+Wi6V/8eLF7PnzZ+1qs2k2m9YdHx9Xp6cne8v5ojm/mIXe+15U2j5kwY0WMAZ1Rczmd32Hs5UXEMqMfK3zKmJ5gfPYDIY0GgyR1+7VZi1vxOt1XSM/Bz6KT7C7mF48/83/9//+7Hd/8C8+np6dON/78aAZ/E41gH9eYfi/Nj0d9x3UfXCLBsMifacnFcTV+PM4vrJg8wpo/tHHP/3x0ysp7R9j8DLKcivfxBp9yj38Qg+qWNxjL9ekD/7by779h63v/90K8I1taar+WPF72/TwvqKpKtgbNXAwbkQy+/5r+/D2rR1xekRlX6RHJO0CTQ5aHEQhT7SNfmLyJJvOohlQ+yTzLnM70qOUBFI6zuSGl1gVS1bUvreUf2mbotz2mAmC7E5qn+OM/Sk+/2cdD4f5Pz5Zwr/4fAo/fnwOM3blRQ6Vb+St93d34e2334Vf+iu/BA/ffgvqQQN9txG5kgAxzYQEBVmpvypTf9kcKN6gKLtFyPEXAFumOpmd1VgP6a3R/aka68Q3QJGfJrBhm16jn0JITN9isYDj4xdw/PyZ9JQZQ7eYzeDiYgavvfYaXLt+HSajcXL5HDScqTqCAYfkNw2MxxPY3d2F8XikZilZNpoNgVBl0RlU2/Y0vSZt4Ck6aPa5cGA9a3aDtwoSnjTWxYoNKs+sXO5DVLMWBovRxVJdeh1B1/WwXK2ERbtx/QY8ffYMFjtDuH9rD25fP5BYn+A3Ot8iBWIcHkCef0k6nKSu+RiNYc4FgsJYxySmpRKgcACFIrNSOJMUB2TMmhrMyHPmLj1jZf9kAXDLKKKUf/jqzzjI7sW2sZXj8RHgY5LIF0H9FrzPjr8S32F1oLiDF0MxVUtYXp8TA58q/b24BStItn5BJ/EllUQusUkQ6PHwxlsDgCLbo72xpEQ/b847H1UEOzsjODqYwOTkAs67LmVUslGK79XQhdf9Sni0yMhqcL7N0sg3cdHBZz2CmS+VZjuUX5uecVKn1VQ0yCFIYJmV8l5eChrZSI2SUiCkfkl7aiCtnbZOlAUDWZdtrtmaJAymF6k6y8gFLHe9POLo2DVLcicbjP2dYteDznVYuU3w4owWu9/F10oAJinVz1zfCIi4x3Kdv4YcR4awfrbSpdkLOxn6nXjR2You1JEcrDjPkvNpxpG4EykugaeahNx0lmvTAEtb4weweWuUvbITLNEAseoAYU2iQEVmIrnXsorEMW0cOJ7GI46e1O4J9N7vyBOFGBhUIuCuYwltVfmqEhrTE+JFiCkvayBcdH3brVcrPD2buvPzc1wtF8ziXhDQyXq92kxPp+Hxk8fh6dOnu+v15mbf+4PlcrHbdf1OVdcDBoJOuGEQ66uk8jGNAANolbc0DrFKRQ01T4vyEYz9vk4sxDyjXGkaxoF0/lbuJSB85vt+GhDn3q+Pf/Ly+A+6vj/eHU2mg2bwo4BuGlz4bDiAxabL9dKr8dUZX0mweQU0r8YXMXhjcrDTwG5Tw7ztpc75RY7IwtSpD4cLzIOmfnfTdf9BR/7XHeK9Ave9MkR4WTmR4rAB0LB2cDAewL2jMbx+fQL3r0+kL5MlZojx/AOVrpQqz9zajFoZ3mSbEQaaNFWGbXB1o/iz40r0bygzHqVkNP42JNxoDKcYhrDq6tLnxaMOCsRKcKmHXBwBpk1dBrmxfy/2xzybruH7n57B9z85g/PVJoX087f77u4ePHj4EP7CX/wL8O7b74qpCAfC+2R0A9mkyGe/xRgGf8noKGXIlaxVeUH02M2kpQD9VPaBaaRCuoYFe4b6BpjcakF78XrYbNawZiOg4GWDXyk4Y0npJ598IjEgu3u7smFtu1aMSRhcHh0dwWQykk36tWu34K233oSbN6/D3v4BDOomArEC0CQwpccaZXyVuNySBsJbnyJpbEtkXGLshp1DydLH0yji1xJwU2kXFu7EypVgZU6YzNJF+SnfopOXJ/DZZ5/D+dk5TM/PxYX2ndduwo2DCWBYX97bb82fMgYiFxAoM5EKFiuXKBWt1RgTneW+9t6oG0q5x2Fbbu5qdr60YHXVMEp27M9aqwq5rYEWwiJ+J/OTCRjrz1hZJjJ3+jZS3Kii1Yrvy8rO1rugPXukMmOTuStQTo7GRsLVERQFCMlMCVJ5w+JGAAYYzX7Wm1aks82gkt5iiY1YbuBgZwSj0SBik3RPovmZ7udhl/vTJwORjZP2VLZtKwZRbAoV7xv3pUa3WTZvMQfitF4IYJPsxmJemFHPttGVgQZSB2CWvJICUsEKlctFFNLEfsu7tcvqQIGwstkIqZ0Bk/vuloBF5oTxmAmHmprCJmGSeiuLGp2KfFIARJ1o7+RhiVUMJGi1AtQAUJ3uZ5wrJLAwPv0NIXo9VJd7HMC6zUmISIgPfrLU4t7JGHfJwHQYk4LcRswDfD8QQMvkqFY5Yswmg1nOwOR+UXIUoJFqAUIP7FCrKDbWiIhfJ3mbTg5YGjJFscqnV3E/aF1f1FXFYLlhGS4BPQsUXva+C6vFMsxn88Vivlgt1xu/XC2qs5enu89ePD84OTkZLBazYe9940PYaTcbt1ltcLVeH3bd5hqniCHiQVW5ff5OVll1lB7HxTqr2/Wmdb6HbtVLJ6mgSbVFlmOOa4uLS0+g1XLer1j6C/CsaYbHXdfvdX3fV0T/PJD/Z+D7R0TCxHbj2s3Gbng+HuxegMPlppvbV8bV+IqOq57Nq3E1foEjylQqOKg6gNU8RVF8EYM33oPbdwF3xkDrGUO6nQD1t9tF98uuwnuvKN2KEb1gUAwx2FjgaK+BG5Mh3D4cw/1rY8nNZGDNr+s1/DtV2NUVkSzPrtjgo4ImsRqEIl+wdOHEbXBaGpPYMAbT0GTJnGarfQNM2wxfNNIpciiLPTUUPaJ4CWzm/ZVt4DM7VSkLeTpj59kL+N3Pz+HFLPbISL04xN32/v4+vPvO1+Cth2+LxNQX4Ihz70oQnDbzZuCyBTSzxi1xe+bEquchNXlyieWR93GZ1SF1exWIkOJjqLiemOSAlPrKCCynTuZIkTGotvTqoutgvV7Dim3y2xbadiOfxZEgi+UchoMBzGZzuHt3BuPxQK8DSh8ryxLLOSPXu5wq2p/Hct5oAhRjI4SJZKMXZplqQSAip5UZWWkfasDEWJJeW6dgK2cioPYo5dgYZpQcaQZJ5GokS5R/hsHm73z3u/D8xQu5bh88uAX7I4Rx5bPLqIInmfMmLzQWEZOyUW+pgl2nDKjVEFJUiM0/o7wx9bESFK+DbWfRmIuHCYiSGCu5BGpt8qQiTPFckOaCAmamVJY2ZfBST7QWkCwehQz0yWm4BLz4mfA+y+Ez67xt3gWFgiAxyhALDCExcPG+AhSqhi0VgAKooHmMgDCUeRvNapabDVws1tAe7sJoqAJJxWa8Bg4gXqOVJ5HJ80afT9drb2XvPcwuLmA+n0eGc6gOsOm+Bogqb9Ie5BynFBKNHxlZTL27hYqiYEZzH3oUjkbWlKSHlRSkc1HEdyRqA8uPjMUzVxie6TV3VFz7IobEHL+1/9VpEcN6c8WESOS+sRtVnylHbKyz7bjGQK4l7ytCHKNz7My6EaYxiEC6VX/egTjIIpdChI0bpOpGvPW9AMnIsPIHdpWrVgzoQqBxvCKO+zCbWE6BnmG5xJEQx4tUK0/OUx92ZIYw2BJLHvEb2jADGqNJ2eiHnVlDZDiZJUX0AaEy4yQIcUo4x++LS8fGRhJZwr+6C0R81rbt8/VmM+827XyxXDyazWfn88Vs5/T4dPfFi2N4eXrSXFxcXF8uFrdXq9X19Wq91/btDhHtEYVdCtACwZI/pB7U18eTMa9cp3Lt7J4TMDBcAhK7zbF7/J6BTRuaF0wCkOP9YIJ8DQgvAWiFSAdcRSEIs81y9Tm56lE9Gj6poDqWMkDlT9B3v+Mp/KBCbLPA3JalK3R5NeL4yoHNK1bzanwZhsGQL1JMQga8ggYsB3odyf8lQEihqpc3dkExC+8lOLB/d1TDg+sTePfuHty7NoHDnQZ2hrW5+0Ovmwpjn3J8hIKPSo1dUl3aHCpzD6BJZiExTCWqTCeTZHhJIqSAqMwsNGnbtsy0BI4WeZFlsskg6LLj59a1tOsFsrHFwq3SNrjTZQcfPrsQQyDOL0NlVTg3kwEUu5S+/vrr8ODBAzg4PJTPZVUZmcOki+YxsVhv7qIFOCquTZIHOgv1DykT04BBMhFhswfmaiRGRBkAyy4FC0rPc9U2mJxXHmWQBNT3sa+sihtQ3qMOmoHkataao2lzSWSN8ncEfRvzz/l1dgHn8wXw/3h0XQsvXryAFXsSAklm22CwF+dH6pfcBkMQw8EBq1CAk3ijnbKdaTeNVcFAq3SscC+O3IgTtu3V7EDtf0tmTT5N40j0kRw/GyUxy7tZr2EwqKHj38+n0O/uQJXceLOTau9DmmMGojK1hIlRN2bN5mZQiXnGUZSIVxXCxTxQfYiTjNLMpcScxmdwl4oQRTyMPlOl2RYVFs/25zg/XIEUKVHoUcobn3B59ksTG92wx6xRlQSXof7FPBLZsWaSSnypIzUWopSladJ9iUzU85BYnyKeI0rz4/tyPNPBZAgvBjW0vZckRjkWldx67W+tbX0Ssi5GflT6Hr6PLp18nzjMngsRi8UcTk/PZQ5MxqMk8U0iDh9zc5PEF0rlgDqOaiZv0IzdCNZDui0h+dRkYAoqtrX3i/mMxnYa2o1GqoHPo1Kbbo1xSvMgLzcRDFM0h4LEsJrce7sQIeZH3ts65ESqag8IaZkLxQPMaVitOENJZogDjRCRi1/FZQtF004hVBJBAuoTJCmSusAF4sWlY0daNaidpIRX8rWkM7EDLYNXHxyB56poh1pHiu3HGFnAWIXx5BwD1Fr8X7lHlMFn8AOIDrV9rAVwTGbVOsA1OmEAp773z7uuPe97v2jbbrrarKeL+fz85Pjk9OmzZ5uL2UU1PT8bnU/P9xaL5e3FYnlrs97sdV173Yf+NUS8gViNONykampu5vSI2CDBmKXIctpRHRtZV4JG66580RiYc0WT/xumpuo4vPauVqnBl7NaEFZE8CgE/1sO8GMi3EOR+uCnUOEPQlU9b/u+a/yaBsN60Q3dS7gI81e/Ea/G1dgeXymweQU0r8bV+HkD7wUPf8sH+MvgcAQa+8DVfa7MG+CMLTkOGg5BHzfwxvUxfPONQ3j3zp4AT9vDeN38xvw/pxEVeZOfIkmcRT+ErT4vKiMfCoo1H0cJPiEDd93wFERmliQWZ77lKIn5OzgrTTP4zA6PtJVNWPaBWhRFZGbyBhn1Oi5bDx+/mMPvfXYOz6cL+TfuB2OAyQzm/v4BvPb66/D1r38dbty8EaV6zM5Y/xyJEWEMcjCmQ1hgp42V0RzHXDVAeQ/XVNrDlzitdMzpqqX8xgxoMltMYPtyTE6eJo+LDrTi0xi6yICK439kYOumlnNjaTADTn6fxXwhTrUYnG6Ihf6CnclYXrNYrWG1XMYe1uEQTk9PYTqdws7Ojmy2b966BXt7+zJnfMG46M1M94wU6NXO5LEh7gsZ5KJu2JUtCuIGG6BqmugI6r32e7rivSH1yG450Rirp/PR5M5sUcq/36xXsoG/ffsWTEYNjGuCe4cNNODFWZRBhMjIUw5mfHhQgWJQxtIMZgxZmMGNMIQuKgW4zzbSebjFAtpD4PS+ewGVQVjLmOOJqYgSjWcKZ9OkHU4EVy5CJPARd/nSupiuCRi1p2CF/7EGrIcKTtpoWa2FJSW5Uo+s9MTysabHOLN4dgQmFY7XJxSFIJNMFmycxawU+bel8VREFgH2RjUc7g2hel7BYr2Ot52Z9RDEDGjrvYp+SGHAuXBU5/ibSiIpGK8SLFdrOD07hflyDgeHe2lN4sxM4acVxCfJqbCdPstqZV6SrgdlAc4V89Ceh5x1Kb4tRUFE8lT5fV2VrkW835Xhqix0RmMx8xpqCpI4rzJgj5FC+vd6PYtKW163sxO0eMSJuNeHgeB9h558GAiIDKHxcjlchcRJI5o9GatDPtYECTTniBcfNhdiQFrHNZEqH7pxnNzQEkqGZSVSWC5OEI24VqGFtIZ82LVnJopN5I70MQ+TBlqhqJkJraqqrep6gVB3cVFxXBlbIdDCE70kosfdanM8W8xOj4+Pnx8fH5+eTaeL6fk5vXz5cv/s7PxwMZsfTi8uXuu69vVA4QEA3qqw2qurWFgYDAcjxOFY1yAzFVgj0FkEh3RLzJIAXiLBE31MdpnJtVuCUYq8B4ATQNzVLFG+Nh6BXiDAeQDcA4I9JJoB4jPnHB//TyjQ/0nkfgfAdx2Sr8CdYwStZamVLv35alyNnzuuZLRX42p8lUfcgxxAwL8TAv7DQPSBdlFFYOliD6P3UZTWcG7mqIHrkwE8uD6Gr93bh4c3d2BnVMeqe6C0+QU05VzelJdMExWbEH1B/AUuAz5IoCJvusufKWSwhexuywxHt4auyILLssgyfgXSvzvb/KeeysJ4pcAcxijKL44SaJArx9jBB3h2voTff3QOnxwvYN2pyRE42N/dg1v3bsPde6/B1772njCbDLK892ljaUxdVcromD10TXaYDKEAwpjZ2tTXlllNMxhJJ6zsGSVchVth+VEFzdc/JPAdOQkfG6jIeiBj1EGoooSON00MNvf2dqGpGwWAvdwXNgXi/kw+xPnsQn6O2SBhRXXDzT2bs9lMfo6vB4NONh3iKBHnBoVZSm6dTGBQ5IBhe65Alm4nYKf9Z8n5U3rldNONWeW+5fRqzq8Fw5ikjUrg8Tnwuc4XC2G1GHjtDhu4u1/Bw1tDuL43jDEsVHLGGdOG5G+JWwURiRUp+izldRCi9Dx5OVt+/fa6FmXBVBhKZUD9s9j6HK+jfy6cR7fk5ckVVaW4hYoAyw9nZpj3ySFKZ9F+1gR36T5GBlEwnAFgKEBPUSAxg53KWf+qgpkUAxOPG1ylwokAW8tKKYdnB+0Rwu2DARzsDODlbCmFtk3vpQdzwz2Xk2EkzyDfb5t4vF4MXJVinUCOL/ZZcuFjej6F5WKpPjrq0hp8msNWYBN2M2jxAIzwzXmXNilT32auAtisEBZe7gTZM+u1p5XSs2EyZiv6AUGS21q+sRkBFWKPDPptXkKWXee1UKN/1FiM9OZYuQ5jTKRowkNkIvkHWznoQEOzdmXZbZx6caGLzrDC0qu2n9WffGYSUqmCDQGIFXliSWwn+ZxEdYjY1UNFgbMwCalhiWwk96mJQUECgFXx7yxZZcCxJOiqGR+jc9XSufpp33cXy+WCNpv2fL1eP10tlyfzxeIF/3d+ehqePH1y/cnTp0fn52d7682Guq671rftOyGEO66qDqq6uT4YDm6yEpZLlFo36SMZ7eqs2UhrTxtYJktCos+Vv78AIAab1wjghjpG8cxorOQS+1xjOjIinRHhIwD3E0R4hNwXC9QEhCeDQf2jpqmfhmV/Nl9unjSj6lT7PC9PsKtxNf7I4ysDNq9YzavxpRqkVvEOv7CjEl5q04FH994mhH/bAfwNbsO0qEw2B+DNXC0yspiJyaDy7dt78LVbu/DmzQlc3x+yfTp4ytsICJSzL02mlQCnbXDU9VQNPnDr2wzTay3LTTaxGIFcxkrmIVucUwEMM/QqQBjk4yp7n0rwmUFu8tyHovMxyQ9RqacSfMZ8w+gqylVq3jS/uFjDDz4/hw+fzWDZ9nI9vX2FC+t1H77xjQ/EnZWBJgMvH0IC3EHjN1hv5jQEHrX3ivdgzNJxP2fe+Oe8AJHPksmUi+tlgfuhCDp0kGNDAIzeSnJRM5tJPa4COJUhxEZjRCKb27CEdjiSXtXe99KXyZ8zHo/htbv3YTgcCDN6tDeB6fkZfPLoMZyfL8BTdrq1kH92peWcwidPnsCzZ8/g1u1bMBgMFKBEp1yTrSbGVjMWgxopsRGWvKfKRMXgREBb3OyjgmfbyMcNNNtCVnp5gs2IaK6iQM3MaMSpVOcLb7D5/i9ZAnx8DJ99+gk8e/YcKgywi2OobzUwGVQCyqI8M36mV1AVtlyMKancQAGg9/r5iv4E4KNlSF5q3SVKrLan2FMIEi0TCSKvrq/CDCZpZgRrpkzIRZXyKdVeRy1SmGEoJOyVC0x2QeWn+y7LB5zG2pDGvQg4qdSwLFY5bJ2g8rkk2tr6kuWJyn9e0xojqBUSTLJQK6PnU3GqfN7tMLnfcn8yEPdsjn/oxUTFw/l8BefzNRztTGA0HEQQ98p7RMDJ89X6iu24eR3t2k3sTVbWk4FdsKqCIoTo/OwzCNQ+VD6HYEoJzQgO5bNbSK3jOtxrca6Ox6lgNbGVBeso/dWlLLpYIwxIx89Ogad2vFFHEe8/6s+JrDMLIyI3yiRzDwAAIABJREFU78StVf4YDX2qqOEniSuB6EATpAdSJixaD4R4BkWgxH8fzYtFShHfR/suXeTUdZHG2HmPwav8mqNEgiMM+v78THXMb0Ypb7RwjqldrnICMqFjl1hEt3IQTtFVzwHw9733n6437enL48fTTz7+yD16/PT6dHo+mi8WfjGfh8VyubNar95cr9Zv9H33TQaWcsck8Qp3B4PhITrcQRe9n51zgyyVF8UPCltK2+GSCMTs7ZwAZwDhjAC+iwi3EegWED4kZLCJh5obzfLY1mo3gX+O6KRGfILOfc8T/QAhfF5RPXU9rfo6bHCIFxBoJsA81hYDXAHMq/EnOK6YzatxNX7Rg7/e2Lxkbx866fP6xQNO/k6rlgtAH94Ivf8bwYf3HcBYd3ZCTVXKGPDmhTMMx4MaHt7YhV96cARv3dqRjEAo8/4Rtne6qU+sMNtJG1CrjOtLsQBzl47VWJT4PWrAlFKvGNnn2gtT7EOWHJZ9doUeMG+Si1gBU+JZBITxnoWKUYvNlKR05UaYAV7TCNcB58sN/PjJBfzo0QWcztZJIsegYTwaw/Vbt+HBgzfgzu07wKHZwmiqKRAUUSXJ4IV8Yrw4yE+J2rzhLy4eaZg+GtOT3FQhCfDMz1bVY68wvOW9MRrFjEfEy0ZjT6LxSK2bYWkulfPk3ND1ciVmQLzhv3b9CHb394SdhNDCraNdyWLlqfRD/wjOLhbxnJOHSPxs/vmnT5/Bp59+Cq+/9hrsTvZSviQUrJ7JO3neyYY+dKKkc5b5p9EYEVhrf2RQuaVcd6+9mxnI2b3QfXMxxwq3Wi4QYCiknhHIr1driXlZLuZyvacThLbt00z3AhotGxQEbMOW82sEFaVUt5TIkh2XRmEk2rJs84R8PWPLbwQ7EawYwE6a3Rh/kgAkRLCrkyW6EhvAMhdW0ppF7u0ki5Wxj09S2Rw3gqn3M/eQEoOjqon3IrTxXIUpV4doCjmapTALyuA8gmmohtKzC34jPZNyj5OJmElwL5mKFcCNZaVNXUkhraoQ2r6H6XwFq2sdjEaNzKfYC5pZYrE4rRBGw1r62Tdtn5haOc4QFQpkc6Sk5ZPIIIBa3SYWPvJ6mKJ9thQZmk9kDKPdF1NrZCY0iWOTEVAorpsy0lQU31CNaG1OoYUJxS7QCCUt4pe4KZahpGr7EV0vvY4+1NJO31QrrNAj0YiABnHNjT2YGB1O+YBH0eEVWiTg1ziIuLVXCazEg8SSVmw81aPi3s1G+xB7nR/MaK65PEck32udc7hixpNp7rrGBYUaQwg7GHtrVyj9kMBVgmnb9k9Wq+XL+Xx2MZvPXqzXqw+n5xc/OTk5PT0/P62Pj49vPH/+4p3p9OLrve8fEMAREHFfZE8g3rZH6NzeoOEElaoVIAs45OMXPynpQb0cIxTLDaiofnstRpbH7iMb/hD+NiD8DgF9mwjvIMB1jf+aIsAUAB4T0DnLaCuHfr3ZfDps3PepGv6wWocPQw1n0EAr6TokWaEB85fB1bgafyrjCmxejX/lcZWx+Sc14gYgNAPofas5br/oIyAY3Dy40/X1r/je/2qFcDc2I4EzTxALzGcp2M5oAA9v7cJfenAE79zZhd1xLWyE9DGlL31Mm3/ICtfsXJpir80wCGPfn21ujTkpM7hKyWfRawjFpguNwVGXS4DcW1ko/XTDB2CbFGMi8348H3iOb4C0UU9AdatfNMNjO89KTVuW6w4+ejqD3/uc+zRXwiLxJe46Ennp7Tt34Fvf/CY8ePNNyZEMhTFM4npLZ0rNPASVpgmjpb2u5cDi4qHFu+ivCClrXI8Z8+eZERDlTTkoSyjyY93oR/YJAbXnLzE8gWC5XsHp+RkcP38BH3/0kcSbcJQES0pdFWA2n8Nnn38KHcea7E+AukO4drgHr92+Bo+Pz+DsYqm5kfm+9mqIwoDzyZPH8PjJY7h+4wbs7u+brjPNmaDSWe4JDf1W+GiUNKtTKRTGN1uGUHbvU09ajp8w587opOqiYZDJaRXZkW7cGUzzvWGWazgaSh8vZyYMm0qydrGcPxSjWtAYKLS5HNlXk80Gm7s22XTObkfeZFCKJrVMmXmSPxQBo2VxgrGhlM4zV3eCtrpmp1koHkeyGKOtZ70ERBlgGptsc6qcN+kZQ4tYCTH+EGKfd8LPGp2UntFiLRB2TiNpJBNW7xFHgXDBw4csz5b+cQjZaMmktQrS7DOqKjrbSu+oJ4lAMVYZ1JwnXfXEjFYwGQ7EcXzJYJniq/izV+uVFB7Wmw0MB8OEJsq+2gSevTKezu650/7ikHNlGYSn49H3MfOw6DRK8j4h6OrMJ11R1TQSUkp9a06k6oktc4ei046PE07cZ5BIRc+2MMR77+XA47R3PfdDRvkpVq6q1sEjZ4xM4t11LSL1wWGNnhoBiCjBllzdCdYPanEoIKCQGiX6g/Vocs0lYlzhsoPyq+z0Ez25JbKDIS9jUycvIRbVBvlEaUiN7HPNmZnYd9F/CMGxNfx6vVltXr44/uzjjz/95MWL4+XJyYuds7Ozerna7C/m868v5vNh226uex/eAKSHBO4+OtytnBu4yk0duimDTAGbWuZTqrjRrz+nxSSx5MPIyvNJ+PgVFp9aTIIdue4930nk3kuA2wB0Ewh2BXgiPoIQPkcHnyO6l+CrKZB/Qa6bOqjXncfZcGf/SU3rxy24qVSNs1DialyNX9j4SoDNKwnt1fiyDRK3A4RJU+fevF/Q4O+zzYoabEbv9eT/OvnwgQO8Jh4N3KNp/VAhsiCDxsHrN3fgl966Bu/c3YWdYRU31KUz6yWGEtGMCvPGN29ijWWLLo4W+p5keVlIW3SgYWIUDcDaZvyytC8HP5i0LhQfWxqnRJ93ylRlZjxLSWZxiinTMe1zt51yRUJZISzWHj5+OocfPprC07MVdMx2VjFjrxkM4catW/D+178B77/3dTg8OMg9rNovBSmTFJKxi3OGbTILG7RnrTzOgkNO1zvt0VGBZ7pkFm2hcRoQM/tqvS92T1GjDASoUWTbOmYNfdDeyilcTC/g2fPn8Onnn8GzJ09hNr0QF1nuVyN1HuWMzfliLuc6wCB9mDd32UjFi+w4nUHqjwUFUPFen56eiZyWHXt3dndl3xRSD6ZLsmAx/WF2TE2AQPv7QsEyWd+lc5jAixj2KGBMcyFxOvmqOohsZEB1kxW9d5ZXLxZLkc5eXEzV4ZNzaBFu7o3gaHcYpekFwEsgFjABuFwQAO1VhoIJywcjh5/+Xa9FyNetUjdVAfHOFPx9lhpTia3zXLF5JeuTt8/cLvaYbDqdh/6cJZ1sMXcJXCtU2GJfCyTLYJivBQNwdjmW+5jlpulJNPaUMg6S+S1FgCxzTUdNuRAmRRrrSc1nJCsSP6PDppb/VusNrPr4jPUhRlfR1nHn4YTSqqSnfWc8hLnE+kQgy0zt2XQKz549hen5Q9id7BQgExKjnOTpduNV3hAoZP2+Fer0uVUhqCzeW73rlO+RSFBdRehqimxhsOpeVooUOnt9PjAyztatayZN0jePqaAg/4+0AsVZlFxX8VF/7lw0Jqd+KH2RsT8T4+MlZZXKsXFN/ORem6ddyGegDrXEiBSVdeVzZ3DL/Y11nPKxxyLGWjJb6pxzwiS2Hv3S98TGQzt13Sw529L3frBp2/rs7Cycnpz2q+Wq63z3YjY9f/Tpp5+efvzxJ8PZbHZ3s9nc7fv+Wgi0kkAUgBFfj7ppduvaXcOoU+ajfYEQ/mdC+B4CvEWAf5cI3tEa1zBPMnK6pqyk1xJpQCDsZKUSm9qa57WccUEAnzmCZwh0QBB2CXBdVVUHAX43BP9TcHSGHj7E4F9Q3fbVoApjmGxWbbfsA6zHo0lPrKbwcDWuxhc2rpjNq3E1vqDBm3uWTnZ+nQOxfwFD9jO9O4Rl/yZCuK6aKN6R1I3IxhxsugB918seY3dYwVs3d+GdO/uwPxpEtuXSTsukiOUw0wntzdF9VT7Pbf+fVNnOoBJsf4vpz2ZY4TCzmKXEMIFW5xKjwmosSPl1djxUGGNYg0rIPVLGkG0dqLKmaVdbxLno4Ldbbzz89OkFfPfjaAjU9UGAFEeBHB0dwfWbt+D1N96Eb3zwARxeO4yGKOpA+kp7KamRamJjbRfvfqZhUpYoFjK/gn3Nr4kf4lK0DGg8QWY6k0EQRaaO58NquYCL6Qxmixkslkvouw7aTQsvX76E58fP4eWLY3Hd5LgPsUiqqy3zHtvk8/2bL9bw/OU53NwfCLs0qDLg4vxWBmRt6xO7xWCRswqfP38uLrW3bt6SXE5z4k3HXQAbhEIyGIJesygLNedY1Lw5MFAdQi5x2PWlImamMMIyN1tlKOT4WaJ6tlqL5PeTjz+B6fRC9pEH4wHcORzB/s4wRmL0efcXsymDZrLG9zRTLiikmOY+WrqSJtbVZN0FeEuOrWindKkP2pGauFgPbC99uKkvV8EcKaGeDHm3QCLpe4JhBZXEGriEZD4F6Sij1Dkah0aEw/2xOJgANGOgagjUsrHSWTyeWIVJ3Cg5m0/b62Wc8yoP1hBHcbw2Caoyg1AUFATcU5wPPTiRwO7vDmFvMoAlA8bOy31lB99g7tTy7Og6Uaxn/He1sJuNsJtee2F938NiPoezszP5laW9dd2A73IkEWijfDRDQlP3C83L4A6Cr7XQJCiQ+x0vuWOHlIQSSF9qYNYp60no+x5Vamp6WCQD5wUuJwM8YpZliwQW5Ho6Fr4OTWx/5FxM9CH0nAnJC0of1yI/ZvBqJHWEjbF5UvIqETZQVTMKVLF5j4r9PTE4jYY4FDFaiOmtWLX1oG5DoImCwM7F5mP+Luq4x9v3fVUPmzAYDFdVw9GYNFgs1/3JyYk/PzubzGfzwbPnz9pHjz4P59PpqGvbwWq5gvlsdne9WX/DObxeVc2qaRrvEK4Bun2MXywzSS4lWov7a/weeEqAv4kA/wsAfB0R3g9AbzLQ3PqmzKISvu0LqXUSM71yizrtcW0J8RwDPqscfIQVfS+A+3gDuI/kDoaEnwXCH1ZILymEXmTIPU6h7wI0a6gGY5iEISw5Vgr0u/fSd8DVuBq/6PHnHmxesZp/MuNX3r3zCpi4Gn8Cg9gjppHviV/U5ZXy6cAdht7vE2Fb1e4UUSzQB8ZoBjWU4OiTO9cm8ODWDhxOBrIx5T432+PgZdYCc0eUIQwy8V9SDymwQZW/pffYlrCW0fTpNwVYLD8fip6lbaK44EkJtkFXwWTa7ipGscSszGSoAaU7IyQVVOrdU9ljrQD3+GIFP3k2g8/OVjBvvfThDQZDuH79hkhm33jwEF5//Q24eeOmbLQ95RzBhDGLbFEz/WDWLQa9h+SemoFxSODXsjGdc6nP0AxBMG920jXleyCsX9eK5LBrW5H6samPGJv0nche+00HJ6cn8OjxIzg/P5d+RNIeVY40uVhcQLdp49taH+crrKuaEHEIftvBi7MZnC/24XB3DHuTIYzY1XhNAjQ5GibUarAim+FejII49Pz4xTE8ePAmDNnRVqWmub9MZah1A+UMtF41sD5TIcF6CMrvkOYdusLdmCzBH3HbPEjpyPQ6keVKOkIyRWJA/OjRY5FOTgYA1/YHcH2fs0exuAV5UvM5Vhh7BEEcoBWMBohBhBpHQiXAsDJR4YJsDrdgBlyg0TcJ9RtLTirLRnO3itcpuREXJjW5cznJYY2ZS62kWiwJITudkhZvojQ25z3mSoqhtAZgsAswOgAcHQBUI6DFcwibC0BvuZJ57qJKg2mrv9WYfC0KKVjia8kFABBJdlBwnB2xzaRI4po4kb9G2BvW8mtQppmBf9t1kqHJKRxYmVVuYdEqsUdRdsvPqGS9ag6txPR4D33faWHOaWHFcICLfB8p/4RYUNk8sYIFBqOrKmbuyEvuKzh7eXER+CY6UcgK2ViRYkUBmRStetAYWlPiW/pmYvHLKlHqhzDWNa3DhtX5vWsIOORMyqgZ4fbK4MT0htCVFZEYmyRAK9b6HPbEoIn7HoHYm6kS5hLqDuP7V0iBaUu5HjFPNZZCvPe+a1tqNxvatC0ul6vo/Ltawt7BQf3gzbf8cDQ6P5ue02effEI/+oM/GD9/+hjWq5WfzxduNrsYt+1moktwUzm3Mx6N3ob4HTVFgpcAdJ0Qd/gcCWBHMyr5Bmy0mDICwm8CwRyB3uBoEmYr9ani19U6Yyi6knHeKN4JkTeeEtE5ELD7K1eenyG6HwSgP3AQPhvU+Lirq9nKu6ahqt4JYbbY9FPnLi8jqNGg+Eox+GpcjS96XDGbV+NqfIGDNwlN1UC/alMP0C/AMKgipMPg6AiBBk3tcDIeUu8DLVcteja24NAujje5cwh/5d0b8ODWrshABeiINtEklvqOYbtvrOy/fKUf0mJVxFExS+8KrxDdBUGS4SXX3pTbH69WVTim2uboVZfJbSOQOArqMF3xIvM6xcxZjl/s96QiszK/VTyn6DLr4WzRwvG8gy5EJoB3j7vjCdy7fx8evvUOvPnmQzg8PJRzkg3pK268eqoJPRf/VS71rpE6oLpIf2STkGj0aPvftKlmACTZfQF0s00C3tabtfRDcszI+dm5/Mr/8d93bSe5mNI3yXmh87k4rG7Wm4gRtNfQTGxkU3+pt3brnCzLDx30oYeLxRrOZis42p3Awe4IDnaGAl597yOz7lxkiHx0Uu2I5Nj4GOazORweXYOqrv9/9t7s17YkzQ/6vog17PlMN++QmZVVWdXt7q7uNnTjBpk24AcLhGSELCFeeEYMEjyAQEg2T9iP8EfwBAJkI4QEDwgwWFBtqMY9lSurKrNyutOZ9rjGiPjQ98Ww1r55q8fqyqzKHambmfecffZee61Y+8QvfhO4vg9SwMjkeUksBUApp0UPGwZxxRxZUAGiwlpaqclQIcmWQoAXDtRd2NhQgfEbaApPj3u5MJf37/b+PLZtA/NiAkuWV05ykc9TTJQdBVfxtHIBV4zn7xBQlZDWKODmiKcOc8CNgrDCzRk3S2L6M3IgqK+U8TdhGzB18COGTCmN3rtIGDoQpIdRBPf+eWi4rtFyFoNP5Fu6kPOJ3CmqKATphB7T8YYRPy4vpf+RH4tC0lkPfcPGxfhcpFtvBIiGT5/xPT7atJH5qoaNibEUNrCmTDFNNbPLPjzKBvkxs5q7uoPdoYX2vIeJyobbMiodmDxkBUPhwTVv0Ljhkvl7sO+PpO8wsg2Eazpyr1PAfU5LPpBSw5uWxFYtJzGShCG5NXyPcZ6LuDBkgFOwzIsmmcGhGt5+QH9pRy5m88jX4hZiEHuM5rz/bHEhGIqfc0rE4TyKd506cjZ3znGikmB///zKQ3RmJFERaqy8TxNWAFCIJ5x7VrVi8+XBBzW7ue1h1vfWWeqp69piu90Vfdeh6Y3bbrfq7v4e1+s17bZbfThU2LaNWp2d67vN7jBfrHaffvyR/eD731t8/MlHxX6zkd8giKpExDzTOe8T8W7qSmGIY/Ma7fOgkC6GTw1ZN/OfAgj7sL35FgD9KwTwlwGl7/KrsvPE3ZQEfboRCZ4S0XMG05SpTBHcOGc/IICn6HALGeyQso+1Vu9Z1X/a9M5kpIfEoOgrf+2n62mcxhd3/EyDzROr+eMZf+NXfv5n4W18QUeQhfKC2XSyuLPOfQZ4/JgHI4RHQPBNB/TLoPTVcjWbaq3o5maHh7qF5WQCbz5awT/7zSfwq2+vYIJOGC9rQ5InjFmKsJgKu+uxuDt5eoZckNSC4mWjY2Z0lK7pVyUwcFSjkKG0sIUkzzsOJRnCSaJENHnS4kLYRcluPKmUPHOR4RrkmDCEBUlgyYg2TYv+UJDGJWgGYN862Ow72FWtD9SRsBENq9W51Js8eOOB/LiL1TcEqcx9WEwEPslzBgMYciN5b5QtqiArDoFH1vTQth3UTROYYiXeyR2notYV9H0fkm+deAq5P9D0nchTXzx/IcCTF8pROuvrOSj1LvYB2EU2ZQya0qmJwP81M9mDOM9GNn0P210D5qGF5WwKq9kMtrygbzrovR5QgCcEUM0AksEvs4Z3mzU8Mk9gwnUx4bw4oeUDTWPt0atHqfcAiCClfMbjZ1VfYuBGhMGw+KeR1NpGP9wAGJClv72c16apQbJYbA5FrmE+5cqTLNSb0PHmRqR83MAQjudYSlIe+TfHaagCfl1k2Yf3JyE3oyqZsGMy3FcqAF9HMXp0OKbIbPIGgsqYQSYPVi3KOlpqYbyPN27MQCTEYkqq0gn8J3iVXifeV/yhZwDaCqBvhh0l3iSQOpoh5XbY5KG0aRI/PxLBGD4rosw8mAYTEx03uSRQKNBD/DQ6zG9LDBg5jTaTNG6WwDM+3tcN3Gz28MbVXIKe8JXPHBtCitizOZ0Ucs+a3vpwXN4w6XvZfNjudqIa0Ho6kn84iWXlVBuKPUfDzFU++VfJ+RebrCZJctX8cM8ykwTKBg+tgDjZMOGUHiuyXAh41CcIKym49D595Y7Ab8icTZ9/nrgcgevIfoYUJwj7BlwZItOHw30YYGFHgBkxWOZJo1QLwL2Z7HTUFkFbpbBlkYP/SKcZm48J4MAfZb2x1DTVvmuavu97rOu23O23drffuu1mi3ccPlY3bMAs94cDbjZbYoWFeMQ9g4tZ/ky9vL4+y7ICt/d3en/YzYw1bOg0jCklfVZOjthE+dfaHNJVkd8uKwJahB0Y0QtTmFwh4qpIcxg5tIdZTQjpulQDwD0AHMj3dz5ViN9GrlABZ1Ejlg4+rQx9BEoflMqJ5SVEjitOWhjN9tM4jZ/2cWI2T+MPHX/9r/0a5KdT9BMYXgY3UQ7WvRVW7FUC7cc2iCsQ1dsA6l0g4hTaSZ5pNZuUcJi0slieTgt4dLmAbzw5h8tlDm3VQB+MXxhWZ/QKixjlsB4I0WgBHuSm7lj2Jot/rUIZWHx0THrEVHIuw0WJLgT+lwamRo0YzSTvjYGjI3Y1LtolvN95j91wNK+c3RAkk1xMkBjWmA6KgeWMSbHruoOn9y08XTfQGCcJrLzoYf/hdDaV9NSz8wvxanVd4wVyYbGbwmmCpBVgOF4dvIESUiI9hTHN1Anw66pOfJNd30HNLGVdC+vH3klmMzmQ6LDbi/R1fzjIojcviuSBjIwkf5//KHk9Fbyag69QjpEX4NLR+FmP7vhxsWcwJsm+fvhzWfcdtL2BsshloU5B0qiD19IYf3x8TLnSIZBoB9vNBrqmg2k5Ba1zsK73KZ5hs4MZVIjAMMw3kaPqKBiMCaVZSGh1AjY9uHYhCDjOc5fCaGJ1zdF7jv5DNp+1rciKmcuZT2eQI8HZLIfzaQ5lppL0Os7ZeC6jZ5YSuA/3wauy2URujdNxj0OPIqiO19KMk4vlPLjhOaRPnuJBYZRps7TV6UJeXFmLvtuQEiCWTst4v4fdjyHoOABK6gO+tRI8lRQII0bPs50tQNsNvmHy/agQQrPc+LMkpE0TxRygYfNp6C/xmy8+t8nXN8VbWYUNKAAPMO3oc9bPXQWrSSnS7vn6IEx0pwx0xsLm0ELTGGDuCtNrYrouShQOmYBN3tAx1lfbxI+m3W5P7G/e7/Y4KUreDPC4iAG/x4Hg25iGTz/0MpCAcZQnEkmksfwDrIrlaCBKG1cwLluVY5MUKFRepxuIUxeZ6UGuH/8V0rxfvb892KJwswTYqYJOmysjBeDaUGeilVK5c8qCU42AO61ariMhkcqicZa2bd9tbNc7qU5R8NIak7dt0+/3B3t/t55c31xnm81Gd23n6qat9vsdHQ77vG4qapt2Y61T1pqls6401rLJ0wYZPJ8o3jRz9ccfTQhEAku+oUk7VKoHTNOF9wkbH6/LXZ9YOE/qQ7AWq3C71oDA72EqJmfy7n0SuS91SHgHgC8UurtAm/K5eErgnoKDjQK81po+tFp9SL3aTF6ue7g660IzdVDzvFKUexqn8TMyfmbB5onVPI2fxsG/ZsqzHNzOAfV/Pr90COAsU/qrDtwjlWHO2+Hr+73aYKX2dQttZ6GLi//FHLKcoMY6sB9xnX4MJmPFQVxUjeV9x788B3ltBKVj5iIEK8ojVXQSRZZ01DKR6kgc+fDKyFJEIBiOx2OPgValsMJAiMwbJklvqmFIfrXwY+wJTPkYvjDex+WDMB/857bu4B9/uoPvfLKGTeOgKLXUwxiH8PDhI/ilX/wmvPu1r4t3k8Gel/J535aASfABHjRiNwVodT001rNnKta1OM8y82PYW/nyxQt4+vQZ3N7fweGwEwDKp2G32wrwzFimbY2wKQyE+OcZDPLgr0Wgw/8fwcnA9MUAooHBFhb2qCNuNLeC5xZD0I9y3ns4PN+oFzGAJU7srJseFmUBl8sZLOcT6Dub0nkHz6m3yLHHldnXer+Hpq5gvpgH1tozfKBoBOIgMYJpfjrPFvN5EDYrghWIxx0zhyL7DqO5AYGVDQFBOkvhVyIpVijyY06jbbrOs8BIUrzHXkwXwn080whHmxzpKBITSWFTZzjXad6iZyU1hp5LoiNsIExnmvdespokr34SeXCkC6By5o17tkFwfWBAPcvrMONaCdDOgDRHpKARl/yjw30esFe4F/l9cj1ShJWefR4k6kfvPgBhHe6zI/lpfA0V0EEKOoqbTKMzOOr0TGzuaLOJYYPcdxiAM/qqIvEtk5dqs1d4UWZwycnBZzPYVy3sDiSJ0r21AjpFeh1k2ZEJx+D/5GvNcunFrJT33rMk3DgJhTJ9hzc3L+D25hpWyyXkRc6SYaS0W6YS/T5E8cS9tXRSnOAcfitSTOLDgjQzn7HKRkznbjxlXGrqtC56wr2dUtS3somivAQXrY8SwuHO8Pcgv8EOiXqylgOBOLSaH8vPWzAG1rm+m5bl+1rjvjdWW+cqa6kVQN8Z07atbZp6st3upi+vb+ju7o76vmF/5s462vemX9b7w2q93SzWm3V52B8WXdfNnHWZc9bNU6ZJAAAgAElEQVRZ68g5x6TsDoDuvIwYGyA8A/lo1vwRMUWCCaFIXD91jq4R6bEC/SaDYPSa8jzWyAqIlOeSt3pBXvmDIVmOz3QHgCznvUVWWjtmLp1xQLUj2iuAj4q8/C4o9YEx5rlzuCdZW0uR0XNS2csJZl3Wt7Ctdv1eZ/XlbNmT+jwatk/jND6fcWI2T+NHjr/6G2/A4uzt0wn6SQ9HUM4KaPaVeIZeZVH+rIN3lrOsWFkHZxIPT9Btdw1ZkVspyIsMMgaaswmtVnOcFD5JEcLKxa9NnCwxVPRMplXfANqi8s0nbYaQHXWUpzGwkaPk2sG/NIzoEoqS1fjcIhvEYQGLKvo0B0kfJpndKPgijLSWSwv6MWMX389nHiyLSO01Y1B1Fj69b+APPtnA959uZBmehc6/r7z5CH71n/g1+MVv/ipcXj0QYMuBQewzVLFsXUKZLGAIZYqJnk1VwbOnT+H25laAC7Oak8lEFrn1voKyLGE2n8HTZ0/h97/zB/DyxUvxUjILOp/PJNinOlThPOARUxkBZWJpw3vWofoj+hg/sxpKEubXb4REto2DhqJ/MT738Wt6eXHGwTBSK0HcfQePr1Zwz+X5dQ87DiCK8lKilMjKP9e3NazXd7DZrmFxtoKCw4CsHQXZxDRVDGmxKH46CKE4cb6KZzaAGc8khp8O3kLv0R1K8cfTwBM7A1uvlT93u5A6ut9uRL6caYJpmcNiWkiIVCIqA/rzGw1hvrnhBYYO1UHimIK1YqVH9CV7TbD3VwaQ51OOnWcUYQhQVhjlw6GeXxdS40e29uxuYlkMoMHUOxrZFzyCyMP9k+bAqALm+J4a9PTjDaoEBMfA+9XNjPjx4Py9l9h3F0KQxjWQUSqNg6dW3oP4BrXfwghwjo6ktZLL6vtQM4CLRSHs5u7QQMvVEQGddFz3QwR5+Fx2UbqqVADMiqUiMCm4OiVuZlgotAYmtvfbnQRtPX7zCeRZHlJ5MYRio+BA0tqfHhX49XjbOW/o9Z/BIqUNX3ZB2spSVtHdMxGtguESAogNhLqVrRE6mjzRoxAvob+AOtJu0bfp/co5ZKyrFg+sVVJBwqBMfJffefny5X/78vrFD7fb3UXXNdR1rIK1uu9at9vt57vd7t39fve1+/v1k8OhuiQST0XriDYCJK190hvzFWPMBTiaoIpUODkfEMTnSbfyuwthxh5K/86g49ucgkEZgfgy/UBr/X8B4l8EwgtgSaxUrPg3EmY1V6+Unq3kLksy6Ld6OeHoFgBegoL3ifCW9xEsQYsq+2Sq1TXHJXcEHyrUnwDCVo7Bi6Ax/EbsQ/pAQu2nnMXT+DKOE9g8jdeO//Q//rdOJ+bzHuhlaAJqskDr/BgGgavJuVsC2BlLZ0RWO5JIeV94nUmyKvkMTKK8mECRl9jlPZCxRzULEbwd47FovIS02KRQIu99hXgE6nzQjUpPEJNffRiRS+BzCAhRSbqXUmLdaxIUI/uZvKNhzeKOF7GQ6kWGxXsEqCqW4YcFfsAX8j5a6+Bm08DHtzX84OUBPr7eQ9UaeRzLJZ88fgC/8ivfhF//9V+DNx69zVgITN/6LBFk72ErqYksXeUQmcHzxp6xQqSi3/3ud+HFs2fCuniGEEQSyQE0V5dX8PDRQwE2dzd3Ip11qbwe0nN53yqnzBphOPn7mYDdGCg0gEEYAQcX2KvPQIs/ROYVVsQSqIKvfN06d5RryoC7mBRykk1goM6XJTy5WsLLuz1sm1b4g3j8/BDuMcxLkPf//NkzuLm5hsdP3gRVlBCbRNJ5DKE+mBhxDB2BLjFwEmzFx6WH2hNKgNQHAzGI9MDNJolofI2UvBo3Mjh0qa6h2m+BrE/mLbSCh2clPFyWUGTae3/dwMANGyCDHDwBrVGvatyNCcxVfLPpZzGBNO8vHpjYONUx3TM6Bm45A9RVIaW0l3PlZcZBSgxGEnJRbJUB4HLIjo2BUNE/OgQeJZZZZWz1BLImsOd6qI2JAbjhXlSBYfbATQ034AiYUqSiggncSUqtv0YUcdnY30rD55E8lSWfriXZNGPScDjnQiaDE3aT//RBOtu2Rt5rT14eH8E8JWaVkj9aK19/AqEXV2d8jjLZ9ODn69rWqw8olHYESTCDPzWk8oisFl0KX0oR37IPFLLK5K2wLzMS1xgFoOiflnysUEDW3gqPQdoRI7cFoCUiFfk6ZblPVdZaVeGDMwNne0LVs0yWZa/OudaYnvq+z521e2vts48//vj7v/Wtb5nvff8HZ73pHzhyCzJu4SzPMLthANkb+yvOmq9aY99wAJyC3qOCgjOVAGCOCt9Gpd7k33pKczeodFIeCKhiYCssKsAMEM8DOOTP1F6yCKRGhT2jAnz5bT4Bwl9CgEeesRS6N/62CDthVCDBFQGxnnePgHeK6HmWZR92HN5D8FFm9QdKdVvD6UeUHQpUd5nWFSK1vaOWHANU/NEfjKdxGl/y8TMJNk8S2j/9+K//3t+D2eLsp/XwfyYHGX+bqqIdF9b9qQcCHXpr7wiA49YfkoJCshLC7j4vJOumwWfXO/jkeg8TvQArqaMM7OywoJV1il8cqqNFW5S8hSM8SqmFYRN9JE1MDyO/cU0q0qLwWd0dJhHfUdLkiABKAT8wZlnHQUIjP176+bS5HytERgFBGBeFfoHN33x538C337+D7z3bwt2hhyaE6fDq7uJiCb/w89+An/v5n4fpYiW9k8w6Hg57CY5pmlY8lc+ePYXddi+hFm3fix9RBbDJrNjz58/FV3l2toLlcgW73QGub64l0Ge/P8Dd3a1IaQ+HQ2J7IIT4jGtHGDAV8yJ8z4QqEQretkgcHPu0YlVIDPQZnajRYzCEsPqQmdfpJL1/0m/uC3MaGEBhgrSv0unCMWWqgIvFFB6czeBmu4d9b0AnPTNIoimTKvx4PjfPP/kQvvG1d2Apcu9cJMrDfHpNEWOg1DGs2CEwzQgDFUEjiXic145Gek2RzwZ5sfVCvNg8wcFM290W2uYAGTooCwWrWQ6Pzybi28wzJQYx7ycdpzQHdou3d2ys51DpMSrU6vA5cvZITx46H19JUhafqguNLTFJ2c9NAXU2MnHMvVRBxhqlzTRisH3aErlByk4u3suh0xKOg6LGJFno3JfPjSQEHd2r44AkSMFJfD65Q0ReA9Xo+guYkr/qoIoPzYfOJpk9jWuTAqrFqBgIwuw41xEGmbJSA3BnZQJnFXBAlk0bHiRhWrxpYxOYpQQ0w1aAzI1JWUjX5jZDyPMcuswCB69VTQv7uia+z7XOWHrtiTCXdgyEuGWGT4AnEfsQxYKotJIveqiEnNTqG3jJ6qgA4J8LAVYuoWwMIEjAJofYKpNlWRsQcGaczbjzledYOZ3UZV4+M9Zs66aprDH3HPTD1SSFzl5gpjeH/b64ub6+3O52F01TP2ib9tIYUzVN9f4PP/gh/d7v/f6/tN5srpTWK62ziQJahY/Te1SYIaoHiLBQWTbRMlGxAnQXSPAXOZAHFFwJW+nnFc8C/uXTIqgNiKSVOLX2kh0n4abkiVyEMOEWHDwPLOcFAH4Vgf8rF+w+TOoMwDVIag2OOgKXuTxrMqVf5Ma9tCy9Jfdd47oPHOgbFisACGtpYfitNLCVp3Eap/FHjhOzeRppMNA8jS/eIAql5Pw798cANv0SkrfDvVkwlICnBSD/Wq7aDj54eg+//d1nSPYBvDH1IIv3la0bEl1tAAA6ePRinMzARkiWYjruGLaSQlFChcLApgXvk9/qT2ymeoXdSW+Ejv8/CcJSGuo4TIWOPZ+v6JniIhsGRW/yYsXnYBnkpFCwPvTw/ss9fOeTDdwcWgFR7HHl8vYs13Bxdg7zxRLu7nfw/Pb3pKOS010ZiGy3WwGe9+t76YxUgQlhn1/d1p455eASBmGdZ8cmkxIW88GDyKCWn+dQVYEhoxTKY2MYzEBvwmQ2g7ffehPefPImbNZb+PCjD2G733rfKHgZr7A1oRoj/OBREE1ihKOvFLznLYbxuE5SL6Wb1QW2S2iYPINiOpVzxLUPXP8g18oaaOsGal4hdla6ZnlwuMr5cgZllsEBOnl/ZZ75ygzwnk0GXZvtBj784fvw7jtvSfBSOT8LtSkmhKSMrr/CY+lpJN+l8F+P5qSS9NBYYwLhOMfyzJBO7O2jcm5c5hkXZbbbXf7yxY3e7w4sS0cOLb2Yl3CxKKHIlRxDKqR4BZBHBlb6/ULpX9z4iMysjf5Zb2Lk8M4EqASIBcQi0JR8taEELTHAjMfvNaY+fEpz4FKXGMsIShMe5M0nYwd1QdhYgGAj9PeMGykVBmshpwHz62Lor0RL4Wdg5K98xbU6UnRyCq4kCifd/eh8ZaWjYk5IvaJuL8wshDRe+TwJm1RxO4pgpMBITO/IYwyQ/Mni5WX0koMoFDhRmAOsmH3n+6Tj7tneAkxGftWUtuvTZOZlCavZBG70PqXmKgn4crJJtNvtoWsaVGdn4HTmkLjAk3iHQEdgP+Qch+lGmFpOBDVrbcEZr4+FYVOFc3CkPUTLvyREKNMZyyKc+Cy5ADPLtojQOWOLAtFlSh8cufv1ev30B89/8Mnt3d39+v7eVZUA4xyR1kVe/jYq/HS33b758vr6N3e73ZU1dmGcKY012LfdW4d99W7d1N+YTifnyLgymEpDeuubkOYRSQwug0JOawXig6V3AGkaote6cF8oIZsJzngPjql2CfsBmvM2LACu0ZdLXgRT7gGI/sCR24OidzDLZ0h4i+DW3GfJW3nKQa6INhbgBWG+zcDttLEvdKZe6KLYdn27NYA3yH1AHmCeQOVpnMafcZzA5mnAf/V3/+6PTJY8jS/C4AWbBs32yigb/bMl1rEUqc44kU+jYnmcdL8JaPRpnFw3cXO3g9/53lOY5gTTd8/hLNcctehTImEcxJMIx9FxDd+IoS2v4MTjh4XAnPhc5IZFaXx2r4ocB6bgHyu5Lxa4f1YNSslnBcKVBBwVviCB+AqDp0pJiD9XttxXPfzg5R7ee76Du0Mn35uXuTAabYeyaG86C+9/9BTe++Ez2G0PwpDw22O2sqprKItCWEyW0PKikJlMOe+9gc7GBNcB7DEQte6lsF4CTgXQ+Tc0m80kQZNZNQ4OykLXJS+ahZUM4HA2n8PXv/4NYVcZaB7qyofc8NXsI1CJUlgvUyQ3bAT4hbgZPLl8jjIt6brz2Rx0VoAuCtBZLswr+yr5TU9mcyjnC3nO3d0t7Nd3QSZNcswsLD1UjWxwnLkpFDk/XynJtNtKeQ9cwct/DXVv/EKfYyE7A58+fQnf+/4P4NHjt+Hx15ZeOhveiCduQ7pp4COCtfF4ro7+47+nhOmTPZVQCAJBvhk2PMha6hSKdY9PLp9Fa4xpt9stbbebcns44OHQQpkjPDgr4SyyyhIukxbcIxadgYxnTD15pYIU1h+cr3859jHGVtEoCB3Sq4M0NbGyPhQoGv9kc8LHpMhSnsTr6UG0CNetTRsyLlynLKQBM9hizB8FB3FvIgoD0m0mPZ3W99djIIAhXI/IYKLviR1LUZON2cbvj1T5ialHAc6gc4fWKQHINty3Do8SaeNcjt24kXwfVA1RehtZ5PijBJMcYTHJBWz6TQlfmcJS+Zo3j+blINUebV7xNSoyhCUHBCHCdt8Ime+EPWVw3+OG/cbre7i8uuL7B22ajmNelgLDrcO1dwFsyv4g62czZJlsxk0oeQzmcopZUG/JZKBIXd9S0zadxuyACru+76hq6nXTNHdt21XTcvr++fnZ72y3m2e//e1vv/F7v//7X99st2VT12XX94+ssZdAboMKZw7omTXmoTHml52lryHgI1AwJyINDr6qlVZFWRSZSJs5Khd2gO4lKDxXgA+Em3ZJd2w4nIedAeKX9Mwkf/h1Tjos5fS9QIBrAnhAQG97NXUst5F2pJdAxIE8jxTQxAF9iHn2BwXCh86akiOFLNBTtGotW6UZ5JLOS+6AYO517raa9Fp3dFBEnSTpnsDlaZzGj32cwOaXdPyX/81/B23lw0OWq8WX/XR88Qd60BPrDTQDvz/9r8RaK/ikyPPnqPCXGvZsBs+VFTmkX7QZ6uF6vYMPnk0lLOPrV1OY5dK3B6ZzoSLCj1hBkBbuskwfAzwa/XvwmsXFYPry6NE0ApqJjQhytqErMyZPDovvVOOemNBj/kQ21MOXMxU7AH06qHcaDn43URny6xkjjMbLbQPvvzzAe8928MOXe2iNhSxDaLres0T8c4bg9m4D1/dbqZFp6kbYH06jtFIU7zxADAt4CME8OkNhP4djHhbYse7De+p8WCIzrryom8+m4n9kEMbHzWBNMRvTtClkhhnS58+ew/eX35enZo+oMIBSfYHpuFSoweAlmngseYGcZQIY+TXYc8pgROclZHkB5WQKi7Nz6Q5dXFwCFlMp4u+qA3TM0rJUUGXQVDu4f/FMNhV0AMNyih1JPcSubmFXNXDVzwWIM9A8W05gva/k+xIgJOX+Kkgpvf9yu6/g40+ewrMXz+Dy8ZuQ5ZM0t4buUpe8gfKSsW/T/yWwXqHH1Ps1LXvSKMbceILMeioRWLLIjEcbTm3ha08Qq+qgObSoOuyhqlrc1x0up1N4cj6F1TQ/qgqKrCCNrrXvfLFAY++sp9bl/UNg+AWM4vAzHD4DoxTn+LMY7iFD3heLyvdWRqWAbB6YQWUgEvqxwiHekwx2dBbkqC79N4LzeH8O8JnSrZccujS6s3Gkp1XBS5r8meFRzobw2IGNxCg15r/bFrG5114aGvy1nt73pkfnESiBSyxu0sUHFjM+cQK6Ucmq/Tme5AqWswLmkwLqpoNOkmUdHKpW/s5MZxHnTKqgSRcASg5aU1o+G2I9TQxwqusK6qaWe1Brpf3+WeiVwdgR6t+vZuMrhnAgX5FEWml01onCXCvtwG8KESmqyVHbNJWuDlV2v167u7tbu9vt+YTusjx7WdeNvbu7MdvNtqrb5m5STr6/XK6+db9eb95777v/8vXLl38ZER+gxhIBz2Sbj5x2QP8cAXGNiWOmNMtEb8IYnAtDc962kk0MVBsiuiV0CwaE3AqFjsrwm0Unob1XsucEuJSUV4lQRifaa6I83BsbQPgdAHibHD2WiGSCj1Cpp+BcRYRPVZE9490Ja7j2hNbK2A9QF9/PtL537b51CvbgsBdFtrPaaG5ZUUahM0oZg0792bZuT+M0TuOPHF94sPlH+S//s69//Sd2LD8N49/41/5VePHRB5JUefXoLVheXMH0YgXP3vsOvPe//e/QVz28/U/+Gvzr/9F/8mU/VT99QzxoVkJ6dK5EHmdoqBb4EwyrUX2oM/U9RPVPlQW8weu0rjPJuyTJmlx1Zxx8/GLrqwHevYK/8GQB8zzzoLc3IyzpBnngSGqXwjrcZw8uLiRTRQXACBjSZ9hQGik8x/AxAk4bw0ZCpYj3oKlX0mmHTkIX6kUgMKss5ax6K+wlVxw0vZUMjboz4sfctT18clsLyLzbdtAHPyDnxnB9h29FyUTO2tpGAJywlbZP7IdI2zgQh9Nlw5tZLBbyhzswvbfxsxJfqZLg5NRYKxH9ZiF4h+cEA1UXQEBkfykAc2Zb7+7u4R/+w9+Sn2ffJqfbUvBSymI5yyEvyxBuolKoSzGZwfLiEmaLpQBN6Q6dL2AyXcBkPofVxSWcX1zAYrUUcNlbkv5LkSPmGew3G3j/d/9f2N5cQ33YQ1Z4lo/ltPxaRVlKNi2fZ/atsnx2kmtYTkvIdSZBSi0ZsCr0I8YA48C03a038Oz5c3jn3Q1cXk1CR6hHlHKtbWDOve9RfHCslZXgHwFXsgaWeeOcM44c4wrjfWOy6G+EbXHCivDvzEZp1ZKlKSt8URx3Cne7bf7s2VN1d3urmraVK3i1mMDD1QSmuY5exOCnDN7MJBtVEPKr/GZKYi2HOc7HLKE76MN7orczVZqkG2VgowVECkDP4mwJtUUu3QdANMh7A+vnbZlp5yh1j/KDVDSvOS+9HYySYYvIjT+OBt/wMKcji64gwdH0eBV8tmGriTXNNHwmpI8Sy3r+LiYeDT5tAWaaJ5e/MV0iwkbgeQRqafjsSl2nAdRyQNByWsD5bALbQy1MOqcsV10HddfJZ2OwpIrvGCWhFtPeGFsLGHCKT9fGTQbH3Y9y/zGw8gnIkbiVDRIn8N5TzV40LnsfSEnSSc5aQ23Xdl3Xt4plrl3b5HXTurZrqqqum912V2zu76fXN7fq+uYG99utss4VWumyN1132B/KpmlWxtoniHiGiFe9Ma217jezLP+nsyybai2yj3uF+jnLVAnpiReYSH8ke/6bQKDbaM71YgA5Tt7JLkDBGZCAUU6B1XDk05XBEVKFZ/jRIJFRgI3SsCFUnMdzoxgsEn7kiL7tuEoF6R9ogN9xOusU74p17g4zunUK9wK7e9M5l+30JG9h9Nkv/+HPZMWbbPmxefg0TuM0/lzHFxJs/kkCfl597JcVfP6Lf+XX4PLRm6DxRFb/zA/0HkhmegyndHL5eqxz+GP++jSWnoOx/1+m6DcQ8bLIOanCLzpVqCjhLWv20W12jYRbsBS073r46hszWBZagARGX1uSp40sZSNp36tbx+NckMhCUqiACNREktBBWHrKAtxF35wKK7RQXI8+hNGFQJ+4PI0Mjo1BMdxdyQ3evYF9Y6Bmhjb4HHdVB9umh64HaIyF+0Mr32fJHAPPpnewr3uoW+8d5N7EwccWex1dYogiiOaSdzmfyi8gmcZgoMWLTl7PceUHs3kCNumI6zo+Z7wALoqU6qk5cKYoJBW33++h61p5XMchOT0JayLgVYEwk7xMXN/egukaYSS1zsGh91ly/+d0eQbz1TnMliuYrc44LAQ4ibiczGC6XEI5naf6mmIyhbwopbohn0yEpeXzz1q6ktlgfqzOhA2NjF5d7cF0LeSTqU9BZbZXZzCbTaEoMuiMB/zs2mKPJjNKudahHxWEVfKgJ2wqaA9M6rqF6+s7u9ls6eLqIalMOdsbluvlnghhb5ckW2m5QJ6l5FmUq4zTNTPrHLEDL0OFPZJq2MjJvYNSnwBakiYlVyigEvRljV5pKpebaL3Zuk8//VTf3l1Liuv5ooQ3VlOYT73E0YaifD4vfNwKLdig21aoHbddcAJpitUNvs4QEw2YT4H48506UKYD94fd6xg9qij9mBRCnuJCn88jS68ZEBlHKbBISCwIN1JwYEt1TLxZ445PWMTztY3t93Z0o7+iUA6HFKtCfFhQUKAGsBmqdgYFrK81ATcAyVGKr39uddSFKt9kf2kmHxQDhI1vJ5wxrxCJHz6jY4bwWebiMXHQTy61NfL0IYGXpdAs+WYf52SS+2Ah64+NZ4zI8YM8fVb6ecyfLRRk0BwyxHNW/N1Z3hVl2SF0uTU2Z20Jm31751TX92a/3fa7/c4ay1Qu1sg9lwCubbrmcDhsD/Vh3lT1g91utzhUlWVWfX84lNWhYhlsUTdNxgFi1hjef5mw9xHQGfkkZckB4QLA/qq15q9ore18Ol3oLJ+FGOeegHbgYBuuASO0OYNkBzRhUVQ4vVb0HCSZSvy2FwTwFlvN2YIdrlcNSHWYEpUE/SA0nDDrFCw9zFY3AO4WFT0FhZ8oUAdiptNRj0hWZfT/ENEn0Ns/YPBJeeYUUm43vVUL1Q4S+lTi/Jr7Ak4A8zRO43MYXyhk8uNIkf0yJdH+1V//BUm6lEXXaXzpRlSFkSrAutaDw9j79kd4cIlgox18y3T9LyDA47zQX52UOfKCmNMW+95FsR9MyqmweN/56A7WmwZ+6Stn8PNvncPjiynMM16BW5GsSVlKAksYQy5C5cKw+jwq9k/9hqGoPvqzaGjapOEHA5DEUH4SilBiLWB4/b6z0PZWJK5WJJhOgEovqadOejF3dQ+bqodDY6Sknf/UrRFQyZZAlszu5XtOft554xvwraYlBOfVew5TVShLXseULDN5zBgKe1o14MgzwsJeCAhrZGHLgNeH6Ljh/RIeefcEeIXu1eXZBUwXC9jc38L2LnQksufW9mHTAKGYTKCczWB+toJ8MofJfAW2a6XagP2WnrmcCsBcnl8Kgzk/vxTQWTKIZA+mzkW6G5GPChJWWbyHQBlmfNhrqoKE0gaZb5wPxWwB0+VK2FRIYTw6sE5OWOB91cGh7uHqHCVsiTsOL5ZTqJpe5p+LCahHHkEJUqKu7/qu6xqW6ilUaIg9X5zSycQeHlSmLTk3kXhTrbfKs4sLhYoX8Ht09sI6c65QWa10T+RyLg4UyS1RKSXwQi3KLVGS5RJK1XPFAvd39n1vNtsdbna7jJOFZznXnUzgallCmesgmw2SS25pCGANGTwqndh5RCseSt/5GKtnvL8ybqLI/HDH1TIKB2mupOPy+wvSeMXMeqwOSZ2hofdU8qRdkFvjADZjQi9GotH6+zYENMVtFp/ge9x962+HmAIdQ748pegVq54FTCLiz0jtQ2bsKx7VcbEEjtjKGBzGwn2umxHGVLBSiqEe2HsckrTlFtOhOsUGJpli5ZKvMGEwKXJYHSS/vJHSW9jvO6iaDpaL0leEJIDjj5/vkTzLYDkvYbIp4FB3AuqFNbYOdvsD3N7d4W6/t2c62/W9pcN+N93ttqqu66ppmvb+fk0vnr+Y3K/veCPpQEQtoGLA1rRNjVVVQ9O1Zd91k6Zus970xhijbW+mjmgSNuI6lt1meV6gpLfCLARKOaVU6FcRtv4q8cc+8CmeOE59nZOw/MzsC5jkCV0EYOkCwzm6OlQAigeTn2sNCL8HpP8xIQNUd4ZOEmUrRHUPRHcgG6dZn2dw3Tt45tB84gheonMNWpw4ojmTnCqDLWV4hx2ZFP/MN5M6gcfTOI0v+vhCgM1TVckfb/yb/+6/DW++/VV48va78N3f/r9/Gg75NH5CI8cOJjCBFhD6wBjYUZfcZwcXZGfvtX3/P4Mz7yHHwPsAACAASURBVKIqHkymesEL2raxAjZ5gaULBdNJBto6WK938L19C4e2h13n4BvNGTw5n8DZRMNUehudeLleVyFxrJ2ihBDj4peCHBh99EVYbMbagiC2U1EC62VrkqBKIIs4BpD8hxfpHOBxv+9gc+igansxDXW9hbrvoWqdfJ9fj9kz/tn1vhXwCaOFK4zYmQy9jwsUHjW6jEeMrOD3zMeVJHkMTlkqWpSyoOf0VQ4B4hAdltuyLJWZDqe8rzO+vkhyM6FowDFAC0wpM4N8YLosPDumNUymc5gtVtB3rYBHBsMqz6EoJx5APnhDAogyZiKLUp6Xf47/5HkpjGU5n8N0NoNyOoMsL71cUwVMGWSZLsg+raNUUQGhazKeOxsNdsKMexkjn4fVxQO4fPwEOvaK9p2cK/Z98rniZE7WzZl5CbvzuQDPae7B5sPLBdxua2gOdegxHFhr8ZnawMxmmqdH72xvQeVFMKP2shOCaFFp6/GGU4jKMoPpnOMAESajOscMi3ULAWqaOUZGJdoGdD+RW8wfAZOYOQeioCaHhK1C1XVdR/vdvrSmDwmwCs5nOTw8K7yENuyKsPQ11bNoZjxziXPmrFhm/EREOeqqjLUf4qftG7+uNi4AxwFIxfMfVbkCPn1SLrjUTItJfaDSAn2gEqOP1QWmMUiIX6kngSDDhQQ+B0mDT4OV5w+AWZ5Z+Z5dirL5oDP1IUAueT59RywEDBNfl45Y0ujJhiPZcPys8ECWLA0HTfGeHRQIEDNqo19bQLeTBOwI1lWYU/Mp4mpWwIzvHd1BywnKvYFD00Pbu9DYSOE6Jf2FvDz7jpezichok5JTpPQZHKoD/OD997EsJ/r88tJZY6/X6zv17NnzYr1Zv6ir6un9/fpqvV5/s22bqfNp0UvyqH8T5OxvAKgl+I5KDyyVMnnO9knUClWvFAf0SOfkFJQwjcxmKvJoW0lIj98TzOnVjzbPVJ4NflZImwRxWrJ3GZxY1fny1ohq582yVDiCe0X4LVD0PxDi70oAEMBbpOick3BB4Z2G/D5rzLbIsyqfmJr33rp+QLrxsg4VQacVxWmcxk/j+NzB5glovn78O//+v/dFPKzT+IIOCrUIE53Bosygsr0siNSP2PX1CzXbZ5p+m1D9n87BN+vG/BLXt0GUiGrf+32/PshiVwdm8f7Qwe9+eAuf3h3g7QcLePfRCt48L2Ga+XZwDswplO92pKiVi12NshL1qbahQtzL1jAA48hIhMATXgg6sWixhNcJg8YsZd34BV9nHVSNgRebBtYMGAnF97c59LCte19ZYD3j6SsLnNQPXKwK+MYj70/cN52wngzSdPB05gHU2D+mHTYxMDhiXZwPGmLpMSvHBFgzENS5MJgsgWWAGO2sUgvC0lcr8joBhgwa27oC07S+ly/LPJjUWn62rWqYLRYwnS8FxHIw0JT9lPMFzOZLWJxfwurySuSuEog0mQjA5OeJvZyeVVOJV5L6kL6VsCgXfa4QA5hw1OH4CrsbalACVknnLc8LWF1dwtmDh3D7/Bl0Te0Br/Iybe4eRElEReiMkXNAXJeS57CYlVCUissRwkscme08k20tNl2Xt21Tmq7v8xxLJMoIVK0Uy2Ql2XIaXHGsk1w4J34wnpxTx/JFH1NbsZSWjJ3zjEFQLoAUlsm2GL20KJshvGNhQmMjcBjLYbfJu7rWvXXIwVOTMoPLZQnTQoX5jVGhGtbRGINq0AcVxbAiD1YUxNoZlypYXKzehxGTZsO1CT2Rr/qaQ4tQqPYI91i4dnwvWDtOP40ZrqOwrVf81LypFFnLxOAHHT0Gz3R8rISGxQ2lwNAjRAVD3IwaEEQK64ka+RG4HN5VAMShQjJmP/FHjSO/GYUxLtl7ARMz7D9agnc6Pr+/5x16uYWcJK2UUZk2Zab1dFJkRZErraJHmiNTrWeEKW7GuADyh25U6eOVBON0iX01UKY5wQe+97334PmLF3lRlDMkZ6u6utust2XdVDfGumfW2hUALRXgSjZLlCrQh5edZ1nGQT2X6COSY88qgzmL/vRQUBzn4SK1vqMSZsBMod+L5BlTvuJ8iBMhGWzJ15A4DHQ3svfSIVvO7wHphqW2vJ+HSt1mCj9VJG1GE2XxQ1Tq7zs0/4jSE9N3ENWStFKkVaOwaIk3/2CwYpzGaZzGz974XMHmCWgO4+/8nf9CFgBtV8Pzm49D3PlpnMafbFAqoAcotYNS2x+Jlsj2kOVwb13+rd7QL5PtL7TWjzM2cwUpJ++or3eNAMci19I/xyFF262F23UFn7zcwdO7A7z7eAWLUgvYZCbgwaqAZcEpprlPYLS9Zz2V8smWmVclupBsGQEPAy4XWEteCDOwbDoDe2Yfg3f00FjYVK0wkiJ/7Qju9w0cWl8ZQiGsxCWvlh+88uK3JqyF1n7Ry+mtHEgz4ZRdv8aKyZGv7Uv544yEh0ST55ksXmRr/3HrLV4OlLATKHUibdXHJNRUls+S2LKcyjk17G1cncPZ5QPIy4mAYt4W4FCfuXgtz8QLWUxn0u9ZCENZhMAiLeAyyldlcay034ig6O3sRhNjkC8m05uKxfijwv/ELMXCRO+7S943nkMaIctzyJjFIw+oY1+n+AQ5fdf31PteUZY/9wZmpZU9iVnO5fjs3axFGh1BkJxH9okSQdO08OzFrXr24rp8+52v66KYahL1LAf3YMcVEVLNAOz9Qk5bmfJ70TqrAEmRtRcsUVRK3XlGxp2F4steOF2E1ldNcKwrTaSqQanGr9Bp0fVdvrm7xe36Tndtg4VGOJvlUofjg15ibYwLmVU4hEBRCLKh4JcUvO5w6JuM8s8I7Ae5qvePUtKXUmTGCX2qNA2M1Gvvf+cRR+polOszbBhA2GCIG1ZDoq+XUFNg9fi4XQBcsQd2XE+UBK/OhS7REYsa92hSqnT6gpfwRvCZmNnRvMRwLDhUj2B8PzhsksgmiPbBTKK+YIlspgW1WWuVVyHkvSOobd9l1nQZEtbO9G1b26w3dkrgpiE9VZ6z6w3cb2tYTblDder9p6hSujSHgvEm2P2mks0sSB2//l88mTbbnbvfrLnHdEkO3kTEiXcD4wIR3ymK4k2Nahk0zA7DhfbVHdw5iS4hbknlhVkk/cHXifAHzkUw2jYCOIGir5IXGLwL1tNgW9WRsRSqnTE1A0qAe0TaA9CaSLYpmCVtHeBH4PB9RPqUOywduZ2ztEYFHcdSTcvipjVw9xoAuRt/1pzGaZzGz/743MDmlx1o/u2//Z+fui1P489t8MzqLVda5FCyHBbNZ14q8lUZ4XdI4/9oQJ1lWv2mQnhEhDkGKZwK/YJxQZmLLFSDa3vY7xv44VMOEerEz8gLyuV8Ao+vpvD4YgGPHyhYzAqYFux90gIweQVjnJeZ8mI8Cz5AKxoqDuGxIn+tawO7Qw2393tYb7ifshPmq24d7A6NyHk55XHwYA0SNqUiM0RpuRsZBoYdtXHwwcu9X9ByEAgn7JIT5tNXwPxoCfKrAsRXvxflhRR6Lhlgc0WJ9tpG6EKHJQMvBmLsjYz1CfwzLA3lZ2dJJh8Th/NMZgt44ytfg0df+RrM5guR4AJ4n6fKFJSzuXgvPVPp/3iQ4zyA77vINYWD569HUorfsw+Bia2jwkgHSktkhpLc6nz3ZuSjRsAgnhAVaCtPrSTNpgeSbSNkIMuKXQLV4TWVBzkxqVPel+IO0VwW83cbBptVqi1RAUj4lF4D+91OXb+8zne7Ha7OziT+hIFhmBy8ELcQIj95WmOy82FPSJ1n5UR+yUmbRmh930URpd4cC8qtKJp7DrlXkw+CsXRfVfn93TWt7+/YkAfn8xLeOJvArNCBMgqg0UX5oQrMZS8VQy7KxblFQnEdySA/dy6yiGqQgCaZbWD34jUb14dQZCVxmFsxgTaG3QSGcpwKTdFzHVWpApIjWxqYTeVZOmb4uFuVE4itpBEZr6B0bmi6TBsvLgFKD6BR/gwTJ9ynNKTThksXsNIggfUjMIgsf1WxEiUEKgng81U2bEuUe1xljjBnUycZZ13bcQEjGdv3eYZUZDm0lYXN7bYpNrtDwQ9B1P26sfbDmypbb+sJ+7fjRh4DzR/QjRxT/pUcLpYTHwxmbULCliysD5V4kSWt9pUPFNkMIdWqTL7zEEFdKk8Ncx1IhlEXDKHbJp4I/688gEr+kMg8iIxR23LSWNq6C7OAAeuU6z/Dc/oPFoTngPC+oGSiC1CSvswpsvcAuBOAie4loroBh8/RwYfILCe4mcvYG53dOlU+h6y4z9F0TbPxScB/yOfjaZzGaXw5x+cCNr/sQPM0TuMnNTx7wAGXCJhxsgmHstiR00sW4LtZPvkHnbNF07dOK/xN6+gdFbr3YtiPYXCkIAEpLUmrKJUpDAZEeosgfslnN3t4+KCBr+w7SeNkOeT50oMh9kxK9D8ilGUO80kpR1NVDdxuarjZVBK+0TY9rHcV3G8a6avUGZf7F/6xXKpuBgZDKRwtmgdAqI5EetHz5Y+/sn7RWxYKFtKDyExHJ1LdGFr0uuEBnV9sDwxOLDAQdsovSmMCLrObUleTw2QyhSzvoOtbmM+XInGdrS5EMsvH1HUN9G3rpbRFAdPFGcxWK1idX8Hlk7fg/PIKdFF4QDkKZNIhwMdySqmAVTeqz3AhphcDEMUj9jIyUNH65oFklGoepbKIHDG02gzsb6yecA4yQS9akkmtwtBfqSFjdpLBMAchCZNqk5TT9z6q4Xl4o0Bzeqs/pjzURzBYd3Z8zOHaM5XSNHB3ewM3N9f06PEjp1GRdY7R3sxxGz4ip3gyw8kL7l5Ujc7mkpIJ6h7QTa11cwShvpyvY3AzEOoTpKCSyOkAzrkxf6a1Nqh0V9c1XL98off7rVpMc7xaFnC1yGE1z2HKic1wDKBiN6UbAC/zRW4IgX41vplSN+Z4ByR5o2HwYQ6pyAMAHcJ0XGCVUboqxtfVpdejsAERgKGQZjYcjj9A2ZzgTRQOsJ6eOatLonqrwHYqzYvRMXkvM6ZbhMLBjxX+vi4lSIEDa4uBfSfh9KxnUQP4VyGClqTVpSSlCxQBvrWM4HwTB/thyRhnu75h8YBxdte0sNtUelubgzX9ne27s/NZtrhaTdpt3U+++2xXfvhy1zWtMYRY9MbN69aWrXFoR4nfHCbWmb342Z9cLeFiUcqxm172IEQJMitL8W0ivvaThLc9DGp9G/7+mJNbwXe4skRVS+wwecAISLPgLFCS8qrk/7vwaZaN5BT8uFzAIsAn3IvJ7Ca7hRFgR8h3Kfa+FAiuFblvg3V3pNSVcxzEA88KnX/PWHvbgqkzjbXpXVXvi/Viahqt4Hhiji7paZzGaZzGjxqnnozTOI0v2ch0CevewRR7iRhUPvzxLpvg/5Ip2LV7s1Ua/7oF9VZKyVR+TSFeQ60kWMdGH6b4pByU0xKKXAkDyazj3bbytQPO+8KY3SwyDZ2zIpckORYtrCI/pmk6SYdgMMoAtrc+Fbfv/cKUQ4pCfWIAJaOKE+kGxdCLP/QUvm5EaZ2KqyTnvaB+Qf1ZBiKO5FkM6yvPGo7cbMEDycciTGY+l5AeBo0SYjOZwnx+BtmklDTYs6uHcP7gofRYMsPJz8zyYWE0rfEgrSihmE2gLLiGZCrSURcK68fYIzZXkBuB3+i1S3ZSHElgfZhsrJ2IQS82spPgRn2JXt6bzHs0uO3iRkTKfApgxIWzxMmezAOW5URCjJgJ8yxXAJoBrOZ5Lt5OZtGk+UM8b+grVVQoix9tFgy+WP8+eS5y0ND97S3Uh4qWy5U3//peQB28bRzuoxLyk1BjYjanV6i4hqEg5xT/CWCA2CvHQJBfKMSqSFwsOVcQIm93bPebu3J9f7fqe8OhLNRazymuphlMJzrNk3Eqs2fiVLiWgsSUC6xe3DxhGXnsiPXq52NnXWQsIcz/eE4kyClRt+Hay7VTIq8Nz3gESFP4Uqgf8VVKmNQP/jqxDFYN3joPvKPJlw9YQrxAYWJbnYuoOBwzHkf8DG+IEvMZg46U98YGP2oGmGfkfa9cA1MC6NKQKtBgoXuu8QHdM1/Z942tq9o0dYt93/SmbfbbQ7O/3lb44r6a32/3i21liQOZMoX07sPF9W98I1uTcw/ut/Xq4+uqOLSmEOu2opk/YmVD/Y2O9zun0m73DWwPLbAGt8wV2OBRZybTEMJkUkqaLV/LdM2GSxgvEHe6CnB0iC8AaI9cUQLAwLH3fK9Pd3V+/2wiLUNclENYo/dj8kmtvERVKkpY9voRET0lZ661Uh9CXtw3KqOtcyZDWz4kbXJVfmKIPmlNr5xzk82+OSjX3b1xMbNhR4I+e9incRqncRp/svETB5snVvM0TuPzHb7uAODgcijRQGMIZshJFHivdPH381JvC93bybT4G+Tw4fZQa8u1FqikM7Isc4K+h7YlsTkqCRPy/i1eVDFw4wUXs5PMSPKijP8IgAj9mMKOjGWboZJAB3bGmJDqKoB0CKJhmeXQfTEMqXgIlQkmVG7Ic32GJRocYMKUBV9qVbuUixGlh54t9YDABuSFspD01SXAia6591WyzJV7RyXhlTsr5ys4u7yUvkrN4aj8Xlkyy8mx84UAz2I6FTCpQnJt8uMFUEfRdweDf42PdcxeYXisg5FMGmFEeYUvBeYwMdqI0XoXbF+jr4/8qvLaCgOIdaPXTCk3KZ2UV+KGIqB1vraFJbyc0DOdCbBeXF7Bfr2W+peYYusZaSf9oFwRwZ5NYfLAh+y8cTaHXdVA1xrYHOrEXku2LHm2iV+jblp1c3uj7+7ucLFcSjqnSA2Vyn3HpjLK58EWTMMqlGrZCdt2SagzecO5VESgP0HSph+oNUQumZKuCAGfjihzXZ9V+41GUyP7kmtDmPFcXuWSdqw1HklTY2doMr1ShFOEowzOwb8YQGB4dEhPHSZ/JM/j9ykBupHc+4jBHl78CLAHTIjJp6nS9T2+fXxqtJ8n7GbdKq+TNkddRThSQGBMhA1zK/lP4zwJ8zOCTZvAmiAqcEpTTVPsaeqyctJqnVNtQFUV9q1V2aHu9K7e8SbFBgBummrf7Na3brPdzqqqWfWmK4zpy6rp3b4xpuvtxjg3JQc/x5sQ01z/o96631pO8rNM63/GWPuuI8k7Q4zhQnK3EI5ywOTYm66HXd3K51wZPqd4I467eXnDbMJqhiyDA3Qj9jhdG75zuMqkIsQ5ALFYpADPvD8DoscIdAmAtUhi/R7PwRHsMIdDpvR1RvBDAOW6zl0622Wq1FsF6g4svnDWvMBMP1dl+Vw5e8Mh3jw3jDDpocYYlfHGYdmZ4eorcnY8qU7jNE7jNP7s4ycKNk9Acxh/62/9hxIKdBqn8XkMlAQJ78M0pGDfV6DajlsKa3Llb01Wy/7hedn0jv4FQHjnUNUXxlImPZy9RU4RpQAUpF/TOKi4uFxKz50Az7Z10IykppLMimNWwy9AfUqjPwk2dApCSLV8lWUMa/NU8ZCW5+TZLf7hXHoNQ1VHSOH04Sbj0JvwBFE7ir69k0KQiQrsEgrTGFnY0POpCpidXcLy/DwF9Fw8fAyT2UwkpOynnC6WsFidQz6d+kW289Qfy+tYTisdk+MkzxQAM5KlhrAWscNRpBdeQzCIhJWOZLDpHaagl6OTOPwoDgwZQgQAOHpo7CNUKSAoeuPoFRDv4vdSqmSss3HC3HKH59nlG/Dy44/AVO3gbZVu1F76XXdlLgt4K0iDJLGEuzYftyvYHVpfiB/AKK/tM5blkpdVkm3hcNjo3XaNvXmT62YYH9eA2LEfTYJPrPBuYngllH7A3Jl+xvwcF+wja0MjvPCr+0woQW75QwEDzHiyUdYQ2aytD6vNZsvF+doEr+1qOoGHZ1NYTYtQREgeAI42ATzwcPiZgJQAPa3F9Hc4umMGrzWEBNb4PUdDBU26zjgCfbGPk4Y9BYDQqBh0mVLXH+Wv+IpkOlT7QACHcjKcFZWx2P4khIcEIPIGFP+si8A43Pf8+dE6I72pspHESoCIvNB30Kz3HdzvKj4UmM7m5HQO14c9bdqDK2dLVxaFu9vu3O1mZ7rWmMP+UBzqOjNGzhrDSeraZtF1/aq3dkWOMkQ8Dzi2QoQt+yUBYcrv5sWmmn3rvZtmMcuq55t6w1eo0NBzjQhINyW7vOEoejlezq6zfoPEWFiG+dyxUiJQwZMyh7IsFL+sdX4Da2xhIMAWAZjJ5MIWJYE93D0J6iUBPEWkcw4rIoA9gV0XWn9QZMUHXV3ddX19k68Wzw77TuHEvZGpbNlVXaeUfZlnxT0itsSSWZ11wRCeptSPEm/Eva7TOI3TOI0f5zjJaE/jNL6kI64pUBgploI1kEGeO+OobbMfHGr1P00mRXN+NvtLQO6bh6Z/ZJzLbW3BOJeYFghJldxzP5Ztjn2BMaXx1YAP7zcczv84A+RHLXqGr+MRjJLVY5FLETt7QlnO67s+/WJfSDiR2AYJqu+08FUn3KNXlpIMa9pOEnRZFmzanp1+EuIznU4kCXZ2fgVXb74jfksGjVw9srx4AEVZQkxu1dJjGYJKEFMSbARh/DoDO6mCz9K/C7IDWEiQB/CoUiIiuiFKJsodR+c4oAoPoEcAU2mISMKzp/GSDAEticF0gfpUmXRHcucnM84j2swHCEXQ6tzQox8vFnpmiyXFDMDLSQlttU8sl38Zz2RzDcpm34icel56RjjPUMKlSk5D1goy5icViI+TwQrX1hiptTFQ7ys8HPaK2+3zYspSw1xkseixUTpBBJqs43RZTd6gynZTUui1t9FEiBhPiuhHOUXU7zegQkdG1dt18fTZU/zwxRo2VQ8l1/5ohMt5LhVE4ntW3rcpaazo0nlzCcmpwCuNlMpRF52oaUiiVkcuScFTMBaFr4dNkvH9FK/HuK8yCSRjaqvOvIZaGi1ArrV/niCn1YF9DPPFv56/rj5wNyQKSweuBdv2wYcL0LRWaock/Ku1ks66r3vmT2laFBwYhXwNGWjWPdHtpkJhsLnsv9zJ3NtWPe3rHpXOM6U11XVF1eEg7LjhOSkbQrQMAbesSeciJk4T7sXjCFh6fy+z1sIebiGDD7hGpGrtw9/56P6f11psmV8B2RPCCog6RJkXWqpR/MRRnk32NHjTWwkra9o+1Kv4ezTLNRddknE5TCe5T6u1JLS4Z6alsoXJ0xUH8RDgDwHpO8rBd7TC76DCa+ew5yvh5DPBdUTZrSqzZ4VS99ZQ65q2g9VCGNRiyomwRUE9O4pty59Zxx+qp3Eap3Ean984gc3PcfzNv/kfnNjN0/iJjsBuTKRvDaDkQo5C9VNCfQZmemEBZ7zGqw9V+aIzbyzmk4s809oS9Vmmmr4zqrdWjxSaR6yZwuO/fzZx+Q/fNh8/fPyzRywarwC190axP9MFUOsXtyTSTQ4PkmoHSVB1Im9F7cORKMjzZJGcZ5AVE5gsVlBOfXOA7TvpwOQ6EQkjyXORvk6nM5gslrC8fAiLiwvIylLYUGYyuWbE+xApBa6w75LTV6XGKACqAXQGEC4HoTy4COCT3PF7HWv3MDKHyTcaz/sA3iH4K484FBxFJY2VsqMqDf9EkTmLnZKQqky8T1aJQXTMf/rHKe/fBZue14fL+JApCrU3Ih+eTNLmQ5SF6gBW66aDm81BEjwXE69m5XqTTGSdAQirIXhGQpqYlnRetn19ew/PXrywX9vtO+4eZXW4Y/ZNUmPIE4yoBDSSs6Uv8NfMKnHcrpJqfzYikpU8Y60yQXZknQrXJ7hEiUGIOxx23c3NTd41tejDe+s9sBw6xSCZxcCkMl+kwloCpOE6uNE5x2NQmYBkuqpD56aLctqjG4cScFcJS/o05ix4MV1o7xd2PUiXvQIgPIUEfPkMJD4dPoXYoaQZJ3odkf3Wu7qDXSPJszCRjQCEzgJsawfbKCsVBh9gW3VUtQa3nC5dGwGeTcd9uSSvx2nKLD1liXxnCBvfu+h8iJKAW+WIAqB0SP4iicxhCCWWOThHoAl5aHyHiDf8dw82IW5pFeQlsTsEfOl/GB90xv069MTPo5X3B/PP5CR/UmKSo5EQQQf/NM+7fdXKe2BpPnvatVQdodZYwhvnU3xxU5AxzVBe6g+Cn39CRB8rgv8DUb2nCD7NFN0qDa0lNEQ8Nx3T93yuGsDMyQbRiIIMc4cvbQMnavI0TuM0voDjJwY2TxLa0ziNz3XwCmTVAzwyxn0DAN9RQFfkaJ4rdwagL8iUZ47sDBTk5Ei1bT/vjblQgDOd6cITMAlKfAZuHq1xxjrXHzE8YFCjtMvXj8jexBeJclL0sZWSbsqgA633V7aaq1kyWMwLkWTyw2aTAqbzmaTpakVQcOdlPgEol1AsL6BcnklAjTA37DEspzCdLaGczSTgJyt8gA1/nf8IeAxMkoBaDvQJntFEI9EQJOTJseC1DHSTipRTTNkM5xTxVTYisMc0AioEY7iX5JYYkzwGk6D/OvtPQSVZbHy8isxnCusZX9exTpOCzxYFhAg4DiwaJYkejVAsJQot1r/I4zlVljs3Rfbqy/zQDe+EJdgcuMJgky5RpLIMSJjlZHYTw/HqkEhKI3a9blu4ub13T58+N+v12j5+/FhRwCJDbi8SxsvCeSysplUqHDypoCSk+B9JFPIwy58tBhySUuNYttlu67ZRzsLPPZwV28bi3b6HIs+kkxYCzeZgBOZDBUm6PSjU5ofzzqdR9lGUZzutG5hQ/jkVpOuoxl7QmMasgx+b0vxLryPniwQIzSZTyDnIq22pa3usew7l6uTEiNSVpfVOUWeYpTQCNlnJ0MkmDsCh7eBu38K+sdI3yWB2WijoHMLdwUgXLo9JkcuxVJ2FrnNU+6oizPy5obYz1BvnL4j3WXMzCb8HpzNxSzpruSOV508orZEIKbkQFr0nl/+/DJOvCrOPKXEjAJNYBpuApt9+8bsNZ/LBA9xtydZrRYFXDgAAIABJREFUzFEmBMVgrELJMfnMsXCHaZHCcop2zpVNWjy5y9nEVzf1VjbBeONE+cogLMoMHp6v4Hy1FR8nM7gUunylIoaocwC/T+D++4nNPnVqHJoEr/n/E0t5GqdxGj9948RsnsZp/LSPceqkWMq8+tBH6UuiIScbvkUEf6El+kVnzNc16idI7oKkfw2ZouMSusBrEasEM+VAcwinIYpkGlGEU0Fm6UYsXMwhSceCKQD1MwMxBP9wYIXBY+YyegMxSkNDZ2OShYL0a2qGTxphzmmtnGgbFvGzaQlXF3MBmHXTSxrkxfkZnJ2vYF4qKNiFVyyhza+gzS9ATVeQTyf+nRsjDGiWl8JccM2ICowly2KVynxFiukkaoYwKjNdAj8+63Q4VkopLqMTFOgRAaJ2rHwdaYhTd+jxCR4PJVUrsaLmNQtVHw+bnlMCj8izqEqpJLFNr5KMnp5VYlY29qyOfbaY1LvadytKOq4HoskfO2bmAiuKIymw9DhEGe8oWrcT8NML0OI0Wj4T00mOq0UpQI5rJ/j1uQqHF/acR8X5U3yeDcunDzU2VZVbxyFA2V6ByxwDEPHLqY6IMgKnlVI+GIV7CsWXij0XnThwGXmtI5Kn9ASe+MtDgnr4v1Vdq832gMxink/noLetsHvLSSkJy0rk6Q7QBaeyCsA6ZAGpo7TYwefKP6dCsJCfG5SSfjX6/kgKqbN8qiWQpvNSYr6mXl5M4BnIHg6t9xwXmZIO3BZycA3AdltD27Rws6vhdud9h514sT2iPtQG9nwdQuAWA0u+77hvspXAL58NxNOHY1H5+1VjJOW1yDLMspaBq/PqavZ6sw1S8cnnk8g9lxy+pGUzIjC3oS6TYq4y30KoVLC88j+6IBWmT4i5Gvh6kaPy7tLcbxwAh+uc+yqQsD/kdxOY+VwCuDUBVDxrHdALhn/ag3bOybF931c9UbuaTibni+kVAFzVbTdllWzBnxGZgrPFFN564xwuzqZeFeGiBJ3S/baYT+FsPsWb+z20xjCXK0Fpzn828KObDu2WC4N0ikw7jdM4jdP42RknsPk5j5OU9jT+lCMCSk6CLDiXxAGdWbKaLEyJ6IkCeIeA3iZybxHi2+TwLQB4kAHNgGx+xGJ5NNkC0oYJEEK4CKi1IlHg0eyVkIzAcHqiaFSj4JTCTindIWDpyJW8oHKvAZzW2iAhdQmgpCAS8VMa+XuW5aAyLZUgAoB0Jn/XmYbl6hy+8tZjeHQ5B2UrINPArMxgPi0gy7x8dV7msGD2kqWyhQbKcmiyC9jrB1Cruc89jT5DFxNXdUrMhZAIS5JKa7wsNNW+DD6tEeweAOUrYwTZBnkqDZLVwbQ6klJS7MjEoUUCKcktB1/nACoHljREzgp498mmyo3AvTRioDwkuUFHINGDSC3P489HeM+h8oSOJLvxNcP7oyHVMmgd/bXTsVPTDfMvYlcGT9ZB0/u+0zLT0hLKTOHFcoarWYnbQ4PsleNJLzU8oX5G5MyZlqiaruu06U1flllPCD1ZEhMdompJ+Dmuj/AGSueoEFyjswoV9srhLLxpP78DepBJ7Eh7wO6grmt92G9nxpJ+fjB4s2nFs/n4rITzaQ48/Vj+6MAN50WFjYEgmXZpi2CYKxyeo5wKzLkH6p1VjiXjxlg01DNSA3YR8nNumh6e3jVw4I2VXMNymkGuADZ1By+2LXDgDsvKufPx4eUcJmUB9/sWdruaYTOzmrCtrQD8Pce8shQ0U8iVQzVXEHl6D4EiA6vkuKZlwf5oyrKCWsOp1h1aawwqbK0j5XpXUKiQYaWEb2dxvTXUBY1CqeTsDiZfLqkBL1plU2euFHSEcIuc1MpsJKiJP5mOL3+REpD8jxcc6ITiw+VgKGYkaY/C6cKOgPbgg7cfEKprpOx/VcpVBMCbbk8R3XMFukdCY43bO8DN2XyCP/fW1dfeenj+l6x1v7LZ14+rpr041F3RGSObHww4hd3USnhXuZ2i+fb/Z+9NY267zvOw511rz2f8xjvxkpekKNGykji25Xio49hOiiSI3SZw3aJBUbQoCrRAgLRFgBZG86NtEMBI2qL9lRZJkdaBA8RD7dR2HbsepEi2I8WWZFmyLEoUxcs732863xn2tN7ifdfa+5zvcpApkRRJnZe4vN/9zjn77HmvZz3P+zyGdbJrVCS6T/3eY0XIwet5GDGuOEO7lW1mmYs1cWQ7MNvWtrb1bqrtPW1b23r7lRqWEFFiyCSG1LWjDr+X5sAhM3YZfCDsJZfLQzL0ZGWaA3FP5RJTIn7CEi4zY8LMQy8tox5fen4SlRrE+pSJlNAFhQfw6cfB7Togo6+O5en+TeGfOlKMY7uwUXzsGjfmhnfFCIN7heW6X7GhEBApvY1dpAMb7Ze0Uexlg1GE0XgXg8kYTnI3xbwnTpHluTq9igvs9ccew+E0ReZOEdWnSFylZjPikJlGQGoJrc3QREPU8RCVHaDEEJXJfUa6c2vWtN+mtgeM3DVpKe6rw3voAvuobFOvHF4zsthw12V+JGSf1/JR39TXffIiAOMANIVp9QCvDQN+DwCp75vsCMUNINkD+NCzGQ6SakjDz2bTQIa6WERex6qomZHp4zCYuW/OFbDpwWPIjITrGd01ePbbY22EwWiM4WSKh/cSuOXSGwoZs3YsBqNuW406mS0EvCkwFYS32BkW0f5kkN0/WdBptdIYmLqP0ul6YI0SZFVVcl3VJsvSxBNpFCv01nY6I2R9pQwXawZn6BtVBrNlmJX+FC4XJ+ZBwRLIc5q+/7YqS5RlGc1Wjk5Oa6xWLfYPcuyOEu3XFEAo8ylBkauxQGXZYl7VesrZyE8GNMGxWZhHAaHCRJaNcpcYZpEeo/vnzKfLRnMdl1WpclMBlpJrezqv8NLRUs1q8tgiS/y5utBYjhbnAhgVuEfYPVoiTiIcn610HwtYj0JSv3xeWGOj55rRvs5Ood33RasE1F8RVdswVcRRTKJ3Jc/+6/3jJAiy9yGTTdoDS90lUrNvKk5k/3c9yD7Es9eBV3BoRa2qmZPAH4E1Z/IpJsjskMwiJd68Wj2bxf/6RHImwzEVHe99Yj4RLK3rQ/RACWXDl+BoB4QXCObXE+KHq7bJYmPOGPbBXGx3lsumYVftj7Pqg1cP42eevfy+bJBPqlX92NFZOrzz4KwoqyZelqDFsuKlGpFJFKjpCNd177Vk68QRRsMMeZbgbL5S9lPVAF4JIZmZ11Kyhwz3QmsbjXwhPWk2nNO2ta1tbesdXFuwua1tfX1qUy3VTe3LhLcgoB1hI7MouozI5kSQ2feSmcdkzFUy0eXK4bIjtyuSLibsg+gKmAfsQZ8MsHMfW7fxNdxZXSiMmLFzv8vAZxzRITH+DMCPgeggUFhBcUZFSO67ECnP2jqlxjPyptY7dfp2MjjR6bXiAlq3TVPWPsfBGO/Owj3cdX6G36YZ4ixViaqAomI0wmRf3F0LjQkZ7e5jun+AOIrQ1JU4ZyKOM+2nzIbeuEfUnM7tgdozoJnDNssArBil/EGG2haoMUKFAg0iz6a6Br2pRhfTElazYyx7Q1Dq8vfXB61nHAODqBvXAc/AKHbIvoORa7DZOYWangVUx0+gz+TrlJZeyuoNkLxhUJDbhfVVw50gmVXWdX2cVOpozdqoR99r1rkWvm8wEDFd6scjpjQc6CYvslZrzV4j3YPbLkLFbLiqhkXJwFuO5Xh3H3tXruHuzRdQzRc+05G7nEWj/YqynmfnJY5nc4zzmOM40oUO8hSHOyPcfjDDfLlS+Wx/XDqHVAeUTY3Z+blbLuc0Gg1zgYlsWPTATl1nNYFHAIkwmIgt28pE5AQO1U0TN1XtXCPhKxwJwjTW1urOslralSBK1pDN9ujhQ7734Ch7eDKLlqtac1FkUx7MSj1/unSTSML+HSvDeHK+VEdW2Zfy+yRkvYoqVYB1EpGCx+NlrSZW02GiG3bzuDRH5zWfryqSiBjZZmFRRUosy25brzk9mVdyAZLsM/n8qmo0nkNdYptaZbLijiqfkZ/FpEfAqfhUSU9vHFnXme/I8iJj2Vhp4mwp+NJwaLRmYUuXbWlYHJvDtSOhJ/5153l05hkzzkTaKmY4HkCSXV8N+tecmI/VLAfKPK5MZ8kDWnrASecMnenJgpxfTuGHkbEPasK5q2hmiRfWygRaO2scHjpDR9biNGI6NWRWtUHaumYSm9YkLd+v7NEdwm7pW5Wpo+x9PEnrr9M8i6v9YX7aRjZ3jbvCzIfLukmXdauy5bJuSHsx61al/F4236k0/CSI9nXmOYaDDEdnc5RyPIBwHVPUEqYEnnbTRfK/pbCm8MdkW9va1rbe6bUFm9va1ltTHaCMgztiHqaubbgOx8IErEA3XNPeILTXiewBK5rjMzUvJPM4gCvEnDM4ayQ/zhu2aL8l9YRbJ0W9kMqoLKVPqtcXn7eEf2yi+Bdax+9tXfNfEdE1kdCC+VQYAwDDsH61Oh369ZQxkO3Fnj7rQn1UZKDvZD2aJjfaXYZ5ZGkuWRuudVlkDGd5ykWS6oBOIg+T0S4mh1cw2NnVQXVdr5DlBSb7hyhGY2U40zRTUBnZIG1l3ytmowgkWsWWsXIOS1cgRoTEpIijUlR0wd0jQWNiNIKBKVGRmjKavIFW+l0U4ka8wDKI3vx/MvATQCzyxi6qxGzsa78IDuxGJ6wNDGAXeIm1qY0JgE+zNCRZo4tpuSDIDRElXd5m1xfJ7pHokw0Qu6ng7TNOXT9dwCHzs2NfNvM7OfRyrkEk9aY8wqjKd0sfp0bIyP7rYjYCYOzXvM/9XMtthaVOB0PN28yKIc7ogQeYNurluLHIo0U7WatRUFs5rhJjBAEV1pLIaM1kmOLBaYTWZ9H36yjVNA1OTk7o9u3b5uj4yO3s7ipfeT4/d6tVWXsXXErqunJt05K1lpIkrsmatq2b5PT0JD07mzXCijI4iSI7z/PiluQc3r17d/fe3Tt7kY2qJEmPTk6O8eLNm1dnZ2eFTL5YY/n2kYCwltLY8qpxqtzdHaTK1t4+WmK2qNSERqZopOdPnFxlFy5XrSaPZKlRGbEAUjkGGp9igAenFc3KmkJ0rH7mPBxrYScHaaxzHMvG6eczbjmJozY4ygp3qfNFmpPbyK+pZzQ1iKeBUddmid81tGDmmJnTRtpe1VbY46DeCNk3uzqn0NT1J6Dx97exP+VZlBPiDPg5ANelZ5wIYwIvfa+kLlTA3hdaphckuoTAlwCq2VDpwJVhN0/j+HNkzJf0InFI1XwYfO4qd6ua1/eMce0ghmkMr0rwgkFLkcMymUruRtDWaq9EcHoP4+Db9MpAjkOU07XLE3zwgzfM2aIaoabxqqrHJ+fL4elshfm8JHHUlTk3iWkRCfMwTxAU6f3kRzfxkicRBlnqz3XxLuJ+YklaWHdcK9uu99qmu0+UaJCok/W2i3Nb29rWO7u2YHNb23pzSwYQmQwoYk6uGsJVR7gCuAPHGNYVRyAxMOFRa9whA1eNM3sMLlpwGhBPCbDoSkfBtj+0k3XrzZ3sVYaivXPiK7CnPvjRg6FlFEX3QbjXglIDc78L92Ng2ctkFaTQOdg9ZNBATDLYOdt7e4INs9N0ws5UM8uzaH8yzYaTHRdluQxJTVMuhZmkQVGYUZaSsJYun6B47ClMrr8H6bBAdX6KxekDHQqmgwHiJAsuql7r6Vkw22+29m823I0OlX+tEaOlAWqTebJSySVVSypV0hmuUgCEvXtstx+7ndVJakXdF0bY8n2RgqGol7PyhW7NtTxWQag165FnMNPxxptOTYgEtEk2KZT29QZHHYjtsviCNZNKRtUoRoPzw9q6i0yp642Iwsmx4Ywrr5ngCAts9nSy5iX2W89ubfoUnFO9sc7aTbdbLxfiZpQBDkY/hqj/HHV9pcFFV+1dbIQ0TRHHsW6LMNRRHKNaLcT5NCgkgaqRzM2Ky7LlYeFbd2UbZNX6eAlag4WuJ1QA0+x8QTdv3jKf//xzLo6TVRRZc/fu3fT05FT6OuWtxfHJsVkuFijyAadZauu2sdWqjE6PT+zsfJa0rl0ymYfGmHmeZV9ix5+9f//e/sOHD74ztrFN0vS0adtiuVoKe687rObG3G+rtqnbL2apvT1bNQfM/OR8kqXGkDs6L3lZtUSGWc1xACNsZmxJIj9Q1g40F+MsNTJS51IB3WlsFLlFhiSdhFXPK+bLeuD0MuW6bYw0CkpfJ3vWkh03ZYjEyRyz71iVa0SMrVrv9yV0aN04b4MUSL1gSyR9rJFrW2GA74U4nH25DzHxUnpXdRKKWRo/ZfIphb9UapCb+3uISFPxaSL6hCVzy1h3JqQeO/t8ZOxda51xRqeObsXs7hKbmGF2nLennqOluYNbHp2X90ej9P4wjRpuYOVMqwXINmbh+28ZCdXUapLIIxfyy6v5Snf782WJ737/E/h3v/9P4vy8sgQzcsR5XbftYlnzbL4y8h7Zjy07lcYuyhIOhU6INWFyxV9HfiJEJhUkAziJLOYuSPBN8OlyGFobjZ0YVT2yfmK25Ce66DU3alvb2ta23s61BZvb2tYbV77X0uen7buab4Bon0F7WUTX2EVPtM495shdhppSuLRuyTObGjSORvVnRBlDwvk8yWZYw8rRoRnGejhFF3gw8r4xnoF4pelwA3T5e7gO0F8DmWuG2p2W3Te3bWtChuBQoFLXp6WMomtt3ThZTyNGLMM0URaqcSxJ6CTAIYkTZEmKg4N9euapZ+3VJ58e5tMD/4WLM8HBMHFGAtbifAia7CM6vIZkuqPbMzu+i7N7A1SLGciutZieVXM9Q0frXeGNODY6HeW9sktb3aVBCBwyBddv7WShG0Y4WLOA3jl0TRFuDvIU4JJn32SgyW5zT3MvjWWsHVtdMNQxXV9lGGwqkOYgoQ0jfQRAvEaU3VkQ6NZuoNrjW+NViRQmBi501l4EwUEn66lt9sZA6oMDz6r1utfOkAgeGEvkpK4rmZ7h5L7HFX0OKHPXr2YVLPVBkho84tvxpO80yQfIBkP9OcwU9KsozKSormWgPpuX9ny5SnfGqREWW95ZVrU6DCtgCj2n1LsAewMkAaVHRw/p9z/1KXN6coJiULiT4xMzm82iJEkrZre6d/9ePj+fx4VE21hjV6tSXXCbpjV1VckVdEpEDz2ANsLyraq6ut00zZKw3DdzMyQjsRnWi9K1qZVQ1Uzzqnk+is1vR4a+qWzclbJ26WQQN4eTlE8XtZ2XravqhsTsR+Yy0jxqBql42rSR5FEKaywMZ6wTEl71vjNIV2ZEKzHsma+apGpc7v2BtGfR1Q1sf9WLakBkrI2Y8qgdrEXXdwnvfhwmMbSjtGWujKO7ZJRlG7FGWWr/Y01wXwbhY2BxeaVnW8IwItxLkmQBUCOqC2I1BZ60nic/g4vOmfmMwDNr7M26aV903P4BSvpQw3xEZG8Zis8kw5OsrGNbx8JittKKGlvHjTS8ijNwXeregNtIJV1fbK/979ddchhPZkv8G3/iCfyV73wfzufSF8xeI5+Si62pk9g4a8nI9S9xMCKlFWOl+bLSvlg1M/NpoP0FJTtTophGeaJg01rDRpUNMmug9xCxwt5tuS0s29Wj663uxTE0s1e0KRRvezm3ta1tvbNqCza3ta2vroYawadUC8ZwtI8a45ZUDnVQRPYpV/EHYOgaEYatcxk7iN3+yINRISkCbvAGjKUYY7CXqyJIWDuc0cOZTe/F8EMT6McoaC7N2qGm+8j60xxYJ+f40qot/6pz7fe3rrHs3J411uhgiYwYCqGQXsooEhYpj4xJSAgECzMejt2l3V1TFAOUJuI2yhDnAxrkBfKiwHT/Ei49/ozZv3QFaT7wPX51CW5riPG/gEFxhDVZBpdEPrpDetYGE2SjOVxToq6rnoFjrBm7Dl6vTXi6Xke7kW3pqzfyQehz1NRFs/Fa/84LJ4ALZj1eThr+HXo3FWi5Nhyzi9Utk0JUSs8QBnlpxzwqI+nVfbp+JgAkCu/VcbXjPg5Cgh6524YAeNdNmdwDv8BiqaHPBaDZA+6uZ7PrPw3DYep8Wdaut11nHnz/md98jats/fLDnlT40rrAxAbAqQPpSEGN42aj7dW/FmU50mKgzG5drdC2HnQa8vshAEde1Q1WdWNrzS+0up8U5DuoLFUiOSLTDbzXkwKy/gIeX3rpJXtyclIUecFN25iqrijPitYYc356clYvFovdxWKhB7lpJO1CbYB0/4pMnYA9ua6cY1EUxNaYVRLHI1UqsCu8d6qSjSagXjl8ZlW7PbOorlhrJgJW5qu6yWLbSlqGkIuSNdl6eSoJ+EgTdUFib57qzzc5VbKURB47E8ObxBqTRFRJnMtCVZ4CP/TAzMJGZwSOvPxV6GEh/3jWMFcic4efW0gYJO+p9HXCkmDKyPGRi+hfO2DFVfyUMZzHsRPDnTPn6Ca37rOs6gpctaDMMd1fNO2RyFRbotLqzFibpblxcRotXGlqZojNUV0v56WlZFWTJHdGjWpIqTMdo/7ao4s3qre85Jy5f7rAD/zJJ/Dn/tSTgKv9ee3X88yx/XKexke74+LywXQYiSnWbFliVdY6ASJgUwyW0uzivUUVBTCqiMjTWN1rrXfcdTbMmtXgkXPNE8QST8XHr8XM1nWDucQzfR331ba2ta0/Rsl9ILLYGeSqTsnitRJHXhoXqcZUdRO8SSoTVQlO5wuMi0yl9++m2oLNb9DacAUl7gLewmAxlcFgll2ILnjXF73i810GgQPypj3+WiEUrnU3ImOeHg6zVAa8LfiQXXsdJY9q4oKJ9gi8K/2VovBiJZM6hiqIHtfBekswPwfgDITLmgvHwo7yowOKjrsJNiRql7piBZvkvSQ8LHH9yDv47/jAdNcbx3hJpLPMbmqBaWIj2DzFaDzB7s4+4iQ1wpYd7O1ykaayKvFwNE4HWUoROR4NhtiZ7qlBT5vkaIopqJgiyhIFpzYfwBQDNfKRES2rMnLYE3Odx06nMBWgqTa76QDF5ADSQNaePURbVh6odEAmxEdcMMHFmmHjHp5iY7y27mlcU8L0KiPbV/JsCiCvz5YUMxZ+5G3rnkEEOSc02sWtTYH8xQXXdWWqlFH6GGNpaVX13NrllteutbTmcakDVhu9phz+TbKTu/7JjXXplqOZmq4L+ifvhNkB1wtbu16HznF33dvZ9i61G4a3PZDu90EnO+6WFljUbn2MSmkLZYHK1cLjNBspJlLps+JN/Ye+3bUQqTYsJNYjxWiQar9j2bzm/YmbuqaTk5N4Npv57RcEtCrzKIrbpmkSfU9TlTLB43kmGqq60dhWCXLdqZyQoSGYdg2RALdTADfhkzenTHJ9rfe4N2Jy33ReugOjOmJa1bVrq4YLq3EirhKmU/QLeSL9oNGiZhrOl3UuTrCdTLmsG91Pw4zus+N7J8tKooiixrXzliCsa0VOThqzgFXrJ029JEMjw2bK3C6Z+RYT7oJwLG2h8O6uETE3hqliyRwFS2/kvSalz3AZL4ytL0WxS9PInjHbWencwjEWLbg2THEMotJAok1qR8yt0ZxSGOfTLyXmRPNUuBMPNGFu7e0tAl2savybH3waH3z2mhr9iIxbzJeCG/dtNvgja+xLoyJ7ejLK02VVUyXmS+G6bdnfebltQtYm+hxiyYvVYWRiddCplLL4axlHIThq5Bzen9n4mcTaL8jqvOqKhn7vVq7h87NXDjHe1ra29YaVXN8CCvM4wrBIMc4ynACaWTydDJBnEUaDBIPdAlhkni7IcuDaPp7/g5fwt3/yIyiXDfb3x/jhH8gx5xhFnODH/snP42/8pW/FE3/qcSAv8Ms/91G8ePQl/NXvfhz/wz/9EJYuwfv3hogdoUjjMGH8zq0t2PxGq9D3NT04RDk7x3w+oyRJRKnlpHNn7/IVfOKjH8EnPvovkRWDd+3OCWkQYqM/ZGcKtJFlMpJtsGKN/6Axs3uGXfue2uCQjEkkgZ6IR8z8nsbxE8ZqT6XsU12OtKR5/kz6mjRRD11jjv9SIIQLho48iQFQp8bbAK3AuE6+L3IVEEXe+997Oq/pMzggrARmmi3HnHu1rWu9wjH4X0hcX0A76lBprPQKsgDCvBi40WSHxpMdU4x3EA2mGE13+GD/ANlgSlGWYX88pNRaiEhWzHqyLEfcipeK0ZupDOCF2qnTAmVRaIx61/DVqPtlMNrZwH8Xbpa8FscpkRYb5ONpDy7Pj+9rtqbG+qs0sxvArSWUHVurRju9pDLIOmktJaVeFsoX40nw6ApxcEZFn6Ppe1dbz1iq4YoHiz1m5Y5txQXAtVbXBtDVfW9wbe0lqf1344KpU7eu64iUDvRuiKfDieQNdkw42B1T+ehJ36WReGrpYp7mOs+z54M732LyQFX6S8lswNIQ+UL9evNGb2fbGw/3xyOwvmmSYby7h8F0V8EmyPdyOlkr37OqmytkowzqxbvTcaSmRIMixaW9EW4dnWG5mimgF7fPXhbNXo4s+lGweN6wbVufiapazNIVVVUVuqZGTLDMysdsmMzjajXGOiHgLhGdspeUv0evRdKYipcAvMh+Mkmuu5O+EZad7ZtzmSeN45tEeK5lTKqmfkz3HfGSmXJraZGl0R8ksTlerOqnlqvqMhPOk9SU1thB3bhkWbbnR1x9jlv35UXTZgLYIxZpL3+JHR6qQY/gX1Bp2IoFrmhxJw5uT8AoHG4z4T4DRxZUedWD080mljZxtTaWq6kUllMBvnFfFnmrISstobyZn7o5lUOP/Amg7JXutG/78ZGc46dnMxwOE4xSYSxXG5yrTNmZc5jkvmvdmbG0NJZGq6qhs8VKmcYiT5RxRzeh17lF91MQci822rOZ57FeM7WYPVmDMC8lDr1PcYQnKTZF1zP/auurL0QxioNdNd1y0la7rW1t640reZ7GY+ztDHG4M8CnP38Hv/fZWyiyAkVssah7Vp7eAAAgAElEQVQbxFGC0Uci3Ll1it//7APs//YtzO/exN/4T78V9z/1MfxfP/MTOD2+jc/cvINxPMFxfY6/+39+BKiNTsD/zudeQr0ocfnDfwhOUvz+Z1/C8fIcf/TSEb507z7O6iFWqwrDqMXq/nP4i9/2PoyGMYaJb2Hid9hE0xZsfoOVAM3l2Qk+/NP/BHNu8X1/8a/xeHeq4/JsMMDHfvlf4Fd//mexOLuLwUH2rto5pA913mHCpKnlj3mCovYpbqIraGzWEFZI+EEEXgHNJWb+ZsA95cjsWA8iHTkBkjRwzInTYPX1wruEi27wFRwXU4jkrZ+W6t1YBB2UYUT/pDf+wYERaZsxtxxL75T2dgawqdaPLbOI8FR0J8uPRM7nvHOkOF866acUCCgSX/IOtojSGANhI3f2OMmHnKQpDi5fddduvJd2r1xHNtkxkJgRcX5NUiTFAHEaITXe4EY8b1UWGXL4xQCjkaW3DNvWcMagDAxl1/fHm11WDj3S5A2m7pVK2IRiPFVwIzEni9OHHlBEXVjlpvgOG9xaR21S54mzgcQ6ltBLVDvSrqNJO6a51x0HQxw169nIxBS4YhB5I5rO6GejX7BbrMplN/ouXR+FQGG47mc61PRIltvUcmH2MSPdRnp/ojWo9P2qFAx5Qv8qo488aVX2x7ByIKSfzEQ9yyoGQ13eZre+CAY+fKHzt5MoG2+O1GdmEh517+x3N5GHGhz2ceiL7Y8QrSNY5P6TD4bYv3QZu4cHODu6r3mVFHowvQRX95mZLUo6mS2wrCYoUrAzjkSOuD8Z4nA6xOnZEvOy9j2i/cSCPx5RpO3HK26bUpMx2RhL0mLMrUzKGG2QFcrNxMYgUwMWwn0y9AUwPwB40To8FBktAWdM/ITISsXFGYwHhmgOg7twqJho5K9TdYmWnTYjorOI6SUQ3QbzLjNfF5ZUqT4Saav5ctXyZ6O6FdvkwyJG6siccmTrsm5HpIkk9nRZtneqpj6KnC2BatVQtIClmVc1CJ/ZGkCZ2MbPhnDcMCcRkQgIyuAk/chRe81ByquAxndnybn74MEx/vxf+G6859u+CVEeY/yIckFmLJYnQmivbBRHLokjF1ljhF2XdliN26l9lilrnqz1MUNB5dDdofI0wrTIFXSuVnWYzDLBxZdHtWsPGbawJOfdVyiJvYlz/Z7BNEUjST5blnNb2/raSi6h6RBmeh2/9DM/jU9++FfQzGf43M0ZPnfrWOcWM3m0iCW2tH8kJawt8ALPEP3+bSyrGvwTv4jT+RH+5e99GtmowP4oUzcOUTj86se/hJQYS3GaziL87nO30Pzhi3A2QiRKMBA+/Pt3cH2S4/yMcPNkgWlmcDQ/wSe/eKTtN/P5Cv/W974Xe+MckfFeEOIs/navLdh8G9SP/dh/ib/zd/7HN2VFZGAaxYn2R3XxAsvZDJ/4/34JxzEwTgZMwfVTwObv/MzP4Oj4GPtXR/DtPu+wnekH6XI7SLxsTN39pFdoH2S+vY2i72icO3BtVbQOl6KYnwTzHpyJ2UgvkzsR+39y2GlFQkck10jB3m1Vh9u0ASS4V2QqkuoGarbn9DY9Wi72UHaQLGPmfRkfh+H5PPZfYZ1z1ElfxfFV3CHFiZJ8MmFGxIU1NhaHTRk0FUVhx3sHyIZT2DgluQFJjl8xGGJ8+Tr2rl5HNhiZKEp5Z//A7ly+jGw0IsRBUtmAXOikkn6iOrCUCP19bQ/0OnAhuyHyLF213rLNBtMLB+bV1KvdDglEsHTBCcPZSj9fXWE1P4VrazXmccBaFvrI4IoDhu8dUF8mO9lkmP3xlA3mMCB8udy2W3VaGwV5O1S9XrjtNnhN3wYtOjoXyj5LExcHsOBNDyKvMnQbPacUDITWBkQbJ1DP0poe8OnxE/DPLUzbCAkPJ9c8dT2qzgfFdyewc30f6qO9rqpjhekZZgXoyurakKfYrvd9AJFrffOahXXukXMgxKzESSIsOsY7uyLZxmo+Dy686+0TgFjVDZZVo4607cA10lBpmO0wT0jA5p2HMzVnEXAv7FIWx9J7iVVVqxQyy5LlYlV+erFc3RS1gmRrkk7m0NwQxXC40TpcM5akgfKYWRhLfErMbcAuYwV0dmUJHzGEXTV9ZXdmEZXGxHK1iJmRjPJNI/Y8xmZMsqz2PGZ3HgNzETuKU2tLZsJscrHzididNOzunMyr+bxJzSSPkMWNtKZWootfVXWUGmvFzKhuxVeW2xgkUS1wsX1EYH/himpDvNHLTGbe6fXLv/m7r7kFP/Jdz3xVW3hyfIrv+75vxw/90J/FaDLGclUheeShx01jEqaUqmVKdhUP86W5tDNSSffRbIGzRanxJ2LepOBS+n6Fje8coMPllUYRRtKLlSY4sUu9HoyRRnKSmZa2bdzImWZstIf3K4D+jXzbbJiiaoTf3nZybmtbr1rMOuGTj3NVGkisVJHFmMi/jeYT6bP9Z3/y13E++SP83D/9f/DCF29ilKVI8xiTYQaxksisTkABLoLJRWllkVOkOck7tsCvf+SjOkV59dIuVk2Ftg6PXSKMikSdICUFuULr5bFZomBTTc0do8iNulDL1SxjuNiQrvcLdxc4m89xdHIMG0fYmwzxYFHhvGzw8HSB8TDTFpOXe6m9PWoLNt+lpVkBUZLUZTl8cPvFaD47qdJi4NqKTZxmdT7MmnNU/Lu/8ovVw6XzeYUEZGWNwXisQPNtV+xt/Z2ydpIrqZ1nln32YyJOiqYxQwPkLuaiMrwPopScEcD5mIP7wRb0Zxwht0Z8D2U0bmJpM2LbyhB+QGwmgZSTEfQJ4B568obELzL4PfKmJHQTz/Tklp971iA1I+1dYiih8s8uf5CZXNvG2tfmYwSCPMqMI3G51Lw6/3s5lklamOFoTMWgYCsr3LRWlIKDPMdkMkUm7rCTPRpeexqDS48jHY6RCUsowXR5Abt7iHgyQSzGPMZ6MshCHYm6uMIu6cTIDL0wY9ZAXE02t5DD1lFwhdUj0fUzdv15X8uYJ6yDMpzTfTR1rYY09fJczWbIrG9ZaijTM5PeKRWOL7J0Clyo701cr9zGMQyTMJ0Dq1+G69WRm32f/js2eiaDkyyZjnntdiT6yZ2Q7ej7Zvki4FVQarteTPdypG6CJFZnOjrJqttcpV56q7kVzqCVHMxO6iqgUgfAQXYTMjupGwFj09l2fRL7DTBdo7BKh420J1J7wfgIYZs05FEZ3Uf2czg++roA4KYGRTFsVmC4c6By2uViBnGA3ZQky5kkJ7iVwbg/Gg18jmuSxDaaDnPIn4dncwWYwyLH1cMdVFWNW/eOUeSpOdgdr2bny0/cd/zLR25xxzpbpq2thPFrgbh1uB4ZHDbcnrfMD01Ds6p1D9LYVAachN5VkdRLc51h+Us06mQaoqhlqq3MK1EIMmkjSywzJaR+rm3kD4x2MzojfQo2kqyP2LnGmRrLukZlMgwyAaAbh2GDTcfmNMM3WN7iz/+rj/+x3/tTv/X5/ucf+O73/7E+I/v3O7772/Cj//5fQRxbrJaVj9O5MEMld3EbDaYja6pEkldMZI9IpNti+FPUqWZsSrapOCRrHFBQB5DGCLk+Dkj6ebMkRhpb/Z6WOlMwMdWmUhTOcG7o0H5lsLlRolqQ+6U+G8nPtG5rW9vyYK3zS8Aowx9+8iH+8AsPMGqBWy/cw0lk8bAxEEdE+vIpKCb8/f/7ozhdLHHp0j4uHe7ANA3YGtTMj07uvcK16DAaDODMRoTYG1RJyGU+mBT4vc8/QIVj5KlV4Pv8i0f40Me+gC/sF/iRH3zW92ktE9i30TNjCzbfReUHlcaSMYOmrg7OZ8fvqR+snjg9uZewc+fF1SvO1a2xcbJoLJ8VJjkqkuxWmfJDGLMykWV+eNybVHwFudVbW4IbIjtMbDKmxOYgO4Gj3cZiR/soa5cS6NDW5pJMBDUx2xbYN2wukQYtyuXfXCPQjiXvpck6IKalNaZ0zuXGiKO9WZHkQrZt7Dk2ygVjWmtr4XZa5qh1bRgjKKVVh5G5gMMo3I7U2bKTwBq1u5eXGmqVIpQbUWsccyZcjYm9k6gYpiRJnERJmqiwSjqmxMUwz7F3eBVXH3+Kdnf3KeUK8fJM5ZKDwQD7u/sohgMsJ5dR7T2OWEx+isgj8MAWVla/1mMMsbCV9rjSd4aaDakm6OJxf1nH1eZb4c1mup/RS1e/+grqTl3nOEsw2jsEuwZnD26jKRfeK5bN2jyHg4FQB3y6nMr+JkvrpJBuduDV5GZ9hmT4ZOjdFMaQaD3Y92zjBnPJIQtUCN8NxpA3JLo679BFTzD63kaNFFHZnetaMsN3tB5c9mY8TsEeKMx68prhdOEhKMuRz0jQvB720MvKIXJEpX3qJNtJer1jrrKfHVjvwOG64bTfj76NmC+YIQXWXVnnTfemToy7GQjLniXyPZpJiunhVRxcfwKz2TFWsxmKwViB9+p8huVyIfuE2sZRo6y+7kTlgEUxNCpS7I4L3DmKcXreas/ceFjowP58USrwztLYRdaukmL4R7cffPqL9cMak2qASB7QNkbl3PNJIhLIGgthhStSajEex9oH+tq3P37k7y4CZj0Fxf35Fk4rlmmd9WSIxl98Bbb/G7H++Yf/+CDzlerXPvoZ/e0PfM+fes33yXXx1/+jH4E1MaqqUjfIl5eAxtolzeKIEL+YJPEDBxrfO5nHs/OlKoaGeYpxkes1t6oalcl6tyar9wOdcwgXVRJbZEmkhiOu0okf4TUjPREs1TKpo30Kr7N0AknshkXFFAzJ3duU4djWtt6Moi4+LLShyDPy/slCTQsxSvH5B3fxD/75p/GrX7qF4ShDJpOeYhr3i59FXTOoiGAih4n4AowGmgXN7u3XVeCUoY2R6sQ76zPz9HyJf/SzH1MDo91JpmYf1fEZvv1PP6vPSlSR+I4DFEM97ZRheGvXews23wWlUk/CXrmYX7UUXTbGXitX59/00pf/6H3EtBfZJDFZRpwmUAVA3aza2J5MrP1U4/D7SUKfBOi5pq4r9g6aMSMasD5KaUnSw7g5Qn+De0OCM26Ha2w4L+ONP5a5mdhB9GwWjx6LjcmZcL1hvHfh2ku2aXKu4Sx44AzGTj7v0MZkUpHSOj+Kl4aayncaqpGPN5UxaoMZRu7CC3kHV9LsSzEDkrgSfZCXURQ5alsB7Lb17pvCw/jRO2tvV+jtC5EoYT9Js1jottMbXyt5k0akhPs03tunOPMRI1laIJNBi6a+M2wcUzYcYnd3H1euPYGDx26gGE2QNCsUs4eIy5neaIokhc1znIyv4LQYoQ02JbULSDhAYg6gUjncNZmHjWx8lU+KcYWam77aAQtQuzOj8R+kzb++tvMh/E/WKylyjPYv6w12/vAO6mpxAbzoYbOml6JiUzq7kVVJuAiQLrwurxkKwKy7B697RLv+SRN6LhkbJkPG9CvMvDYo6tkpdbPtnFo9A+zQOeya/vu79ev+5g3TnjWGDz1evR45fGbDtbZDip5h5vVmBhDMHVtLawmvBmhssKQ9qNbv4z5WxqFjZjsg5tYgvgNaITvTz1msv6dnfAMDLJMrg9EYw/EU1iTI8gGuPvlePd63nv88VjdfJAGBZ/MlFstKWOEExgp3IxwrD/KE9qcFJsMC86VET6zw4OEZ0jRSAL1YlK0l2Mlk+NiNZx7fL3fbL63ur9yEBnh4fISbdx/C2LgH/trgHPpFt/X1q5/70G+/Yd/9ax/5JP7Cn/2WV31deixPT88xESXPa0xCaYsV4/MG9OE4jp7I8/QaE8XnZS29xNibFNgVKR4zzpcl0sj6nOAwoeBbMFiNzrI0wmCQqsFHVbfhEcFyiyiYMEhaQ7bKXiUm+StXkuT6yYYbOF6+naaLt7WtN6Uo9CyWjvVZQBzpvGjDDp994T4+8dxNXB4v8eM/9Ql84qVTPHVlKkoTCSbW1RkkJC1EKn0VsNk09TvqQLGqJgx2J4XeZ/7m3/sXet2fr2r8T/95jmdXNdzyAXhRom3PMcnHGGcD1M7opNdb1XOxBZvvxOombzwJMXKtexqMP3fv5gvfZUBPxWl6GGf5bpLm4mp602n8BZ6gppEn4pkVsUDt3GzVqNHNeFA8JGtemN+ZVRU7stZdbWjw/sYleYbmRYrMF2HtEUV2k/b5mncc+wTGNI5snrgojqwRT5odB7fPjLHjdgKmPUMYUVyLJPaZZY3Dpc4G0wGIr9rgjunVT2sdoE4Ux1aH903TiNWu1f7ITUG7J0tiVhdJptZn98Xi8eiZKzE06WMrU3Hr9RJG7qSLuv4BaRjeAAZxEms/Ggc5JRlDYoySjyaKoorRFE9+4E/j+nu/WTMH5btkVjpKUgm2J5EVyncPpd9yMkUxHMEksQeSsgXDKczqVPIdsGor1CbGjGIFmBptGIjGvqeU1yzlBk7xu+sVjiVtgrJXOnaP9jW9wbNkwQBW2wRl/4z2LqNtG7jjuxqN0gEc74baOaB2TNorTIjQBtwk6sEhgnuputz2GxH6JfsMyzWI7OSq6Pog5XPKELoeVHX8VmdaI+DHwPdyeSAY5LXWS2CEbVQifAMor02A1tvhGcymjzjpgKGX8G5kjvpXw+umlxFTeC93USUb2+l6ee8GWO9+1+2f1u8zG8C9c2vTow5gr0E2elYZQQhLQW6sUzTs1Gl4fjbD8vxMl2liCz/3062j5GlyaFhWWtdqDzP5TLLJoFBG6ShbQPo7b955oLETErYvE9JN2xSTcbF7aYydv/yD/2E8nh6UeZHiV371N/C//cOfRN24l5kebevrUz/7a7/3pnzvr3zoE/r3X/7eb+9/182NXDnYQYwWxtWI+NUYDGX0RVB+N4/x2UWW3t3dGbur+xO0dSOmXOzjm5jqqsFS5HYDVhMQF1QXcnm24dqRnM08Efda2yvhw31jSuAnHeJ9If7X+Uavs3rptUh1E+/wvK1tvUtLzvayaTFf1LgzX+KFl2aQ/O+mbFBSiWGR4L/5hz8H+4+AxKbYn6S9EujdWDK5tTfJdcsOpgX+zk98SNtMEMYes8UKf+lbn8Hf+re/C7fOzrHkFBMTaTuUMetIMz8memN30BZsvsMqxD5Q1DBHJWdNXn1HPT//92ycfU+RDXIYEuJqAvCAgTkkJJowAKFkVpfEI4AO1R7f0vWYzGfAOJkUeTO5HGFet4Ola76tbssfpOVi6EzyQjuefMTk0e+kO7snneNJc3+27vl65dqkbrrxqwl/5LyL2tbtRWn82KX9yT6D8rap9sq6fnbp6qeZaWIZElGwF1kegSlhR2lQNvoB8CuSrGGQLbEcxjbab0WtZZZWR+qlrZ2EkTxjpUttQlagAEWNSxBP+iTVLyzLEq6pFLrFSYZMw+VbuSIjYRc9kwmkWaI9kjv7l7B3+Rqk5/t8dq6gcGf/AHvXnkA2nKCY7uHqM89i/+oVBVQytCBeB+3LOFgkHkLriphX6FPpr5Rmc12fOEcm8s2oUlllK7JASvohSg9aLvRbvsNusKE9UhH9YIDR7iG4rVHOjkKGJfq+xB45rrnHNSvYSRw7J9wLvZi8Bp5dXZCQ+igU73IaeirJ9Hu4Zx+Z1yzexsd7Hx2l510Qgq5ZFBOy+LzUl/ptWa8j+u1CJ1ful+3XTeR6XYxK2NqeGu6lth0b2617iEeBd2HtmUfqaG4K1DJvML6ddLiXUYdVC454fmJlc39yP9tBwTFXF2v9THK5XGC1OEPbVFgtazz/6U9qf6v06QrT7xlkdmXbLhvnTCoqB8mg1XBLQppajAcJhlmKs/kKi1UpdrNeTkUmatlMwbQzZrNbL+fpqY3L4+MG3/Nd3466dvh7//P/jlE+eSPP2G19FfULv/a1yWb/OPWLH/44/vz3fbB/585kiL/7X/w7aJtj8NlXNn8VsVoFxBQNeToelHvTIe4+OMHxsqJVdY7zxUoNRK7sT8KzJfiRC2uuwNLp9R1bif4xPo+19TOAonzRiRTG9YbwdGkwBHD0tW0xqzBIXcS7ibBtbetdVNLqsHItPnfnBF+4ear53YMiXiuPwjNJTLnimFGXBK6+ca4EuQ8NxfRoo3YGCT75wm386N//ZzoxezAs8L/+9e/HUtRBVbsxv0xhMvaROLavobluCzbfQRVFsblz60WO04xtHKOuy6Sdu2k2GCWGWRraKkeCTyj4ZPKQid4rmImkv9jalbWmaNs2b9k0wyKz4yKtxoe75z/2H/9w+4/+j/8Xv/Bbn3zSZtH3plH0rREhcq69PK8cx3lxB217FqwpX2mnbf5SMJLEAUzCuSlMvZz1Y4D2wO7Q2Piy4/jqsuG9VSt3AcFTfJ3IfIsBXV2f4BxUjmbN4Ci7caE9KoCJsCJ+oC6Mpji62rbtdkeXdxj627CWWApgk8Xv7x9iNJrgwYN7ODk60vgNK5p/mW5OMyRJhp39fYzHOwogVJMbJ9r3lqSJvjaa7GL/2hO49MTTIonF7PgEbVVCJLGT/UvIR1MY6a2JCKGF0693SL1wJqgNWbMaZGJdpa/YAI+yRcskkzSF3nH0rSjtO3wrQWsAnHLM8sHEs8QgrGanaNtqbaISAM2aoKN1diSvwScFsx7054nbYBDDBIbrTH+ol6KGpXrLcz1XPEPnQlRJ79wa+rMI6xx7DsvsOQYKy6lrlUHLgFCX49amRV3fdC+/BfrX0PfYenTrmmBn1Uv2AnMagGHHWnqpcRuYyLUSoOut7NlY/SrrswM1AqbxLG7oIXVhf3lH4LAqxhswy3t9mbUZkbpRCVvZohEzE/2uVr/fRgnStEC1PMbZ0QPYyKIYjpEOhuDFuUgN+e7D+cnOIC+T/fF+EtmRXioBGIvhShLZoGRHWGev5pVd8fBklr5w72S69+CLo1GZn8kkxXKVYnnyZZjo4oN4W299/dKv/85b9p2/+psfw/d+55/Azm6B//o/+2Hcn81fz8flQn9gsfO5JLIvpbHdkR59ydlcljVOz4GqbTEZFdqnaYRVtN4QzoXrWtOOjM/bTBIbeszEbdKg5Vb8ACYE93jblJLhevJG2Epe4DXlOWG/1iVua1tf3/IkgEFt/CS/YS+jbWnTHHBbr1Syd6zeg/xzctk0+E/+8a+85r4qz8N9kgiLELLw1dxGtmDzbVQyoBMm7fL+47jz4Ms6I/pIuWw4wid/41fw4u99HFFRUJ4VYnguw+McoCkxUhZW06vyREczaZ2T2PM4sjZNk9hFUZYURVq97/Er9Y3H9id7h9MP5HF897HHLz0cfbqYnMxmUZybcxOZKeCelCapauGeX56XzzuuziJ5WIYICPhBrDCHIifNiXDQltnTzZzeQ7bcbaiMiPXBmleNmzBwSMSXAbfDwFDcZInpFIRzBu0w+GBz7oRDT1VWCNBL1MhhuVxeCIqPjEEaxTp3XDWNPuw1NqRt+6c1YQ0oIgnFjRPUdY1a4jVcC5HQyiA4TXJM9g5RtQ7zxRyJsJQHVzCY7KgEVmJkRpMJ9i5fRz7Z8dmBJkKc58iKEUaDIZI0gx0MtP9Savca+7B6GawruPCD/VftPb84QkC/oZu/7fTAIdfw5UY+G4DkDayvFzuq50FkkY13POhqWyznZ73hzUWGu5Nz+lSBfrDXR512UrNO/9u9Fz2D109ccJC+hrZI1xvGigicgzNv13PpfV47s6BNwx2/sM0olA7UGlAUBWlpG+SzCAg7GBN1jGffI8vdHIyWwli37t3sz4UAanswGVxtKeT/yRu5n4DRcEvv7tubB7VrLpXWsuKuN5l7IyRxmfUy3r6PNjDAvbw5mEl1cl/5PlEQ5MMhBtNdJEmKqiqxVIBZeUZfLpvGmYcns8HtYeJGReZ2RpmT+BNReMhDUyJOYms9e+s8S5TGMTQD0cGczlbTL75w73B/b5zs7IxVUiTrdv1wF//BD38XfvqXP4o0eyVjmG29G0vO77KqEKVLtNS8ni2Us/gBNeVzkY1vDweDZ4dFFt19ONPIE7mPlGUDAZ8i6W7T3g9apfLqRO1Iz9lhlmBcZHgYi5Oyk9YNhBjn0lIbR0TDoAB6wx1+TCmBXtth37beSRWeQWkJt7RIat9jv4WVb0y9mgXLaw31WkkO026mSCe7jyKvWGIFHamSNC/dunnhM9u7ztusfF9HgisH13H7/osXASczojjWnrXV+RzTYrDKk/hOWbfPObgb1pgBgBmYT8G4pJkbVnLgKKudsw23Ns2i9omrh+6Z64f2qccvXdqZDL7H2nj/ZD4/uPrY3m8+fePS2ac/X32yYTeJGVeIcA3EhTHuT9Qtf8Q5d8ZdtAPzTkz2xv6weMYxrrfAeAG+RK55rzV0mZV2E/CLWECwnKOGRc6rPSlDaV0M/XWTQNZ1ytGwudyDBte5+oUBcxjqeqfNOEYxKPR9s/kCbVn2LoBWB7WJgjxh5TzQjFGVSzR1qZI9GfgKgI4jCccGXJLg4Mn3Yef6U9i5dBmP3Xgvprv7ClDFKJBthGKyg8F4ByZKdVRsY4masdqb58P6O2AjMR7qwe0xBAKT+QbcKV9mGHrhxXdhT4LuS4t8MIJr9lWaVp/P4TQVgzt1aP9mz1vS2h32EcfQC7vNbIJ6CrLZNSjtjXH6HivXm+t0ILL/aPemTj7q0M+4EqGPHVHgroPQtm96pQB63aZhz+Zywz8kR5NCtIozXUQJ93Jh7poeH9nQzhnTBOa1i+Lpei/X9Cl6tpC6J3uX+do78a4zTV3PMNMa9NKaKe4ApkTXdHtRVAKD0RSTvT24yUT7Nc9PT3Dy4C7K+dyDWBCtahqfL8poWZZ2PEhIVQ5yn7QGRZZobAV30uQAwgWANsL5G95v6ubxxaLdSePmS03rE/tlwueJywf4oe//Dvyzf/ERJIP8azs3t/W66xc+9Ftv+U77yO98Gh/+qf8Os9J+NT53bUplk0aGdsYFiTmVGP0IgJS/88yz7Ky9xg6261poAucAACAASURBVK3GWl0htwNxo9XzNjKopJ8qtG/4qCwT2aaxb9ZIWkYTl2uHo8G2V3lb74CSyVHDGIy8F8W23vj6aoeKG1P2vT/IZnWT1V1twebbtJI4w+X963hwckeZNwqM3Euf/yweHt3B8InHwFFULtv6s9kwWhGSB3XVfHtbCVvXDJzjbx4OiiuXDqZ2Msysl7PR2eHe9P57n7hqb1zdu57nyYiIMm7ba/P5ajgdZqMb1w6++OKdo+Hx6fnEOT401hZBa/inkwg/2nL8aQIWbSpyWLy3ce0zBDxNxuwbI8mPlDK7PSbOOtZ+rXglx4ZPJWDdm6JK+of4nnIszKsANGPWtu3cci8NFEazkbyjEAyvw9DwQG6qCmVVI4qjwBg2mnumD3ErdtaeQSmGE+xfuoK8GODo/l3MFwtly1TaNBirNHZ66RL2rj+J0e5lpGmG0XiC8WRXWVWo+E+IR5+NaISRWhuK6mXVdljGPTJj1AU3oGtleweDQd4AbURvuazWJAny6b7+PMd9lItzocAu9gqGA7J2YO3Y31fyuPKSWBBfQPC0+aZwJzXBvje4gvi+XzLojKP053CsTWAiHfEFINTdpKnLzdR1X5v1IEhUu15JPwYNkllhyGm9Xh0E7lsksf6BNwAgP8J0d/mkTBvRNQFwquFRWODLZdPkJ1QIoQ+t/3XYld3+8BNCJsSqdFmmVmZeyG9HXBR+4qBucD47xdWn3oNL1x7Hl/7wD9SRlkOvaVm3Zlk1WdW2YsJlOEjf44hQZJEO3o23YFIAKjttVdV+30WmMFG8W2TxcG+aW9e6pgPGAhK+4wNPYWec4x/8/G8ic5H2sawnFjbOFWyeO52Nc8eKd8ksX/PZ/Q1Tv/TRj37dNvV7f+Rv43f/+Y9/NaMszVslY0wSx2Stf3bsjnKMBznyPNbJMHkOmZAri6Ai6NQBhvykZBKyNnsHbXFjl9NXhtSWFt4K+s0pmc3drdhPgr45BOq2tvW1Vejtj0Qx5uR6Wm6trt5xdfH+ugWbb9OSAU+W5BcOmAzilvMZqmqFpChENsYN8zkTPru62z5E23wqnXCWDovRqMi+47HLe9/39BOXnzrYHQ8iP3h1gyKvdieDNEukzRPUtC4R5puZ3weDeZYkN9LY7hHwjAOGxj/05Dx5PxEuW6JjUQwxYUyMQ2YWaWxBGuKoOEyesq+kS/MpEEQDjSNxzhpmkyaJDkhF0ipgUlnGKNJBqXx1Wa6UpheZa13VHrBFfnDp2lof4kKJSi9Ynk8wKUZIVyu1Mc3yQh1hBaTnxRDj3QNcun4DO/uXPfMpkQ6xl+BFSQZxix1OdjGYTJHmmYJJcQ3U3hrnZ9l0sGA9qHRYZ9gjgJnNXtLA2gYGaw3S3Ds9X2GzX/atBs1hcCauvcVkX/e3yqBFUhv6Cahfv82IEF4rUF9GaWz0B28m/GzkRvb9nD172rUBy3I9sOKNxsp+lu8RR9mLmxFY0abZ+IYNO6CLmTQby/G9pO5CoMsjyw99m70N0qbUOuiBuZfTdm6yWAPD7vPUQ9GLDsaPxLuYzmhoI7IFIX6l+0/hqZok+WMkJkDSD71aLXH64B7G0ymycL1KH7SrVrqYVqW/bLxKR6PvjQeyPvZnMsh0oF+1jZchE1TK6HNFo2hV1Zfbyk0//PHnzN2TU0xGeR+43fWdPn54gHq+gFsRbOwzkmQ3REanyNBAnHJTRCjBvIJM6nnGmjQmKF40aAY5mqQzVSI9L4Aej/YAddO19xVe7l2VX/m4vsoB39brKvPVzQxw5MzMxXRsYruwYqJgDGuGggGVje/fFNmsodBazWsJfxABIJJnUxxpn1kXcRQ6yTMGDR1xZOKXjdXesGJ4p62WarR8Bui88HamZFtf35LrRbuz4JU9TRwhkQkdiTLZHpt3fL1lYPO/f+op/Ldf/OK7d0++CeX45TOOGsjqCG3I6JLBkqvRlGfu1viwvjubWzrYj+33fvD9d77pqau7o0H2WBxFkWdKeGAMiTQ2ah33PsdhIn8HYiZk+DIRpmRwSUyFyFNYwjruQ/ot4QMxu7CR7ibgAwo0XoQ6s54uzj3I6MKI1STW2kQApQnyI3kQS6iurIjEW4ilqgzAhRGqq1Jd+0Q+LPJXMRQpJhN1fGV5TXKSpruYHFzB4dVryEdjlKtG+0lF5pplhYIRWXaaDzDeP8R07wBJIeHusR9oq+mMUZZTQG7vHQQ/6SskabsJINtHGCVsvH9DgkiMHnh0LNLFYhjXqsHPW2ny80YUG/r6DVDCzrdZioL2VYYqf8rleehJpHXf5YWBe8d80sbP8AeZuriYtdEPQh5kZ9ajrN2mKVBnyBP6KTu5bff9aqaj59aaVex6HjsZKkIkSp+r2YFQ3mDREExGghGRv6aoB7S9s233/g4Rd2CQ0Ztr9T2koX+VwySJ3/bwjRtmSV7GT3oNdd/jyAWW1vQAVR03ybO0PurHnx+ukxsrIHVr2a6x+pk4jZFkuR6/+ze/DIn8kb43ud6ln1pWM7LkImsqMsYRkehoxSBIhvPIsgh704HmHEoup6yE3m0ck3xv2zjMFotrX3jx7o1PP3+3OF4sq8O9oQLYrmR/TwcDzJpKRzmSg1v6uH2YAJb1DkGSvV+hRQ3NI2694Yq8z0rP93KllvucNJrkK2pISVGS3l7ZdpgaXDs0zmhvuRxTkX1IvIvslyoYgSUxgdoKhvKNPmMvm4gTA+nAb+pX0+K//UHDr/+rj33d1+Fbfuhv4RM//+Ov92PsqH0A2NtxFM+TOClEp30+X0arurUa3wPCyXSB4SBBRKT9mA6un2wUFwGRcEuvcRQHtazcP/wEZNo4fjqKzHtg2+eYePlmbDvQr45qE8l088lbae223vqSW7F43BURYxQ7LJiwMpFO9K1d5rf1Tq+3DGxugeZXVxI8LkDED2ATZMMI4z2rjIBnJoQhYIyuM9LMtXe+ZPNDgt2bDN3jV/ZdHNtsVdamVZMCmdDk6Voqti52nMfWHhZ5NkiSSCjVXC0p11d6N4rZaIsT1SxXfoTOcUfh9f1jEj9iIy9rrds+z1CGT2LGI9RqtVrqQLaqari61QG9ypMkb88Q4jTDcLqvpj0S/J5kBUZ7exhNdzVKQsDn6PAqJpcew3RvR1/3ElpGkueI4xSNa3xvJjwjlmQpbCRmPRtP3d4FdE3scBh49wCkc+xkH9TPQVKJfoDeEWq9A4yXU+qA06zldoFpkgFqXJfKsDZR8s5nPN/KCoAzTlMMpof+mXR0B9VivnFQKTCdHHowVei8ASBdD+g6ZunChAE/Mnbv8LXjTYrTgyudMOh5RAVi/kW+6Poa3mEQ+iXJBXAZsi+D1M51USQ9qO+YRwrqZX7ZMj2Io96tdu3I63DBhjJEA/X9lAiGCwEI6pkaGEIOk0Zy7bbtWp683lHUx6WoFJlNEJv36H2DBu+hs4I6+Q6ZAJru7ePs/hSL8zMc3b2tEnWZdBLWSObSImua1nFT1k7XIY69Etc5rsQIaDLMkskwM8fnS5mYcm3r2pXf6ZGA3bKqd758996zcWyu7O+MTpNY/Mwuaty5c+3l18qM3bgVboB5/bzc24ShbmXyqNXmAdcY1K1nPjNZdtxqBJJDhLpt9f6XKnAnVHIvdP5YxalkhNZYNHO4ihE7I00GQBqp8VEVzLUF2mjUTJhf0GkNW4eJCN44N3rbqu2wbaOs/Sr2hm3uJxQ9Z030UhJHY0OUlVVNFTWqgjkhwsOzBS4fDNX0SqdENtQN4XDpcbRqbGX6yS75NbfumSix39ow/Wt2/OJbcsgkfsVsTsRta1tvTclkzFDaISJCZgm5cVjAoIFBrHlw2/Px3VJbZvNtXvPZlzXiwTMlEWx8jslBHJgK9KY5yYjj1b34iTSl91R1M/rDz7/4nvEg+8DVK7vjyEQuRDKQ9oZsDLTCGNM555okirA/HbeT0aC+dffYadakp7CY1iymPC+lIcp6ElOW56hn8fR7WNlIY1qkhRghJMiKQqJbUC3nqFTmGiHKMnWSlYFv5hyqxVJ/3jm8gsnBZe1/SbMchzfei4NrN5TNFC1dkg1UFpskkZqMJMOx9n9FdmOg2D2/11GBFybJlOx0nbR1TQg9Wo/+an3rexSJPFqkgz8b+mFeecSwsbLbev3VAc4sw2DnQA/gHPdQLmbanG5Cf2AH96iPz/H7XgblCPLRDtRRfzw65s/3HXrMRMEhds08bohusdnn6QKgugAHO5Y1sKFd9iRvnksC+OzGeeU6QBc+LlLuwG52p03f+9mtjel6Sl2P8Dx7H9akA6wb696/KutjzMZ8ieuvKbOx73x/aSdbDj93O+OC2RI25Lbd9pv+NWE1h1MvXV8tFlien2u0jPamBnGhsJTzVR2dnq+ixbiO0iQScrN2crilSTy2Ns9SI+tXqxSfnJizNI5ty47qxuXni/Kbsjx7lgnPt7Vb+ViYAADk3lOtvvYL0Ad8rns5N9s6w37z2+//FpMn8dCOpI2gl077HSV7dtEuENURYkrhBGzmFuJgxjWCEJRAqozwkybqx2AblSdHiNG6RldH3LKbplKWNopN71Dc3+/0GWL6VfxGKXLpV7OlJ2TsJ+PIfGaQJzeGWToSdY5kNDdti3lZ4ex8icWiRhan/p4T8nnX2brQvM2oy4omdamVgyKZ0IO2Me9hbq6D+Tagp8abWjrBRDIJJ3eB1uf++hNjCz639aaUPNZk7nISxxhGIXeWH1GQbetdVduezbdxSczGye3nUZWLEIkgA00BmtnLVpqZs7qN31cU1fctFqtLn3nuxetV27z/xuOX06sH0/pwd0KDPE1epclanpOrLDaLvcng+GBnnH8xiYfnizLZJHA6CxXpt2TH8hy1ZOQ2YdA2NYkUjqzcOGJEqVXWLx+OkMYJJju7MFmOxewE9apEMd3DdP8SitEEaTHQgd/s5ES/6+pTz+LSjafhZUkWk8PLGO7tqaGCc22QBVp9YFM4gzmokbhdD9r0xtVeICD6trxN4HlR/vpovUrvFF00DLzY39d9nwmuoi+XnPr+Tos6ztaf39brru78FBYbe5f0423ToFrOVO5JPYupbbz6Pw75j9yfJBuMFXegdJNdRJDEomeSsHG8TZDBUh+b0jGK/n0qH93M8cT667o8yk3dgOulrt1XrI2lNs6gHl52brvURZNsnEtrVfeGtDYs82Wn/EY254a0z8euBFUCOidogwDIuZcJuyCf9WzNGiR369P1SXZZp8I0qzxeGCBm1I2YgNWBKVZAK+N0U7sWs0VpHp7NzcF0gNEg4yQW6tDFzrERx5ZC4pisxayuyJLEEBtJtlClqmvZLsvm2bqZfVtk0o/XjX1R4i88iwwN/F6VC2RZ8paPcl5LJObNlTZktF2Ez4VPP1qEJGIkLsaSaj2GooIpV3NYEta0QKUZqhRwsYChGLFdocXA29nbvsXwDa/f+Pgn38zd+brqAz/0N/Gpn/pfXu/HmibGzTii2zvTYbu/M8K941Ocni/1fO7k+56Fd+uJFvamW3KP0HieJFL3WunblHxOMlRHxixa0uSfHVB7KLPL/BaATb+C3skvSy0aE6HWGaYo5HBtn03beuNK7lqF5mISpkmsz4/2Vcdf23q31BZsvk1LHk6n9+8AszFMnfVSNbzq4ESMXpeGDO+xow+crsrHPvP8rZ1bD07p6ccO6Vvff8PkVw5eJksI42MZ8WbOubrIU9qbDpo8S+vzRdlFFvTkn9ceMkl/ZhSnGE73KEpTrM7PBHBCckCL4RTFZKQmH4PJvmZgFlmGeDRVGaswUcPdA0z2DjTAXbIspYTZkCfycHcPw8nYb5Uo0uIQJ6kz/saTPV591gPMnjgKvWcXwFsY5L4RgI43gxIfrUd/T75T7VWXpSSPfdkB7SW5m8t7VMz88hXzDNPr25x3RXXz70mWgaf72vfXuhptVYU+x7UKoKOaTCebFhbQuXCOtAHWmT4rslcB9ADt4oDf48XOACoAuL73ssOE1Pct9v2aPZNFAXh17rCbx3Tzhw2zngt9l9z3iW7OplC/LuhzQrHB3a6p//VguDew6WIauGNNu2VR7zKrkzrW9NvOHhz2jJ7Bminz9y7Zp3XfP6ogJ6yPsJjVcun7szUQ3yBJE6RJwlVTY7Vs7bKs+OHpvD09X7YHOyPKksiSMUNhPWNjaDJIsTPMZNBPVeOMjQzLgF7UFHXdYllWl1Zl/S2D1D45O69funM0c8KESuTg4a5vV3jXXA98cSqhm4DwcwUGK6TaeiGyNZFySqt9njzAcp5hWRLaSP7IANCtWfdwrvh2DneRrX4Hl3Hz173yjtOEEjMcDotsNMo1Zsexj9MRw7tC2jTMelpprSRiH3sludFpjMkgR5omKOdLbp1rLNHSX+Xt2DGNW0rsaz0/3uhq2LclJJmEvcvEdgLH5TYof1tvSHXPvNwa7EkvhILM7dn1jVJbsPk2LGVR2gaf+63f0GxHMdH4SsVsyrpyx3C8tNYOAZrMFyuzWKxErhM/fmWXruxNdRDnQI8+QPQLWsc2tpaHRTYv8qTx/gbcNXvJ0KMlY+ssy5ANJvH04Iq9dONpEhnc6nymfZGDyQ5Gu3tq2iP9lmkx8tknTaPmPYPpPpKhSGBj2Nib8XSD8k4a2P/NHmj+/+y9WYxlWXYdtvY5d3hDDDnV3FXdXd0km81mjxJnmhZlWKJAWLAAD4A+7Q/aEOAvA/4SDBgGBNj6t/8MD3+CDdg0RctUW6JFN8Vu9lzs7uquqqwcKjNjjnjjHc7Zxt7n3PtuvIzIzMiMzIyqehuIqowX791353vWWWuvpS5lZYOMFyutz+2lO9XxTranUM+QgbyfBXvIex8EhD/K1VGoisR6XRhO8pjt76Cqygju4plxXEIuujUFSi2Ai1yTMOityU9DhTeMZrcnsuEI6Tjg7OC4YyJpxiICh9tXOn9rfzl+YmubZXShRWNudOw7uX1jV5YbbLy61P5xDNvk2LafbbYxgsEWJEeWbfEaBZMfaxWsq7V1apVK9HWpkyhBxrwwS2KfRmWChyWroNI5iWqa6jqrA7WvdDWtNZz3MvbzIL6tHdNoUvqjaVGUdWWYtXNTPFjEaIU213q4ujnkO3tHNJoVlHKS2DzlLEtD+3glEMm+zOzftOS+18/NoYDNJPn4XTLUMS9rZB7Mpr2GRI4xL30ws+FgECXSbmnhJzZIbNmqQxJaqKYXjcPPeQPPUo8zAjL1JaLeVWNsJn2aIluWiQ3l+D2ra3odM+aC8fNi8inkRAOpSbAxyNDvWYynYozn04ppU6PCjBtbohfAlDN4/Ez3RzyGSTNJ5nJYKp/pKqzqo1XNLUESloXH3EwTBZmr+njVCmxe1BK6MU11dvlRpn7E8cJzXRlDZWLMnKyZ1OzFGCidFxXG40KzKPNe3oxIZSJzKmaPgOnL0JGZ5H6wnuc5enlWW2snzH4gYz8vBo+udlk/9y9+8jP+zS/+dX7tzV/Kr33iDTtY31BWsyprpJn0Zw6R9XsaUWJh4oDGwwi4TK3KGRvfEucW4+qmJaxlK5vgeSw0XQ+9R50w0LnQElV//zq3uZBnWc5JMt6PSVFnkKuh6WvryrIJmJkc7qAuZhEsLoLc2/7BphexNc3qsJctSPItQxcAXycWRAeP8e9R6t7k6zXmQArROgzgYiW4Netpf4/8ERMfR4XRwMaYBbuJJRfZ5pxpejIlR1JXySlsiOuHmONjj83wNBEgZJqJHnFTdaiqqb6eZj0kWargUjdFXF/FeEvYY/atU28xn6Mu5zDSk5b3VN3QSIBFZi8TUmL+Y5NUB+mzowPMp2NdZ+nflOXLYL0sSv2YxiHpCFx6NP1sOi/8ZFr6YZbWZARsUi9PEurnmd9c7/teL7VuTxrNPSpTY0pz9o4pz9J5ymnma/eZLKk/8eK6O5Snn9yTxNm1Y5+0qma+4USnpMUUSU+utZRRZIyyMiBhwwqP2nTuX8eMlC5e8cbZe3Wp6s+Nw8QAlU2FhU+Ri1S5dpjNC+yNpnhpXuqt3R6LuelI3+WhGltB0jQRd2WZFUnjhM96QnStZ2hIoN3nu9NknXuYmXrl1bKqxysObOYVk6hMf5WX+fGsFdi8oCV9Zwfz3daE4xErT6xdE2lUL0+mtp/35mWdyMz+ZDbHvCyxEaDbhEE/Bfw7AK4C/EUmuswh0T3J07S/PuwfZFlybz4vNx3jigxDvfNWBtxXXnqj/IWv/Rbe+MUvYjDMdUDrsfAjOWZYKeNQB3gT/FLUKLbmKPU6vl1+6S5EsU+JYo+Y/5DFgzxpnQU6dsHTR7c6OTIn7YN20oLQG66B8DKYHca7JVxVBRucY6Y8oagxdml64jpgcMEYotPn2Hy2kZhy47IRlteErVLTvRguirDqS4ZA8rEOG0pL/ZQtExv/uOgLa74zClY5mMRIELb2+sl1IwNZdZl0qEX9LnNJtumpDIDVsdM+TemHlgmjsqjgqlqjPYqiwGx6pCqLNO2pUkHUCAI6rUlUzXCwt6sXvkjjBXjOJmM1RFFWU9jNJLChrna6HLmdNb2dwp4U0zHGe3sRtLLKOtl6ZaTr6Uw3VFo3E0t1QmYyndZ2a29EtXeUJzaxxtqe9H0mxuWpdev9nskzq661tXNUz9V92A/yfM7gy9MSX00s3sp65vrM2al/kH/Xqh56z2kvlxCHpS67h3WBlBiXTQaXD+FkdrGoYlzSBavTrYdP3eqqou0U/naWmsmwn19KZIJEJmbqGllilbUpy2hyZRK9Hn3DJlNwktZsXm4AZzAYkuvCa2y02QcwIqpxUXbZS5dlvS1mxaq/blWPXvJIHWYGGezqvPmY1wpsXrCS7Lnp+BDf+Of/JHIijzznLo+ljbquXwPwyuWsn1zeHLL0OoUYSWE+HTk1I0gOCfxNEH2LwZ8nz68BflOYTSJKhr08v7IxGK8P8jtVIdkcPCQi+alcVczKYio5dplGWltVyCrQXITmHyfZVFrXeah3sukfWmwe8Y2n7JAWJHR1jRepLsAqdaWlZ2KBOX6WnjZ7HKJmKFJvfqmPaXm8GLxtCPlwDUP3ElxVYHp4oExcI2qlpfXtZlzyMeYxMJbB3JUXoljtg/OttDsw9fFvnd5F/Z8POZnLz9pF3yXu/+72TXTsFOmutspTJT6hc+FxA17VgTawEf3UoARhVtVwZQ3DwWRrPpujqgqVtIsr9NHhPg739nWiS67XqphjPp8qGxkkgQIeLXqDvrpAj/f3sXPnloLIrD9Ux1PpuxQ5rAKPqlCQ2facdiS+HB1ntWFc9q/KacvoQntsIiA2pHLqvO8dTef1jXuHbm88zXppmltjSaJMBnki6gtjyVC/l2E2LzudriS9n33PvOnZc0HmV2uffmDgbzCoACA/0rxXo+lLX9U518Xkjrm4cubPGKK5c5PDLLGzQb+nJj8CNrXfWCZZGJiVFebzUsFno4gQYXLTX910PEgEipqR1T7m96pm9aazdL32fkoX7ExUx3cX+jtXTOeqTiqOz+DNXophLud8fR+xsKqPXz1TsLmKP3l4yYNJjHdk8CVSs7OW8zysa3+prGvrvHdpatFLEmwO+kjiDKolL7aLl4joVWa8AnBfKQR9WFoM+qnfGPZ00GascWqFruSPKeqqmh1u3ent371ly9kXSCREwkj6hoA9YfbqPiDyiE+pBUA8Zn/yyKUD76b5s5NxuaqlwxENZc6a82lingwfY5yfRtPW4ug/6vH3sR+xt7YJX78WwNXoEE6C+yMEaaI8fAMSG8lbdDxenHELh9cuFuS2FyvE92iuatuA3PRWBsMhzZZccnkKjqs2Otl23WLj74zIRFJ0ol6sr8hLBeR5BeEUeidFG18WaowkvZFwNdbkgX/1EvpJiqODHext3YV3pS5jcjTSnNt80NfohZ2793hne5tigC7qqtbvkQkqWX9hYSR+SH4kG/dodweTowN19DVZcJVV0wf5TPysl/uYTVRaL6Bdvk9ApQmZHGoYNhgMo8zWK2BV86CFo5GJSuGkBl+aFNW8cHW1Nza5JZv2UnX1FObT1LUnkTIKk6QMKTMSYyWP05R1nfkw45WB8bm6Mv++oWRsyEkO0x4zbjnHYzBPyOAQjEMGpF9uFsFo99Qznd/NCqB+eGu4vn7mdSdjy/H26ABIRmv9Pq+t9ynvpeA5qyT7aFKoO62ci1F8AG7OEL94rlE8c8S11kmqvQl3Jrn6mEzqa40dNBfp3JLNsGzhjW9Tile1KgDthOzlXoI0Gxw32FvVx75WzOYFKpkVHR1u47vf+CO1R/dnU7fLA+lGL+/9tCjrz49nxQuOvemlGQZXNySTMoj4QpD8ZRjzO/D4EgHrILrahBnIf7I0tf1+71qWZjnD5J7rVAzyKbGJr9xgtL+T795+P5kc7GF4aRBktC6yPefaI9lhQx9zCXwMqp7yLcwfWyD6KNt+0nva15bnEU5y0j2HEldGPkv/V+PpYwwGm5eDibJnzEZ7gYFsgtaX0iTCvIRpUzbRRHooEDWLB2jnc8Htk6PzcOzZbN8T2NgWPHUYVNKeShPf6iODy8F4hxYIhkV+Kutbe2UOZ9OpgjaVoB4eoCzmGm8hclhhIgtxdRagBcbG+gDVJ16DmHrdeu89XH/nZ1EWm2E+k2XMkGaJrIs/Ohr7o8MDq6BMMbdfgF/JwlXXaGE8C1VfSFSRspMQmZTXHkwBmOIqLZLcEMnCMNK7nSa6rbOJU8Y0GwwwHA6VLZVtEpmzAurlc+p40rzMfOV1ybmwmMKwDMVESAyqPfNoPLeO2aSJXcgrSI88QsyoKYgxAvyLRP7nGDz0jAJkDjzjsCq9EMAH3mGLGDt9S3sEbNfA+8T+FjvJF6ZNgpVG1DGshBNik0A9WLMP7yeeeW7AMw0sDHReGRnTVV3Acu7sHWTEXJLJ9oycI/10fml9rS9u67NZaaXPUQAAIABJREFUiVJAI2pM5xUqx61aIah7uPUhENM8G2OZ5BoKjKcJZtDAG+zxazX8LSR2h4lmF2nPNc/4cH9iNX55ouKuS/aqPowl5/YVISdAi/zY1ZFcVadWYPMClTyYZLB2dLiHXn/trCsmT833YOibRPhSUfr1qi56VeaxPhBL+1INP9gxvFU1jIQSvhg/20bfyw0iTZNkY22wsT7s97PU2Jn4Z2jWu+mx9b3J0QHdePsHeP1zv4zNF64hG/YgkW46MjYXy46Q6cFA8zyL+DFagJ5zNaDwQeDwpL8tXlv+21PaAU8ggzapRX/zqoI2MacpJ0et5JWo+2Ck1kES8QHaSrGDHWyUtTYDx0Wu5nH2c7G08IJv41cCo2Fa8IqG6GdGMZuhFvdc+bsN4LFytTKylTCCRYnp+AiToxAzVMymONjZwmx8pFI+m1jMpnPMZ1PNqJVesP6gj3s3rqucb3t3B9tbO/r6cLimPWbTyUTXV0xKHDNpn2NVhxgTXuwLWRdxjp2ND0U9oWBX9fkKmIMrrdy7BDiqGZgxbeSK7DFhXDkO7mXbSJ1gcwXmxWyiUl0F6Sf3ZetHKdxdpPm0Jq8C+6xyjqazStFl5aVNQNoFmJpJj6qqybdSZD1r1wFaZ+CKRGySgkHe9MAgHgzRfJUWPBFDIgBHMPQzcvX3fMWHxiQvEMzAW96BwSEBnyKHlzix24nNppsw45rrd3zJW8ymB+WIeQ+kfXjCnI4ErDzWifwRqL/zm1/DH/3ZX16IDfn+//rfPtbnCFSTNQdscIeI9tPE5oZIJkL0DBMAJtfWvAyTRGHGyLeflhL57HCQY9DPwrVi1ClP1KkzImwY574CS297Qz9gxoUCm8cymrgKPeAxa/i00nZ44qV5JAqoO2dM/RSblDV/eeabtKrHqxiEhWu9HJkxHS3QqlZ1vFZg86KUzOrbIFPTvo/HG1iPy6q+Q8YcACyjSMzLKtufzMzB0RSz2RxrvSxI86TZUtuoOgNjFu5EQgUSu7E2NJc2BqafJzwvSqF2rJUmTTFDKObYuvEObr79A7zy2c/hhcEnQNKqclHm74/hXXooADwvFu7DBjSfRl04hriJaEisxu4IIBoJ4JxNmj+3M+vaaun9go2Mj86WrWVuZbLUzCx0XGkb055FJm6T2hmdahGBqRczkRLVvNCfYE7kMTk8wGR0FJg+V7cxK6OD3cBgTifq3CrMoixPJo/m04lKxTfWhrrvj/YPlLFMkgDoRocWu9vbwQzIOTHNQS3A0HkFzFVVKXNb1bU0YJNk6gpLikYRQAENi6umyGNL7asMTG+apu2gIkh75wFoSv5mN5PTO8ymE30P+xCjImyObKu6xlblg4yUdbgu3mJxt0sW+F605b3smPuzoox8KEXJYqcHmduQU3l/yiSGaMHgtnPoM2JkgYjljIgGTHSpQ19/FkRfBmjE4IE6+IMmYOnz5BeJ1EBtpm7B5Cvj7Xcd81sgcfLmFwlU1kS7YNzgNHu3hr3JRXHLe97Dxewk/5jU42WryllhDXaNcbtpaqo0MSRsjrg/e2XSxVyrwtFohuLqOnoSXM+LYbgAL5kY2lzr49rGEHf6R/Kcbia84tSTT6yhl4npNbA60l4cdnxJCkJi2kAGKWVRoXG8ZKJqLTGonMFMmF8bevDFudB6D2cJta9wVKQ6caV52oq9pZ81vJfjjVrbH5oImagO0YHH6uH7zCs8J4HLWYqeZs2uMjNXdXqtwOYFKZGaHe3v4J23voXE5o0zzpnLM5IsMal2YHpfsGc7m5dmd3+Mnb0RBr0Ma9ZoiLm1nRt0uJnLZ1xiLQ0HOQ36mTGGZLqWTZJykucSf2JcGQbJB9t3cbh1F5dffFmleM7QuaRvUNPv9gxZyVV9hMsHxJfkGYaXX4CXwRG2UZezIJFtbWRi5o6MJkWWahaGPQE4eYU9apSj8lcfIkrINIElwR02spgL9jQAUFmGSF9H+7sY7e9hfHiorGQD6ObjKWaTEeaTkbq6St9lYlPUVYH5fK5GR+qqG924Gpnw2toQg+FAzXh0MCamQZFZFLalmtcq45PtkXWTf8/mMfIhTg7I+7yqZRf9jt2JqGCAxAEwBiYUSZopaNTezLpuwXgDNp0P+7IBmdJfKiA4sYmyP8V80mid7zNs6hRFRtNGz2AbhbXCDlYE9LzzEWg+VIxHS888jqBz2b1GDtw0/nsgARlEJD8+DqZbdaQsr5mwEBAeWtfpkjCe0UXqBWZak9Y8YtyFMe/WxO8lPfvjFPZHxtCRC42902hSJAdXDYtY5LgrkuCplc16j71oz9UsI67Xer3epfU1Whvk4oiMisPJWjud6MW8rtHL06BW8KGHs8FFeWJxea2HzUGO0WiOyns5x4fxmPcIeMUwvwHmtxH6hy9m6XVeIxVH6yVeKzUGH0xmGAwS5JRgFpzLQjSTXB7NPRKEec3oJ+ESDa0IYXaJEg8rwNMbTKoKVe1xZZCjnAeH/SyVmDUTlBZtp/1COdIEUK3qjKWkvIatq+tA430Rbn6kx2TggF5iWqXPqlZ1Wq3A5gWpNM1VTnbzJ9/D2qWrj79SMggjLpklP5N7MgYrywrb+yPc2TnEpY0+slRYjwRWQG2s2G1p2TNRAt/vJXbQy1NLGsrnSP7v2Xox4Egy5P2hSnJnB4cop1Mk2cZxec1jr77EANYKOJ2EvD8OU7Z6rqxqqRSfWSAd9LF27WU9Ryb7d1UOqrEEaCSti0meILGlOO/jYwDs4vQi05hXkcY6yMApgK8q9jKqqRZqV6OazzGdjNSgZ/vmexjt7mA6nShLqWY+LMygV6mqryvUlVM3WakkTdEfrmPQ7yuTOZtNWzMiK06WzmE0nuj3yArK+8UMTGCajz1p3Ykl9bi2TcA8L/JEo0dRANLcGidJhi9HHaBR3SxiiMuijrOJQMMKNrEwsvzMSgZvD3meKUM6cZVGlDwQZgYps4zhRd6aRdR+RaSuwjBSPA5BrMiOQvMsh2AJpa5s546w7IMdDmDX9Cncb6SJbhxbEwS1D+Jfu3CWRDop8wf6emA7mx1wCcAXhO2Mi16PO+UqHL8K679ijTlMjL3F3h2VhZeTZYc8bjMwqksBnb2dBP4Wwx8AOIi9n1mchZtcIB3Jmet3f+2L+Pqff/+5rsN3/rd//ESf974uTZaNenk+u7w5UEksNFcTLM9XY8OpIpJ0GazbaEimPaKNczUDeZZgc9jDXn+GybyUvM0EROvE5hOAKJTcC0xIL/xQvpHbN25G4rRrDLZnJXZmBV7Me+gvTeks/BTC1i1iXnj5TUhkx9YpqqLClBkvpwn80RTjyRz5lXVYM0FCCQpng+8E1aqYkNtVRgaVDzE0i0WvwBHQRG2FYVt316gpZAqsUQlvUxRIdHKgcAVqV2GQ5LDeYFizzqKtalUPqxXYvCA1PtzF3gfvYjgcSCLRk6zUz7zHDzzT58G47GMw+sFkip3RGONpgY2hOESWSCWknWJ4ReiJs8GJBVWepXxpY0Aba0PeO5r4uiot11XotNCRqzATIs0dq7tkGwHxSKv4oOyTyKbwwgv042zg08R+BDnw+eeMBifaaPjwEd/FHAFnPhiA3QsQhl6dU8N5jciPxUGP18mU2HQIIx8UECYS2Pk8OKomRh1YRSKngLKcY3p0hLH0WM5myjCmWaYZkke72zjY3cHh/i6OdrZQzqe67Czvq8GP9GT6yCaKEc/VF1/QdTrc3VGGMMsyDNbWdMa/1P5HE/I02WMuhj2zuf5NmcnIrlIc+C2zhhJfIuylGpOQj9vFCnobRpNaAKpgz3Mj4yMFPDpoboyTlqt9vTP5pLLbPBWjMv3dt6zvyaymMkHGKOJlz2OS5jBgKPngWPy0gnkBpNE5Nv4aWMdHPTU6y2qUz4NTlhGI1UCWFCAcgZWNWrzPqHtKEsCuNrXV8Xf5bN946jPzyxXqnxMOPQBlc2SIboF519coyaa7Bv4nztZjBxySw4QKGpTwYwZ+aohugukogtBVnbFms60n2mXMfoT66o00z+4kqf1kkiQkbI8RPXZmISZVIlWXSBRxmrWJaSddKE7tuhjJJX/LMstlLVJTL4BTHrAbDFzyoNw6D+s/PPDIxOt9vy5xZzILzNgTPr8XMtrAHC9ktGHQYTBBlm5iNJewOEJmBGxOkJkN7SUs/VTbk3wS2x30cqwX443m5xHWszGqaxQxerRgLnwWTDvq8lGoLfyBMMJMYvWt+00nZROgP0hgMsI6ShibYweJ5iLPx0eoqzHy3jVUdehvWNWqHqVWYPOC1O6dG3jv+3+G4aUX8ARaVPnguwTzLzzjy5LFLC2YsrjSec2em6sMT3q1amTCUmZpzGdYfKc87BJj6drmOr/0wiW+u3vAR5MZPJk6TVOZ0U+ld+xg6zYOdu+pnKWlChrd/gNuvN3ZtJM2wEscBH2cAeaijETVODHBtKiT85UVU0fG1EhBP9LFgauSTdUMzssvqkS1daglE3Mvm8zKODCMgEivo3mBo91dTEeHsFmm0QnsPKbjEQ72tnG4s4XJwb72VoqsUlhGAXGTo0PMJ4GRFDMcL7KzrIcs7ynwEsArszzChAzWhvjEJz/Zft/B/j7GR4dqsiM9logsiWI5R0FyBm7ZyMb0iDpS2C6ARAR5/UFPZa1FWaiDbMNCdkEkcxuoF5KEeHHVLoPNNjOTF2ZJjcpYpbnEKgeuyloH4nIDaGMhOiXvTdNU+FQ5KgKBidlbhP7L+GX6X5mVq+LYXTC/jda+jZ1wl0t5kHOZj6BNPp/GE2Fj8bFj7xOJq0xD9CISz7UXNGyqMKtlsEnT/VQTU2jpDWvulfxiGhFwm8M6fioCiyse6JPBa6Frr95zjKtIk76HJ/KGaY5+bfw9Y/xbCZl3me0tA7rpQXehDKuOntX99qLLBv/t3/oi/tm/ej7s5v/z3/2Dc1iKH1WU72SDfFaWlRczA+kLL6tKj3STvTkrQwwQi0qH4/2b0M7MyklRVV6VDL6Z9Q12105dlInSvKIkKz9cbYkyEfZ2OYY3eGKg+Yjf2JhPd858C/bUZvsKLhXDRHIiyRUJ9TyaRssFQzrZZ+sqSp0JRlomrLgFUzt5JkL6xIfPlIZhfIVkWsAgKEk4yeK45XlNDTTgebEnmgmORNfPqHIMkuuaMco0hakIaSGycmGARRlDWM8yzBzDxRsXt/ZPoY1kpZpd1VnrmYPNVdbmySX9T5T2tHfqCasCmZ8Q6h974KuG6DJFsxN5oMlgVjqLhC2Rh2CapcefBARxpbTSa7Gx1uerl4fUyzJzOJo6JOSs2ATpANlhIqHu77+Lo70tXH31VXXYa7w4HvRgfDCIpPtYto8z6BTAYEKuDJywUYbQVa08CesbIiBNY5NyLuuKC368mrw7McnqbWyiLKbKMtbFNAC4GH4X6LJwPrtK2AmJ+5AJlns43NuGq0okWYaD7USlraO9XRzs3ENZzIJ0tiqV5RSZbNN+LXmT1tg4K251/wtI9SIbj32exobeSmpZwUDSleVc5bW+GTiJDF6AnVzbPpj92Abkqej1+H2kAZoij6rrAoNBH2988g0Fwzfefx+jw8OgjrV0jN2MO03AV7a8L81Sj/YCyFLbu5nG7xRgOp8VOtFljfECJo0h41xNCjwbQKxOnYmaD9VVKdawslprcRBum3mqOFwv1C2WRPAFMfRZjyvQmjU9YnkmkaWKFFqApEa/yI3x/l5OwlQOm2SWgzEAU9YIrYOlNxdxBQYqe6VgxInGl5Q0KULw403twyNlUNfjEe8Tc19ZBzEtCoBWzsopE6+z5Q0L+iTYfNYBhTFuZK15hz1+wDVuA7xDMFvMdFizE5Z3EveRu4i6wd//N7+KP/wX336m3/nH/+gPzmdBzKZKR2laz9PptDAafCQAc+65dp7k2ijLGruHE2wOc+R5Evq7O7MOAsLyJFzHRe1JejxTY7Q7UQkoz2JJ/4IztF6lvP1hApsCtK2hMyQjP6OSXlExida29xQpG9QiD7Vyr3ew85mOwzg1SGcpfH8OU2Tan+jIwlYVUnHdBuNAPu8rXL1+Fz61YXbrldcknwl1ngXweX862NOp0LOhtxeZe6y8ZDBXOsFRuxJ9qmCRY9Y3QOGappHTZbQrNLmqc64Vs3lBajwdYeJY+7aetLicTzeH+Y4DRs7xZROZjrJyGM1KFFWlEr+qrpD5nBo2oint47IQFz26vL5mhv3cGSLvnRO7yjTrD3WgKeHx88kRxns7ym4m6wO16VsF+Z5TNVmBsRrJa7eeDNjFh2Hr6cAnM9KNpPkBX0XN5z8s7CgHUDPYvKqM4XjvrspqQcFkR/NohYUsCxzt7WL/3l3s3fsAe1v3tCdz88pV9AdDlcbubd9VZtDXJZI8R3+wrsBSAKSY4gj7aLVJEsFlVuNPEnDtlOWUQYKJEmm5FqfjCW787B29juazMZIkRd7r6d9Kdap1oRdTAKYNrGuWprhy9So21tZxcLiPne0dXV4DAJPoMCsumQL+Lq0P8eYbL+t33Ll9S51tpc/Tdiblue01kwl9hvMduSmhZXu7eaTQ93mV/W5ubGp/5ng0xuHhSMPrZX3zPOXBcCCLo/lsTsyVyrdCwxcry1uCyTmJMvEyKNcv8mpPSV25m2xUnxmtvpwal0TuHun2T6dVQhz6MjkAQCE6BmD0lj4n5HOq7riEufZidq4KIWrBlEZ+3ERwaVWW0Nosqdy3B8O/KGktYLrWrjYackvFgesBhOLQEyZiGy79qMzYkPhUWWevUar+VwnYIuPfB/inxqd3C+elQ3g/I/MOga4z874hmhqieWRmn/wh8yEtZ87n2SSxOaUrEl+4ynhX5anJJGdWxO3CpgnQnMwKVOzx0pVNXBoGia1r1Qas13OaqoQ23Hb9wrCaSUTknDH7/jwzGc6/g+LpFQPv709hLqwtD7f/X/B1ocLEfNNyHiYH8iaiSsQVejnXkSkNP5xYsA3zUoO7H+jk3/ilV+CkZUDu/+n5S2wb70R1wOYQ4mTSXBOP63mNfX+IeX0EKlOIKq3Xy8KdajU2W9VzqmQlVbwY9Z1v/nMMhhvnMg/oCbP+YLBf1/VoNhdDWq+T75L7tXcwxu7BGl66aoIrpPete2ZT0TjEi9HB5vqQ19f6MhA1ZV0nVVWRq+ZI+muwgzU49hjtb2N6cIDeYIDQRvbIzZurelC1sseG6T3/a1Vljk0GHNlOFIi87uLspwr5Hii1Det38UdEzUO6icfMej2k/aHOxB9u3dMJFAF/VVWgkGzL+Qxbt25g784tjA72MBtPkIoKQYx52GN6dKDOssI62iRFPS8wlnw9UQ64SsFV6OdBzJFscjbRRoi0gC30KapUdm83gkUTZEstWxmldo1kVtZBlpNlOV5//XW8+Zk38bN3foadnd1gPBSdcCVqgX0Y4K4NhrjUs6iPtjCaF/DlVKNhqJH2xX1l1EiIlOysglyXmn0Y58TDGJla+Z9+TsCsVL/Xw2AwCIxmjIaxoSGWXFVbH7uQRXLoScCk3KaC260YkXluJzF0SsTI8Js5KPXDd4mENY8rwrKXyAhFyaT+SZKHSOpuZGLb2LGTN6oZOc7hhNEicV/lqEwnTcQ2vZyEAHCXlqejuaxzIQTp8YL5jFkxJIq2T4boqQZocqG9ncKGMloWmcWjg6U/VZvrZzH+QUCwDSISSsH8CoyRjMZDx+5VAj5pkcgueN8z/xjV7N1Bmt6zSX5v6v0NwGxF59vlu/Qzv2v//u98BX/4L7/zTL7rj//xP4C1jxd3slyCGy38LsFvZQkV19YH2dXNISbTOWZlpT3cZC02m0gl04TzRCWKCaISMdHJsxRZaiPIDP5dHHCOzCjtm9qPP0weLGqItGQg9mGus0yhig5BfgY7W4FsdDXGL7wEXrv8+ECPO8/k+FLet9oLPBo79JUhj5310ewi3OyarOMP00zFqj6qlUzX1lYH9wKUyOLOC0ww8zxJk5upofdq5z5RVG5T7ldFWeHu7pFmbW4MeujnjdyuE3HQTLHr2M5wmiScaJZCZBNY0k+c+BXAl3Ns37yO9c3LePnTn8Pmiy8jyUyTHrGqx6yuHNWZBD412vvypGCOuux105PXAYktR6O9KR6JuIUaqz2jRoMOc+2nPa8KTK2Psumn90DsAsxmXkVwX104TMeHONjews7tD3D3+k8xPdgNzNpsivF4hEScm8mgLApl78VQSPbi/t07avojRkAsA8vo/Orq46YTWN4uQUquVoAWwUJr6qPXnQvhAY2slmL/j7i3LhdHz1WZ3RbGc/vePQW021tbcT2aqE4HIW0VBLLXgUlVTHHvzm1MS6dMaZalClobBBnGw8JSsra5yvdEnKh/s7ExVBCejHNsBNCSQpKnwQhI4lumk4nm+6YJaYZenki0CgyL664yp0b3sdyHNH5Fup/ETCiR3DZpXFODIC8gMjGWHTtTVc66mo3nkFkYdzZxow2Lqtw4W8JRkyz7qwMQqVG5NQ5IcXRMmTEk5pdqa+v9UpdoYFPve2hKZCnJnS8wnzbyVHbpom10yXJOzU04+zMOWQ+JrnGYWegu2gaT02a8qQmvLZhtpMdg7hFhA4QhGJ/gAEhfB/PngeRe6bGFwu8Y4rt9wz+pkPwQwM3IdCIyrqPnATj/9r/xJfzxn37vqX7HH/6Xf/98F+h9ZS9t7HiT3TCODy6t5esvXh5ga/cAB+MSng36NlEQmSRhYqoRU1O8/ziV1htsyPO4n2M8KVBLjz41aZvYJDKvco51/yHAC7LOEvlya2d+0b1ynn5RuJ24JEV/fw+0u4N6OATWho92iYlpT+ZhZDJxbJAmNV5dD9EyZU3hNnQ+3S+rWtUzqWRFq1+M+uyrLz+SE9qjFANFAt6ujdnP0qwyVGuPZlHV2B/NsD+eqEFQkPU5GKe5geJyKLcx7TXi6FGbJsbmvYST1HJde6UY6mqO6aHX8HkZOO/cua0Db2FyUolTqZ/HkOVDXny/hDW2+JwZiEmPpz6kSHo8F589TcVw0uuszF34bG1SHd56c74jnoatfRrqCuoAZ9NADAPUJasb7P69O8pKzmYTjPe3ce/6O7jz3k8xO9xXfCjyzfHhPoYbG7jy8qvan9k40wprOZuOA0vhg4FQo72ihzDQjcMrx4GnyFuzPFd5rTCa3V7J0EO6YDHvO0WaGWwxj6pr3LxxA7dv30YtMttI5XHsDQsZokGWLd8znct2JJhV3JqThJQXlYZRVIU1hEwL0kXuK68lFECnxslYi8QiTkgYXR8buoxROYdUuMcsQ5Ya9NKQRFILzEJg0iUfVMG/JwWafZUWhu/yTFXJVHoX0KDzlFQVSAa1go0rz6aq1VNXwXHjDinJMx6sDrCRoWX2zGXM5IyMsqpTmTq9ZQHJSXKFmhFrkurClLj5Ie5oUSkeeKZmiBl21YknQPM13ltP5Kwxevp4z0ntvG3B48kfS9COY0l2SQhCCSTzZagpnF60/fhcHxCQU+hlfZkVQMusCHYSV79dO/5LT+ZbIvosiyt9dskHQP0+gMOHXmDnXP/Wb38Z1hH+r//vfFnO//o/+j189QufwiTJzhUASUPlPO1/kNX1Xw1cfYNArxgyIgAK14cx6mFQlDXGkxKbQ6+5mqQGy4tZXbk2RUYrESga4ePC301QGQyY6LN5kryRJvZtZn4im/qnXbLOlSgTMF3lWi6VsJ12PseVosBlNZ8+RLm2eZ/UQi7bXmYxLQ5QVAXW8s2LsPqrWtUTVwKZbXnG9V/8+q/jH33jG6uj16nf+JXPn9uyJHx8NivKW3f3ijvbB2pRjRDmoA806Sdz6khbwhY2mBMRVcxcenAeKAtSejNPLW8OezTIc1PMSxZwykUJAbDyVJUeNRneiIGJmKIEc8ZnXR+iXsGTKox6w4CXuuDw7IsKslhejGYed5UQGtOUKDJ2ETBxrnW+QLNZxeB+GlRF4vQ4HwenVZGV3rv+Lt77wV9i+9a7mmsrESbFdIzp0SEmhwdw5bx1ihQTILF6379HQdYpwFJiRQLDrywiRaa3AYUnrlfX2XWpNFOkDoZduvylszmY+jRb18kXokVOZtOnWEr/p1zTFKMwI+juJYRUpKoC1iJoKysPEZwKYOsl5JMkFQ6R2Hkb25BcLzWcp8ZYIklfQF2z8mp5ZnTgLAyMbL7QgMEMhGIEZ8zpZNkmghrPKrAGMlFwECmD07CSQsJ6bxS8pYYC+2k4pgtY6ynxnvnQOXfknVlztX3Zse9xvGHNK6ay9sF8SLxeQaSMrGTBky7T54n2O1JR+xD0Iz3nDC4rT1UVeukEoFdN7qiGaAoSjtQmL2CkRigSty3V1hANxdkFMEXtbd1kh55iDpIkcomShDAkzN4kTTxMJzzq1PMpvNVbQ3UW8kVt7XxS1xrAkIns2RjjtHeWvfXqzEsphazPfgSsLxrCJ5nplzzz5wj8DrxZNygx6NF7NpHoLN7pZJTuAthCE3/zFOv3f+2r+MM/Px/ToL/15V9AlaUYpyl+5VMvqjPseZW4ktY2OdggupXW9b4wl3lq1AxIr2chuUVmPy9xMJ7jpase/Sw60lJzjMPelWuyrEOUUDi34vNXzaH4koW5athmnnl2UZ9yss7SpvPe7tEzcp/9EFZ8zhudpKyQjo9w0LsEQ4UYQwEzpxN14mzrwSvvi1V9pCrJn9z9dFXnUF/+1GvnthvFSGJvNB0dTYujW1t7teTwcZyPZ2U2HKbzAmXZh7WF972eU4s2mQUnkyEOYtmiNta6PMvSRG00VealHIkY+pvYuaWSw70dNUlZv7Ie+jbPMgfLiwHzfbLDR9ne2Hf4OCzghaiGD2lEdo/9sGaNSmn6goKTLcf9ctZlUkcy+/DPhuYzVkeUZ6mhaoyLiBeDNzl3C3FeLkuMD/axf+cmRge7mI2OsHPrBg5372EyPsB4f0d/RB5FtSxCAAAgAElEQVTb0IZ6mouxT13p7LyAUbe3qyCTuWPjv9S/+qCBwTHzrTaKJPRjGs2NczrgbGW1DUiJjCYa59fYF6jxkxwkrtZYTgXsifNrwtQToEaMog4S08QQ+plRttCrBIuVRRTmJU0CcLLW6sETAzFhCWVT8wRFP7OzPLHSEjSoGkAnYDOAUInXEOmoGM4mwoaysYGN976NTPHdtBIKAy15XUBliE5ByHqLXIiJ72OOsTOGE7I+J5jMGZN5y4mEkwhvK4NxjY4Qa1YX9kcS2pRkbsAEhgjIEphEe5qAmLgSJMEMlRFPizDR45n9vPIonTKkRvtbc6MTFvJawMxh5eR0t6FfNaC8xFi55ZURnHLs1mxtauP5KXLiLEiETV0HBrpqmO72pI6w8wSnX2MoZruwkeMuqDn0yLbMLKeJxJ8QfEUZvLayilQ3aw0BxdWXaIOg+aDXALrFRvnddRCNncc7AG017rps6D0w/wBEPyXQvaed7fl7v/01pLXH//6Nx2M5f/c3v4ZcJ0AMticFsn6OX/yF13Fnb3Ru6iE5AXyWubSu92bO7VIxL7PUJoN+pteYtKzM9Tlb42hS6O9r/TT0a8tdsmHL1UoqWBbLv/OQfa0GQ3Xo8V4rq/qqd+gbtrOLKhnSWJHKY1bXATit6tRqRjvkHWoW2+n4Cn/IJ81XtaoHVDLInwcTtarlujzon9s+kYdWAtod5tmuIaNz+Y1ZiDSVy4NP5D0y2Cudk5y9yqaCJ42E20VBoMoDRRfEjZxPBqU2SXx41gZrRGEzBWzOJyMd3D/WY0YG2Kp7cyo3CTmbj37T5UYm+Oht/Bfvpn4Og6DWxjzGaoR94p8AgD8G8F+y/3yaQyOKzJpNwmBHwMV8WquZjzjHisxbzsm9D27g1k9/iMOdu9rLmPfXVLY6G48xG4302hB3WHWMtcFQhxvJrDCc8nfusIpaHZfgB7CaJxVHsClU2EY/QT9PcDAuMJoFYCly0zTm9BXRnTqNrrOayWFj9lkKld8NUqt9mL3cKIspg1lh6OSSF3+izGqfpH5xpS67oXkxAE2QNZImB1OnVgGbXO6JIWstJUkImPOJ8SZN1FtfmEw2xBVL8mMAXkk436i1zVfWN54F1Nlfaqmv7j0LybGhIH817b4JetTwb09Uo0fkLhNLBAoL85pF6lDPbFHm5kzHzDx91LmayHTrz0Ip2zZuyn4b5gokFfAXlfOl8yFEIAnHQRhdOQ7CxlY6Wcfam8ZxYk9dew1TXYUrppcG8ODCvo59tXFCJInRNxwAuYBf15FHx+Ad5hAQuOg1ppikoOe57BIvDayQKQTH3El7YRaWM8YORtlvMDvi4ycvKXNJdI2YL4UVQuqD+dJnJIQwkLdcMOGWYXwFzO9WxvwEwDclz/l8ruLT6+/96lcw6QG2tPijb3zrge/9d//Gb+D27j5eu3xcdjjsZ/j+23fxqeEAv/5Lr2PncHp+gLOaC3u8XTH91Cb27qCXvjnsZZjOanV+lwuwcA5lXUfmMhhkqTy8ecYambyxkM8J0KQ468hxUoaZ1mvnXyDPeTPhdBErhG6YFat5plqJjVf18anEpufjzraqx6//+b/6T8957+koeX8ynd8EeM9Y+gSig6VOVVe1ZnrJzKkVwFlWSd6TCE1L7QxbYBtNL0vM5voAa4MeDo6mxD5I0BTERLmmr0pUxVxjHPgJcJMyGT70GgYitmHooqMCBUapURV2Hfq8oUcARxwlpg/OAf2wluwr3zQoUujeemwX2xN6SJer+SvH717uNntaA6MGZAbnY6AsHFxRqonO0c42bvzVd/D+j7+vrGRvMEQxnWDr5nvYv3dbrVYGgw09d2eScSk3waynvZM6+K8dyPkWPFJ0y9HflDXuaiMXcLo7gG0ABprhhInnXGTwTVTJCXYbZAbrPYOiErfo8FkBh4PMqjHUvAoN0CJbFblqYhqHWAWK6GUJ9fJUmRy5Mkw0fGpy0ij2gDbfnUUuOPB8jY1svLKEKbMR4xjkkgOu0S1xO21jSMys2ebMvocANCnIed2x7e/GorQnBEWTpO58jzZc+nBda65IGLRyoBuJg51Sj0C9Y3DJtGkRum62URh7GEsLESjH7ziW3Rv/3+xTr+ZGbHI1c1HhRtjPJjKiHAyW5J5Z1qw9o6Fn1qgjd+UYs7nIoQNjLCBSWNJajY8AsYpV8B8cm1A76VENDrzNagkTJ7tfNMPSHr84NLE5k7u21ISq9i1tGsC0ghRTOZ+SD1JfY6Lu9gSaNBwehTVJjCXl2PMpjOeQG9UA8RUwXmV2v17D3yK2nzXEf0qgd0kktiEG5qnW3/7ql0A5UKnMEBhmBs5blL7Ucz5PTp6sTVOLnf0x/tm/+gmocPjq517Bwag4F/GFrMe04v2C67eTlG+uD3qf3lzr087BFLOiQu2dTvaIigDx/DExeqU7ISfy2ktrOXYPE2VBnef2eDHzJWZ6g417mYnvXMT8Q7lWJ87j3mS+ApurWtWqTqxkpQt//vXWjbvnug4cBi7lrChuJcbcS8j42oj9fxjkjWcl7x5O6ZWXKhmsGud86p277ykhgy5xqXzp6iZeeeEStvdHYu0u9h+LthKRLM6mONi+g4OtO3hZguKlN6U+W5ibgiRKcDJa9Wp6o/o4VXZSlM5CjXAY9AjmNVFiKsY55qMbL3uMET4zo8nRIZaVDb0wd4a4IlHhGsIkPFBMSxxu38PWzet6/pWzmbqs7ty6jjvXf6YD6bzXV/McYd/FbbUuKlTzosE8asKTpKn+yGSJL6M559I5yDEfj2I+XogfWeCnJj7Id9sSGmAFs8jXMEFKqcYgiVWWMoHHRs/AchoyKlODYc8qY+fFvItYQZF0YoocVEEQApCQyBK5RtUJtqravs+uQVLTG9l45C6cTWOUHB+fOMCivSgB0MLGZp9x0LxKjEfaQMZgtukW292pZl24O8JeaimOZp3tL94QLd9A7jsffTPR0fozIUbjLR27zgK6f+ssUKdojIJ4FaqSaWlG/b8YBondLTMZn6EzmSAxd1aBY9HzysrqcZPzsw4g1ClIDXJf6ZUVObIckzRpFCOREY7zaVYnUqwCDrnt+QgBWWXRHKJiOGiFF5MijWxX39i2bbM5accd3z3UnhPa/GDCfIgGe1bhKCgz+oqsKnu8ysSbBv7zgHm39niLa/yQGbdAOLhoUEP2z6CfYDyt8C++ewPylHvzjUvKYj9pybY64gIeB8bbSWI9GzKkEtqyQibydXF5Frfo2rfXmfOLi06u6l6SYNhLkYsrM8/V4IsWOT1i2Pdmxe5N5+q3nraE+XFKJklEPrtfzJHb83MrX9WqVvXRqY/uqPtDVNde2Dj3lZWBaFX0xnVZj2fzqq7mRUYIZiRH0znu7o3wiaMpNvo9GUGQj31WrZlJHDiLtHBjbYBrVzawPuyhdkI1yAjEk5qaOIfZZIS9e7ew+8ENzMdf1L7NENAe1oWXWMiTSmM+6PiDijvrwk0egwdSXyn4VNBoAD6T3POjwGqGXszzdodtlv2onOSzAKMNi9m4ygaQOcdo/wA7H9zEzR99F+/+4NvY+eC6Dr6FyZTzVthMyZacT0cKQlUqSwEUKiCkYGdjIjMk57H83NcsF0sYKbFo5Wgk0zjBtn22CyYiEHW0kHCqjD3KVvMEarDTSywGvQTD3Cg46aUJLvcSBZvSwyVANLOxR7PTY6iAKP7SZFdq4kY0sGmyPH005aFoJNQFWbywUW1PoPu2eAHMGsp6maikk0Dj2euUD/tuFzPuR7AnrO6Ji3rYfWfpd0OLr+JFKlRwO2qwIHWAaCyZDMiMPQb0B94oIxoUJYHJnJUes0qcwQWEJgosBTwK+zktnB43kSvL5s8qx7OiJmFB2+OJMPHAnW1WVjP2+jb7KpDCYT96biYgTtyHokXWmUZS8priqazTCpLFOQZImM7LcdMGYP85AD/HcLPSmXfY0w8T0F+w4b+oGR+IBJeByUW5zcqWiFR9NC3xjbdu4vNf2MThqHiETz680oydmwxcXYkXU4HpvMJ4Vug+F8VBliSYlgWmRQHv1xCAfTgfoheVyrUHvUzjgkLkkTt2thvwax70RsV2IF5CF+nxJeeUqxxGRa2S81WtalWrOqlWYPM519f/x3+IL3zm/MyBmhKwOZ0W3lV+vr0/rkfTWRas1pnKirE/muLezhGubaypKYpm7AnzJ5aRHRMT+YxIkfp5in4v53lZO8m4K6tKG9vC7HutOYSBWbk/ZHPRafK4+lphNG1wuxQA7GokwlZlBrUATn6Uh1xjeHOx+jXpBCbm4Z954hH+CdXIi02T5XDOyz9DNYY/ZsFmSs7DbDLH4fY2tm+9h3s338XW++/izns/CS6yVYX5dILR3jZsErIxlZ/xDq4sw2RFnEwRN2aihSOL5GrKOaznP7Wtcl2WTweCwj6IdHE8D4PJNAn5mJXzGpUS1N5hIGkTg7XM6Hsq35jXhJ5K6ekT0561vlGbe4OFwZF0UzYwqyUnI5AIIvi4Tg0eEyBT1cfXm7kDfrH4wOIfjz6bsDgNHk0jHZW53J1d6r63YQRPan89hXU8qfn3OHDi+1lTWuofbvpqT1nvrqz3vvUK/zZNgwHxyc1Wy+ywbQyCWlxOEAOndWm3dKzKY7H6lQkPAZTz3KIWm1prFXyOZrUsQwGna2+rrCZE4vLbgMhEZMea0xp6SbuHmKPxTBdsdtVMraBWfKHEFYmCe7DXQBUON11I4yy5qCeXNw/jqbfBxNfI4/OG+Msl4yue6Xpi6F5C+HHNfJ2A/WfhYvuwkk2WiB7Zvvdu7eGla0OchyTVGM9kubKg2jrDavgUWdNGUio+CWL4U7kKPels1Bta6LWVu4bEWK8P+tgY9rC1P9H3NnL92Gd7xRj6RG2SjdqbC8Uei4x8Us5wNC+RrFjNVa1qVafUCmw+51rPclB2/utgJO/L0Tix6Z4xJLOhg9DrGCSo86LE3f0RXjuaci9NqCoruJ40O/FxwxPphUos8jwLM/DOGRcflI3hhAyXrDEt+9KFQgoOfamspbfLGeePWp1+OBkM2RQiXXM2UTOcs+Gij0JPySmRIbyIXHhksNiSXjGpkJ6TCUVk7xqZLMUeN5HKCjs5HR1if2sLt3/6Q9z48Xex98FNjA8P1KDqjV/4Il54/dP44N23ceNH39VIEzlnfWwClMFcd6OUkIxOu8rox+A7irJjiqylj2Y3CPkRKmGVgWLtrAJMrQ6AaZhMuXaGvQQvXe4HN9OyVndcdWG1rNEembqSGnVPlWW7eL2ZODXjW1AQXm/sdhRQMFojkUblyk37pbKaXfrxfny1DIpOBHlPwlp2gBt11vu+lXmcZZ90Wp8GVk+7BJZ2QJfMPsUQkpp5ofZjJ6z7MkjtXqIcfxfjXWuogiVBFOoQq/6kFuhLsyc1LKiw4JZ6GXiWB5Oi4Cbs1WlYAaEP56iJ/aquNWeKsuAocZTzVs4PUbXAHd8+o67A4uobXJlMg0mDfbkwmhkx67QNiIq46KTZQP1q4kuO+Wve0ZtkaJKy2bfMP5p6/+dkzL8Gs2R3jgBU8ee53GKEEd4/LPB//slN/Mf/3pdQFueAgdXcyldJ5uuqSF2eJiZLrQBGkvBXYwM+l95ruQcMNZ7HBjY6ZALpDh/0clxZH2DQO8JkViCI7/V+xAr6nXuVvHnVWnOTLlDvkzhf6z1s1au5qlWt6gG1ApvPsf7l//APUZZPJ6dZgOH29ujgcDTdct7PDJlLTbOVVF0xjsZzHE5muLoxYAl6d66mFAvDqGasmSYWlzbWsN7v0e2qNvLg1EQ9BQeht6iazXj/zi0cbt+l4ZXNaA8qTcEOWTlDZVNU1Id/AqVNAFKEOkkfSZr7YajHkfSe5tTbGdafCVQzN0Lkht18djuudeG0UYLK0Y0FhOloijvvvo077/4YR7vbmIyOsHXjZ7jzzo/U3EddbAYD5P0+1i5dwWBtLQBLjY4wQXbbnPMUXE7bzkVqNKKLXsZGRk4hZxEUWSKVijmP6bSSGA2s5xaVl1y5AAGlBzNlo3EeKoskxpXNHl65so5BBo1WkT4uH1l+GyNPZEf7xuMlynl5wUZylEPSMaZq6bg2ABlRUsutSK95wylo8z7E+aCDdMJ7CfcDUzSTTc0ePYX+Y5ykVF7UScs99U33v3yWTTu2Xt1F04nffzIMXQa7XeB63+rpi2Lf4xtC1sXrQAbuJh5Dddi1ElkjmCWB095Nr6ZDIsPVnOQaeg5GryJE+yrtFQ3LCxMkWRaUK64Wo6LFGdSowMUNKUyqBJFIcC4WCbDEpoQueZnjCDbC+knRbduIv4NIO6i7rzH4Wu35k5XD50D4soX/GuC/T8AdIkhsynUAYlIwj5v+zIojuO5lCe5uFVjr5+cy9UiWaxAfMXCYZ3ZzY5CZqnJJ6Ov2mJcOk1mlDrVddj3G9eqJI9rsfp6p0kF2pAu2wPJ+CSs6gueBNfWriQQxqiPCBSmdGaufz+zBqla1qg9NPTew+d/87u/iP//61z+2Z8p/9vd+T40LuizEeVdm7Xy7nB2W3s0a5852TEiMeVHzeFrOK1fPnCuzqqr6WZ7bxuwkBOELd0i4tNbHS9c28MH2gIq9kQBMne42Rp+MGO/v4fpPvocXPvVZXH7tdQwvDTWsHZpAbpWJ/Giwihe9uiPeR9jfTdYb7EI33fzhKZYu3QQmM5jbMMZHhzjc3lJn4yRJMTrYxk+/82d47wffRjmdwKQpZuMjFLN5BJBG5a+3f/YWDrbuYjo6CLLCvAcbpYXiSMvRXGq52jzITjVZZz7GVkg8Y5qSGrM4X8M7gzRL0UtSDDKnuZFNLqZIZoW1zK2Ykljk1oE1s1GzKfVLGuapC0h8K/lUwKv/M2QiX9nMLiz0sy1QbuNDENe3ARALwMmtnnSBwvhRae9lsPVA8HfK++gBp9KjosJlNvTkttpTQR+60tHly4OPXykPXJWHbP+Jf6bjxzoeuZRJ3dCCN2mcZJC/OW6NipT9kpBTkwYzKu8N1hyj9kFm2xoO6WtBql1UHtPCo4xsvBp7+9j7a5oM0wacBtfc7jSViT3BsmYuTvqEvng91fvxzc0Mh8x1lMrvedkeajv1DVGPwL/Inl8F+7+WEI0Sa7Y8+K8AfB+gH8b4lMmzZDpl20azEv/Tn3wff/B3f1V/9w+QVz9i7bHjtw38rfVBvvbSpQ1b1Z6FSS5rTzI5Oy8EbIY+Wo5O7hyvcA+n9yLnwwPamkburP+rDKiAocoTe+/mF+YhKuu5P6uwO6l0cntVq1rVqk6rFbP5nMrPJxg9ZcP4OVCOE39QsR8bpQJdCE8nFUVhXta8MxrvTItL1zc8v+ace807b03LlujYV8U+vTy1r714me7sHNBoMlepTwhND46KRTmn3Tu38ME7P8GbX/rrWLv8aR16VLCozTAM4M/gjqqxb3T6SPXhrObyKPlRvzcO0p+RLCjAgvP5rsdd54WcssE0T3fsR2aB/ST3si5rjA728cHPfoT3vv8XONzdRj4YoqoKPZ/2bl3XbUuyTE18RCLOFALSBWTt3r2N3ds3YZNMAakMmJtMymO9gQspo46J684g/9j+EIbVByAsrOV6L0E/DUy+9mqmBoN+jr70k3IVT8YAEJIYlyHLrOYzNNJ1g2OAst3v1AJB6q7iYvTbnI4BJkSAeX/75LHt6ARK3o8Xm7a79oWlg3PCATtt3uIUqW3Ts9myOKfJW0+S7NIJvy+fjqedntT581lO4RO275j896RtPmFdHgqAOYi1gY4TGi+9tTF0iu4x+jHH7XyDRrVo/3qcYMhNzAgFqprVyVTYzpk4ybjYP8hBgSKAYG6dspxyDmn0isrIg6lVc61wZKeb3RLv2vIIybouUSFdKaiLPbUKCR/biZMoiN8EmU158jhXC1L6aynRHQvzLTD+KYB/DWDrWbKcRMGHwFlWJvcc0g63yOOdxCQHa/0831jrmezA+HlZURqdZlkdaWtlp1WN0Kjw1QE8oPnm2UMRyMlV75hz9ryuXtZUj4lcdVEmbYMK5FTjqVWtalWramsFNp9D/cHf+Z3QR/OU53MlgtDCHCRkjpz2araNVPqQqFzF2/tjtzee1dcur8vsqmE+ljfA3ntnjPF5ntHm5pCG/VzG+EZDx5tpbDFdSRIFDvvbd7Fz5xZefP3TOqTybAJzc4ZtFaBpXdX2Zz7Lh+uzBpuPSkA+swoyuSfcphOa1hCHnvFckD7G2dGRZl8ebd/D/u493P7ZX+HGW9/G+GAPWX+oaG82OlB2EgosnQJM6ceUnkvpafIxaF9jNyiYVck/ndquLNxZm10tTKUM2B13iNwTNlejSVITDF16CTb74g7bZFAa5DlByEp2wdDHxMY/3wA8yeyM15JRUuikXlhu5ehd5i2wSLHhlMkHMS01eHXhLst8DMvg2L87eSB88nE9iQPn9qNNaOXSd3QW0bz3vnmdrjkPH2djuyJvPrbQ+HrMMWE6hW3qfN+xbNPOlM0jSXSX/r2YcHnyuZZj23ri+fUwJo1bebUykG4xGdHeR0McS3BTlnNLnuQZ6/1WgafIviuHso79+wbCevppabXhWwyspnOHsfREix43HicBp8q++WOrc/x86uwrx8h0IiRo1MHtmKKZalg8dAjoAfyyB15ytXuTHH0msfR5EL5JwNsA7jyLaA999tUO//0/+Qb296f4T/7D38BLVzf0tccqRmGE4SUSptoUwmSWTloyabOfIzVG83un8xJlVSNLk3COcDtPpb21gzzBoJeCJNrGsb7mnLOeeWCIh9ama0S9HODzsdJ9ghKzs63xFLvTStdzVata1aoeVCuw+Yzr7//eb6oJz5lCKB+3RCHkaGSc34dBZSylTd8aBSMRM51XL+0cTDZfvVr219Y41VFGtFuMAwirSi5jSOSDWZpyniRIE8kEdJGdYZU9yuz5eHcLd975MV7/uc9j84WroXXzrNsahykCOuXx/XjA73EBEz9GL+jjS0+fGah9hhWyOiXCwwR3WyycZUXROlG57B1s3Xof13/4bdy7/g4mR3v6+uRwX6M8JGakMfZRMx8K/YlJkmmPprEJaokTKEoFemyXZNrsTzZ7ibEgjUy2fbuc6Np/FSSzeWLRyw36lrCRG6xJ7mUEUqFfWNxrqZNhGeSJkn/YxGeYCNi6cULokJgh1kTlkmqEG97EC8+h6PcTCKMu03n/v02zkxtqqqFEefFdC5SGYzuGlnFEAzaXixcTVccYzcY1s/m9kQp3WMpj8SsNBGm/oiPxXQZip7Cay5cN4QSQucyonlTU+Xxnf7RAvvN3dI7LfcvrymWX/3imibZ2wuGYvLa5ZzdfZjsTAprbSOGAKEikkNRCvkbPhpzWoi5ZAE9mEqTGgq0HcgE5KdKS1ICojnLcELOymABxLSd/39qSZnou7e7uabF8uPT0lwRPoksM/HZd88+Tob9pib9D3n+TTfIjALcB7D76Xnu8EuA3Kyr0kSPzCXz1mI8NmRBK64ITV5APR1CApsiQq6rW/Vc5h9F0jllRY62/6A3XHSKSZ0tYG/ZwdX2IrYMpDseiiggO18YYMZW6klr7ySwxmwRsPeV56odWZg32ZyHax66IzVWtalUPqRXYfMZVW/fUZYqdEn/9O87xe9aYkU2SK8I+VlXIHJSnXVm5/u7BuH8wmuDKpXXtG0m70/EE66PJylovxysvXuLtvcvwvI/xfKY9Q4pPjUi5ahztbauBy/bt97Fx5epJrXIPX2kZYUukyTNB5MvffdZZ2gCsQoSJ/QhkeD55dWDM4l8yKC48Dna3lcGUc0RyMu+8+xMc7twVgyk9fxT0iQGU90HaJz1kiQ39lZIVoUCWQo9TVcK5ssOVLdXSS9zJtiMKJixtn6NI62xwiR1mpAxDL5UZfMIgs0hDAiEi3xiXtQB9DScSeuIi27QMik6QmzZIIpi0NPLFOLAPMxFJ+/blTeq8EqeHFq81wLD7Xibct4xTfj9RIdsFoR10emypzQc7MzZExxZwDIw9CpHYfL4LJrnzHadecqfTvh1g3AF3S9u6DKhP/KJT6eGlHbn8kS7wXl7lTu9t8/8A8sOZIRMlVSU/PricOqf3YIlEKUqHWpi12un7hMUvKs/745mwnSQmb9I/LKxnP8/x8tUNXOrlcNLyUEkGs8O8cqEH1JFec66zdp3NbE70xXbFiJ7o4tyk+XhebBIT2uAYQaoZCK+zxwse9AWy9Dcd3F/5mv8iQfKnhvknAKanHN0nLllfMQv6X77+Xfw7v/VLePnqmkYbnblUjezvWkO38zydDfv5IM8sjoT5O3Dh2DGwPsjDsep4ssv9rZmIybMUG2s9DPMUR+MZ6rpW6XOSpn1L9hVL/rXxvBzsj8u2r/N5lHxzwR7T2ilru6pVrWpVD6sV2HzG5X39zGxU5aFQOxzUNbasSaZJaq4U3rUjI5VoiZW+ONHWrpS8wbquk7r2KrMK7V1x5CwaqF6Oq5fXSWZgZSgh43/iILSrpZVEg/ONyiD37t7Ca5/5RfSH/ePjrUcaXRo1qDg78Hvy0uiPMzzHBYAY31i5XDRN7Bnq8cnZthrQpsY93OmZlID68Qjbt97H+z/6Lq6/9R3cu/GOspjFbKp9mFakfpToZIcAyWI+V9ZPZvzF6KeJAqklE3N8pOspstpFu2PT6Xj85GqOiKVFHISM02TArf2VIgGnYPIj0vZebjHMjPZoJhQ+JH3JfGzqI34LR3aCOq9hEeK/GGIvrVNcKekt5dhFDTSX23FWMRCj1EptOTpqttgxrkOXEaQQVHEMfGo/nWmk+3wCcFvkMaITa3TiebEEkJrP+QiSdX2IYqQMdcBiGHXTKerSttV0Cbe2x5hOWJ8u24jF36jTG9t9/b5/34eoOw7RXbaSFwzwQ9sfHoLmKTLozXdzYzhM3OLTcIwDrThoBTsAACAASURBVF0LcJT/M2Na1JjOKkxmpeZwjuYlpmWlJ6dIYMfzShk7vW7iMZkXIpcthAGlQZ6ilwW5yFo/A/sKn375Cl5aG+h3DPMKs8K05kMix5UfiVuRnwiUdF7NMPmaJTYlXAYiLlCVgdwP3WJbjXCZckuQ8E6PQjYLQM5BTFDFtKVrnvEigJ83jF8pjP9KkdHXCfh/iXH9RBX6OZSs+O2dI9hEerFFalyfeYJUD5/DETHuWOvHw346GOYZdkRbWzsF61nMChZZv0oVmBfu0/HeZKKcNtF7IcX+br0/WSJ/pfb88qyoN8Xg6LmDTfJwsRd9Vata1aoeViuw+Yyrmp42RX7+pWDTsylrlyWmSrTvLZpDNEXBTJZnZV1MZkXVnxf9VCxprTUxKJ4aWZ88BLM08J7yUK6dbzLZdGBp0wRZf6Cyx+nhPiajA2T9fhhYs4OtKwUitUlas6CT90QzArz4j7LAwkb33mf0/Kcok3weYPykopgv2Z3k1pZeHwagMrLc37qLH/7Z/423v/Vn2N/+APPJWCVmVnswgwQ7nJ9O9GfKyrjolmm0r5EVMOqgWyZsWllhAFDWNDQKtYxj0z8p6yAusSbG8WSZwWY/QV8ZS6hsljjsz0xBaBskGAEdL5FcdAwIhYiV5n3NqJgX4KT9HBbgswUxmplIIdu2s3OPgSle9FA2AJrovmV7LKJamq+h9hPxfdQBb125bud9jYSzMVm6TxbabF/nSm1lptT05YYv4fil7ebw4vdTiUI6/nsDGpv91wBr6iAQinC/3b3UcUfqspgnnsBLc2CdhZ4EPI7NH5wAxrtML3f2U/u69DrWoZ+Y4oRK0pHI+shQCmt5NKtwMJlhVjgURY3D6RwHkxLjGKUhbGYVwajkbooJjchqUxODa+PshTB2ck0JUOllCV1d72FSlHjr+rb2310epBj0MiTDBK4HVD4YDEnkj4DOw5nD0bxSEKoAyOrUAUvqhRouozW0WTD+cbJEYz6N8Y69caSxHXVQrVMMXiZVmhKzKNUHIPqsY36JGb+c1vTLAP4pGN8GcHDKEXyiGvYy/Ol33sF/8Le+gM98ahNHo/mZTG/0rZ7H02m9fTRx036WYL2fQ/I2G4dZNW1nj7JwOnlgVVbRmfyJy8mzRBnO5gKV1yudH3YDx/7VPE1e/tS1S997XnOaci0O0gQ3RyPszoqTL5BVrWpVq1qqFdh8hvV3f/PXn+kuDzOj0o1TzUvvK1+YpQFteFBMpiXf2Rkla4Och4MBDdY8ER0f9nEMnO9lKfpZFmbdfRgs0YIa0Sleia6Yj8eYTyfao6c4wjtkdQk2Fk7GGKaz4JPW/UPzDKPz7bt8JIaxAUfn97WPO3gJWauhB3IymqAq5+j1B0h7A/27CTn1ONy9h/fe+jZu/vStiNCshpsPB71oLuVk8kL/JkymNcFgR3sgI0PXgKzwvcEapRnYNsydjdl1rM6dAZAIYynAspdaBaV5IpJwg2FmkSTBPAUctqGBVtwBSw1wOplc8RpJsVxtyHmXQWwYOxPYHx+bmamrdGhZvQhWIg3ou44tYhlkGhDWUTM2X+n94vMdmXDjsHvsuxp27f4jG+nXZZbwBOlqawPDaGJBj7GAzf5rQFqM92j+Tkua34a0aUGmQbuNxw2TY/C96YC5CHSbc+VY5MlZubEl+vXRdQvHP9QeHgpS1UlZ4WhSYl5Wev5tDntY66XKfE2LCuNZif1Rgd3xFPvjAnuTOWbzOoDL2qEQtszFfaPmbNr9GwxuZJLAkPZfuvr+/afgRZQDzunP4bTEpeEhXr+2rhmPaoJlCBkE9YVjIEB1kFtsFAlmldccz9qxUaArk0O8OL8ct+xnS7QHU3Ov5jkA5eEhyE2TdRL/L3lGczB6zJwbonXy/FUG3qjJ/RJl5g/J4k/hNS7lXKW1cgxu3DvA//EnP8bv/42fx5tvbGo25qOWnK/ziifTgrcSbw+tsWoCJJOzOpmgyh+v7LI4uZf1Onp5GuYCnG/PZzmXBaiKtNd08n7jfVF26ZX/n703C9brus7E1tp7n+Ef7gyAAAhwEimKMjVYkmW3HJfcdjruKU6lyu10koeuSipvyUsqXXnJayoPXXlMVary0KnKQ6qTdFKpdJx2V9stT5JsS5RIiSIJEsQMXNyLO/7TmfZeqbX2PsN/cUEABC4ASnfbFIB7/+Gcfab9re9b3zdI9YnVfhKVxDbYT36wEmRznMGsck8L7x6P43E8PoPjGGw+wVGUkyf8jQwIq5FK9GWHZt1W9vzBii0v1kezXG3sjNJTa8OkKAsia0kkT9hoQ3klgcimKXEkvSex0cIceemPlgcjW/K7qoJsMoJ8OgaqrI9qCAtXp6LAaN7vMfXk2N/P4mAGjp6QFPuTRs1oFnkOW7duwPrHH8J0tANLJ07DqRc+B4OlVSgLZrm3YfPqRzDeuSNuscpEAhYjE8Nw0BNwOZ5k4MrKAzA5lwAWklgWX7zYYsmgXwBSAzi6SkhejzGQjEP0CC+Se7GXo7FcdonjS1It/9bo5HVGk7A6DHblM1hOzpQTNvDHL5rveyq2clGieT6+wxX6DyJq0FQDBGu0e9AF9J7D59+2ACqAiq7y9aBklCAAYw/U57e9/llLdaKax2Z4P7R2qDvPHMM1H0XTOYBzLOu9PqLzdyk0BOby0K3Ce9w9DkiB79L3dxjUBlofZJvvhTg/AYliAIXMEt7encDVO/twZ28mLCOfmycX+7DUTzmKCrZGU9ibFLA3yWCUlZCX9hP7CKWowuCGHZqZMmQTIO5xZobR+j7n2ti4Hrwdu2NvaMrXFhvSXL8zgmHPwEI/bQouKsRkMTUZKw2Lqa441nNW2GhS2micibgdcgZTLjC1gOGe34J960D5Qo7MRnzIbihoVO7zNsQO6AQB/TvaqJccwZvg7PeUVu8B4iUAfCwmQjyHy4s9+N5bV+DVc2fghfPnhU1+0BqiyEqdyotqvKVofwtCIayWrwtg53vYNBdmmmXObMzrr31sCkPonNwalIKQ3dsG8DhfoFlyiGdKdIMUyt3HFZn1MIMVIj8ZjWE/LyE5ztY8HsfjeDzgOAabT3BM8icNNhkjumwYL6wnxqyz9b1fVc4vqhhwZmXFZkFstV6xiZBzNtLGIBK68HrJhuMM7Ijlh8Yo7SNPXBwnxLLbOO0ju4Raa2G8tw1lNvVrBwnO1pBHRhbcTAQ9Taz0xONNHmY88CYdxbbfn7vx/Wateiqb5rD+8QX48K3vwvUPfwpFlsFgeRVOnnsZ1s6el9ffuXoRrrz3NsxGIzBRIuCKWUh2ySyKQhj4QoCe78Hk05QLF0v9GFaGMUyLSvrOZiGjrpZWUnB8tT4mXYBkP1Fi9MO70ouYuQxZmYkWBojlvY0EVdhMJ/1TVPdI1RLPAL5asHF4zAiEsHwKTXd1heagm+kcIqrxZi35rPs0qbN87Lzfg9gWAt1DbdvBTdhs+3xx6fCLrum1PeTYiylTF9XhAZzmUYnz2TPeubqD2+6WxXaQZcuWzqPGbuwoQevQWg9Z4jaAvYuwO2C5IxeWY3rYwTtgVtT9+Zzs9+AM3uveVc9PZ+eF+Q8s1d6shI/X9+BnVzdFCssgkpsYr22OINYG8qqSnsvqPiY17SkWCi8sOWdH0wAOpTfwcLq62acawHKxZXeSwQfXt2GhH8MrLOOMtGhjXYjlgaBeNgrKKFLTJFL9XqXMILKYGIBx5mhWWKwsihKBpaMCLsN7G1MthHuoA2RbEx/e0jT5dkyZUZN1bxDBaQX0NxDchcraPwGCP0KAKz5S+tEGz+Xqcg8+vrYLH7xzG070jNyHHmgw4Ed0Rhe7e329FY2Jokihz8r0H6C5fYTVH1xAKKqOCVj4n9Dn7EK/LhcM+HXOtfOFgAsV6bOlxaVVXe66p9BmMlAGUo2tcuN4HI/jcTweYDxVsPlPfuu34B//8R//whwnJ5mRT/g7kTt51K4mWCekEXLIdmdgqGDzAqEsK1eU1pWVVQxAtdAHJOv4Zm2jFBkTuSgyyKYuURRVSa9XKR1FUdKL+NE4G+3D9u3rsLN+A8587guQ9FNZuNBcD9SDi9Ie72AJqg3yV/0kv/jIx6OAaL/wkaXO3e8Pi++agKvJ6WJawObVj+HCD/4cPvjrP4edOzdBmxh2bl+HmxfehXQwFKZwtLUJ09G+DzQPUQzCPoLzjCZRWJxiu9APTFCel9K/Jr1PwZFTNUDD91TqEILeSzSsDiMx+PG9lEr6NdlJNtbeepZ0Jyw/yFPLMveL9C44o3mg0S66gxlOWEG3IE3Aneu0I+JdE9id7IYurSNYDunZPPD91EGfjSjS/90FQbEKfkL1r5p9mR+dBWwHuMwjvvYNc3mRnczcRrYqEkicAEGP16MHY3VboFcbK81vUBtR00pDm91rdr47MfUbXeejsNn+xugoOAM3rGwXRHY+v4bw9fnXANxmA9rvmMtEfYAhW8VSyMrB7b0pXN3Yh9s7Uyg6gHKWM8v44NGJc5E9InP17rEY+iW5gPIgW1iDdnafvbk1gvXtBTi7NhA5LV87ndZi+bNy4qXV4wSiRCOaREthZ5hYKErDUlIYZRYmOUHh/DVdF0G4/mPCdSseYG0h58D1Ij+tgost34pUiODiw7MCgCuVhV+ytvqSVvZ1UupfIMBfIuD+o+ZWp0kCf/XOBXgVd+BXv3IWNqcPrlQ1VUVbJ1Y3ttbOXO5N7+wP+vESO1pPZrlXE5iw79wDW/nCmpE5piB99npn7mEfpAksLvS8ZDr3wFQUGgpNhLBoUC9ZswDuEff3YQdvw+3ZFDIpkjzZ7z4ex+N4fLbHMbP5BMfDmA48riFSJqXWnYL3ENxNAZtzBpl+ucVs5v4kU/uTLFkrSuReEk7qJh8XHiw3SBjNYT91SRKxSEtZa5FdbJ11VBWluIVm0zGwm+2tyx/CuS+8CSfjc6CZ8aSuuQ3AgZXck5yVp/S9z+6QBU1trHKARlJNtiJBkRUwm4whm01gd+MmXH3vHbj87luwvX4VZpMJ6DiWBVORsyy2lKw4WV1y76XWQTKK/lwA38+EnWuD/zBBBsv9ZLPCMzXC3KAKPaIYgKIHvsxgDiINKwMNK31mZoIzLdbREaFS0pH2Qcfh1XXCGbBxtG23hxrgSc3v7v65/NgFUqYDabvTWa+usQVdDZM6D6r87127Hx0ha1ea7mErcmd0aJisu63bY0i1yVA377PZEo+sGlOfEO8iBKWaU/02RYg5N1k5FFgh0IyAYhsIVYXUzE8D5jrb0/ZTzhv51Mr92qG1/RXN7U+NTef43tqyFjv7Bl2JLs4B8Aa7d199iAS3xcpdZ93WjOguenmusODfU1gLm3szuLM//XTxGp8wpERELjg2P/j7KMho+YosK26nKGGaVbDQS4NTcgvmw57HRC4Wi59wnfYT7odWrAB1eeEojVAlEeI0szBlN9sg42WFAfdNC6taElWOzWlFNnPIdJMV31qHkTc8bTp/6zhZdtB5oyI6hUTnEXCtcvaHkcYtROnnLD5NbhYfUzYL2nUabmYEQ+PZzUMzZw8M9iUwljbSsnzbxOZyL42/1EtiZDMmBvMi12dFh+YeTCuxJjqKO9e0n1PuY2ejpmEvgfEklx7fqt4GlKSRZdBmeURDkS0/ycEZrX91a0sckaNjCe3xOB7H4yHGMdh8kuMpgE2uUE8qeydFeC8yeNU6+kKXuqr/lpcOtvdnuLM7gTMn2Ca/AqIYg/ujx4jschgbWFtagNWlgb1yQ6npdBa5qtIqGPfLAsVW4ja6cf0ibN+8CqsnToJRvi/OPfWSKMt4f74YTWjIskeZ2wPzEnqHZJXnPMM42t2GO1c/hlsXfwZb69dh784G7G7elmzVIpsImPT9uw3HJABRsSyvy9xRHeMR5GNdBo9QmEgGOLwA5vYmPrlYnxlhu4Lkf5sIII0UrA4iWO5r6Ic4E9UFRjVF0nQNQsMiYoj7gI6RTL1NtROrNz6pTTxCCLtq0QVR4xXL/zTUcr9z4BI6QGDO25awzdo7wFrNIdqGuKMag9bACOv7eOBXQ19m/fsuRRre0FBKBKhVA39ZA+GBuOf2nHNUWwR5OpdCzIrv6hOJrVc195XChJRSSA7FkbS7DsbO/nTnpZEZto63NRBFhHY/YR7ENcWJps/UNXAEw5w151bDlgZJKIVj2cmE7B4VpPkIHcQDm3/wEsPOth0UBKCXG/L/MUuf5SVkVXUXa/2oo47MeVhmry54ODENsrA7nsHuKIPVYQ96iQGacyIOx57aXEgXCg8K0GoNE50qjEw0WEg1jgsLe5MKJrmDrCLPbCJCbgmqcP7jXR2aMvh8izwpTbq5LAFdA+39JrMgfg2IfrtytJTbyTeWh/EFkdUSfAQANz6NvHYpUfC/Xd2Hj0DDf/PmKpRKPxCTzSqfIrOZ25ldinrRB0brV3qxWWDw6IoSssKJyyx/FDOWHFOTmM45I0Udr/gwQdXBrxNQb1Qtle4VRbVa5fu9YrrzxJcTUmCx1VONXTkex+N4fDbHMdh8gkOlw6fyvQRUEtIlQv0hWfctAFio5X/1g5SrrVleid3+LC+gyAtIkxSU8adIkPtUCnDW68VuadhPe2mMJfd3StZaYCtlkaklBqXKc9i+fQvG4xGYpNdBL8cPq/uNh5HEeqBDj62YwYdJWC0Oj59lsLu5ARvXLsKtixfg+sX3YOPqRzDb35HqPAfI87FPBgPo9wbgbAHZZCqLpaAhvEvu1f1n06/YEV56s1oPOARwWgIj2ZgYFl8IiVJi+LOQRjBMEBLd0K/gQu9aOz9doFkHqTdsZ63R62zBfDQH4py2ssPCHshy9MbMXc1nE88Ch9SaGAB1335wUVsD9sMHBTDsL+SD7WU1PqXO1jRSYKValleiFSzM8kriNfgnidFQOOcms9xqhdoYrSvnwSX/jp02mW0Zc/QBIQxSY3pxrJNEI/eaMbAQnq2OjKH5769BM0LtYN1BPnILabpE75qPLjva6nm9YdBh8wdQx7H473NN5uiBc6IGqXOfQZ1t726nZ9ddTcshNZdf9zUNZCVsigoS8QMPJs+sGer7gZ1HlY8C+EzN27szuDOawbmTS9BP507mFk4jBBI9nJsB0CMhR2UpzqqM+ByJFQwjDePCwX7GMS6uiUWx1qn7bLM68Gv0/st+d0OqkfVG6LgIQF/XiJF19OKomJUa8UOF5s8I4IcAsPcwLCdv10pq4IPbu/BPdQlf/+brUmh7oPc651KbjxAGO7FWlsGlbKj15x5fFyyf5cIuKzU4a1U7kPxUEcG7UETT2Ew6A00+d7lGwelitrLPOcIXdKRStlp4Uk9S3v6PJyOoyB33ax6P43E8Hnocg80nOBQ9vel2jjNQKFNgy5Zl6i6euXenhJ3RFHb3xrCyNIT+YACxabfZr89dHusIlxcGemVxgJyVWJbWS7icj61I+wNYXHsOdJQI6zXauiPOpFEcP+6i/s/taNgnXoPcp5JcMzWPOjBIZnmBlE8y2LtzGzZvXIHbly/A3sa6sJl3blyGva1NoCq4yrI8TPIpE8lXLaYlFEXe9sDdY4hzrPHRAAJqLUEZ+iAL5wPQWabHLCcv0mNEAZuRUZAaJU6zHMcg4KZmH52DblNhx6unYTGlKFKjXxKvzjwspGOegRrutFLPg0SU395uhxliJ34k6DObrrkQ5TJ/sOrtC8xoyOGTvlFHc+Cs2ysKB4DbPM6dl4XWbGUbCdNKYOVvCkXiuL0/hetbI9jcnYmEko8JG8WwWc3Gzgydc4rvARKbShZSraGXxAImd0aZuACzkRPLBVeXenB2bQiLaQRRpCFG7wZsA7h0vum7KS40x6UhebFRUMxLURvAz9rL0qfQiFRZw5y5UZux2jKcUIPGKvg/mfrymu8f7855PdcdoEctqKw3yvcweyK7NsS56/4Wfqi4v90YeBBNBXbOrceCJB9wcOwKS9fZpIj7DdlQK0TQNuDHqwYUT40NlRBpmSaflFJz0hBzQchwZqQT+ay46jonfZ37UxQQKs7Tjrpa5nvurshqiZvtGXQKYtc1g40+TmWVCJcB6KQi+jVXVF+BJPlXKlJ/BEQfPqysVmK+IgMr/RTGD/omIlImwpLQRFolRgclBPqecWYEub9WnpcMQKmpdDXnqjw/kwQGaSRGTaVVcs8rlYUZm0eRfS6Jk68bE//pxtbOB4vLi4/j0N93iNpgWl+bx2DzeByP4/Fw4xhsPsGh8gfP7jqC0QcNA9SI9aK2O/hnzG5s7IzhzNoiPF+UElMBEDUSTQKIiGDI5kBLC3017MVwW6ztXSs95GwxXpw6C8VsCmUWjC8OXYn94gw66JoSxr2mxKkn2BMjvUIAzEpYC7C/sQtX338brl34KWytX4XpaA9Wz56D/tIqmI1bAThwTy8b/hhZIM5C3I2tiiZ/9bDRsmzYWPzb0LRUO1Xy57GCLFIgZj+DREOaKGEvWWImzGbIx1TBdKZm8FTdu1evX7G7eG+Zq458VpHXCzb9gtBAtXZb6w3DoJKlORYM58DfPGfWfiaEnr8mtaRGh3Out/c4QJ3PqWWmzed2Mkh1HZkgzpbUkQKTMJjsOM0RMrz4Z2BxbXMfrtzeg639mcQzcF9ZL4n4GOrxtFSWnBiBQeDplBQKPGDMSy+I5PB6zqjn2IyTy31YW0zh1GIfzqwMYGmQiMSwBit1PEb9eV2VAzbgsKOXpc4MIHEv3ga3jjO4QE8EeYUvtIgIVXvsA7nIDNBIAXAP4JAQI//6uu8SW/AInfeGc6CO7RBmFLv9v9zX6mQfmImvz4maOe+6C2NoMrgfS6kC2HXzFOld54M3yXKfSj57r8Fs9a2dCVxe35VooNWlVKTxEoMViiKuBcN8A1AN3mMQ6Le9AO4hdsS1Bs3FC8OZuQlyhqYtLSTDtDLjGRsJWTcrHLG01hHpVvVy0JgJmqBQAswDgktq0TQQseb3ZWY/SX7OTZ90CgDPoXVnFMEfgDI/QWDceK+83PmRRAZ+dv0OXP7++/Bf/Lu/BpOsuO97+MFaVk7t7+XcnF5FWgvLq+Q+40DpWHoyy8LCdJbDsBdBLGZM3iVbJNfaZwIPepFcNzN+fpLxhLJCsBUuOWe/YKviFSL44PEc+eNxPI5HZ/CTgKs4vVCkYjm+DTdkXtBWx5P18OMYbD7BYZ7ybBujcmOiaWlphWWzc/1ILKWyFcyKEmZFJSYWni3iR7oOzWKyWEriSMHa0hBXlpZA3dyCyhXcj+LCo18V0wnsFQWUeQ6nX/o8mCSRyn67wH52xpOIQWFQpm0FjvtWcR5EPu3pEMOdQLeM9qawu7EOV9/7CXz01vfh5uUPRC7LjCVHlvARlAJEAElOAt2DqYwLvX6BvTr4HRhkd11TnrL0kl0bXCsZQNYIgVmAhVTBat/AoKchNdq7WYaYkprsrR0voQMQa7klA2JFXWDTIpB6yxCVId/Hp6jp+WvBG8yxifONed1TxtWSQ3+ZYM3G1ZjQhfOgRoZNK1rY2rZntP2KFhjPXzf1dnZNlbjIo8OuMYPE0RU6RHvszQoxp9keZZypK/mOk1kpxaXxrITdcSby+forxrPCbxOS3+Tqk4tkReXvJaNpKX1/670Ebgx7sPNcBp9/fgWeW+r7hbJP92eIVKtPHaFzCn18PZ9BGls5dG3Cg0GfDAoZkPSJSHNijatbMMN8KuWTZxq9pZDdrrKEhSPHGDBSSuk6qqY7zVg3nXeAK5tlOd/wK4yTEYbYn3/S8ykZiZpfYz3O5SqEw7rLFjEESKrm0rjv9e4LEZ9cfFDqoHHV4xksfb91ZyzlRWbWBoMYBtrnKc+df47PDF47BPeqeq+YqFRQIiCHVKLjvKtwOkcKS0CsjHYxM7xLicknhcvHeYWT3CZZ6dS0sFiU1F7F81pqJesVEiVCxemfcsfxZ2iCAP1WiS0n0AAIvqQqGhDa1Zkp/nmm6M/R2UI1OZj3HmLqVFWADmFw9jyMNrbva/DHjK8usnwwzcaZVlkcm4UoUnKCckRwZC3oEmF7MoPBfgILgxQiE8m9jMIJwhcBF296USTXL19b7GSsNV8/ots2ztrnkOjMYHGB3d+rJ/EYMU+y+Hk8jsfRDlZhrIY/yyC154fcMgA8r5V6GYDOgnMxKBw5NDPrbKJZ5oP6EoF6l5dL4JU16IuZOEaJPjuk/+N4yDgGm09wxItPE1rQHePUJW31HdLl2aI4QL6ESj4vrrivxDrbLoB9Ww6vp9lE0JhI6YWFPqyuDCFNYhhNZvxCqxhMuQptWWA+m0keY1VmUvRW2FlMP+WBdX/pHHtwNLcI6buxJZgqA2tSKE1yT5bzSQ4vpQxgr3Cwdes6XH3/Hbj6wduwfuUj2Lu9DtlsLGwlFxxufPiumDxNx/te/lYDsdAs2PbjdR07w48a0qldbTsxyvCW//x6ZlKYwYxDr2bCPV8Js5pK+gQ1qqZvznUaFJtVfUB11GyDZ5ekXyqAg2a7WiDHD4c8AMy0JkbrRW6dj0jBIKj+zFr62l17BjasCm9nlyTVgoaOTU+3t6+dstYHqPOZdODP7r8k0kP6uzwQ4m0tA1vJgI8LRgzcmMnc3M/g2tYItvdmMCsrWUT7YpKfQNsB7BCAs1SNRFfd6Oc/+Twn33s2nlXCnO6OvZT69MoQnlvi7VTEjtUam/pO7cDrlD84XsIc9kuUFM62n+0E5BhHtFwweixJVSK9ZmU/2Fij6yWotMKoCkEwjBUq57ByFHNbMQqzya8JsmWGihpI+5sTlhUhny9W5iSw5YGdZISaRCzhRl/0kE2W/mCqKtsQmc6jU/IiD28fGpGSE0N35Mz3m8v7//5+bPjDD1EoFJVEtKzvTuD5kwvQZxebpkoYigB+XlTL33dUKw4iEJUtahJZs79Wlc/RZBdZzX5hrFqIYwPDvsaycGqSW9yejt9eFwAAIABJREFUVrA3ZTMdX4BqjLjaBRyXCVJvYks+09X/3bfjEuyEbvMFDhsBBTEQvkbkBrmtYkRXxdp8D4DKB7nfp1EE12/egf/xf/lX8Hv/4G9JFvAnHhduJ9A4WhpEN8el2l8cJicXBwns7E8gkxiTEiqWE1sLk2kmkto5Y7DgsM1sMjOrLF83Wsk9TK5XCNJbhSkoWF7SKiHZl6MdvIWXt3fl+n4ajvrH43g86gj1ywEofBkJXgWiF1HRsiIsK4Q9lCWGOwNAr/FrgGCJC5WOgCOVOAzcIGCltLsOQB8T0h6pasfmsIHaUgmUW4s5Wcitc9tawQYi7Mp96bhxTMYx2HySI3pKJx0/rCvcQUeXCGCD1+Bao1Hoe5zqBx4/2/nq4jBpNgjiGBP+fV1Ir1fUtRshV2CT2PBCF9n0QSsirU2ozXthJMtoyyJvt+MT+nKe5GCmEXkxiwqsPrrLQBg27ukCzxAcphB7oiMoF8Wq3wCUBcHGtctw4Yd/AR/84M/g1qULUGRTz+YYA1HaB1dZGG9vClsmQFmhd+286yYa/EOCNLHtmOsApwCoVChuMNgdJmz0o0UymxiUKr6YjJgA+MJizIXzbj6wvo0PcUGG2fbw1a+gYLBaM2aNkwv/zYUFFNVRHLU9TdNO1dlVUQ2GBXQDdNtfYkiun1O5+i9X86ZDfsMbarMLZA+8GTo70gwVZMgMDpidnGUV7E0KkcPuTKYwnhbSB8t92JOsgv1pISzJw1x7NdByD/Emfg8XESjQu4Z71XQH5dcS2lZDbJgrd5aQYzgoNJliKHwxSyv9vIVUurAiMnnl5DsEbAZxUy9SNEgVsR/urHD8O+TfW46hIWJCMpyyXmqsjAebfBMUs2QS9okKiZoAnNtn9PMda77neYZTzJ2kAucRj0JU3E4gRkp1M6OATe5xFbctkYvX7rT0CAuQuthxVIPl1Mx6+yxQb2xTOWz2q7Ml9a2e/GNCfh+FVElsRAW+vhcTETWCcxIcpaNIIRmluAeb/+N7wPa4gtGskp5iG97B/0UGSSujmDGtHCkvuw6VJmLQRXvBojgR7am/urjwcx4c/T2oXOLALWtj/gIAtu43fVzEGU8zuHjxGizqEm7vjT/x3s0C8cqVoyROL68t642yKj63PBzBejSCLLcCGivrichKij1NCQ5c7WxNvqiWJBH0Ui+lrVy4bl1z+SRa69VBHC8S0eQoa7goETca3iu2Gh3G8Tgen6HBUtjFosRTZNRroOAbaN2XgeAsEfZFwa9cwZJ/QlgGojUETMNTmx9IJRFMAXEWil2vA9FvgKIZAnxEDr8HZE+U4M5TJa/dIoWbkdLrVLpNsrCBNroKSFcAOaLsFxd4HoPNJzj6xdOToqAT/5WdQrsNKGCqEBdl4aO8zXrtCsrGBeNpDtt7Y1haXoQkHULa4+o2al/NDstw707J5WURJZFzyjlLSkWg4wRZ1sdr8sloV/r5+MF6hJjuoYZEcrC1Hz/EgxG/NfGRfR/LZ8tIzecoPPYxx5Xd88MZZCrjNyWbFrB++SJc+OF3BWze+Og9yNg5mJs3xUXWQTpYAJ0YmJQFFDYTAGrCXHG+KpG76/sOtps5aIFbXRhnZinWSnL6TgwiAZtshCFMXS0P7RAqVPe7BYxSL9PmvZMCMOVaoqrjL+ozNjBmNTsQNiMsTAPaaxgaF5bEilld1/le6DCc0AHP4U8dgC8GaW6Q0raLynaCpPbgfIoLyVXUZFN2Z7RuYcOmo9Xn9SHC3iSDi7d24cr6ngDNaVF5kMDRQ3klYMySOxSYdAH5XB9r599EDw+J6mkZ9GI8udQTo5mCt6N0yPEP3pG0KQxIjkpFPu/RWb6DCAnIs8b94eSBpcPCM7Hci4fsEmQdCiCU+FVAzK3Ts0r8U6monOLe41CcaG2GVcssq0IkT04FSbavoRGVlUh0Sc4fMccJx7yW2qpKyga+UOcPVaSFURVWtOLADgod7ryCASssPV/+sxJazfozPDDc5amWEB+4bWF7rvALcp5hbq+QMlSoqdTAFFuMjxiqPOSzYLVIZkJ+ChsKGW08exwp2ImVFElmJUFe1VE6SLHRQnfnpY0qLp4AsWy3DJEpiwAuB+nrFCZV19UnRDxDRH/HWsu9nK8C4J8SwAdENPqkI8HOy0VRwR/8vz+A3//N1ziL+p6FAo+qYeai6FKUpB8u5Auv9dN0LTIavQGYOPGCdSjXQcsVdwJbQ7GJC7lpbBrVv/R1oo894kwyQLWs4mQAdHQ+EHyUuN3mo60d/+8j+6bjcTwez/DFLjhBLKMH7CkGlRZ/uQJ6BQ2dVAivoIKXAHAgXTa+uEyEoXG/SS2TZ7asD3wLvmT/MggNkRLhLqhhAwDPE+FXUSpFLMnFDWdhm5xj8+Z9gPjHoOj7UFWXAdUOAv/sFw91HoPNJzhM8fT6ivlRVWiTWzR7hDlXaRYPMhbeOMjB3jiDje0xrK5MgPsy017ciN58/VaLs2KaRGiCUYu13H1lsbKeOeCXlmUOe3duwc7t65CNX4doaflp7f78QKF2BWxGNvcPfGWOzpSHl576aBeZKlTJD9uHGuQxyNSRNwMa75Zw6Z0fwbvf/zdw+b23YPsmH6OxuMtyPINEFJSFxNeQcT5eQmv5nVbGO6k66yNODoxa5Upz/Y4gQJJ7fygwPAwwVwYaVgdG+jVraW8tKZ033GndZJv9muMK54cslFUwcZk7zz3i8EZCAkWi5jeqyV2s8+M7ABcaRlMp1fSddkcrLj74JFFzPyHJCAy7wL6uDtu4mZoGDrmQc/vLK3TlmcLdSQHvXrkDP7q4Abe3J1B4VvCQmTiMgT7awcepn8QuiiIc5w7tXikMZV46zCsnymvXYUytSPdlx20gMhlPa166W0uqtD4qwmhtleAa1Dz3jfcwo43AZFJtM9SJJ4UWCEAHQ1dM2FWODEn7pfxWOz/1TivFwJZBq6qn0cuy/UmtsV73y/ZjfW66oMXWXjVCnu2UywoKhxDHkeycs0/VLO4TB8cNTbJS3Gn5+laRogOnUbeV0oJP06kvDxnOtUZNB0enLVcKWvw+BlMMahlo6qERSf0017CfORhxdAqz1QTKVo67blufJpReq6LThzUJBh4VtBdxHdOyRkC/WVn7CgJ8wSj4v+NY/yUi3rnXfPB2sWrgO3/5EZxMpvCtX3pJVBeHXWtyArGBbBTduOXSd9M0+Xra663GsRGTLRcYSm6BZlmsGOupToJP57OYFWcZLTPjBVoPNkWOLUUs7ttMc1soYX6PCAbyPZrJ7Wt7++KmezyOx7M0uooprs8AwUlL9EsO3NcQ8TlwyFlepxDg1xDgpHLEzEJKAMOOwgia6jV4x/LgsN19aLNSY1HUEu2gYCL0VQBcRokTRO7hXBTQ6SyTO4uI6hQA/BIQfg2dvUwKPrQK3weADwnoDgBVvyhlnGOw+QTHnn1gE/UjGLyk7U8RhmNWn4nhgPOV1trd0pu1WBhlOexOMphmGVRlKT1rbBIDIaaCFNg4juzK4lBzBMotsyPSK5TFt2e7BLhaC6PdLVi/9AG88Nqb0OsP2Z4Qnt46y/mIOH5wM6DiHa6efv/kYxk1A0advsXAUsg6gQF2ZSGb5JBNp3Dz4kfwznf+AC689Rewv3PHB/YHAOdc3UupvKQ2yP5UcFaxtuwAIX9P7vZD1v/TyFADWoyYyYx9VAmDzoWehsW+lriNWp49Z3pygLX0f1DzgSqwfdT0k0EHmtYv7/SaNbb9HSDaODN34+t9i3FrlNrwD1DHBh3aW0mtVFwFx9ru9mODdAKa7NClrXy2/bCGaQw/4unnReg0r+DDGzsCNK9u7N/z7PHH/27n6UNf+xh7sRhM8e1gcz8Hp6fQT2M+dxg4Umkt3050680qeuvQgyqTa7wfT22uRCwlpMgoiiJdcj2grKwuKyclHBTTGA9ew1HE+rzr+EX5+QvYEHxGY2gIlVsC+lxQT26HcBZSvuI9v2/1h/KXy/tCgYLa6BMPnBTfZgQSO3KKN5edSNOEI0W09O09qyMvLGzsTNz6zhhOLvVheZhY7jYQI2G/qAtT50nJcHKrOe+toDAI2NvbGDeItb5Q2tc3EnjycUd8T+izrLbnYDl3MM0t7M4szAprbKgqoHd1ioNmny1b+YLuE4npB8vjc+kT5cVfc4FhAkSvEdCQM2SH/Sgqgb4HRBv3qsowUNRGwf/+p2/D3/7W10ChEyb+4OA3R3wKRenepBpeyfL96dLyAJcWBrA/zWA6s03PeVb4TGvebxMyWylcr75XXMk9pL4OsJYw+5NYWWuHeT4d5tAnzjZ+7CMw26M8h+gYaB6PZ3BEwO1JjvP0XrCAX3EIX60cvsl9l6gUu5XnBMSpaWdrvjIIrbgsGKQZcn254A9XyzW6D0MXHA/NXRIhpCEQvB4q2qG8LfKohBS/Xpw1lxHpBAC8jA72HLnbhcOPUONbBsyPENlRWkBnzs+mUK/8uRzHYPMJjhye3gKDyLkY7Xas7E1FajeO9Tk2D7EHGBG++FiyxoHtLKfNi4L5Bom4CBdb5ZxbVwp3lxb6J0+tLq1dubVluHdMHDmlC86FIHWEIpvB7asfw/bGTThx7jzo1MgK7Sh7jpDoUHdZkcI5Kw6lfB+wSothj1PRPV5/+Oc8i4NCLxhI7EjoqwzMAoPMyf4Y7ty8ArevXIGtW1clO/PahZ/A/uZt76zJCwrdmo8wslFcVai4GOiCI7GHZNzLOyfv7Eg/PantpaOezWwzCHtGw3LCjDj6HD7uf2tiNfxomNCg0YO6p5Jqs1VswS123jBX56wpL2h0k00/ZCuG7TCmDaMJteSvFrl1K6BCTxE00S4NkJ47lwNKxYNL6zmGsoVCdX9ovQ1UMzFt0bZ+BCpQ0nd5bWMf3r20CTe27l+8upexT3Ocj2DwVcPM2M2tCUwrgNXlAQ2SxDEACxJaCh46FIB7jXVZb2hiY4gzCitr5RE+HPaoF8eO570oKyyds4pX/KgiOb/5eHiJfwNpDo7QHculpRy9MW7sHMTEgFXeKic5C3HZ5ZY3TEPjg0tzH4l1VYbmW3YbkNv4CxFSOHkxFFiYmX72TVYItkaZu3x7D9YW+pqzbPs9U/EtwU91s8N8nYgy4F4FO+qc5E05qGMMXfevUjA88jPuX8cgbBhr6EcKFnsaBkkFOxOkUW6pqHzIjANigYrufL0W6Y2PK6g8S8GBInWjdnNFn3YOfsNojMuqWrVOfYcILt3rouBjxu6xP72yAb/+y1+Sz7GHFAz8nuj8DJmNm5menFxbcCdWF/Xmzj5MZ0XjmM05thnnZpYOTKKaGwUKza7kv7pQxIUJJb3GqhYlRwh41lbuFafcT3Nb5p/+WB8+UAzHSri5szOXtX08jsezMEiqXtK10i+Avl0g/SNA+iJaTP1tiXrhPlBfwVLR5OcGIvvGWcNFT385Ur0s0Acq1/eSTtUPhNQ/RuXGZZu3IMv56RUvw23ezuZEXAg764C+qAl+NQJ9wSp8C8i+jQ4uRYjrzrCnipv8PJ5kx3eRJzh6pvfUvluKy9psK3TvRpH6OIrM61VF0fyrsHnA7bPZyO5UQOca5+9Ro6QqCeiGc3CD+1lWVxZWTq4sQp4VkJVF2/ATakZVUcL+nQ3Y396AsiwgYbXTESr7ZAVIQcx26KJOeY2XYsmphjJKxUXw4GvRN+yBxc9GVbe27sBafhls87O9PdjduAU3P/4ArvzsR/Ln7uY6zMb7YItCQKbiQgJSGwDZ6ec7GGPS+UL/R9Pf5//NrIQslNBLCdldlvGkESMgBUs9LUYgWvtesEPLeO3inQFAgaLZJWYk9N33/u7JdPfxpgM/bSAkdX5b/72R/OLc30UfONfPWLvR+j6sdu3d6e2c09PSXJ/XQf+g+nMP4r824qT+t48Xubk1hts70yA9vfdoHoQPMO7nNvtJQx1gT60YE1WAKoc4MzDIE0wjo7RRpJV23NxYp6P6XjbPovCUsqHQoJdwKj5kecEOnsTnUxQZvsdEeVliJShUihBMQpaIqiJyETuV1mcDztcKAhBULGawgT0XUIIwx5RzyyWD4dS3Ks5VMprP6hD3vpOwTVsRoMltqFJucyy7Qm/qKwfRM+mN6dWnnvGjH5V1+tbWBK4v7ePqQqp7sY5R+wTRpvLfmZXmh7UA4aARXOfkpmBny9TxXHUgHLjG8bmGqEiQRHwviZjxLONZNZ3MrJqWNMwqYHO6xmkLWtVBDJ0CRAfptlIHxNOFdd9GpJfysloEUv8MAW8dNrm1TPq//Z//OfzXqg9ff/OL0kri6OAdjO9atrLZaEtF+tbycDhdWuj3jda6VjXwScu9kMxueoY0uC8HFI6hVzQW8z1fTHG8K4rqbYlQ67OO8E20s+8TuZshuuGxDNGrBIM3c8xqHo9nazBmWSWABQs0sgTLhPSiI/dFJFzz7q/cE0kFEPagSRoLJgHI3WSqQguJGMf5gmDzXJd4avEg8a4PKC7pbXNLmIlaCqUaSwr/97qOHSPgqn8IsVGtz+gMkNbw7cxZesGhfQGU+iqQuQDWvR1H8FdaqR9aYCNPMSR6duUvn2Icg80nNP69v/nlp17RRoe5Bn1JRfCBc+5b1toT88Ve/1dmECYspR1PxY2vrCqIIBjooAjLFvkC0lotLg576tTqEMaTCYym6HtRONLCGGaCXDkdw3S0q0Zbm5DPMhgs36NW9LhGI388CDO8Nb3XSyhf5w6GMXAYq9miqcN//yyynrqVQ+azHLZuXYPrH/wUrn3wE7hx8X3YunkFZqO9II0mMMb4eIvakZgCxeYtn3w/FQIcIHYOHSLx5MiSIIHj/kyWUzL45IXigBeLEUr4f71+IaqNJA+6XEIHcWJt7+m8vFW29S4i5SBTR10mJTwbpO9pjj0NfXZNtEkXtHc/NzxRnAvMZ3drWwcavwkdTvYTR019HnAEwsYgCVvDVr89eWmll47/VCqA9aeMWHhb2a2yIttxrQ1zWheonBi81FhENXQgKhSzI6GgnJfrhxk0SgnwzKGEyWSKeVaoqnKY21Jia7XsvPeKQdQcolmRc6GPT8Ty6OpJFCmuhijSkSWHHD9BbHUljrRKIiiEfSen56TT9SlEPg6l3t96saEQM38+URL6fMDLdKWxztTOL/4QMw+nMTKaONZimnVcpp7NgZOsgM39mR1nJZ3itBJqWVpsMkFbbH+w5OOFAPX9pbnA+JXWXymkqZ7o2oip8wFEjclTw/4PEmW1isY9o8w4d+n+zOoJgcor1yjmsW6dBTjM9a0DO2Vj1pTCs2VpuabBcQX/EoAjVA6/gp9bXYL/7n/6p/Df/5f/Obz+8ouSJT334UybF4Xb3dyZuFP9rbiYFHFkEiYshYoN0n9mNLkYaF0NIP2JLwEn5JUfi/0Ehv0U9qa5N9AKcxEw+qIFelE7e2aWT2/TESxM2en5qPpBj8fxePihBgj4eQL3rYrgFedwp7S0pTjCBGFCAGsAWIRHiG7v3jhTvs8hJaKIHwxRHKE2jsqiwqqqpXZISiM/DHxkMDYt+By5JVXRYJamfOeGNNvU95qm+BiKXHz3L+ulA3EQIC+nsG1zkj/InQDAFafxVGapbxxgP456ubU3EWDdN3r9fIxjsPmERqoHz8R2KMQdRfjhpCo3LdEJCguorsTQBnfIaV4I2GQpbZ9VCQJMuOcFXmVJYRIb009j5CpsEkciyc2wpJm1kPYXIO73LVUV5bNpvLe1CbPJ2Dec1A1T9WL0sYI2DFL5w0cNMtuXH/7dLqxuDmMgatZTWJAHMBWqDWqou5J6zAMbKSZBNp7C5tWL8OGPvwcX3vo+3L7yMQN+MfTRxkCsjfTT1jgSW61mmBvveliHB9asmzowV/U6kAFkFKH0E6a6zsb0brM6gE2OjGiC7Wscj9S4xLZHry4beuEqOkhkAkNPWBungo2M1y9XO8EMQY7WGsvNsYpzLrWtE+s8wmzWxnQAyAYaq2ZfoGY9msiUmvTpfC60+BMb/W/9mRRiV+uezrp3qwbDbbnUOkeTrHCzolIHpZ1PazQZpkLBtPPMuK+SwpOtr3NxaQVxawWKIoNJFMscsNunMLjKOyDP8jwY61gxHWMGyFIuwNR/nQcwoc/PGOVihco5pXhilNaGbYiMK0tdH0/uk4y4ClKWcREcelkazv14TC4559CSS8SZSKohcsrJEeY+dvSXC5/j5M2EUHZRLLmJoo7pE/k8SV/1ZsmWF4YidyKoNIl1wva10gT56fCBCue8N+I6OsDKTk3jWWGzoipQYawNxlUB3e7l5gy/+zZan7XYFHtaTl/cngM0DPa8Hd64vde0ETrQ1P3Q9GMcJhFiP3XUj5Xbz5B2Z05Pc+tJcwVl+KioYTb9os+G/+qfq/AbbRC+hrH7h2xQ5Qi+gwR35rTv9dYRwNLiImxcvgiL+VgaWbtFgxp5YxJXce/UpNqbVAqVUWHPXShuFdY2RQ7o3L8pyMJZFcJgc3mYwp29sTyP25oUobMVBzYvKqVXsZR13GNr3OTvKAlgv3Sgj/4uEwXnYCudRvcQuxyPX/BBuABu8OtK298Fsn+jsMiZmIVSuOMhIA3C3SYCn0ZV9/0wNCzDM5wjtmICiPuJJq01lnkl66JaweWsky5pE6lSazWx1hlb2qHzmURQS3HCJV9yizVB64k516nj2U4tvZzhGXjYQfTvp7Oo4Cvsrj4u4Bzq6Md9bf8yBna7faaLkg88jsHmExr3C4R+goOXTRNQKuNenALanM0adEhJh5z0mIwmM8iLEnz8ndc1gs/kll6ONI5FayCGB0UlC0xeYUX9Hi2tnbS8AB3tbtJoewun+9tgqxfBxJGXuzovT3ycYHNuCX5XQ/dDfM8Bc5ZDXxIWXIfxcvPbhKHZyYl0916AEzthanQv+eo9NlXKapWD/a1NuP7Re3DpJz+Eyz/5Ady+clFAPnctacmeUXPgEmpDk9qB1llwVTXH6GqFc8CLOt/LfVWSj5do34fJxh6JhJvLv3U3m2SeEOxITGFOetoAt1ChnDMNapSqXTaxO9nt58+5kXZBJbSAspb8ioyzKwtsD0ojbQM8ZPVZywYbZR51P77eSNXdv0aKGEqczdd2vhupZT7J56MwcLfWUTXJC52XlXkWAKew39VhYRCERVXSLC8oLwp0LpW9tp7lwr6OYdiPYZqVUFSVj0YKIK+sKmKZITtck6cxwaACltbq0Jss7r0ov2Y76QkfGqVUkcZRZbSmoqyWsrzQtZQ4iiNK4ojlvCJ/ZKkvm74wKztIWudY/ngtC35/XYiLKHm3XD6Vtfa65qK0qqyqHme18Pss2Zm4izrssWshn35aqVIZVRKg5ShiRWQipXpK6eiQM+mBBgbDGl9rcXCXivMxDOwUTLiwMS74XNOKi4rsVFvVdtPQtkEepkzoxgN1X8GAMVCkHYjZnDYH+5vD1/iXKgEnbpmTtYaxsj3D+aqakriyuxM041nFUSkuCE80NuJTYQhYXse6G3aO5OrvoEOHxlC6Vwulvk0a8gjdXxHA+mGz2Ysj+B/+8M/gP/67vw5feOkMZMX8s12qD6aannPV7QxwEsfRyTSOpT/d52tCIETamxXWQLwjdGAGfNCLxViNGgMqrF2RE6VpkRSlVeHUo8jgDw4MyE+pI7m9qKACIF8MoPNEcA5BcWTNDfBznh3FFx+Pz+hggiM3v2Kr4j8pHf4tBcjLizuiXEB4w/mChUV/2nLcUcM2hDtML1SElZOLz7GyQJIUktRArx9BFOvaLNDEieFnk5j17O7OdmezYptz5MvSsiomcsSxXI7LbplSuIIIQ62URi2FxbhTVWtNhdrOnDkBSFPIRpb94/NI0FNgX8HKLsZxtF9Vlq+FKfwcMJzHYPMJjN/+1pefpc2JNKhhL45NFGlezumiqAvBLdvI1+SsKGE8ySDLcmHClJhbNBeI1UrbYT/BQT/VjhxOs5wU1+wRoMxmaKI4Pv251wA/JiyLGUz3tsHOMohM1Gn66eCOxzywUTZgVxj7yEMgIdbPywcc6P0tD1H3NoMXPqYqvP7CJOKae7/HvSy6NUCVE+zdWYePfvR9eO+v/hTWL30Ik/1dqMpCyvY1zUfO3r3NsopG7ybcOOWEzDc26einYuaT5aXkOJbBySbRGhZTI66yw1RBbLwJiu/b7AhZ76Uq7TjV4oEXUcOfhH93b9EtUdIyH50XN5ELVB+h9pfzLGf4jYI2KL8GlhTYOmg7LvHA9nX/dchnM5FXhL2KGXu0zSNz2u3mcdPsh/RodXMvJV7DRlq7tcWePX9isRxNC5MVo4QzJe9zihzpILr3VeBdNwvMyzL0nfmMTR32i5mdSkxWENiDhLMFDaM55NxK5fNENUuvldXacMaiTiIjxticyRhpw0dttyztdY7kTJIoHvRSExm1lmXFSikyRf+9kYkwTfyCvyxKmLHxGfgoGYndQcUxn2Sd1VXlVOE1hDaOjE0irY38PwoILaoKp7Mcs6JULP3N8pJGsxkXAAhRyhfitcXhm2xgkxUVBoNuBr1cNfcL+U8DFGtHcIAjAZoQWEWO95DjV1Zma39mdia5gB9hALwjuc+4rSXw1GjI6u1sGhow1I2oVQygD4s5MLoMYbdaE/6nbaWWOQieS0iDBF2kI0w1wjYibk9tnJXWZ7C2QNg1kSjBg6iJRPIFpTuA+JPSketR+dsLBk5UCv41AFw9bI74uCdJJMeTDfW6N+rQaz3N8ulFAPw4jvWJXhL1kiQy3NCrKivxUdTmy87J6Os/41jDcNAT1RDjL3INhwISx0C4ZozpJcOev6s/hgccT1hWVcAS6iOAmgkCsDPnSfTOm9xy8yvOqTe0jjAy7hYC/MiR+imAgIljlvMXfCAXgdC+mefuHxDAbyPCMhDuAsA2q14JaIBOwGZt1tEAzfBs4qVFTB13d1aV9HuRguNHAAAgAElEQVQxrJ0YwMpKDxYWUrmWlVbcK+0WhokaDKNkMinj9fW9d6ez4qfO0uz2+l7y8eUtNRoXLolUpRQyQHwFkM4651JnqceSWIVqoI0y3ZYLwrkl712DLS7AK2QWAXHI253lpVNanUbCdxzQZQCYfZbPhqcONv/Jb/0W/OM//uOnvRlHOiS1/BnaHNRUKAMbUaxPE9FqQZVuSZj6Rb7/cjTNYDbLZdEWdZCmI6gQqeQH6NJC3/TSBJMop14aEy/myqLAdDBUK6fOAPdr2qoU1m26twdJf+h7W4444NwvdY5gLd4wXA/+OHaI92RWu+woNsYYn4BK282QlxScibe3Czc+eh/e++s/gw9//F3Ix2PQcSrSRBScQ3ebdjSAyy98ZdEzB6hAFCm9NIZBaryZhThyiHYRFvsRPLcYw1IPpUcT68YjZlwOgvt77Uqnmn9wzPF2XWhWv6cGnAc/wLWvb0x2lGpkqnMzH2hTnJP3+rUy1ivjxlMOw6LadT6hIwMO7aXeYkBklRV5w4Go00GKHWDJL7fellWkNmFdjvUjql5gc0BfzoTWycWUUK3tZBVVe9PqxNbeZKFLVD/ONsAW7H7qD0UGkxy+z0WIxUFfjE96aQS9NBGX60HPAi57ti6JNMRRBGma4DBNPBBMDN9TCi46a6XSiEWyRrOEH4yRlbd1RLtAsMHmQ9oo7sF7riorUzM0zJoyg5mmERi5r1VO2FTyRri8KNDKN3pW1iEX3wpb8T2vNEoXaZIkvdiIcTJfAnlZiVKF2c7KVjCaZLizP+5zcS6OGBQbIuIeQjSltdHOaArTaeYqH8ipNnf2RNYrgYsPORp2+HEd5ENGo15AhGnh4NqdESxf3wEOjuP7AJ/RLDfg+RA74DqRrmkgxqALFzgXyMOW5mztNnCu5NIWntqKf60YoM79CObOTUGemg1d14aRsIDGlJqNmqeFbXJzwwXXI5HYQTp3a/GL0pIQZwiwFIP9+tTir8wQEwfwfxDA5sFZGvRT+Gf/33fh9/7eb8BXv/QqTGd3GcKWo2J2BZX6C2fd1ET6cyeWF14sSjscTXIpsky9GsjrrpUSxtgFRQkbt/WiCE6uLsLJE0uwNykg48zj2r6ZdYJaLURxvLY8yOOTy6dnRplHPjM45uTm7i68dekKJCZ6gHfcf4TjumyNfc1Z+8UI9GnncFyBGxHCN1DBmw4oBjbdhYodgv8yNvhHVWl+Sp7VOR6/gIMAWXnwdXDZ7xPQ3wXEFV7ukHiU02kBaIBRncwW1FhNHckb6LnwlFYQJZqdzeHkiQG89PIJeP315+D551dgOEyk+MfPkzSNKUm0KLKKyrnJOL9DBG8rgEuXLm1lf/3Dy9Pd3XH2/Pm1NNH65Pbu+NXJuHxh/fZIX7++PZhN81PO0Wmo3GnUuIgacwKccg4nEJ0ij7nuukh97QxSQFHF8IpjiArOOKJfd6X6w6Ee/F8K1fv0GWY4j5nNIx6/881fBv2MTLN/iFOmCG6yzXtV2ZeKslpp0rhDtAOHDPKCm42BOB9sbzJlV0hIkqQV/JHsFC8Ay0GaqEEvifeTiPPxGi8IdqId7W7DeHcLbFnB+uWP4PznvwSLp05DlBjpi3y2PTKezKiBJgPSIk68VOpeMtq6t4cZljyHbLwH65cuwsaNy3Dr0gW4dekDyMYT+T1WlbfRjyKfkel8kDh1aBGqAZfgmYNI1KNTjsApqkLkjrLA5Jt2L4KTSzGsDjXI+pOCqNgFmdg9xhydd7/RYTPv+76ufLYGSM3PApjsrGzrv7ZxI93P8gs6R9Q6o4cipTTmdRbVUH8Xtj8JH6dCJR+E1Tyon/FvxNo7htrrU34ljqVhAR58BbjNa1JyxB/pWb8/wNXFBWLDIHZtPZLr6DF8qPRtOoKVpQF86XPn4MTKohQvekkkILRyPmcQtZIFNwNJBpx9rjSzdNYojBmhcsenx2oyN9q7HvMULSFnmCGuCmtF1HcOVjw13UIVL73FxlWUPwsCme+Pd918Ha4R3w/JUo6Ee+7mpOTgGVrlM2kpr0rI2WiirBg055GAYIpYecuRkJMsg1mWq+k0p2vr2yj5w9OMbm7soPuki+Veh+WRj8onDz7vi2CawZOyu5/D1Y0JmCiBtWFPlAvsLm0MQSpS5DYDtmatqb1Vdan7OcMxgq6TM8zNb93bPNfiMad1COUf9PceCMnrS30Ndd/t9rigSWbR+oKTlt5ACgwntnK78A1rBPBbQc9xuiSqIgs31wjfs4B/dtAZks/BHe6lLBz0TQxO3XUc+YylQtMdY9TlNI1XiPCFvOT7aAnltJLs0BmLeqMY4lhxZUYKI959OYIkjQFMDKf3ZnB7awwlR07V+y/RViZCcOdOLT23mkTJCKUM+GiD524pSeBzq8PH4kTL58TV3SmMlD0zJPhV5eBNUjQARx9bV20RuLFSugek1wiF9fxCVbmXtFIvmQi/ZwHeIoKLqMQA5ng8Y0OsBB8pqBx9aZraJmqhJxWsxLb6dpHR7xPAtwHhNKJvllck/ZmL4S21t4QKOVXIaxC+17MywFrFBUs4cWIAp88uw/lzS3Du3Aq8cH5F/lxcTH1bQigaKyUVsjKUsdITJ4evodYXkdTV02dWb3/xjTO5RXdisNJ/Q5E6l49nS9NZpa9e3dq/8P761e296Tu3b+/jhxc2ztzZHL2gjSmiSI8VuC8D4CpxC4FEcMluNj3lYSUch/9QpL8AiwrgnAO3UOWUVz15Kn0IgM9MT97DjGOweYTjd77x1Wdqe0SFE5lJlKZXs8neponjKI605sXfbObBRBvp4B9r42kJm9tj2J9MaTjsi6NikCcqsnTDWvdepNWJtaXBlyeT2WBnf4KTImepGN2+elEu2b3NDSiKHG5d/gj2Nm9D9ernWQJ35Hmbn7khjKm+52ISWzsNyGeFuM3e5P7Mn/4Qbl/7CCbbOzDZ2wajI+kaY8ksL2aTNIUoSqDKM3CuaFxXmxgPFmHVYAm7CzwUFo9NonSmoJ8iLPS5xy2ClWEs0lk2B3KuZvVqqRyFCBZ2+ydvDlmbJMEB3TQe8m86gEq7/8YDr7vXZHUZ2u5JNi9a9UxlAN6N5s/72EiR1Lku89zOC2ELaO9ijGu1TodWd2FljaE5q/5EBDSh3IDYWXFzraYiyqal4z45Nj7JZxVNJlmFWQULpVO9wSDt9ZMYJQv3CK6jx/GRvg8YaHVpAb/82nk4f/pExcY6bDZL7P4qOMDDEtUYIqMsekOFgCfS1EQvdYAJ+L8PHT+YSXp2POxHSroCdAxzKpXu2k8KvUZdSbw3Noo9/qFkYfqe0KjWQzcS6xAKyqe75pUJKooTQ4tDpZ3z+ta61609rxa5lCPs5sKgx72semNnH7d2RzTLiqfed3vXqEl3njPfQysM3N6skvsHS+Sl6CSgCAXkcX92L9LQSxSkoVdbhWVUNxbHE4v3PrMOdWxvrlUE6oh1a7WByN1DGjqD3YUeOqON5XvTtrZqlFWqtE5uRuxHRQgZEvX9ArX5whQJzpOvP8jZpxycB6JvKuW2lYb3OTahG2LE50i/1weDfXH4PmigxuBWq/zcyZXFr545sfLapeub/b3xDGZZIUUY3iu+Ny8tLUKaKCjYrES8EcQ5GZI0gZKVc+EexfpxJfck8swx0SISvLK8NnhOG3XjcUQl8HeXykB/2/dIP+rgfTy31EvB0QulteeUUuc5b7CqqovZLPuxq9yGinDVaPObURQNlKz81ctsXohQfhMB3kHSf0KkfwjorhHBnnXu5yoS4rM4UIIlCXaoAniEWxhfS0OKoYpLYHnKAmhMwJ3JKvubztF/4AC/TUCL2BaeTBBK+Me1AtKyBpF+aGni52tnaSmllZUBsuP+2loPvvTlc/DKKydpbW0Aw0GCaY/zvo0vwreAleuAfJvgBYtFwBQUvISu+hoRzuJUnXvu3BJLbj4PAF8BgpWFYcTA8c6Z0wv7b75xZlwC3B6NZu/8yb/54Mf/4v/5SXzl8p2+MeZ0nJhzkvHp05srrSXSTXeimbCzugnZ5Oj724k+T0r/w9lsOixt8b8S0XtHIdg76nEMNo9wRP1nw4G2HuQNURxGZoswuR4pvbWyPDyTRlF0e2sHinFdMGmr+LzI2N2fVZPJbObE4ALTShgG4Gy7C0TwL9Mkfml1aeGFza39we1qH4qS2a0c9zZu+Z4pW4rb/Wy0C6OdO1DMZtAb9h9GhfoLPwRoKh8aMNrdgluXLsK199+Gaxd+Crubt2C8swX5ZCxB3JybyQuhiuXbzGaWlV+w2MrHSyg1h9Nc5x8HO1u9KQr3Y2pYSBUs9430afKiUtrOnF+USoQKQ1NqF+18I/exF64FmQ8wsCbPm005ZHl68AefRH3WJGe3J8x5w5eATg5Sm53/rYEh1fLAtscUWia17g9tFsDzstaawmnfhs38ogPPovKBYJaGXUCLytGscMUoq8qyZPMaqGbWRbPcsjZ6mRm3NIow5R5ArUXi3DhaNq6/z0Ylh7F1L01obXkRVhYH1bQoc8/osb67Rn9+NKY3dc9eHXXTTF9HwO4nUfkKN0UHgx2xJob9aN0J5QC5xqFhfpY6ZYUQ5NkADIUt4xYYUDEMolBqkdUBxAJY6oYhIW25KKPRJqQWhymcObEC506twcUbt5GBx2Oa5cDY4yMfd74uWDPMhSZmnq3vfJLJzK0FV1iZe84W4Nojfyc7xvViC4NUw0LCJmEa0si7USusVbbzVmrdv9d+ZIc6QNdSmbmiTnuN1jkD5KEot7oXvRiqE2i0MRjpMcDuuMSsIMP4Dv250NHwdreJAkRGR0inK6DfRcRT1TT7P1VSvBVBktXvWoz68J0/+TEkKobPv3oO8nzuWPJ5lytUvaXFwWunVhdOX765CZNZJs9UMZZiR3DOIuY2hQH3jdkg+1dyTTPYM9NCfsaOzXxvZ5JfYny04vbgfmXpVFnY5aLaNQQuf9SHKh9PVkrY0khE2KMPgoEx5/LSfckSvKSUOlHkGZ862etvvHnt/Aufu3rtxqXzNy5ffHk2nS2jNv0oilKlcMg1KgToo8MTJdmvA9DFXqLfG5rBVUe0h+T2yLrRY9jI4/EpBl97GRdB7Kc/T3hlMHAINiIYcWEL3OqCc39TVfT7DuHfAoSlkLPNtw9d151caFdhYDlYiIG7FsbjAtNUwwsvrMHZs0sCLBlwnj6zCK+99hysrfbZU4QTspSTNgeCsmzqFuQDtVn1gFPilRahQYd9IPgcISVcfdYVq2bgJBAus/SeX4sIqybStHxiyLKKrZPnV5LVk8ObZ55feuf73/0ovXZt9xvTqZ1Mp/lGlpWnnK++GWiY2flKtfT2R6oEVBk5tyG+koiv50UR6wQ3tdI7uYX17vqkHuoZXlQ/E2Dz57Fv8z/9D/8+PPf8yjOwJfMjME+FhXi92p/eKsvq87ayEV90jf5Vhl+8BCmtG08yqipLLHELg5+KSRTplUE/TQe9xKLWvort/GPfBXkFg5XeYAGiOIHZdASzyQSGq2ttf84zMC8POh7WOJc+AWN111OfNGo2M5vlcOf6Zbj87g/hyrtvw+a1y7C7te6X6Ewf8Y1fq9D/VMfKcMWvlJ5Zfz/1gMlonyVixU2TmsVep91RwG0aRbA0iKGnAfoJwiJXBKMQPeFaGNhoQWvmz1v8Y7BevPekHNx/6vRydVsxD0hqm5/jgfd2Rhfw3bUInzO26Ur/qMMuKmFN6j0MGLhdNGPdB9rRAlL7SV1Xo8BoMs1rw/NEO4mXcJBbIpZbchEnL4l7zWiaW52V1CtKZ5wPhTaWKHUEPaMx4cVmEkVB6ua3nxk5dljlCKLqU/QDHtUobUWzPK9mRSH76axLKue4Yov1zHZ9SbvOw/OHdf5vtcFL657azHZboAgkNh1kzag52uFN7QnZqKK9hDk4FmNbWPAXmHymtRZrp9AgxWK6E+U+WEvVySNico7DPWF5oQ8nVhZgkKZwB0ePpSjgc1eVFI4e9PPude/1124QHzOQ1N7B16h2KSPGu1DzgD5QruDoq9LBOFMwSAmWUgXDRENklHe0DpPRzlU944fdDNt7UjPrjQR3HrR6U/PaqMiRFZCPKo1AraIihUZcKrfHFRalZSltEtKtDkGbzXYaADzN8j3n4ExuZy6K1EiDepcbEtgtmvsb33vvEvzymy/Cr3ztZbi9OemcY7ymdWNUyW5kcNZLYxsZpQVkVtIPDPvjKaxv7cLuaAoL/Z60qdg6n5YpG0Dpb15ZHsifO3sWrFIQx9zXHPN1rh3RYHsrW9TJrgasHryid9hx5wK5UZAX6PM/7aPfQxRoU4F73gG9AYhfqKqyl2WzGy+98mr+e//RP3r5y1/7enrt2uWF7/7JH+2//YO/vnrj+nVdFLNlrc0JZUwwYYHPO0dvElBmjP4wQvWDvKx+bJPkg9hEG6jUDMBOP1sriZ+PESozn3rU7xeGE7HvSvpmRu63EfCL/KPOYqEjHEDpK2E0yD32/BgZDhNaXe27s2eX7ZtvPg9nzizqXi8RA6C11SEMBrEU4J11GAQoXmehuk733sYnlJVVfdchRDbteR2Ilq0jdn4eK1R8sUUOiFmTZWetRrQGCU5iobPlteHW7/ydXxp8+SvnzNUr21+8dWN/eGt9b+fOnTFub08WblzfWdzdncnue3bVr8P46+PYFGlqmMHfmGX0Y3A0AKTTvA1E+PeNjrcM6j8kV25F7KUSYBwXBXNVPNoBOcJxzGwewfiv/rPfhZXhM81zV2WOk3VLs+l06krOtOvYt8vFpnzlmheuu5Op2dob96ezTLERUAiTZ4nZm1rj2qCXqoVhbzjoJeKcV5SVzxrTkY++4JJUFAOrX6bjfcim0xB18PgcYo9i3AUs1bwj6dzoZvN3YjcUBb6rI5K4W3EZfn4IWKrdZvNpAeuXLsDPvvuv4f0f/AXsrt+SN1S2kIo3f1DaH0AcxVBkMyjzrAVuNfhswJcTwxSOHczB983V3++laAwyNfR7BhZ7ESz1I9GYRZqAg/CllBDeo7CW/84zF1LUsA/Ry95dSDZol+72I/wk9P6pRwtM5g4itMEkdwHVTkNaq/yd1wA34AcbtMm9YlPy0tLFrCIcZyXMSplNNk/FcVZFk9xhacmF6YjAy0QFzWuFOhagaSiLDJuE1OyfMMgqpD0+LlZTB3MlMaX5FP2FPJjNub6+Rbe399y5U2uciWmAxIySd5FtL3fDVbNMdGgQ/6cd2DCaB8+ZA9UM/9QP9yMXCibyGzmpXQjjV5z76V2aa/kVtf3BjRbaU+DUOTc4UojvhYNeCgsDlrUb8j1Gj/sO+Gifh+E+wKwbzwfHWy30UujFMd+LhODkxRGzA83XBQkrkwS8n1nJfaoWJomCpZ6BYWokDilSdWRN53B0HKkP3Zh6r8Ic+4WZd+SVxSJSx0BbJLv8rqj2BYs10mpPy3XCq8HtMVVZ4bjQY+7xhBa8HpyItL++6SXS9Du5jdYjgG0L+obTFWinJE/6nYu34PT7H8NLZ1aBzaXIO166vhnuTai8pZXaNsacM+wKxVJgNlhCLQzntfU7cH59FU4uLYh5Sb2vksGKCGkSwYnlBVgc9mF9czcYLFHI6HSoFS5XZX6mv7zcUxHuP8qx57mdTmawP9rjJplH1+T6W/qyIzgLCCfB0XAymexrrS9++Ze/WX373/6dvz0YDt4498KLL77w4svPvfb6G/i9P/3O5vs/e2e2vbUVV7NqQRvdR1QjpnG1Vi+6is7OqqqvkHIXRUWUqDe4i72q0p+giq8TuOy4NeezN5zT/ZjsrzlL/74F+AZ5BQIb67D8Xe7h9a3CC7MQSjYwqJy4jL/22il49dUT7vnnV7Mzp5doaSkdKKOR873ZaVaKOBWplk9pby7c9uBry2xwgRyCIpYJ1pFxvg+oLx4lvki1B4hXCWABAV9GoLQOZA7O+H3Kqs/bstJxL/rW2RdO9M+eXl2ZjrPB7t7MjEZTunZtl77/vY/xRz++9v+z9yZNkiRZethTVdvNd48196XWrO6qrq7q7hl092CmhwAGFw5PFIHggAOuOBA84xfwRBEKLxQhRSg8kwcSIhRAhDIEMYNB10xP7WtWZlZmRmRk7L6a26aqlPdUzdw8Miu71p6smdKSrMyI8DA3N1NTfe993/s+mE5TCNHeyMUiT4mWLFjwoc4OFEfXZp3tALCccQhlqV5jnE0cBxLg8s/8XI9DGyiUDCBjxWNraE/D+D7Z/BbG/GT+1J6b3ddRGTFL81yiWiYlRcJUrTHspc5lZiB5fEhn85Q/PJ5wFETotGOscislSbBjExjb8Dx3utbrlJe2BtloMvMx2UzznKhYWMlH+maezGFUZDA53oc8TZaUr6fgmtRj2Qe2kmiaAFQT9UmTeIjtc1SqSedbfs2NMBpXJThakequ4o5RROVL8R+TqPGldUfjPKpEU5YapscncHD3U/LOvPnWr2Hv1sf0XlGnB77XAqUmxuJEuOC6Acg8J3dhszSvMsYqBVtFiape0bphJLLBSHFyvRPAoBtA6KBIiAKuTQcnHVbp1ZNdvVJf7J42gvOmLPljErVHbE/OsCXhTI73KLD4OcnX44ISfeYHyniQVJROC/xqtpTK1WfuHYPVfkGonyvMKcmNgyqyWs0WSpwkhcpLJV1HUMo5TyXPShJ+VpwTRYeU6bTWZPwFJgEkWw0hOHmFYSBNvpRIWS/0o0WSJ4zl3NOPXg/igBpvS43H/+KHXRmLrOB3947cz3YP1XOXtkUrDoBLog7jIUdMo+gBxMBYjKzCL5aANdaOM69f1q2NeMWyEPCYecCYYuZZ4Yz6dDkF2qym91JnuSR1WagVD4Et7VHPQKRnxamW8wTpjxGqjAaBdgSVBdg3JZdtkEb1pe79k66yQY6N12MU+Mb2BDg+DaWlrnoaVuMaKjgpU4xCWneFdE5TBXHAIfZMT6fvYJ8n06QlR1Rlc7mX59NUqV09y2WSylZe26Q+M419XbS2kcUxChr1A64dDalgen44yb2kgK46I8NmWQpmobM0EBuXcqKxlfpPOajTUgf/VyGKI1+hhY0Pf/bXn8D6xhD+4MYPYe9oRBcOW5I7Tm86Ob39mYiCQ8d1S8cRHj2vjiBUFI89TzJKIqcXF5Rsrt4+TWwFRDGxkOs5LhWUClJDzom24nnOIAzkOc8fhLqUFpL8agPnp5pLyOcAjv/1pyXeFqlQgFBcB802ZVkUSqn7F65cvfPyaz+BuBW/CgA/BYC18xcuBYPBMDt/4ZL3xn/68+KDd9+GBzv33el0wspS7qKVjNb6KqqQcsbOgWY/EmXZ0sAuKq7bSsb/kZf8DV/IA875SGuJmfk3xVH/fjxh6K9A3zRrhaGRY9OzlOwnhVL/NWP6nwCwNWvzsbAvbTdzFXxGcW1Ce6a19Zb+8Y8vwS9+cZ1duzbkvu+id5ZyHW6DCl23+lQD10ospmVZScKXuH+SUrnDGdmha+2h1ybGrwhrouicIdJYTX9MPlHcRy/RVr20EUDvv3WtVC+d54qzwhVciDB0ddTyMmDdYnuzS9ZeaCX30Yd7FBPi++FilEGJa6eX5WVPltpXoIdIJQej7XCC5Hkp5Y+ByQXnUDAQ/0GBnkG9nrMzMdnTM75PNr+FMTp9uu1wtNZlquRM6XIMgmdogu57DstLSQ+foUgacRhTldcwmaUwnc5BlhLVIgvGWKa1DqSx/pr32nF+YXMo9k/G/sHxxCpkKnssBXmWQjkrYPTwAcxHp+gxABwV+Pi35xf3W0cVZzf67sxQoEpj1q7KApQqyGcUex9L2uQlJdBIhwJLeWK8IRjDDQVKoCE9Hg9N5CmkwsTAIVl7sAk+Fy4JI6JiLKK/uPAhkomHQkuT0cFD+PTtN+DW2/8ZjnfuwWR0BBwL5ErQ+7tBCH7YAlBT9DYluiyKVdQZ60oUbNQBSSkYaZuoLsvQW8okwZ7AgNCFQduHc8MQOrELeZZDukhrI/eaMdqkLtr/EbJQcxdh5X3rv1kjRF9Fg2rOo9ZnzruJIH4e9+/s8ZvjSb/zhNfpmlK5gm7qilC57CuziYn9ZwWQ4a/iE4RgxzyXkGSlSAuINTZDaWCLXBfTTCIDmolC+lhFLZX1BmRWzZaoi4zMqJmyZoHIFLCNtkijDTyHaLPKoh5fprJvxHgYSK0em5DLqt/2a8AFiNEenU7FrZ19sX88gjjcwLnPtCQzerywbSCj/W94P2Jn5t5ZTgElPObG4VpFfbzcXHxs/OMmqaQmH60UI1VgZgRpKOAxP7dgoPm6mWgy1lB9IMEsQ3WOkN7VjqHXiiD0PY39Q8z6jn71XGHJTvjSl6lRmDlbRsI1CvuisJePKi1KF/Y1TtPTrvnL1l2Tgjq0TkmLEiYpkFdvG3s6AyEjV6SBA9JxeSAY85pow6N5ZrMH+tGqUjMxFaxWHDbbihXyQlMQ7Dnn3MPwUe6Pi3JRYp9v9fiyShSEWySFUHe77CC7AHPknxa0M5YlB/ZvOcApnkMnDiBNFOw/SKHXalGijYdNi6mUi9kuj4JbrsNvOEJcdATnhKQwY8eDMwStUNBibEOeKRaYWUU2QajijIj46WROe2f1+fOijFK3HHqjWZQdT5kqyi9XbVp9O0qC+2EIR3KKu9JXOk59PADmOaJblnBNSjUQXEyuXH0m+eWv/lHnhR/8sAtA/W94mzAp5GEUix+9/vvr5y5cKX/80993Pnz/3eLmRx9kJ4eH2TyZnZ4cHYnZdKwYsDYw/vvCES8KTDS1cBnXvpbZ+cgRKTDnqJTqLWDOh4CtgBjD6+++Kf7TOnAhKNiXzesZBGELsiyFzCmvlLL4J1rpP2HALtl1G1kFE81YCkwH+DXtp1btHnuXL17sweuvX1a/+MUz+vnntrgXOKj0HRjLE13b6DYVrYm9JRXM55k+OJjB4eGEaLXr683NnaQAACAASURBVC1Spl0sSpgnOYoH6V4nhG4vYGHkM7NH0vPQBqVDsJBnFRdgBwNxj0w2inuBVz2FyPZQJTo5Q+AIHsRxULzwwqbK84IHgcs+vXUAJydz6h8lrobWAiny6A/KGHTNh0CrQXKRmDLGzstS/4pzUXDGTzWwXyMuVD7ltrTfJ5vfwniaVzXchwqpy8mifNCL3ffysnhdcxigbBe3JuqsScXjhl7DwSSNVlCjgm+w4o+sg8hxhB+FgReFgQlOOANVlpDTxljRzjScHuzDwd1bcOnGD8GLNkxi9bt6RtgK0FCvPrQAESokKbmUMifl1iJb0B9V5qY6hgmmLOyhMJgqKQmlgF04jWvGjUiPbYLEPjqygSJJbk5oUa2GQddGQBB2IOoPQTgReRPPTieAar4Pbn8Ed99/E+6+/xZMjw8oIXX8kDz6MPEtc7vIcw55ngKt+Zo9mnBR5ZqRiqSyPZqIYpJ6pO+QoT6KevRij/60I5fotJk21hVE0WzCo7UfXhN2BAMK6Ipa20R47F8rCSGrD1dlk01xoCpYbGKdK8WBZj7RTGBh9T2fOB8aX575KFXJtpaasW1hTZmh+h/4WHGTJ3NMJrF9tpSaLUoJo7nUo0TqBfmWKETuMPVAc4lMKi2kZm6hlQO1TRjZtDiu4C2kMZK9hkUu8QJhn4qLliGCq8j3Ofb+oSduilntl802jCGoXvGkaFwQrfU3soXhOjCeJjCZLajQIUwfJC4Kaxr0wNjDPD5KZmfnkjm5L/S+rBJQMMaPjfzTWKcsJ6Eg309S/SR7FV7Nd8xenLo92cbfdeGhkSXpsxlb7ZrDqjoKnY3vCDbstfiFzSGbJykV9VBU7eB0AjL73YIxZyZ045umaEa93dwawiosmXG38arqYzYemUaybTmpeE1xG0B67SKTMEkx2eTztu/IdgjC9xiK2VbWA/VKsvJsG29JbfszWSXUBNakFt+KV1QR8/us8sdF2BTPgWsIWr4Q0KUEHx5OFFRuIpyzBedsij3RWusuBncaYG7nZAc10DT64CESpxH700cA8GeYU3aiEP7dX/0NjBbH8N/+8z+G49m8uh6aR85MA98BgAMhAH32QmxNoa1UaCpcYKEIA008J9Hwnq4KF3Hkw+ZaD/q9NqCarVSleX6Ih828ksOWnKfrcRzdYoxnX4eajcWrbDKBk8MJuOLr+WwqrcT5reF6CWoDlDrZ2Dq3+3u//IfxH/3jP/nlxUtXPBt/4oSfWyXdSAjubp8/L7bPn1fPvnAjv3/3jrO/9+Dq/Xufrb/31pvF3U9vqiSdu0VZdpTW66rQuN6mXPArnLGtUuqWZuUMuHiN8/ATkPozDexTJvjHRIH8DvsUPm2jeu6xxWYqxl/q7HDuXtp4Bu7v3mtN08UPhHBe54ytITsW91KztkJb0/xgtriL/c5G+n5zsw0///kz8ItfPMMuX+5rzxNaFooraYR/VnayymTcfleigXJRFijoNZulbpIUHAW4JhMP5vMCjo8TmE9TFsc+XHtmDc6hF2fsgx+4GCuhv5OrrC8ZghLoK4+GKVxwjYpk1oKlIgyB6TDS1FWE1HdctQbDGH7ys6uwvtmBP/t/Pob/+Oc3YTqZa6T8urY9hliGxgwaD+gYFiKboYagAFTOhl9moG77wt1lrrib5E8vhRa+Tza/nbE/e8Tg+aka+Cx6rjgK+vH/B+PZi4usuFws5ABFIHzHoWCw8mOs7DFsPwzR9UBrjxvBEtPfA7qNu38U+Sh+oX3PNWVwXnXzMAoOuCtgNjmBux+/Deeee5FooEHk1UqH39ZY1TyyATQJNRQmuSwQpcygzBeGgor/zlKQRQ5aFivoXeUBx8lPuOrZUhSDUlBsFDoogaReN5kDtnkhmolBAqZQnHsUZCCNpCxzENwDwZBu3KMMa3JyAjff/Wu49eavYXp6SOeJCCZx2NIFNQ5ou/gWeWaogkouF5rHrDeY62PgqG3ghar27dAj9Ujf4dDyOLRDB3yfg9AFpHNJSVZlh4MUK92UrW2qeK4q8awiSnV+ylZyuGWyujwGs4Ik1bf4mdebL5ZHqRBRVt2blTc4cw6PRTcfB7+eGauo7DI5qdEr+g6Kv+8pxXSp5TCTupUXmqHQzzSVbJpKvsgVCfVqc31w85EVjRJxNCGYtYnBeYGNGhypi9SPUhSSkcepEOAJB6LI04NuS6FXpSMcfTSasqwo4fM8G5cG+I9+RkJMNWOf+/m/cFr35IGFDqQQUr8f+TdaNNFYOfLP2SJ1RV/6cqNS07aNe1VSYudghQA3py31ZDrcCm1ZmnzVH6iry7i8TFYyH+8jBkeu9flZPUvO6vevEgf8GwVe1vptdm6zD4cnExjNF5Bm6d+KevDj35EujHIcUQaBzzzXxdZIjj1PSmhcfsx6ZwsxAhp0/Mcc23QOEOuB5VJDkUpnkZeteSbVPHfc0OPk3YmenSg+5lA89yhJoWqVpfYObQoY2G8eOGQVonB9SouCJVnJMKHDORd6LviegyqUQG41GkTscVhveRqX46OkABTlMv3QvKCgTlqxak59WtYoygCJmBAxrn6qpPhUK3aXgf4Ice/QR1ZKAAdj7G9uLf1yOSQBV1MOijmOEI4VcDNtDCXNN2SPIDUWv+eceRC4kSkzew4yYRwGqmi2RWjmaHap1YmuvfDM9Xfj2M9o7n7F+RD4Pnzw6R14584dENHXTDYV85hkG1qpDhdMrm9ulJeuXOsPh8MrriOQilgQ6liX9Qgpr+wg5GC4lvcHwzx9IXEfPtjduHj5qvvZrU+9/b1d/8GDe/zh7h6fTSeJlOWhltrXnKOgE96zhCnVZ4z9XAudpZq978Xh/w1Z+ZcAcBcLBDa5/d0/cH8Hh9kavxwKjmtiWRQxk+WPAeBXoPU5ALgPmuG+2Kb+fSBkz9d2QS+xIKMANrdaiGjC7/3sKrt6ZQCuJ5gRuDTP8SPsJxNoEFWftgLOtOd7xfpGW8WxJ9JUcqP4jF7yQOvz3mwMh8dzmCYZfHb3BHrdELa3urC51YFeNwLhmA2mKHI2nyZQFqUIIh/idsQc1zHimKbojojnArROwTCdAgAW4jowGEYMC/3j0QKOT+bw0UcPiNqLcbaUrGYpGcyCWjsuaKZ7oCGwqgAXFahfSMY/FoqPGMDkbEn2aRrfJ5vfwhBPb3GBhjaVklzN09tCsLeV1r9SUvU445xsNNmSjocBMj7ks0UOk+kC5oscoijE6j9vUpiwqizsRopUW2zpNE6alAo4VCUHD1RRwOH92/Dg1kewfe05CFubpLYqi2+Qat5ArKrzw4pYKQuQeWoTSUwoc0rWMNEEZaix9Ae1pqkkb4qgDFFKE6YuK+6sonAJI5jDTL+XicLsm1tozthaKGMBgsckv0yHFHtxkebcVPSxqp3lKdy7+S58/Mb/Cw8+/YiSUcfzjY+bI0BRE3lRv4fCZFafbWRcuRTGUtAWCwilxBK958Cg40PgMRBKQuhqcLmi+5NVqrWVeT0mqq5PVTwUJKIk4cx7UOqplspqS0RnmZVVwXqVkBu1y2pBZQ2q6tJiooa9q9+t39MmrzVVmC0T4LObTRN6WbYogq7bLFY+yOo/LdrYwFXtC3QdDGPBNS0VLDLFFoXiaaFZWkhKNhelIsVfm9AoziFD9MEK/3gSFUpR8dMKxgBjpZKwUEryoixDMjhgHDpxCO04go1BBzYGXXbh3AaLw4D9+t2b7ON7D42NAth+4ccknU9KZH4XSQ4ltRg5uC5R9ZRJNJfnAEt1WXvVSyuO7OmziOsThkkszdXmj0thm0UPixSDvQaYzOMzbMzWbL9urYhf0U1Zc7qZHr/qh+zRunJVmaiKUuSkKzgMux3Y6HcpiHhweAzJIlvxo/xbGiY80loFnjvZXu9PL51b4/047kolwzSXgopcUpFgFPlEulyjNyMKdcjGvFtBOzVoRzA0umSFAm4UHSHC649FFNcBKnihT2fkItMCk0QOgbAemlChj0bVFq/TbJ7Cw9OEFBi3+hFE4PJ5UrAHJzOG31/kBYSeA2vtEDZ6EfTbAX3NbYNox3cY6xGqD6fTEmFMXwEMwNZBbA4dVbi4Va8VlgmzLqT8pWb8LjBC5e5Fgaff+vAOLLIc/tU/+xOYzqndTAlvMmJya9cVHiGgvueAtmsHkmhLqXVqkuYm6bqePITTaoOepFlGyelyvbJwsILzWugbrhf+h+1LyYkbJKDVV6PAxlEK+4sFNct54uvNR8W0KjhxbUrGxTrSprjgkXCo3Te1SWVkX17FohKWuyhC2DKMYrh87Xq4ee5i9NpP/4E6OthLP3z/ff7+O7/xD/b2isl0LI8PD/uLJPFA6wUzzl5I0Y00MFxnz8tStpmACwLcTwHYHQC4DQDHNuH9Pun82uPLBW/YDj4ajZ7VjP1jAewPNNNtRKBp2iBNlVEDeqq1djVol7pIlNa9fqRfe+0y/OIPnuFXr62RPRB61trCol7WnpdeZJxBZfpLa74QnMeR57diT+lhm4qZyC5IU2MvNBwm0On6cHw0hePjGdy+PQPPE7Czc6LW19v62tV12Nhs68DjLE1Skc5SlWW5ytMMz1HEnRgczzIUatHEigtj6Bf4WWSBzaEOvPjiJpHdzl/osXff3YU7t4+oxSkIHBSmk0WpsN4kmGYdTeq3UGhGCL2rNX8hk+qPmCpPGbC/AaL2P53j+2TzWxjn3e9IX/o8z7MwOOh14oM0k5eLPPWLrFJPrfqXAJC5MJkv4Gg8g2SRglRdbEOk59eY3tudQRmag7HSQM58RXcC06voOURLnY1P4eD+LepH7K6vE+LxZGzli48Gu8qqKmIlPYF8MaW/sa+xzFObVC6psSQOYhMlgluY8RzR0EiErGR2pUS5NII3OZFm0hq+c5sQFqbih1RUQgjNoqhY2aDrmQQrXUxBH+/BZDSC2+/8NTy49TEkk1M6DqKtFMghzcpZPrKmN6kyylwdVbBn0CRGC3KhDH0x9gUMuwGsdQNwAAUnNFXPDb1WmUSTWy8roo9y+9mNbsYqcGkpgjaLo3zbUsEooVDmMtHvc5Ncoqi4tr/DbfB41u+Q8wqdEku7BAaNgLyCm6BOYtWZK7BEpJcXpE40Ybk/PnL19GNz1dork1t6Hor85AqFtrSeZ2p9kkmRFcrHW10qpfJSM6nMZzFiWaIUHBaoSktCOEyINMtYUSpMMSkQjVuhCj0/UVJyqaSHPprtOJTDfo9f2F4T59aHsDbswcZ6nzvCg5IJePPjO3B3bx8Wi7yZsC3P//MKEU9APL+NwSqLDusXW4sT6YozsDxlVs8ctlxEGud6FpNmVeGLVSUI0xJQq8VCbfu08nvQSDiJskRFM2Hnew1prlSZq2NZoqZr+05NsmltUpTtV9eNPwCV2jMn0Z1W6NMzholmSUgct5P4b68+7blCdVqRvHpuvXzlhSv5c5fOub7r6tPxtDyezNR8kYl5kvFJklh7HcbUio2QpelbuNoyFZiUmnpe8aoJDplgkGBiV5QqKkpwS6EgzyWkHleh5GVWCh37XISuEJ7DSR6ykFj0LGGelrB7NIN7hxOyYxlNU1zL+cl0AfuncziepPRaTwgYtAO4sN5mF9faMOzF0EX0wXXAQyYHZ7BtRY3GiwKJH4IhcqgZQ+YLtd3bZcd+OgF1SqhvlKr4Uy34VAiO/ZsPcfHzPAlBex9SXVleqmwx92ZIUFjrtzXa3jw8GlNBltP9liwvCuO/meVEY23Ot4pVgus+s5G0sPZjWMDBvTYri25W6OcTWVwuld5xNcu/6iNNxUUFEEgGvvp61V+sJQgl7xaM3VdKtfMsc5RWHQAdaykXClTCmPAY56H1Pi3sI1Ilm46xv8BtS3hRFBZhGMz7/UGytrblX71+TUxGo/Dk6HDtvXfe8W7f/FBjX+ciSbBiZ3B3RiIvgdbqmubaZxJ+D4BNNStuA9NvAMBfAcAOPKp7/v34ggOfiA70vuCLOWgvZEIctmaj9FnO+MuMw0WswZniItKiWctgNlTSd5VEZTINg36sX/vJJfUP//A5/uwzG+D7gtYgQ5TR1G+ZF5K+73vCLk2YSCrhkOevMDGIIqqtW4El5HXrOaQKi3FWrxfB9rkeHOzP4PatQ727NyrG40X58OFMzKaZm6cSxuM563Y8FFPAGIoXpcyKLOeqxBKL5q1eDJ7nUdkdtU2AMVJZZ2CUdQnE0cSpZegF+uMfX4b1jQ7t34cHU1Kpxc9blqqYzjNHa81RX9CIuANarFQMgEiDWmOgN31gXa7D6dNKFf8+2fwWhpM/3TTaamAa4QfdUavlP2RIQMqZT0lQ9QIGlvamIDN+mzbZLMEF70zaYV7vegKQ+meSKFLmsuiWqpueijyF0f4enOztwtaVZ8DpxaZ38+torVe0K0ufKxC1zDJ6rzyZQp5MoMwzI/SDlFM8OVLgXW7u5jhVx1DFEzPCIda2vZHMchtMqpX311X2bQNbdHmwxiA2ieU2elGU4CHlaT4+gdnoBBbTGZwcHcLe7Y9hfLRPPZmMkGUrtiTECid/WcZrXIYGNVBb1VnqK5CaGB1IK1vvhXB+LYJ+5BNymoKkirm94XVyR8qcyoiW6CKvk88mbGFLE3VCXiWQ5s2NQuaSvqwsemlQ06agZ83AXRE1YvY1GpoAUvX5amquZlWb6CpVt4lcNrg1FU72CIl2eTqro0HHrZLVTGqYZ7qcZbKcZUokuQrLEj0XqWhZlgplZTAWZgLVm9HTHydSKXWAYKXrCM93hFDKIRAPk8qNXgfObw9Zv90KmGGc8sDzNHrwDftdWFsbQCuOUKALfM+oU17eWoMXrpyHN97+mJLNL5M4Pum17BumtuPxZosM9o/HcG6jTx6Tihu03xKomwkLp72JGWoxZ0ubkfrc6+MagSOT71mBLsaWva/N+dpENc8872DVEZHOzsnjQ1u1RHMjTJ9dhXBayjPROjlT1ku2nn9aP6YAtJyc+EmxRxrR6n4cQuT7kJWSJPqRRbLIstXU+9GreeYqfDMDbauuX9hQ1y9ssUvba+FLz5wXz10+5wSe609nCzWeJfJkmojj0RSORhP0h9TjacLmhMqSfBIJ9KBKJN4zXGe17fGXWvOqNuU6YuFwOChK5Sp6AIDlimVSKeTQ8VzpcpFrSDIB3YjxToTJXwl7JzPYPZ7ByWQBh5MFTJMcPMbh/iEnJHORl2QnUvkGz6GE6SKH2aKAk2kGg/YMNgcxXFrvQK/lg4uMAd+BeaQ0KufiZkV2XBqRxrK5HKxAjrYEEQDo17nSiVD8UGj273uOXuzc3YH/8X/L4V/9i5/DycT0brpONmaOPo5bfrE26Pq9wzEJ/Ui7dyAFcDSew3SakBjQcj/StOZiz/+gH8GF7aERXVpkOk1TXRiKOZU4hAvD01PvnGaj2I8mOUoYfZXhBQ44bgKO4ijp+7UG6W1pRBY11lLGSslAq0Lliyk7PSx8KRUujoUrvFw4jmZYHXKE4zouoHqvfTjDRpyaItLJBOOD9TXV7nVShR6+i4W6dOWa89EHz3rvvPkb55MP3tcnx4dcKoW8ZbqvXPC+4CLEUrBSSAXX/0Bp/Vqh4GXQ8J8ZwFsaYO8bfaD+Xo0vWpgwVXyV+RcZK18Fxq5rTfcYk6cL5jWmDAjmESAkotf24cZL2+znv39dvPj8NkPfTCmVZWwpmIxTODqcEq1+Y70FgR+DttsAJqBHx3N66043gij0zG6jdc1egWovEZzEhzDxdBxBievGRludnCblfJ6yPC95kubw2Z1jaMcedNvom4wUeoYEJUjTjLHRjJ5Z0cGCp6DDkihAo3XIgjHI8KD+zFbHgytXONz4wTm4vzuC46M5dDshf/Bg5GJOqut2EuLnirrLCCm6DMYY05Qla4kC2SffJ5t/b4a7NfiufFTphuFDXup7oPRUA2sZzY4qUDOMtMoCBalBk8kcsLE68Lw6MahiK0TNwsDDij32hLJSKRuA4QKPfWiSNOTRCB0VaUdHDyFN5hB346/HoG1YhRR5CdlsDIvJCPLFHJQsCMXERFNaKh+3Zm+VsCHnDkhUnMUSWo2SWWNt6t/S9e/VAW0TNFtmSnVSxawpp6HN8lphlfo7LaKZoR3MyQEc7+3C4f3P4OThA0imE/LJLLGftFJiQrTkcWCHbqIvFeN0Kd5DqG6pCN0MfVSBDAjR3B6EsNYJwHMEJKkCWThQ6KK+LpVUuInPuVHnrRAgvRI3L2+AOSGD8NrrBRWVtkKHQC0b3ms0SdXHaFxCgyyx5Tyka8rqXrn6mBZypPetE9HKtLlORm3dQDeSyuqvsxPvMQFW9VEqKjLaOYwXUo8XEpJCOWmhvbwkvWEtOJP1o4OJtjQ1FFSyYyimpZnv+YxFvs/CwNP9bqw915W9TgQX1vpie23gxJHf4gSzcOGS6IBgvucxV3DIswzSRQKLJKH+Kmww2+y1YL3bgYeH3yCD5htG1tBD7OBkAnvHI7iRFdAKI1NwsEkma1rGmERQLBFJqBkHZ6mqJhkURjxCyrowQc+v0rYoZIo7jC+ZBI8mc5UqrSQ0vTLWp3nLmoUMU4aqaFvmL1YXQ7QZ1Q/qP7qRuVDfpiPg3HofnrmwCbfu78P+6YSopFiawmTzSaNmT35N2i367GIBa7PfhWG3RYnO81e24fLWOht04nBr2Au7rYiFnqdbYaDWBl3YznI2Q2XyRQKHxxN2a+cA7u8dwXSREiUW7wUK2qBVynSeUvJnz5fZAiTlm0pBrMmrmYK0kjM+s7bOUam0m2YlXwjJkkKqkxkr54s5v7N3InYPZ2yyyJe95FV7wIoKsylAIFSJ1/t0nsIkLSA4ErA1TiDNJVzd6sIgDsAXHHqRAKSyJjnOIaj1RJrK23pVBrei+seg9ctloX4PGPvA8+Am/u5Htw7gf/hf/gL+5X/5K1LxVnq+y7T3Lmf8R3HgX48in40T1AcwVHLsPUX6bWGD52ZBDaydFqrRvvz8FYjCCD6+vQOzeaKVSeApQBZCB3ladBczPwg8VMT9ajsqU6hAPkO1JVBfMzzE/jTFnCtaFduc86zf7+3341jn80lrMT5uq1IGXDgLIbgSjlc4ridpyXNd1/UD5vuRcny/dFxfcFNx8m2QjWr46E9RAvhJEMY8ejn2L1y+Js5fuqz6gwF88N7b4vjokCcJqnyWyKvqGgqvKLVWviwlwsODUvHnNIcfKab/PWj4EK+mAnYADD6y/aTfjy80cQAebSJ4dCC/SS6SbaW9XzJW/qEGuMyonmjqivQLZn0mNxXUuogDD65eHcBPXr/EXnhhE1qxSwsF2ML2bFbAzs4pPNg9hbjtw3AQGcVwpTmyJcajBN59bw9ORwkg9fbGC9vQ64VGg6RUjXhX17EfjiB0YXu7y9bWWl6eS5bnkk8mC3iwN4bTozF50+NeoYwKtoNrHwoAZUkGk5MpHSPutkx7RsWGsS1TpG1uCpZGnLJUpIB7+dIAXnrpHNy7e4JtCWzvITgoemSXemEQX9ozS7KGYTBhmnW1Yi+Wgk20lHdaUj+V8/b7ZPNbGCz4eo31v8MhGVcHRan30qJcVBV6vvQVrEMyLE1i8HAynkG6yKHb0UvvOpNhSbRQiYNARJHPsUrOJYE5xGZVUiOOsaSNJnNIRieQJwklUtQU81XQlErBlAOUWQHJ6BCmJweQJVMjEW8zGE1RhEIpFmtVwkgYCHjZoBMSi8MmWLBELe2/K6pehV7UtD29DITti2lZYMwhmu6y54tZX00AuUjgePczuHfzEzg+eACzk2OYnBxRDykmGrzuE61ozfzJGUD19g1qKAZcKPjUjR1KMpFS1m170A6MEJS06sLUf0iCRkuktKw+R4MiC6vo08o9aAJFSEEjquzq6T2CPq4cgjWTCqj7mmAp0HJGq5Sd+Vs1OuRWf7J876rv87dMMmY1q4jAqQ04b2izgL2Zo0UJp7OSzTPlZBIcpRSnPl/N0IWZKakcaeBWLSijIhETEUcx78QB67aw/zKGwHN1HPjQbcey141ZNwpE4HmoZ2tMrImSbXJ+FBMpZ3Pbn2ueo9z30PqADOFfe/Eq3bx7e0cwniVP/nxfYHzTLE5MNk/GU5gkC5NiYg7YqL+u9PidgZdNoWHZo7tSeGnM97NobF0D4swYOTaow0vRMPbIwaRVmK7ni9T1uqhYU4kW6vO0/UKFWfCQolVZ2LDVpcFaSSH6tznswXNXzsMHn+3CeLEg65OCkqgnX/0mNbh55l/knnEmyFNu0A6h04qhFYXwynOX4MLmGuad0GvHvBtHrBX5rNeJDQVfkSUo91xHe47D2mEIG6pNSHynHQG+bu/olO4vBncuKXHzhhCTsZHBpMj0RwHSGjcooNRYn+HIYXUZF1iUxGKko6iXUWUH04VIkwU7Gc/06XQO8zQXqPJcfZ6zUjjViokB6mYvomu6dzpHr1fytcMAM3AFtNBHDxGT2MX+UNYLHZCyhKkxiq9bxZvljpVJsryffa3lzyTwHVSXdgTck5mC3b0xMOkifxhXjkNg8bse9+64rjhPaJ0tyBELh3ypCyOcVSmUV+uWVc/zhAtRGBIijsVaRAUrvBWphLN50duIR2ujottKkogr+dU03gPtwUk6BYHIrvx6cYztzedSltrxvNP19bVPN9bXhND6uWSRBHSzgZUo3cK5kzmuN2EK22d1hwnecr1Q+FFchK3OLAhjwYXjc+E4juuoxnTHkxR+EKr1IFxErd/X6+sb8NyLL+nP7nyqd+5+xvb392A+nWLi6ROVV2lhbI54ABwucrRSUWpLK72n0DxfqdtOAX/NgL0BDO6tfqilPkL1f7zH1vnii18cpr9QcvadGCRciDT35PM3eDs4sMB3vZdScP4LAPYKAxYxRr2z6BXZoT8kI2F26cjjsL3Vhhs3tuC55zag3fLp+aT+bYdBmhawuzuCDz96CKcnc7j+3ZTuqAAAIABJREFUzDq4nmNjOEnU2t29CXzw4R4cPJxAnpWwudGGXs8Hgf3T1GqlVlbPqgCOGguO55S+73DGsF2CscEA17sQjoYhJNM5oJ0nUnS53awFJZwSktmCYlJ8jziOac1P5kT3R4E/ou3aCUVrjUSvE8GyQT+GZ65v+EmSs/s7JyovSl7KqnC5opkorYozhifXAXSfMX3KHP4bURTJt7CFf+3xfbL5LQw2+c6oa+OjNk1d77CQKlNWwAKRPpzZ0iozUsVfAiRpAcfTOUxnCQwHHUDVLULCDB9g4Qqh2q0w7ndjjlVyjb1ISjIFqlaUpA1USsjTBBYzgz7WXRpfcntsJld5soDk5BDmpwem/1EaJMPu2Ob1daCG3eYVYtdIW9gj4h9VjlUjEhXL04gsNbYLKwJU9x5WgQOhLtKia4ZJlkzGcLR7F3ZufgQ7dz6F+RwT49IEtBjxNRwZ6tv0228kuI4JkAuJh9DQCV3Y7EewTommB1HggucsFV+NZ5UtmYkqU7T0X9BWfRaW3oNVMlgH6LqmzlYIYxUAqSadeMl5XPnV1WDcIlJqeY2hRr2gvh5Le4lG4tvoyauThAYJrkKnqgLBCmX28y4nI1U8/OQCi5+LXOl5JtkCxbJSCZMFUssVNy0U2mhEGQSWY2rkO1iR9SEOA43+eINOi6/1OmzQizX2YKKSrBAO9xzBosAXYRAQ9xiTjYrSrGEpxGTmbVlNPFIryXlBH73fiZH+CIenY0CK4zeRbH7TgwpMeQ4oMa/sg14Jka3S2KsEsHHvmighmITP4vhElJay5HYC2mnWoEzbajI9s5bq+Xm3fynQomyBiNc6E7zSzLW0emIbYLSjyrrXUy97BXT1jJ0hvdfzD48eBR5cOb8OL167AAejGRUKUICtGti/R89oExZtnm/juj1uhcDnFvstMUFBim631YJBO4ZBN2Lr/Q6EQUCCU5hsbgx6UEhSccUSPbVCIPpKO4QyZRfsdarWMYc7uteOwfc9wHl9eDrW6KE6niwgyXM2W6SmDzEvqOefVYu7UQIXCqqCCr2FizaYTGtpVxC04cpLWabjaeLtHpwE09kCkW5OPd+Wxvy4KVpdByy0tSOPrtssLSAvUro1i6ykvs5W4FEiWq61YK0bQje0iZUuCWEsqnVI2WXj8WuFZJqCvhcV6D/NlTjW0ttjLCtQZXLQiuBoMkO+Qyl19pnni8+67fjVTisKT0YzhDRBctPfmyQpzJIF9Zq6lTXWUgaZBEMOj07h7s4+jKZz0NbOF+csPlfJwus5oeoWmfZL+dX7sFWuIVtokEKB5F+vjVExnWlV3mPAPgGlc87Yruu7Xc/350EZDLVWBdqhIpVaA0ePJ1cpWRZFge4UwNnMSZOJWswnqR9ECpFP4bi+FwY6DFql4/uMcRHYOBY3UBnHLfbcjR/o85euyOPjQ76/tyt3d+7LB/fvOZ/d+rRz/+5tJ8+xN9ZVWZayxWKBVPie73mvArCXpConZVk+AyX8MOP8eSj5n4GmhPMQNGSASQI+IlL3HOABB2ee9fqjfNDXoii++MXB56BY+qV+14cuGSjHhyRZmET6Mc+LoqKJ6HVC7+W0KH5kxaFQDn2kASYAqCkJERibIVpr4tCFa1f68MLzG3rQi7DXmWLJKA7oGXm4P4F339mBW7cOIIp9HQQeCwNj26YsELFIcvSx1IhsHh3P2K1PD6idanOzB2hvgtR54825cjdwExgrxRaM6RYD3QHq7RTWk9OHGVLfscCW5abArijcI2CABIfmKSSTBBzHgbzQ8PDhBBaLDAaDFqyvt6nox60VnS0OlWHkscuXBzCZJvDw4Rh1K8/MEeNTxqjNhK4fXiuc/2tcqU+FENu65RzZ/uenanyfbH4Lw2Hhd+hstfRdNo0CN+HYGYOcb/uk1/udDfaxr+h0PKeAdmtzAG3XMYGcqUJR+1IrCtRar0V0WhQVKk34wOPYn3LHUWmWh0opr8gWbDGdQJrMiGpE4hhfclT9mUhHnR7uQzI+oB5NoqpaSihUCAcBgxWdwQRwS3VUZQNK3rDesAExmDeq0V61hBCrpLK+khUllC+RUfMeBjJEOu9sNILd2x/Dzqcfw+hwH+azMeRZav0nOfAVm/QvhlfUMvvA7WcACD1BfZmX1lvQjX1wXYsIabpdsCSL2LZ8m+83UUG98tUy+IeVn505l7piq5d01hrZWRbmKj++mprcuJ9mylWBfkWRrmi3dfpVV5mXVEV7nCq5aCCiNVLw2JNu/H/ZlknRHILzaF9ymhRsmiqkJWNErBDtKaXkwhEy9DyJLC8huEBVWd9zSQhkrddm/U7M+60YE0LWjgIIfFc7jkCVOay+OvbcHbw5WNyp+txg5ZQaM8L00iqTuHPmcqMLPZ7PAYP9JM3qQPVJfZe/a3EgsKgar6ip+nNx8pVhaOSscb5LPR/MvgmloFyPWtdqL9iqp7ei11ZzpKZtV9dmWUExtWyaKNwUiOoqWGPU4sjMrimmgm5JuE4lE1TZICFdqkboYUkhV9ZbY2PYgx9cvwR7x2MqEqDqqLR+i0iTRuo0Jm24RmIS6LoOJYLoZUyU7jRfERhCyjwmK1EUYBIIW4MOdOJAteNQb6712dawyxFZxz5RnG+9bgznNweAfq74NaJseM24VcGiRNqeviZmslVdM81UOg48LJbAsNeCi5tDmCOFbJHB0ekE7oWHcG//iLwhDTVfmue3QaMHIySGnrQuU6Q8Sb6y1D7JGRF9s7wQ0vR2wpIY/+SRZCUcjlPokm+wT9TZWWqoqqNZBp8djmGcpIBWLMjywP4rpNPi0UvV6NJUWLxTzJ43TSdbhcAbOmcAyJfraq1fLUv2h4yJ912Hvz+a5+p//Xd/Af/0Zy9Re4ks8n3H9XfX17qLC1tDGE/ncHw6AyLdgyZxoNFkTveyE0dLejkVIBkVaE4nU6LKozerWFKSabuJfceZtiKxcTwtwtFE4Xx4bMT/W0aEqND9AzjBvmJCfr76+oC968DhocOdT6XSnd2dXf3xRzenFy6cP+IchoI0E1DNmBfCEWj3FGtOATTj0rS9l0XOZVn4WTLLBRc4ybXruiKIO2UUdwovDFzH9ZlwPV4J0wkhyk63W3S6XXbh4iV544c/ykenJ+Lu7U/FO3/zm2A8PqUH9t7t2+7Njz500XYoCAauHwRuUeQOQI603fVCqmu6cH6iGLwPoN4BgDdB8B0tWMQ4f8lTbBuYeJBuRH9TtDszN0u/zMUBlaR/d2SJcI92PAh7OcwnBSVJ7Ez4gqU+IVifOeJ5yLNt65+MIUOMFGlNliekAq2x07HV9uHqlYF+8YV1OL/V0gJh76JknvApHpinOdy7dwK3bx/ROnjp8pASQc936rYb7O3s9yNERPXp6Zx6N//TX95h77+/By+/fAFeffUiDIeUs1mEs3m6kGFJoCrAkzu2beVBmxNhPYEnpxNIZW6LjsrEcfjMo0L9NAHHcyEvGRwezeHh3im021NIkgIuXOiRjRnjqH9EhX8PKbdrazF74flt7NvkR4cz+izzeUbLbuORFlY8K7bK2S7TMOScDUtj+v59svn3YfSfvfZd+pRM7d0ueeSRKf1UZkTlYha5A5vEoJYgPoynk4REPq4kKbSjqJIQw4gLK4yp7zlZtxXzKPAc3PByVULoeWqt3z1FMOboRK4noD1UWV3MpjAfn0KWZRC54RfngoFZ3PDhy+cTmJ0cwvT0AGSW2AfdqVOGZX+gSQ65pXKZZMKK/KjV4z7K21yVMTV5oUFGz4oaVTEKXjNlI2OzCWqYzSaUZN58+6/g6OFurUYLlspR2308AXZb6eeB5r81Jfa+YNCNHEI0L6zHMGh5FLAi3UzqRmxtz7ZqgcSeAfQVNMnqKk1vNan+vPPTS8jmTHy+RCbPHFeviIHa1zZ+mVW03s8Pmpr0yiWlGZY73RebT4pQChO/OVUwSS4+SqVJLp0pegMWiNCzEnV9Qp9zRHV67Zbc6LaV73nc8xGlDBTq+ww6bdZtRxz991CUCUUHmN2ySCChokLagF6VFfLDYDXvXX4AXhdCoCRGJ+cushOxinpwOoWdw1OYJl8i4PkdD/TxRXEGv1JUroHpJdWyLjY0VIabL9bLIhE398n0TLJaCRaaQj415fRssmkDnaoilFKNkGmveiaIOVpDpYb6ahqSoVpLsBHdArE1s9JoqvCqEGURanb2noKhsDMGUejDtYtb8PpsRkkx9m+iCBuJaDGGiDhKt8F8nkC7FcGV8xtwfn1AD8bR6YyEZrLcxBV4LCzyBY4L2AO8vTGArUGXUUIY+tDvtlmvHRJyiVMPfw+r7hibYN8gM4E6Jbuq+RwRmsvqHumqyKOo58io95p5jign0O/PkiEluu12SIhtkph9JcHeRFSQVKq+J7T+WVYNvh+yYfJUonF/Ky+p27lBIPktC4Id6Du7dzqDrPAhQqN0p9LT0KZoOk+hKLBewWl93B7G0Gt50Aq4GkoHkVxRWqprWjJCRPEx5YJsixxNaBxVGUrLsxhqUD/Xkt0R3B2Psuzev/3Lt+Cf/9EfA6rNuhJGJVcP2rEar/XbFzutEMbTxPYZM7omKLSEyAcWpZoUS1wTBec1NRmvvetR0QoREfLiHXRabMyUP72zx+N+C4LtIUpif+kHHIP4S3EAPwtc+OCdW0RJ/KoDo3XJfe04ealkEd28+cmVPCvWL129rDvtVun7XhRFkex2ukUYxzIMIyf0PRRU01y4C1IT1SxUpYyUkk4hy1LnoIoFQLpYFMlkzFzPZX4UQ9ju6jBsa+64Zg8zG5t0XU+5ric6nS7f2jo3vnDxymgyOfWSZB699+abPMtSvnP/Hi/yjFlKo+MIJ/RDXzhpej6V8roG/bqC8g4w9pZW7Ndopc44XAYFN4CxCbhCu3n2FpNq+kX7gBjFHX+3BHDx2QxjBlw6UCz4I3s7Ct04WrdyprYs/RlstLBWKxATpVQxFOa5dLEHr756Hq5dHaA9Esd9DoX0MJnEtozdnRHcuXMMp+OEksq1YQxIc8WCGwm6ob+KL2BrqwvXr6+z2SyD/YcTOBklRGXF121tdqDTCUnYcikYRAP3lZYGHRsxsGURs4rV0F0h6rXo67IcUaGO2O02jMMjZWkO7mSByplUSN57OIG7944p2cSt5+LFQRnHfskZQ3o3WqHhWsW2t7vwyisXYDxeKPzc9++dIh19NSQwNk1gq52nwNgMGHPzLPeon/MpG98nm9/CuPrKje/MuTLO5cHeJ5ksVSqllkorp1nrrwIpYqQqQ0M6Gk2JyrM+6JHPETOENiIQCCFkK8YNpOWE3jFRAj3XUch5LwvszVemwVkpmE1GcHr4EBDhjFohvYf8IuuvDXjyRQLTk4eQjI7Jb5IJO50t7RNWgk4T/FVS8gBV4GlSZaYaga6u6KGNtKpuAGuoXeq6cav+OcqL4R86JkrsUqwmiWoxPj6Eg527cLi7QxRiL2xRAA5V8mqDUm5TVnXGtw4Te1wglW0qr5JkwY14EQZUndiFrUEE5zF4QooIN5V69ZhNsEIMGV+itHjVLNrIjS3JEt1oohHMVvUe1z/W/LpiRVafoRl3V4Aiq/wLa2o0a1CYWZ2UwpljAbCVc1q+dyXuUqFXsLxv2pBQlvTcGrk37Xh1Q6B5Y4fxPPDcIg4gcByJdC8U60GvWd1txXJr2Odbgx4PA485rtCB56kw8FngoYW7QbUpxdTS3gfylBZVgM2tXO5Zf9El2svOXl9tlZGNryHNCQfpuhAEPgWl2Cf8tIUxzHiJErXYs3PeCA3a/hjOaxscboXFgBklbINyN4sUZHCN9CGXG9EfVtsRNYswFUKlqxpIg7K7WkspTa7b4HFYxL2e441nkdmJRfYtDcS9qncAVGyJVRrtUgDJUHElKelyWOu34fUb13UnCvXFjTU4Hk95hv3nWUa9vYhiYnFuc9iFl5+7AlfPr5texMMR9dGTAFhQFTVMsB35HrRQAMdz8Rw5UnKRFlv5VJIKcOCZ4Il8kZXpnacCmEB9anNfoFZOtAwPg/pqWweia4PFSW4gdpzySCH3OjH6WqooCth6r8Mm8wQmswUcHI/hcDSFJE3pHLjtnzcX1vxV5iXsH43ZJFm4eVnoUj5ZpryqZVV3r7rmGKSdTFKYeyXdZbRCwScHzx6RF0SLH57MjNL6IodnzvWwz7Pst0ggl89TVMfVFMmlmUFhuFk6jNsLg8DGlq5Vl7ogNftHqhQPAjf8330fkr948xa8/sPnIW676UzOb+tC3+ZMX3W4iBGBznlJ9xKDZ9xbZTX367ls1gNEuhFBxudn5iwQuVatKNSLBFsd0cCK+fLodHt3NNqKX7l6s/vD66VafHmdEKy6XIh8+ONza/D2bz4m+vXXGKwE7rmc9blm66PT0/XJeLJ95+6dtTAKhOe4Qb/Xh3MXzhVxq71otdty2O/LVhRzL3C9MIxVHLcc1/OwmVdIVaZa6YxkIFSZZYtZuEgUd5IJLJJZHoZt5Xi+43g+c31fuJ4PjuOVjHO6kp7vJ1efeTaXsuymyaI1GKzJwfp68d7bbyLq6Z0eHYokTdBmJfTsgwOgXLRyZZz3gfGXBBO/B6B/LWUxk1pdZYr1RZq1hSNZydkbzOFPrPaxqrJZfveps48bCABgcSeXuGY1k00sSknHZ7I7L8pOs4xvC7ymVK1I80H4oQOXL/fh2etD1sE+TanBQe/d0Kdk8/BoBp98/BBufrIPiP65bheBBBZGHu0jJNFhi+soBnTjxjl2fDyH3Z1TMqPb3uoihZbt7I6QfguXLvVpHUVlaLs3VMjhMsaDM3sQ0mpxne3FUGQpzGYL03pmqWK4ByClfZEsIIgB4sDEnYcHCXB2CI7LiMYbhZ4WDnYpmHUWPyv29G9v9/C81K3bR6XjCleUSthLdLamMQVgaN+TSAYDKSX60EyeNn7298nmtzCG3fZ35lw556rdbY8XyeggR3UarX1RA0TNJnZG9CwMVmaLTJ+O5pCdK5lDzdhGrU9p7WFFuBUF7rn1HhwejeDweIxHEEcn47U8L3UpERgSZBcwn5zC6cMdmJ2cwGB9g+hCv9X9pApI0hSS8SEs0IuyzKg6T6qvFSWW+vTEUuTHBoxVwiOEW/teVkM/Dmqr3rMpRmiPxUjN2oQvVSDGjFYSCeS4jgd5nsJsPCLK7OHOXZjPJuB6HuRY6SpyqPgmrKmyapGHs9RPTh3iVc8aWIVcBoEnoBW40IpcWOv45J/ZiUw/gHxMMlj9LljqMCmfChsuQaXyxmuZCmUDUWgkko8ytNij31vlYdcJ65Jaa3vhrMVJlRvASqpf352zN+RMV6uuQU1e3a9G4kqbXV1xJC+9ZU+y6TJx6BNjKx63QbXS3PUcxwucohWVZVoodDIRcbvN19YG5Wa/r4fdtojigDh/TEOJlDC8m0pjcKSZtj19leIqYiCKaJLc9B+SDyfuZ9xozJCS6ir/tb4WdaKNylOaSRPkQxwFcGV7HS5vDmE0msHENG09scj+u6TPgp27Mc7RMLCWQ5jgaUMhrpJsVikS85pgW1kFATSFdmo1WVYhmnW/sdK1ypWqRL04rzMEO90aRRJ658Co31YzS1mbIwYrlQ5YBh3GB1TopYS9JaLXAkSsfnbrOV3RvavquWWkeoLDubV+2Q6CdHs44EfjWbxIU5inGQU/iIrHcQBbwx5c2hqSciye0tagR0UkH8VuhIDGg2XeTxsVcWnvNSU1lKg3emXrc6s+oVXrVlB7hS4XolpZd9XOtUKQlaTzwuSVCa5c15Hn1vqiG4csLXJAJcedgxPY2T/WD45O9Xg6Z1ku2VLtm9mCZoEJN5xOZ1Cp/T5C439k/pqfCyo+WM9nDNww4EtzaPkuFeKw5xp9OlGlu3AkCRpNFxmtP2vtQK93QlTuZkjso70uK+lO2wBPSIm2GYa8gsAJzh1mqj8TrSFRoJ/Riv3TUPjvurp463/6P/8PePm1fw1+mys5Kz4upXwzy8tXlFIx7RdWghd7WxFprlTPK4SF2cIFFhy67ZDEmEaTxNi0ME5NrGmesf1j5cXz9Jl80LpROs57bJwcQvHltSMYiXlJKJIFnO96EIZfPdnEh8NjWV5qVJHlfcd1zpWlvDY6PQlPTnTGgZ0excfheDr2/CBE4Zg8CkMdBL6IW61gc2sLtre3RafdYb7rlY7rSMd1Mi44N5xZ9JhCXaeCLaZjN53NJZq4Oq6v/SDgfhg7QdTK/TAqHc/XnJNtqiOEw+J2Wz734g/kuQuX85d++Ap89OEH/kfvveV/8uH73tHBoVMWhYOosxGjYQWKIHDg/SAMXmeMd9NFcl/nWay1HmIXENflgRLqnhuGn8Fj98flo4kngT3B3xkZyS85qEWp6iqu9yzFBOcbrue8XOblprf0PasEGxBaxkKQi7/c74bs3HYbMNGUSkvk3/qBz/3QpV86OUr07VsHcPv2AZvPChgO0e7EgXBFnJPEyAjdvHJlAA8erMGtW4d6bdAuXnn5gg4j1/3ggwf8+GQGjvNsI+G0LVRLHYGzwYc9vNlr/NCDVrdFFP2UbKCWxUlq88ozskIJHQ7DfkTJ8fFxAlwc6c2Njtja7HLXczmiNsyu4Rgf+76re71Yh6GrcI3HvaAstbQRjWH2mHMrDA1ZXymlnnDOPgSA+98nm38PxuHdO9+ZD4mb28XN7VG50PtJIRdSQQebp5VF+ZaCLECN1Lh4oM/QyXgO83lK1C1mRIIw6MIeNCdCyej1Hjw87gNWtOdJyhd56WE1z3GYobLiQlCkRKPFPygcglLTT+ozA7uIY5CdTE5gdnJAHpTVpm2hulqoB6ygCLN+k3RYDL4oo13SLDEoM4uiWgnSWYU4NlA4sEEriQNVyYsy5fQqMDYhLIFXpLr78O5t2Lt9E072HwDSfLauPgvJZAKnBzuEzrLKBMK+r5Jq5RpwtuxDxCDJMPwY3Q9UVhx2Qtjq++RFhQ31gWMCLqXV517LpZJ/5XJIfaWsopJUwR99G0wS30RHdUMIafm9JWV2de40ez2rP6YgUKHOupFoLu9BA+1czfZXmvfMj5doKGv4MrLqApL9hZEpwQA8L01fV27azzCgJSDSEaAxeXcFfXQuHO53Wj7vixi9SnUQBLC+ts7XhmsCAyPhmIZCKzyFRRRuBWtYNZnP4L6GrkMJfPWMMY3XWpBnozbzEWqaZ/NX7cfVrPp8OBdQ7OTq9hpc2hzAB7fuPxbF/tseSPlb66GxfhvVSjEOlTZ9FGCtRJBKbJ6hZUJh6OiytiF53Dw+S43VNf1ziQ6DFfhqilSx6vFizKufh1ocTNn1o/Ha+rwoGcWCd6ktowOsjJpZb8zJcqXOsBOWVNolGqeJoouWOd12lEVRyLfSLEbqZZlLmMznlJD2Oi1KNgLXAUQ9UUgM6ZMkq88ZoX9KmcQNrNhW1f1MLIhKhcsilfVz1jgjyqthmVlBkxHB7XrXuC7VqJkjFUvBFnsUtRuiRx5qfkQwwN7lbhvOrw30vf0jeefBPts7GolZkrEqAUeBHER0S0tVO9u/rB/To91gZVAvIyc2x/J6kw0K2sqg56ItxGDinaTm2uMzh16caVmyUioPQY3QZdb6SsBsYZWIwe5x9TWrryhepsxaZXRBq1eKbPaq4/kftDu9/KNbt+Cl69fAAfkApLyVFXJM6rh5Tp8X1wJUlca+M9wHmyrw1WdEBkyvG8Gg34LdferbxLZGjr6sCfox03/tSxub5190Crae7U0O5VekaSrHAT5O4UrMwQ/FkzfkJw0Ea8rseAe8hwrYyz7na8IXXRJslupIM/WwlKU8Oji64PreBmjw8zwnwa9er8fOnT+ndu7f51EUs26n4w76fb/T7+gginzf9XzhOBKZVA72cmoIJKobSUhlkRf5Ys6Sydjxw9ALo5YOWp3SC2PHcT0hHAxTeI5sq3anAy+89HJx7dkX5Es/fEW++cZf6vfefss/OT5i8/lMjY6P0XMVTcULfL+o1RaBH1yaz7ze6PRkXhR5zhjvSa1/rEqFQkiHTLO5eqzquWE+sS+nW/udG/ipfRc3UwZ51VcM3NUcXpoD/MrTsIUtVdbKBqzxtiBhX61FEArYXGvB+jBCtVdVFrJ0HTSmdjmq9M8mCVqdsPv3R3o6zXTgu7Cx0WGoMotJGa6XyymrqSWi3Qng+ec2Yef+SB8dzctFWujP7h6LDz7Y48O1FqAKbLvlQa8XAxJvyrJSjmSPLNv1kU1ASXFS2G4Z8Tu0m8tLu4Rqu56i4nROMWGn5VLyu7eHrg4ZO7fVY1evrkPU8oi9gIU6bQMix+FsMGyLc9tdtvdgLOYzEiKSjdo92MsdatCXQUMfpD4VikXNcOppGd8nm9/w+Df/8r/6zp1zEIWzOPTnnSjMcbJnhO6d6XdjGAho6rvhkLHj0wmcTCbQ6UTYsG8DEV2i6bLruG6nHYluHNEGXyiDljkOs0UsICQSkT98khFQlSj9jlXUs4BW8xRsxbpIZrCYHEOezI28tPE3sgjEEuGqAqQqmTF+C4qkqfE4RpSoEdSvJGBLCh29NxfWz1HX8X6ForIl7GeDWU6LxnR0AMcPd2Hvzk0SBZqPR7B19Xm48oPXQJUlfPybP4e9O5+YoFrpugoIdZLIAAFbowrJyJwYqZgY7bikMMmhG3nkm7nRCyAOHUrkQTe8Jj9nVOsVoXiCVwUDi1pUyIdJ2ozlA7ml1ttkfV3O9GIuVUNtUn62yVQvMcllP+0qCrdsmW3sGMg6NJU8rhuvq4NbPJZa3sbVFk5N160ojUfmPFOQZBIWJSad5r1xw8C2LpcD2pTowOfkaxmVQnBfs0Ho65bn8m6nzdbXhtBqtzgil2jRQImJuVQIdYW1mnDzBqx8Ty+pnMyk1hV6cyaOb8z9RxEdba8BMr42hh3YXOsRVbXxyD4VpU2cP8j2uHrkakckAAAgAElEQVR+EzYGbSySaEJ9zUNVCwVp60NLvWlgAXyCfyvicAP3blylFZSLLS8M9XDaZ5tBcy4uJ1nzuhqklYO2oj9QkV9rESu9LGrR4kLN7HXyD1qj6TyugSFo7dW4aVV0eBzDgJtfl4r6PWP0AO/EoVYqIJp0Jw6o19r3HKIf49ollRHaYdhfqXShtM6kQrof9pxqO4l0LYxVJcrk40OistomzI9eQ11J/9cBlq4rRIT4NhPUOslECrS2P2M2cdVkfkT9n9I8HFXBod+OYW3QgbV+Cz7d2YedhycwmiaQoocs+iQXJQWN6L2P/0Y6LDzmXM8ODYbJgUXN5uqHL08LSWhS81iF7WlkVZuIpThW/sY+B92NHIZrBP4uCgVLpSyHdjmVLMLQp0QTaBnJleQ/LaR+Kwu77/zr/+6/l//zv/lv4AfXn1PHbDpfZFmO/ZmIZOL5UFuE1tSzidcBKbX4TFvXE7rOhp7skr81odg2Mcc/OA1RiZw7PAKHbabjRV+qjCOv4nMfyicMRIeLeQZeK4Y8K1a8jb/MwHvQS9mxcPhfa6Gug4QrGvQeMF4ygaxTPtbAPk6zbFyUZYdzhskmLxE+XiT58dGROtg/EEVeuHErFutrw7jX73vd/kDEQei6vsfWhsN0OBhmfuAXaEosGHUBcbTPKcqUl0UhsmTuuLOR9oJY+VHMg7ANfhgy4bgOw3IIY4XnecWzz7+Itins+Rs/0HsPHrDDg4fl7v17bDoZB2iZ4nqu6vb6JeOieHD/rjufz9p5linTO8NcUPqIl3qHFeKtrMQywJkyI9IusX8X2Uzd4Ctd0+/KoDCJG68OUmllDLsiN4pSbjPOHM0gYwbcrar5rraJOKKTnS569ZKqK26O3EU6j6Xc7R/M4ObNQ9g/mBDrYW2tBefPd2EwbNF7ymKVsYbJJwqHn7/Qhx+9epH95a/vuO+9/wB27p+y8XhByeEbb3xG6rA/euUi9Adh3Ztp1tMnXHS7NiKVPaLnpSSlW4CGR7s2QmOG+SEhTUuYzjKYMQaf3TmG3WdHMBxSuwS6GhHhzAqcw/paxK9eHfI7d45gf3+ipVKCc1vQrOugOrJUfrR2KEph+7eesvF9svkNj53j6XfunPXxNG95AtV1ikopUZ0pjVTVY9zMF3kBp5MZnI5ncH6rZB62Nxg5+lRpveAMWq0wiLAXKY5CQkGph9nG2pV1AH4DFVoRnaSfLGO8R4cN6Qqkl41OSMXWpB2sRjDxQaZEraaRVb2QQPtBFS5qa+peKSNqG2Y0kxRYmi3a4JbbgGSJiehGZsPt842VLJnnMD46hr27n8LpwUM43n9AKrSyzCnJxEVMuA5gTwkj5MbSnc4EwPhWDqpS4jItgZJNoN4TDp3Ag17HA6R9YaKJKoK8UsD8IhmGTQrJ05Mvqa3QCOaMiievE18T968WIR7B7ewlqXpBWeP9av+q+j5UdEZ7GfkyGavokuaYyC9hDbBvFWEl2k4z37ARNqKXiFxgooko5nRRwjSVkOaK7GEI1ajAGmv/4nBgM6bBSzn1ngV+zkDkIvC17vVarNMfguuHWHhBmwhssEA1LcGoaa1CuAXUfZfa+uRUwBJrJE0MqmZVREQNttq0VF3NBlYzaDuUJT6j+uj2sE9J3YPDU/Mrv40m8C0OLOQg7R4/a7/Tgp/94Dq8fuMqKaTagB2vl0BagCmMGBTdYIYkz5MT6EcuIpzo+Rps76CluLIaarNXk7Fl3acqfFQyqo2k8uzXK3OpiUSeQdGW/cDMwv3A6zlq+oELrRRRmqo1aWVSLh+ORr8wq+a9g/3ySjIwhAKidIMbBeT3JlVZai2F6SWytiqazmqhQY8ZaJQw7WmDFC+fRajFl+rSRpOfXyn9Vqhv9fNa1EvzujBSrxENA/e6uLTSz83t1Mb7zG27AlVUQHBFvUrDfku0Ip+j5cpn60dw6/5DuLN7AAen/z97bxarWXadh621z/SP97/zvXVrHrqbZFMkRYWUY0WybCGBnpyXJG+JY8AGgrwbieSHIAEMxQiQIE82oABJ/BAkL3nLAMiSI9myJUsipabJbjZ7qLnufP/5jHuvYK299znnv1Vs9lBNFgVusrqqbt17zvnP2Wfvtdb3re+bwzIvpYC4PugLvXQ6X0JRfTxKqH5B078R252qfgyXizB8DziBCzxDRmj29tEkEdDGgJN6g2dgIC2QZL6uglP857i+74gD1OoXlqn+tbBDTwZdOFk/uEmbt+/Bh+PZLE/zGQlyEdZepHxuRjfF/qSsWLXatoV4j2awLRTck9vrJfKesB0KI7RMs2bl2DgKFWm9E/WSK4N+3IWgWnyaahP/yDDch9GVIfyL/+8PP/kBWsMgVKMg/M4Mqi6AOVcGNwzCBgH8HABuEtKIObEEuqgqXPKfk1CV2phsOp1HZVX0yqJU09kMz87O4k4niXvdPnGBNI5jdfv27eTu3bswHA6rbr9XbK5vFp0kYQvVSEBjroBrTWaxNOl8HqggSJJeP+gO14Jub6iTzoDCJAnCIOTNrlzf3ArWN7fgi18paT6ZFGdnxzAbj8s8yynuJGowHNLRs0PzB//sd/D+h+/3DJkECSsFGCkI/y1VBO8S0kNAOHou3HdtHaBevUTg8x7GqiGkSuGpAboNhMllKUHLDUXxkmX9CUYJq1IrLiII4SVQsj6fnc3h2eEFzGaZTFae+wPb++iWuMtrgEUb404Et29v4XsfnETff/tQ2A7DYSLFnbfeeixrB1ubfPWrV6HbjV+kl/DCIToDAUHcjaDb70BZsDJtUe9MQnyzUt7iyxkor7lBMB4v4PBoAnfmW2Zzs8/aAVy+jo2hkPdQvr4rBxuCvMZxgIuFtHtwP492H9ZHFXy9JaEpRBRCvXrFjJ8lmy95LLMf2XX4yg0CYCPjMyJ9we8lV5lWmo1aFX10ooyVrcCAqsMOeSkjG1OogOm1O1trQqF6ehg40T53DFlIjPQynj97DMuLE+AYLcA6Ln9uCBW20tKjOR8fQ5WnUtmvFWU9xRU8bVPVFDKPZPhgMXA+ZuR7vNqonLpM1/PdYf4/NgBoIwYSPCobqGXLDC6On8DDd78HT997V2i0ZWm985gSePrkQ5hPLwTZzRbThn7rBrrKMkpfh/PBJNtTaQvaaNUmhx24utmB7VEPktgGhPoTJRZUR47S56Rspb9m2DmzYS/g0r4+uvwFuPzFFpDZurcrNifuBy3KAjW6ia2fb6UBAQpSAjUt2uYc1MzJlm8nH7OoDMxyDfPciO1BmhuLZJaNcAuiS3xdwYFptpVQqq1nFm8ORVqBwQz6Q4M3kgH0uwNLd9GiqMmwQi7rqIFYWqj8Z2tRFr3oUfPZnkt2bBmjNn1tBe5t5M3yH9t5tU2QnDcfU9dvHezAuw+fygb6k0w09zZHcLCzAeznePNgB37hC7fhS3euAvvvcpAs5Sb0zAJfhFA1tdT65tFUzEUR+6jUGrE1xkpjpVCK0FZ5hTPRKn74BAnrgka7gNH0/zVJE6zeeftfl4xJkyK/963kzBnDOx0RmVc9m99R6O89tea6J/JbCcHaH2mlVcGl2/V1y6dCKIxRGZHu2LzRCiKhXfsiJNN3Ca4S5kbNDGg+DU8d6cGsrZvs7UEPE/ufUUHdJ2h8tORrI/xeBMqK8roX1KLBXoLaMldU/R5ysUH7Yl3l+vpDXZKIcLOa8/X9HdgcDQXx5FXvw0dHcHI+kWIVf73fTSBleunHTDZ/1HjRKyEWMoVN3OxntpmkscY2VTdWeqMfYm4ozCoTmB+1xRP1SMHdCNSv9PP8z9HAv4gGG1myfgWi8O1JhHgSKEy1MV2xM1B2RnCvJLeo5GUJA+rUVlTkRM1YGIVta1j5dzydQ14UrlUAJZhezDNcDor1fDvf6aHqKQOLT7sCVJzwdjrwy3/9l+Gf/94/fyGF/RPc8wIC+ONOGH9ocuxlWXYdQ3wMGNxSZDoAtKdRTVRAz8CoPgAxklgVWc7eapyHK95zy7I0VVWFi8Uy0EYbZpUsFotgMp12et1OORgO4fr1G9XVq1dp0OuXQRBI14RTp9WoS6zyIq6KPEwX0zxJekWnO1S9tXXVH60HcafnN0XNSf3m9jZubm+TM88np7gf9odr4Vvf+lPGUUNGj1E0mbleoLbyYvkmKXiNVDQGwE+u0PTTNbAVEjw31VCwAyVFsxj0gDTtEnDPM2bYiAJR6zgQRIo40esmEUoR3CleK6dxkHPReJYDK8vyu5Nw0X2jB6NRR/oiX3wlUOtxjJhO+9oePH06EfbZYllYpHGWwttvP5Pkjlljr722C4NBp/nZHznHGT0NxBIlW8aQp4XEuHVrh7F027Uhq4In8mGXaQmzRSHJ83Sa4fp6n5NIro0ENjcj+Rn2R+feab6uMLRYMZdVDVfy7IetbAlMds9SkZq/irPoZ8nmSxz/0a//6k/ldS9iPdtIug9Aq5Mo1JUKqsiIQbGVtbfebQh5SUJ3tZGeFrl8rsYmllrKEz3hvgvZGhGZS4+DbmwXgdK+dKGS/QMqKJya7DFMTo8lKRvC+gvBf69gWuY55MsZVEUmrxVXhrmQz+goeaEd+xNy3T4Q92iCT0xEW4BFGLgdvfIV78uF6ibjkX9i6m0dZLqEhZNd5+2lqxLSxQLOnj2Gpx/8AJ6+/w5Mz04leAmTLvRH61Aul5Au5rBcPLZ8UOm3Cuq4E1ZWX/sn7l3KWSQkUNKfGXQUrPcS2BxEsLHWgU6sPP31E4023ZTvFScIBfhEHRrRD2ff0MSjq+lOnUDVAanPs1YlftroSBuge45GS607QSsnsaQwbIRNVN1vKhG8DdgqRt7ZU08D+2IymlmI1YL7TK1E058GHVLjFVH57yUZoeNJPx2LYKlInpdHUt2ckV4TDv1tv2Z7zqLmAJtnCZEJ0cOedJly7AVxYEWI6fI0dCThStA96YZpJqwkPYGSfrh7N/bhrR88hAfPTj7ZhPiso3U/1wc9+OrrN+EXv/wa3L66CzubI9hc64uAgzaGEw55WqYlUoNtlFzgfil4cSli5gC5AThKK9r7aMsUVrxZrUQ92FDgLVU+sGJQztuyoX1LYax+Lv4RmVpIrEEnlUsI/DtAK4mYg8uMQ7cQ6oS0rVDLulEIWNkChJxPCihiCK6EBuk+FTmBVnsebhxmz0mtSaGjB8taZLQVN0LVIat6bdV7PDPN+lX6+ptyNTLfKEB+kZHkNUBn5eKeSevl9Yqx9f3h++d6mjyVFut73g7OHF1XknJib1lil1oOHu27UkkvItNqmTbL/s3JtyJB9nhMJgtJSJkt8nFRhk87uNBWGWeDxZQ3mSZG2qoDVBfdGKeDhNZmqdnJqyqkS8yf1iDLzxHHveuTRfHVIFRvvfPOt7OdzgBiwHR3b/P0cLyYjWfLbmqsByyXqbIsh5Nzbk+Zw2jYE0VKXzgQIagokp7njbUeHJ1e2DULgcqqQs+iSJfpZlWaKypIhkGgTj7LLRN1Ya3E+5VVNz/rLQaAp/zwQxU/DoLg/ZLy60j4hiE6BE0DgXcV3APC1xBwjRDmgCK8xB9uoIWPzeJowng4QzLp6enp7mK57MZxjFEUhR+8f7974/o1c+vWrcX1G9fna6MR0w47hjSjnWUQhbk2ujRFZbJqYcoip6rKWOBNRUmHX8nA8atMy54DXTWFbwL3kNLa+jqFUcR+OGhbTORdXkfErwcB/oAC/IAAn65MDK4UMcz1yjkg1uO5IAiBpNOGQxHLjpWVhqcd0/b7bMfqnm0hv1tUQbl+TJ59S1mAEW4D4s8BwW0EGghsj1gAENNpY2PXTjUcRHCw24fdrT51OqFt6amsQjMv3VlaidAYs2M6ndD6DndiptIRM+zqjf4FGScL7PD33769BacnV+DibAnmeGL9ZMEA90R+77vPJLHjr927tyt/9urdHzncJsbij3EnljugXYIpve9gk9HRMAEWPnr8ZAD30ws4PprA/funopZ7cLARhaEKtLaewsa1Q3F4GEUSm7AAIc9Ntk6LvOqtbS+SLaMixAuqqFLzZfQRPMGfyPhZsvkSh/kUvlavwqhYYw/hUacXP+oas8jzoisJnFNY7bN3WxzCZJFBVtilRER6FktI8xzWhn0JxiohnIPLCIhYoZNtIqLQKiVKr2bCL2MCZRkKwpGygM6j9+H00QNY39u3G6xuLRVumeckdzk9g3wxsz6aKrQonLM58Zu/N8M2xq55NpB31WonPuI3Zgms1CVKbCuKoKa70H3BJWJ1ctjQdxfTCTz98D149O53xc6lSDMOEoXa2un0xJaF5bFlA7dMuDrRauOF5ARsLCyGEviwYmKQgBiUbw4TWOvFsNZPhGpCnyLRrG+t206st6alPJp2/2nr+zwqadoJ+AuQAg70vUfhc/cP6kjX/lHZQNa0bCIIWsJO/pjN0VfFmxy1jOcpVzk5wZwsS5hmFaSlkcSTG/35RyJl6cIcelem5adFFunm3rjRoCPoxtksq9WGo4BgNOzD3s46jAZ9axPjEnDb8qo6NhA2WCt82gmXyuaroIukQnQ2FyTX0OrZBFXTjj3VmEzTc9KiKRcEdOESq01oiRl6z8aNYQ++fPcafP/+Ezg6GwsF8dMO5TJyQ615D6s1gPqRKoCN4RDWB11RSr15ZRe++sZN+LnXrwm1Nw5CmcpVvWk72V2fYHsBH0Ouz0fubeSCt4T7YI0xEbo6h0sUPXdT+ZpunRx6qic2pHdv40Mr/p3NZKQ6jazpuS5kcYgzteihxmdtVkkUfbLp7pFy7AjtnyO1VjMHlBrTtKTWzYKqfZsbagn3NnGfE38IL+6DZJFHvkqeVyG/R2j7yH1w46sqTrjTXQD59RJtMsWAp6nXUfteNIJLl1/fhn7rk/ZGgA2dj7H/Ni9MRBaGjizPXCRn62IT958ShOK9ykrF3VZSw/6iXGyzdcLPN9kUXTn34vqeV4cuIynKokg9HPWC7awIhhWZfl4Y1OYFTI/6holn6xUV6F8IzO4fd+PuhIzJe714cuvqzpM0zU+n88X2Is2599up0mqYzVOYzZcyr6wtVmPVwz2v7AnI/a5rxxdyb1hIik+XRNY3EAO1Pzk/u33z6u7o6rXrssd+Gipt8zEAdv/2fwz/xz/53+y7+Sn7N93w9ZoUAR8ZoCON5kEUBX8UA6+TuAHG/IIms9QKOelU1sbbFrBloljnpxwRHiHiXGu9MZ/Ne+zuw8vr6ckxPXr8gA4Pn5lllprr16/D+vpI9bpdxRUbIkw5YTVgImYb6qoy8+lECas/iKrecISBhY8cZb2e/aFLoqr+YFhevXGT9vauqPHZaVhWuk9g2GGyR6BSbeiuIboGiCfeXF8eYSQcnR9bsmmZDN6/lroI3Fds6R0oTAj538Q53rCFwg4gCpyHQLGBcO0w31iDvAww1vMwwskwGkVLWAvGVZUFVG1DEdyaswd7xyxDgznOOoVBE1YGEiSqEtA596JrgNcA6EsI0APEnoPsC0AqmC9hHL9oOEhwf38A29sdiEOUQhznuR4l1JqIxX2KvFLCSMgKodOWlbbOdwh1ke+5++H2n+GwA/de24Wnz8Ywnc5hPM7qd2Q8XsIHH5zB7u5Q3rVr10byXhF99PpjkVOAMFLQ7SfQ7XagzEu5bquyzY+DTBKH6mBvqK5dW4fT0yWcXSzhyZMxq+rC9esb4d7eUDznjNtjjBMT7HQi3e1Gc26511r3mYVv685WvB7tXL0CoH4JFD3TPXXMfrD1Yv4KjFcm2fzv/sbfgL/3e7/3ClzJpxt/6+/++9C/vfHTeOnQ5Y6JMHiIS/NWhMUvhYHa5gCFZeP5fyxCQIX1LfP+koWgR4VUi1psNDfke7Qx7BzEATnVkqM2mbKURT4Ho5Ws0sq/+OWMkwCsTbY9kgQy2kA6m8ByfAplvoQgjORY3OuJpmoCxtWmy3qg2+JI2SSBX14lAbpqxD+oRS+rUYs663TIqFVoRIfuiZUGaciWMzh6/ADuv/0dePbBu1AWmaOjNcfjhY6/bi39g9bFtdOxpseON/VQ1I+MCAFtDRPY2+zB9lqH/etE2CfwycBnGJ566tUaK1FDaz3JSyicp7y2L7/9pZV/b6E+z08Pl+i786tLfJwaCPQHbYsNtcRiWLhjyfYFmYbxknsyS0hzatDeVo+WadEsJbiT52KIq52cqF3dWsMpG6sXWsRYNkYDofHtb2/Ate0NGLDZuiLZAD213LaroVUf9vw6++Bj1nVEI0kTur0I2o3JbbGV9hx+kZos2c0kRgP+5O1/k59j5JA9GL/55j14+OwM3r7/1JnGf7LBBRLuA+P5wPRCob2+YJ5xn1uSBLS/tYk//8ZNeO36PlzZ2YDt0QC2NoYwHPSEfUDeF7aGxmk1Sm8ObtsyXZu1AjaggB6hS4w8MtyA/6FSWM8ub6Hh76MtkLjz+vmDnq4t6QT6ooNdOyxtySaAVjCL+8JbZNR60vtkU95tq027KkDUWhOJWn6VjEq7SVP3n3qLUN3I5VOTj3NChqKK7+iUeOlds8cxtayZRXZrEFORYyaICnid/HpjI1KctAQY8s2UtdHU7AblJi3V52sr/wLUxYOVeeiTcmj74BIp8dWUdVTVx9RS/NFWGCQOYdjvQhgom2Sy3C+v7wZ/NLIA/tF88qSUr7AbxdBl5kgAcKkBPCCD2yqgoB+rzmYvjHNNOKVKqPpO3La95aBNK4B9NiJE+trm2vZX/+E/+j+/8z/85lb++q0rZ2k+eDAaDU67nY5mGw9u+2aaMPfPG7JCZozuJoLUNMEzPzFOQNeGPbHCOZ/MxACfT8qm9FzYRVLRYplua4B19mA6X0zpsyWICCoJ4a/9zV+Hf/17fyCegZ8x4fSDH2hGAE8A4YlUIQjDktQ7GMO3UOuvgVH3AhXsEejtyhDTmTbd28KupyP+OwD1vbo3RxuVNjrNcvMofJSEcRg8e/a02NvfS29cu0Fra0PodLsxG5Oy/xqb5I8vzsPjo5OkqnSwsbNTXrt1Bw5u3FRra+sKkAkkkojNXOIpFklxHMPNO/f0wfUb8N6774R5UURcWAekOQKcKKVSQtMtqkp5MSleyDhuKT89G/nFN9EWc9bctVmCDJEJgiDYHG5dBY1bGlWcFrSpILtuCHIKINUQbqschor0GQJdlKTWqqraJVMNKoA1MLBhIB8CcG+lQSylM+PcFq942WNxJ9oCoHvsAaWIltzWYKSUqoy0l3BnDxJfFwfGQwTsAVC3tXvxDR44n01hLqUpe84ymwMNo3letq0u6BFVeVbp+SIP54s8iCNro8eUWN6XFb7Agq01uIjD+9be3hp8/evXIU0L+PM/f8I0VvkmZjg8eTqGP/uzhwJOBMEt4MSQ5RSq8qM1Xn15PU4S6PQ7kC4yyPMcnFYjF4tLeYUD6AhUbCwz8ORkAe/94ETUcne2B8g1ptIVJJiZw3oMg2GHe7UDQT6FwSI2zVjrU1qT6CEQfc0g3Cek3weA45c72z7b+Bmy+RLGf/Ob/znc3bn60/sBOBdUamLw9EE1X54YYQ+y8TxJsDpfmhoRVK5iP0szmEyXkKZsZFtByAlgk41IWBcHrOrJHPiozqPKMrO0VxczS+myKmA5n0K2mMtLeqllEsp0AcvJKRTpXAJ6bomy9TmSAI0RRJFt9Sq6TrDHBvhlE/35uMhYSWq7l7zAIgEaKpwdPnRSdo8UH0b7q8hzGJ8eC3X25NEDyBYLG/gpUwejnFCHUST3iO+VaScALuGQhC9AV+23mwiLaXACsbvRhRu7fdgYJpCEqpWYfMbH7mlynLiG1m9TGRvUGK+c44NfH7G7O/FDAzov+e7FWfgZU4uG2OqfEy88MKvrN/pEwF9jq6e2RpCsy2GujaCY5/NSUE1GM9kQmUxD1/VDEgdtf2cUiM2f+Z+zvERW+ux1YrGl6XQV3Lnahf2tNdjdWmd6Dgy7HUFdAqVcIG5quqANgo1sCtYfz9g+TcV2GnVHnFyQ1iXUlGSnQOmRdK1bSc8Lhu1xwZG7WcHl7xCE0CgYDXrwlddviEgQ0/G8WNDqLV5F81q33kcrLQGn1SCfESj29VwbdmBnfQQ39jfp7vUD/Mq9a3B1dxPkfQ/dvZDow0DdCGwpmysVFqznklfiJVOLr5DXGV6ZXv5S0SPdja1OnYa2jtskmhaVhsbz1NeOue3Knc6CfC0UkFRN4ydNzkrTMSR84uVOwk581nWXn0Vb5bq5r3wS10Nmr4u8YlbbwkXVIrDoab2u0q1cv7nvXECHILaTX6AV2i97vZItsvn5Ru17aZdMjoiUJchJLcTdR29jYmeEWin4tKeqr8LzfhEq9/yx8eytxcFQmtJdrza5z2eT/EG/C1d212FjrQ8nFzOHCHz8RBPcOsrFIz4N28G8SDDo8uB3l32Kh71YRMGwflb2kgFpQAR9LhD0OgGulSQiY6xSi65w4qcIWLM8aV21lEPciZPwjfcfHV75h//of5/+xt/5D5cHB5tHUXyYx1EY8r3SCqUfq9uRgFLWmcIpagp92Ti+swiMWKZRqKygkE/gs7yweyE70ffXryyy4s7Z5OLbpS7HoPFH3oMfNXYOdkV7wCI1LyXZ9MO0fmdu2KOAxXWU+gsDsFdRsR8oda0TxXcBcLcsdFfragsDPSIIthTRQilck1fYtoRwdauaLeb03g/eC4MwMmvDIby7864+OLia3nvtbn79xo31fr/fm8+K5NmzZ/gXb70Vn52dBevrI/OlL72Zfb38Znb1xm2lgjgOY/br7IUqCLSrEgbW3q2D3V4vUmHQdZvaBBDPZb4YuhEouEaGEk2U+/c4FEGMjzekSIaOPspbFv9CEaLvALLVBeggxHSj39kiwi8aDVuIpkMg9nMpkYqMZtoq3gIDfc3sENBMX80AYUFGDQzRhg1WMDOATAmuSHPLB+wCwJqcw67ZkZjVECGlPmcAACAASURBVF042gmjpCkh8Pm20NbBtKedIMESEKdcFCBJMuV7ooa6VA/WbWRLAsUOYtz8mmYl92RiwVI3qKRhkwkd/O7pynD/NlZVZb8CiFwk7/ViUbANvI3aSgRxabJx0Z80dJIA7t3bg7IkQTZPTmbyb4xMMlL69ttH0h/KNiUsPsT2KKIa/VHriYsJxJaq14Wkm0JRyHvJFml8xSw3G3BbT+WEIvmcaVrC0fEMDo9mcPdeCf1+XO/GDMiwSm63E3FdqmcLpqpSAVVkiKWXA6p5g6AIYYBAu0hmdHnv/EmPnyWbL2G8uTH4qf8MLAg3TqqTbDE/OjqfpcbIBusCCm1DDafWJ4F+WcF4uoDzixlc2ysgWotqzMZ1YQVRGFK/G2Ovk0AYhFBQKUGTNnbJDVRAUaergyiGdDELlpMxjra2a7QMpRfSQDabQDofS6LJLx856xIZaAVGlBPq8d6SQtF0irM++bFiQjag8slqe9SiF20CpxcgYtSK0UovIBQoyNIUTp4+hIfvfBeOHnwAi9m0DhCaCNWGaVzVDKMYILfJ9uV1wJ678VdE12+2Pojh6lYf9tZ7Im+vzaenzX7Es3e9uSGQPz5ZP08vVNIEfC5Ib1f/qfVP1P5i3ZjpHtVlbzx/j7EJ8GAVjVzhzZL1zOOYf5FXMM01XMxLmCytX6azAHSqwasJsY8dWUSJPTQ5ueQeqDwpBE25tr8NmxtrEiswpXZnfSjfE8WR7Q12c9FVEcEbR3h0yQb8tX0MPz/0ypYyn9H6SPr5hU6R1Ae1HwONwY9ar22RwkigerC7Ab/0tTckYP+nf/wdmC3T5470otNJHuAUZEtH0Wtf11q/Cwfb63BlexOuH2zB6zevwN2re7izOTJMQY7CAL3hP5eBgOlu9lF0nErqc5vfC4RHlFPY899QJ1LgUDPwCFqNuNfHIGc9ii6QkSo8CI3UILX6ZUkaI11C1praeBnNh8v/1nyKS9dOjupXry2+yLCC1teUK1pBWttoPbRxR3e5LZ/aVffPGjh0a00r+Ub0PcKm8d10lTTlW1U5WQkD1w7Q3Aj5Xv/3Gl0jl+yCFM3c6Yz9rKg8WiviQtBaD1aFipo55W6QpSIHgtjdubYL13a362QzCKJGCOfyOvGCIZQC9on0HqY/JDj0wh2MSHOSOVqLoZdE0jbYTNO6f13sd/ignYiVwAOYZ4oWubZTErG1GpDvWUObx1PfIH15ZzR689+8/eDh6dl0effW/nEUBodRFC5CpQYVcxrjGIZMQ18fwvpa165zmqClHSfXwn1p3U4sewyjv25CSF8l/2K0ZLQ+3E6Xy+v9xWCkgs74eXXOTz6yqYGf/+avwHe//Sew5FaW4Ll610scTK/EI0BzBGDeNoDDtCg3dVEN4ijoBDHs6CrcDEPcJKVu5bq8mzB6RjgghINQ4XZZljDOCiHmj88uwsdPnlSPHj2eTiaTp2EcqXt37+zmaXrw9MnTncdPHseLOfsepkE3SfTm5obuxgkEUURBHAeD0VavP9rUYcg7h22IztI01FUVi0iQbCuUurDlujFmJ1bBkygMvg1Eb0vIQS9Wia8l1qHeO7sAtNmLBz1VJd2yhG2V0DYDZjoTDgUjuducthrAozBQO5WGu2xkS0DbhmiHk00ujZWVuQYIOwDYs+GIGrOWFIpFkumyY44NcGSPYjGjJREvlmbgVhBGJ2eE8D4gPbboKX6B6xnSz0pYCbXLrl2Bq+KVlkJO65YuLUtBblFMW9da+fyuqsnWIawEyx7t3W5ESRzJgsJJWQCouZuXBXHyTIdlabFCjoW4bi+Fml4CGAVgSu7lkr0XW2Zyq/dcZM5RxHzu3duBo6Or8PTpWOizvEez3c8sz+BdrUUwaHtrAF8f3hAxotL8cO9y/wxFEKkTWbGgNGPGHu+JsXJqdq4bE5VjRPF6xgnu2dlCkFYWJkL0gStprkh1unEQBJysGrZ2QXn9qFbzbQU6tl9Mkfo8X9BPNV6pZPOnkUr7T//Hvwejz9w7/5MfrBrY21gbT+dbp6eTZZbmxUDEf5IIeux5xnTFtJCeNluZB5ilBTw5HsO1gzn0+l35ujOStqEWgmLOAFPy+PfKNPRBqb6HIVvtUZGlMD0/lk1MhvKGZwBVkUI2n0CV55IscrLJJu/G+fHZ4NOpLHpJcWNd+gR9ZLqk/z73nvvjiE2IqVoIiIf6WpwoH0i4/iMunPJGX+SZUGc/eOtb8OS9d2B2cQFkqkZIA8C1lbmkQldQyiK3Smts01CZwmEk4GfF2QDW+wnsbfRga2TRIttz8HKnio+dmVrCCnycULHqr9wCZVWDsfaeE9SnThEvHwt9fgk+yG76Duqo2QeuHi1WNTWyuZ7apqbhI0r0pmxP1aIwcMxJ5qKEtCChskkRRK0q2q5cGzQ5MlvILLKCkiiE3c0R3Li6g1+4dU2M8xm94k0vdn6VHqfnOaMlxTQrR1V1z6/rkQFcESKC1p8Cm/Q4wK5BxVaptJ++ksCnZ2owP0cWCvr3/upXZcv9w7fehaPTycozDwJ7Xz3C5Ad7i3KCzabzjBL3O4msAcNuF25d3YHXbu7D9d0tuHllG24c7MB6v8fiGqU2WpfacAU8dB+Bk74MbeU8cD0ljbiSvzutuo7/B2wnwy37kZaKapP4+V5s+3ctcjvE5xNIUniwNglyCGBtZeJAVFH0s0O5+Uh1Sy010rB1fYpqlHHF7qO2D2kxJKz3kp3Prc9hWj25cr6WcaMNCv11eusLZV0wWoxen0wBYdPvS40wknaK3JZnpbAugqi2+jU5WquHQK1VhBPfre+LT1JrnLmZvjXno87wXVsCC6+RQ49X6M3ePskpL/vk3SiEJApgf3Mdru5twHuPnknBgzwC6hgYymbIzraqhdG34nXb7mGD0ZXhP5jCOlliNGTU78IaIxHsF618Y3D7bRFBzCUQHLEMQS9RO8NOEE3SivvCyYkU+0kdKFpplOoCwBtJlHylE8d/okJ62OmEJ904/oFCOEVGTdFByMZAWWj5ZVxPrmqB+3xvY8X9mSF0YlanDKk0JdZa7E69vCrz5OLCdMLAqGFnAfplFCcJ4Nq1G/DdP/8zQYzjzzXZbAbbOQDBeZoX51XKRe1EBVEU6TKMQ2ViHQUb3VJf1YijUKkdMNW/o7X+FQJ1w8W33UrrWFf54unTp9M0TbHU5TsXZxf/ajGfv37//v1/Wxflbr/XJ2NM9+jkmNHO6Ob1G1WcxJlZQkVVVQVBiL21dXSCgFoQp24v7PX6xXw2ZVSuzzUDlmjg1kNjzF9RqP4sVMEjADPDSxs3IUQIsJVguaUwZMZzjjHtG6KbhamuBBjvxiraK3W5bwLaZKU5KLiQwcglo45Clj/WCB3ug5QaLMEmIG4AcOKLmhCHiCLa6EtllRAvADuoWGBG5rQ4Z9m2D2LUlqvqWhBSgJgAuaLyDADeRyWI5z4nnYQC1BoRwhMhJULXEl85QSAWD+oisiIvhtCKAdqPt6YEsABWEuLudh92dwbU61nwQno2SaOW7YVMnlfBcllGeVEhv+dlZZl2tkaENmq0jKKPzG04aVOVhsEggTffvApHR3NYLgs4ejaRns7N7VCKOUdHM3jw8Axee30XOt1Y1g7deqFa9XL7d7KKdVyXSLqxFIfKomKqb6CkyxKJtF2vGJWNY+WK1dx/WorGhMM3eJnLEGkeReFga7vf2dzsK2ZtZMtCGMecUrIFM99/rMMvWW+zAIL0h374n9AIi+Wrpc78D/7KL8Hf/6PP5u304xr/z2//JkAvejUu5iWMpBPP98qN092LyeJsPN8Wc+kolN6roigFzbTIEUqvHJtQM03v6GwCu1sb0Ol2oLWosowZsmeSsjBjrfpp6qTEqDxNpfdyfPQUF+MzljcXaw9y8utsdZIvp0BcwpKve686n/FIM5LVSWuoTxKkaUf7aurOjrrrhYPwssJDu7mwXj2cH2UgNEum7BbZEk4fP4AH330LHr37NkxOj+R8nIj+MOWcqiqtVkCr0i/JsJN6I/GDtKjcsBPC9qgLuyNG2LqiQqs/h0TTXie4XjXbs8ks3bxFDWthKPVAZxlS3+uPYefY0GFXjrTy75KoU0PJ80Mpp8xAAJNUw+m0hLNFAakgC7aoAK1rNTW63pxFNgkO8rVHboEVH+mNWwd45/oVmb8cQOmylF3b5418HbL1OqxNHoG3y6k9SJubUYfUIrgELbEfdPRLcEWOVaO+zz58LkSGN9JuEgVfff26pQYmIXzrnftSLDqfzOW9jYIQ4iSCNM2lgOQ/G/eOcY9IP4lFrOXq3hZsrQ/EwuTu9X24cWUbtoZ9QTkZYeFTlroS8QPhjpMENKIQSyLsISS+ugzUSlf8g19FCB1FvUbAWr+3f8bfc0t9ZbIlV8+52gNcYQ8AGrtdT0Nt/zj6xkxPW/WX4D0kiWzOZk8WkiFrPISraL1/8mwN2tinWMquqdWcW3P8kgWQJ301CGfr7XASjE57WRRsbY/p6tz2yK/yXrbGN/ZqV1hTEKBF+XxPE3kbFFHGsQmtUkFFSCW3MQs60c763YmMFxyhmgLiO05FwtZmXS0BLp+O+gTcP8vWWkD+sxNCL4lhX9DzDdlX8qJleWIhIqco3ojVcFuCPxtfnyDyLYS6fZ/aVyXCSgH/ikR0jjtHC9ejWk9YchJeiFMCeF8b6AcKh4OOite6AeSVMXlFdS1LNZYOvu6GklQkwVdQBW/8/r/+3pPd7dF41O8dDnrJggPYpNOBUb8n13t4Opb3insy+9ZjtbGyAX9tKIkxJ5xMW/aINCO6g16Xi8NYURhWugoCWADRy6G9posl3L5zFT54r4CUBfBeLp32I4ffK60qN7/rlp7KLuEDE743iVBlmPcjou8HYfhAofoGElyvtB7yXFYUdrTW22enp/inf/yn4w/f//BdABov5vN+GIY3IwC9WMwP5vP5wcm103le5GnSSZQuCkpnE0q6Q0q6fa0SybLVcDSqdvf3s/WNDX1xcRYWRckiRh22aWZ0rzLmgJS6p9gzEeqJlxiFI4O4G5bmNih4I1HltoFoTARdFdGbYCCpjGaC9AEivB4grKGBioiTR17XhC/MVNgpM0aQJNnInBospy7ayRN0HRLJ5524l45tR/pOZbdAqxSrvKWLbc+gDBEyIhEMYhGhDSB4EwCutVa9c5B+TVEIT6D+fOj7WrX8Wa63EbJrLW++4m5lL9hUuTBi/cN71aAfW21tzQg3t3qbpCxKXebl2XKZJ8u02CwrHfoilrNIARECcQrfH2dwssoxz/7+CH7xm7dgscjhnc4z2Njow+ZWD548mUCeVUKnXS4KW4RnQEWb5tNgLQSBzSZsC3rcPsYKtNSkpGzYg0WpkWny/PqwbV3SieDKlRFsbfVt4a5qeCGMQYRRUA0HHRiy+0AnhPkcCi7AuPyS+2bjVq1tAkiHMg9fIXEgHuHdb7z+ClzGT9f4o//rv5fr3TjY/kv1ucIonCyy4n4UBKxidZ0XIhYqmC1zNteV2ELMpvlFLSrpGZnMl3A+nkGaF6LAhU6un1dErsKwwEoceuVCcD5svgfFcKKpytzA7OIMpmfHUKQpdPuRZBacZC4uToCRT1t2Zyd+8pLv0ndpV2D7QnPQ4YM526OnWwhBy89RqFuVQ+qs7LxVZLSbZx28Cc3O/h5GoRiUsxjQyeP78ODtfyNiQIvp2CYkHjldGT6way1/NQUPnM2GFfrhcImX2LV+BAdbfbiy1RXV2USUfPEjqWOfdfjYOXCfk3sKrNIiSv+ablfuoM0t9J8FHWLXKlXWwfvle0Ir96AOALFJGhsUqPmZrAIYpxUcTQuYLJwnnjuQaSGjbeqi/wLWCB5IgLaxNqCbV7bg9dsHePfaHqyvDaXJkgsdtaCOS8A9vdA4eSGsFTvdPkP+BvortebNiql8vDG5Pjab0FCN7PrPufpUP+MzRknsuJcDNVHAgfsbt64Kmn5zfweenU7g2+98CB88ObYqBXEsybeh3KImLM3OVNndLbi+tynCSFf3NkVhlpPNnfU1fp8pUEp0ILTRgRD5OBFDDC3ZECuuuyBywoI9coxVN3UcsMV3SKZ9DY+gs5yx0ytwd7JhAdT9jCt8Vq+KKlYImcgWIva8IjBK8ON4vT5Fkhzf1D689eEc6kUt6JXtDDzOXh/Brxn8McxqZ5BHTv2cQ6uSVvv3+sKDoCPo6KbSm61sq1ebouWo694DRSxF0TJKarQP/fyvp7q79gZHNFYhqF4XwQkKtdWYa58T9BwCeeOptuvx0lYOPa17zskTTW11wfdhQ0sEzT83X+CxPaWtwgy2EHaOVJMEdjfXYXtjCPNlKtV/szSy14hqq2NFiKBZEFhv4EuMgBetle1VyArUael2Ygoe96uXBmGScTJeQS9mmykU83UP6irplaMDwwgQUch02o1eKOcfp4ZKYynUviwg3aaIojpChjpxiG8m3eSbv/Mvv/vntw52T3/1F9+8uLa/tXx2MpbnurU5hNk8g8OTC3h2egHX9jdhd3MNVBxJodXXUVv1VN4/RInYekmTMBK6Qv1PwuGANkZ9vc7WWB+jbfVjDgO/8PU34eToGCbTmTyrV2HYGaDMssxm/SD+41Gn99jo8g+yvLpJqDYQaRgY+GIQqS9qou35fP716XR8gQrfjePkPIrja3z/siwfh2GIh8+ePX327Fne6XSvsuVSUZWdbDknXRYmSjqiVNvr93F7Z08P1taZjk+ueTYUFM8vINyKBGaXgxUDsIOG3gDEN0ygXosKc6dSatsAzclawtwgzYqtcIEEhwQ0JJSQgP/PTGsOdTq2wgljBDwmqzs0dEJLhbMdYRXkyNpjSQGIa7RTMDRFgG1StC32LsT7hCSCISvCAvdZArGlD1fF+fgpibqpYU7nTbIyNCcs6ISIC7QMbmayBGRoiUoVfJ/JFvpmZGCMCENA6rl3T7uepHrJwlp7wKp6LxYFLJelxJqMY+o4WNpQTsW60mGZlyrLS8XAh11XlQhmRaHyCzBasoK3nPKeWGolDKkRQBf4RJ0Q7tzbgcWytOilY8ucnS7kWhhxnExS2M3XxArl0rpCYFFl5XUOvf8yJ5tshcIsIu3k6vmMvP6wwGaW2rYyTjK/9MUrcOP6JtfddFFwjyeE/Ly5TZxX3OWyNPN5IfG4q5V23OIbuMWNF+0xIr0tSs/Uqua9IiP8xn/wK6/S9ch4//f/Z7j71/72K3Alz4+3/+9/DCMW1fpLOBKM549LeFTo6ixJQp3ngUqLAtLCJmaRMzB3bzRDl1Cy5cQygzTjWG/grR9s2BIEMOh2YW3YFdQopUI2Pn75bBUeHOWQzfMXkC2mYCor5c7WKtl8CkU+d2bqQS2jH6g2Vb39y5mL+95M18SH7O3ZNikHXyXnRM8dV3nRD5JrkCpWGAL3k/JxmHs/Pz+HZw/fh2cfvgfnzx7DjBNNoy098gUSaLUoiQsGPPWupRZpES5lJcpGvRCubPekR3PUi2v1yM8F0bw0fPAax4k80yq1jAd0PlErN64Ja1ufu2U60cr82j+52gvbHKvhTT7f18J/zQqC80UJx9MSZlnl6HGrOe9lAmq90jo7AxbUWJdEagNuX91DpoHubo1kforola5EfRma/jj0tEjfDGoTRt8Hd1mht0a42pfgs6WwtktpBdlND93LGHIg5VRrJTLg/hLuwfzC7euwt7kBDw/PpQ+VP3eWl5JoFroS6m0vSURF9urOBuzvbMLV3Q3Y3hxJHxuzG5hOy1VaI1bqZmkN+mHgyOuh+yyecKpaqUTdf0m2j7JAoJR7kICwa6/V50iNtYhFuyzkVj/pmmqKK76uFnwSnzaLajZNO7W1UU3WIoLmQWBzviZBQX8OT8VSqMRszntGQp08OXXY9rxzSaJRzfc2k7R9LZzLNiipL0bUr5BHRY1zC3GFsqbP84W927j6RXt8XRfdcIVy2liboEfxQ0ulu9zr9AKFxxpxbSGXHvnEoO6tr2/WSmXJn7NhgVjKPUG3m8D6Wl+o29ojFy5YrD+z16dEZ//3Md6hmu3Qou3xj/ViFgbqSp4wW2rpARskCobdAPqxIuZqWsMkGrp+Nfm5UCla6zDVjnOKEidZBY4gII9L2fb6JbkkAQluRBF8M+kmvzOeZyfTLH+8u71+Mhr0zOnFTOVZKd5+nPwuswzGs6XQ/bsdqIXW/P4n9llhix2D1heUax8seJZmul/k6Y6uoiHvffASWRTZNIONEds2jGXNDH5MdNqPM5QVpSsDxPtZoR9Ml3mn0x9GQWB6hNUXFai/qoC+GUUw0mzTgTjhRKwoi10wtM10Ua2r9588eTJ9663vjHq9PuzsbPOOEObZ0uR5CnGPbd4CE8dJsL2zF69vbETsgpuLBXIs9FinKlaggdcB4W8iQloCvqYqejOoqpuAuMkgvqvjHroJvEFS3KIBIhyg0FcFQeQEruNMUzwBILE0VUEphy7p04JUKqa+yh7AVYyUNLchq6cY0BNjYIREN4w2IxZ6DlRQME3ZAE2IzFQhdhVgXwNNGAdAgiPbbw8XkgQD8P2aC3sFsEMEO5xQKqUuDBnuE90htjUx+AjFBxXY/uUOeWVFIqbgbrtymBdVsA3fAXt6kKjCTqYZt/NwLb7runG4hyWsSr1blhVWpUU1OY4Uux9f6A8snSbQNu6S4lq7RccLE1JjyyQ0dT5OHDJ9F0ZrHXj33UOh1Z6fzyGJA/H1nM1zt8NeijTIYxbktl+y+xlvityDOuhAuuzAcpEJQYL3kSgIWAwBF8syYDowb1iMWPIzzLJiZkynCjHkZxSjgthoyqbTZXVxsVTLZYlaUxQFuHVpnea5dEYA94Fwog3F8Kolmx+8ffgKXMbz44O3fwv+3f/sN16Z6/mdf/xbr8BVfL4jCgKaT9PJYJAUO5tDxf174+lckjSPvpmyqntzWExBbEnS3Eo8m3a13UiQwBs52yE8PjwHTlxZ1poc9UEwjCC0m782UBUleKPaIsshZ/VZFyj5uEZCHGPa2YRDKRtUrC7lup9D50PoUQyLKAYe0nDqovb8ZZbCcj6DbDmXYKY7GAi18uL4GI4efQhHD96H+fjc0Ze0owG++LW+XFyydFxHizJUd+3xvd0axnB9pw97G13o9yxdVwqTpoKXmJF85PAWKJxciNiENENi01e2IqCCTXe6Lbdrp7+jRAwAm/tfAy514H9JzdYF7R599Jw0fmzslXk2q+BwkosgECeNvRglIc6dFHnonq3v0aLV/whVZW97A+7duAL3ru0LTY+TTEY0+ITixWkLERaZs/F8VLvPe3/FF6G1Ptloi8+AzGd+G3LncqFkX6udeVqcTf+zn/aZtf7syOWqPqzMMS2FDO5N7Xc7sLM5gG9M7sAiK8Vegd/fIApg2IlhfTQQL1FJMOPI+oL6I7NokPN5oNbHdkaLBLXVIkXyhUuZELnvdZXYyKp1rva62D84OxFeEEjQNbL5v3ePsRk/utzWrTPKVoBbCUm74lEnKg0yudpXi3VhaoV66ZIz9j3jdUpzr7ipasJ0HSu16IT+PKRrkLEpptQqvxbqFUaodYUiqPXraeV9b7cG+ImNNW4KrmDhpKjUCxYiolqx1B/E07x8cugZFsZUCnzSj+0E1/5kjS4HjW+ym8uN14rcbuPa6Jt7Xl9VW/Dp0nVaT+eEmQcw6ltP29kiF1TzcmLN7yw/i1oArr7XTV94i2fRSrLRW4oCq1DzfGfqKe8H4s3L+1nJSKchxEiHQWAiKzgWeoKHpYYTJRGq9V4ILEOzrIz0mPp0Hm0NpWOsUBYT45JeJ3kzz+jLf/bOh392cHXz8fXd9YecH55eTHuM7OzvbggTaD7PYDpbSEFobeieq27EnZgC30s60Iki+0562rkxOFsu+X0P+8P+MB/1hqajIlC6fFk7SKZL+MY334Sog/C9t98HzrFaBZ1XYrh1hZQI5RhG6KYmCk6pqD6ACv4VYrjRSYJDpcxpWXJzI/0qKLytMJiCMcvJZDx69913v7C7u5MMBtyXOQjI6EVVFgWJv0aAQRB09vb2k4MrV+Nup4OsDahIimjsGxkjQR8QvsL0U7R7I9N5ncoreSEz3rFYbOcApVJPDmmU/snKJTI8ORNsGDRGvsbH595MxKeEFKDBKRE+IkOFaxE4JQMXLOYcEX6oAnWskQZY6V2sql4QKRNGQRqgSiut55UWBVmWRx4G2kBuKBWkE7A0qE6BYMG+pGh9h/NKiuRhL4nCEbJwTV5mVDI5BhmDODQE84pohz9boJAR0x2q4JcB4VdYPIid41w/6ITVbpM4GBhNajzJ4OhkwS0yahOxW3GjprEFszwvopKBDxYzFMXpQp4271Os2g9JBJQXUFX2+0vxgi9FqZmpsPJWqqaNRd6lUEGnF4qA5ZMn53D/wxP4/jtHMB6nEk+srXXk+1glmumunvHWUshGh3CsBHtSdAv42B1h/BVZiWTXWhOHqogiccXpFrkJT07n8P3vH8k1ffFLV2BPrbmCNwsSqi7vq2WhszwvtK4MVzTCVvMJOdsT7ttcAyL2fGXEJv2xBY4fc4SvnmZRM373t38Lfu3v/uQSTj6/H6/yfXppQxE3R59vpYPTyTjlQLkHtZmPC1xq9pWxyqh5AeezJUxmqcg5M21A66Z6zC/o/s4GXNnbEJNu7hmrnOQzOk8iw2IszP8rckYUpSBepDMssrnjWzTqGIE7ri0itxIhvsYgcIGGkT8r531ohILGPZc2DldOzIXjWV6UOJlMl3OYnB3BcjqBktHc5RLKPLNBWKXh/PgJjE9OIJ1PLV3Xyfvby/hhmy3VqCZ4Aq+LJYWmqgj6SQAbgwSubvfgyhYH+l4ZElwt/jJm9+Lhvss4dUiFKxbxHzkI60sjKQ6zMlxURM5z09ndXOoTg5V8TkAaY5tSjXLy8OgVH1fQRPvqRQAAIABJREFUD5cEOY9KZyXigkJnEI/SwA+wKDVM0wrO5hUscyMAI8e5nJxX2Kh9omqWe/L8TLEiYdXZBK7srMMXbl4V0ZytjZHQRX3bpdQ2qLlKdCu3y3osRdpZSTT2DapOdMBl1wY8hbCF8Ijc+yqVmnwkZJxQ0GdINOFjzAybwNtiEfuEDroJ3NjbsmJTrWpvKGhJYBMxp2zb9AdSTfFkaiorbLpky7buUm0yKdRTRzem+vOuqgKG7hju6w38/Ry9uIVCijK+eJIgoZfraeBNxOb7W26qjZgP1f2ZTkzHFaSkAs4hlK6P1aQn7evheWoudQJLqoiWrtuuP4C3UGo981bGKNY46GU7WHKLLPUKxZQYbC2NmiLbpfsDl2i5HhEl8kni6qyo80FEqJvdXzR3amtP31/ZCCoJWuuSVk+n9bdB1IP9u+vOry8poLYLb61n27qXFq0WkaCNNXjtxj48OjyBuRQyi0vHskJYbbo9/5kpdcN+R66Tf84bwctcd+cPXPGMUZGhWPj0RQmTfLGK3Hss/5Eiphp2AhWGrjrBpD5X/uBFlum2fabdhuj8fRnS5B8TSqLDHzmQBwpVtDsp5r+otf59rc1ZVlQPoiicElFvslhAfBEIS2jMrSmTOSyzotWa4Iqt3K+ZBNDnFos4gEXh9jxj92O2WphHGS+g1wA6b1Q9/OPBaHJkzMvrrywohV/89RtAUQHf+sP3xcT+lR9MGwV6AASPwVqIaIbwjYJNXeF9RPgaIm0ZVHe11oOz8/PtR4+e6Dt3bkO32+cFP6rKcma0ToNQ6KfYXxsmuwcHNBiN8OjkSKYGCqMceRrwasEU0qFw4G1Bhhkg7Be6dKx9frCcDHdFWAi5wCmOQYVTNY4tjE8lAfJLsADAc4V0TgDPAIg9ShfMpsaQpkWh7hdFcJ6EoKMeprqqZglHMUV6aC6o6CbhIOolHdUfmTzP8+lsyslzFQexUVFiclPBdL6MOhgESdjLjeXjcONS5V7YwK56pG2wYBntJ8tZnECkEwhEwD5A6/BCBD9wli1EkdkxSBUW6jbXlASNFeYPPeUQLgzVbaNgyH2b42nG/s5MTy6Rk17UaMh0s2UWcfsWz3tmADhiHFg/ToBqkcNknML56ZzOzuZ4ejZnGxVBJpkGy0wBtj0LIgWdbgSdJHY/H0DSCWF8Pof7988ExWSkkWNGjiN8G1a2zMVWSb4G2BYg49vi1C89s8hqgzBiykJB4SyAIpdWNAwjjOIooCRUaqFQEtt33z3mWDl47Y29IV8PMD3Z6WiiwigIWUmY7RBo3SuL0AppSFrL2Xf2LpJZUxiUL1rif5IjLKWP9NUd/+//9Pfh1//OP/jcro+P/0PHq1Ww+9wHlz/7/Wi8uTZ471F4fqyNucW7cKgsEmmoCRKMq5hnFcH5dAHHZxO4neaSPDKnQBIK2euVUKO4YjvoJJSmubGBgFbCnVA2wGUPr8nZMUxOT3EwGkGVLsAUmRUGwpqT3xKscAqUdSLsvgwuGPPUNN+a5YIg3oyrLJXzlVkBi+kpLKZTmJ2fwcWhNcFPen1Rm51enEGZZVZaPk+hZC8zofS+OKh74Wh9j3EIro+he1EgSCbTZrfXO9BNApeocGjsbL0+Zq+mReOEX6dlEfK+Gz96oKN6lS5JjDmg5Eb9qgol0OP7yIinFBi0Dxqd56Anwlqdo9jYk7czjZanoXtGLUpc217CCmOiqKBOcptkztIKstLYZJcLEpqa4LEBBmsBSUmOw4AFcmB92IMbV3bgtRsHcG1vU0RtLBXU3WOHLbcEHJTPFbBtju8Rck+SsWIEgmzLLAycJY5X2bXzLaAG/F19Di5IFlrmpST+5e8Odp5WzpMkcDY3Qd0tA/U94OBce2QOGmr1ihWITZOCBrGqGQVOMwp9kx46spTNquy9MeJWaX82Av+8eOqgTd7r+2eTVify4/JY59nt89e6lkLU6tNzxF2vVkre9qiBlOkSCm0Va4X3atvt/Gdu2ZhobYVqfKHBgjmhO7URD7W6VdLP+ZYhf/vpCqxh4dkGenPvg8wv9MUYXJk5fsZiiz5p6udCNa31sp1M23KlzTyp6aROyRuVl0H09/MSwtogreTTxrZC7gq75AU1uBVrl/rQTUJOTtiIbXS+dPsqXExmoor8g4eHglK056Iwaxx9jlsueHA/NvcaM9r3+OgM5otshb4rVGjXR8v9huvDPvS7sT+uK5vxJBUfTXVqCHm9KU0EG/3QRGz4Z5+Qkpkp7xPTbgNYS0JYZNZ7s0UadrU4O2kQcRiH6pvji9k3j44nv3d9b+twa2N4sT7s7Y9nKTw+uhDtA66qsYhXURa1eq6/V/yRWXSv17VtFlWhpSfb0wL5s0k/u6l2yIRvmCo8IIIjkdB6iaMqjPNH/fGJBL2kYRk4vIhhyF7bFxVVz7QxkxBxExHuGEJT5MVyPB7ny2WasMI2G+kXeVZVZZlGCbP/QUdRXO3uXTG7u/vq8YMPWZgqDrjbzgr2THgrQ+5bJxHmYVpjhBb55EAiAlu36KFUxeEJ5zPIvpzs16lwxqQUNDCASGjTZ0j4gTb6PhGdoglmRHhKQPOq0roIoOqtTRdxrrJ8umUQDRfnTCCle5SKW6D1Ury863ZFrGMlqFdZzF5gR+WHvvR3nt4lU3HxxRuXccJFfPAjCOA7gOoBIr1m8zTOS3GNCM6IaEEEPbakLIoKsqyiStMSA3WBRAkjekWVRYtZCuk8B/4eXv9YQ4Qv9/GjCzg7nVPJ0kwG6PHjC3z77WcBW4kUuQFOPNOsgh57VvYiGI26sLnRlwR0Nksl7uNjXlwsRQFXxYEATAygPHs2gbfeeiQMPhYT2ttdE29Paynl68duLXTcVvK9xEpgWwhZlZbZf0RYVhRxz6bICAYo18AJZ5aWGMdByL3oWpu6kFYVhEVhAmfa5xd6bYUyIGh6XyEixA0kxUJVPz5K3MccIVNJXvXxz/7JfyVX+Nf/k//6pV2pP+ZPw+f/cY4gUONer/v9tWHvYSeOb8wwU6Yt/NIa6PwDl1kOZxczmE4XsDYacEWKW7v4nZBmba4G8aYk64Bh7z1JRmJDJrDiBwRFuYTjRx/Aw+9/B/r9HlTcq8kIok8qnB60+HhKdd2JABnXN1WbrGO9ywu85xRsy2wJ0/FYEtqxEyJiclO6mMH4/Bgmx8eQLxfQGQygPxxBnqUwvziXDV+5XlWgxmbhkw6fbrG/k3BjEgW76z24uTuEnfWOBBDQ7s/8pIJAdvFWTnTl4yaaftjgyHW1gRN3SpLY0VGMqDYGQuvVTV8l1aAECOgs1BgLMWLzzzJa7Vnu77jq5eBGoY0ozp7NChgvK0gL48SK7DE4Gaq8w0hdXKhXd+cHy0JLCdy9vgtfvnsLDvY2xcdOgrHKrATSXkTAePRJmJteYNOiox6pEbsQch6DrVvgLSd8j4g/LkrpnOCyzZ1HeV5INX2Jo02t9AkWB6Pa0yOpOW99S55jN166qtUcpG7T9A+h1aOJl1FbdyeW0vODuFYnnLiqSIs+PkeLfDqE3HF6zcqka/BsQptLKYs0r0w88rlv/WP1fJT1g7xhe6SoaVD0vTf2tNR8VgTfq+mIEoJM2uwRn6cQe1TRX34N8Mu8Jq9i0SR+/tzuPij01OLmBtUUXn8rUDUNSS3EGuo53kIV/VU51Leuk68kpfU/2LJ5y9/U2EXMuPNZfLa2fPIJCMJlf8eVY9Y9zHW2XX8+TgSvXdmCr2W3YbxI4XQyk2Im9xVzEYxpcb5lg4PBRizKaoRIIcnYwgk4MSPfPy+tHd1EepPZS5etCaDpyiCPgrPaepFzP6aklWyoqIcdjBy5pp7bjJR2YoS1XgjzzMCYKqm6MfrBiapFS6nj34cwCvbSorg9Hs+6qPXZ9vpwzOjq2WQB8zQTBXamsPP1aldUU25Ok6vhMOWPEVxWizamat1PW4SoypKPFRb5fJOytf1qstkzRpRHX9qYzQC+8uWfB6V78K0//Y5QBX9ahsh7mRDSQtaVMyDzoQK6IMBNEoEfzI3Rk9l8Wk4mU54Cht/qMlvE2XK+Hnd6kaQEQVD0B8N4fXNLdbs9LIsJP2OGxLjHsgOES0Kmo4rK68D1gIeOO89J3RIBl7rST3q9+FsG4FlZmDPSehIglmGsOHhJikpnxRIOoyh4pkIca005WiTNSzWvLrM/fPwEkg+SopyCRCPSMUbmkamKGQv6Kun1Fx9QKksukJDWxgTpsoDpOIU8L8NuNxyyBYsxJuKiUp7lsFjmUmja2RnA+kYPxhcp/Mmf3GeEEu/c2cLXXtunLCtRVwTzeSGxFcczHMMMBnFdfOQCOjsssDAXn5ML6UzP5+VrvrTMCP6ek+M5vPXWUzg/X8LNm9tw505Gd+7sVsNBVGrNVGOInTAS2GK/GCEoMEKJhog9vbsxVJn1DB6Pczy7yKDguCpS1t7FeVxzy4vfQf1+NZ8ug+PjyTBPRaWT17HKiUF52o5Arc7vdaJUcKI6vekrl2waU3yMb3s1xu/+L78Bv/affrbeST7Gz8ZHDp0kwbPN9bWztWGvPB/Pk7IqV/Qc/PCWAVyluZgu4eRiChubIw4W+GXjnIB55BH3gK0NukLsrIxGcoAFgTfdNq4v8ogev/sWbW1vwmBt6KKDVgCsvEolriYJRC0ZEU+TspTZIk9hPpnA2bPHcPb0EVycHgliqYsC4m4HkqQH6WQCs/GZVSINlNBmy6qAUpculKWXIyJtFwpghcC9zR7c2h0KshnzgkPwnDjOJx2eEfkpELK24b6lLTp0U2v23mz6ca3ghG5ZGLjAHrGZIy5yU47J2wT2AE2naivA9kGSAZhkGk6mJYwXFeSlqT3i2nmQuDW0E57aON9SWrvsn8lWHdf24GBvS6qfNu601Glyz8GyD9EpA7cTq8tZF13qBWvQyRop8kqkrYvyx7eM5tUJ5L//Y6Hjn3B4QRE/PDqLrbYScuq6ljrplWBbqJRPqlrX5wVKnDn1CpUSW0C2T7pfoAqqrAebMsqSb2ubkBrlbvrPXJLjJ4+dQA1TuU7HuGnTsI81F7e8CJhH1tsIIyHVSZEKkMhRg1lREetEuY0qtjsjeb6gqBL7pjCHBATovSNbfprQIMHtaVTnV6v8J38/9QvyfV+Y8POwTb1u/ly3OZiGXgq1uI58IlFHIqvYiO5B1YlkTSVXdctvLXbUnuv2Ok0r+V+91trztO7ff/FqtIqINesAgEUgWRX5ztV9WGYlzOYpfOe9h7A5GoodyMOnJ3B4Pq5RZz/4/X5yfC4oKPckr1yZm4/r/R588c4BfOHWdWFtnE6n4hvpUGmHmjfTXmuisiSaZxVyG3YnDkzi83qBw1GgBVaw7XcQFoVyrQHuk7lg0JcQAoU6JRNO06XOyvIsiaOLOAopiWOekMK3BUmiNeSsuqmN0P6tsrXzelYI/aTD1k0SnHKg7u+j+AFXBqaztJgszmdr/ThHPXJMzpc8ul2oct4jmP3S/VzWss9z8MOuDI1VqN4PEQ7Liu4qFPpqyvY/s9ksPjw8DO/evbfoJB3UeTHM5tO4N1jTQTjIFaqy3+8vN7e3YThajxeLpUxjqbii2iCgCAzMEGHuFEu77HyhEA+j0DwsAZ5qMvfDIPre+WLxfi9JLkBFM9BUKjIYRRCyYgBWgg5mjfv4R4x6z/nJD0ttT6AXJZbVhTSrsHhUkhmjwgO3rFZhiGEch1hWmnRGsFhqOJuksFzmnW4ScBUj4BZqtinpdyP5NexHMBh2xLrk0ZML8abd31+He/d21WjUxfm8j/v7QwgihNF6T+4ae1pGYSTvFavKbm72RRF2NOqIxQm/N9LnuSzh6HgOF+dz2YdY/fbocCqo59OnEzg+nvA6Z9780pUyCDGkypcl/RsvG4UQzfh95CSy2+tAmWZwPl7C0ekC0rS0Lg1koBBv71CovUHdFuaLIgBHxzO8f/88GU+W7OrLYKmySuteo4F8uZArb4/QmA8B2R7o1ZgHfoTwQ/DvV3X87v/6X9ZX9mt/67/92FdZ/9yrdf9fuSF1qEBNBr3O6bDXyaJQJWW5SsHyA7ERs54zujllY9wUWX5dgVhxcogddZOY9rc3cH3Yx9PzSaJtwCMBkEVApTROZZHR+eHj6vzpQ0q6r4WIgfO2E3aoSyQ96tTQqRgdlV4iFTgqmFiqwHwyhvNnT+D4yQM4ffoUpmcnkC5nUBWFKMh29VBkbnTLMoXptUYEKRz9LlBALwiWPu7wwSQ4OW6mXO1t9eDGzhB2Rh1pUJdE87Nmmm58hqOoy+9GGFp0syqt9YCpn7myZhMrVMQm6fD/WRXDab6TAFaWHd+7ySgm02bnmaPOelD7Be9sG7iiGoEj4j6Mve0R3uFEc3sT4iiUgI2fcR0Ar6A81mtQ2T5ZrgxWlh4mKIbyPaueWmmsrY+j2a6ihs+Jy7jmQts3hrUaZiMC8/kuRuTQK0tTxdXEzaODHiH2SbM2TbLROlbjDVlXEkTJz1yWSvaUddt53ELMJOpWxhD3JyWiP+izJN8X7gsQz71xDin3iRY1eKrLtkwNRxM9F40helVCv4k7Z1vnDCm1Dlo1AffJqn8tvfZOLZlf95XWH9ulz6tJpj+O/x7095G7xWoVWn+dDe8aWs+E6s/uEmiPMravtu1hCc3HdcmO0D+NVzN7QVJgFX59gtpSw/WVHFXTw0kJndSsCsIZs/K++yeknNBQ7Y/sS4IrlFpqGCnus3CxkP1dv/aFm8DUv/2ddU4MYJnmcHoxrX0XpWnAiTQxKrFYzlZmT78TS2+Wdhexu7GGB1tbYufD7R8wfe4d5De1tMAsRUKrJYJpqnGWaxx1qNzsBaxSa+3Y3QKVhAoGnRCmGcn6xahKs960ClXcCEYYp8uimC2zwzAMDhkh63VjcXDkz1AZDVlRwmSewiLLhfqvXWEkdIkue+GyFsKDp+dyP+qiq8vt0zQNpvO51ltmnqg4q0x1+XN+5qELA6/ffVOe3He++21IXhErlPaouTPuNxGOZ+CJBWa4PYSwDILgma70e2TKW1I6UmzuSBvLRaqOj0+q6WxajtZGSikTFkWmqrJgXqyoXg+GQ7O9vVusb23hxcV5kGe5cXWzEKxXJd/4xwDwF0x7BVJngOZ+XuJD0NVxmZinIUbn2lApirJ4aen7mFsEOi/wuFtCWaSgqwR8ClK/y/D5UJ75+OOzE4h1ANwJxCStnM9ZGgi7fTBra36fmFKgPoQwOFUGPJ13oQJcdJJwaA0CjBRPJpMcJrNCra11vC6AIJMb6x3Y3e7CeJLCYl5CZTKYz3Po9WPY2enD9lafkULxd//il/bhXmlgbdS16GYYyDNnxkCcBJJwMrDA9FrpSWd19rKC8UUGP3jvGL77vcdw+HQq/puMcC4WAJPpEkoyePXqRnj92kYyWufOS3TtRfXm0mqfIVm/up0EJiqAw9MUHj8Zi39wlDCyCk6IkaDTiUTjRFnHAiGenJ3M4fvvHsH9h2dqOsuUc8NSBO1zuEmjaAaofoAlPayWy8+huvTZRmiKn65ksz1+57f/i1fnYv4SjUDpcazoURKqcRAEozoobQUINiC30mk805l3nuel0JqkuEesoEaSiqogMDvrQ9reWMMHj48DY5gGgdwgxT4KShqerD0JLsdnMD49MtvXbhL3TqKnekGbdul87pyqIoahFQdi2pGI+yxgcn4Khw/egyc/eAfOD59CnmeysNS9V0kHdFEJylmIQXUogqrSRhWounuf8LNCjt7yhMTX8Op2H25dGcLmIJFFiOh5u49XZfDjZpoZy8HAYil0WnQqARqbONQbzNUsvxdkhx5ZaIeVTdIGsGRhgAUnmlosBLzR/QvvqF9psfkeEetIOnCwvU5v3L4Kd65dwbXBQP5Na1MHw3VC3Ip9lZ2jEkhXlS7c9YY+SEZlPf0qSVgbhLpGvVt0wFWVXefdKt+nLfplms/wMlkujqK42lLnr0KQKFU7J774vO7JoPNibNGMfTLqFUo9bdN1HtZI6gqSV6v4wMr9cb2NQRubtJR3d/+Mr1U0lE5/iMs9gTUvk8UUmuoG1I2EPtlXNqHTqxYncigGRJXT1ze+KbTua2ySa1l7FD53DfWMvpw81aJXzYuB9T2h+gVoErsmNmiQ0cYX0/bMqyZ89pRtbHw3WxRuq6FmMz4F3s/UC9lCndNZF1GXwHqkkOqI136zkWI7VYptaxBDpYLEtSe563PzW+wIwvoZeDouXXovyP0Pm87PlWnoOoYlwNteH8A3vnwXru1vAXtSPj4+4944WOt35DzLZQ7LtIRc272HA0q2MGKK6c7GULxhB/1O7ZmbJCEFAeLDwxNh5JBuLIlac0o7P56Ik9S0NCatbKrOdckkYNlOZW+qu3fdOILNYQBphbDI2QjeyJrE1Pv/n703j7EsO+/DvnPOXd6+1V7Ve/dsnCGp4TLUSBRFa0RalhQtThjBzmbAdhIoCBwgRiIjFoIYEBAgQPKHgfxnJECgOLYRybYgWKYSRxs1FIfD4Wzd09NrVXXX+urt9767nXOC7yz33lfdPeSQnFFTngP0UlWv3rvLued83/f7fb9fUbzBBJk1pZAd9AvkWXZQqbg3/Io3YpSsKc9Vpn0/EcWcBHMYziJAeWwU1UPxJEyeERHBnk1Ued9c7UGUJjCZhOr8qWL1MCwQ1oMwXp+EYZ3AiHPxwcSevl9RFmGgktnHI9ksnkUBHkoRcCuYUGLZZBkw9EF0lBZFn0u4SwjZkZK0USgI6wdZlpHxZAyD4bCysrKS+b6XZHEkkiiS1SYXlDLqVyqsUqsR3/UFYw4jNGZ6Cis6JQr2I8VyDAL+0CHij6mbHnBOp5lkMREKE1MByfstO+LaMD45hixLVVUfuRmzCRZXHKjXte2Sq/rh61B3M4hw/qez72vPwc9MokDpWFCvqvZUjNfQdPM3/tH/Da5HFVe7QgDqKMcrAX77H/8m/H+/+b/D+sVLwNMsTBjcos3GLYjiF4ESZL45PANnHmdumnLFace5PhpF5LgfwFKvCtVKbl0KlQr2KzuQpAJOBnNTeBSw0mjC+fM9OHehp54NxhqwsdnSPrqMSofRTMcUlFFVqZKnQ1rTiUMgClPo9Kowm0ZwfBTALEyg4fqKYRAh7XYSwVF/wt65vk/X1lpkY6OtkElTcF7wwlIFIoeB4zsQRhx2d0ewszuGySxRx62V8IlS0+10qtBoVDA7lTTjMggScv36EX3zjT04OpySJEVNq7xkXTputZcGFMS7lHpXicuGjxeBVg+H/dD1d380PtCBMmFMhozCMaFshkqNQIupjRXnXKXSFFLxywR9MSMtU4/2fhjD6V429QCKatWXK50mq1erMJyF+HzxHK5QrxPKZ2k+ndDxybGYh4H06w3FGdD9UiaAEdL4zNmIXUlwq+brcDxUirGYQE5HQ+jvbcPxvbsQhYFGPU0g6TgueH5F9bvM54GqjqPViFIwNbQHoRKL73+DNiE0VBwUA6rB+Y02rHYqyhCQl/uwPvjxvqE0S21EzzspfJDzRFONTfCrWk/KhEBZqiRbiwWag1f5RmfRT/v9JAMYInVmlkKQmiICcpHEg3ujPQnHqIiishFuAs16FdaXOuTymTV56dwa9DptVYBA5FzCwiHa6jErNefZvkqM0z2rskTkYiBKcwXQEiMyF5+i6rPKFEqVpKAYEecPIoDvYzyUiHj6mzlcTE69RFFrUCXYkZK4BqJ9GAPSNDmDbYEsVE5yNIyUxLH0zaFa1QkNHko6pwWyK23ea9BUQi06WEwyCae2zlO9g6eB4CJCUD+nkOc1D7Iv9MuEoZoa8Rsr3aDNIu0d0/+hpd7HktWPLZLgnCKqb5GXjrh8X4wo0YKirl0FrChUGf2U+fyzYmcPZwPYg7HGoipT1r2csJBo2sOwIlyFbpE01QZSoLNFA6lNDEl+bhaJ1Qkpakwp9IUUAkaLRZOcaqson+VzN68hBQsArIp0kXcXZ2t+T5gGyXajpmx7ljstOLPeg7Nry3B4MlavQPTv8GQCk3CugjdmpKmXu03YWu0oBWbsAW3VsC/eIZNgLm/tHsLN3QOYR4kpRiw8TzhLNONLN8aRMBU0b9ONMua5qGtHoOlTRXvDLapa9aDaoMApVQyNEUlUcSMTMr8Mat1C8Q+ZQZrFmLSHtXrtjWbNv+UyuqrqqlRbBOAHIsIzmydKuC9LJTRqHlS8QiUTi5e47mFiHdAIhGkdMQUTFkXpxu7B4eou9LFw9oOHNg3NHfvo/PYyyDj4njUNvu9hlc9RgSfLlGhS1XOAmuftgQVPSNVTiwwdIUWXS4niQB0A6AGQFrZfI7Q9m07owf6+t7Wxzl2ni+4nbhTOZCPtpNSvScpY1UEfCyEcnqZMKF0eOlfelET1VTpCEqzN7ns0e4vRLJplnlIt/m4NDhQzhmcwmQ6x0gFJHAOfRPC3f+N/gqWNDVgCgH/yj/8P+N3f/i3Y2FqDRkNvcFRdDlepEqUOGoXUtFd6vhc/OvjAz0R9C7QYE0RBCpDFc/jUT/wlOPfkM/DWn70MraUl+Ilf/gp020144Sd/XFFDhsqXBdQx4UxobGwCERz+6P/6PzHh5EzKe4S435iR+HNUkmcIyBaK/8wF96VQctp4X+RoEpP+SQhBkKhk0zIE7BqFbCtEBFFzBdcbkUlIU6FilVarqtSaVSsV/kwLz0W695bU0GoF761dAikxIhNKKpap/spzZ7qwsdmGpbt1tSW4rqvRy4QrgaLtuwMYD+dkfb0tPvfCBXn2TA8txKhQbWLFdcTfxWckSjkcnYRwcDjDRFp51+PLUuWl68KFcz24dKEHzYavwwYh5fHxjLxz/QC2twcQhYnabheSY6LKKCdUygOQsCc5vE4debvafPxsT3A46GuVAnscj+2j8ec0XIdJLyJzz/dCxzGFGqG7vEjevJyx1cOGAAAgAElEQVSzutT3UaYdRQ5w81/jAmXoiTAVdyEEc10mV5fbdKnbhMPRWCZJQrQ9ng48hanuR1FIRv0jmAyOodntKcTJ9g1JQvMgSQe9OvCNZjM43tuB/t4OHO/swHQ0UCIJWRoDYy5Umy21qSB9VlWh6w3wazXtpWlWMFVRdj3dF6YsP6Bk/fA9DhNkYDK03K7CudU6LLcqavHhqBgI2rDdKkB+gEOWHC/fN3dTJZwVX3krx1GU+y3mqAyUo8VCSEX33oFCinVisug5r4sUEsahgAEqz0ZcVWlpSfznYVdFBVSODvRcymCt14ELW6twfmMFNlc6FPuDqVKdNX2DUhr7HNzikXJJVXMZ7j12zzW9blTLzevAXCfLBt0xPZaqd4prGxabOBUJwoL/lhGwEka4Ch7opTx9TqBrL5YHWcoay9RHm3HJAikzNhWmIrP4rgrmlFjVpdLyTx8gqRrQq8QioHkfpzWUhxJKbySMy0kTVTfb5DI2IT1VjLBYmYQSpdgkg1YIJ2e9FXNFz5vFfsgCKVMeFLogZX6ubWpknuCodOyUB7DFGVUxQlrMlywkiRSsH6cokMDSM2F7Xu3X+TW3/eP5E1dYHgmLAJd6Fi01Xc8TmVvoSEvZJkZNSPXsUQ1ZmsKGLBVkSteGmD5s3Tosilr4Ikptp5pGJpWvKjGfb8TQdNu8khdX6pkCPTSUrQMQI8pk3oeqlgPbi0ltkmqLkg8rAgCcQothsSUNqW0olKPaJBj0Og1Y6jRho9eB8SxUFzJOU9jvjxSrBhEN33XVcSPy2VSWHHptx6QMKfWT2Zw0az6kPIM794/RYqEwpzWTX0rld5jvcQgSGS08EqeCnAS6lx/VTJsVR6GXru8oETKidAUYjKYx9CdzOJnE6n7RHNmHmBARASMM/TyH4/jGPI6uAsDzmLMqOzFUuUZIDC3B8qKd8Qe1VHOh6XezMIRZoBEez/NUoQCTXGzcT9KsOZkEKxhgI4XxoTfg+xyqauc44FXqEMxG4HofNrqpW+RYmkEFPGW+kcTaMsZRVmgPWW/NVMwkqYEUT0uR/YIA8pOMyIu4/luddXyOgiCAg/19OhpdhFa7LSjlPAqnJAoD4vo1gYq03e6SU6vVqZCCqJYch3ICZJeCPABBziv/Q0bXCEOLaB7VvRQ8ziH4DrVsXBOSKITjvSPorp6Br/ydX0O1GGh0mjBhHnz5b/xNaBGCCjvw6rdeUc/Ag+LAxTMomAcRauHqHFgVLYRM8wIfTzMYHh9ClTjApAv/0a//9+C22mr2JoJDenIMm+tnIcg43HnnGixtbMKP/+JX4KIDsDsHYC7A3Hx+QJQPGmydvwB/89d+HRqVGrzyu78LtWZzxHn2CjDyLRBwBQWQ0JsZlzPGiMDWHaTpx5kgw1EEs2kCvW5F3UtcGlxKRbPuk2bDJcHcUXoMuM+OxyG8e+MQnn5qHVofr4LjKXQftAAlqG1fWlk3fV0skcM6cKmJwjJB8HfrdQ+2Nltw8WJP1uueHAznZDYKCbKbgpmAG+8eQ63mIuIoO+1q3G5VZLfXqGAZSuaVa1BrIrL+9vfGcOPGMRz2Z+p4kdaLxY6MCOj2avAsUn6vLEu34kqMP4eDkN68eURv3TqC0SiEQqTT3lX1/wxA7AOB10HSbSHYO0KKPaCzx1KIxxmwFdgkQ+DEhVR+BHN+NBRqJGoVGbUblbDqe4LSORWaRqQ2sofFyzHKN09DGI5nEMexUtTL/QdV7EGg16nD6lIT7u57NI5SJZlCldW9bgfE+C5LUzI56cuTvV3RW1uX9VaHFIGRLAJvY3GRzuewf+cm3Hn72zA42oNgNIJ4Plebs1vxAWWnFcKVCkOnB/CrNXCwvyQMVTIqiK5+YiUMdS9UZZ88GOTrfivx3SeGikJBldosUmdRDMihAnhWFP0flVA9bkM1uVeJFnMKYxWslVEbM7i5l06JCJmrtebaOrhKKvN0CZNIwHCWwSTMVKIJ3wVrWQVejKo5ttRuwZMXNuHK2U1YW2qBXwrQCvN7WbLuMQxc46epzfyZKYqUeu7A9HIybc3DH0AmpRXi1H2RBvFbeIUwSU4J733YWBR9kQsTghpPL2tVQY2oC5Rpnca31Z6cFeYxfzPDb3PACODAIyDuslgT5Anm6aF7f5QidMnXWuVKkhp/d5PU2SJROREqdVuCdX0kC8wj/QmluaDBxkcn6jJHIR/Mz0mOrp5aP+zVkYXiJy5IwiTBuYCNVY81/a5l1M8msKd3TUsP1y+iuVKopb/ShcKMpfsWSXVx/LpIogsc3ByyXYch73O1bGJqBNQUki5zzrS+D2Y9E8YX1iblChkVuogoSqhjfl1zrxVwlLqi/gE3ikOlgoBmhZTuSn6p1XmBVfMu9/kWokPS+g9pFeJC3MnQ4EFIwzxhykYLg0yb1K90W+pfRPjwNbbooCpLXD97OoglKgm9cnYNYiz2cQm7hwOIsAhpLrp1QLFT0RIwbJEODyVEtWyUoHQoeEyCVymuQaPqyQ0gwmOEzqKYuMgMEsVjLUGgjqmPBvjBLIE44aP7B4P94SwI51FWRVodo7rQiQVJh0jo1X21/mLbhT0/W2CKkhiiSKvzMsvgMyJOqC1UrXitpXbVz74PZsV3GuqeZg5U2DKMB5NHFhY+gE9WpI0EHRuBQ8VQ+xW9nz56X5WCOkTIK1Skn+MAXwSAHyUgL1t1bDtUMTRJxWQ8luPxBJWCU9dx4ySOqvMgcOutLkXEa/PMGbF1/py4evUNFoYB/qZLCEMk+QinJ6HyDBD+dMqdNZq5A4WUoaowUEhJCNakGswcmgUBhNkcJuMErnzyU/Az/+7fAF7vwk/+4lfAPxnA5qWzcIJxDQCMOMAKAxgMTiBI1DPrEZBVs+gs8GbxKUy1A6YqyDBWBUq0UwD6jNN6Df6Tv/v31cPOOMBX/rP/Uh+TNvgEOZ/D0fXr8Cd//EdqjcGCfjAewWSp86i7A2MJcHlrC1745V+G/+1//V/g0see5DKSh26luZOJFCtGnlqKgAhMNpEqTh1CsZCCSOBRP4T19Rq4VZzTRHiek62t1sjmWoNNg0QJ+4pMEuzbvHnjGK5d24PzF5agu1QzHtNSGl+4ilnxH5Hk6B4FRC9lLFUT/8ZaAy5dWBJxzPnR0YwlcYqMPaUiG/ZnKtnkXJK72yfk8uVlaDar0im5W6AuCO7f4SiCW+rYDuHoeKZQ7VrFMUKHLpzZbMLZs22lrAuMJsF4nr59da/yyivbdGdnYIoItuBaIg+h7YmENhDoAYVdRtlQcH6cifgDYTF8v8O5y1fBESlwkcGmG5TAdaT50e8QJn00/iIOfJ6qDoS9TiNuNxt8MAxolGl1P3kaGjPGdwqtQw5/OId5GEKjXi1Kw8Yis1b1YanbgGatCpNppDuB9FbMdSUemzg5n8+nyWRwJMPJCKq1hq/sS6QwQT3LKXoohT043Ie7V1+H3ZvXIJxN1fHph1wHXuiXSdCFmfkKuVRUK9fVAXqWKGRDJRKCA0tMAGaVOkmBB+LnYm+KCuT4d36W8cyQ6tmpu3ButQmbvYZSneWCQx7PfHjqfT+Ah1gHe0owKBMqEOJ5x5qBiwgaT5NUSf0T8CBHv0XB2iGghT5irqizo4DDLEZaWaHg+J1OhKkeKV8FmRfPrsGV85uwsdRVCLJQ1izSsiGt5okJx4lBfET+lZ6AYjFQPqW+idEfVUEvzxE2lUBLYpJMg5aJEn0Sim2+tNvnfqCPOje74Japlrk6qtBJv4TCk9AGdjY5UmJauZcrJqRoSUMeydiSD/1fKemw6KlFb63ffZ4vGf5qfpkXkccHztVWaKmxKioJFxUiSwSK30akR4l+0jyBK7+d+RxqmA6Qq9hS44Mm8gC8/PqcbqrpwqSw6bBWK2B+l1i9WIUs2qJDnoya3sn82LVHKMiFMyh3rxbSvcIcK5SS7Zx+nSvpFscrS1+DFUkyHsjSerxi0ieLZE2W0UUzNwUhUNJTMomuaeu0SohEWxhZ2jgUvaxlb6CFZ1aWkkndJ1yctSx9pa+dXCg+mSpJkdyXe2cNdR+TQ4HFfC1MR6wAEe4nBSNZf44wr7fzGRFQYqi7qGh75dw6JHGKXgtk73hofm5+m5DiAudnUAwU+wpiCf1ZBh4KovgU6lzaYlDGGOHKSy8VzHWIJELR/qzruwuE+lkGThhG4EuWSklGUZqF8yhZ0lw5hgisUtbdXOvChc1l5VWdZXrucS1gBvWaD6tLHewfg5NRAFEcq4QUbR4MM0B6viNqDbcQMPkAhlLIdSoK3R31h2q//cA+q0TTETRTyFzsf/cbHKLTPHOeBiH/Q+DZzwpCLhFCa7ZTPX+dWXkyzrNpMIWTwQnD2KZSqaSCczeNQge9t/1aU3Z7y+TcxSuyt7QiRoMTKoXwEcmUQMeSogm/XKJCXhDC24gy9x2VHREAn+o5ykQCNUQCwwCGswF89vNfhCsXL0CQzuD5L3wZ/vKXfhZuZAB7b70JzTiB8bAGk+UVaDFNb40B4MUf/wJcfe010t/f3siS7Bm1yqAYD8A2quvm55VfKAEO9QBYBbJkDkurG/Azv/LX4a/9rV9VwRhWJ0dK0UfL24dZBmJwophN5EH4FIp3zbd5Zj5OTAE1gprw/C99Gb71zT+FWqUiLnidkMt0iix5syyiKJNUytACSBRnCrnf3hvDhfMtRYu1rIx206drqzU46IdiNEHXAMFQjXYwCOD29gkc96fQXapr+6EMQWZU8yUusVu+pqoUm1WpRokHn8a6kL7UrcPaalPevNkncZwpKxXqEKNsxJWiLGNzetKf+cPhHDY2M0w2UYtEkUHwGc0STk4GAezuDuHgYKysWFzPVagmCophT+ql8x1ot31dXE05v39/FP/ZN+6wb7+2404mkd4frQaAjpXxCPBI8EFblxKOsK3bc+Uxz+RM6OrLYzecTDrwdnQGRNoHpxKCQIUuylT23XYicCB7bAVMPhofzBASUs/zJqtLrWBzdTk7OZm42IvJH8bBNMqB2EuC4gwo3qAqrUh9QuEAw7XFwM1zXSVfj2b7/cEEklTmP0XWD1FMMiJ4miVxOI2TcIbeKA5xXB0JqQI9VxRKSRnEQQj9g33oH9yH+VwLJKjcAKXiHW2MiyinSkwrWR7ooeKskrSOY0gxyCAEab+afocBpeo7lXnAs2B3YYmo7/FMWNXWepXB5lId1ns1hbgpa4NTsuQfllz8o+iz5CH1g/d6E5c5gGrDeH/xPms5fvNeJsMxsXYBrciCSieMmNQsFnIaZhDEnGRcwKMyzRwosz792lhfNhs1cWFzVVw5u05XOy3qOYzoIFfas1J6eCZl8lS/GhSUUEKKhDLfHR1NZbRm5Xmwj6gB1ein9fUjeXAirWd7bjtRvqPliFU+onSX0z9LCkY2WdQ+kLYH0nxf8/5UQ1mZsmOLIw8gC/l8fVhqWbrONphbEDkqibqbZNLSgxcprTKXcyKnznJBOEgWyvyL/a3FMdoErASx4T3RTpmslIidSh4tmlVSMVL3xIqm6Eo3z++tsPY0hORq15DTcC119uFnZY+B2GOwxwGnkvE8wSz6zFUyWio6FD2h9lroYgYzPyvTlS3DY6EYskDrNTZQonyNqZn3ZXSrFFiTAjG33DKLlgr7XXPfVOMDoWCVsqwwFOSqlzoxl9aXlFg6dPGM24Q6R29zJLf07NHS1TZCSRadlYbGjtdUEJmjvbmqLS3dF8hvq0nidWG006jDlXMbygYBj/MAqbhadt1emofKk+FhYl6OYPBkjsbyQq0btSqHWkX5EqhLlWLbBteGOYxKbBRXdGYiICSOO9rnIrozm8Inllfji5fP7XMCw3vzo7MofIJ9XKh/gOJEZ0/GKjFuVCtalE1Y5J0o65NLZ9aVGu00iCHAhCB/WBUNO5lOolkap8kHvs3gHpskIMCFh3jifN/DvqOHBb5Uz1GKMcb7ZOMJBj0Q5CVO6F+lQJ4gpbu8sK2b3mueZXQ4GpP9/X06fWJc73baHqHUT5M5SeIo8mtN7nq+t7F1Vq6tb4jd7dsiSWLU42kQAs8BQBcktjGSFXDkOeaEdYiCmX0WGinaMc6BZSls/OgXVKLzt//er8OnnnxK/RwTybshh1kwfmSShwnhL/7yV2BtY937H3/tv74ynQVfchjvZRK2CeP/mnL5GgCJTv+eFukSEMxm8Mkf/Tz853/rV+FqBuA7Otn8HuYMqwK0mIROCtAQ+hkazSQcfe65H4n+/j/4h/DvPPcUNC75WSzSMZN0kBFYUXI9UjrIHo9EpuZ4yoXkksj+yZweD0LoLdWg6jIipHA9j5LV1YZcOQ7FZBaTOEkll5Kgwitak9y5cwxnNztQqaLouVorMqks3qWvdOe1tLitDJNcqcys3ZqNBGD00plmCQgVp2ixNoqJJMH4chagiNCUnpwgoy8T9Trqf9AMW0+nQUT39kbujRvHZG9/pHrElRE6I6pwhIKL7ZYPZ7Za0Gn5WMwmw6Op99bbe+Ttt/adfn+mkFGXsIXinJ6nxO5yePuvSylfkTy7SUGm2dx9j1v05zccXBorJFWlj5dny0AcFxpuAzhx4ArchzU/NFViO0E/Qjr/og9sna667Hi5JQYXt5bSvcN+dTANVJLgnEIXcONV9B6KIgZcKQOGcaISPawCESLzQAA97pc6dRRvIDt7fXz4UGiBmXUNTY5jCsRNk9idjSezNJyNmMw8RmRd0elMxqFcA5BOJLiqygn93siVUsckBYoVFUIuDm4cCo3UdJUomKj/IwUG+1w88y++FquXiF5oh3hWQgCE6gGwGiy01LNaHjZgq3kMVlo+bHa1mpoA8TDfwT/38d0mmnmCRsEor2mfckw4FRKsnS58LKabdIjIUqJCTbKEAViYSJVkor/U6V6E8iCg55USqOK5OiiS+ZJGvRJtrfXEWq/le55T5YITiwCqBFhIJHVy0E30blHFJOUTMt+yfYcapdI2OnpTEsqbU4JgOqlUyHo5uD1F37RKpItFhFO0yYecpz02pHxbDxpUTrZJjw00VI+hMOiQ6a/LRe1zWsric7eQqD7q/j7i+3kSKjV6m6NSkCM56rBVIlf6HVJG8Rbyv0WqZrmYg+iysMmijfiJdeQ0FGCbHJaP26CE2suU5MivPQpZEqExqsP6D4Yftgddt2nl00MXnor+RfU6k4gCKRBAyKmnxX0n5TfK50jRmp0L5JhkjdnkVyycVbFelGwL8uTSCn1IWzzR74X9PkrUzVq02J7iEpl7UYDpwcKAPQYN4tqktpwYSiPuWZL3LM0Y5XMMLGcCnH7/8nEV19DmeaWeb3v9sB+UyAJxzxHeotBVTlofOCIDYuRzhWuR3l6nQZ6+tKnYGkivvd8fKWuDBK/hQ6tC6r25znklRZegUcgJoSk4XgSu55BW3XNcRli96tCK78go4UqDxl5G4KLvter7b4+ns958DL/wzOeSw/7w3ng62x8ennycS6y2miJDmkEYRMruJWs2DHXWoLWIjLmeKtyi7gbSirkqlHFI00x7HFMQJ8NJlgQZ/+Cprfq+OE4F2mz+A003pdk/fOpAUzjAA6medRm8/6A6acCTgsMLEsRG7qpadF8n5uN885FUcunNsah93JfT6bQmhEAXfyedJxAFQdJoL0WOw4Jur8u63a7rMrcf8/iOdKAOIJ8l2JOoutlpF0A+SaTccAm5Ie0zaMS2psM+/OJ/9xvw2bNnAXfWkSloogDeQ+eivqEuaDecqgCoVNc21j/55Z/75Dsvf+3Z6Xi0lSTJs+ByxwM5hYxeBwLp6YcD95jO8hJsnD2jlGS/x1mCp1DjAGe7Ep5PBFwOCdSohJQCbHMK35wAXCdRHP/MJz4mjw72wwNvdKdd6d0nkK2YbRFtYuqmksbxIfJcSsMwpXe3R6r/emu9id9TokBrKw1YW5nB9s4QeMqJ1VYYnYRw/fqh2FzvJJcvr6aex6SQPE3iFKI4FZSSSsX3qOs5c6m8KNX9qRZiJIIwl0IUStg/mJB3b/Zh/2CqlZ4LCyOi2xBQhFlA/2QmR6MABYoIY5QQl2ZxEMfvvnvovvz1286dW31ydDSFNNUeuUittziM5zGx3K0hBZfMo5S8e+PQfe21HRd9PUH7QSuMmBp2IBLmFjhDBBn98jqX8mom5cjJVcIfv+EUswVhfaGahz2SKTXIa4MqhN0qrHdcZbjOEHon4qOE8y/4wJ3RpXHScMJ0uVsna0sduH84gCQNTnEOiE7kgMA8TrH/BJr1CgTY04c8Ic/G+NYbT+JDBUu9pvIT4hMUaKGEoe8ISE8i4YhSP0sTZzoa9KPJ6MCjMmMOO8+l8ITq7dFPPD6DqOLVabeg3qhBMNKUEkK1oAQGXYi41jtt6LQ7ygMsSzPVT4phh1+vQq1WB9/11bodzKbQPzlRqKdNCmipIm575uxjzEzwnwmxEMTjdysOhaWmB+soZV33TKAp3jPY/2EYRX8WUZ5QhsykLARQhQ9BjTwIJzI30lKlQyoJriHzVCh7E+ujKR7RiSet+SFVGuUyQfEFIaXvsajbrE82lzrRUqflVjzPU4iIlNY/0aAq6ta5WhgHNHPGRqKE6sSyZHuggriSwmg5c1K8G1OssKiMRRBz38UiaS3f5kcslCQv22HSgKgpl0UvoC2x60JHoQBjKYq2V86gKDqAtilrLtBUvpiLSQFZPL0F7PVh94FAiYJp0f3S19QquBqKqXbRNQYsJSQ/fy97nKeTnlMUVEuXV+2UJXqN+S3QKvMlddUS3mwTVksPFVxYvmiOWJd9Tx/MmWx2UFJPLWUyOYoKkK8LJpXTAaTNl02yKUuKzNrz3dxjmc8btd4Igz4yg7yqd6SOvSrERhw2mV9IwOzhiAL5LM/NXNG7RHG2cYn6HWqTMvlAkljMfVPwKBULipzRoKdGIKrwZoXcgiZHzmWpJaJ0zOXeZ7JwHwhYNSRCrShW0Q+sp1/5XAubIcr0e2SWSkr1PEG2yebKErQbDdha7cHVO3vw9q1dOB6MTfEi90DFC4xkL2JYOJiJCEXUFtIZzVJCWaAQC+xarlcd0q570GtUZBBlkEbpnEuJ1Bo3E27fyZKjzXoYLlVTCGehSMJwDGl62G43o7qU1XCewHgWKNsGm2gQ42tbMGP0PEOxNm05o1W5U6XKmUEmM6ApFnJpNYLMfWhd4Ac8VAtLSqDhnpLN/D4H3uc6daDloa5IqTz6/oPquqD+U5LGKExTgdx4S73ZHFDQB9R92gBJGjhrcFeTQqSz2YyMhmOGzC3f9xkXMYmCaTWZh7Ffb2SM0qle++UtAvAWkXCOELiCYbXUC0dHpOQCoXKj6osborxCSwmrl85BGgUwAIBlaVXqHjGkxErOipBwkUqymhB6ZkJga+3ilY0v/dW/ftEFWH7r619LJpNxFQj8BGHpCZNkwl1+Ty/ZhQXULBzBS1/69+Dv/Ld/D0bfW3yCgtHLxIPnOMCLgYSfZBQuo5i8BJhJgO1YwspAAHvy2Y+/8z989WvzX/3ip+fRZH6j6mVvuJSsSBDovYxtVFXQavEJpcRBZfjRaC7fusYJT/W6d2azpcR/qr4rlzq1rNetkuFoTkSoSBfqud27PxGvvX5vwDy2c26rOyZStIJZ2J1N0QYIWo1mo9Zo1kO34kyByAYA8XV9UZnfIagijk9CePOtA/rKN3dgd/dE2Z4VrSqQ/+t6HtTrFYIoKj6vinkjpHPv3rDyp1+7Rf/wD2+S4TBQ2h22+IptRGiVtLbagLNbHd7u1JAZ7xwdzJyrV/fh1s1DTIy1mi7ke4NaOjlX7Uo403Bjw3k3IQQGaZLO5Hv0KT8O4z3J9VVXws4ghuMYDVEr0KFTuFCdQqqqLTnU+T2XQz4aj+dQAEGVOlB1aa1akd1WDXrNhmpUVlSjPPCQWuKeozKeVOJYs3kM83mk+inJA+GrJKjgh1XYiq83UgwCqc3ogHhA8MEndD4L0kn/6B2Hx9stX3kFn4sJzfufqlSC36qAe/ECpOMT8JBPMJooOxMDBUGz0YAz587B1tYW9lpAmqQ5bbbWqKuf85TDwcE+vHvjXdWPILIMPEcnyQlPin65gk2mEl1qxFkwIOSlwAxbRZs1BqvNCnRbFaU6VhbS+CEdeW5SDjIVGqyS/0jRp4UoeuWKwpvazfESkZRLFsy5Up0NIuS2vPd1ISpxQT6syjUEWqK1G7XoibMb4VMXt2SvVW8DyLrgwjA8jWCK9sAkesOijko0hUWa9L3DYoFKNUvCGTZ4ZQq91HNTI55aaESrg+apjkZAFcJukDWNjBIoJS8WrbHXrDg5Y1OAwTUyA4RBgsQipdiKvoBB2aTUYYIpfpBCYEUsPGsPECZPI0zm74Uq6SOGResIlHpFbVJi0CtS2gaKvkNRUDKLqF0nybx4XzBJlrA9kgTyZMJSa/VGv6hKS0iepEiD/BLDIi0mrE1EuJ5rLKe+6mReJ+869CNGvdIKWlnaJZgCS+5jar3Uij5N/AYSQigxWKn1kcxR5VLyQ5hRcgZi0Cih5hzkSZnt0TX+sOhJjIRNqRkEui5SBD9QEi6C/LjLNiokTwTLiWhZvElfZmFsOWmujCNF+Xrruai9ig19/NS8W1ALNwq7OaprnzVzUdV5O8xQmkWR0JcUgYmx8yhYEvae2D6mokvMsic0VdkgyvZcTTIvjfclmDNFhKHabqj2jgoq86OKOfovh3MwLgvKhkBo2wRUL3JMti+UZQLWoYRkw2kKFAKljn1utSlrFTdtN/24P43QH+84E7yPrA+v6t6azyb3hZzPZ4MAdl69JidxPE/SpH/+7Oqs3ahV7x+ewM0dkYtniVJxgZf2IYxQifHmxPmNiac0omLKZ5EyhzqsGcm0nsbz4aMYJD/IgXfjfgiwVXN/YMmtg3p9mYDJ9/d+eDkvcSl/DCR5Uve7lQtY6JXGH3cAACAASURBVHcpxxJIH7RNzFm0PySUCsbYNI5j9+DgoDocDkm9VsN+HR7PQzkPZx2/3oiqtfqg2mjOmOsAB/GUA2KTAvrR5hTxKgjWlozU543caksNFDn8b37rFWh3lxSo8x63CdWgvXg83ozj7CXwKi9VatUnZa22MvdY3QNwVrY26aWPPx/cu3u7zzn3mUefisLoL9ddcmeWhphPThy3op47MPEL7jGBeJDl8JBRVDr09XS4lGsJwE+5BP4K5XAJJKxICitAoGqm6YYDUEdHnwmAX2l33vovfuurs7/7lV/eDQf9P+kuL5+TlFwEoWxnqNn95kZ6wM8EJ8cnodr9vIqD/ch0c6NOkLrebHi0gzGg8pXFXkitZzCZzOXdu/1Jq+W/LjP+RrflrUZh+Hw0j5ZlJh3JRY8waDa9VtNxaVVDlRQ7COk8mIudnSG/ceMQ3r1xTAYnU5IkmWY1sVJBT0qJMUS3VyVnz3VhaamGKKXa108OxvSVb2773/rWLhwfT3VrF7P7nC6addsVePapZfnUlSXhV710d29Kr13vwzvXjmA6TUyhyxS7ML7Q7KFMuctItGeDtpDyiDL4BgN4VXI+SoSGah/X8R07uYmhDLpUwETW4VthE7xsBM/UjyGTllL1Ubb5F2lgEOBzLIr5CWOM16oV5V00QmN/4xloe3HKPUT4NOHPZ/MI5vNYVmt1cFzH0uAKOi2lqvcPKW2Z2iDzfrqMAOWUOU4cxeT+zvbO8PDe4bn1Xrvhi+VAJDXp+CDx92UGFcagtbECHe+zcGZzA46OjiAMQ/VB1WoFektLsLq6Cp1uFzzfVzQ9G3TZQG3//n0YjYYw6Pdhjiq2jEGtXtcLw1R7OWkhCpMxGUTJIiKWumj3CIegeA2Fep1BreJqOwHxcPTusR86wLJ5RJ6u5KxBqkzSNRMFm25zETQbEObSKCTDnoqMQ5hymMcCEsUte/S+qilEADyVFPcdyiBp1WrppTNr8MzFM63Nla7HGOpyCGI/JZ+LxdvIQlunpLKp6JMlJNAEx4rGaZFBQozXZIFm2sREx7oWUVtM1Yj1oJQkF1mBsqBMifJHjW6PfY/yZq+Vc3PzTz0fc2qoLBK1MppXunalgRM4M7mHk/MxH3LlT+Ob5IGfLKKipRPKqa35g25fSwp0qvzmhBhepBK4odhvS3RipZN9YZR87fXTRYDyfTDUbAp5MaegCxfIHBafbE+tvfflREgWB2TEcMpBV27gaSHuXNK4SJ7zs9TtjGDFdUrXQedPXOrSDGa0tCzYopWQi55RWaJLml9mKqFWND/1jfw+lueMNIkHMXYhKqHK6deFivaCSFKhnmU+V+S3W5QiYl1IEkV/56n3yhM7izvj59OSCFFO4zbHlN8fmxTiPSrRbo1CMC1MMDNzz5lVwtW9o8T424IJoCEXXrJIRxwnah1HpECJ56iDodjIpRg4+LOK50KvVZNPX9gkqE9w9/BEhrNIJKqhPKNGdNg+cBj4EVGIoKgerFGQwf4wxLYS4Xs0mEbzCc9Sl/MsIIQOiYTIc/itIXjHk2OX844AuKLSnBlkdFD1/bDVqsFgHGohMNQVSBIl/INtKfi9EoyseBy1WgV67YYyhZ+Gc+0TTaiyVEgy0SYC1oHz1gMP+wc87ocpbNa/194x3GxTLN/o+4kY2ffvqMKAeFcgST9JQTZOr2FS0TjJOoAcAZBbJqFfV22ejEVo+TMYDPjwZMBWV1ak5/uc80zE80B51XS6S+SJpz4mX/nTry2PBsNlIWSdOmaBF/lm6DqMshrGRKJ4buOUAMdC96OPHVsEMXtfunt89LG37lz76Wqt8VJ3Zf3Zlc3NSsfzFKomCQSO7/WXN8+IzYuX17MkrqZRRDIuP8GAf1HG/HpreXP20i/9++KFL/8s1HtdBQpAFMP4ZAD15aVHfj4StiihvhYKly5hrC3T7Mxsb/c50Wv/AhD6OSNaOwWp+ghd3aYHqALbEwI+HQpouQ50Lne6/4Zn2Xwaxl+vRnHHq3o/DwTOGJUHfHinUsiUUeIxbJHJRDSbpdGt7VGlWvV836ewvtyQnbbPlno12WhWeBDi800oAiLYP4nLg5BidvfW0a1nnlh99dxWY88h5LkURCMK4xXXnz9Ra1S3/EoVV6OxkMQJw7hx48YRefnl23D79omyUwFVVHe0InZphyM53QGFJrnMUqx3UoiDBN54/R782ct3YP9grNYcW9yywoXo/bu13oDzZzpQb1Sd/iChb71zzF5/4x7c2x2q9ch1SS5bkLN61JIo0S9b+WtKDtcoIV93PLgGM02Rfpwzse9BNkxAKDx4J76sarh+rQrPj24rRVsw/W02ILEVb1XBtbyhj8ZjP4jqUXOipAGRpOgpLZUfGUrEW++3BzBL08AcxRwG4wCGoylpNOrgmMq1emikrmCrkhhz1KbINTsJgwgPqQQIQWHVKkuhs7930L31zjuvfeLpKy/3Ws3LbpY8w6mm56GqKz7piK6tbWxAp9uG+fyK4tZj5R2TvEq1Cn6lkj+AFh1RXppCQBAGcDI8gb29PRgOh2pjd1w3T6YtKqIrUpA3OGKPqpIRF8rpHOxDjobJ9QqFZsWDuu8qwSTxHuqjj/9EQEPK4itzBTJDUmMaeGPg+YYCicUGVJGTskAZtTAGw37fMaKacw5xJopl+zsOhIsINOs1+uT5Tf/5py66Z9aXmMPQx5UTXXGkOeJiBUaUXYSacEZBVhQ0WoUUGJQlV5c1SQgmOBioY+DKDO1PicyQ/D0NAqQDWdvDu9AzhjGGSVqptbrIkwhunh3T+4fJK7eXW9tXWJphuSfUChcYcmrJm9Aq65Z8E0uX1BxGgkxkIATbgSr63hVoZF5EOHUrCnTQZmomzS73pNoNUYoc6bI3tpzUqEDZflvoGkCuBaALt5owW3ie5r+7OCep1P2ogpQSdHtNpFWg1VuPTcx5Ttm0yCVjJBfssYRSqbxVS4mk+ZnqV7U0XAN3Eav2CkWlwF5LiyiW1HXRyVOYJVCRnXL6bDkZNMUZ6w3LjO0OopaY2kg9eXBOsdMCSTmV2arclnogqYX0jFUPLRVQNApmihpq7kGu4mvtSPQ6WCgFP0ycqRDe0q9VPbyc52u/hEIoKMeNZW7WmhckbMxg6bSaQoaoMbV3gGGAjGIbsWGhVH2NoqXKY1KqBNJSMMZBCAf9MYRRDBXfU9RZbH3AvQfbMI4GU9g7GmAflTy/sZZ2Wg25tbbsDqcBnUwCmmaZ1gG297c03RYSbWPndDgI4Wg4JWkS+3GadVJOXLW0MGfme94uBfYWkWRP9aNj8ovFAQrTcRDuB0E8QS2DSRArfQT8wCBCSi2awKdQqxQUcC1UQlQvGyq8VyqeWqMye81RgTdL0cB+zXVpHWl+H+ZGpAsD7+8DUzHXLATlEvgDP1hsp2jq6EYhRI6+nSQ2n1YxM3FPANwBCUi57OLikWUZ9TxPTcskS0nGU+5JF3iWsiQKwyyO+o5XydbWN5tLy8uNu7dZTQheAeEwwxrA1JIzpqpG1IkqwDk1zyaDX/3N/xc6axvGvurUQRMUqpVPRdPJxw53bz2xffPaZ8ZHh5+lAL2lzU1HiM9Co7MM1ZqPoOhdh7nvrJ05v/HU8y9sxPN55Xh3J80E7wUnJz8WzebfaPbEfeo4E/RGRTsgdXxpBpA+QmGfQKUJcis9Prp47Z2rT2xfu7o8uL9Nl1aWu+2l5cvjk/6ZeZhcrNYrdUQnJChhXR3t20dfLxWrALCZcHC7ne71f/3VP7jz8y9+ajCLRi93/dUlSllbgvAMwokuPqgRJJQIFCWpEDw5Og69O7WRXOnVeK9dE816JTm/1Q6Hwzl6dHZns4QGYQKjUeTMZsnqvfuDH8lisffGa3u/93N/5crvP/vM2rxa9T6vAA4phoJnJAiiaDiKRuNRuHZ4ML7w7W/v+q++uusi9ZXatgK5EArpYpNSjs1gNp3jshQtL9WZ51H33ZuH9E+/flt5asZRpmJAJK0g8w/fB1VnL13swBPKKsWDvf0JvXpzwK6+cwQHBxMVg2p9khIrRuQVT6Qsd1F0iQJ8g1LnXxLivexXyGx5/cP2t33/430nm5ZelGJxFY1ewIVX3XWoMweCMATpUIjTRPXINZotxU9upyE8O9mBhDJI+Udeno/90LSVSAg+yATMOOerWB1OULlP6k2SkLInolVY1BLzkyCCWRBChg9OtWJ6dTQFEJPMWrVCkEqLtAOUiWeUJinnTHDuEy3VgeJC3TCKntze2f7j45OTa91u747j157gSeag5otjaGiKhkux+oSceb8I8AwFSytLcvU67Z9JVf8d9m6enAygf3QM0+lUPeTEBHZo5ExKfnA5zc78ZelLouSfhqhLo8pgo+3BWrcCjapvvB3/4hRYTIoxM6fcAIkG2Nrihfqaeqxk+ROtuqt7CgWkQsBU+WlyCBKR97SdvjKnEx8TeJJ6xYML6yvyuctn6aWtVeb5Li2CquK3Ne2krJJqk7FSw5KZrwU1cFFtWCfJJRzXJKqSFr1jqjuU6ERJQIGG2bVRa+hoURlFA1QG96oCanquikSx6GkzNholohKRGgGyyUeBDp3O1EtB/6n5hoxhnVzSxHiJ+Yu/9V2Ph/OsZHHjzP2ybmC2VAOa9inywo29jgR5iKoflgtbMJZlarDpw1OUdUV5VeRBkvdnWmVbvO/SVJCU7DB1lCKr1POvAIMKNE5YemgBfxP7zNtkTM8+c5VEQeW0QhGi7F2YXwez/tjkT58D3hhHpyi0hNWWXm+GEItWLebGKpluxHeUFmwJJs8TNHPMVtQoz4nwa24Qf6qpprRURLE9pTg3sWeJnkIjbXJZIjgU62IJ6tPGhjI3Q6I2ESf55CgpwpZQ24X3IMZLkyhaaBDPYRaiPZXAu+nhvcAC0mQWwb3DPgwmM1hbasPWcheiLIPj/gSGs0AJ/OD6i0no4WAMt3YPYTqPoeo56rlFBVq8C/VaTSm57vdHSm3y4tYKubC5hsgmuXd4AifDqTpXTymtOyr518GjocBrJQ5D4pAU1TBPwjmcjCd0GgTVuu9Wzq53iOswbzgOJgJmJwOvcpMwdzybBfDWjbuALSrjaZgIIbZ9Ru/N0+wZngnHFi3Q0uTgaASby13lI2oReTD7D7EtAeh9gIVWrNia/jAiBQOH1hzHq3nRh+/zjvKnHZG9N53WFG8S7miG8gc4CGRzIvltI0ZzEbcWSRCBU+sOXqB3qITfZVL2JSGXJYEnlRSUEL6UaFlD0Acc23EyWSXIGKEY4yDFtubX3Eq14lVqNYJrvkgz3WCrUXyFDAIRrShx1gRAZ2X1YMSRx8UcaC6t6PVC5JcEYyAWSzifUnhRMHgpOD58dufqW+392zeXwyBY4nEM0/EIlrbOyEskw/Imwl0ot1ipNevVWqPpoFBiHEfM8ytAe8tnuBBfoITc5Jy/Ik0/hjy1/pRui+sQOFcT8OJB/+iT77z26sXr33r1Sn/v3gqec6PTIUtr681qtVYRnDOzjDhG5Mdus/hNrDKiF6RLpEr4z8WEPLG6snJ3ODiRbk1uLzfX/jmldJKB8AiRPwYAa9h1I4T00wzlZ53McZ3MoTSeTmO2e39Mux1fntlqi62NVpJxHhBCBtdvDdrD8Xwpy4THOcfCwicEF+ztG0eh36C/t9yrvv7UE8skitjlYJY8tX+8J7bvTdPtnZEMg8ifTObsYH8Mg0GouPGCmK5+YlJOU1RUPT1CJr7LkvW1NnvmY5v83PklGgYxvPnGLtx890jZoqCJgqW0K7ZdhcmzWy149pk1srHehKOTiLx19Qiu3ziBwSg0+5u2WbPK4naX0MZnkmlynRwT4vy+I/1/QSQ9wNlru20e5/E9P92mX1X9GxA0WHZhBglwwgAJATXiAnUrarGOpIAD0QbkvX12faICsDgjj/u1+bd3qOwJIsiSfSLS+8xxNhllyroagxnXVOSFQXkWhtR9JUiZnCcJVJV4qA1gJDiMyVazJjvtOq3VfAhRmhSwCV8y43mne6so8ylNl09OBvWDg+ODC+cvjSlzeSZSRwsxOOq1KoBH8QTHykNrLzxqep44166ZedulClgEzGYzODw4gP39Q/X/Mn1OPECnK3qJ8PwzXor1DbsOFcbaNQrdpguNqovKhJZi90M/jQqmgm7VtPm1/bk01EHPRwk+HyKqaWWawiZhFKKnZgZBooMhak2sS5sctR9k3lEa+ojDKHTaTXr57Bo/v7ES1So+yURWKSiBYM1mDHpVbuLneZ9lGY05LV6iUBQztwv6pSh62YwoCjEJRG6kb/vIpGWLEpLTLgktzP6xNGl4uwW7skT9tF2Q9iKDTQjKSqc5laZ0HgsXDezOaJMZPALU3CJa92CIxQFlA0OsOf932bNpiMPll56e2eYyYPh7hBoIEqAjQaED5lIJS70mxBpUF6I/lhJazIAccTQoDjHGqfn9XKT7EsuNNBfZVtZzBFiUDtx4phab+eLcKBcb8kDM9oyqQF/kRTbFqMed3iS26t1ysNTeD5N2Ed0n+OgClJmPOHfQhkfq81WJmw5aWRn1tWimtEJN9jhF0a9ZDibLz4A6RCFKvpJWDdfSWs0lsKj8w5gIpYIRMVNEW/VI1f9KZLnHN4/awCpH2kTWps4K2RG69/BoOIE7ewewezCEWRCDrtfo40Yhuv3jAfSHI2g367DUbkMQxXDQH8JoEkDKM6hXKyrZnIUR9EdTJbiBPV0YjSOyodQlGVOvRQotJpOvv7vtvHljVy3omLAKrntzkZ1Tq7iiXqtkvudRh1HqOQ7F5M81+w6ud+E8gv2TERwPJ4h6EdKuE6aKm8IZjGfBOJwPhSSJJESgtcl0NgUu34anLm2mUor7Fa92m2R0Rhm0sQ8de8VG40AlzEiRXYeOSizTnAmhBbVUEqwCW8Ipo6nLGE0zjlAcQsJemmZtkmRu6KJ8z4c38F6FVLegvBfEoObve77iBzIyKtJdSsmOkPIsoDqsYiIrT2ichiOQ8o8FIb8nCa0gwokVLKLWMULjKGKHBwd0t9cTGxubstPtCeylznhM5rNxt95sk0a9Pmm32wPPcdN5kuClVv2AeT1awpk0I88RJr7metFIs3I4ZEgZBh/MbXVDgLWUkGckgc9FKf/C8e7287s3rrcPdu7K43s7JIkT8Ks12OwuwfL6WVKp1vADqBSwhYA9cxgmmAILNlmakG5ridBOr+r63hfay2ujdquDBaurqA+kqi4luVtpvBszgI+nHL50sr//xavfennz23/yB7Wjne2qzLjjeD40uj1oL69Bd30TMJmFYsksTzGskOFT3QKi9oIhJvUph3pEofZPvvoHwX/1n36Fp0m6W/fIH2VCnBGSbVGQT2MN2+6VmHQyRmMhJBuM5t7V60dYABjVKs7tJy4t3Wk1/e0o5uPbO8NLUZR93qF0zRA4qtRhF12X/+iNmyfX/p8/vPXK7v3xXZ6RFwbjqHs0mF/YP5h2h6OwKYToIQEmSTMlw0GxZwBQeFi1+evIFAlRXKYZ59v1euXwY0+v809/5vzyZ164sFmve7Xbtw7p/v0RRHECnqd7LrlaawXUGg6c2+qIJy8vy16n6gxGMbx1TSea48kccsE6Kwlotx2mqHyZNpvWhCzHJ1erzP1mJpw9olwA5QNh+OM4fiClJKqfWuOvhGin+RoDAykglgB9cMCVHryercH0ZAqf3cSeOwERJ2DW9I/G4zQkpESIyCVUtBpV6LZrcDj00QhbJQDC9BkV1KqiGI/JHG7WKBqD8u2soLCp56nTqMLmShe27x9jcOAIjonmKf0UolQ6V8Jw/tThUT+bhVFWrdUySqlPTqlf2qgnD2Dx+2h4jLQwZRIsdFVf9cFIhbJMplOVbA5O+jrwQIpkluW9PqdBnHL1r5APwZVIgucAtCoMWr4DFQxoLHW4LEDwENTph2RIA2CrnjSJG6h97Iurr79BqKKq4fxAMSaktEwToczPJ5GA1Ii0PLSDVQedSGQEZMFh8Ibzpt2oy621rlhf6ZB61WNcILOGm36+YtLpZKZIIk8pxJp7b5IBE/SaZs7880mu9FoY4tv+u/J7MmM7kaPfhn5JytRCFaVSpABzKXgqhMBJ4RUZWwFhqrmeCw0VwX4ZsQRYVNsEYQuti/OzSJY00EwIVb2CUh9gSoxPjYWTiE16T93L8tfEysaWebe2P8RiO8UBR0BIRABqEmTdvndO/xNq+zbFKmMxZJpXde9sofC78MxJabs08kQ9P16ieIqIixFZ7quVD/q35hR5sNTN4v3N/bbyQKSMPNuJkqN95m/b96irFeQUim5RPqoRVlLQzctFg+IeG7qqOn+qmRH5fSY6bjUFjeJcIEcLhbHOIbSgWi5cRzM3yoisoj/LQgE2pwUbkZ388YDiPw8yCooih/0sl2lBIZ14i8Kf2PjcqXvJQdFhh2GokilcL2bBHMZhAPeOBnBz5wDu3D+BaTDPqzSaxeKCSsQmM3X8aGGFzxD2a4VxZPozmWJcCFkgsTTWiB+yIgqlX6p6qNBOYTgNyWQa5mdGDbKBnzmdMVmtzvGzUBiE4OvbtRpBr0tMVhHVRMouqsgiAwhHGCWwczhCPqA4mYTdeZI+i+2M+sxhB602UHl2dXVNxlHU90HepTIbSUJaCeckEzpQxeQbfTvVwUi7RuiiHc76XqsBn3jyrOg1a4OD41HWH80aGU89FSFzOCu4eC5i5NsplTsfdpyFsV/zPaBNnB+uan59BI3zBzew5+IGZfJawuETIGUigcwMboIRBd6X+4KQCLk5RMLrIMULEiRSkWucc+e4f0y3d3bkxUuXvM2tTeJX/ERyLtJ47gqeuI1Wy1nf2JrX640sDGZVLkXAJLlHdHHvshSwxCg/QwhZOZl0QXCNbHZQiUjXWRxHwvmMwYsM4Atxyj9z++03L29fe6uJnuBr5y7A4GBPTsYjOPfkM/Cpn/oyXH72WfB8Dws1uH1hb26NOQ5f2th0Lj/3SUiTSMyDGdq71VfPXLjSXV39Ob9SmTDqDCoEbsaJgGQaALgu5px+xYEnHYCfnI6nP73z7vUX7lx9Y/3m66+Sg+07GkV3XOUCXGs0ycb5i9BbW1NFnId58Zu7jmt7Ax9dpMYSXX9KE0bYC5/9HMwmYwQK5PJWe4fOk38VztJVQWQHKFunikUlpjxTCl0NkKSWKaAgIUkigoyLV2bz7F+ubbSuJ4LE8xm/nMai79bpXyKEbGqxeylqFXcj5eK5b357f/b29ROXAv0U5+LZJONn0pQ3OBc1SdBChmSEkpBQwtH9jxSNgWrJwx7veZy+1e3WvvrTLz0TvPjipefOne21Oks1KrOMYMyDSWa16sB0arRPXAq1WhVWVxuwsdrCohS59m5fDoZzsrs3gck0OtX/TgsXFkKk67A5WgImqewQtGFznGucy38VxdFVkcXKhBn/+WEILT9Y3oIZRBkC6crrQVyHwXgOPErBb1Thic4cun6mKltYsct+CDL0fzuGjB3KwopXoZ166nXbdahXFZNJBfT4YOVCHdRaFGjUEDdQrEYnSpgh08GA6dnCMg36WrYbdahXKspnBftiWOEvmDMHKCFrQogfm0ynnWkQbviVKtEUXt37qVAOQ29UfU1QmOpLlbwQ3ScqChRMKfplKYyGQzg6PITJZKKOUZiA+VHb4iKKI/PzrrgUmlUG3aoLzWpBtzqdXP55JZqWxPYwYOK7GjrAFaXi9HuWoDHo8z1XJU9BzCGI0eokgygtFsSHbUzCigkJooRi8E+t4sPaUkueWemhCbuPL8hExgqLQ2V4JYzdCBpqEYVAlgxDiPX4LCM7YH0ZqaEFarokITJHosyQFh+jtBBu0QmOIWDa+SY1gq4pj9IYCFHzxlRatQtFgzMUYJ2s2l5Nqii5ULI2sXQ5UlJizR+R73g3lS4qp4RGKJxHgCxLpOEoGq1cyIngYdjmw4ojBUT1APKvf44NlbJHgOhSvVZlItJMGpuLgbHBoCUhF4Ci/zS3uygl/DoFo7pooPoJRY4E2/ewwkJCLIj6qPe2/TdEFL2Spgc2t1sxaOqpohfJe2gBLOWW5vmlpo+K0utp6dm3RThm/UIXEOly0UWfr52PwojesBwFL90sYa6NBrbySo+5BTkNt0QcsM+LsC6WeT/1Ag0XSvStvCCRX19j1S9lSexK5tdCmsQI/+9gHzeu5wqFExBHqSo8Kjo5EC0gl6TAMwHHgwnc3jtU/05nczgajmE0C1SP5WgSQh/967LMTmltN6B6WSmgkI8WMZqrz3SNmjAY5NoK9OJpILVU6QeA7vdn5n7g2jCfJ1ppPV6kmor8vSTMuaBJljHMPFHQCgtOYy+E7rxO2vW6Or/RdKbexw60A7t/PFaILRdiAwBeAlC9a20A+B1MOGuVCvS6XRgMhoHPk7uOxw7TiJ/F/lP8DAxWFXWXFPTm0rKk7luvXYdPtS7JzdUl+Ppr77KjwZhlWYYoJ5KAVkHCpYzQDs3EzunH/IMeLhAIRiOgrgDq+5DOBcTzDHxsr6lo5J6pHjX+IRwNGTiO8/UM+BWeyfMg5RIhEEqAI/wDAM8SKf9jCXwPgOIB3ZISegTFawipZFlGBoOBc3iIbKiAoz+r0iOIIxrHc16t1cTmmfNyaWVNnPSPhRA8ZJTugSSoarCOa6LjoFMn+HsnLbUAYKzgmWSTE6gwAh9zCXx+Hs6fv/HWmxde/YPfb/b3duHMxSeg01uB1tKK2kme+szn4JlPfRq6nTaKauR1dyHAoQ5xemur8ulPf04O+gfkrZf/hGZxQjor6zAPgwvbt26c4xn3K35Frq2vw5kz5yAVvMIBnpz0T35qdHDw5fu3b/74jde/2dq/ewdG/WMVX3VWVqHWaECt2YRLz30S1s5fBK/iW5b8A8PsUl4p9sDTxIbZ2RTtzgHgH/yj+KDrmAAAIABJREFUfwr/7H/+hxAnh5lD2JvSEf8sY7LqZOJHJJBtCeQeSPk0z+SLRocx4EJ6/UHofONb+5W796aTWt29PZ1lMArSPddTD3kXfT9Vl5+2VFlxHfrF+Zx/ZjILlikhlxkjW4zSmn6cmW1qV721RlDBVYw7o8POuUiCMPlWveb/05e++PRrv/LXPvv86mpjnQhYFiBokkrSbHhw/lwHsN8TfyeKM5V4tpsVaDR9bAmg2/dGgFYtccRz2yLb0qFU8o3iLYazzKGY/OJmR5UkP5HHVRC/wxPx+2mS9e3+kT3OErSl8aEkm+XhEgE+k3DjWIKfOlCptuEgDJXv3mYLYKMpVNJpL76qWGY/lIjQD/WQABkBsudIctiseaLXbtKa78FkOs8RIBRnkHmipwMv3KCnQQwHgwms9ZrQ6bSV7YgdNqBEcQZETJGKlBqblBz9tCEpJVVG6RM847UkyXpY+SMEFvjs1CBPSs0xL+AbCq2KUQrJfEvJQ8VaTDT7x8fq/6lqyv7uNJUtoZBI3adZ9x1YrjvQqbnIyTfJprPYX1caFtP4btKF73nogzQZj/rifXGUbC5hAVxMNN/P8SpkRTBIBIV5ItXzWyQAD3k9MYQ7gWRBqihwKOTRadRgfalDe+0GIgk6zcrRvby/USXDiJcIovv5imRQD2ll40g5eQCdkgoorFqoSfwM+mJRUgxoMZGhRuVT9fUBMwgNLcSGFhwrjaAPEYZjqdQrFRKRB+vq/5oNYtXu7CmJUk8gEeKhF/80zfuB5FAoRxVXGlVAMw9IiQNrsytVO4GHMHMBCjqoPa/8s3NEMxd2IYoyJUyBooy6lY9TlL2ayx6ZhYqxHVooJvc7LIRtVLIIpWsGOQpo37ew4ZF5Egi54A3yj0RutWpFmOxEIEb5SebJlShUAcvkY8FL14Ra9NK0imp+NSUy90y1qJRSlzVGpDJXe4VSEUKYIgjkFiqqX9MsoJb6rWkHueWPlLkRYyHIpx8Vi5IW6HF+pfKXGg6DRbCtnQiQhfVsQSDI8NWwnEFNvz2eC+4BiFYeDUaqLxL7+C3lHamho2CuxG/6I+yZHGLfokI58XeQZ8cMKslLqinMqNgmRswEz8NRBQazvrgIIbG8J9cObR9SUnM22ud675AyCGOkHzzkCVscaHGC91t7g0klmId/xpNQ1VXw+Hm5Wo6bKFetF8QE2pvmyt4EgK8DwO5gPJXffv0afPzSZgqE7QSU7KRZ/CNxlDFcazJDOUdROoexUoJfPIeIsjpC0DhLW7N5TDhHAVXKqe4lqQDQnk/cBqEfOFX1gREFU7jy8U+B16xCivRP5kOn1YHRySF8+7U/gUa7q5htH5KjAT7MdwUhNwikyF1E2fkTAuRQapLSS0Dkl6iEfULFPkhoaUEvYLiKUEIzFApyHEfkVRfB3TSOvCgIwk69Pd84cyZYXd/wb9+8LjhPawBkGWukWqlVToDIsRQkYYl+6ByhC9bKYJKC4wGszjN+4d0331h/+fd+p3LrzW8jOglxOIfO0oqs1uuwdfEKeerTL0Cr0wa7dpQGBwGZ6zLRWVnzK7UGTcK5+v0oRjEb8Ah1z7/5x//mXBLOblx86rn4p37pV2Dz8pVz29ff+fnr3/zmz+y+e/XJo/u7reHxoWJ7oRZGvdOC7tIKrF+4BOeeeIace+ZZqLfauhe/MBE+PfJlxnSDBATw2kI/AEhQ+5ZwCqkSF8WKDE2E634DXEhZFj8viXSlJBUg5BnT842VnMxh+LhKGE/jS6NJ9Pk05jFldFhr+C3PZ1sA0lUxC3poSnAkEZRI8rzrsY4jWJsQ6aiuFt3bgIeWqiRTiQDJGghVI2Em1suk5MfzKHmVMvrbn33x8h/9yn/wwvLGpaVzME/XM561lUUapVGrU3OvXF5miIG0mlUYjgKYhynMowz2D2cQBDEZjSL1tZpUjvZbzredMnNG7z8UUdcs40JkfJBU3K+2uPgdLvmtjJRCyB8SWuiHnmzagY4JdYfDkHfhZEBgHsWQEAc4FcqzFpWykPucpjFcWWFqc8jER32eH+YgQhyAkDe8mnu82uusN+s1srN/oioyiDq16lUVRIbh3AiAaOuTeZzA8XAKh4MJrK/2oNlsqCwRN3uMHRilslGvQLdTJ9WaD8E8ljoAKts06/QTabMgoSOFaKpKkw4aJOSCHmV6nQ2CdQKB/ZxAbV+QFgsK5wEcHhzCvZ0dhW4iPSUX5Xgfm7HjEGVx0vCZ+oOJJiK2DopQUHJ6A/jzGLJkt/i9RBkWmiHl/szvNIgRdQkjVWmEWSxytsJpqmbpS1XWVkg5AVhqVGFjuQvLnSasLXWg02oQ5Vd6ykJGIzuCav2m07xnnUAKI0NoaKTECqQQkxGUXDZVXVP1m1mqoKUsloRP1NxSpGK08HFA4Huh+JTUVUp8Z2mF6xSdU81OmvdeGh9Fi1wS04do6Z35FXnA/7Pwh1ygukp5KrEuP0GESSEquRO8zL+f9z8aE21h7vGj94NSlQQeldzmP1dVYrawC5aUO/G8haUo58dskO9T1Fdy6ty0yJc9++I9uaHkgiw8LIvGl1JyVLBb82shbBZuky4JhQpQfrGpobjm3OJCzXbhFw1abQo9KusWwsrlQGkeSNULRClSfymYfnP7qJYTA8LsmqbmMc17WxenvDDPEVNFG3oqAjG9kcJQzanxGAVTsBM5ZfhBEkS5I6AQCtIWNWA6GlzlTUtgnsQKzbt97xDu3D+A27uHqo9yHms1c+xPQ4p8lKTakxn3+Ixrj9nSdDq9fmrEVBd/uNAoGBb20BwdEYQkS4t1vDw3bNotQAmV2fuG98P3kWpLIYxSAx4/stVBFsdR0JhV4TJKVJwCD/OsNUUMUlau1c8ZKm5irO1PpkH06uvvwLNbSyKM01HC2CCMkizl3Md1BpFhvFYqFlKBPzMCZEXhE8/73kGfvHHtrr+738frKR3GuDEBRfJH3au4Lcf9/9l701jLruw8bK29z3jnN1W9mshisTgPzWbPrXa3JLfUmiElCGxJiZVBvxLEQRD9UBDZSQw4QRLohxEECJAEsWU5sey2Yw2OFMmSuqVuNXsg2ZxZrPnVm8d737333DPtvYO1h3PPfVUcikV2k2xuoFis9+5whn32Xmt93/q+8HVKoO/OyNIx3H3/o/Cxz38JiJe6s7sDC3PzcN+5s9Df2YRhMoCVq68Cb3Zu3VrxLoxCiFgojDkQW0cRqkkJDPUTkrDNOW3wz/ABADWkBJEhxEpBg0qhyHDY7XaKKI7CLM+8oij90A88xhmTQgQkTHb85InR8RMnBtzz5oo8X1QKOogwsoxi7bZDzI+5wBRFiEbLqKwf6qd3kkuRrd9YCV95+puday+/FIwO9vWV2bqxAmVR4vnHn4DHP/sFOHXuXh2vFHJmxtH/5jQtaYfivhcTGnrmvgfH+zvb1w+21/lo0D+pJN4zSfqPpJPxd/p7/e3usZPzSZl/5sVnvvWzl5799pO7G6tBMhrrZzKKYtU9s4R33/8QnDp3Hs488LA8c+99GLfaWIpSx1jEwOD81hmPrtUiUHY18gFe6jJ4uguwRuJR/8//+3vwv/2d/xIKmcPCUtd07iJmUMJTqEgRWD6OgD8OyE9rpoxZ40LdDsJReMDIYuYLPGJ3cQY5SnEcGKOCzqJ9vto2BCBhkBjJN5VjigqpABuYLFlv5lKbxOtnkxTb9TpLXKxtn7FXi1x9IxnnX3n8o6ef+8V/9+PByftOPCwOR/eUuSDH1IjU3jnn40Y7jubyMp6bn/DdgzGMxinsjMcm0UwK61ksdSGt3mlRb+Uoy0olH3XrDfnqCLkZ+f43U8X+GSA8B7p48f7LhL5vySbYJ4OjgJBL8AIJ+xMOW2MfWoEPzUYTtg/6cDA4hEZIBSgOiw1BbVAWbUBLzP0w/Xy3BgKnBvKLHvde63Xi1ly31aYiAG2wpqLq6yAlZUZ4zdAgjT8iUYgGo4kWaFgoBQROA9Migq1GDN1OExpRoP8tplQuirpzbUNA85NWKil4IYpAKlLjUsqItXBjZu38+KqPr/kkVg8zWuNrAXt7e3Dx4mtwY+WGFgbCmsT+WxnuVWHAodf0odfkujLJ7HcYq5fXp82+q4imGyYLQbyNJLE+qrjL/uytPmEaAmUAeUGecxnsDiY66XSxu2EyGpThyPXRrXt0W43FA6h2M0Tq6z1xbEEtdFsq8DlV7NEps+rc0bQAs8osfqapEKYZEtn3KEUbvQ+oPEJDtKiLybVq761sLExgXwuAlW30nz1up6rrxImUpTFOLRzQ3QmomdW7d9vMxRZQquQLQN08f1xyVMt/qrSghgweDXa1OOstb3FVjmYGgFOVrg3UH6fp/akYBdWzckQJ1V2n6Ttff+aY9039eV2i43pOFcxuwm5oBoNTBa59lcs/Xe+go7pXP1PTRM/8WEzzQxfqahUaU6NxPnjKXnBmqLcWBK+Z5LEKz7PHKsEyc+n1Uhu16ORaVjR9m2tWZex6AueuGbWxKpDTeysrCrd+tbQ9lVA5DGhwTRqIQAsJedO5ZOakUejlldpulbPCUcpybSrV+5+ZoSpP1VgZlLLQ+0GZC0gmGewMhnBhZQO++cJFeOXyqi46UiGSEsM0z7Q9CLrk/1Z8+lsMrUrmEU2WWYExOcNEca4EhHg6MRpVDw1ci0TVKmuuqab7eh6SrZc06LHVLVNHi3MuGKWikXc0G5evM9elK0bM/o6SmvNE2QQAEmlZpR7/69t7dG2w027p5Jt0QXzm6WtFYkIbO304vtiDYwtdU8wU08IJXd/9wQh294e6X9TeOufFSonHpEDhS5ZTN9P3QJZWQVHksHTqHDz8qS+C54cwONiCMkshnyTQ39uFbm8RvvjTfwP++F/9Y9jZXJvxIX43h8f4QinVcQmKM8S+PV5KUBq1B5kZVBM7jqfODH1dpWk6Wbm+gqIU0bk048vLy6UfBJk/ITVREbea7bTT7d1AxmIhZWgnX6CUatk9tacQ29ILAqlERjfrD7/8ZW1K2Vo8lvlxkG9fv+RvXL4YiSKjJJbiHWz35gh9hAc//hk49+hj2l9VSiOqdatVX1ICFAWDux56ZOz5/tPP/9VfXNrfXL2/zHKaQCn3fdVgnU7UbMLu5vqnv/3nf/JT26vXHt1ZuxGk6cS0c2h2WAnt+Tm4/6OfgPMf+Wg5d2wpDwPfExICzScy+uGqJtjG6gfDEArO4HKA8N0A4KsH25tPPfXUNw7iKIT/5df/c4ijJnhRE0aDFPyGpz1PVCYITtxCBZcA4ROgFOWmhRW3c5t9zBlGnCEJPT2KRlW4pZTSSSnV1FDpy0oW300TC6mRSzy1eJPxK/aREkZAn+pYHICatgcK1UUF+HUh8BtZBt+N/Hj105++Vz3+8XOPqTK/P0+KZeT6c31aN0g0TCoG+4MUrt04gEuX9mBzYwh7BwmMRpl+Xomt6/sMwtDXyXmeaxEy09bjWmcqz2hdKu9zhFeR4VPo+1/FUfaX4MPw/YJkHh3f12SzPpSV66I/AZfgMwGRJyH0EJ7e8EGxAB5fSqEVEtE9gJhlEEBe+XfJGWLQh+OduSdqwoFdD5V3LY6C++e7rTaJKYyTVFe9tO8meU0KWZPDN8gQUWNIAZDEHagXJoxCa4VhAibqr2nHZBES6Y2mLEqlbPsPNe+TgibZuAohRJ7lsshy26LnMqmaN5s01LCqz8wGFpXiqLVNODwcwtWrV+HiaxegP+jr11E/Kbe9PkUpKpuTN7gmOuhpRx4sND3oxrwym9fqckeEgb6PA+8ksX0773VvmGQCdg9TOBiltj/KXh9LE1RiJumiqj/XIT0zOxbdB8rbyL6g145UsxFJZB4nH83CIhiqFPY7p+qerIZgGbEgI6piewipUklhKSdaG9fkNpeiuH5dNi0UOL9BSyVEl0gwqJLJ0nmTIdrsogK9Zi9ITYzE/VvVkJQKKbEUy1tSktQtav+1rGBWV7Webt+MJ1uHEpeIGS23m+83PWMFxY0mp9abOK99lJPixbqg0psN1yfn7hM6Q/8Z5GwWflQaNQZrak8JnzxKz9LonxV0qaGP05lZXWN3vRwdu0o2qbmH6VxNWXNcrOxuKjTYako5MZypMi5Y66faXUILpE9vkyMLuIunnIjxkTtn0slKDVnTtJzQUIWuVv2fEpEVRg1XB0+FPjIpudM/NtNZ2vntlICl7VNn+vpWSb9BXLFKzqSo+tOp0EgJmvY5FiSWkcHW/gFsbB/A5t4AVjaMHUl/lMArV1dhdXNfn5H2NC2l3if0v4HZ/ubpNeFWfdkIbk1nK31f6PtAVlk+Q93rmZs+G51c0vrC7LNPx+SeV45QJTClbftwPstUdGSWF0yfYVt2DPAwfVjqz4OywS67rZjp1klobJPNTwLAswCwTvXTNCf/Rum1pKRGS+npYqqxlxsnGaxt7cPp5XlYmGube1Ar0FChi1oP5rtNdTimAl+BRVlgUehHlwL1STpJPZFqdOhdTTYdY2Nx+RQ89smfB4YZiDK/ia2QTsbQaHXgiz/3S/C//+Z/DXGz9b3YNpkXBXOoyuMKVNfcCxYYOyhtEDSmxBCNajd3BTj9dDNtLtHZ2dnhg/6AbaxveLv7+/zE8rIi4cLe0vFyTL5vwIYH+/trSsoeZ+yMQdcUEbvm9QOo8C6l4FSiihhQZX6Ww//5P/4GwOY6nPzYJxd6yyfPsrJcKvLMT5Mx5FmKcaMBZ87fD4999vNw/tHHodGMwbWRY60oZ9sYSF2XDAbXu4H/5+2HH3p2+/rllf2tjQcmo/EyGVJyj6976FMV+IHxcDB/8cVnv9DsdD6BqFgQRIdKQcgbPAwbTWjPzeE9Dz0u7n7o4fL46ZMF140D+vGCwGfulmdWlD8gYwDr9EHbufQRtnyAr8jJ5Hdv7O48889+6//Y/e9+4+/hPcdD79y9D3HShxaq0EwMVNCWChbsYkT8jFPIiEoLA0Q8VErNV1u1sfAyrSHo1nVIEVWiFDZAo5f6p9p6xUCYuprbAFQkErNvKSlzdjccMYTDEtSKQvVcxNi3GHjfGqdyNS+L/PjCApw8sRyAYl1IRcsGNbTWlsaMircG/RG7eHmPXXhtG65c24fDfqbvShwHVgHbrOlk+Ud/dNxL62JZTq240ClX4A1A9acM4U8x4M/2J/l1SYnwWwRF3ovjPZNsvtGgHk/mKXh+FSAMI/CXluEubwPmRR9KFoDP6TWmOmmGq/p9mH7eyVCocqb4HrI48X2Pd9sxdJoN7T9Gm/RglBh1PydkMhPwCx0YkLrgeDyBRjMiL0y9iWuZfk59mwHEUai4x+mJUzWriQCM1X1aCrE3SZJBMpk0KLjn2sCfGYRLiFo8ryojcOlWOwo4POOtlqYpbG5swKXXXtM0WgpuOp2OFhwh1IEEHigYqlROj4wKJSEj7YDDXMShFTHdS+NgGM7RVu3fMwnnG453sn+UWeopqdvtDzPYO8y0RYET61TOFw5vvjI2WdTAnocMGqGH7WZD07QpsS/KklF/g7aEsH1xkuS+S1mh2DoOt/QTl8hVisC6wkkmbkTtNCqj5NenU0RkRo6zQnigljTYQjeavkGoe3ZWSJ+lDlcJalWZnKJ9NcGaukqxm2ZVf5kzzj+StNVReqg1VurvsnYaeDTddMbvLgGb+UhVL83hkTRW1X5OCo4HSulncXHqoWbgP+tPyY8qx74RS6AqBjlLkJlr5c6gbo0zo7RHfVOa81AVntDKvlP+6YHjFU5tO9Tsta4+C6dIc6UEa69MRSd11jQ41cupLq8THdMerFDRKGeSZZ0UG/o+MtPvW010rCsYY43xbt4roVYss57BDnqe7nK1BFoHsrp84qNt6HPXlltfzSrRr/vrKYO6urXT4st6TmlqHLOBozVyGwxHpNoKlNQcHI7h0vV1uLaxA2s7B3B1fVv3LYaeD+PJRB9bqxlDi1olklQHVvQ9JHhDgj10cBRo5bkT+sGZeUrH2ooDCIOgUth1QZmbS6JUGmkl5JTQaiGMLQ0du/bsBMOk0KisXeulseDS78/LUgeDJC42SXMm1LQ0UBvMCp24WsbbXS8JEte0TQVwH5g/zyLAgKNHpP5SChgzBhPP99u0P9LxJuMUhtTTOslqTAK3ZmimkOp129BuNTEOD+kaq6xgOCSRJCm7COoezMXJUjo17HdnuEnXWzgGP/IT/x5kqdKe1rcKM+kcSKgvy1LoLS5BmiTfC85Pc4J4gil1ArVQE3aMm6+2akrMvVGBwcpRmNb56pmia+9nWd5NJ6kiX+7ReKRee/VVL4xj1p2bL57/7nNMlrJ35dKF49S+EBAdhyFROT2SuNCCNaAWUakzvizmpI8jQsU6vXnlMS842Fz94cP93R8Kw2gOGRNKCGx2Onj+8Y/iJ37sp+DRT3wKuu2W5uLQUVPtTc91ppPL0orbUHFhEAK8EJfFP7x68cI3vvknfzB/7eUXPiFEeYb5Ac2BeQD8vAL1ZFnk3rh/cL4s8mZ7fn69t3RsIKU802h3Fs/c/xCeue9BvOu+B9Ljp04nDHT/f4P+BlbfY/QmibqI7IRuGKMK4VaSZl/b3dz4+uXnnrn87b/8SvHUn/+b4/cea/SWlo8vIPmdKtkyiCWizNVJhXAv/RwZksQ07TekCksG6mNjQwOhdXnycGYb03vUSCkcaV4vFd4M8ciWuakFC1tm7ili7PVB4RBQ3SCzDIa44imxmwFczZE9FwG7LJU6DGm9oA5QrsLVq3undlc271440Wt6OS+FEBMtRITol1IGO7sj2NkaQjYRZk9CBQ3S8oh9/eyOx0Y8jNY7Eh3TrQEe06rE0/0TFTJYZUz9ngT4v8cCnm8h0bkR5Pucxfm+SDbdoKTS5wqavIBrwya81F8C1urC8iLCY40NKCSfqvpr64yaoff3//Dff4NiDwF7IlObXMKoGYcLrWbIfE09ygmCsl5vUO3BLkCixC0rpe4zoQ0lzzLp+wEFr6bojFqaTVesuRa2sN51JrqLKJ9knPelkv2dnR24sbLClpePy4X5BVKuJaU3pnuhtFeVN/W404Grq5AznbxQQDg6PIT1jTUgJTkK+ubn5qHX60EyIZrDUFu0qFs0WtapfLS2xR5q5dleg4PHoUIHqMpHFAm8VTb13h3C/vFupy/zpmtkQ3Wq3u0NMrW6O4b+MEWKrXUCbjsjlJXur18eZYNKN2+oJ2mu1dRm7eShR6aM1KsiymlA7FAe3d8tTABtetAoLZKIrJ40alcMX4OSNgFRVj2z/r2V0mstUWLcIiFON8UlKBWSB1VSMj2hKabokl5nYTE9Y1Uli07R+bbirKO5oX7zVC30zT5ruj8D3CLRdHKannukSeBCVdnQNNBHeB0UtvoWl+g5Sirq/lmAutipmrHiqBRiqyR55jPqkrymDxEd821azDAFJwFQ8+11xQerMVMh2dIWEEyuK6fAYp3e63JW2xMo68dUmw+zpz89fm57hA2C6xS0WZXswRFbG0epsjFc7SNnM2alYyidDgb2h2zK3K44ujPUZFXLldBan6haYcK8xVrvuSSNMZUXQq1v77MXLq7AxetrsNcf6VaJQ233MYH13b7uzZyit0ozPqitwCi/Oj9QCbkAXWjstZu6H3Fnf6DZDEcXTrNuoN5LRCG1HgAVBes+pZberJ8x95xJ22YjK1a8vfd2DkaEp3ie3pu00B3nIFC+kRi5SzYF3FnC6Savb/s2j1M+7nk4OL4UyTQr9ouivKAUW2EM5zqtyKfnYZLkMCkKyOh4pcPm68UZ0EJ71NdOfXRplith6PzEcowZqLuAs2XOPP9dzeeUhO7CEvzkv/3LMJ6kIGUAR1uH64P24XZ3Dn76l34Vvvy//uZtaSa8jaHRMAlyjrp4QDG/Vm+lW++ZdUIxNTUrzu1CxOwjpZVKqfJJe02/31dlWTDOPc9f31AIL7XyPHtEyfIUotdA7i8QtVOnD64KBtBCUPeBgo+B0snUBJQqsd16OCrKX1CoHhdFGUgpx535OXjw45/in/qxnwke/PgnvU4z1oucU6KmvdRDlEqqnbwUV6WS+wxxggCbpZJf/+5zz/7Vr37hs8OzD91LdYcLfhRtMMB7AdTDCOoR7eaBOBayxDydCCXUpLd0LF84cXIyf+xkev6xJ/np8/d5lDChUryU0hdK+WVWICHT2USj1hSDRbSPZmnGskkKjXYL2r3OzmBv/9mXvv3Niy/81VfbV196/kd31ldp+W/dffe50+jhshJFKBUcY5zF0ujGtVCqY8Cwpb2glSL0dQ8QBgax1eftWcpRYogYmikQ2p66yHo85/b/PcsVqmqPqBT5KO0ohZtSwgVgeJV7cFnJYmUkcTgH/qGPsJt7IIu01D1hnsdbo0ly/7e/c+nRhaXggR/94oOduV4cM8aaCgSpRbGMxMGEhG4nhm43hnDzEIZGVEzferJrorUpTQVMJiUgltX6aOycPPo9vWQDUf0hZ/B/gYJv4UwV8v093lfJZn0QIZv6N0Nfwt6QwR++htD0EEqi+jAOy8vH4WxnFXxezNbxPxy3NxQcKlCrwNRuI4pO9FpNL4o8P8tz6w/IrWiDi/RsP5Gc0sykKqmqTZZUwvO4dk9xAaDPjfa01vJRCjVSaIM/7X0pymhvb7d78eJr3l13n9mdX5iPGedNTYK0/VSuuOBUCDX1ywVuQsBwOITVGzdg7cYqZJMJxFEEAcmwk01LOoE8z7TZNws8Xem2G7WpPDFzToUtpzV9Dq3YSNIzu5hQdcrQcT2TTL0/kk06SqGmLiFva+j7RLwUIdXeKMeV3RFsHSSQZUI/d1PEb/ql1QEovZBDr93Q84isDkjw49hiFxbn2hCEJh7QiYM0IiJOnVTjaspSU1VNSZQxQwOsqJJYiaO4hGxK2XRxuEPDXf+XFeRhJiHRxKH6kVeCPVhR/xw0yYZBAAAgAElEQVRlz4TrrA6q2XzBJWkuaZCaHjlj8F/1ItZQwhqqzizNWNb6wKoeTnTp4M19oTcRcI/2V1YTgiJuXSVXljrLpJI9IiMwxAim1w6dkk2Fzh25rxWxqSac4ixHwInRONGdGVTLvJqho0FPE0tN/TRSsVp51S4AFncEaxdiv9X1IIJJRBzjwCVWFUo5FQJS5Lqj3SqJF0UFLGmRGUdtVtOeXtsVaosVVa/mTBLArB8wKZ2iFfIw328prTNIap1ajLONT2ATYXTfZe63VaBF519ctRUoVYkF67UZan217gGgY7NIq0MtaR57vqefSSruUB/gcDyhu5X3h2Px3IXr/lMvXPIv39jQjBUSRDu+MKePxVDKpwUVC31DMkl1zyGhmg71p+eJegsLsi4RN3uhukHzfJyl2h/T6DTc3MtMUScVuegbaS2unJd1W0RZ3XM0njFGZZrOj8z+qMCIoG1W6PkXb95DeqciEUzT+szpkhP/ogKY9yK+tnh/pCaJ19++nj8tR/5nGOKDRSH86RqB1b2vXy2slXwIMaZ/jJOUaUpxNc9ZAJz3PAVdFPnmHRz/6w5CKeeOnYC//gu/BGmaWJbjmw86tzLPoUMqwu9+WK0KBIoIPV1GJw9kgzbThWsoQPLeLO0TyTVF3aw3qQJMQBEdFpu0QEglhX1WyW6ryNJJUpYiVUo0Pc6PcU9TPH1CM2urOb2ho0B9VINamXhFI6pKLkjEh5GzxxFUjzw9GWdlq9fL6Zoy5qnt6ytqhyN6UUTsPrq/TIkSPd8rRv3+4Mbli68N9ncvBWGYI2O7/d2dlYvPPXO8t9CeF3mZRc3WumJ8VYE6j4qSbbfmIPU5DqWQfZCiCOLI6y4uQbs3N2Gc+f3dbTYgcR7EsMgz4gjD/tam2tvelPl4rLJJwqWk0IdDmWeQJQk0Op08arcPd9Zu4Kvf+dZDG9eufEYI0Q6j0I/juMkYXxBKRYqRyC80paLrZK6S7YaXdqUiWf+uVogFrATs7NqdEreNQgiabCYPVU1Qiq55ZplM9DqiA0wM2U4mkrMXue9d5lLeYCU8z7n/suLFTlGIFMHTndDSsk0ilOCBCNIoeGwoxRdurG7f9ZW/8NpBxKPPf/7+Yq4XZ0KoFmmJZNZ/vtuNJfmOFqVkZUHPoBEGIiafY1665Y7WG2oHajYDiJueyLLySpGXfyQlfjkX8lnOvgdPxPdwvG+TzaPD0fUciZFYOl95KYeg1QBRADzZG8JdrRJyZRJPfaM/TEHfyiiA4R73+E4jDse9brPTjEPtiTal9E2pkkLUe9CsLL6Wptfrh1cFqaissoX5ANJ94jaRUFadAxk0mWTHsjT1t7e2xjvbOwf3nc/ngihukGQ2PcDKKoFqZUSwlgEWTdA+b0UBe7s7cO3aVdhYW9MJSxAEMBoPoez3TaLJGDRbgZ44h2Np6G5oYtg6QdH3mLY6iQIGRoXeBHDcQ/07zplphzKhwfdeZ/72h1cJIryNYWJ10j9R5eGkEBv7ibd9MOEkCqTHG2gfUiyIiGUU+OFir8uEDUCX5+fg7uUl6DUbOlqVdo5JORXrMeiUqFEfjb0AKXvq/jDpjM9tciaknivGW9H2vzGXEMqqTwzAMB9djxpaf02P+2CVHYnHqW+wLqgwtAnstGe5Tt9FUNMAG6BKVFwSIF/v4rzRQFbTSJj2ZMp3Zl+iUo9fmxPMojAIcNNiibc4+lsgPrVXacopq/3IoXl1YaTamVkk0PzKJGkmn0bFuacnhhKyipvQKgXr/k3ObCKjIepK9RcqhNB8HpWMpEYINaaa6Rdre0ClgT6nHKwTQDefcFrckNa7tzoI5UwDsdSwPJArhUKppmslJQJkeWOo2RbNxTo6au0tGM6gvlJOUcgquaxhyDiD1E+TTUfFdT6w3M5RYQWROKsUdDUKSX/2x2P47oVr8PKVG8Y3OUnV1fVdubK5A/3hWH9mVpS6V9D3PZC3mg0U0ZcS8lswRohuW5QD2/95a49F3SlRSu3lcMuBDi9SIApx03un6rpgk19TkNGFUTUthE1q3phHJm5pkX7/DZ6D2xk6N9FFFICGkPIsKLhre3t04bf/8dP5L/7ik2XYCVZUAleUhOHhKGkVtteVM6fcfKtrYZJoY7vFbH+Y0KwS29ZC/5njII5JgAt3cPyvOwyluXwb75PghRF84m/8+/DSl/+pWZ/fHYST7mc2LvEwVTBhqGmz+1Yw5oRGHFGFCnACCjIS1qd2WV1jZnqzIbSsDYCxLUdqayfOOTfMbDXyff8A0aNG26btA61TEVy1i2ioRJ+eU0jImJa6fAKUOqbtNhQWmv3F0E+TiXf91ZfL/e1tiIJQBY2YBc0GBF4IsiyFkCRuAHl/Y7Nx9cJLd/V3t6ltiNadc+PD/kPJwQF2F47lYdzsK4Su7nk0vpPTAiVoKJfkMvKyKNLB3q5+LjbY5XDl4sue1XHjxL4tRUE0M7m9vkLeqVSTw0mSoCgKPc/CRsN6sOfUazo/GQ8fz5KJ74dh2AhDeg3RZSNhK4jaaMSxLlAvIagFrJQcGdQSO5pGqyCyhs00+QulMNMtMQbVjI7cZIoAzH0E2EOALSXVdsDZQPnshhDy6XJcXlUo9gOf7QD4QziCHtJ9LSekcM0g8nEuY/yHANiXZFF4167uJs9857p/77nFnV73JKGk5xHVfJGXfHCYent7Yznop4qADOabJ30yMddHq2nbmELvT0pCIw7gxImeiBrBy4OD9Mv9vdHvTdLsVZqr78ZD8P0cH5hk81aDiq0oEEJU8PQmwIs81siW4AweuecEPCi2IRXaYOe9d/DvlaFUwTzvhhcHr/hBfq7ViHqxFvthOtAzTc+qSvSnsSJqatQ4pQo2oTiCVEOZrbZqTgolZ57PUXttNgJBq0kpFCupt8F8ZIAMmwwxT5MkOTg42E0m6SSKGx1EtWB7lUzoaG0DNBplcwe0yWue5bC7uws7e3sGTeOcFBMgz4qKflVKs1E6lMFhF5Q829QXogChGXGNamqjXxvw0QKrgwET94r3SaKJNrm4LQ/N2U8wiVeSS9gdZbDTTzFJS7N9vP4VoN6SEWM4pMqpQlgYJSkZpkOn3ZDn7z6OJ47NUcXWFIRqJvhQQ4YUTAV8wLa8uUy/3kNY9QbqJIRPaYXM0VinvXlTUVCD2OseUY62kEE7nQCy2jM1UxNy0PwohPMRxZonJJpuHyvGYqBJtAmRvEUSjtNU7QhUWKUgyijWauXbGSqq8RGd+j3OsHnfMhbDDDTnWTKuvRLM6eFUtMybEVE3HWZ/Vz+E6j4Y48TE5DdI/S4e1+rAzPj11iiqN9tfMMuc0hMPlWRVwmUhdjMPJEx9Um0xQqlpsgYVymcVYmWF/mKlGFv1V7pk2M4ZcL4c1uZGOf9W2zRsHXOQhFkMOtxC+6nTrlpXcJDuW6d3WbmEdZaP4xLvKd3WvF+4BdBhrQ7prgozzPY62kTbnn/VC2pRZI96VJTSieTWfh9eu74B33juInz31Ws4TCY+MuZRr1FWGH0Zfb8UwN7gUKOhSqP7U7q4Rto4h4jWdqK/5oYx4gZBEqK4/eTk6MR7OzCjrBvbvfEoNM1xSn19J4aygGwbFNxD3ptKqSBJyvzgAOkJH0tPrALj+8jZMgpjm0PXUNus5NRjejObgCxc4sjT6KYppFnJRIWlYjjW6qtCdjwh/RpV/o6GLlgUOUySISyfPQ+f/dlfpiTjbWwmSiecH/vlX4G/+p3fhvHBPgRxQ+spvMM0oTQTuKYY3wAp9gBkYmmyS/b39LQQ9VIrkhpWFyaMlFFBZRLwjNWnXnTzgdgfgEiIp296BTGwtFtZK8jVq2k6KFEkhAPqPCCSx2PXEKdQL+zIuV4Bhv0Dno6HknFP+WGEXhRr1rgsSMW0IMKEErL00vFoYTIaNouieHhaA2YyanVYEIS5QrWtlwrA0/r8puuNMssYxhJUdzwciPXLlxVeu9ooirzBlOb5SJpPnu9rEGBCnvjjIcVv0o8iKEjYKkv1eXbEvAqiSCWHAz8ZHs6DFsMJqQicAeKYYiN9/jM0GPNf1ASMqkPes4We1M7zwCbqdKyuqtSgP/YNY5BqoED1keHIYzAqpbqBCFsKcJdx2MpysRq0w7WwKDZVrsZlqFHVakGi9Wt4eABzvWPaB9PFESIMTjJUj3MpHwTOxnleDDc3B+Xm5mD7wQdO9BlTc7QlC6H4/v64s7Y2CMjKL4p8HR8XhbHvpHiC9CegELrPnAp6zUYAy8vd4tHHTn73xPHm77zy4s6//PZOQj2kd7gwvjfHBzrZdGgn00ozALkkuWFtawtrCcJrGxmcPrYEj/C0QhlckkLvKUhp9Q5Lme/7oaBEydbBw+t+wNJup4EkEsS1MqAwzX7cqrBiRUDUgVmSl7B/OIZk0oEiM/5q3LcsRIVl4Plssddid5+ch53Ig4PBSA7HKbfBv9mUdS5IFI7cHxwcZEkyniwtLW1Rj4UsSyqlBZTomQBCVlQ9Y9qN4AeB8QKdTIzVCS2Avq9RDy02ZFGz4TjViqKyrpZpVzyKkXyOEJG3JhmHc/M67atIPUmcWaVIHQRTByre0nDiPTYcy/PtHJVL2kopMUkLbzguPFqkhXij4EAH6wNAdYUhlYHgbJ6XbGN3DzzPV/ecvFucPXUMe+0m87yAGaS8qFBKhtbfQakKVXaIjiZCyTpaaPvebIInbb8oxQM6yHcJn+3rM/fQHqOSQLSYcTbW50N9UAawMr+nfrNuK4bA9zTCzYXplXJesy7/RcvRUjXxG0ODlbfu86tRW6cY15Q1YIap/LvzqiiVzEok2HdjjTxb9RbWvuOo3Y9Lwuz/1xuPK1VjrH2eKRo5XLlKi1zXn5p+jv57yqk1j9RYGiNtEn6Y2YPYlN1QFQ2c+BfTsZizGKmLgs1Snw1SLac9ihKmib9Lfp3QlHT2IlopMkADVrOqnRJhRj3Xfc7UNqRKME3RQlWZYajRQqaQW3uUUgi91tT73Kt+0jp92vZtYs0iaHrP3LfW6MB1YSsnUSlrhRiXBDv03iaaFNJSoqiTBgVa9Oc7L1+Grz93Aa6u7cD2/hC2Dw5JdZw5exSHoKOlZ6cUgudCF2WOPOr6mQoDDyicznTlF6eWNO/AsJewQkduqdh8ZEyFkJQiD8o3eCna+Ciy/ey3QO1vf9hliEQHaLnpAkLLtN4xwDICrkTe6oqtuSjc4ciL/cNxkOUFUNvKQX8Eh8MEuu3GlNVtnw9i1pCoGvW5t5pjLcon9Hwz2JQi1IxREfQNF+i3POgajgf7cO+jH4dHP/NFXTiguVTo9pq3cV3oHNoteOInfw64F8Jzf/Kvob+5DlGz9ZZUrt/iKEuV7/i+v0v4mQI4ZhOZ2M4gR3EhhLGhUHkI8kBK+IrH2KucwX1C4udAqvsQ5QIC02I1tNtrPoVSi5qWSuI1AAPTEwlNUDoxwtqUBTu3ziFArrt0QHtxtszPMSW/k0IUvhKl73OmRJ4qNTxEWkOKLKP9iyFDzZrQfeGeF8VByKwwggYw0RTb6NdU8CqZUUJmVTeHuY00t0OlZDfPsjhPM3o2otL0PZXIuDLFTaX19KQiD18u/CAac2SkINfknh/Q5xGLTJQCad75JslEU/Az/ugoQavCIxil2Jp2N6JL3k0q6iEJNBk/SebWDTtTBJGplFH/3gMFWwrZCnK+gihvKKG2MgmrAYMVWp5KxMLzISlzEg7S11pv/HRtqDCSjkcgMYFOrwf/05f/CP6rf+vnoNFqAaceSyFaqZT3MlBzHOGgBEUWao2iEEmRywynVtwsDIPA97WYCKO4oNMKdbLJvGlsTH3nVATyAwYL8004dWpOnjm79LVPPHnmt1Hmf/TqSzsbd0jTf0+PD3SyWR/O549ZL0AyS95JJTSlB99KPOBxoHv4SH2K0M+hQniwyeGeEGBCSWr5BloYH+ShVyU5AT8HH3hrvtviC70WxGEIWWb7dNBSuixM5p4WovIcjlMYTXLtsVaUOfjSB8k4uc+JMPTU8cUuE2VBjQ98OJqQEzXWdnWfFibqoCryYkwemYN+v8C7ofA8L8/zMjKvo4KimFbqbV8Y/Zs2aUoyJ+MxaLoH0YyseCNaM3YNR8rpF9fvs4nlEQLOoBl4EHhYyebrgApNVdlSfzRa+H5INN+JgRYxTHOBZFqcleKtKKYpq/IXlkJ1yPaE7sNir6XOHJ+H+U5b99RGjYZmNKaTBKiaqmm0DnF26qEVXRVMFZwEm0pZvbYKyB39kYoDriezUoA1xQJ6CaEHZDNwcDiE7f0BrG73tXegMVUnj0CiHAIsdDtwZnlBe94tdNsw12lp7yyPG3lzoVSFcimnjASOClpD/uq9ma+DCh59nUHohBMfravYVQ2cR4IzVTVM1uS5jwjvHPketOCXIbtjJcV6FE2qJcZok0rzY2ELfdwqAqK1oAEjN68aumfKilJpumuNhlz1Vc6kuKb8XffRdNejAgXVtMCoZhI1XdafEctxCSdMfXlNyloxLxg6X1FngGDyLHnTfZu5dubLmfF700g7Kl5L+uzQve7SPgpQqRS5V1aJshOiUrYn/qi68PT+uXTf3CBnRFXdM1VrT7UFNfKapeB1mGSwurUPV9a34WvPvAzffOGy7mNsxJEWrsi1NL+c4t228EN3VFqjcnWECasTWClhTCIizgv5LSSDb3kQ7cUnj+MQChLFyQt4s5ZL0/LAlM+57rdLRc7fhNXh2xjpTumz9cFcDyYjGiCqZaFgOfT9/hPn74ad/UOBUiRLncYwywo5yXI4JCXjUsBgmGgRpuWlnlHfrYpQBjlpRCE0GyGJBSli8wgh6LnzUMljCOwEEo2ReXduCE+J5mEfTj/2cXjkcz8Bc/OLkCUppNnkjj5WErtlcQk4D+HJn/oF+M4f/XPYvnEFGp3uO4ZwskImge8d5oiUKJ5Es1Fwt7DZRYoQMyoCUIBDyj83hgK+sRB4L3BQr2VFeRoVHNN2JoqFQsIcMvUIorpbKvJs1IW0HfsYNpSrxZnCkrAUSdp0IjPHFCUuAl1xjoIZxMzUuZhPhH566kmjxNcKyz4WpnjDjACfESUwVlUqN8kueXvqVYDg8oaNa+xaXI9xqsWTOpli6mYy0gdsohhPKdHkyCIpSyGoSRR55IehCqI4U0pyLEvNGCKtviLNNNMV6diZR5sUl0a8zFMKQ40YCzXQBRYpQqEgsVT2yC7ehd23idFG55AzxIFx4NaJ4j4irnOGY2SM/DN3EOAKgLzOldiSJW4XAvYxgBEJ48+we2pPL8WHW5vr8ORf+2vwH/2nvwYbqYA4DuGhRx+Fv/9b/wT++//sP4H9vR1ozi+0AiWXsZRKIl9RCgPO2cm4EQzD2BsrszGS7+WpbjdqHzvW4fMLTaBYSFhUM7JF2dE4Mx64CDA334RHHzslHnn4xPOnz8z/o8W5xu9vrO/tyzfvGX9fjx+YZPPo0KUUZoKWzQIhjDxIlYAGNdjnEkaUYAQRHGQC5qMM7lty6Cj7wUM6mZLIZO4J0BsaibeQwTQtdJM0s/5p06jP0RmFIhptDgPy/ppk0E4zCMOQNkbCKHgUeKzXbuL+wVAjZNL451FEUtooy7e8QRAiJ6GgcG11rTh//rwfRY0Gom6mm0kowNLCiB0yHo9hfWMDbly/DqPhEHzPh6gRgxcEOomhaqwTDnHVp/pwdDSSNIoDBs3IKM467hslHuQ9xz3fJpsf7MXi6EAw/U9JVqjhpIC0EKY37daCXBZHhJjoYxJkqKSK6fotzffgobMn4fTxeS8mP70ogla3py0PkoTA6z5MkkMoC2F7lxxuZ9MO+8k6aeRoUUtRJV7ahL7q0xPV0aG1q2FWJGRzrw+vrWzCa9fXYVN7Bx5WvU/7gyH0h0aev9tswFKvA0sLXbj3zHF46N4zcM+JY7A419KegGj7MYUTwlGzF8F999HEcxaPfP2hZE2Qq4aEViSk2i5rwVUKQHyb3M3cGic4Uqflzd49m4zBNDEDmE1SHZpJWsDoPsB6SNbknJ3FCu05TYtEMSMiJWsqsrV+1HoiDDWo0Vkk6fMUs+uOpcbajtnZM3FiRhVyOE0QHUiuqbaCojgzN4QUNars1PLELXd1FLa6GGbeTTswySdUYZU8VggyoftWPJUur52lXM4kru4vVSGqUzTTqfbWUHyEqi+57slpKTt6zaLngQIusjC5vLoOl1a24OXL63B9YwdWt3bh4HBk1jee22dryvoBu766HsJCidm+5Nq1LrQauawsgt76QN0HGvrcCAkJebSAYl/jQ7sVwSghm6XyTddfS5tHyTR+Im8mo940jiaZ9fD1bY3ZghI2ENi9APKe4Xhy7Xd+/y/Sn/+xT0tCgAippF5ZLX6kjHgRIc+kAjyaZNBr+zPHTswaT2sGkHdoDqWSitoJiWmjpIqQyzMcvbs8z2shY6O3e/w0yiKHex7+NNzz5GcgavcgT8aW0fKm1/NNB/V9ylxAe2ERnvixn4Xv/OnvwubKZWh2uu8IwklqpELCAWjVVkrEzc+ttperxnFbaCAZssD3WDTOoBxkaisOZJ8xDCUxF6AkP8eIcbwLkX8BFTyGoI7Rz4BBpkhd1aClE1TakiSwj++htr+hJMtcr1BT9O3a4vnoaZseKXlZ6ucISaiwEXkYRz5kaQH9UUpEaUXzhBTZC+oaVZIS1EQh+jp5BQi0Bp+HHtSsuqq8WncQaKCPHghmtPKY3jjJ+JIhyxXxtIQMTc1QOwOVUkivyPJGKcqgLMsAjOaBFFIxq92mJIrSLDvK6KiR2q/emlUCGsnX9byBTxVaui56w8aJkMp2i7Ax57CKgFelgm0Ky5HBLiCsC8mGPqAIuT+USu0LlY/JgDtNVHGYSxU2ELyQLNMUcBIZxKktH13XjdWr8NEf/3H4tV//O3D60Y/ASQv3ZhLgc5/+DBz2D2CwvwdBI/bDRtgBjnNKqgaSw7/Hy1Yj2Ah9b00plUuja8k8jy+eOtk9tn9uqUc+wAyV8HzGilxgOil1vyYJAbVakTpzZn7nwQeXX3j04VO/G8X8/5NCfuATTfhBTjbrwzd6yuDbhiX6N5n7DBWHUSahLwRkcQQRR7inMwJi3yhNs71JM/CDOXRsy/Z46O/xTJxvt2I8sdjVCPH2bh8ORxONaDGGUI+TaPNJc6GDdFIv7FG1vFmC8n2mOCcLFWjEIdFaValAFUJK0/6JygWFZJpLui/IWGN4OJxfXVkZ7+7sjE+dOUN0Dm7orAIq3RFlVWzLEjY3N+DFF56H1167AKPxWCvQtjtd8PxA033KonhdnWJ3X2mR78Qc5psexAHX4hoOAXH+bqTeONMn9wMwmFUdHk0K7as5SgvrHXVz0j69pCq1oeuCBoW1GjFXx+bbeO70MpsnhDDwdEEiimMIw0D3itAGMQ59SAidpmBECI3I6Loi4zZJUFXwrWmmwmGstr5sBVmsMI+ixT/0fbLwh73BEC6ubFJ/mhZFub6+A6PJBCZZCYG25/F0QEty/jQo4Lu2vqPp2M+8cgXuOnkZHjp7Cj720D3wwNmTsNRr60CYZnGpjFKmYTROUahKrKYenrnXHAnup7+eJpMziqoul1BTSQqcPoTKJkuqor8iTkUt69TS2q16EweVqrKkbqKUWiLqLGXs6GA3Y7g4k4BjlRC6/lasTrKinc4ckRPBAcAK9Z5SXo1wjkHnZB3dQ2ZROi1Axer9lMz27UoxlQGtjvF1ro6qBw32eytEG50qrgKL9CqbSRrzYKKbWSR2Cj2YS+16jF1poU6eQCcQpZibRHpoar99gXacZOZcCdAdjid67v7h156F11Y2YGfvENKsgDD0tOgPIYUk4qOOJEi249BaPr3+mqfcYnyLcau+3/qg+9IIA+3RSbTRcjK5+ZMUQJETKpvqhPStYqbCeCmzm6b8Gw971pVw1tu2iXLDzuHY495Z5PJsnhfty1fX0jTJdL/voD8hBWBFTAu6tRTAErK5c3Co2Rdz7dYMNdwqI5MKJ6nwBpRhNNtRWuQiG46TWMjymFLyk8jhzzzmP3cHBw7pcASLJ+6BVncR8jTRBdd3tNBK3zEewdzx0/Dkj/wcPPuXfwxrVy5CGDcgiKI7SjpJD6qUsA0AO1qMxlraIGBmV11/WsHU84OsY5Z9xrpJKbdLEg/K06wUCSX0AJmCVstfjRqNrTSN/9KPRMwRjoOC+wXA3aBwjZZb7UWJ0FEkDKT/iU50mpoHAykUMz3r2nvRiyOPKgdsOM41ayAKfZ1o0r4FkQ8doDamAoVezyRS8yb9CICVyPQ5UN4myVauFQcqFxLTrEDHLaHihX4WqM5FPaQUTTEU5O9ZCkFYqhcGHInVkJYlZ4wFjSjg2gerFIEoU26eI1VyjvRRXjMOIAo8RWq6hZCMal7Gno6seXweRrwJEiIpwW80fTHXikW7FeZBwFKPMSqIJkKogVJqvyzV2miSXs7S8pVCyisAcDAuxWgwSEXWzwCIpRZJEL4HJXmf92K496E5WFps6pikf5DAQhjChfU9yFIBhSxAjHMYbm3BD//kT8Kv/vpvwCOPPgaXxqYxlIhRsWeqAH/zb/9teOqP/zVcf+VCI/dwIWo27gZZLpCybSnkajIurmRp+YzPvagohadAjgRAMDfXeOLB+49/hMKPaw1/vL0zCoaDSZNC3bjho+/zsteNV5cWml/1/MYfRHH4NYBy8wch0YQPk83XH0rvKAoirmCUM3hhJ4Re6JEVB8FuOlo600416qV715QVtPhgwp60gO2XvrfBOaZx6MfabD8vCZGsNC3cHuAM/imQo1Vo/zCRVEVfmMtYpxQVJkUBV+D7cq7XUnPdNgb+HgmNacaIjggNk61kyARn3C+KYnF3Z3d/d2c3OX7yRIbcazC9aAojAGMVaenDkySBGys34OIrr8LG6poOxkJtdyJA5hkYnfF5whUAACAASURBVJI3uFk2nI5DBgudAOYbnu7ZZOiQFRvEcnyX7cHeA2OmLc/1QSOMcgF7wwwOhhlSMOQQzVteVaxlRZZ7zRlXJCAy14qh22ro5DIIyZYmIrVRnRFRQt9styCMQhU3GpiMxpBOqLiRabEZZaFUBVTBlxUVmug/OvC3alF1uiVaRJq22rWdPfjG85fg69+9AK9cXtWef9Qj5SQeclZAkqHuF9TVZiGd+i31ssH6Dv05gAtX1+CVKzfg04/fD5945BycO7UM7VasRVJKTdMWxrMQTZxqmKNVv+BtmTO5XtUpvGZ+Luv8Svt7NGYuN/kQMMu0tB1+ttXQAWE1CYmK0mmUW7XSvzKSvTZpNZqvUnK0yKnrQzRvrlFOa5939HysW81sIiKd9y7U3mmQa9dz6X5+lDlcY66Z89X+qsbU32Fa7qgcd00nmDRvKoquMj6e2p/81vG0Kxq4dcH1Q7oE1yGZWBMcql2FKik3bOVpEYJZtoW0fZKkF6lqFQCXlGLtuZK2ZxjRIf7mgDUVzlLFqYiyvr0PF65swFe+8yJ87dkLkGR5dT6B4tYX1CouY0UNrl4jbQHgjUKkW9z+tz6sQi0lkeIWSrbuCmTkPVncptbNVAX6dnfqO0Y1oVLSNiwBAprDwDvued5dUJadcycXdsimCxFHu/vDvbwoc8/zY88Tmo5M7ItRkmpLF2aqfaa4QusU9WJyXjabsey0mr4fBGz52PwkmWQ3rl7faPYHh2dLlI9zyZ/gDC9R3/RtHzxxE5MhnDx3v6a2FtlEK7O/G4Pm8WR0CIvLp+EjP/RF6Cyd1Mrya5cvgOcFd/KN1Fi4CUxtKFA50U2t0uxY9y6avkZzukR5RlgqSvFwM+Z3R4D90XCY3/fII3D2/DljrcM47G1dH7/wZ3/8/DA99mKjV6qFuV47T8uzwPhx7rHAZyxQpDsh1Zwq5UlQeEwy6HmMcj4WqFIuZ4XoopKh5zHqr+wGnEVaE4AZH8aTS21tC3ZwmILnMzi9HMD69gA2dkZ0+8mfWm8nQqqAGEOE2hJgSUWHOPIV5ELleYlUTCIv6yzNMZmQyrEHfuCZzJdY5pwh5aSEL3oei3w/4M1GGAYe58uLLen7TG7vjZGSyU4roCR9RIr0QqpWrx3xRhzILC95KZSnPXY1moiq0wx5rxs1SWaXir1z3UZ6fL7Z7LajKIy40Cg8ACn9lqhgsLc3br5wcePsyvqhL5VseAxvbAzT3Uki+qVIEpVkuiu/9GID4XZjeODxE/CJRxZhdS2BFy/swkNzHVjrDyEdFSbZHCQQBDH89N/6D+CxRx+DXQE3+cDSXvYzv/K31GB/1Vu9cnGhLESPEGoG2FAMxCQr07WN/u7q5sFFAXKBcz5XymKf7E9Iy2NhsSXvBwyazTDtrvX57s6wMUmLkJ5SRNaPQ3gaIfjdJIWny1KN+A9QBvZhsvkmQ1nJzoYnNaL54kFH65X5VMqIPAg5SafnsBhJaAeEdhqxhQ9SzmndPIZFkuz6njcJfT9KixJ3yNg7LWTgedrrzfTm1JEao6I3nORiMMpYkhaqLEvU/UeEPkqQHses22nA4lwnDMOACxgZvRONbypbh4dSe3GWZXOcjOf6g0NZ5KUKQm+2f8pedeqv297ZgfW1Ndjb3dMopqdNdSWMBgMD1QpZs1xw73fna/5Di0eD6LOBlsDWXf/uBaiOYivq1tHo+39Iy1XkTlXPIdhkcXIwziHJjHgaw9e9AhYVoI1dWRcJpgV2ep0mzPdaWuCCkCRdvY6NmrnzimMMZRCGBa3mfhD6YTJBfzKh3mosspTo2mh4PCaYZM7qomrCtQRQKr8yQqM5EnJzeW0Lvvrtl+DPvvMSXF7Z1L57bh6gtTShOS20hY8x4389phhRD5964SKs7RzAja1d+Ozj98NHHrxH93VyK3Ne0ShdX7G1s3A4Ix5BkW7+qpnMq0IzZ5Mxg8IYf3JtUM6m0OeUgupSVIeA3vw9R4bjYzLtXOmUVKlsXSoTPZtE8y3G5DdRhtFy2CqkFKsCxRTJrOOh6shp2/tWs4hQMwmSmqrKWtEfp+hasUzth7lE1ongwC3A+qMCT2j7to0Vi6j+bYR6agkbViUZCrr09dLKuTednzkjmie6D9lZXhzBi+u9omj79xz9VimH5hMyVsIomcCF62u6sPLcq9fhyuqmTjRJ8I2+h5B72ssMImpEp9zXHZ0Rd7LSvRkyRfeKEqtJlt2igPJ9GVhDM9/21s6ZKU7apMAKOKm5dhyea8fdZeT82ub2riCl7kkOW2EYJFHodQthRehs8QKP0sON8g0p//rHF3vqoD9C7vnq2NJ8MhyOdzY397IDBWcU4hIgOw+g5sEort7WhS3SCSyevhse+eQPA3oxTPIUIHhrfppvZ9C5JuND6Mwvwqe+9HOwfvUSXHvmKQh683f2uUJsS4/tkB8jJRKGEKLRwOAW97crJD7sy+K+Rrv52nzvRP75n/gSfPxzX9DsGhFxeOX5Z8D3IojbZ2WzIeEbf/h7h0mSPt9sNcD3JAbcCwV4QZ5T0qE6Hue9QrIoFzLzlPKlEifbkTe/ONdc7nXjjzUi/1PtZtDoNCPK/rSP91w71s/jzn4CgW8omaQ+jIATZJhHPmdpVrRGaUEEVsr/tFVxuxWr5aUWK0qpkiSHbiuEqBFCfyTgYKSA+aHuyyZvcpQFcpXzjJT6iwLazah9YrHN5+eaKEupGrGnV8tep6HZXOfvnlfdZkDNo2UpKBxDbmlwGPghtFqhpEybts048BRRSBlluqgwDv0ginQ27SprXFN4lZqTo/Q09ThOcuEfHE4SBbjmMbgxHqaXpBCvqpCtKRkOROAPEdgAlRjLUsgsKdhoXJRpVpak2kyWRkZc0BLmRQnz95wDiBv6xtroj1kRMKI1z1NhNk/LYXfxZLR44vRD/d29eSmKApEVqJCOD3f3R8FzL6+XvxLiVUQ+KTPZVmXZKRXsA2ev9OYarSjy1dJCN9/bH3l7e4c8SfJBURb7rU7j4rWrkxcJn/pBa7v6MNm8jUF7eMg0S1tfuIvDeYg8D3Z29+CBXg7zDfP7lg9vKlbwfhrMNBpkZTrqY6M5Vgx7JfW+SEnqLsTZobIUpwWqtCqfWguRgiQy/JeSZWWJaZZrGxISO+AkcGDjmIBzjIJAd7tT/4FRswVr2l4lOcTxD4ui6I7HI5nnuef7AShp2zvBBFVlmcPe/h6s3lgFEhSivjn9XbYfzNkrGBuMKeDmLFBocSIhGAJJyVOzTXLyOsywwUHd5w6mAYCa6l8o95EfkNuv2+pQq+5BZXFDYkBD7b9XUE+J0UBAdVMgZq9UjsY8NZQUczFK3n0Ss8CFuSbMddu6WkvU07gRg+8HJh63qIY006igSRHHDS8IQ4ybDZgkE0zGY0yTkaZEG1IQVF5t5t4yvUlLWwWhn2eFgFevrsHvf/Vp+NNvvgC7/cPZg7YJQR1l02jIGwg5usTwxuYuHI7GsLnbh+Ekgx964gFYmmsblEr3XonZJAWnqrpVA+UM8DKllUItUHctysqqlrp3SJPYF5oapVRQibrU8pCjPMxKG0cqJ7irPfLtO7lDCk3Pj02WzLXBWh+oJevOqufOoqOvc9GUeVpqultTpR95BPmEacKPrHZN0J3WkZ/VPE15jco8Rd6mirfmAK29DDfoXmmpbbfiyc9A/Q7SPXKSmupYQ+cMFdYWqmzfbXUesuZRrMO6KT1Z1einLjF21QJlz5tBvZgwVUsapxbNvLYB33rhEnznlSuwfTDQHpZEy6NzdXUKKY3FjxMjug2bkCOo6y1+P6Vxv7FYkKVDv8disTuqH9M5k+WWaQ0x/eTaNikvQwB5X7fX+mgjCq7vbO2t8oAfRq3eulKqnxX5Mi2dpIBNxTm6V1Q4kNZn2DEcaLp2G1F4aqkHu3uHsNsfy529Po4Ok7k0zxYYB2PYpVTM8jy8ZS3rdU+bPDQL6PTm4SOf/zEI/RiG+0NA793f4rTQVFlAcjjQz8nxU3fBeG8bvDtLcpNCwS4A7ilQxEj1EVik6f3TiUv/Vxg7WvTTcd4K2lF094MfH7Q6S7C/vUXVaEgPSwhbHfgP//4/gHO9RSBjyP9WSviTf/kvYDIeGW0c7qUl8LQUQLZpO4EHUEw8GJUF9FDAMB/Dlz73ZPDRx09/pDsX9gKPPRoFXDUaIZKWCDEW0rTU6PbZU8I6jimc7zTg/rOLI5CyD0p2xslkcTBMy6JQWlyAeRwXj7WjsyfmQ48h0r5H1m1+4MNu0YVdOA7C7wIn1kqRgC9GwLMdSAebqshStjDXZOdOL0KnHcL1tT5cWtkNirRQdx3vslMnuvDI+WO4uNimvtM0n+TZcDD2kmQSkEJ/q9VQcRwo3VGqpKDGFo8846KI2V4krn3EqKJM4h5Ud2PME3kxt7bRn7twZQfWNgZwOJxQYeZsKeVBnpf3oxBPQuglpe8fMmDbDNT1gESDSpkk/YnY25oMi0QcdCNvAICHULcR0VY9RWVrRYAwY3DSAzhPHXJKwb0lQNxbPL720JOfE1dfePXJyejb50SRK2YojO0g4O00LVpXLuzyZ5++sf3RHzq3jcIPZZE3QEmfoR/xiIc84nmzExYL8w1/Iw7k6trOQEKRnjp3Il+5fuUHLc/U48Nk8w6GzwT4xBcPJFw8JDqFgCfmC3jyGMCkrs6nXP/h+3PoQMYLShX4u1LIbSnEMtWjwsBXLvmi/3JueK86hrLy0oROckReFlJXzqli3RbC9ZdRJ13D9zzVaobYbkRa9ZWSPaOvUVXtmfXGaxZ5Pjc6HI3SNC3a7ZYysbuh7BLlZDwewub6GqyvrUIyTrQoEFXhHB0L2azAkwtOqepMFUQtaFEqfRy9lgfzLZ9aJGbsUFyYRLRK0685g7EUNpwMb389URZIQRdbvxcGhcK+QchM/EqU2YNhLnf7GY6SHIVQM4KltWtRT20q+Iy84BpRAM04hFYcQRyEEAcBNJpN3ZODtWtt3q8FPUJNddSAEe1Zke3tjGB06APRa8uy0FQrororZzFhv9xjHnCfaeTm0som/JtvvgB/8cwrOtE8GnUxKxxE/BzxFlAVmgfcqtnSGIwmuveT9lTq+f3YI/fCYq9tEE4pqwTK0SLR9fWBpWQrZ+Mym6QcmSnm0tyUhGqhHt0SOFV2rd2SOlVVVSluOcXH9AUjbiL5RNIDHk3dJ9E87UpZ8FFTdCsmUnWMtUN93auH1nTGUe7tD8FSlBGmNjLgnjkESyk1P5SiltDa5EXW7Y+M2/pUgbYGA0+FiEyRSYsu2SQAp+tOVSSAKomqJYS1G6HRJTHVYla6pxiNSjIYoaGZq6Jqx13do1m8Sll/TIeFVunZtNZllbEtIux5Vb+6K4KN0xQu39iGp557DZ559QpcurEJB4eJpsoSakJx3uFwXCXB9Ql2uwXTN3t5Nd9v4z0flOFslXTBs7qwipR+vUlenM6K8jOx71/xfG/LC/0R5/yyUupqkat7hISIElW6Z5M01Yq05FtKPeNuMO1piNBphFoxfWfvgDxTF7KsWEizjHwo6CmgLDVn3u34bBqf4HZ3Dh770Z/SSV5JtOvvcTwjigw6J87AE//Or8DT//wfwWQ8tM/W7Q8qnCtR3lAI1xHxBJLn6c0zUWh6rRaugUlzbr49t7B4b6vdDDzfG1FvoxRSu6t56BXJYT/Z787nOTL4j/+b/0H3Gu+vXoO4FYFP1HzksLm1DbsrV4CT0jr4YLUJtcDc3/zpv57f/WgjAJh0QQgqvVFxNQBbrConORwcJLA3IJs+ga1GCOfPLCiQpT9J0niSpDxL8zzPCknFXwEs86NwsrTUnVte6nlhaHiqoDUEPJg0zsOg+zgkwRKosgBPTKAlDyAaXZbl3iWhskPeasUsaMcw3BvB1u4hfveVDY/27s88fhruv3seFhdagLEfQlawIkvLYjJB3V/NmZRFAaMBWfxAjgxTKiRHcRg3GhK5b3rhkTOn2JdpVFmWAa29QcA1Aru80FLEKDoc5zgcZR4Cbykhz08y0eGlQD9QqeexsceDg1Dynb314f7Lh/Kw3fRHpxfDq4fj4lsAsEr3kYwJwNKqiP5MsrcS4Thj8CMxhx+WAHdZ/9QWAxi0u72kd+zYsTCKeuM09zSdR9upM95pRYVMc/F3f/134Q++9msU8mV0DlyQaBl6ImIyJzpKmUIDQuiEHSjEpp5UxZ16C7+Px4fJ5jswFBhl24ZnqsyTEiEzbU22d4eWlmmw8X7bYIn7X2ImPN9PZS4zzlA2Ip8k1lmaZUGqJZ2Z7gWgDZAkn7WPG/V1CmnEDZJUCwUdjhOYLwrrJqypflr5jJKOXrepLVXGaeYomS6SdgfCyrJUaTaZlEVBiWoHGPNNb5UySWUp4GD/ALa3t3WySUEneW0qTck64m1YAyt0C2chNTJLQWngI7QCrlVoOU55dk74CHVyyqeUzakCKKoKWrjdanhNvPOO79o7NiqfRattAqOJKDf3J7A1mLAkF1jvqsLa3zVCpUu8lQuOKTCn/28GIYR0nRtNaLRIWCcw7633apmpwqCutGpRGxIS0n5+cQzZJNEqw+MkAZGXpo+Xujklapou3a+t1QP4xnMX4IWLK3oOtlox5Gmu77sLApWhAt1E87wVnxCtMichRNqoXpjjpjn80pVViCKiKfnwyUfvhaW5LpRlqZFVtP12Rrl5inAx2+sniFEkVJVIHCWnvgEqRGxvT9dyVKW3qQzJ0iKXaube1FIrd5ecx631/XBitFa3Vbk80SU506TQ5mM60Z0SPmuiPxX8X/P6dMUiHVfNWLFMr/Fskqdmfl9JpFDmq9jUj1IZcqoplpmE0t1j97fJ893DOhVgIgaEe7brwmczMx2nlFyDnsrqfKRRx9AMCuezqqyarkuG3UnUE99pQaCGDtbp+y45tuetVJ1qbF7K7DxK0xyurG7B17/7CnzjuUsabSd108DjuqBHRkVS1J/ed3/Vcff+B21IrSVTTvt5bRFDaoqz6BV58RC0mo9ICJ5TUq2JXF3wYngeGXzCAxZlWU6CMLpAsH8whiQtIAiDymtXP7Cl0oJqcRRSwY31B+OIigkcKzYCz0vWYn4Rv9VbrdlcjTY8+vmfMT3Jovz+tAgRKpWn4IUhPPLzvwx/9a/+IeRpan91e0ekAPotj11mQm1L9fpLO8XHWpIC8UxvfuFLH/n4x88++slPrbe6nYPJeDxKx2MflBefXDq53e70LiilrpMm4raC8u/95v+sV5jMfhjhsL//p38C/+Tv/hfQWViAzbWB8fT2GfzWP/h10Abk6VUfIG+CYr4h6VhhCZKK9SntAtw9HIMsJDQjHyIPWJ6LDogiYiDTwGN5wAPq16RNceL7Ho9JQrHMxyqMGkjVdGpDZQFrNCJWNmOQxA6DAHxoQiAi1eSHgrHdEnI9SVk5yeCli5vwrRdvwJXVPTix1NH7VKsVAVIbU1ao0f4w2tsZ4CTJqJWKNq5igjmFdb522uRMlYHu7DcOoJ6nUfowJrMBNDRW3YqhY0G1NN/G8BFfLR/vFStbSXF1feyvbw28ZDzujqlpGYdZUYogCvmSx/k9GpwAtacYrKAvs0E66fSvT/bHorxPCPkiMtjkDK8JxC3OWEJ+tRsS2h6HJwqEH2YKPmsZOvSY9hDgLomsIAtTIQSXqkSOuq+VentHEmAflEhanQh2twbQsb63dpQVjcdaQ9WLxj/I48Nk8x0cAVdwbejBZhYTI0AHrIHfhGNLPtwnrxmqJxg61/tslD6yMo78cFxmQRyFskFBPtFOBdGBqE/eGZQbepvutxMmgM9EQSJB0GoEsDicQJJMiGYBZPul6W2EDrdiWOx1oNGItbQ7ReNMu/daEQ2DxOwrgHWpVCaVDI1XH/qEYgkd6MXg+SFpc8Ng0IfD4RAacQPCKNbBI3luVjBbRUkzW4xOFGyzGyUODd+DyGcmxq5oirYXirignrXNmGUSMe0R9fbvL75XwzAdVFNlPS/k7mAitw4S/P/Ze9MYy5LsPOxExF3f/nKrzNp77x4OxRkOSQ9pUiRIjSxuEkVKIAmZhmFABvzHJrzBNgzDPwQY8C/DECDbsi1YFiSYEoihLJimxGVkivsMNUvP9FZ7VuX6Mt9293sjwjgn4r53X1ZWdfV0dU/3sKKRnVmZb7nv3rgR5zvnO9+X5iXuEKUgHqYWy2iZvMRoX8G+wcYuTqqxSLfGgBepYQFtOACu76EIEAHH82PRs5W9JURyPZ++sDKaZSl4s7lO45ihBQBWObmF/eNpBF+7dZ+sTRB8XtvZhMOTCezlJ9BUhDtLHWxWY85uG2RLUFX1XrlyuCfTiCqc3XYAW8M+DHpd8FwPpMrBqOcyiimawi9gBWEIKFvwwxr9nQ9XOGsgV/97cazNubTyKw0Nv01zb7n1GbbVNhfrZBby8PqcmO6b+hytngmsNuLftV7SN/mCNmpOG6sRpQm4V8vXzNg7UOLIfiB9TmV3qQZbA0qr49+4r8FSUpkF8CTMRMI/anEO69chUNhUwYWlEi6pXMOSilu//wJM1hVHewxG8bb2CDXzuBbtWYinkSIsX34mq4oLC89fuyLZ96HzIJdKuPVBmoRME3ibIgH6yGL1ASv87+wewm//0Tfgd//0DX33cESqlR61L+C6XT40l5+ND26ctX9htscWr1slpVtV8nkt5aeBw+9VldzzhJyD5BPH4yk6DmJFROYFlEVJvqfo59tkA+BKggk8RzjUA4+98AdHU6gKCVosqCUYOG+XUu1wpW+cs5ytDEyCeWEbPvtTvwTFfPaR6A3CZDKKrv30T/8N+K1f+weUvAMl31OaBG/LkOnDhLM9qSARVNl8aJDVLjNhzRUG+hJznJeyLItwscKyJuMCLUZEVRX3Iau+zNvwdeBwlwO8E1fqQHFWFtZkcwwAP/Bjn4Of/bGvwhfffBP+y5/7WRDtNukVpVEMIf4s2T2Q/BsA8BKA7FmdsIIkoLQOcZ5Mp6kcTVKW5zlP1gNs8dFKqhJ9R5TCdRulJagg2K4q5WdZeZinRd4b9C50uoEnfEdp0YYcWhzdgiTXIK3YlFsUrCpSx6tKpODwKivhrdsn8JVv7MHJOLEZDcs8EdSbAmWcsWgWszwvSLODsoyGz4++mNjriBk7gfFgnpVoh4J9/k4uBNeqBUE74IaCYy2suMdE4MMwXNOdrXYxfLWf7Mx46/h45s3Ho+HJ/m58cO9mlMxGWquKx2kJJ9Mck8U9wflOEATReJZ2D49nV7NSPp+m+oB5/P5Gu/3l8WH2Ba31FxljsQB4kQH8EOfw57SCIXmjckDrGCS3yXg6DeLZmCjcjufNHNcbaaUSBdUDpeFY5hCdZCn83Of+NvyjX/sPYH3g2mL4s/Go8QxsPuXBbAWIm/w/VcVKBfB7ox64Xh8m0wP4we0Shn6jBey92pB92AOjtEJlzGEJ5yL3PFchBZJotEZ8hUAEqXjCKpBjFsAleQmzJCfxnoK+CuDCBWN5xgh4DHtt8k07mWIwpBhvoDYyWuc8V1qXWZa20iTu42IDyoZbtAhKYxmvloChrqxoEmdYAoJHZdc5VakRwCAlAiytb5n9tMZzpHzLeE2hXQUn38rxrpWvb3JQgKo0xEnFpknuZkXJFMqtc1YyM4EDvarZ8gjIrSnwR3uFtV4b2kFIAZKDdGfewKtPOuoeNqSsuq5uC5F4jlsGYRgmSeJjtbMqShjP5vD2nX24t39CVUekuqK1wziKoKwev0k0q3PnDUpUPOI1UN32j79+g0RYkEr00tVtlLG3oMeEiZgIMZ6fhhZW/62W0amH9TbUtjrEmtW+M+WvRedw3f9org15makzfpsr16nGROYWbqjSPs6uQpu1jqhRwJjUVroVlRQpXJN6Wa8jHzaj+sqsQBLohcJxDfnMfaVg9TPaAz5zLEtqfA3QTKCsTVnCOI1WegHy6k+52gPJFi6izEZ5VAkFCyqtWo6Gpr2JpTBYj9QabKqGdK1StUozXxYu2WqFdrm4wBJJLs6tSUg01Yrr919JarFaZ8Oc0/3RFL749Zvw+199E27vHxq2CXkkyj9bHk0f0aEabAUEiYWsWlLJK67jXAFgf1KVyCQSMWMiIYN467dJiRPUVRGrfRYLux4uoNtuw9qwD+32GIEp3mYVZ2gGxdqc65ehYq8B119E0b93Ozva6hx8lEZ9z/7IX/l3aC3/0m9/HtLZKfAnlPakVpC0OtBB8A4T+hQqeeFcWrCmfnXMpLpVWegkmrfTJPY91+syk1TGNRiFC69WUr6qtR4lGu4NlP59AfAbEuANakewAxPzMw5w9ZVX4L///K/D5//u34Yv/eb/C1qMAESEJiv3IXe/BmX+/QDqFWAkWJSA5ilebYfxbpqV7O3bx/rBPlfZc+vw/E5H+q5TKdNbH9S2UnjdUMVfaSWiJMMeDt/1tGiJNshWn2XBDhSiT8wIrixDRebAqxRLyKjszrCC/vatI6qiD7otmEelTe4K7WEPWSV1FKUsjjOKuaxXORcc67CslFqXmkGJ5CLcH6WUpVLUbtXRkHuYm3RcFP0TpAKoRRdksKWV22da+Kz013wRbvMN8N1BXjhFlrJotMejB1/nxfENFk9OvMPRDB4cRjCJcsfz3c2KBWuFLHWcSoc7Tqsz7A2LsryQJtFlDfqy4zlrhaz287L8kVC4P8kYvISeqHYLCvAryzLn9GgP5pMT7LMuwlY7RymPLI5LzXnM0F8zT1AshADUL/z4/wD/4Ff/Jly9svYMbz5mPAObH9JQlN43wcCXRi74Th9KVHNzGDw3rOByKwZFbp8mZa0piP3oaMxgYQiy/A5j+sh3hVxf68LGWpdh9QY58MsKgQ0g+TI8oiCMej9MIIYbpxHeUDWMpGwlqtIOuqFGMFKVpDDKltl89Pnlw6osnZPRiTo6OuZXzhVbFgAAIABJREFUr12XSNvF1+JGCI14EAgqcTPGINQIxkiz+BoRG3o5fQ5+0JaChpVNT9SWCHWfETPFB7Xs8RT8fUr8P+2hG4zA90bhzaw6IVIwu2eBYi0OWpRKzzOpkkzpUmrcybgkrayFbx3YC+roM6W1+kcEVGh3MuiGsD7sQL8bktcqAjFtq3rvCSkz68mqifOC2ejUDfxT7jiDsNXaLPKC7R0cwuu37sMff+1tGE8TEuY4nURwOo1sjx+34OXRb3z2L3whTvN4WiD+5eB4Av/qy2+SgFFWvAbf8cJl6LZbBFClqgVTTA+iUbxljddXK4CambZoAymVamjsLPvhHvI4tP9mpqFTwDniVWeTFDXQXD79TKW30Sta/52qLWz5eZAFpg0OFOaY1bJaZ2VyahGaRlV1AfnqSqVagKtVH86HzjX5U3Ij8INUZCkXdOxK4X1t7lul2EJUR9s+Ot3wCV5QfK3up64JwarxednqdW9WrkhBVqlGlZMtrzFWMm0VlJmCwoou1CI5Rt/k4rPW/qsrqQW9vDkpSeaa19w9OIUv/MnX4bf++HW48+AYgSbjtnKqngHNb+lo2vXUo2aNcMHXNPozKrgsXL3PHdhlDI6qSr5YKeng3Y6iTiUJ8S39e5u0a5zztec1JV7NJFVWrdjXoDsS+KZkDlbOokctemVZUFvDn/+rf8NSrfV73FI++EFWVErCD/74X4Mv/No/geO9PWqneJINBHNAQx9vCy2L8z8ZigU5Zl3VSNll8+k0yJMsg57cF5yVmvFNqWGjKvIwnZ3s+EGw1eoOrjOHXaw0vFAq9jXF4GsC4G3cBrSFIjgHXN+n48cjLcYjEHmAbLDKcV3pYaO/g2pQIWgeAHPcBMVLmVO287xwTk8jONFad30HeqHjbA3DNq8zYnVgY4oALmi9rqRkeZo60zGqs3LgvRZUXh+kY2ImZvPyqFahGMYCUsiqah2eJPDgODZq8yS0yGDYDWBrvQO+6+gyL2Qcx26BYq26XuOoFMw1Zx4Slow9T50e07oqK61wOWKYuE654wroiQ6wzhZkrRcgDS+CdNpKcYdL7ruVQ/KMHBMJXtiCYXfgrG2ttZ3ZcyCTGZ8lpT6NChhNM5iMpyIan4jKP4JIBTQ3Wr2eNxtPvGQ22+HCCfwwaGfzcTQ5HX2nd3HnBc7BUxUt0x4XhIdYNJ3yw3t3YHJ4pKtKaS/gaMx+EbMujhCn2BwRKyP3S3OJcfiFn/978L/9z78Ir33m2reXOuhTHM/A5oc0WOOrwGBHCigqRlFY5G7Cm3kfdh8cAKpBh60BVXxeau9ZKwcGleLfUtoTtpakoO55DO47wplvDjvd65c22MlkDlGU2CrtkhZYB1TKLtaYiMUMbpZL8karKmntE4TBSBxk6LuyEwbCFdzJz8FLjLFQVpWYTmfJ8fGxSrOM+X5gDPwtyC2ylIJ49OLCRTRLjPiF8exbVgTqak3d67VYozmD0EXRJwb+Aj4xG8QC2a4b4MkX1infBvFbZpk+qFxwDqXIVHmSomLzOIckLVVZIu2RsqnueSaRD/3CeHHQFwkEhQF0QgSaHrRbLdx8FVvyy58QLBO9aMFQJHyrNH4G5TjC556rfT9g+YNDeOPeEfzR127CLEpo7s2SlKjXnLFFz997GTUN9t0P0QCk0/EMXn/7HnRaAQy7bXj+stcQrmEUIGqmrA4Bq3MwqElggEtd7dTAEZAvANiZrkuwIP+sTYj54dHZq0d/+ocrzWfDzrqPubYRqcVp6BiVvdlYg76hrdCPrUgyWxGEBb21prrWPZPNvkZu2X/LRtImBDMZfSPMQ0C+sWrqlYqSXnivNoWU6sPUy0JsvVgsPvlS79UcV12RboLL+t8roHzx3suKKq1JqnH9zlAtV867Xv19Q+SJmk/xx9NZBF/8xk34zT/6CtmcoGo0JnfwsWfEKT566OHPwDhvr8C8kuC0+q1rgE9oYK86fjAPfW9WVirJ8oouNbYeYJsB9uKi2N5av7NUsjY8AOryc4VD4mu+kVFH/1YEqtxKSefmsaxtudsrtRh8vTxLYDBYhx/+qZ+jHski+WiKmizuKcbgL/zML8AffeHX4cGdd8D1g3d9rtLQlaraZAAdZhMx5wxu1wYdRxHMxtMgL3I0Qt1F51yudJ88h6sCitmIJ1xzoYoOa/deFH54kQH8MGPwDangDx0FX/EYvKU57JKIoGU/4HX9e19I4YK4j1Zr7rDrwUsvXc2vvnC94J1hoHknUG63LWTq+UMB7c49um1H44i9fRcFoVzWbbter+0CtgTLplo1Zd3MIol042iKbqwt8AeFWXe4BZp2OSX/Op1jszkK/8E7u6fw5s0jmM5TWsux9eTKxT7sbHaRR+xMxmjpkTPDLtMra6lSyjMtINhNpmxRHturJKN2ePo51wkm9nvbjHdfg6T3PKSizZQ2lQryXCsVr4oMUHCIHAQ041r5rggugtu6CB3uQku4+oJiPJ7PdTQ+1fPJhD3Yf8D2bt+CeDLm8WzWxg/oOG67KsvB/ZtvqZuvX+u2u323223pkoFXd2bge8aTCcwmp6hwz5SqvDSJhVaqYhztT5ChrFQVYJtXPWE47B/PIOx4KDeMNL4Pctp/bMczsPktGIZqqxt0WwcyBTBNNRSYPUdtbOnD10YBeEEI42kMr/XGsB4qmtgFIr/3ZAP/VIaSDE459x8EIpj2Ou3t7c2hWOsfwe6eAJlX4LsMWqS0xiBJMgr2oA6oagEUFNVhhjqI9CDHsY6AyKtwReH7XksIw4epQWEz/sJ/VrKKkzgtcrypuzDXJG+thkpKP8tSlqaJ6aPD96iqZSjaUN/hwlQLZE1Ls1r7KPTU8QW0fE72J1I1Ki66Qd/7sBCmKaygiiDWbRyAFXbx2ce+t4pmHWwDQ8XRPs48rc8HjpSBLRTMkoLFqD4nFdPnF5ge/XZm+oLvOUTL6XVaBDrDVgt8zwcBwhZEn/DcMm1dNlZmSYiJY27LSbM4gtsPjmD34ATmSOMuShLoIeqsFej4Zhv4n0ToxNzrHPKygr3jU3j9nXuwOeyB77uwvdYj309oVDcXr1h7HLIzVT1Zv69evEGjWA+12CxYNeczBNSGWcITf8p3/ex1JbWmslr+rQ1nFCwmypk+adPsXSsPWbebhR/msgccFvcuWyrKNubdo1ZCTGpIWd/vNulvb+hFH6VeVuC1pQODbigFN7p1dZ29P9OzSutHA8av9tE2b0krDkMgmxmFbNU8y2zRBlBXu87tK120uzIrxGb6zFHp+xs39+APvnoDbj8YQZaXRL2u+0rPzNdnQPNbMYyglK7nODk/VJrleckqqUMh2PPA2ac4E7d91y0El1nlu8TlrKvgeJ2n8wy21ivwHXeFDo6bQyt0YdAOSfHbcTiTikQ4Y2BwqhU7djhztIY26PwhzXNZFnD50nX41A/9OIStDuktfNQH7vEb21skOsO0pP3l3ddm5kNVtJRigbYMeczhIe5fWVvMulOlWVbF0/GsyhK0FVnX3OkApENXFWaVKx3IZqcIyniWFd2gN+j5rZ4WLltXHF5gEn5YA3xdMvhTB+AtzsUDrXWkpMwOJ0X10k/8HNz+yldaN+/c6U4cLdJ+qLdYCNpxfA3tNa91gcEFT6xfn8Da5gEcj+cwmiRwe28GOxtt6LZcWgOU6V/Vy2Wz1pmQUGLCPdWQTSLgvSnwjS4JTuHxCyXBlRPHKSctWZTs4HAGb7xzCPcPp1CWClzBYGutAxfWu9DvBJDEGZtNYiYLtdKrvuC5YG8mqa+hbRpaClMh0KW5it7VWCkFzqR2dRFcAtm5DonThQKT2EVGnpiqzKHElqsyJ6V5bhP8GlusQFWB50jfF5hT5t3Q1+3uRba2vUMgcWc6g7tb2/DOV/9Ujo/2XRTNQ0+Y+fiks3vjbegMN2Bt84K+9onXUGirdp+ihFw8negqz8BvhayS2MdaOEpSr8tcMHggNNsTUslmILbVC+CX/+Nfgb/1t/4qfMf3vQCQJh/L5emDHM/A5kdiGOkgpGUKC0AxKBznAgLhwWkSwX7nAuxOMzjJCviEKOFlKEjPmXp0UAIN+AcdPeAqMtFd+ZYeyn1H8pe67VCgEAGq0NLiITgBCFxQCrShyEpL7zF0ucBzKABEEIh9myh2gPRJEhdCVozjOJ1OyPA1ojjThqHAMcoTmtJdtHoqVco8ms2y6XTqrm+sJ4yxRGsdMAY+UnrTJIUETf8xG1ZXe86gojoOpEDRxq+c6LyYQeamp8zIPdZ+JNw4IzTMChdUuw/2rJvcAjXch9aA+JzBKuN6AefSJM99ZcTapO2DdBsYPvJzWM4eArSslDyvlH4IaDZVYh/9UcD1HFgfdGFnY0A9uh0ScApwo2SGVqifHDAbi8m6EbfZg0iqQxjIHZ1M4Pb9AxhP54ugfdEZWFNWtVgkFKR6evyB2u8F3zcrFCkz7+6fwr39Y3jxyhZcGHYNvZxpC2QM1Vwyo6oKC3uPpViQ0qpB5FwBfUYmttFrufjDoh+x6eO5BFJPDO4f91kb/9fGZ5ctlJsXB9j0WVxSUWt1zpoO3KxwIuV1pVIINUUMFpe9htU1M6Hu2Wx0OVobHCDVYWj8Xuu6Rrl64awHzFKhtv7X4t4HWN4EzBqHWyabqjNUS3p2DW5r5d26D3NZL62PZ8m2eOgc14GjBeQ1a40zRnpGUZKJw5Mxx7leW61QRZPaHJ7GVf72Gya38LCH6lMaD1WPERAELrI5Ao3ioEma8bKoiP5aKRn63HkBJHxXVVa/JxWbuo6YOIKjsIqHj0kLIOsa7AW/tLMOgeMuryuVj0xvNyrSIosCLVMwUAeGqJHPNIMTBTBzKV8iV1TNaN9OY7Jr2ti5DPPTY/SM+lhc8zSK4Ds+8/2QzaZwuHsH2r3+4wGnhkg5zgMANWagt63AtrT97I1kGd29Sks11uls30uPeSAvbysx3NCMu4IaMUyMk5UKiihi2Ffpp7Fu99M06PRA+P5VzsV1xuFVDfCpPC/vJfHknuu7N7vD/hsgs9vh2oXqhR/4C9+zf/Pt75sWyZWv3B67vf0Z+IHHvF7i9XeuwnBwCbrPfw/svHQIo2kCRTSFk2kCN+6PASubm/2Q2oe0tKsEMVmMOr5ZXQUUvAVFpkEfH0FbO9AaroHnOeBXJxAWB5zJiEdRCnf3ZpDnEjYHbTidxJCVFYShC+vDFsWo0TwGVEg251gs+9HN2o1FdpT/Z9ownxxWF6G1EXukNZK7oPxNNhNbkCUKsmgKZYZK8im5iagip0RCVXsHkzARWsZgQFhWReWWoCRZvWOplAcBCM9lKDYYhC0VBh732x2VZrmenp6oSlXC8TzIsgzu33gTNra3VX9zEy5cvmiIIVoztFA7PtiHydEIqgJdEbhmHCUQpY92NEqpe0pX++gh34x1UCnqD/7gJihUmucorhs/y+adGc/A5kd4oLgQfrlcQcr7cFQ4cBQn4CtO8tF54ENScGjrCj7JZiBt4FWoRyl6vs/BIVMb/EaSFm+1vOAzjiMGWJlqtwOYxSkF6mmaUyBENEMKDDmEngAXbVAqgOk8h1k7g7AVQKuqarBHBvSuEO7Oeo+/cPUCGVdPpnFlXUkY2TYYywBcZmA8Hruj0ZG3c3FbtNrtgKERqFIM1UjTNIWiKIhS4pD642pVgApElvqhF9UkjTQW8tcUS98V3IJyggAaQlQg17b2Zpw4uH3+BxfG2QULS37OY0GkpmI3nq/QPk4vIvJliWXlGdoos0v7HPfc17VPRMyDMm0leTCv9vNZFmhlf+FYfRX73No6wnTpdVshXN4awsWtAQVDnufRFxdiYbHyzZ2ilXNBX0iXxUAbr2fg+/RApJ9h9lI4LrRbbdzMDa2T+ogrUizGuYOKuU9jaDKxtH2DRCPPIU1LyPMK5lFG8u9IezOn0OzYyqqyLu0xGmjkMUMbMYuHH8Ma1btv4sQuWgPBMlxr3KWbj2t6/RgJHG6FpJv9mnxBma1fd9XWZOFbufCfrFV767+xxeO0KVmay62WQj5s4WeoFnR5aXu2637KOvHQ7GOsj82w5oV5Ldszqx517izoY8tSNImRLe1e6i9mElhYzULV2EVP6rImbIZaAFkNS6/Ls5XkOpFhurR4mRZFOY2SoCwrLhrqt4RCF0p0dK+n9nvrcff8s/G+RpOXv7i4uA75QqBlGBt0Wxp9pZFpMZvHSPsv7cUegoIXpayuFlX5FcbFSVHJPC/KFs1xpWmvHU0iSDGZ223k+Zrv4ztkuWSVk5nDeQsYbAHoqZLMlyAd5mIpENVOzURN4hkML1+HFz71/fTzh+2l+X4GgpLBxgX47h/5HHz1d34DDm6+A+FgbUXRemUoPQMvvA/A70IlLwJjvkFNdDdW1nvYY7Y9AxhXQmdVkOz7YbTBZQfKnLVU5QSO1NhcgyKwglgqWJkrywyKLFHBvFd5rRZ3/JbvOM5Wnma944P9775/461IaXXv6quvfunyy995ozdcY2sbF75va2fnM8d7Dy6NHjzw7x0e02tx5xD6e0ewc+15ENyFYG0H+pvbkLscsvkMbj6YwLDrQzt0yKqN7DYsU0vb/k1GDAgHKqcFqfahmkdQqQdQ5Qn0WgI6egSuHJPAP8ZoR5MYel2fno++rmhJcml7ABc2OkzmObZOaZxXzLI8OG7fnArLYH2vmFaKm7gN/8JMqKeMoxZQr0sLErEGaYxMuAOQVQFKFlSMME3+xopOc8doGNTWVliNZY6QzNWSebxiIfKlQJDYkRV0FIx119fhuaAjjg720r2bb6N1X9Dq9Dj2I88nYz3aewBFmhj2kVkUdZakenx8wE+PDyGaTi3FhBTt8P6MlVZzySDzW/5DBYzrz2/B//r3/gD+k8vrcO25TYjGo4/+jfMhjmdg82MysKzmop61UDAtAd5xXKi8NpyWLnShBC9E5WwOZV7BtUEGJBamxRkdw/cxTPSowIdTJw12tUJKLe92Wi22NuiyySxh2EuCPUPcEPVpE8QJFvqC2sWQ+4/V217bh7W+JM8uzNYKRdiEIyBF8/vnL2/BeBbDPEo5mldTyZGiPoYfBy2Lw/l8xo+ODtvz+VyEnY7wHM+dTMZwMjqG2XyKVIoVIZOz42wPdx3Aob+m55r1xdL1VK3jgWVkJrVVDjW04A+pWuCT6fFjwZgWdcxsI3NJct7Gc9HQkutHLp8kngh+4M5bgi4rxahgYmprBQNUZULjf0Q5PDZ7mu6sgGJzEUwA5Lqw3uuozbW+6nfawmHEZVZCCCNP+tTGEmyEvgtdS9fF2IqM9xmHbrcLW+vr0G63jVVPgUAzJ7CZ5jlMp1OI4/h9Cao0n1r7NeIxIeDFDXzaS2DodMF1lr2C1Meom3kCK1Kz6D1cJZazM3W59wLYv4kkSUVa/WY+eQ95QdZ0eVuLXShS14BJNSurq7H4qt3M6mdcAsPzUiZqGdqzxdxs9HOZv2HPDwZb2F+JGN4IqZzxh7FCPrW6bA1yl5RZ66PK6oooNN64/viq8dszJGYSorKiRTjduQ1zlDqngZIUuW0P7AJnSyvCwbU2Rs5WaKgoy2o+nsXR8STqTKM0yMvSWXkxM0rbm41Jpu1nYPPDHYteZl1RD50rhGr1feYIprNCltKIH+N125RKvVyWxX0h/LkCnTiOGDroxQjWxgcZ2DSH1XK5NdaIRKv2yAOa12wG3KxwD9lEVKRA7ZZa/4mjHFZTytP5HNa2r8F3/dDnYOvKdcjm049VZYY+QxzB5vZF+ORf+mnIf/s3ILr5Fvjd3rlVa82ZZEzFSvF7CtiQMbgIGnbqP59JGDiasWFZSlUlY+bO7zoBK1PmXpCp2/UU9zvAPYeqbkjXVxjblCyL4qDKCnBTX3PhyCRO+OG93e79Wzfdo/29odZ659orn7j+0qe/dzrc3HE7nrPlXdzutnsD3huswd7tW3rv1g12urcHx/t7cHp4AK1eF05Px+RwIMGBrNIQn6Rwc29KdNprW21QnPbpuu9gkSi0WwkpJ6ByfxbPocJKoivB9eYQBDFwKXUU53o8SflknhFdOyskrA/a8MpzG7De8yGNU7QxsSeqtvCxKUduKR0MfIMXtSWHmDwoedlhUIIWYMEazEUfKblQVDFoVdrqvMAeCFskQL2CCoBZjQNM1CGoFC4XwvV8R0HAJTNYlBspXlT8YS7TAqqg5evB+obqr23wsiy15wcUG5aUVC6tLsKCLJMWaaqjyThIo5lT5Bn2iTLHcSUpNzNItbUxxjaYsyMIPPjtL7xFd+Qv/xd/EV58eQPyg+yhx/1ZHc/A5sdsEGkBQRGVAxWEZDUJ8A1/gERUSPIEKlcQ5aHPZ+Bj4hKDXM1A6qcAPDNVbPcvjOfZ9NhxxIVhr+UOum3Hc12WpDmoShkacKMbpKrQ/qWCNM1I7GOWZNRPVOYFVGFBNFzcQAUJIAjrNahZpZRTKkncfmYysKmBe9DO8oyPT8ciTmKKLIXgVZYm4vDwEH9Pm/lDfXCP6bOr/UFR0RH9NfEwsP/CAj3NGrb23Jr4YxDYVLD8IEYDPDz+0jFznI3HKVPpZM1qaDOax/XZb7g9PPK84ENKpRjpltfNgAZ4SAKzpjQjlnzP1fNNCQXHgV47hK21ftXvtKUjHG5jfQyg1XLNfwrDHqDnCui0QwoA5nFC1XKyXQkCWB8MoNvugOu59CXaAowvJ0m0Q7fVguPTU4jimNQfZVP+31Isn/Sa4wlDijlWnHDe74/GsDcaw9XtTUDfw1optOYPo7iN6dWr/RuNyjJmkk2j6hnrj3ehw64c53uYpvXEWOBIM6ckzSn9KFhrn2VUCM09aKQSDWjTDYDMjDo3wNl+xIaKq0VaD/lsLqxEjPjXopdS1oq3tUCZIkC6KMWyxnRXCmqy7EK6h9VG3JLoBDR/8VhsxdNgW5vC42yZpbKfXamF5edKSqCu7tbXa/Wa6KXSbEPwZfkQbt8CJUBM4UApY33DSZWT557j3C7K6mD36GT9/tHpK0UlN2ujcotG6q/Afn+KyZ1vajx23fkwD+IDoAGd2wqAt3MhK6gyUk/X7SDQfZcrongzye2c0JzzjtLwcpoV9/FqC85n7XZwCdcQ3F+RhotJNFHLoZ9NmCBDx3Mg8FxaA9PM+MZSizuDNRDwSQB+FUoj1IIgbbB5AT712b8I25degPn8hNaqj5vyHQHO2RRa156DV37qZ2H38/8XnBw9AD9sP7Ts4QphbMB1Tl1IYKkRRs7dYcv7xd56wONUDqZx7shs6nRcXUI+T5i7XhX+mkLgpMnOyfRdIxBES1BMCqmyKLN4LnZv3hRvfulP+f3bN4h1s3P9eWdze+dSpxVcKmZTmAUhcNcB3/fgwuVLyvUchJRcVhmPxmM4Pd6Howd3YDwaUUWzSHOYxwVURQH3DyPYPc5gc9iH0PdBavORlgoIuGRoqWXOPZk4KLaDxYgcE/4pMn0S8Nwp8GKij4/n8ugkglv3JzzOCmKBXdzssosbLWJjx3FK1nR1ew2dOKVZg0WF6xun9c4SyJSV8ed2XS7Bg2nhwwydK0VsvLiEZaZQvGpqG0KVUGH+XHjEUnNkCmGRyoD5SaAd3ZY6CArPU3wDShgYZy9dKqnback6Y1VC6Lp+2w0DtypylkRzhRYquErLqmJowUde74anU1ZlpmVVeeRo4Dh2L6NvLn0pVkHl5OfdGRghXLqwDr/zz98BWTH4T/+rH4MXXtwGfX/6QU/9j8V4Bja/DQbexT4WsujelnDztAPhYAhXhIaWnkFWOhCKEnp+vgI4KQmk3/N+nzJHHTKuR54jinbbD9qBzzzOqbPe+O1ZT0tm6BwonY10iFJp4v5PowzmSQaDrICgKMG3MuDC0m/HkxhOxhHSHdlKwGZorfjCQlZSp1mmEUBoKeOqKp08z9tJnECe5xTMGV/Cd98wCSkJRup9PgHeZS8VVQVtXK0Wr8UXSpgfsbFonbNGLQFZXVjTQiuDJM589HeZAIxIkZVSOisUVjdrCCHqKWQ35raGZprdjLoHD4N+P3D15rDHBp2WqDGScBy0tGGLat5TGPW1dxzj34nV8ZPxFE6nc0pCBF4P2mFIu2CcJBCqADqdNvjcp4o8HhzOSTTans/nMEclwukUUPUeB1GzkZJUVk8oLmT2YexrTrLCiDtgXx1RmyjVvFRKXjzDzDBtwdFZywRmEsVMP4zvP7BhKuSM1RhxwXyFJv21/qYXH8fCwGUvIrPpYSuUU7/GQuSEG8opJzVa2wu0aPKsFWXN3Wm8MO1xNASIuD1n1hiHAgcMzhcgvtm7WyvLIiiteyYbr8thqbhhkkv8YfBr8G1l9XexyUc0DSM00fbVorql7Vp9diyKvjZhV5/TRcEVTB9WTb/FPiN833bo3w589w/nUbY1jZIOaL1xzv2N6fi1Omf5oUya80d9ap5eguljMEwiy8wZwXOYRBEvMeCVUqMJB2NkS4Q4r6W0fiXNy5nSkAauO+eCYfXaQ3shXH+Q8l8ZtLSi1kDzTDMSyel3QlL8nkUZCYLaIhemfjYYY1ckK7pxEs0H69vw6R/9S3Dh8nMQzyfQUAX/2A1S053PoL++BYMf/Un48m//3zA7HZHVyJmhzA2oOwzgOaZhS7PFPcEXkl+2/5AzOCwq6c1SOUwr7Q1Z7nrFyNX5TDvFWEg5BVUNQbotAOGB5h5ai+AlKpN5CuPTE3Z0/z4/3rsP05OR7q9vwMbWJhusDaDKYxgf3geGzAvHA+w5dDxfo73IzpUr2nOEmp6M2GR0zB7cvgFpNEdPaWCZIG/zPCvg6CSFN+7Oodtfh+cu9rTvBcBVypCWCmQBBVoJjiVXEDLVSgVMCgcU8whMIp10OjuG6ckJe+PmiB+eRpDnpVaVZKiz0e/44IGCNEooYQqLJGeNJmsm2HJttmMxmbgFm3mlYX+awp34Psh2CZ3hOvSVFbthAAAgAElEQVSHA2h12iBcz+TeUCCISaNjojkgYZdj64uewLDYVx1HRoHysWtTCOX5Wk2g4j1QgiPnQ+XBhUQ6nYlUyi3LIsjTFOLZtEqiOan0hu0uQ+/ULJrLSiotHIHXvMWFo8NW2wk7PUjmcy2LAj8PZhF6qJmhgU9L7U4fyWmTANuXt+ALv3MPpP4t+M//6x+HnUGXCipPqTPnYzuegc1vw+FxBSHP4aDYgEr14fSUw5qYwas7SL9wFiqxGPh0RL4U5LArg7Jq/OdsOfiQ5CSe7bkAJy42gvi+6LYD8D2xqChRFOFwlAQlM/GiMmEb9qZVUsM8zYhKiH1xGLAjNc112MLYnjwsmbFLacjFYKDUocqKZlJKqdIkFUWal5gwTtLUieZzUspwHEOXa9LiHjXq4I54Hw6HwOH0vrUULlsElXVA3OjDagqvfATGajVleW832YXfTGinFZRpVulZWvCsqIQRoGGY6cMqM2YEXAATKJ19bk2ptGCKBZ7jtHx3AZ1cx0FhKNFAU48dxiry8RmS+o+oADtBP83xFD29aGPGuYAedWZTVKavszQCKrUFB25sOIeG/T70ul0CpCPfh8OjI8iKAlzukLE60aXOcQUwfYNgbYDM7yq50JdHo3W6L0pZUbUVHcnqeUYueRKoAsuEkSat6aeYwMGMco06YFG5b3hsvvfL+9ihVxELFvuo6MHp4q9WV8lbVxtSFbcP4hbwUf5bGNpond3mC8sBrJwzoxytFDjCI8o19YBj4qjSaCQO3DHUKupJVHYqixrI2b5KsomBVRVf4AslQ1JrXPHtXH76xTqx8ItTC6DPGgJEdZW2Xl/UUtRI4uXVGtDBT9jEPyxkbxt8X+M+V79GY9baTljT42rqvngcVojIMW9D9x8l5+j0gvZUJb28qGRRloleGsmzc5JL3wqQucqZXoLNJ0h2fWRG89Z6X8eMC1hRVXw8jdh4FmvUIUChvXrSMqo+6+dxaeKVvCk4i7jiSSWlh3MNq6PjWQRHoyms9drQDr3GnDVVeNdxIAwDaAWB9D03KSuJIkOYfAyZggBtP6Js3Ao7LfjM534SNneuQoLUWava/HEeBDiTGNZ6ffiuH/tp+OJv/lOIphNKPDYmpNSgJxp0BqjMCyxgD7GgSEo7ZQzmnPO5UsqP09JPS3CAe64jKq8qEmBZBl41BRZ0QHkdrXgbmBMWGfdP9qeyurs7Gh4d7Huzk2MSNR2sb7BrL78K1156hXX6XajKDHJSL6WFhKFiv+OG3HU9xgXXa5sbutUOGFoYyTKDdqdFLQHHew9gMj4ljYppnMJbd0cAXgdy3mOXLgyh77W0KyLGigS7EZlGa2zm8pK7kGsHG7NQLAM8OQc/O4YsOoG7+6fs7XsjMZlmOvA4OJwE8AENEqo8h9yRxoqr1k1biSj0Izcgs+YDCeXtHqfw+t0Y7owqcHuHcO2FF6GFwpKiT0lG1E+g9dVh4PgtcN0QtN+G0BWwVpzCWppAwOea60CBchRIplkVg+PlULIhU7ylJHOF5mxDKhlOjg/L8eEhj6YTpqqKea0WQ7DOOS+01nN71AgmfSPeZgJP7FNVnDMr8oYfOdMCptKF8vE3iYat53rw//3Jfaj+238Bv/zv/xDsHc7g4oWHEh5/psYzsPltPARDsdEKWg6Hccbhy+M1cPyQqooY9HVaHfiEf5ey9dLSTvGWCjwNgbOUWj8ztPKKo3zuHQQBQ/lp6LUDCEKfNioTjRqKGmaiEESWsiD/UNwAa+sJ7ANAxUStK6VkhZlXFP0SYeDB5e01fXCyqaM445M4bbaCOWD08ZSsKj2fz/R0Mo6V1lWe5/rw8FBF8znVM3nDu+9spHPeoDYqfHGHmYroosSwDIm4beCsweZHMExSrEGbq2l5dvD3Wkmw/CGd51V+Os/1NC6DojJADwNeY8TB6irJ+aVem8RA8OSbJICiYikaZnEuXNdlzFKnnxZti0RYpIQ4jWHv4BAe7B0TYHRtAgL/hrTaXq8H7XaLFJEXno+2Rw+/8PeBV/eKmP640+mUlJQrMlZXD0E8fCxS14gqhI+RaiXZgRRZzBQLmp/K3gPOyjzFYBSNZZgp+C4YqzW4WSD49zDeDxCtn4dJoKUFCcljGX9Qo5dFQNA2CdUm8qisZauvhgpaSUn3P363RVACgdKCMnzNOCuhqPAapXA8nkCa5LS+tAIfWqFPIlO4nmBCCc81Wh5wvkxWMceASsL3sLQNMdVEtsy+1LDujE/c4nNbyi/YNqTlRT6nF9xAJ1w4MDUvSBejpgAvQK8wwJiKJUsBKK3r6jXY/ieS8mVnqtmLy7+wmrFJEaUhuD+avPjmnQfl8WSGUog7HwGabD2U7RXlNmHYrGiuxPaNv31Ux5NsJY8dZo4S9weSvMSECuOc8U7oO9TQayvjXDM0OluTUo0qKUvP5XHgewP0+sc5MItSuL8/IqG1lu+afnB7GnHOYpsHtae4rHA4zytG1vWo+x5iorAqi4BxD37ir/9H0B+ssfl8bIDF6nX5+OJOxsg2Iwg78P0/8YvwL//pP4I8TiylHyx1Xx1pULcA9BHamdh7WDbuHbwZc0tj3yhKzaZRUUZJkVZKMwcTpbhYlyXjMmJeVSpHpIVSY0gjFR0dl6Ov3Y7EjbvHg2g+Q51Y5QW+3rn+HLz8XZ+CrcuXtOs6TBn1ao2WHlpZ/3EFTBYZraXcERrFC3uDLvivvAJSVjrPc4b0YBLAu3sH5tMpCUfdun8CEPTZtHDgua1Qbw/6IFCAD04peaW5D6VoQc5DYJqDX80gzA7ALUY6LwtIC8lQvK4scMYYo8xB24FuYMTSSiM2pms98VrU7nEbDLOMEIwnj8aJ/tqNEXvjXgSpEtCRQFVVVH3lqDKEiX8HK7seFTDCdgdEewjc60BHR7AWKyz7Y/ONpGbOCkU9VK6ZD6U3cJLWdchE51Q5ralmMMyi+cmDG2+L8eH+FtYTvCBg2GpRFbl2Pa9qd/uV65AkJNYv9Gx8wkb79yGaTDCvIxl6B1Ul6mHESsOuADjt8EfKxa2M57dbcPfuHvx7/+H/AZe2u/A3/+1PfQAT/eMznoHNPyMD7yZUtRVM0Xdm9X5eH29Bu92Fg6NbwL0AKh3A91zP4LWLBRQVI2T30G3F9GQ2kbtS8hHjbK0VBqzfa0F77BNlkShoUlphDbO4k5F/4FAQWBQSoizHCo+WCtGmzpVWWN/hnivYzoUhvBhtw8FoqmdxxqRt4LJMMmk3CidNk2p0OmJJHDtSqXI0OkmODo/aUZyIOsDX0LBaOGfURUz8nChy5vKGfYGChcAI/sy4XmCipa18TSX5CAyG50aXVhHWqw/Ipsu/mQCJ7JjnWcnGUc6zXPIlbZPKVTXh6qEAcVnRNGqeQeDCsNvWnSAsABhqiofCddDtBh5tHPrweFxVs54h1GNZloCWaLP5HGZRBFWpqH8Ts8NY4Tw5HdN13FxfJ8ost8AAwQplbm2VAEERAkvX8+Dizg50ez04Gh3B6cl4NSHRGIIbMaCqtskAWPAg8USheEfb9wg8+Z5HlTpVv5Zm+O4V0p2YVm5d7lhU4/jSrxKsWNViPAWwXr/e0rCaLcASBrCMNY4F/21Fc9AWp8iJVoyUQGIYpOTJmtMxhygIoSSM5xGMphFaG9F1QNEmLGyjDc6Ll7ch8F34xq1d+PLb9+BkMkORMMraU6uA59DjsNd12OuQyTg+ftBpQb/TAmRYdFoh2UpwU/0kPGdsQmwfJzNZa3lOJm0J5JcVTnNZaiVdRlkpoxZsZ72yZ4j6Q7F/WS8KmmzFD9Vyj5Wl69reb1jcIwuqPqZxyI/dYGDzc7OqCrUCJDPzaZ7k4p27B1tfeeuuf3A86Sittz9CoK1WwHUbvaJLtvjDQ78fMPcBjveVXuRWFd0PcH6GVMBO0oxsGLREqo7CPYwmJ/rWawYxQ/8EYIozLoUjYiEEZmgEBssxtgZM5yS6t9Hvgec13oyZhKkj0GJT67ys/KqqAoZpGLw1Ud/T84bbl16+BMBGZVk4jLGg8fm0TRDUPG/c7gr7szgDRqV9rDpzfvRjQOuHtl2i7YsftOAnfv7fhX/2D/93qNBWoz5MXcUAcMQYnyBx06InlNYRlpWPKwaqNrexX6+sVJXmVZykpUIWgfDdkjNRMa49BdpHqn637SulpTw8OIVvvL7fff3tY/9kmvq4U7rC4a63ARs7O3r78hXpBQEquiKFhZYakpF2Pa7rRC8pSVdcViUtZMJ1dC8ghzJdZDnzhMN6vTbb2tqCvd1dOD0+gjzP4PadXUiRDp2uM11uQBD4uuv0ULfVR7IoMiJIjw/3uyoGkY8lyDTFTF+75YUb/dA5HmcwmRfQbTl6Z7Old4Yt8FzOrfEWO1sFftyg1gPgMJln6taDqXr77qkzmpXQ6fUgQDeDfq9qtbvKD1oucxzmBW3yl8fihIdFDL9Nbga96BACNcHppqnsqak7FhfTWDutTLoDV7qDXIvWl7TL31FaXzi8dyfcfefNF7I03mx3+0QWmZ4cQy44+O223x70uWtp00mSqoO7t/nBvdu6SCPF0AQe13szk6eas7tBpfYGSQnqPcQsF1wXqsMY/vH/8iX4z/7HF5bdTmz169t9PAObf8aHCVjUwt8Tq5qv3wP417c9ePWCA99zWUJ2lirIIPV9dY+BvAcAV8PAC9a6HdUNA3FSRKSQJ0jkBCjzponSKMm/i0QSyhImUQazKIdBr4QgqJjnkxAQvXgYeGx90GP9bguFf0CWlWGLUdCiE9oMOPOLssyPDo/Svb09LwxDkcQxRFGs8rwQrq0u4ULnNI7l7NC14BIKA3m0QVsq8cP02JVOhNqJ670TGGWd7HvaM8/Gq5wtD+j9LGFUIMwKyWZJGSRZqZWWzTiRNQDt42mtnJNYRccYjZMvILpbEt3ZVqWe1iBPS6RoZzFkaEqnJHRCH4EujKOYssaoQpdkERR5ueBFdzodQ7XkxrCjaahvXUUhCDFQbOH8hNDz4WQ8IQGhqjmvaH6j1xb2Z9V0dbOr4L/xNT3XhY1hj4JE33WW6qfaaikA5AhalJSOwUB8MReFFsaz2yqkMsYXQBkTJKpJB60B1IrhdqP/sxFa1uqrzGb+60si64qgVhDlJfWton8t4St6PPFYYRKlcDKeQ5RlDLPi8yQnZWqkwSKYRAYEnvfDyQxGpzFESGsuK2s8L+C15y7BpY0hAcYbuwfw//zul+B0EtG9i2yI+t5FS4cLawPy8wWyK+Iw6HZge30AlzbX4OKFIax123TOcc6tDzvQa3fIdxAfK0mN2NLrbe8mhhps4efCGpVG6+GpGsmqxa1uL4NWpkfdWKqwhRqvpeEakMiXp5sUrZeVVS4Yyfkro0gryS+XTi1zta4Ng5ZFznoeGHEyTn7Ge6MT58a9/faDo1MnLYquFTX7qIQvdUXTeYJjej/HrOza+p4ZHE9xnFv59HyfRMlarRapYON3fGiWprR+oM4A6qVhf71JSGlDbgDQfhg47W635KCKaDavsqzAvY/uJ6SZRyi2V1TguKYYt2gL4Q44OLmU9rAvFNcT1/UkMfplJVqd3qsXdi7/FQB5rawKjzG2YbSbdWHZ/5LWIZreuuKuO8U2aqhA4BLLHeYgu1IrFVeMj+25r/cDarcBgAjdpixQLWtROfvvWj+g/vfDDcxPaWDiG/shf+Av/zX4w8//n1DmCa2bmJiVzJlrBjlyTGuBILYkhHJK3KJrh3HdJNZpWaF2gQTpCvy9VedGYSbOvcAPpnFZ3T8t+fE0R260G4Q+OXJgRbvT67LhxibDCpuldRmrZ/zOjUCi2QWU9fsldVumTV8vlt+ojwQTmmtrA+j3O3rr4iV24do+3Pz612D31k3IkwjGLIc7OoLxeKq7wy24vBHwrR7ngV+Boytw8PSrAtwqAiGTQiu5j3I8AOwy58wB6w/dbrvw3KU+bG92qGBQqYVtVTOTuiKUxhaJfqCYD/1kp3Glbu5Nqzd3p2oclaqqpCiLUnQGa7Dz3Mvl+pXrqtXuCea6wvVDqmzSHmzvZLfItJ8daJafoky3ABDrRvqbFQDimEl54hSzNBDj28pz/nksgq9Ho9H2ra995UenJ8ffCYy5FNdRy4wCr91ineHQ8f0AxaAw+aii6UQdPbgr5+NTbsRB0M5BCUs1kZzzqdAw0VhUfa+rC4pPeuJM28bq17f7eAY2n41zh8s17Mcu/ObdFtHRfK7gZ64jJZYCnjwOyt20zO5yrT4VBq5ot3zlCIHNlJT9Ei63VBXjdYl9asQSQnuWikSC1HSe6jTJnDAInCD0maNdK4gCRI1Dikkt327XN5SexjJHmwPLq7K8f3iwL2/furm9NlzrJkmMu6swFRizGhja3LJPDCmUy2rQ0usOg1ry2AQbfDO2fNeF8CRb0BmX0PNJBGIWQzfi+6c/iI9JVifve9TWXGkuYRIXLC+R94hllsWuch6RcEGF07UOHm4UQlDg73sO4w73GUeVPu0YD06xmCfvZ9QcQ7y+RZET4MzzkjKily8M4dL2Ghy9MYVJnJi+Pg2QJDEcHhlq5/raGtmgIHVWNOiNZJDuG+EgWZXQ6bSg19okRUiUUR+NRnA6m1ElFSwYwNdrhk5YRavPGGZrB+0QLqz1iH5Oysuy7t2j9+UKlEstiZyxpjUHUGFNWZ7lEoHUyq3ArS9kgwJaV1SAoszKVFe5pYqzuv5sehfxuFPM2me5LqXkWGlBqjDei1lewc37h3Bn/4jsYZht0CZDcMchav4szmA0mcHR6QxOZ2gEXhpqOlaLtYK8qKjnFftV63sLvQDxO1Z5rl9chxev7MDRKfbYZpDmxUMzAile9w9OANhpfUro83TCAHY2h3BlZx1ans8QFKOP4UtXd+CFqzuwvbEGHax8hqYfFJPt2GwspcLVwXruaisQtJzZVBUVphe07tPkVmhIL9gOfFmptB6iFjyuXLsafNYdtoansbRK0VrjBcLmKpcKWowXDAM/rV3bNrQ4bwjQ8Ws2TeDm7gH/xq3d7vF43iH1okUxna3Qsz/AcV5iSzcAYPiI5Jo+A9BWXoMzSw19srWhrsaJJqvjQxyqUd1bUJgxsTTo92FrY4P6v2sxPMy6Vp0u9PsFxHEEVZHR/YulSEEW+LKlGb/o+kHQ6Q8SVRbt6XTO0iKnfj285zB5FacJMTlQjLzWXZDWJcwPXN5phdz3fV1IXTqeXylZCikVmgs/pwF+hgn4NLamKQ1DYlRofcow0cXQl5MVWgIa5vs88OaYk9UV1eGnjsc9pnVHSZXkSo6xRxkE76MGFsIYzrMMeHwM4J5IJRPOWBp4XiwYT/KsmMlKSlkpX2qZyEqO0P/TXr+IbCaWfb1VA5x+06NuqfnsX/5F+NNf+1XI4xh7OPNMwHHF5SHTKmaadbTd6pfWxBr7+hAwo5mjW1SSZbkUpDQLWijQyGt2qONBAlrA8Rt7c+/N3SmbFRrCTlsI7TAmAt1fW9PPvfYKXH7hJeaGLV7luYN4E7RERMlIN0A+rDNBWnCo5KiNujZOEVJPZYw5wtODsKP9VoeRvVgrhNnpGPJoBrvHY9gb57AZc5bmPR3PQXVihztrPeb03bLF8qlbTTWXucOBhViSnUcFPzhNYBLltJ4NuwHbGoSsFZjYzOoF1Gwpm3C01uOs9jk2XBzGWSkEy5Osyu4dzqo37k6c/XHFUUDJ96uw1eu3L7/8Gjz3ye92Ni9fJpEsbZOYYLVDNLU3ae0Wp+Dmx4pJ4z4CjLVMdhbfXcVQjW+5sX4doPWvxnDpKzfu3kiqw73pg3s3foxxvt5qd52yyiFLUiKqdzp96PXXwAsCPNpSalUlcczyOPHwRGMSgHhdyqqig6o8zueO62ep/83BpjEw+O/+m38GVWUSlP/4H9Y+2oYd5Hs+tFou+q+sBFPfLuMZ2Hw2zh1sIdVubvxxquHv/+s5cB5Cb9CD770QjUGzGImkQeC7YRgWrucgksOtCySa5NuAwbGAEcWBMK2PoixZXso4LWReVE5ZlA7Sbz0P0Qw3/Xy252RhX2KItJ6NPAIGvFSVYrPZzH1w/wHPs5zlWeaQiAjqzdgPhcdSV55W/PrMXw2YJOVSZnv6wCqMNhZV06Rl+/lg0S/1LtSHRSGp8TsCcIuXXCaiP4okCszaogItj5JSFwX2k+i6ge38qqlRLiHbf22iafr4rudAOwwo+A99ITAwqYPxJ9QFeqJBlUhUfKzMF+5U7cCDzWEPLVeIQqtnEQnVOLbSGCUxWZ5EUQSD4RAGvR6EQUBvx21PoO96FBBiha3leeCERjGw2+kQxTZDk+vy8ZoBuAHj61xYH8D1y1uwvtatPcmW1E3Ki6AsgfBpjnHL66wrk9o6R7Llya/pvoyx5ZVpSFdhwNBqt8HzPKKU4XnBDZR8JlVlbT40pGkBh6dT2BudqsPTWTmfJ06S5g5yfHc2BpAkOfz+V9+Gt3cPoNaQxeehCrUfeCT2g6Jgk3kMk2lEPZd0XLYXtj7OswOBJ1bMbz84gl/9zT+GKxfWCbSGoUcbcJLlC0GzxsRcASCI18bzmIDsZE79PxAlCZ2T12/ch5eu7cD25hDWBx0Cs69c29HDbluhFaHB7rWv5jJRD7C8MfEz1GJINag0lijL+FfXXpzWGw7Yslq84N4vLJJqCu0SSCG7QlG2gXkatKNBVxigI71RqspWuNniNZj1VMTevbfv7LM3bj9wp/P4ocz5t3DoMxWsR406plp5TN2Pyh1m6NnqXbEGs/HMt4o+zJvrIiZYkAnRCgLYGA5hbTCwFU2jSgvMAFHqC/d9SNMIDfy1YkIyB/kL2lcavKooO2WeJ6Clg72djk0eYfJ2Pk9QaRYqLeto397b1DINvU4btne24GCW69PxHDcvRn2bGlhVFF40nV5K4rjjuD669mPzYGYlkjEb1GXGpxl7PLdQ00gD2T8gpslAc1Re9gyfUhUgUSCOdZEpTIlGxSvOOILG4yJPK5cJPuz0MFE8m85neNt5+HE0yASAjYOgM82zLGOMnXiue4jlPE18czhhjB1ygAj9RjVnE2VAaZPq+2RNdDZ59AN//Zfg9/7J34X5yb4SvbV9Bs7vIr7QGp4DgGsaln6bpjWdYXeNKCvF51HOoyTHB3NBCjKSgg2Hs1QpWR6ezIO3bo28W7snzmiS4C2tO4M1deXll/UnPvNZuPbKa6I3GGLdkJXJ3MmTRCkpmawK8h2ndQO9gBFbUr+/WoIOrZYMFSYYXgBdKYb2IL7H4dL1q9BbH8LpwRHce+cG3IszWjQkd/TxJFbHB/NKiGM12EnFtVfYze5m8Ls+L3Omix8EDZ/EBD52jmQZCtcp6HdcuLjRgWHXJ9abVMom3lm9d+UA7AjPgtZ8qLXytFZZJXWiQY25YPtJLEe37k+9r7x1vHNzL96IK+67nuf6ftu//son4NVPfx9s7Ww7vsMZpRblqnUyBhRC5cwvjkCU2LpCk9tWnSkIE7RyqvwW5ONfd/3kjU6Hl2gY9GB3N0zmsz5KQ3SGQyiyBPI0p3OMvaCdwRA8L7DhJVC51PVD7ng+7bVSKckYKSThY+ZM42dlscnnfXMjSwqTFMfYOmtMWWwaLRWd18DxILC/bjl4kgsr2rncQ9SHk0R8quMZ2Hw2HjuWeiQaTpIK0H+fdTi7M1WbfYDNdqADz3NZv99l3U5bO/xEV2XFlLYiGKig2aBVGAI8w8oJpKXkZaUYgsGyyKGUPgscc5vhjhZ6uBE75BNGRTUOIWX9NTU5BMDYC2VZZQf7+7M0zZMiy9ccx+k4QvCmL2L9cw1sVrRobKMGNxuGFTnRUMe3tcmWLTSZLKOug+fHUmjP75Jstth9RJUYuUHTVaW1SvPSTfJKFLLuNHuoN6ceRl3KonBt+x4xOApcF9b6XVjv9wjwEWLXy4vQrNC9n4FHWJJhc2n64jiHtu9SNavT8qAbBiihvBB4IXEepahSV04mBFBmkwmEYUgbPga7GLAhSI6iBAppKLViZsCHtFSnWvm4zvoC1J6tLs1jUp4lyxUXXriyA5/55Ktw9dJF8P2QXkPjZlJJaBbcCaLZwJL6Iit8b8Vq37SmZQgsLEXY0gXV+G8QyMSKLQa+RDHOCuphRYrrZB5BmmQEJLH/687+Idy6f8QOT6Z8Mk/YLEoYHvOVnQ2qEr9zb59EKFYvugFjaOFCDAYC+w0QpmtFXrb6JHvwddIBqcfv3DuEO3sjqv7WiSZhvTIf9tmEhXWKttY6qOx7OpvTc7AvCem+7+wekKdpuxVArxXCc5c24fs++RL8G9/5Aru8vUEJgIpA99KOhaoHqu7PxiKTXCjC6iXwt8eh7fnXjXkMy+NjbNED/HCvDiHMhX0O5xh8I8PN+KIwpn1t2AqsVgyvJ4lJ4AGcTmO4s3fE8PqRlyLj9lhrZhtbzo8PbjxqpXsSSuu5vZDM+IeCJwRIrp4EbNZ0XXiX9/vgTgIzzXeYtMRE1GAwIEDZbbfJqgIsLb9OfiIgpYSWh5pSLcAyNrbqErkBgYxUQlaln6Ypw/5LnH4kZcINvR1txLD3OUmNF6JrbZRoheAArXagL2xvFVsTKefxXSeenvpMG6o3JnzTeM7zJBa6P5Sc8VShuBX6cAKLMDesQbuWkWH8mpVCT0qPlMiNx3JGVWTzO/SIiFAIl/7NdEb6CpRc1WgF1AbOAg0Kqbc9ANZHYGLLY56SVSYYj9EbWqoqZ8uW5xHn/BDJDzpX4zbkd9pKRag+qEGnnLEJYwwrjyMAQEuKUnOWScbT86qhJPzm+/C5X/pl+Je/8ndgdHL3gRNufr7I2R+5Pv9epfXPA/Y8WxKTRko6A+RpYbMrSwsFs7RkaV6i3A+5/WrGcs308TzOx7sHUe/B0ezidJYG82ksHNfT289tJp/87A/m3/ODP9xqdzpOiXsPJtm6fRSb46qqoJhnUasAACAASURBVMpTFK0xCuTIBCkKUqlFAIoJVJozdh0R1udXa+sAQKeQQ7vbg1Z3QPYnYbcLvfV1QKsPPImT4yN9cO+wzNIkHY7mScaDPw7Fzq90g2oeujCWWoxkCUVeyFAr/V29trd+baenX706qNb6IV59h2vG6nWLMV7lpbw7T7PfL0sFuVR/Lk3LtSgrp1kmDyql71VS3TyeZPNb96cvHpzEV6TSA8dxPYczb/3yVe+Vz3wWrrz0qvY8VyHDRFGVssEq4WYSeFUEbnFaMpmazWRpvIXJkXsA/A/BCf8FiPYb+3u75f/0K38HGCtR2MdLZvOgLAtGBQXXB98PCM12+gPorQ3BcWkddQXjPGi1mR/4tI5WmFngXC0lxTHxx2cO8EK9D5hXs6Ye2a0u0eR1mXejdVDjcZP0HBnACtd/OvS1D3k8A5vPxhMPwTQLXdjRUn7yzon68xfb8AnhsgDVbjeGbb690Wd3dz2GFDisctQG6cQvscGvtrb0UmoHe7YKtI9QUlVVyWVN80PqpetAJ2yRwTGwmBYArpnR4bbsKsZZiA8dj09P4nk0Qs/GSla+Nj1Li3GucuTZf9bgktc9V2f2KVNTWqjQ6sWT+Pkv+jEfWiuR5RWfJyVHufJK6pqlpRsf/OySyRcCPvYvCEL6nQB21vuwNmgTMJPU08jIygIpYe9mT/OkwyTZFShpKnhI0UVqCvb0YfDX6YTg+y4FZ00AA3WvZ5ZBURQgplMj6IJgtWWqE7THcQGT6ZTopggkERShyi1WNpu5C7A0cDR47rXbcDAaw8HxCVXrXn7+Cnz6O1+D69d2qD84z3LIs1RXZcGw8ojqtXVvHx4TSSOyhkrXoiewMb+Nx4ih51mAhJFRXYGbjCeQjU4hznI4mSTw4GgEuwcjOByNCURjxTbLsSo4h8PRTExmMU/zXEvrc45VQ6wCY/9lfWmbn9dQTLHMsEzqLM7tssm5cS81f2xWKBXIQtHxONaCQavViujZeWLa0jRwaajAOT2XEYjE74XUMEOrpTyH02kED45OkA7MHhyfwr/1b36KvXJ1x9isIBCoFFGcYBFQmXMtrSBQHWgtvDoXIM5kpLVeVc1YAaV15dmImxl/RG7IemxJrq8ViWz/HiC1rba6WYJYZkB2mhUwiWL7mQVUok6EmXmC55DOq3ziwo85Qm6Ez/RKsuCJR5MW+2493ecCzfrckdCXNKrlT3ro7+lIn8KoK7AIGD2sxAqjNI0Ac204pOtCNgs26aCaSYnG2wvuguehBGnpyJJqVqZGjup5eY6ejUgxZJTYshR4TK5MpjEcnkxgrY8iWW0zD/E/6hfn0nO91HUFVii72pZkjOuyBF0VWhk1zwyYQEDZ1aADm19GRNw3uViytfr/2XuzJ7uOM0/sy8yz3HPufm9tKKCAArGSAMFF4iKKkshptXo04+6JmHb0y8zDvPl/8Ivf7QhHjDvCMWGPHW7HdHSPW47pkO0edw8Vo9bSQ4kSdwAk9gJQ+3b3s2am4/syz61bBRAESEielpARJbEKdzlLnsxv+S3cFjqQsOYC08iBk2ASSsM1p6RUm4M3CStO3NA47YPQWiEc3KOVlRUMFaIgYuIomZH9bmqta6Zqhu9Ti0pTBy3joHtcq23GWZQzsipxauUQjRNReXSDcd5LwM0qveFOQ6mlBPRqzhjCc3scRWS0jpXWEpO6qfnD8LV//M9gdflGfPGdXyx31pbixlzrlD3vg3MKn0XkHkpcJ3Z6SBcY8Zl6iWFBnCmd9qKk/+mtTu/SzR1/q5erUsmDWrUM1fYMnH/1NXn+pVeyZqOu84Jjgumr54IbhnSvFCFBctDc5FvILcTjNEVbLKKmSuc5cvkVSBlImZlNgTtWpIGbRBV9XFoK2keOwuLTF+DujWuw9MklNopus8EocpJRzPO19WQQv9PbvNva+f3nalfeeKbWibj7g7W7fT5I8pONqiePztdeP3O0IRbna8NK2RNaqTBNFR/FKYsS7HzqeLsb95fWe3K3F7vDONO9UQrDKAuVggpnrJmmcrE7TEWU5icZY4uuYG0pUyZElc0uHIXFs89AtV4h6XJZoKYmn2AF4Ooc3KyneNqPiKvJmGvnVQKarQCTb0mZ/5nwg1/C3GwW7GzBzZ/+FFr1Mmih0ygayTRORJZkgErOOKqNFkwdXoBqcwqKeYgdexR4wmc0i2KkzEjmotPQGMqG89STOfGqvxSk+9EHoyQTil4N2V79eo/gcYwnyeaT8cBR0OWZhnoQVs54rngpy0Zfl4qdG+V8OslUVHYYq4eeaNVDhsE82IAFq7C5DcqEMGujiXsomGJZLnWa5rlEvIJUnswkk54E7hpBkUYtIMVOGAduhuKGUBzMYBkalGst8iwP8EegnBtnSk22EcbcJRiHtgcTA3yQPc72BdETihzFX/Y6E7CXdO5Fd/uG7dvpvVbE35NRXKsky/l2P4HtQarTXE4ml/vgYvcZ41og3nrXcQnGemiqTn5w2gZcaIWCBQVG4k33h1g+6tiD5RoepOEnCoJPLh5qwyetOqxsbMMwSj6zU1LYnhQD+Z+ojIEBZeh7ZHeCSRe3Yg5YlUbBq4PHL8ibrAg+GdTKAZw8dhi++twZOH/2BFQrAR1jFmZaZpnMskykacIw4S2KLnmWkg+tMpUaywO1iQCzcExm/RYFK2C4lKAhpDROE+iPIrixvAbLazukAotQ2dWtLiVeKC5CXBHfgTAoUWBPnMo8Z0YZ0zwxg1E8Pi9uu2vMwn5JdTfffy0fx73M5YP386LrOKkcXHxrnuPfM3pN6CMPxqfuMhbBBmkCV2+vwWCUYMELnG8yOHF0lh7hta0ObHb6UA59ONRukrKtgStJ4x3KCTHHi47mGEqvTQJYFKH0uJCh93UX7cUZiweB6WYiTgNxA4jb4kVgi6eolB6jeidFnbg1uBsMI9jtUSFOOwJ9XzN270r06MwfLKKgSjJ24xPizT70+7UxwafXfymRHsw1aA58zjyYEJZh8IDk9XEPTCJRoRoh94gcQF43JpyCeJfKcL2DgLqc+0A0bA9CXXS9sWuFOR+KiaMkipk7JshVKmdZmnKV4581R45zUUTC5w6fb/TbPDTdoGeY1hyCeAOpyY9SxUajmGdpzJjttiNsnQTzkoTHURTKXLYE7tuMIefXMVOWUsbQJpEobYZVwYApjYkr4ptQqaw8PlDApFLjLupaRVffJqSOPRzKWhmDOpj6CVJiSpqRLy01bRmDAAssSJfBXNoAKllJ4d9AJwxYWXGY16BTBkwxxnzuOJiApUpLbpTUUM4oHyil72rXueNw1lVKdgejfLnk+suMqx7jfHvU392eOXysJ5kb3/j0Rsnxl0/laXqOu14DLw23hRJihNhqkOCcgKudXgqbu4kTHVJOJeQai6dbnbjyydJOcGu116rXKv7xo7PAghbMnXhWXXj1G6w+NePFCrgsJkJRvLJoKhSIwsb8uO7k+qCDsJg5WHRAD7guxjtKaU/K3ACQSLXJTntu+J2cJB81qa6ypSXodjowGg64EI52PEfnWRJs3rn11Obq3dNXPipd3fnumSt/9C+evyrXR7zeLN1+de7w4lw1PDld96cq5ZL2PU/EuUq3dvpyabnj7fQTL0q0u92NDy9vDmq7/diNk6SRJHkJC3Oe48x4LluUCksZynccVhGcl4D87Ryottv6yKmn2dTcIaJLSQ1csT1oKuoOcJ0aiVjsXmfDdZH1FKh0DohKxbuQwxIcrv0E6vAXIknf+fSHK+mf//FPQEVDGPTXIR2AbDdm+plKdvI8z3GfzhKzPzampmBmfgGqtfo45otzybbX1mBncw3SJMJiSW5uPxsh9EhoRTDynNO8z34da8xv2niSbD4ZnzmsYbmXZ/lTINWrnuu8Alyf0FIeAQ7NKBfRINNp1UdnOxYKG3xCsYAK43cHhfqjTfaE5Uii58kwSvUozZhneXZSKeVooTzX5VPtKsckZXVjB+X9tZXowc1pkquBrmVVzXADVAGSXmCiC4DHgBYJGIAbjZuJaMRQzcF3UQQGIVskPTnGiY6blxOwvz1bhAdcN7Cn+sXsRv5zGCxJle4MEzWMMpShLXSTCtLmZ4FAwNITbZCNFf4Q5qdalPAJLnSW5UZQHpMw4Tx2YllRTiCTfwt/RDXaZ47Pw8r6LnX0Nrv9h9Y+JDIHwrAxcQ08EMhxxOBQ7y+AT04RHCiGg4ktCjVi1Xp+ugXPnj4OZ546AlPtKjYWIMWqredrz/N1CbV+lWIIJ89lBjl6UWLilyC8PAdJf0OIcG65kKaTic9ZluXE3UOIK272CEnd3OnB6tY27HT6cHlpBdbWd0jEBxNHTLb3OIgcwsAj8SC8u9gt0fv4yvvPURcXxfqOmp+i0/frHcSblPd+r7ZdUrDKNMizHcr9BYbVrV34219cglrZhyjLSCn3k1t34cbyFsxN1eEbz5+FM8cO0zXOAVAhNjU+hWSMP74iBczeKPlyO+fuMyO0Fbw40KW1MEUJVINB5EbBXZx4nblRdK91we1FnKMyokt4z7D7pyY/WxWc0keH0H6ZrE2Y64D1w9SiHLwvwqNkNuF8iINnsMfhcw5YqzzWgXPdtxzLIAwpyUQeZhgEBJfltnBWFEAIRm/20H01x/1ertokmzIDrMeio4PNOBBKQv8l01QojurdjFRnsdyE34FABhToKmDomJy7Qk/oLWgeZVBC9ROllTCes5icIBBTQhyN2LDXCdI48j2/hKolEiR1ISu2AYcf5qBTEHlXaCyGYGWLtgGsKpdMW98YgxHRzaI6sVvJGBQdUatqZinRRsKBW48Xe+90CAzqRm6GTt8Z+2qzcffTp79pSBmq0hsWo9GK1jrA5JdRpijKiWAtweAcijZLrVFAdt13nTXG9UhpvRbFg5tZHq8qLXcOLR51srTz7GB76zlQuso0DIBDYwLFIywNnnQLUP23P0rlKM6iXPoq19qNEzmHbiiuw91qKODQXAtmT35Fz539iqo167q/vQGMu9IPy9rxfaOvJotgwRbPoPC5gnEhz84YyQVPmbkeJQxblPbHU4pp+z5UMGKmNbt88zZc/PlP4NoH78Ha0i2dJ6l0HT/O8wx9WyOX6xow+fTFmzvv/s9/efPq4WOzulbL5MmnvI2ZYOGTckmtKKXaozgv70SJs9HL08vXd/XN2xt6GGdMKeaNEjmbSZhzgwohSHgqKdl1BCs5jlMVTHO01SZdAKVwpY7L1Vp27Ow5dur5F4NqverpsQ7GxED6TR5TUS0X5VXJnbdASx9AvgnaQUjQTZip/uVffu/t739ya/XS4blWeuliB77/1kUInRE05uug4lR7tXKWDdNOniQRMN1AX1LPD6DabEG11QS/VKKFCffC7vYu3Lj4PizfuKazLM25g4xcqgrh5RxpYF2l1TCFJH8cxdTfxvEk2XwyxmPvIRpX5ZsyVy/mufwmB/0KaHVcK1z8OXpf5oOcQz8Toq0V8y08COWxXWvSP0lqxm5DAcmi1zgObpB8dzBy6sMSq4Zotot0DTlUjiM5Z+VWveoePzIDd9e3WZKg+qTOjPIJq45JCyZawyqq8ZbURbBhwmIhuMYNGVVyUwqoYc8/D4y/JnJdaCN3GBHhx4sf26/yU6TLhbKdAZMWHdB9CxDf07F9uDEOTSdBaL+iwYrGo9VFOfht+A9o3pxmioYRfWdIhMTrLyxM+TNPzlYAiOt4aLoG060alDxPjSmNtrpPVdnH3Pg1nUk5DrIxUcQuxNxUE146d5w4fEtrm9DLDPewwJXpz+nIcZKdV1BASw+mYmPBF3th8HW9wYh+r5VDODzXhhNH56BVq1DyKdUYoshNR5JxVxjPSYOUwp6C4epk2OFEDk+eEewWVXG55Sr2RyNYXt+BT2+twK3VTZNMxgmsb/dgZX2LBHZ6w4REkEgV187iYuB3jaLEKMxaD8o9YMD+cxwnmuP33j/Z+3WMh9rvGSNeW6c3oIQMz6BkO0PIuVrb3oWff3xNdwaR7g5G7PbKBlvZ7ECzXiauIN4rLBIgl1tiN6awwdlnjQNjUTGmATN1VfDUDB1VW29YdvDQxt1ZsldAAVDYEw/bzwG1CmnWZgZs2Q07aVjMcR2H3S/Z/6JBEXbTpVSP9H57rIiEZEKg04NO7dwoLE8e+UG/X8HjM17Gvqxa6T0fSqrD1oyLiqMOdTERjo8QWeJBl0pUjBC26KKLxNL66RZryueJoDmOS+uL1ZIl2gknqJz1hNWKY7qFdiY52SfllFwg/71Cia5TBPTjOYVWY6OUsQxXFLekHccVubUqom9CyLqSkCSRSpMYu51I5oxtPaNkSqWEHvJNDokqscyh5JPmqTLQWErEGLcqztIkqNoxqsg0LfNiu2UmB2aW18mY5XQaAVPObPFFgtE/8e1tMPkXwieN9Ki2KstGDNos4agkG2uDpEcYxkAwHYKGGYXnwph0PNGQWj/FjHjSKM3izTQXAy/wh4eOHesnUb+cjuKTyWjQ4oIM4ay3FM0r26XXeIIil5INojTrDbPRVF1KJngrDJzSTD0wfs4lDlP1Ehyea0IQ+rK/u9WJhv2R65ZajZnDfm12TgiHWZsqiwTS+9k8sAeYIrtqdKVTmooA7ng62eI3U7nZf5hA7iRsra7CpZ//nf7Zf/j3sHn3DtPkyQtJnqUreZr8XCvY8Xz/kO/xI4da7LnVjXj9b95a7f2zP6pC5ifOzoZS/VqexJnimzsjb2U7Zis7sX97edvd3ulgpYOE0vFmBhUsuJRAY7HE0gMcz5OO5+ZK5kLJ2MH9GC1bhBCq3poaLZw8w6YOH/EQGS7Hmuj7Hj7Q3NMKubBcrHDH/Sl3sSghXPD9eaiXf7i6svNn/90fv3Xl3Q+X4PlzJ6A6MwULh5uQDbHg5ZYYU42d7u4xFut2lsRQrtdh6tAR+nivFCqvFEpuYLVMSeX0tjdh7fZN1dncTFVONjPYXUfILBZVIgUQaSV3HAoin4wvMp4km08GDU1m864NgnjIHWdRa3hFSfkGZ3AGCf0Io6GKIwFVmZczVk8Vd5VWLnYQEbKGFgQedQOKQJTZoLRQaDRfZmTbU9jtR3y2nppODqIdsky6novkbBZ4HsGD5qeb6LmnoyTNLUwwOBC8oFi8cxC6xchXX6dKKiy1+6DvV2E3qo7Ee+AH2pj3AYsWAcSDOjl2Z/0iWdSjpppFgPWonQMNe1AQd78SpKakJMnRj1LyXKENB1pjUiChJmC09/9gm4SjAu1Uo4r3TtcqgQnIlRw3fIUV4JlM0r70mJBMN11roziLHW38nsX5GfjqM8fh2tIqfLK0QvdQMJNE5p9Dw8DksT+Mxkpw915QEzUgR7Tku8SBxJ9KWIKTR+fgmVMLcOroHCXgaCPCxR7Xl+jIsOdPhpfYQXYyEyRmUdLh2EcTYb0J+fONYHVtCy7dWILL1+/A5RvLpOiKarBojxBFmbFguc+RFpt7ce0NlK/odBpRHhTooUjxvp8B9n36V2mP9+UHFk3QZskKsgS+C7imILTf+OMDrGx1VGcw0mhZEMUpw+Qc7Vt+GJTg6KFpmG5W8J6KKMsDygeLCFDv7xgiJBqUzrlx0UQZJ3qmLCeN1CcLHqgZzCrXEuIeA3bB2UQZg4uxlYos4AS0RgmCmeFrMHGuVU1XTX4+3HQ8xiq5E/WwSSi7NFWaL3T5ca0ApZ1JrawJS5CHftgfMVEuOpqPxV8T1ybsVmLXEq8t/o7F0ZL1ykR4rGfXrkLgqkgyWXEtH7KIRgVa9NpFoSi05VKWs43VB1V8Fnr6O0AQAsYVA0nLRyX0TaAPzAiN2U45LtSjXOt+5qRSBJkXVjzP90UamTlFPEDLVFMSEYZprqTCNhQ3/prkHVnUWjl19Rl0NZAafN1e50ybro9gxb5q0P14r3N05dATnE5m/haDgdUCJZQMMmAoe0I+lXqP5aJ50ePTCCs2vzO2t9i4GmlsRHGmWCBkpIxKyWdma1GGQ4pdStARFabHWCMWMuDHbBdVeEFlWGtNx6XwdjMa9mqMGOBQUGCKOasMQguws5nvdOO8049LaVaGssvzZsWPjh6qu2HJdepVFxbaLlTZgEXdNdkd5IPtrW3kjDZGqJLtuUl9airBmrxUUMYCPkzUwCeenfH81hN2Pvvo8AXSATPoYQQ3r34KV9/7Bdy58gkMOrsaBVXR05MD63HB3s5l9n+ko1TKXH9npPLzGvKX5mf51Zkp/4Nuxwsv3kou3L55+RUncA9J4Yokd3R3JNn2do/vbnd5EsdWvEuA53v0vVicQsuVWhO7hSG4JV9lWap3NzZ0PBoRFJxkY81+w3s72+LW5UssP3Eia05NIWsKdxJTuKDBQTkhMkfiANRmyWE7krG7OoMozjOxvbl+8c//7Ke3uv2oenJxptxuBQx9raNRTJ4/OoMWSPZi3Bk8LRz3BeE4ZT8IsWhEXM9Ko5VVm+1UOAIfFT9XqhaPhiwaDXKZZhny5ammrscWOGWzT/NRTg39J53NLzKeJJtPBg3cNIMSaQPUAPjXOBf/iMn8eQ2qrYH7hlBAG4Nv+nlY8WE+WgYlOeiKz4nrVK+E4PkupFFM3S3k6RH3J5Mk5iEIAqRJ3CLmDNC/CSEYCPlCbkriJmWv5HNXkF0mcTbLYUn7vquSTNoGo9qXFVoG27iYbPFtWAFFCGiUyczPJUJw9uxK2F5SCK6LiqmCVro97tfegsKgMDJmYyVaU0k2MNzHgaoY7zJs/I2f8wY6TmV9MCYBZ58b0BWODrb5PB6F8jB6vSVpruM050iIt58siBNDvJzPbDqMlS+QN+K5rq5XQo1da6wWGx6lSXKQy+mYjOqRrtODBiG4HaPwWEwQk+RqgpqWPA/OP7UAX7twihLq5a1dmofK2nQ8aBSCMPtOlk1w9ZRh9mHC4HsuJWmecuDQVAtefOYpeP35s3AaxWgcBzKZgccPWAHeR8qcEj9DtKLj7w5i2On2YHunA0ur6/DRpWtw8eoSrGxuw8Z2B7Z2umQ5MjkEdZBNcWeS14hJcbvZgLBSoUAZrV263R6JJGHHA7stFFmpR+twPe6xJ8rzcAnIA70lkQiGPNk8t/6pHnY8eX8UaeRwcuq4IyxZw+Wby/DuJzfgmRNHYHE+5E6uINNWCGhStIjtCQMRl42YVJo0RY0V6l4yVyTy2kL1Oanm7q0neoLjyZlJZFShgIwewIzZVc6I/5RcbjmBBlL7MINZ4SBBHE9JnGmj3JuSQukXHXoiCUcmBLOu+Q/foHyosW+5mvj/x7aIYBBdr1ah1WpBvVajBLOAL5trJ+gHJqDaAPvn5sF5+sDuphXRQwoJdqgEU+P3FIyPAoWLnLsSFXO58gTwRrUE7WYVKpUyWUsJ7mAHHiLpQF9yGWk/AofnfrnCvVLgEcwb4dbGMhnvP8sz5IPmRkZcM1QiRb6l1EaTxJjmM0rg0H9Q0JFpOsgMA3tTm7bwb6ULm4jE2N6gkJDdsA1vU9pTzkGj0wX6VzKUBy0RqJSNkzuyCLbxqSmMGrQFJqicuqaMHlepDQwAuZ/IFc2Q12mofvRQZjZhkMCwHUzng8cfUMJKHpoKwU0+8hL9oMSN+BLtdUWlnI3jCsYwC5EElhjJuDdIm3Gal8olpxt4TrZ4qNY4MltxQo/pUoCfvs1i6YrhVtpcvb1aHQ6H7s7W5mgUJfnCqWeixvS08gMv4GIsNKsO7N9FKPKZezpZSwncHxQsXf0UfvL978G199+DUrnM0PIKERdhuQZTc3O92YWFS4Pe8J3L77zjry1d/2qa5RhqzT216J9sTfHs1m1+4uNL8h/e2tp6I8/dRYQtN6anWRBUtMxzhsJ7riOU5/usUq8zSi79Eq0lYbUKzalpXZ9qQ1CpO52dHefT999jo8GA6B/ccbBiwjpbm+4Hf/e3wertm+6ZF76an3v568n03Kx0PQcr0kIXmgBkugkyT+PuykaP7V4bQm957era8vbonQ+WxC+vLn+tFLgnKkFpSkklZb+XYKIoXHcgE8UZON8UDjvrev6i53llRAWt3Limpg4fSQ4/dTKZOXpU4r1EDSKptUpGQznq9SPkNhurT1JgxulFQlhcQYaGqJKm0ZNk84uMJ8nmk4GDO45bkVLN+77/vJLqu3mevYpVH8bYiFGiiZLo2jUme0wULb4013KYMV53OYmezE7VYGmtRPwx9CisVgOC/uV5Qg8pJpwFr4UULBFiAcomBFI4cSZkmIPyfVphlbU1QGnsPJe+TYjGQhimuElcTqn3lOR8EhAw/E7cGT1Fm9TevdZWIh4Dr7LHIUCNPGbAQWMZoH0Vaj1OTqlsSsmmhC/juXTPOODhP/5iKA5oYhh8EUd0nzl3lJ5XuAn7nxd82SDG229ebb5Ckbm/YnEqWWrUnYqCsBoHduYNeEOl7Yzuz5ysJxd2c7Dw4HsuY/cs0YWVzONLNvGzPNenRDYeB+6cuE2kMpfncGimCW++fJ5O5oe/uAjX76zv80s8GBk/yJYF/w1VBwUTMIpjCuJGSQK5kiRwhYHg/GwLTi8eglMLc1AvB3RthKUl3e8zi25jAVHNtITRKIKbd9bgg09uwOVrS3B7ZRPurG9Bp9ujO4PPUH+E3E5lEnhtvWIRi+aahD5WmRVMQl/cAA4fPgzPXzgPJ06egCyXsLyyAst3l2FpaQlWVlbRB/ex3pv7XDzjyf25L0O4qACZ7++y3i/vLLKO+3H9GKl3SkKC4xJWr4bQqJUhTXM2GEaECSvweziiOIErt9fg5so2HJ6Zpk49cmfZ2PpUjw+EutqU1HNHI+V87JtEqwYr/E65FRQyHeOczsfhgr4Tu+AoPIV8MEowEV2Q5WMNsiD0SWTKcwwiQISm29ashNCuV6go1z9gS3Pw2hTXgRJXDmBSjKJg9lgDKIS+xBY9EdyzPnzx8SvlwOO1qdVqMD83Fc/aPgAAIABJREFUB/VGg/iZeK2NTZIynUf2+aHmQU6mtj6q+sC8LV6G8wARC0LnIJwCubln7k8SVTJnJArDhS4FoS77XFcrHjRqFVarIaTXhVQpGA5z3UkdvZM4KtaegyBXI6Gg94RpuMBWOgjMETX5rThc8BztLEBxa6WIOws9KmZP1dAwvEI6asckmtTFl1Q7QeiseTRwB42tmq6rTfcQ27UOGfFTgZQ2Tdd4MdMcEYYbR7PQQsm1T/+tSKglM01/+jxMcDODBqCHPbcVH+y0RtrAQ/BsR4BCQozVQUMAWo9wy0GFGmwm2+4hblJDWjewres4HhPc2u7uWVnt02HHk/f8hInSIM65n+bIftVKOMyvCOE6wlfE7FSSi6yn/VQwGKS1UXdXbm3t7myvrY221tan1u/caS2ee1YdPXXardQqioPoKU5LECbLJVbUbiwI5bPmfmEP0t3dgmsfvg/X3n8XttfXYPrwEQjCCgtKIcwdPaafe/2N0nOvvX7GYe75v/6z//X2W3/+J5ubaxvr5VJduX7w/Mag/w/ieOfsQKbz5frcjALh9nY3eW97C7JyRFzM1swMFidZvdmE6bl5aExPg+cZYSwvDLQfBAztRJpzC1Swq7RnqfB7+9NLEA36XOZ5aTTsuVmWikF3V+xurbPt1bts8ekLPKzWOK1rvoswV/BLAag8zbZu33avfvju2ZtXPj29cWeJb65uyEE/mvICcbpVry5yxkq5VDvA2JZbKklt+POyVKkutKdnFoTrtUf9Du9ub0M8GvATzQul+adOOLV6tYgjuVKyP+z1tvs72zJN4mnOEOQM9FmWPz2QXOy4Mo4rcRcUe5Bt8JPxWeNJsvlkuErJw/Xa1FfSPH01Go5eYMBOoEI0MBZp2idxkSduhimsa5O4Kc2yVHOIM81zxaCMBtatGgVFK7BrE7I9DzqECkmbPJLFCJZAkxz6wwS7aCr0fZ7lGUPJez/PtXTQbIVhVxNclIJTiunxzmkG00Y6A+M3YBCTKIERJ8BNWkhlvN7u4aqB6b65yMehk1Goh7f3CqtqyqAQ5GA2QTL/rMawpaIA+tDzqMAe8gd2IS2kyAYk/P6dGjovWUhx227C545JXYHJ06WdP0eriBy9xCBVmqk9RSUMIFPcqPGCK7OZY0BZZQSv2n+CmMQjn6xeKTNUUDTqKGysxlmIqgDcv+0xmXeP7VQedFGtxyCKcni+D0J4WBY188z6fiKcFgPIU4vzFMh3UFRnFMPmdpeCSW4DRW1VIsFCWzXbC/wO3CNrYbF3DspK1R+dnYazx+eJp3l0bpqOC7tPHnnr3QvFKWw2ODdzcDSKYbfbBzRjv3l3Fd5+/xK8d+k63FxagdWtHSrJHjl0CBYX5oiPmOcMwrAM1XJACMjBcEg8TShgv2lGCc/s7CxcuHCBfl566SU4eeIEdDodWFldJSjmjRvX4fvf/z5cvHiRbGB+ZaNo13zug8NITMkUerCTZ7QXM3mv9+K4KrOvm2iBeTZwLJFljcBWBvG5HcZZNQwYgvEGkeG7AvHoBHT6I7i1vA7nnjoMM606dSWxc0wrgS7iTztPCt6esX0wFDbLUccgCp8H7BziZyJUFwsfUZJSAowiPz2rLIsQ3q1Onzi02MZBqCx2IMMwgOl2DZqVMtTDEBbmpuDooTa0G1UqZHxw5TbxdT8raZx4mIzvKxhv0QIs+JgFng52G/cREiYVWYvC42Md7F4Ow8Mk06jAO9VqwfTUFPi+P+GPaqN98eAgsziP/fxtm9DbeZvne7xf44mq0WsRouEQBMuBBy4VFCaPmSiNCt+bsySK0FNEo9hrUPJ0bxTrYZKxza0uokf0SHId80rGg6b0K46rlC5lacxVLhUSBblwNOntIHrBC5RfCqkuJaWSjusozpln8ZwDpkkZFx3+Ms204dwjptf0W/0Cj8OKlIcSPkK+IMxVUWJpRLUKYCi3LAeshHkaE1i0PNH0oYzeYyzOhM3PJXUfEbKLNBna7zTWSBJUi9WmKzpCqgxxPhklnBkZFwPGAmiPQmqyKF8zRKoNnRODhJEaLqG1YgRaaXwtqvFiqkiyQ4Uat60ZMaQ+YibOkGA7FSte66Wc91POmxLqLiXHmuUog222DM5lFHtab1W0TP2sH0S7W9DtD9zNtZVwbelWsL2+DKNeJ589cjRtz8zuBLWKFkLgefmEptZ7tTO4T4ENW7J4kXY7Pbhx8WNYvn6FuP1o8YE8xSCswpETp/T5174RPf3ya2GrVv12CcC98OyJX2x8fGzmmpNUM+21N7eH57c2B/NSqbYTVFRrri6F46k8Hepht0uToT1/CGYOH4bZ+QXWnJ6Gaq1GasxF8UpZ+ElQbUG5NqXDWkWVmy0oV8vs554Hn7z3cxh0u9wDj54OLKitL93iuxur3tUP36MOKRMOJZnIr0Q47rDfDVZu3riwubpyOomTSq6ki9JI9YYfYGkAHyeFMGmGysQk2evleT4XhOXa8afPOc9+7fVA5tK59sEvVZ6koj13iJ17+XV//tiib6QgAVWz+vEwurF2Z2l5d2O9rpVscI9Mnp0C1c04TxjnXVCiq7hrmMNPxiOPJ8nmb+9gtur8wjDpfotp8YzW7Dhj/JhCSxETg5S0hpqtTjKjMlcwBCg5wd8hV2YH4Q6DSjmEdrMOM40B9KOYAiid28DQQobABmdolo6B1nCUKLRAUaC40spJ0pQFRChHKKbD6pWybtTKGoVOEE5oF/OCcaRhT4AA3bEdmyPgHl2eSOz2jUKgw6FkhFNgp42bvjlGXYip7O04DCaCsi/eCVDM+oTqB8BjJoR79MFg7cDg9oY4+t4g75EG4ZWkhM4gge1eTNxCVdC6sCEHVAoXRqGBlAixw+2OD9nuihgpYFLXqJahUvKN+iGXZM/BNBt7GD5oFN6E9oMf6nprC2NFTlUcBhAN1bjDZ/ZCBcjvx44Q8ii/8+qzEPgCfvbhNbi1vAlJlt1zqYsbdc/tQQgkN4b8UmVQcgQkVhgIoeRPHZmBl549Ccfmp6FVr9NnCscjY2nO7w3uCZqJ3bckhd5gCLeXN+Di1Vvw0ZVbcOn6LbhxexX6gxFE6AWKkOBSCNVqGer1uinPkniIomQTo8JOr0/PCgoMYdLY7aOlRwivvfYa/N53vwsXnnsOjswfhjAIYTAcwLHFRYIMbm09R9dqMBjA9evXH38icOB+fd6gjrQuvLw5WUzggz3E63CfYyu6t8J6zuUTQjeIqghKHoS+C81amdaqgRuBExtBM+wQY3fR+jRAtz+ET28uw9nFeWjUy1bULLdSIQfgkdjekbaDiZBx0+4x/Ff0lpUS7mzswMdXb0OvPwTXd6DXj8huZbcfkS1LdzSC7mAInX5EHHYsPBBMFotzDoNmtQKNctnOr1l48elFODLTgsVDU6S2vLHTfWB3s7g+cvK639e56UuPYn3w75N4QrVahZmZGZpvWOjY2toC9JL8Mt1VVkDGMbFHPiUptGY0/x/2c/G4ms0mdWomOawP+/7P5J/bazxOMi0EF4W/sLDa6/cgGg2g4rsQlpyJZX//wAA9jiJI44gN+wIG/QHb3AlQ6JNUqtGyCLnnpXITGtOHIKzVeZ5lTnd7Q2dpqg1nDXt/kmpoUmZsNOjz3e117G3qoFyR6H7PHScTjhNx7LNy5mGOyjWPyW+eER9X2X2ngLly+7fckJRJYCg2okI6xM6pgbRSUdSxlBf01URYrs1+C5VZFKjFPYU4lsomnWBhtdp0Iw20lyxWgPUBfTiZNuI5BnGDr2wzzcr0+QBDgvASEodavAJhwiRwxDRySVG8R7h+gO5G5PW7J1uP67Zjdv8chUiJ/OlpJng39+Tm0OGtuhB1XzEqTkgphKmmKCHzdQdGH7Y8GTWd0UmddBcH/X6I6jejbg9pQ3l/Zyefnj/CTz73fOvQ0ad4tT1VCsohcw2RkSk5tkIaTyYjFsXRAgDyNIPbly/CL3/4Fty4+CE9R8T1dz04dPw4vPit34ETz76QVxo11E9vpFnvZTfZOdaquOVmszLb6ee13jBxozjHe81KufCcUkcF5SqvNlts4cRZmD9+AqbnD0OtVScuM3KMcW3C+Ygq6XhbiT5SrkCl0QbHLyGxc6XRrOtzr3x9Ns+UH8Vx59r7v+jkWSqkkrOQJhWE1+I6OejsQh6WQWYZjIZDyhq5I1Q8GJaS0fAken4TVpuJLeaIITO84S5o+BEA3AXQc7nUzSzJusDU8tThhfBr3/7u6fNf/1Y7TdOsPTvHZ44cVUGlKk49/1VerjVwiR8KgJ4DcKe/s/3unauX1c7G+lc442aDNm1PsytrhdC41BFeqsXUE87mFxxPks3fzuFoJc9Gvd3nS0HpZZDsZQ26bDj6lCsgmMRwJhjJk0vGxuZfWG1EVVIPEYq2bKjRlBchYe16RZ0+Nk/Ejuu31zhaMExCzvYNDVaMJocUgwKrqIcLKG7EuOkHvgPVIICgFGDyyZLMKGpYwVfaPfcyE+KUClK9Y1pZyKc1/jQNqkkRWddhZIsirFULJwDTA4CsfK9LIi1nU+s9HujDDGJd6f1dxQeNh3gd+wz47aMcE+W2GISOsMsyQoGZnJQPJ/RMCMY00cYNDFx5f8JsBBQx4UOlzJKV5t/jCRW1ATZ5Be5/8I/ULi4+CZNA1/cZyppnSQRJImlDK7ra2L1D+k7Zc+CZ44ehHPikVPvOx9fh0vVlKo5IlX+utCWeNPKkUAkSbTNQFOgQwhyFA5VyAO1mjexe5qYaZLaOiWGpXKau62TwWiRGcZzCbq9P/Mvrt1bg0pVb8P7l63Dp2k1Y3+pS4IwXCrvEU6061Kt16r4Mh4lRpaxWEBIKGv0/GUCtWqOAAOGgqDY5P38ILjz3Arzx5pvwla+8CFNTU6bj5rrQDtrQkA0KzDFR/+Y3vwmXL1+G5eVlQEGL/79HcbnwLibYoSURq8/mF+K0qoUBWVRs93oEUaXnFv1H44Tm4nSzzhbm2gwVg1FYaTCMqfBV1AAw2dva7cOlG3epQ33y2BxM1aum8zyuQE10UQ2nl0ifHI2gkM9t8AY0p64srcAvLl2Hi9fvwihOoVr2IUsl3N3Yge3ugDqdihRC9T4u4GTa0e/HcIdtQ6Vcgo3dHqzv7sLZY/Mw127C4ZkWzLQa0I+S+3gJ7L86pATNJtAZv5rw6Z4k08DOS3D06FHqqh87dozm16WLF+GjDz+E5dVVmoNfZOBzVatUoFatEu8SO/s7nQ4lcw8zUGkWeZoYTINFQnzZURQjijmCRU3i5+E6G8fQ7XWh1+9TgI0Kd4Fbe6CunFZmLUMcap7nGtedTnfIihUY53We5cwdxN5oNERLJY7K1Wnh3askk6lkynhuMJ0mPE0S9F2Ewc6m8MIQhOsoIVzm++WKW/KZ53nC9UvSL1WY47muQOIowmfZmLJSiAMZo0jTVua2IYsVF2mTy8i+rmqlcAYGRs9Cy4tLLNrGYcZrlh4ozpjNvrF7ybBz6TK0STGvl1oTNBdFhPC7sX+MIkYIwa0jHBWMj6fPQLeAvDtpuMzQPxAOjAguzgXzhSBVLpDWq5IUgosDIcNN4mZQso42VVHGoZMH0FchIzk1jQIHOQQ84x6nBLzEQHseV3fKvs5Cn0cuY1O5zGfTJG9trt4RvZ0ttru+xrkr3GQ0YrVWm7fnFmDq8DyUPEGtN1VY84KVKUKFKE5/5+sry/zyOz+Dax+8C4NeFxNjqLbacObFl+HFN38XFp+5wKoI3QAWuErGbrQZell3AVTeTDMoJ5lycik5apth/zrBopuUYm56Wh899QyceO4FmF04CgjHzbMYRr0diLsdTJTBSDiYiMkLq1CZmoGw1sI5hMjwBCnC9UYtff7rr/e1zH886Oy8c+3j949x7vxTBVBBb9EwrEBQq1FHMx4MQeYdiOMIn79UYhCotc8Y9zB555o60thEVbnSdxIpf5yk2XKeq7N5kpVlml2cXzxx6+Xv/tO5l3739/7Lku8fz0H7vv+qN3/8pMLOfb09LRnna1LCzZKAdVfKT7ob6x+s3br1VJpEL1eqdddiqKHwGjBRMZbzH79V22/TeJJs/pYNxliTaTjPNPs2cPaslLIFGqYo2TSQmMAs3gRl0RbRyIwmO8ViBckfYz6H9NKV0MNMqYqreBj4rN2qqtKqT/kpBuO48hxUS2TWIF4ZX0ARpzlDriBuFxIxPVLSuoKqf9VywIi3Jal7wByncO2kTYhb1Jou1DoQJmOronseb9pyEwtYnfWEQyk9bIJwNIXibLxTSjAWA2zPtcB8ITcYI+xU5LnxcaRy6ENWwL9s9/FXMqwSb55KGEQZ/eRajnlGB25b8R9sv4otjJVaETKIBuPVMCAxDWU3bMJJaZP1j9U2i6vyRQcbs1tI+cHY7zhQwu5mNIQsyazEfCFEaIJsch33HOpATtUrcGR2Cg7PXYeLV2/DysYuRAhjVBIGw4RCIJzHBJXUxkcWE+npepU6XNiJrFYQ2jgNrWqZhLKOH5qB6UbdBMC1KlRqtXGiWcwTvDYecSo5bEc9Si7f/egKfHz1Jly5uQzLq5vU6ZpMPjzHg8WFw9CoVWEU59Thy+nvDsGajLJsRpxAFxzq9uBDe+zoUXjzd96Er33tNRI+2dzcpK5Se3oa5g8dosTVJL4C5ubm4OTJkxR8/yqSTV4I5YDl9j7s7UfIISUjal8+dbBmQd6FWQ4pRfa2l0HcOGP3EGMi2R3AVG9AxQi0fkEYKyb0RRMdjw27mOvbXbi9ugWdzggTVILX5pnxtGSF1UUBlyTnQE5cW5lLlsoMtnb68N7lW/CfProKn968C7u9ETgOh1q5REJcm7s9VkB373+xkE9uBKdovUFe3iiG5Y1t2On1ib9LMG1HQBm7toGHqoyffU0ZGRzSmqUKG5WHvPxfdlQqFTh16hQVM1599VVKOhFevvTyy/CDH7wFb731A7h588YjCRXhnK2aJFM2Gw3dqtdFmucspuf3IRNGy9Vs4DPqukat9QsGlZPJZfGsFzB+DM5xXcEEc7fTgU5nF4ZD83zVKwEU8KEHFeGsYBYranGZzBhje0UP8t1NE5bv7rCxT6MFItG+Jk0hzXB1STCIjXq7MBr06NknoXDXg7BSF6VSQCu3WyqVao0pP6zWmOt4HJNPLwgkd13DRjHqXYgvRCgpxglYf0YcS9nklUigYayoM2J9GhC6SiUZcHEiMsZSTBgtFSSzR84Nb45iDmn0WgzIQZvaEy7j2EV1sSZOxwDoH0nv9smS3/BFmVGg1dwcG3ZoqQuLH1YGrStkDyKEW/JD4NrYcgkXa/Lo/50zOx8oHMjTRETdHd3zPLXRbUBYa0ItZYqDyn1HypoYCU8NnWiUTvej7KtxKmc7MawKr7rsellH6whFaRoyz/ggSahzWf6kLrAogB3Jcu0yzB49DodPnIbp+UMQumabNYpMVKVNcSsdDkb+0rWr/Nanl6C3uw3CEVCrN2D+xGl45uXX4MSzz0FQRgcYcBmKoqm+G+a7aqHFNo8fqe/eXN6SK1ujcpzmLhYiPD+QmJSdfvEVferC82Lh1Fl/em6a4b6CcyjPAqOGzThk0ZCiI6S6CmwKVBoqqNa143mmsw1wDOkcnLO82ahfOvfaa2+tbi7/9frdW2f6W1tHaqXZb/tBpVxttbt+KegM+91mb3e7kSYRVlK0lirhjOFD49r7mWJAKRxezlK5NhjGP3Y98eHRufpgbrq6Mx06na9eWLj10nfeGN51vnbCL/krKZCCchjWq1lYLWMSmTDg72sFf6sFXPMBtgZRvPTh2z/Va7dunHddvyxcvDWkxuyaR5AloNmq0rCupEqfpJpffDxJNn8bhiU8yTy7IFX8msPEs4ypY5qJaVCAlcUQF2PTybQy8pbkiGyvAkFKSZwp9xDpDBGEWoEYpQy2ONOe4LoRIM/IIapcXvjSfcbGXUAbB2iBMoj5TJKiITDmmlmeK+Rheo4jWLnsUSBl/MyYNrA0lMQbm1TtVyYkn7vJbXs/bMzSqWzHSyjhCOVQGZMpwUWmHdRqyD0MGA9KHppap+nzykLV7+95tavAImcSII5ziFNZ2Pl99jD/WJhOjjvG2C1D+xuEzxKjbQwXZWP36bHx+WPgiWl9r8kAVnexs4lCA0mUgEzj8VQo1Epza8WDiekM+oD6RoDl8HQDbtxZh94wpo7ldqdPlhmteoX4dVGaUYKIwWG55I9Fa7AjUq8G9Pd2rUKJ5/zsFDTrdQhR1TIMDHScVCA1BXYYViE0dnl9C969eA1+/IsP4cNPbsLSyjps7XRMp9JKciB8tNWoQ6vZhHqtgV5mwDMjSmRb7HZmmngOIU5RhtxA7LqW4OjicVhcPE7wRUqSpKSuj5ZyHKgi3PbOnTtw9+5dSj4RUrhKXM57g/9CTAU/41ETUvPMqIe2h5gc90sgDs4ifA0mcHiv8P5M/juGvViH2u71qduIN6EzjKw9jpEVm/zE/iiCtZ0evQa0uW+UDI0FQ8yCQp6MgiHOTsZx6uz2B87GTo/8T99+/wp8emcVuv0R8SVLvoPcPUWWO+baEioEYeZ0vBPPBeHjkcNH6mfm3HHuDqKEfna6Q8DvadQrVCoMXBdi9gBIquUZUy3u17tu6SAIGHKFv/Od78D58+cp+cSi0MKRIxjQUwGk1+vC+vrGQ38oduKRY4mJYqVS0aVSScWdjkC4+cN2SbGr2Wg0oBwE5tn4nCSVTcD6TZGhaO6ZgUkcFjXwPqG6c2bhvHiO/eEQut0uDEcjKm7gd/m+Q/DuUsmn+UUtwfFXTN4jktgDK/SLLMrxJq2sf1Kh+KZlTtxtbESitwQuDQhbLKynCt6sIPi4ghwhvammdcP1fIFd0UxwQJVOMUQnahQQYknu+QhpFH4YAhdOBgW/UVsRctoTzMZKP2g/Zg48MMgoXViccFPX1dZXn1YFi1clqxVuO6DFThRYwT5UnCUujUIorSk24+uwsOzZJFQVe5OpQKOQEXJH0Y+bPq+ocEmyckNrWMfRYbmmas1pPhr00AaDEnGwaxWM77ApHCAcdDToq7W1TZmkKAjnodRS4rmgfDXydLQjBoPIGQzTKamhmjN3ZhSrTc7xMHiZCCVIC2Q5JNEQlm9ehU5nh+xBsHDUnpuH3Y0NOP3CV2H+6AILAp9MN6TRBxC4ew36fb67vmHUw12X7IpqzTacffFlOHHheahUQsqsieKEvdckkkLEvelD4eVDM/VlwXgripOn8kzVypVyb/GZczuvfOcPSme/+vLRRrM5U6qUOefcCF2Trp1gYbVGe49MErpVHEV9HKGF5+UCIR2mEIDQZdcCO7anGHv30PTM3306t3D30oeX+1PtqX/d0nCVMXVIMzVIkyQZdbpPJ8P+ec10k3PWV7lepS6H69bplLla1gyWblzfTbv9aOWrz879+A9+5+zKs2eOVGv1UFWqXtCerp4oN4QKYWVm1Pdz7U+tSacUA2fbmosA+Z0pg/9HAvzNYYDNaGM9+ZP/7V/lf/Wn/+Yc0/pEKSw3SZfCFNEdKww+0Awu6Vx9CLns/WfWJvh7NZ4km7/hg6GsgSePO6DOSSVeYUp/lXPd1oxFdvVsYmGVmY2Bk7w5wtRNckBVymI/s4szblcOWHcRbQy32CgXOla0Z3DE2JNhdJbTRrsfGjRhP8JMMjKMEuItRWmOFX/c7Vkuc57LnGGghRYF9SqaB7uQ5Ckzwgq51YjDKp9G0+NJFVb3cyMprFBK3LBQjI8Vnc8EHGfLIZ4Um1ZSldk4YSpUJ+xOyUyzF4MF8TnCEY9x6IlQ+LEse3sySEY4ZJjkBCfEXGzCit70DMfRD1NjjSUr18asbCZulqjEit0bDMoxeOYokIs65jaxYUYHENRjOIX7CoGQd51LwWxOQiuSrAWMlJSR39TWgJr8EQWDMHDh1JEZmGlU4MLJBeQRwzCKYaszIJGhKlkLcEik4ez53IECnIuJaNn3qAqDkNZ6uQLTUw1otJtQDquk2Gfgl4ak61ClWMOdtU342XuX4d2Pr8AHl67B9TurJA6D3zsZKCN09vyZ4/DyhadBCw/uru/CYJSQ6q5L3rhWwWmCt4pzEuHA+G9nn34ann7mHHU+MQivlMvQmpoimHq5Uhl3mJGneeXKFfjkk09gfX19zH+cTDbxPZgcIMcT+aKYjOJ7et3uPrXYootczNh9CZ9VdvhV9dTM56t74KS64GFpoza7uikpcDfzsqAG7z8mVOpFGCx2PgkPyGEsblW8lhXXnaQ/geUyY3fWtuDdS7fgo2u34frdNRKi4jQvBb0jQSsBU3Chpww7kjhnMbnFIsfkMSMfD4jbYLT6i+OkDr2UsNWRlDCgmBDCcMk25QH+p+pL+Gh+icGm2m04fvw4/UxPT1NXG9danEezc3NQq9VpPj/swDmOcxmTxTAIeMn3sVJJ/GScyw/T2cR70mw06Aev/8H7f5BbbiDIVlHdtG4o2aTiTW4UhhFiGGcpRFFEnUtUY0clY1yLkBee0ZpUzB1G0HssJpWJLuLTRkSfRZb3e3Nyn/u9sklQUeWj5FEYJXD6d7V3CuOkda+NXawR2vqE4nNqvI/pumoshKBwUZ4mThpFmMwoTMbCah2E53pJHKHCKBX7uOMIvG/CdSRjohCrM7o/UPhbUqczt4eD1ZOAJIUMmMhjMBafxz3F39t6rJItJpKaBIdSKzLkk3cnKdBCBRHaVMpE4Qez6FglIy2MwBz9isloxkjBlringmQLGU+F4wk3CJhTKik9GqBCKZABzL65gCwF9CnlhLtFEZl+p8MG3Y4BSTuuS83iNHbT0YBnea7xdcJxS1pH81LJtsqxxqRcqRlzPV97ooS8RzXs99VoOERslaPyTA963cwNQhRycu5cvSzQZgQVZhvTM6xaDqnY39/eFJvLSxANhhCUa1BvteHpl78Gz7z0KjRGzXuKAAAgAElEQVSn2mY/kCawyTUkvl/+OfebP9i988Gnv/hgyb213DmZpjLG7bA9O3/zhW99Z+OV3/3OyUat1soAjo2Xib1lDpgjtC/K5O1ioiYOFgBl7OdQ2IGbtqDPYK0E8FcjDX/xvT/5Xz751//yv9eLRxc6fuD+jcrzd0fxqJ1vbQjOhKuzwWIeyZNpls+4HpOs5O+AZokaDYVkKsokX+v3RqvPnZsefOX8Yvfs8Rn98ivHjs/M1J9mOcwxwWvAoQrRrlfJe74brVez8AiXwdx64lQuxtrdUWH9WrNz42ddqC41GtPqg6Ub8D/8t//Snwv1THvu8DQXwkcrnOI8tKmNS8b1Old6mcnHErL81o4nyeZv6LCwrlqcypc1y77NPfYs026dAasqjaR9qgSic22FIcSEqJesyB2wupMrjYbMZFTFzKZFqrTkbquNBaCw8HbKVzyB+BSloyhh3d6IPAxx05pMxvZBfmzwhAEd+W4azhRnSrl5mjGZSWJiICG91azCVLNGSUCaSeoPGdM7SjTlgbzjoaCq2nQ4WZpJngvNhcPJtkChqh7iSuxHFcFdIahXxB8El0P7AscB/hCS+I9hmJYEeyzm5WZHtik0bipodzGKjEKmGmsUTVwtkzygrH2szfwJDh4Ewhcx4DIm6Fip37su2vLvHCYoGR3DvB73RUJIFMIPy6Gxp1GSfCRxLmow6nnm7JWFnZo9BHmXod+EuXaDuhJoY4JJXZwa25BaWCJVSeyYEYZYGDGSRi0kJWbsbqESoOeXIAgD8PwAHMcz80eZzhkmjlw40On04P2L1+F7//5H8MHl67C2tU0JkLxPtxeT3Hq1AnOz09R9Xt8agKmLCKuca0RftOWncesHiInU7KGj8M0334Dz588RRDCJIhIKKpfL1GnBpHY4HBKXDruZ+P+7u7tw9epV6jRNJr0YjL/44ovwnd/7PTh75gx1NH/2s5/B9tYWDPr9fQkMdunI1kOqfeeEn4Gqn3jdCO5qjwE7i2QtAQY6qq25/bgvfj814C80OYo5ApCqvXOzBaf7fgd2JHf7AzpGl9tiCbNmQPZ6m04RE2mai7vrO+yXl27C2x9cIYuaEc4XDNxst4rgsKAY+rGidY5EaygsWiFMxD6Q42dGKYIOI5pDiIm4b4JrSfcbVbW5UevFefjr8EdlViirWPuKokQBJRUWlo0DOcJnz54liHYB2c4tMgTnHM1HKc08esiB8Nl2s0ndUQe5FZyTr1+n26VE6fNPgNH3ogItPhNwsGN54AdsNkQFgCwzyZhN+FGYJS5+SMTLJJVGpMjQLe53PwyU3NhoYSEDu1oEb873Q3Fh/AyYIhlM3N+xKaVnbI50mhkRKKv8Dtayx4i62O9V5hxw/UMIflgSIByOyREaCkISYZJMXTbtlEqZF5RRYMUphWWVp7Hc3VwTg06H4sdSuQL1Zguq7Wnt+2XjNKtNYRo0SltpUtbBpdemzJhFC1usFNYGxQHNRqQmP9ZewIMvWDBjYT3snBHfElXQyWLHFJHws9ESJdf4fiM+JI3VCnN04dtpLFLwMFBMwLHWKzgfmSk8ZUrJjB5p4vePq2aF/y39D3UShRBc5SlL44ioCwDCR/oPKupjwRypOeUwwCIRCiDyPEsCopiUHNQyUH5YVY4XQpJLOex3VTIa4b3NlJS9JIp2dzdWeTIaoPBNyfU83p4/4p149jn29FdeQpcltXLrenrn2qeiu73JZxeOs+e/8YZ+7uvfVHMLR4g6LnMTzOFMCTl8VHXq//v/+ad/8n+/+8O/4peurry5OdBVIWBNqOxioz1z8fT557yFWq3dB+igWCwoaCATyj7eOd0vi2WiAiUf2wwjPxXTb0wwYx9gQwBcG2b53964eeNv3v7pf/z4e//qj52rH12qvPKNV51cR4MkkVt5pPMsjusOZ4lbCm6ADn8y2NoKt6NdJy35qcpFlm6uy9RRSavRTje2Oun/9D/+8/yZZw+DN9QXSr7zOjB4BbQ6AqlsAigPxY0hHzl+NtJe3sl1vLoRiHInUXxNw9SlYOfmreohrn/wwxvw3/zXfwp53PHai6fqvuuFUimMZ3yqsxtFY49phpt/Vzkwyp6I0H6p8STZ/I0bVHX30zQ7BQq+niTZ6wrUec5FW5PRsk6YxsUc/ado2XWs6GpRAMVNIWGgIyANRPCpXUmFSlrAC0luXjT4LCWPSxAwShPY3B3ozZ0eM8qN9/Sexv+HqxYmJSSdrRUlO5jo+C5wTFqyNNe6pAg226qVoVWvspX1XXxN7rgilUqjRLhjNqhHS74K9ULc5JNU8MwjEqon82xKaQS5Zb41kh6/QR/4Ekw2EUKD/r8oNEReexYu+iuK8x4b3/Pgh2RJrkejFKI4Z4UII9N7LBtGc4cqyiPrsckMtMm8f7L6LtALDo2fy2VKvqTaDw0c+xLeywl9wPEWHTI7Va3ljG2o3veaIJy2XKmCwg4rQmBHA+IsIfIK5xwZ0AEf8wa1ZaKiD6Yw8CAIfZ+CfSyIYIcdCx+UGAiHAlRUhXVcjlYaxnIFuwui4KOOFaXod0wuMFi9s7wB73z0Cfzo5x/B5et34O6aUcK1Bm8Eq8PXI4yW+M65gs3OAC7fWqPOD77WfL69Hsx0aMc+gLaD22zW4dz58/DMuXNwdGHBCDl4xqWG4HpCUKcT7wX+HdU4UbQF//v27duws7Mzvq/4t+effx5+//d/H37n29+G44uLsLGxASvLy/RZ6p6OEDcdNrbX1cQkA7tbjXqdAlxFXTvjL4nBOnZzCmh60UEyxYB8zH0z/G857sgUiY46kFwV8P2HTbgeBOnuj0awtdOla1WqlLHIRbb22gTK44Sr0xvyK7dW4Ee/vAx/9+GngN1NPF7DT2WEHBhfJ4RieC6tf4ylVGxD9WDDd/YgzRR1wwzsVYGtfFGicbCspYzVMAlRBSUJwyiFh5PFefBgBRIAYGz/UyxseM8ROu7TnOdk1YL3j3jwVhEWiwoEC0S7odOn4YXnn6euOP6bJNiyuW4I30aRnGhk1JYfNPCzMEGsoKJtuw3Vcpk+B4P/AqaK4kAP09XEgky71aKuJh7reL5M7FlF9zi3c5OSTDtn8Vhx3uLv9HdMMFHoScoH0kdgTF2wInm5svZFKBKV0+9SG/VugWhUJIzIAh+grU+ryeW4TUBp6cqMqSYep1JqfL8mr4VZ5xQlwCQ3j3D5epUE3cj+CeHcXJCoFuMuBLWmDOvNrNKYYqUwdOLRSG6t3IbN1SU+6HbxIYZKvaEZUzKsNZT2MbkjQG/OmOYWtYvVldQK5iCCagRo2UViQcZ5zMYUpgtKKZ1mFmJrG3NG7NvGrByK/2YMpYMxmU0tFMeY8pudIUXkE1BhlDqZmsSHUDhIIyqV1k9h6iLCEdy4XWiyN9oj0BhNBhMM4L0pkCCmQMCZHwTgSR/yLBMIVHa4h/GA9koeq5Q87bnY+caGmcfKJQ6NikFquWEdlBPq3WEOd5YF31rXTCktg3KlF9Qqq73dXbG1sqzRvsZxHW9j+W590O14yCHFZfXyO2/LlaUbLE8z3p6bhTMvviSPHT9GiLVEQdnuafjoftRw2J/9u3/7p//XX/7F36xf/fTKGeGJReGW+I0rdz9mafKjN/5Rc/2ZEycWJUAUAdzSGiLG4BxwmMV6CDP8WMdanxVFalwAkXmA+IzUBdiI0/STuysrH2+trXx468qntz/+xdvi0i/efqmzvhocO7kwk+VpW2rdBcYGXEFVYWNB8yvvXV76WTkLdtd7G/Ct107BH/1XfwA67YPcuIspJFRbTYh2Enj19bNYuzgOKnsZRtlLoNQLADADBjSENigxcNcHLQOW9QTLRzWP86ardQLaucgG+rYbiM7lj38EP/1PF+HM4RMl369MM66bWunA8KH1LnU0gShmy0rzZcVZpJ/Ya36p8STZ/M0aDLiYVhpejYbxdzToV7TWLcY4LRZao+IsdaNQra1qz5xMk4n6xmwF0aysgtRHCS1Li72VIeeWUkIkamSPIJ8eUs3Z1lDC9nCgby1vwE63b5j8YqIcpG32ovcCcLQ0MtVcoA4AboK+a6wCspQqr6gixlA1tBKWyLMxzaUWgmmZUJHS/SIJGGd7mbKyWRUoFJDMKlAIzxWpzaRmw1gOH39yQIF3DPTRCxSPzXHFw2dQjz6YqeA+rk8z549B0iiKTSdamWth71rRqhLEozHVPqxnBswo09KwrjakPFsth6paDRl5oVKRgrrPxrts3E1kBGt9RG+/SeIMWMN4Ze//PdeEkhNEyLoOlKt1K1ClKKAl+BtVZy01uQhPrOCLVHvwNAyqA4GJ4p4hvl8yHcCwXIWwXDEeeraLs68rZYNX6vA4glRIr9y8A2/99F344dvvw+rmNtlr4HEJ68OIr62VQwrI8Rki43/XA5zqd9d2aY4V31Akp6bAUcCDzZwtV6sEn33mmWeg1WhQIilcc8uKYCkMQ3o9mWlbew7sVmJnE7uak0qep0+fhj/8wz+EN954g7w6ka+J1xGvAwbc+4N7RskbwgALcSP8fOSbHpmfJ8VQsF1NMsu3AT0G6phI4+di8E8QXvybDeaLpBaD+8IDFJMP6pIViWphl5IZSX5tpRwz2326H//08wY+G2grMhhE0K5WsUmCEmZF8x+dTgiV8dHV2/DXP30f3v74Klma4EB+L6mqTVwLmry5IhQB3S0F42PE7qRPcM7cJJt25BMCawefGpzbqIx86ughSPMMvfzIy/OLDGbhldgFp46hndOFjyjYbited+wsImcS/04cSSmNUBUVasycwnuJrzv39NPUDV9YWKA5g/cHE0R8340bN+Cdd94hSPaDUsRqtapPnjypT506xeq1GnpOwg6atcexuedpSvBZFAf6vJWFE1qgCjNTU0aB1hYoikQN52KRQKJwS2STyvEP/t3yMA8WOg4OtofRnnjd3kohyOLGMWTF1IjpEf2ACyo8uZ6APFX0HBB5zjG8PttBJJi1tOu4keMxwI1C4E1afjwiDXCdkbQ2GmXreq0CM+0WNGplXKOYH/hQq1Y0CpGB8JV0y7lTrqMXBSyvrIj1pevu+vISi4Y9jtcb5wJag8TIQU3ixCtlieMiKhb5l8ZhRe8R9qUlSybGL5NVmNlkM7uGezYeySd2XEXERjD0wwkRhsTGryhsmGiyTdE+p44pVSKljVkKiASBCphZ8F2L8NeFiCAW3txSSSEahZ6pCa2BwuhNGrEgauFpq4iNsOLG1KwqBaFM0wREnvBA5OC6HITrsbKrWImnHFmvyNWuhRwaZQ9q1ZA5QYWl2lVbnUT4OuaQRDBIclZpNoKwUvN7nU406Ay2lM51EJZK6JW6fP3KTG97E+e+s377Ju/vbvMgrDDk3EaDnkYHuZLj4BRhORCt90Yo2L/t7K5/799979+sLy3damYavpKkUCml8Uff/OaZH7348pmlN799HlrlcGdd6RuYZDmcKc4AbfCm2V7ZdBxn4YXNMskhy5TH5ZBpubrb6318+YP3Pnn3xz/Zuv7Jxcr26upr/Z2tBZ1n5XZ7inmlsJXkWV0qHQnGPYztkkR+2u1mq//FP74gzp0+BaO76/A7//Bp+No/+ScAsAugljiBWhEljVLrG4Mz0Iv+ASj9Ckg4DUxPAyoQW19XoNPmtuqqUbOpgo1dBvA0DIavQl7pAgTvB/7swIfT4OlyOU3iWTeQTXJeAOgxgDUSmGLo6qdWleIbWj6W+t1v9XiSbP6mDEaQh6eAO9+QUn9Xa3UemA65YBEDNgJD5SrT4mrWSl404Wjh32OEFNVD37ZlMnLFAMOS54Zzga8rStA+BiLo5rzRj9lgdYfdXd3SGNjxA+baxaardWGFbkqaBBVDc/PEJCAFXAgX0DRNEd4Knu+yRjWEZi3UaZ6JLJW+Lqqm8GidzQLKa5pjHDyC/O11XAss8fgPRRlaG1EhGOdpjLoRRbCBMCXH4fsCicc9HtunGq6M4WlGMQbUzHLuxsKtttps5WPJrBsjT7zurp0/472fkQdiCdVXCR6Y5ZlKkwSjPhSsQ3Nxrq0uA1nH5BOwyc+ASLICBUQVSz2yc7cORgtDcROUOJ91VYqE0/VcqDUbIFwBDlphDIdGlXFCpGhPCcoeS9EZsAbOY2FHCrpNV5ACaiwwWEjoQbGoseejI2AwiuDdi1fh+//h7+D//dHPYenuuqnUMKNeWvT/MPE13pBG3AY/G7uBrUaTAn8MRDkvTFj5eNYXnUCwSeSZ06fh66+/DqfPnIFmq0WBPRtX5E2Ch+/BLhF+Bw60Ovnxj38M7777LvQn4Iztdhu+8Y1vwLe+9S3qfGIQjiInq2tr5JGIwf7B+0ffMfF7EIYw3W5TFwnXBUwqJWGvBCUf3CbKpDaLcAXHsWqZJvBHcROCh6Luf5ZR4F1cH+y6suI7i+LJaGT4cJ7h4WECguq7vV6Pjl8+grUFcs93egPysNRaGuy+koYQj8gypfXWbo99cOU2vHvlNoqdQb1WJuhrJjV1xTl2OEFSQknzH2HacUL3HT8Dk1LCqkkNUWwS5TFMlhJQh1YlfF5Nwmz+1bMCV689dwZefvYkbO12YXO3R/ZFqLL7eSsGL5JIK36EnUqcE9gxxHkk7DUXhRAOM0G3Y1/LLWcRub8FZ3vclSYf5ZQM4I8cOQILR4+O/TULWgLyGm/eugUfX7wId1dWTCHoPgOP5bnnnpNvvvmmfPbZZx2V5+Kj99+H3Z0duqbM2olgV1M+hN0JzhlSn7U8ZkwgkyKJxE478i4xwbS/05yR0qIgHm0V5raItY+3bNVHEBGDVjboj4uoCWVVVUg3wLzSJKIOFl3Me9BntV2rEqICIf5pkpvsKyN7E5NV/X/svemTXcd1J3gy7/qWerUBhcJKECTBRaQoSpZo2ZaXtrvd44nomJlPM/PnzMR8mv/CH3tmHNHTYasdluy22rZsWbJEUZRIcQOIrYDa3/7ukjnxO+fkfa8KBRDcJNKDjACJQlW9d99dMvOc34ZmD2vmpaGDeQaGZrG6w4Mh0e22qNft0IWzZ+jcxilaX1umleUlj3s3T3OalbXfG1c0cpHZ3u9Xg507fvv2B9FoeCjCSxwjnlHyNB4O7eBgz2d5u0iyJDKONZaY4DWuhGmzTnYdyMPWrgUZFICF0GgRc8FFoopV+aNw18QIXGrVIsJo9YruUsK5nF5tiOTBEMon1ikTbORJ3sczsmmNVUUeGxkx06ZK89zlrRY0Max3tzq5ciNNNSBoXvIchPNaldTprUBHWa2ePouAyLprRvl6OkrTqLaOImpHhWnbKWWxaO9jXEsbU55a08qMrX1lliJv0rplWmaFPtgZ2ZLK9U7iXev0qXtxlAwOD3Z3vUOVa0bjwUF97+YHpyajcVoWs0gfYtq5dZN+9o9/F9skaV+++pxpd1qD2NK7RPTtVjX7v177+//ng6TsR8Ph4BmTpGuHh9N3Xnrp/Pf+9//zf77+xAtfYxC4P7xepM7vddJkGsd5WtmOmZqUKk5QJ1tVdQTJxWwyccWssIP9fZru33Or6Wya+MngnbffpX/+/j898YvX3/zK/t7BqZrMSprmWd7r1BQlfiJMnT2JTq7OGXI7nSx9f2kz+cX/8b/9TwebT3yNqL5t6WCvQ/13e2TqJdo77FFMGbVm4FR1aVD+Nnn6Fhl/joxZk/WfSK87Z61y1roRManYiwBhNxtE8SsUR30q/OHFU903vv5E1xV23J1WZTfykbyOowlZg9zWtu4rrXF1gn6wNrgfj485HhebX/yBndYTVeW/Vdf11733XzXWvwAqrSfaV3vxVS0eE9FHwJ2NporgRaG0UFPSxkbASLO30qICux2rof7cRTJc6PHKa7Hhmk0KPxhNzKysOSEkbNADBY4XU+0eB3pYUXiqdHMCYwxZMCWKA9qHoihMCopiEtPqCgLNW35r+yCazsqYxL68VPe5R0I4G0McL61PvG+KrmaT86EsYR/6sb4xODnpxZ1atZWloapKpA/7GWkRHzIW2+UfPvSnkPMHneBkMgNazG28SohE1ovB/vH5QV3zvfx7MJuwxEU2zmtRVAYyLXaashY7c0k/U53Rxxho56PTuEtkNjzRinan40fRrsq9J5vLCPTNLKNJq02j4ZBRziro9pReRn6uD/ShCFV0TNxhgjmI+hxqrMFJrsT4XWwasWH9+dvX6T995/v0n777fbp+4/YDjxdFCOJOGHEnQ6fWVunMxmlaWuo2pjDh1J9k5opC4cpTT9E3fvM36aUvf5nOnz8vRdpCoYkNQ8E5aYaLNaBU+HdQZ//+7/+eXn/99QY5xOshE/E3fuM3uLhAYYlzCeTzBz/4Af309ddpMBweP+tHvmJd69ISG8HgPcOm3odcogVNWhbH84aUflYcH9NNlXZJeU45snn1NYAEpora4ndQPFguKDK/1O0YzDkoboFgddtt/vx90DZB2XyEwgHFP9yJB2PZ90aRiTWJDeZiniNS9gb+YFQaGHVcWVqmU8ttRlv3DsdU1tI4QwEMF1JkhTq/QLW3pDpioqIWN+jFawst8enVHmuFd/b6jKSfXukh05jOrC/Ti09dpG+98hyd31yjdz64Q9fu7LJL7bXbO+xG/KDB1Olul68rUCoUx4jowXVq53mjRRd5gFyfWlHoONZNt1Kccf4NBfaKjEAZRqEIA6CzZ88ywh4HhB3XGy7QvR7/DI7hQcjz2bNn/e/+7u86ONmeP3eObt+8SW++8YbQcbUJhOs5UZTzYSPoSVF0be/s8L3BOksUl6ohDqj7J83bbC7jffeZZ8p9t9Om1ZUeF5xWXYdRXGZ5wg2H2ayi8XjazC+YT5Ah+9SlTTq1uiT5u2zuY5g+jesBi/jxpGSzKUhTGj16KxdjvMrRKmKZljrSrDi9Spsbp9jteqnT9kr3N4NBP5r5iR8Op6a/s2dm4wF0sS6OEwtLA3F2hjKy9tNRn0b9wwwonzZJrC6nRlUlqsFkX0E4y/KKC68/bd0WelpAycQGoQqAmq7MwalWiDeSnz3UU9yRsGmAX5IHblxALRm/RKYRjosdDMWZlgd3bTwiU4zxkEbwOhFFYoan/fdg5BUjz3h51SGeZHR4QOPRoa0q0I0j3+72qm5qZqsJRacTn7eiwkYeTIXIZJRxvBru1FkdyZ8S3dqZiY0zrZjoidMp9VortL6S0t2DMknz+lRneT0ZXNjcvbM7mhzs7W6Vs9F1VxdDE6XrvXWz5pxfm5Vlq5pNkv2dbfPzH/3AjCeT+HBn5/D85Sf+ZXml9+2lVvxXxe7b7+2/8d+ybn1w9sVnzp12UbJdzKpf/i//w9c/eOLi6YymN9b8QbGeHt642ErSFyluvVCZ7vkx9VpDt2LGlNmyJnt4uE/bt2/6/u62n47Hfnywa8a7t6kbzdLIzc5e/+D2mZt3dtIstr0LF87mjmw1KeqDyaw8nM2KSRzbveVu6704sofjWfn2Siv54OsvnP/7r37l4kHH0Hka3sxo93CVpofPUTx6iozp0MQskfU5DQtE3KyToafJmLNSCLKl+eLeR/eAPnSJfdPNBtBi7GXK6gEN6NrTG6euffNbK6P/+oOtNaJ0CauUMqXxPmfIUE+3DitkaR0GVAsAy+PxMcbjYvMLOrxYfZ+OYvclovqbs6n/QyL3ZGQiFJ+oBmtDKDgNHNg65F3MmgdjOEfIkB1oiOSqtPiYTapwH2/wJyLiN+j8WSk8jAm6EfJMxY10KXHFZEzj4aHBRhJNIqYoKo3NqjkBO3LWvjHdQbfVaEWL7v9gOuNQ8pWlFqU+IhgEFUzVEZOgnCmExmKzhk0GBPqS9OArLj4+AroZsvSQsxnCuCI1A/Dqzr7wJc1dAuZ601CTLtKo/ENjuT+TAW1BKWZP9KFWjkYXUCCy2MRMZ1PtgBNN4UBYnaA3FSx75skMxcFv0fnXN74aQI9ALcqTHi0vtaOlTot98sVxc07bDVQ9yXV7iPGL3IsuRIz50PAQr+T4/miAk8c81xI6yw5laUZp3qJkMKDpZChZnHXta6hjHO51YwIKb3QvNNcAYllKKMmyxjnzQfosjqwwhu5uH9I/v/4u/ehn79D2Xn/+8R5w9CGTttvt0JmNDab7mYXNexihiUNBs+gcb9yff+EFuvrcc4wmJqrRDAOb6KkiOV7RLEYGjWGU8vr164z+kRYjiKj4rd/6LY6GgEYThR/MXuBW+w//8A/02muvcRH1MLQHSBYQTThuekVVAwLGiOZCpEnQmQVtHCmtlKEN/K6ZxzaETETigrBuXgtN7QwmTXnqsywxtRONcLyywrmnQNPubm/zn3AeHjaAPIMCPa08tTpLlOQV2WTG1PA4MjQYzqg/rand7dHzTz3JG380ryaTKbXbFSNQg0Gfbm/dE02eE41eGE6fRzaQSmLWDDN1WFH3WJtXYtbk6dz6Ov3u156lV567TGc3Vujs+hptrPRY4Qbd5tn1FS5O7+0d0OHw5GIT1x105nNnzlC706EZCm9jmBbbas3zcc0C7XoRvZ/PdzR3Zg339UKzAPfQ+qlTdObsWUHYtUgNiCnea1Vp3sedX8PAa1y5csW8/PLL8XPPPedbeW5v377NyCPeA40InFN8hvIRjIFwbDi/O9Ak41ooPfIzMVTSc8EFO8x9/NHv5e2cVnpddrEmNf8xrO9O+X4oiiG7tRPHNAl91pDc++dPr1EnT1hjiVsETQ2O5ahrmk4LvmeLSp61LI2Uqu+Zo7K63COY18AxCPdcnhjCGn5QTFHssqPyYDTy/cHI3rq1Y29t9WNjE99bWfF1MfOT8Yg7zvwMR5GHYY0xJmH0ES6ibBAEtR37C5ZKUwHiWXAAEfYTnm+sWHcUE24ck8kRa8yGPoZVchmJZAfFopicygKNhwF5k7HSKBnRVMSTxJ+CgszGqk+OOO4DzeSyF8JYLjgxkbB5kDVQlb4AACAASURBVIniOk1z9JPs/FLJKyZJRkvL60A/vasrOx71mWI9HgyiejZJWy1QySaxpZKpsynLUp04r3NzWxklXHNbkgQl+Tvils+sZhyzdXaddSlJnNfrY5N2VtdW8v3Bem/QHxwWZb3X7rR/uby+4pMk3by3tXXlnTfeOLt37253f/tuMvvRZHawdfP6+QubP790dv3t55/sTai4+WTcap159Xe+cSFO4gvtPBqf31xdObe58uz4/e0zaUJfMvXk6WhSXD6YuCe2D4vOwcjEgyrtDcu2GVaJHxW12d3Zpb27d/zgYJ9cNaMs9uRm42g4HHeLyvWKEmqthFbXlujMehdyqHowLkYHw+k1cv7dU2ud7Ytnl6ulbubKsn53Y7Vz7etfvmQvPLPx76j2V+n2nSUquSi8Sn52kRzA4bjUin+ZLJ2Sa0Vh9TwuYHfzzdmRJx7/1oLhEeXmHDlzrhzU3XFZGR9HlyNrnjGelrwwi4COLqkGdGKMZUPGB/lCPB6PPh4Xm1+goanIxkSmV/r4K0T+D7N2+Wo5M5umNuvGWOgNQRWZkPE9RJqQP6J1M6p1mCkqGMuDzPRI54XFZ03DHwmOs1bMMBisRM4Vt5MS+VlTgboz7u/H23dumuHBDoeKV9oWwiMLLR+e/4o7/DX3nbC4IluMN3KTKReb6MxOwLxk0YnofWAUxJs6IwVsHRZkqfWMZnnVj1R1LGyOTKBGImeMQ9jZ12VBD8d/amvNVGuOvEk8CcjSvH5qfufXMBwXguIO+/CCW78Dk4PpeMqIJrrcjhEVaOGclbgzNmg4/tvqHsiL/FS1LpGcJ6E9cwPApmw2sbq8ZLqwaGfKkSyyJhR+vPlSSPQhRhr6r3ifs+TdOhYBzpj7GOc5ULc5fiLLaAmuqHlG03GLi6XZZArqNzYRWq9YPV6h0yJPjNEy3LedDnU6Xf77gwZ0WNjcHQ4m9Ob12/TWtTt0OBJXUaYZklBsod076QNhgw2XTFD9mK52DDk1atoShtNiC8Um6IowPglZmkky70Hgs7TZ2Ei0lpVqzqDTROTJ1tZWs+m/fPkyffOb3+SoiqCJbKm+DT/3+s9+xv9/2CYdx45CAoWN1agFu6D/W7w+x79GAWAWKMoSXWObZrZVWqcLua0NgyJS4zFpZUUI50WjC5RbdcNlB9HphPYP+4IOPfzuYU1mRREtra1z0yIfjRipx3u4+JBsljP63Om0eQ7b2e/TtHS03FuiXjfl95JiUe4LU82bLKxvxdxeG8raKWVpjGgUfv00Tni+O+gP2aDq2SfO06svPUN//NtfpheunOeYIdyyeI7RNOp2MqbVLrUFmQyxRscHvsfXBahiq0W+25WfYsqlNtxUjN2g6XodmmKyKaLuR/XDzwK5BKq5fvo0Nz7cAloYCk80P4CUD05wosX3cR8CXX/mmWcssl/BSEDj487du4wU4zXxGv3B4Ej8zsNGQC8/i7HonBzWCXbeRjdA3M6bc4T7dHVliTpJTKPJhLW9XCxOZop2e4kIk7PBv4vi82AgJkiddsbSNIwWrkVkyMFgqFM1zIv5NZHCBnICSABgKY/iE6h7/7DgeRzBiihqcY3GsxLaY/PuezfMjd0Bmazj6qo0tatMoKED1cuylu8ur/pWe8lww0f2Fo6LRsdI5RSO93gDR9TXPcga53vz7aZetZZqiVODU6w38yxOYVmpXwAmwymjksZz85Hv8LlGR0NPuUNaeu9jdt7n29HHTLmVO9xp4WI1rJQHPk+U5j5Sw6nQUFZHHF/OZrauC1/MprKnKUrqH+xFo8Fearsdk8Z1HNuCjXDlyqGudfNGJQyETM3MLY5wY3cdceyGAqeTR9TOE8+uw35qO7TT7iTLFzfa7c3J2upkUkU3k3bnXzY21n++ttq5tbuZ7WfT7SvX7OTMdDLtTqeDYuvdN0bV9tub+Xbv959LLzyZbSy5MxdOn9uk+My5jc7ZzY2eo0729Xo4nWzfPdwcjctnrDHn++Pp8rWbe9Evr23T3d1hWdcesSRmUhCzKsbjCU1GU1x/6rZSWtrokmklZjhLIzRSltsx64JPn+7S80+eoouby5GJbDkrqrtZEt/e3Fhyp9eXNvJWshZZU7U76WXma1fuRZqVV8lVS9wy91HCkl8LRNpN5H7xHSKjC5kp9bE6vt9ZMJlqKlJSahoMoXKqYChV5zar2mnq25Exz5ExV43nuB3yR/wf0CExuyaim2qM+Hh8gvG42PyCDEymaV2bbl33TKf7b6eR/V9p5r5FhV0x1u+Q9RNyphTbb4p5lqMgY+B5zyk9Fi8FCkJXwTunrvxQjs2QNGzkwYwbUWOwgjZSKqjksbbWegBXk9nYHO7v0t72Fg36h9wt9eLWyO6KbACEr6E38mIkChOgpXbGNJ/R2POi5zhKxPMBGVV8Aykrq9onmTEoWuGeF8cW7u4qpMDCxKjmIxvnhCKRVzdYwFeyIi62yWS/4Gfem761NhWtYthAzDMSQsdLNrufjHL1cYZ2ctk63jxCoQkEBU6DMLyoRXcmXWJ2iGIQxYQasBkSU7YkLsZ0SIb2iKhHnroMA1oLTJDRQGzme52WBx0rT2NlN80D5I1ZTFB7pPMV88ZkfjAf+1w1WmF1bc0YnYw5oqTozNCltlVZ8tbOq0Yn6ONAcWPtXJaxKVCWt7hZcRxtDOcaRRYocKA1/svP36PrWztUVHBcLTRuQCIqoNE7qVgDAoeCMdXCqKHsHis4acGJFUUDzHuAhq6trTWh8c1nVy1kKD5RdIboE0SY/PVf/zXduHGDfxZFJQL4X331VUYygQAiG3FldZWLgls3bzZZnA8bOF8omPF6RnlrH1Zokt4nKIhJkJOm8PFz7oDkwGvPyB95LY59wjxi6tr7JGb6ArMyJCvT8rnCn9FkzLq2DzOrgr5ygA1mDIfOJeTNyc7GWjqcoMjrUpzs0X5/Rof9Ee3D3AnZl3lGh4czuru9Q/v9Pkm00vHPSo0RCeeDQs8M6kiSMnLZzhPKs5y+dOU8vfTUJXru8jmmzMbsGipOyVzER5ZWux16+tIGvfPBBv38/Vu0R8cpzkfvHVqgZzKFQGmy3BB4wPUJRlrB/OqkWJpQlOIew/0DDWiIQTlOc8X9BHMg6ICPD9wDKDT/4A/+gJ56+mk+We+++y59//vfp5+89hrt7+7ysaBxAjr3R9HifhbDHHtObbi+vuEENQOoOBDoq5fPUV1UdP3WllB3DXHskmizvTRHnLhNTzTXeTSe0nA6Y8p1ZMSrygKEcWLO5YJ1GHeOnerCUeALwwgFJoyF5GfFR4vXdeR0sI68Ql6puXZzy7x3667fOexjqbWurk3lSgo7B9BpsQKkiFPqLZdZmpdgshh5uUK4Psgu5Hnfsau5NxOA+d40Ibye2TJe/AIaxwSJNSM1l1WPGkYzK9aAGupys5mRTxPrSRdlqzRfKx9MCETzCSIT9jWSPinsMCxcaCj7KIpSY6NY13E/916TGaecTcz+9i3Ox+XcZm3yDQ/2/MHOPTNZ3Yxth2w7JpMxGAc41qr5YMgMibThGmJVNC8Vi3CtP8eMXz5vvm0dtSMX1XYSzeI4H1VmtfKDU8nB/oW4irbP+mnyjStZcWXt/OF4WpjtnX662x9urnazS08+sf6VU6fW311e6Y1tMjuTxOb08nJ7jdpp5GdlsbPTj65vHS5t7wxTsDPu7o/og9sHdGvrgPrDaYJ7Za3XYsQB5oFoaCWJNd2sY86cAiuix+ZVVy6J+RHmKTRIVpfb9OSFVXw/zvO0F0Xmik3jM5TEy7JvcLCQ7kgm2QyLYEscg21gj6GYHOldgEZBRt6k+r1KtZPRCV4d9kj3fz5jleL7wA9CRjXUFe1Tm+vLaZokF+radZzGVJvFfaQw/Q4jSzdBNvm1Tiz/CsbjYvOLMfBALXuiU5mNX0rI/I+V979PllYY6td1xEnWFJy3Yn3iHFpkPMGLzhKzNgrJRNG9Wq2xXeP949nrJxYQyDRVgjE0IuNn7P7GMn87he14VRVZf383Pty9Z5HNhZXCmsi7SHqB0IqQFo1hZ8jwalHRQX8sGWTsaCe5i0DBgCKAMsu5V6DSzmZAoXyexKbXAT0uc5NZNdLP1Fb068gIVP358EGGqZb+Cy6zQQB3PGdNep4J9K98MkLG2cJcxl1n1fRV7LBacyzGZ45yymauZk2ucZnxJnpQqSnaKi7audCczUqqShcWUfYkEUc+A/5QZGuPut/OQwCk6eDJIXcK90DhvG9Za8ucCzZrJkWRALXOk5hWum2z1E5ZuhpiRZoOu5EVnI15FrICP3Q8gGL30U/bsY2ztZS3ctxfFuhO0Gl5PW6nwXRW8wsjNQcK2asnjeD6eG9nn37yxlv0T//yOr13fYsO9g8aLWRZC/ubTvDfRBEE6iE7yKpJjjlWoC0OJ46QtLG5Sc8+/zydv3iRqZHZMc1ZKPKAAuFPyNaEG+jf/u3f0k9/+tOmEOh2u5yL+Morr3Bxuru7y1RavAIKzdd++lN2An3YwDEj/gaFc3IcnV2gAC+OWqNNgp6UQnMoICaKePiFk2/M/dR1r0hCVdWcl+e0yRUyMrmwqmvWiT+KKzIMnrZ3xJG2u7LUGBrhmZ9OJbalPxzT1r09Go4RiQGkqKY7d7doe3uXbt/bYepm+MxzandEnVbaREBBL8dmQNA+tHJ64akLdPncadpYW6arT5ync6dXCFEKeJZRAIdmB79q7VlucGFjnV6+epF+8f4N2t4/5Ibe8QHEG00GmOpAx0zshFrQEJRooGR5zn/sAiV2kbbt1fnXL2RRHrsAfI4QUYImBe7HWLXDi6+DAvOtt96ia9eunfg8hXxOGFPhWfjg+nX62+99j77z3e/SL958k3WeD6Xi/xrGkWMxShkKbBzNKMQ9ifP79MWz9NLVK3R3Z59ub+/wfSTGWNI0ARqcxuIUDX097ldcO9HaF0zHxpwbJnJfaRED9QL3qDjft3HbqbSR4Wo1R2Mpi5UGsU7KuJrIyn7/xl36xXs34MRsQFkO7vJcBGO5ieTz1c5xddfqdou03UaxmXjvmE6LPYIHouSZNIW9SA9O5ooglVwAwLhaKvSI4V9VZpAoJ62WnzM0vI2Y/mSqxdS9i61MeAg4RxOaTJNKnYg9gqnVTLCSaRs0XlOyNlRoOQUgYRvF7ZgnHV87V7FPfeMVqLFLnGWM/QPuZTRoHPSqw3pwsOcGw15cr7aSxEYmMnqeXWBxhms/ZwUwX8yKcqmZFrn4b/Yg3OlPopra8YTa7MkKrTqdLYf+1HDgZu2UZme78cGl08tllOTd4bRYOhxOs3YrjdfXOuNTMCpLojrPolUy7N2R0KRgJ8YkwtX0Blr0u3tDurcz9PsHYzTeTauVMfX6ifPrtLHepoP+hA76BTeTV5dzOr3eplOrHRg20sZam3pLOTvxiw5bNMdYk5T6usr7y1lhudsXKKnhQ1veZzo1dTKavZpykcj3qEk4F1UG9FnZ3Fzj2DhpHvBcbO4xskl0hmr/1XYrvXf2TLdYXc6zvcPx27XzS9aYDTNPacBRFM7TMJ66aew/SZv78aDHxeYXYsTWRpfu3L77R3m7/dWo3TpTOnfRONIHyMx0bm4T8UMY6/6dzR6FfmKmMIyDNTjotfK4I6xW5DNqEw4tQ5v5H4YfznJOU+Dls+LoC0z0nGdBY8SpFNNJdrhzFxbc7BhJQglhHCHLc3a7gy5wVozm2V/Y8M5K1pRwBEksYeQ44MMRkIExtfOUssSIxmwyNTlC8jHRLXVoqd2mw+EYG8+hbh9D9lYzZBPUMPt1j9p4fmqmoiQRR3EIdKd5o4z3TyZXzYgJ5VJY1Hjv6kNRqiis5rHNHWn1zU5GHDnH0ht6cBXx0GGCh8yYFxBoEo6dg4CYoKs9nZascWI6ZOWazTiJpUJc1d7NqtqVdc1S2/tiRQUxAgAMMwboGtpYyNPEVkkcJ7OyBH/JtFuS29bJWo2edU5Jld2B3JzuPofGX+W4b3OqVNkoNBwWj83PzYDohEzHoy+jzqLe09bOPv3i7ev087feo9vbu1pILVAS65PpjdCwrcLMKJiu0AOKbT1GGK6ADvnU1aus1zy9scHf5kL1mLYTBcUbb7xBP//5z9ll9umnn+avYQoEzWYYoCsipuLcuXNNMYfCFHTHf/z+9+lHP/whF6ykVFlaCPUPA8ePYhPFjAvatXDoR87Z0WLBa/xMQNxI0TP0LqyN8I3EIaj9Q+4eIdGK6WX4/OH/FUeqlEe0kw8bmK9AZR0jhc5LsYXicTaZciF+684Wbd3dod2DAf/7ZDyh4WhAwxFcbMfkFq57GN1WRk9e2KDnnrzA8yRYB1kc0dpyVyjRSzk9dX6Tzp5epW4r50KSjGQsVoqs2IVzx7t2jR85f2aNnn3iLL19Y4vubB/MT4j+LM4vaKe7+/v8GrhOMNfZ2ZV5/PSpU1wMGXWfxfkSirI0D7lBiCBF8IhPuDfxfVy7ZRgOoUlhJc82XnBNxnj77bfZARloZTjEQLdBMwQutmhy4F6DTvPHP/4xfec73+H7NzRufl36hZPGEebBAgWZwtyADFJtFiwvdenMqTU6vbbC9yJMmSbjGTcGfYD8fMRaQchOIhPRwI/FcRk0YFcL0U9srRv37XCr21Dc8td2XvHwpCCUW3V+h88Vdy2xDoynE3vz3p558/oW3dzaEzMxNNBMeFbl86DgwtyTtdqUZLmP4gQsXF87/gCR9hWtQ2QFL3UOD/OSJ5MZw8UFvBZYlgHWjDfI4eSmqfry6RqOrg3RxAMR1X2OEZpVKVIgLpDhqB8xq2uOTjnFX8VTnemY4l1IkiOOm7Hjee30Lopil7fbVavd8fv4IFV9lKOJD6QyCEh6XIjzwvWYFSjQXVFmvvaCDrMZoohY1WgrHJIIAplaHZqJXsVJjQV8iAaIWMGUIBHa1ibjX4HxECWupsR50ynLeinLq+lqLzFnz62kXuimMOdDcb9B3ufeuSR4bviq5mZdEpn61Eq7drWP1lfa8fkzS+bcdk+MprhxFdHTl9foyQvrPAcc9Kd8ZMvdnNZXWzw/ddrIl475vMy3TtwUhLFGRBXvTRLSiKdmAyV3KBpyLqgiQgqAAxXau0xs8fm8xSbsk0Ie60cZov2FDhO54F2qa8i+uxfOrmyf31iqh+PZ97z3m2Ts73t2tJUbz3m/74m2fG3GVD1UoPR4PMJ4XGx+fgdu7XXv3UtZnv9OHFV/TERPs4hdkKiCJ0njE6aGeNNS6ggP7R/NOJBX/F3RfwT3fWzEKY63/lhyhEnDk3KuvjdDpaB0JQSZZ4UOC/SlWGAzAOgjismkHvYP3GQ4tHVdSuEFt8I0pc7KGi2vrlK1sw1o4Hj1sli48eQGqsbO4ZD1ALB3j7qWDxQ0N++QXZVSr9v2nQ5qZpPXbDRi4pN6WbIKLKjHNbAlGMWic+x0foQ0hon+TopUQUAF7vQN//9kbR2FhcF5RQ5L7v7CPMY0DWV/ZJrSV3HKFDYfaxoTSNaq25/xx17DaLEN6gu64Iy+FBVD3XIJhTcscWy+VXvvJgVcO72R2uS+HWS4fD2SJAREmdST6cxObRkVRcUNg3aeAXkWvdECrYwWNmK88Bq7eLCfYMP4oFr+o76Mb4phOv6KTZF5nAh3/wjUqN2DPr3xznV6/a336M69bb4GjzJANwSaA2TRBlOgB6C6IeoDhSaiTr76yitcINpjP69mWk3hABTpb/7mb7iwvXLlCiOb2PSH6wMHWhgDMZqEDWUccwE76PfpJz/5Cf3VX/0Vvfvee8dPD2+eAkpo1IAGxUaSJI1Wd/47R48xfB2OE8VNoFQ0P8t+RjZCsgL03zPVmzbeXQ+4jU40nvH+ZAr0AwYQJ2SiIiaIKonDGPb7NOwPqb+/S9PhgMbDAe3ubnPO5RS5tcWUNeqLo9vOCewMhLpfPrtBrzz/JH352ctw2eZnFA2C5V6btalAFhAnhH9j4xeck2AZDpT6WHGvVBYWUuP1n750jp6+uMVOuqBdHj85oJ7evXePjXXOnD7Nvw/zpAqxMmqYw47haCKiiM2yRj9s4lj4fvIg2/CzONeclzqbccPk0sWL7IiMRkWtsS+RbtiBaqJ4xD0l7sjiysuHCeSv1WK9Ju5DoPw3b94U+uxPfsKv/7kYJlRG82tw9L5bMFxTag3WOpyDzdOrdOXSJq0udWnr7j6lScrRJEDFuaFV43maMc2SzbAw8cJ0TKm27NoX2aaJarSMkTkj+Booi0Sp5aaJPgrGbkZjdji6y/SHU/v+7Xvm3RtbdHf3gKN2wlpCizR1bQZF0CqvrPnV02dM1mpFHHHlOJ4kNca0vJj74GK1dEGsvORRhEqNxHPBVHMWjdopEDOpJnpbY6+aM5vJcOEZy/qpggeP3E4s4ya1gpjOBOFkbwmS+Dah1RoyMeLe5Myw+yiap4WxdpbmedTpLSd53raT8VDO9f0zyoKOHEoisT/03pa1s750lEUk2tGIJJYnMKjmume9L/TF5w72qGDZjdcqioci1MNTIdbKvJZoc2NjWRxq79Oqdkld+Yp3TJaNGeqq9LXzLne1S9GckvdwQLwxcdSRtQ7FZq+ToK62lfMGsUvIesWTjPW810loZbnDDZLA4OI4L+QbJ8FPMnywppLE/vGAjG8RmXWmODfbJ69SAvFwcJUzldLFPJlm/eQlxNrMcqipl5i+xVV5rncKmPBDNgEm14JT3LW8X46IXmznyX6nnd2zsM2v3Bkysv8Nz6r39p637hYZP2Fm88eEBR4PGY+Lzc/j8JR7Q19yZH5rPJn9jiF6MYrsWeQwI05MgQY86qk2yfAgxFq/KWEMlBF/4KXAbIvmjrUSA0almH7K9JJa0U3pIcozHynyhsm4NIZzi1rsdurg0GW8sTaZFVPbP9h14/6+d66kiAssKbySrE2r65u0cvoUjaYzMvsHBP6HXyhAmgUMOk3jqTQSjj4YF+xKm+cxx5J4DblupdCZpcgLM0kS5fWMszZPKtoUSJuXmiSNNJ5rDFfSrilyvWabec3kOrqwkupeGmSEfQSYvRrYIMqTwXHOpqWsqS3LRh/e3z8JNpsTY47ozT7KCM1rLJR6xMocNg3Fsygcbx5R6LAxk2/WyYIc63YZuQbzqigpWtTL6mFpuLYPQnxxD5ybX0SzooqcTvR5FjtxS3SWu7TY3Lh5dx9WrwFRjcxCPfuJkIn7Tq9/0DceaSwYe3yko9A2PlCJazu79M8/fYv+4r/+E/3kzXebQvMkbdviQIg+sigRExJomj7ch4qSWGubDxbO62qvR08/8ww9eeUKF4pHToYWASH7EIUjNu9f+cpX6Gc/+xn9+Z//OdMYYRBEWuyh0PyTP/kTGLJwQYBisdNu09bdu0y3RQD/orlKg9otnLVWmkqu5uoqoznuBBOZBw12mk1T3Zv4Bln2EpdgUIgBKzG1UuKb5/DD2dbh+eDt4Ue4RWBUtn/QZyrtZDikweEhawRRdLbTmM5tLNPm3Tbt7hmaxkRRDiQopQkVnHmIRwSI5SvPX6aXrj5BZ1aX6fRql85trLNuD0UD04exY06iJjLQseSgXDDWsk3REM5PY+ijx4pJfamV00vPXKTt/QFMXujtG3fuu6nxO2zOA+ffqmJkjfW6iENptRiJnE4mTLVlCjZTmTnqx9e1pEgEgynMz5yHCron5pzJhMyVK7S+ucmNChSOoH9C5xuyPFE0fve73+XmB+k8e7Qd6RsjH9yHoHkjmgcI569iPMrcHHO2spg44fMfE7sf+ZIRSTYb8NTOcrp6+SK9+vJz1Ot06Eev/5INWMR0yaghnmH5yXQi+kqj0kUg3OI+Kx4I7G5aa0TNAnI0v0dIg0hUEOkX+ErBekjWXnP73p752S8/oPdvbVNZl6zxBLoq1M/wqeSV0VQGVbK7vAKDoNJGSel4XnCxMSb20thE0WG4qDMwF6QB5yAStYxQaLGGtbzxgZ0kLCryiRy5HfHXhlaN53871M7xsjB6pGj1hucGqwkopfEeOswWqRjG8Nxh2k68KYgLV8wgQtGt5fKYMoozavWWs05vxZTQtc6mCxOLb54bKUZqPXdkbZp6Sjt1aXMqXe0T/q9kgMfGsYkRXz8+gFqamtY0md/N65KZGmsm3vuW8a6F6jmxIvuBEZgwpY2LjJeI23DCSmfGk1lcW/Gxsa6yzgEd5LrUO+dkc8fENcTXxC5KbZTGNk3iDJPILLIxuM85PKS87uWM5odT+OMX1uujkiLwsofSxvaYsGJcL/bCYI0r5ojKV3UtBr3eh2aq5/WDzd5k54r3i9jsAyl7jM97lSWbIzs8OWsPnsSNQcdyprrPJFwsZmhVPi7KanVW1Wve+Re0EK0YQSdqe0+DxJpbPqa7VVQXdfYY1vyk43Gx+TkbxpqNmurfH48P/zvv6Konh0n1gueMS+qLrh28cu4cWgm4b7Rv9RzUVLtvQ2MNQ875ehszU8cWEXR47jy21QioUu+HTHM5vcgmOJ9KSTjoA9kSM8jocD/avXMrmgz6UDxIXp+X8KxOJ6dTKx06tdyi6VKbDrKEeN725v6O73waF8pnWdAU5iYoCEFXwWRV1GTbBh17g2y567daBiiiQ5yjOhkdu5L3zQ5YddAIdtoPdc7wRpC7vEeO4+gXYkNCzURrGrOHxhVX3pCDzB0oNdKlty1oUc19sjwTeE10v+fro48gQY3Ce4djxeYMHWl0yItiJnb3KlrhHDMtjrnodp7GBZwHaz4vkZ3TpMQfU/xu/LzYvP8ce6FUtfPcwB0RRbb0ejWKQhG4gObyfcuin89iAg8brV8tRdeoUypQrR+89kv6s2//N/qbf/oJ7e4fHjOvOXkAvTq1usqoJhCkRX2jUb0QNnceSOOC0Qo0XafW1+nS5cvs+hknyX3HFdCokI/4pS99iZEimLK88847TI0NXfWIHgAAIABJREFUAxmL0GnClAVaO1Bra42G+Nnrr7OR0O7e3pH3OI4QYtML6iTowDkcSIOz6cPOHx2tFAN1d/G1jbXcHZtVZXM/R8GQhRYKUz2GcM5PKhpSLqAzOujHTF/9sIFzANOfrXv36PBgg8ajIU3HEz5koIhXL23yw7yx0qWtvUPaPxjR/mDIRlzo3XVaGT1x9jR9/aWn6MtXn6DVblseKNX3Yq6z+lmYIruYV6nnLkQFsdJeNBAuNJqQZWsVNYDpGQr8s6dWuOB889oturMDLenJaCBT5A4OaBDHHE8DfSW0kAd7e3z8U0URcW+JntAZV9eW6bxlKQWhFpzha1zvc+fPU7vb5XMd5gIUoaS6YVC3QeEu1Q1YHHDl/zyftNr882iKAH0H3RaOyQ/K4vy0x6POH8bcf4dZdaP2/ijl3ik1/MWrl+n3f/NlevrJi7S/D0dkMW2DCy3n0q4uMbtnOBrTweFQ1iivBllGpBEF1j8ueGKlYirqtJCp2xzf4lyrRYQsZ1LIgClwd++Q3rp2i67d2WaHeM5ODqwORUZJP48TRSJvG6IocZGNJ6LBhFZSIlC8MLsjfRH8HRv5fWJnWhSKHIqFk5QrpUT5pFqGGYLeEwVjoZsPo1hW3ax9rOPkLKxU8zedxKHofkdayV43RZlkbTLCGWNvow1AuNaCOJtEURzlecfk7a43B7tGUF8hDoXuqIB4ev6a/GhjC0qSsctsxRHmpi4r0FitAf3Vyuda0OEIJZfzN2ujDbumyYt9l5PEMMxxUnPV0sjXXpOfNw208AeVF/slbin4CrpZiCZNpKHn8/uBz0/kfG3Jq3CkpiIyvrYI/mR+tKEGfvWsRzChW6038nx/xMF5XDTOMJfCcMfX9XLNKLePmE7sKleyPp7EsdiJhAknnfsrUagkDRtAWjgXw87D1pPIR+hZA7GOPkKwXK06zS12sxeTwSjQZarapZNpkU1nZSpyDVNaY8bOA/GG4aIvYmsnJnaFl3n2MYv2E47HxeaveTDaAFdK7ztRkr6cpq3fc3X5x752LxNZUF63vfFDmNQQ+YERoXQPxaYQF7ith2IQ7Ar2zVQrOtRgXRMcW6W8TNhuHOEpBKoLCXnUU7CSdpjkxK1WuaNiiId5rmBRv+XAk1k5ndBwbycd9feQ42miNHV1VcNEx2RZQuvdjE61HC0nJW2sdGhvuath8lUAKpoR/g6DgxkVNJwkNBjNaLRU8uIZRxKBglZhr9umJ86dNtdvb9Nen90HS2NMJQWyfyjRwammJZCOnMazwBUQZ8Q3wk3NVTHUREqHLE4vIpmF4lQ3t+F3rCzy6EZjw8EmGzaa02YWPvYnufN8sz+fvwxT3oqSM9ZwngNtrfkAvChjYWWjBOtV01ZUzs9qiUBRtrGu7T5afK/7hphdwKQcjQaz1GmZpU7u0yRBn92I9sKL5k4Md4QeBbelRXTmUx1+TsT5FQ7Di2TEpi3XbtymX7x9je7t7j/SAeCAV5aX2UUWiI9V2mST0af80IaKql/j/gUCiAxD0A1Bpz3u9LpoCgTqIYpMoEx4LaBMAdEM99Lm5ibrOIFIwkl0Y2ODi4G33nyTN/tAQT9sAAVF4dzpdMCAQPV05D5tDH2O0aubz7lIUQiFpDYsJKNSabBqgORUI+gXKG/NptvMqYMVO61KDM9sNmbk5lEfQrzuaDyhnb19jg2SvNBai3lLZ1Z7tNxp09XLm7S7P+A81T4jVY5R3uVOi9ZWurSxuowMWnaaDRreqgo+GDLZLkZjaI4hf14wRxqTHfJVVVZVVZW8cRSeAGylnZ4rx8d1cXONvv6lJ+nOvV167e0bD4wGYWolZxrLORwOBg1i6TQbNaDs+seEv5+kYUZhDCo4Gg5oQor2ftKgmoguAUKJIrc5x3pP45VQLMNRGb+LYhP3KnSaJ8WjfJYD57DbafHzDSpyeSwuBYW9lxtSXW3mcKLc4/c7i6yv9eiPfuer9Hvf+DLnot7b3mPdW56nHOVC/PkT6rbbfE4Gowm71co5ctxILJ1b6P2F5q3kQy/eP/IwNP7N5IxrbugQ/QWN8fbBgN6/eY/vWzQce90WN2GKqhb9dOAEWaZwqgst7k2J3/YQ6SjzhXizLrke2hh3Wh2hadkWVhbvViJdXYKMBz+XqmazkhayWSbZexTqI4GTUgH9AzrKzYkG+/KVoq+AxECzrY062krgJVNUlV0sL866T5arMvKVwF2fpc+uinE4cCrH/9nIkB1kLQXxYbi++PdqVtjZpIyqCiJEKi25ODN1os+1CUiyDUU7eJ0WYZoRF5qyGeNPg/TKDqRRYc6sOatc50WhyfOl8E4qeN6o6b4DFH9sPJySUm2g5wYqLzZ3UWSCUZGXuRJgQWYtpKE2akTTUso6rx4cARPmxiWyuStxrubolqqO6rLuMXJfO2SwszEbXI3VgZ5dfK2JKE5judq4kyRRDLcTnjWx2rDonUXBQNzNU+o+4pap6bAb22y+pJ61de2y0ahgfSrq5MRYwM8t51FYcgcnq7xbSjxX3u6xYPOTj8fF5q9x4DmYTCbZ1t3tS3GafqOd5X9iyf5m7aoLZGwqnUE4aImTLAwKxcRf0EwS5KmUidhXauttVVdZkJj+rGo2YqmUeEwEMyMT99J8xeIAQ0wppYVNZvBImbdta57D0AUtZ/XB9t1o5+6teDad2CiO2WNsVkzZa6e33KHTyx0OnK5N4m1nxXW6yyZL92xVLiqt5xvLxhgC9l+TGe0OxrS23PGdNKE6qoXSUlbU7rRofbVLG6eW6f3b99x4Uo6tsWPnXU8XsgfOCkybPWJEEvbugXq6+MMLxFHN/TQ684lI8iTsTL4GYopLh7xRX5PLW+zYahs4kT5ZNTSnSs03FbUGeeOPdBfndOCwCZqX0Y0bP7eIOWOzchIKfhQj1vicZg81J6+EfYyVIivhvBS22DdYPPkPVVrQm3DT4kaOjn2QL/zgz2YFkbq3e0Af3L5He9gUP2I1jaLu7OYmF3hAj7jQ1CIxFIsxu5VGDR22VgrbqdOnmT4LVPN4oXn0njGNYQk27D/4wQ8YVVrUvuE4QLFFgRkQURQGN2/cYFMg/AF19GEDxcHG+jqtaZHhMObOWyYUFIsmTG6xaNHCxs2LGtnMlELRlAgIKaa4eAQDgjc8vimISKovdng1igIz2t8gbzUXeIihCUY7jzLg5HzQH/oZskuxU7RKITaCTMM4A5v0jZVlevLcjN8P75/HMTvMip5djr1UExgn58PpRkoMc40Ygwu7LGJ9FJ6xNM0k+inL+bOOhn0DnWi9gIQCxHBGtLPIIwfq+qWnLnIh8cG9PS4mHjZwnj6Ngg7XHvcz4nuAvqPRiMIS/44/yGeFTvh43MkiBx5oer/f558Bsrl3DFF/1HFcMx7G3PX1wQ8pins0CKDDv721J/4Bi4wcNoGRf4AxGJqLmPmGwzEXhcdXI9yTX3n+Cv3W116kJ85vqomVo26nTWsrPTZpAmPnsD8UxLiu1OVc6DhO0xshL4FpUKgNwrPPOF5wCVqc+MOCHhBzrzmP3tPu4ZB+/t5Neu/GPRqOplzoo8CZFEUTJcOrHTdXglmXpSiNPQx10rxVmCgCkpYKoilhyIwimiaiAv0JQRutn2mvN+P9i7EzA/96Xm3lgxjjCsUTW7rTH2lyUVcvZzDGyxRfNOyrz0pX4jDvgCUqgbgSwQ6v6KWoTik14gdb69IItpi3cVxFcRzFUUJpuwXDIzMe9KkunVA9w/qntQwe3+lkbKajQ1/M2tWkjEw3tnFmCsu1q3geLRroNBTVIG2oZc2E0AToW+qEHu+sTNwM/5Hia00013FjNec0UsWo3EKd35WeKmfX8rwexULAtRz5YgFmYsqMZM51Um6pm3dVMdlVkmiUCYZ7EvT+SopLdiR2dZ3L0mUCA0LmV27oC1gNZnHDehLGC7tO4K/SRzMN3YsVEt60rDBVPqpiEj+/omkF+RFTIUWGKz7nDAKgMZ5ovAp/cmvtoXNudzJ1ez7wpT/LoZrUk7ZEThsF6BNK/1R9RUIXpVleP9/jcbH5axxJkpwtnPnW3mD4h+00ezmNkyu1q9f1jpuoVm6djXnIgeraZaqsTHSFMb7wcJo1DP3HC3UBO/urnjPRJmuFPpI+x4X+P9bik+PvdO2ZYB8HM0Ajv6u2h3K/Y96aDPrtnTs3s717d9JiOmbNirq8EaOay106tbpMcZrT1Me+dCX7VqDegrW/Dx1hN3ckNTSncKKbOlGdEptzo81ZllQDAagz3nitLLWBcppZMbDskiuLT8qf9yFDE7R08TW8oavdHM47qd7k31EP8xACNkc0j/50wxY0opmsqylP3i5PeZNlg7zShELx0e6/k2MiPHcOudNdlkybLUvXdNnnv2O56wBRChh2cj/J9IbPPqucQcFZ+3nRH95W/x/k/V4nY0NivMAzHTqc3KXQDbRlVT/NNzcBndNV2rv7u/3HRnifQGD+aO5zv+IRsTsh0a27u/SD196kH/7sl7Rz0L/vM4amhvPzC4/fBdoI6qIg08WRm8IGdG7BKAebcDw3QH5eeP551mu2VG8X3iec94BuJ1ywxPz6r732GhsEgUK7+D6XLl2ir371q2zogp/H7wJR+sv/8l9Y2/ne++8vfhy/cF80eWdATeFCXSo1E8da0xyJDAgjU4IDYqaIZa1UYXY/1e+RNvylQJyb1oR5I7yODxmqCyZU4gAp9M3mdz5BDuO0KGkwnAAJ8lknx/6ab1NBVYmbNXEUs4sq8jU5Ngc0Q73va+5ZGQ6A8AHx4lA9OzOWN5YwxIg5cF+tRJMUtPSM0kzmDxRusaCikXOVnU4mpmQtvGxErTYJgglMbGPaWFuhl5+9TLe2D+iffvYOR7R8guGoCdh/sGUGjuGUxveEexfHj6/H43FDoQ2OxscH5ojdvV1G3YbDIRedn/Yw6rP0sFsChSDYCriPKvfwPSdHvXRaHNsFWclwNOFLjCgkjv7ynp5/+hL9+299g168eoUbS6ArIyoLhnIcf2NjjooEio4/4jMwR7hQbKax5+YeUDE7F8zwCPPu4gghmqTfC00OoFCj8YxubO3RtVs7dGt7j7V1CWusHa/D+N1YM4bl+eSilw1i46ztOr3lWbvbK22cJFwokQSuaJTaTFSKnM/cLK8qdCj0yAvjOBuzZTjbW7EnSdRH27bU2SyiedimFQ8/foCkSLWghTICBuf9iTTafchmrHkfI29s1SjIiWkRm/CMyZsCVFoTkU1bbb+0vFaP9veNc3Ww5FKNpRHG70JjC82e2XTkZ6NDP52uRNMiM1MTOxtZl3AEzLz499oQMJE3vG5a8VIwEo+juhOp7b3qPNlsD/T4Rmy0MBpJkl/04BGadOg1aMxYmFOapjjmpLJCN9BC3gg9pePNELvJgi0Xl2UdlUVlOJN7gebutTFV6T0S5ArcwDAL1H+dfym4IvsQciJfVLWroyguxG0WxZ4JsXOE8hdl8BEzwUcfhiS7NTtyxlSGMB7NCGZIyG5v57Gpam9qNjjDntjghOx58m9QZW7+qkzzKynaj/yb0QYVbsPusqfRWOIBMW/BEQvZulmC9lZNn/eC83Gx+SsfPJvkzlfPRjH9+07S/u8z57/inW8jfcIbGrF1t3DHa460EA1lrLZmXkTP6Mb5yntfWHbs4VyiWH6CaRaZztk1T9qeRdKRTmORTLqmDvx9vU3x0BdaScXeGNBd5Dg4NNpW2DxOR8Pe4HC/U0zHFsgCePmg83XbLb+63PZn1ntmbSknCzlo1TftYmhNNebFoZl88Hho7kctigrWD7K5ENusywY+bOSdaygbDpPvUic3y+2W2dkftJxnFzE338EdGQugnjBYUAqxs5pSankR90JV4RHm47l4kfxiJTrPoqYjhNqGHuybuJWqdnY8mcEWjrLMcWHOtF17PJ/Oh/6xFr7Gh2ZoIGg1G20t8DDpoMhEASEbgQXsshlhMdIC1/tgE8vfA3W5xGsF7mw4jIUXCBipMe6e99BB0LIx5gxrG7w3HHlDMU+KS50WRzpEkWnWTUEPhHrEhbc3c8OT+xZPPu8wGtjir605Q2R6C/y0z9eTrDR43HrXbm3R3/3wdXrjl9cZYV4cASk0gfIZik1sJHBS9/epf3ioC61taKaR0iiNer9jMcI17y0v08sw+nnlFaa+Htd4hsIuaORAscVAAfjDH/6QTX4WkSIUiTAN+ta3vsUOtfhM7737Lv35X/wF/d//8T9yrmag+Olw2uDBKtfVxhXfh/xZ+n0pIJ2zujHzTg1NAhIZmk3AGGoxnuFIEfcRnWJPGux8RnSfY+snek2wB/hZc6Ci+djGJpxrLvQiK26hcARFcQEUiAVivtHcGY078LUk38MMKIpioAsujmIPx204eQckm/8eSbYrP7hqEBSbyGR5yyRZxiZFrmaapcwclu8h4/l816wXffmq5FQizuDvfvzWxy04vYTm859Mm3v37XBwTqD3hcEUNMCk91fOkgLLtFi4ysJd9kEDxc5wOKDhcMTzW1U9WkTNRxmPkq+KefbOzj4Ji9A9dAoCxXYymVIrTWhtZYk34ojW6XZyLjZBi/29V1+m3/vGy3R+Y52C7k7MeWY0GI4YPRdKvugyG2V+uABsaudops1Fxzv3WBiYixdqcdMaljEj0YSurv14NjWD0ZTu3N2nd25usSQFTctC85gbbSEFFQY70hhXV/gDq1DK807d7q5Ms1a7hLNpLZktEuLBhEg+qEIbwF7iRhiQYTMWD7dZ+XkUGlXwBVAH/Ij3I7wP4gW3IwYuRgwMve+IgoglPpEys0JVUkuCJWv8gGhWgdCsTuyp5HyaWBcpK/mc7FHYAl29vbQM1Nbv3rttS6WXN77DavoXaP9o+BazqZlNp1E1m0VSlfKaKzaCZqEYW1iPcR19WTG0qq7jDe1V2wO8YGAuCAg4Gu6NMaEiq07XBc7SLmtuGPDxxZx5rg08x/OCVdTTKn23rpAmJa3gglkibmZtdCDaVrfqPLVqFKFOuLQgOrNn+6LEw8zZSwF180rjx/zmGBhlTpPhLM5IdOp8JqyvkySdJLFNrbWJjayBAVaSilzAfrxC88Ejsrhg5d3tobt5t5/UlYvyPKHJrKSy8AEHMOxkbH2RxXFpf0U2tBNX8DU8IjnB4pqmVFcxXdys6OatiGaTipIU3iYAb2paW55R9QXIZnlcbP5Kh0mMjdbIm1dqX/yHuqLfI6LThos8pnJURuiubZ4IZaXBhBOxtoDMSKc7K4wRXgMS8Y3mbEySnCrWQiQqHCml/OAJFv8AEX9OMvsiGFO8zsQZDstvpvL1WizLvQvNRHQDi2LmxsN+BUSTqRVWOn34k2cJnVnt0uZamzqJo7IYU8yN+yLqprIJmpZVU/WZkAKqxtdciKCT6iJFxUSJxLRBJ0IK8maSZ6lZ6rba3XYGrYGtqQLVoqYTiK2iQ+USiiki7B5oDXOInbgrcZFbsanGMQ3ZQr2p3VhaNEJ/qOrQKyFIKG1mOitcVTkXx5WNY2tjprJEqitDQR3qYR+azzPdc6W1dzH+4ipxfMSmFwimmIHIQuIX6KpNnayFL6d1+2BpMJfLCaIkiAv5udkRd33F5c8u5F/hIu8T+TvaTT7j9YWweKDLDhoZAsaxoeV0MX1NMBl547Sgxgsau/sHn1W8z9tOkPUlY3xvLoj6HI2GS0y8yN++u8MF52R2MlojRQkdvWcMcSYjMg/ZjZQXWL3btPC0Cws5EL9yNqPnn3+eLl+5Qlefe46D8zlfMZojEJWGwwc6U0A4r1+/zqjSrVu3jhwbioKrV69ygQAa7dadO/Ttb3+b/vRP/5RdQ49REI80IwLC5dUABto8pbSaYz/3hR48B+GWRsOITW+mvB+0jEZJIwkFIjd+FE0QXZU+J0aMizwKxlgomkmeUZaleRwnPo5jG8lrqNWkbfThTQHuRJOJ90HxhgKmGI8ZQcYigKtsQ7YtI5y8yXWnV3vu1ZeeRvfe7uz1zY9+8f59XfSPOB54PdF4fPbZZ+nll19umhyBBXfjxg36y7/8S3aVfSBd10jcVT2pjoN0H2s8yJjrUd2Rw7X8sIF5GPE4OK+IgOJNvqtpVhbUabXoxWcv0zdeeo7OnTlFoFeiaAMmgbljNJrSAJ4GZUGsV2Oq4dG3NZpZhvsLQfyHoxGfp8zHzed5EGU4IGqgWB+MJnR7a5vu7feZGgxEE+gtPmYTWbX4+WvG1JjJCG8DbL7z9hItr5+uOisrwyiKS6pdj8QtVmmwjKsFW71K/o3dQRM1IMSYCa0V+kpfKTErZ4Bee3GitRRcUbYsbGgYcdB/APKkkEPRWekKl5m5s21w3oHnTGks74ckc1HYX+JFEdBErlJNGid4HhPDLstFofKFBfe8xYvCdNEyQtOHqsKlJjFZVEWxGtYvFg+Nc71eK+ghQ9zWEaZogHBhvJgllKZC2IIrcdDzUzCMkpNAbMBTqdafJOcWewyvtNda5w6msYppHNoBBjnnkawb+PhVFNMMLLjaVZGwoqTB1Ry7k41JY8bWFL9EZsE0ke/XiFP2Km7mY3ZLYhMlsRybjXyURiZN0zhC586r0Vlkm0L+Ux9C354Ox4Xb3h/ZwWiKLSSDG5Vk3/HexzvfMpE5byKzRsbsfep7jyMPmGRuN7uzYzOrU2ADRo9Oddl+4f/MzjvpFz9n43Gx+SsYGi2Se1e9FMf5H5RV9TVD9JIhc8qTH3iigeGwYtPSGQSq+5x1lx4aBC4QEXw8kMwg5F/6kjt93mTeInvTFDLvCmVFAUTvw7QlAnprRNPpGwoUFgDngz8F9kNtL3FOM10zsGwUxiIOycWT0SA+3N9Np2OpewP3tq4KspSaThZRN4OZGdAK6ZCiS7XUQf5iTIdDQf2ChNEHOE0iq5TlUNN4VtJ0VjXGGUKlraq6rsdpmkQrnXZrdWWJ8jS2xahse7ITQWBZl7FIu7SLKy93hpUKojICKitPJUKM4+MP68KmPyCNYrNOwVxuLowMdV2AN33j9KeLS11VZe3qKi7QO4ykkEhQIMRRQ5fhZUIkGaD11HXtIle7mO3DXa2mInMqS4BTTUPTDOVM04UPInv2v2uKzYWJqQ7OwCF9MxjuHTkJ/P1Vknyzda9zB3NVYH/fzgn5p4xoquOwaRjdGmsggiK5Z2ghCmNhYTESjYKbA0UmAlU1Rf7z+FybZmE8OBjQex/c4WiMk/bwi8jj4nBMP56xTutR0TxsSlbX1rjYPHv+POv4gP4E5DsUlvg5aC5BW0Rxqaim/8UvfoGNfnMDoHN+5swZpuWyIclgwAXm//uf/zNTHU847vAPIWel2SW5T1bAfK6HNHAsJWlO7XaH0WtB3OQhZy1UJL0+RUIYPbDs1mzZlTVOJasyTlITR5FJFMW0avyjE0gpG3WOj3jAfSNZjXwc7UkTESLGPboztzL1A8lCWkk7z+0zl85FX3vhiruzfUA3t/fsoxZcOowimsnDqO2s293YYMQdhScG0HjQYb/3ve/Rd77znYeimg0S9zm5GayZT4ofNrChPxyOWPvI6DbMlkZTWlteoReeukzPXL5ArXaL5/DIhHzmkjOmC6x3uF+MVY3m/W8WaPhATAWNqQl2LMFY6sRjlG4F3x8wRLl1b8+89f5turNzQMPhhCazQpDRYCR0bNMaChqr9GybRrR8esOfPnfBLy2vRKxF9nVkJNIkGMEFgaEWiNBDSiHJzvaemVOl6M6CVIJptEoKNk2RqE3vUl3R8Z2p54LSZIJuGlnfPNNu8bilGmPBz5GmebljzWMjWeKmUGdcy/scmMrCBRc+qOgSe9bvmZMch00ownw4v3ipkmNJYiMgWuNEb+YSHtPsCZTub4VwNJcN8EI50UZ5nsRx0mqlfG+M4QOhOnNz7HiCV4I0nD03p6NKHKwZxazrRutnOes16HzRbK9CFxrn9BRZoL42hThK8pPJeG0YS6OEKxyROZEwN8LVsjaaI+O4GGlCULDCpT7JUtbTc6M9iUwEcWwUdRu3/+am+xSf/qP2GugZZAAgnHcRQIY8YYkxFUwW9Ij2mzrhya3Z3PZsEhM9AgviUUbDkAsvh+s/q5hh9nnc43ya43Gx+dmPzDv/pPf0tcn48N9Za74WWbukugNY8e0yLZbNfWDPbMJizuR3Q35KPAnyNJEidkkn5rCLr2RCZxQoFmM2caKWxH8TOUZLuXOYqFP2RDcNS17MHJ2RRA9p32lYh+ewZVYYFJZ9yn2rmI470+EghWGHNREvKCiCMMG0soS6rZTtvKta/Ot4Vo+Iuq2EVjo5HRxOaTwrtNiTbKXgqEYqKcCCjSDyu/sDWl8WQyDJmSriqiy6WZ5StwvHyyVqt1J/OJzA7jSio3FzYZhFhIXRCRXM4/BLNcepGBwUsumic4106ZQWu1iomXkVZrRoDcWmTsmyTLsmmIapPjWKfaopMJWq0MWzYjJjpJOOy5SLn4qLnRr3uEDfPe5kFDZnfiHgPpwIE7zwGi4tKWDCZTlTiJ1oTd38ElilLtmFt0ETdMUYDwvxxCpcLJoCyzb9q0sdSuNUqJLajXNhgVLNSuh8kp+bGxxhKAv6eUocl23Y3H4+q001SMI1vrO9S2+9d5P2Dg/vo7Q9bIA2M63qR9nINtuW1dVV89TTTzfayjBg9hMMJ7iAUTMh6OJAnUVsxD/+4z/6999/H12M5voC1YReE4XBu+++y06hQDV/9KMfiYb0hI99BKb+QgxDauqqzYCPfj8xkwMfHoY/eZuyfMoBtYESL3EXgi5XVcrXltFq7GbTlNI8ozTNpSC1kSCdZmHLuBAU3/DpH3Qs2P1FEedhtntLNJlO2LE7zFyMUoV5CcF6WBu8t2fWev43X3yq2tre9/v/PEyHo+KjUgbMh2moWbvY7R7JfMVGGjmZuK/Q7PhVxZd80vGgYv8C4/dHAAAgAElEQVShw0vDlNSoCkY+zz11gV5+/gptnlmjNJLoB68O6Ky7r0rxDggNw4W1xDTNRFIpJDE1GxpiZFi38rQpHI6PkCmLV+pPpqzNfOuDLfpga5c1oVjHKTgeN+zbozRRr1TGio+xpla+5Hsrp8rl9Q3KW51VrFcLjJ8QSj1jIBXeD9IsVlIWCswGbcQHrnSvk4sjLEt8nJkjkiQeE5xv3dIyZKwrRcdLG9hxEWtUQsRAj2HTREiHtAntRcOHgtiU3piJ6EB9rnsiz48wv5atIjhMRFFtbJQITfeE6U69e6xxTANCV6d2pqw8/PPLOEUNbHXdDjIVnSvCp4903Q9GaBojUDjvtmCAbAxtWGNOx3EcZa2MNYYFcpsXmvDz45H/eR9kn4KCq5FQuJXkuO1cyhN+h62nnIuFUWQab4VAH/cLDSxTi7OxynxknxRZynLoyuMGDY2j2AAcSNLEpClsZmXqYHpsANGDOOyETPJP4xEmT2N9kOColdRVlU0YzKg99idB3iG1MTqFfA/W3vmyKGe1cbNPcfshD0cxNpRYiStKoljIiP/Kx+Ni87MbeAKzuqpf3t8d/wfn3L8hoied920xheCu20T3BS1PZhn0DkHlBM00ojVQz3M4anHKRMV5VaEDCKMgZFEZ0yZBUOO5e6hfjFRElxyU2wmRmci+nzf+VlEqLzQUM9N5BQYwKFBr0ErAQC2KaT4e9GM4rwEMjRN23CdbGerkKW2sLdF6r806gdoFjYJllDOLI+q2ckY4C364hZh73PfGq4EMJoPtwxEdjma0gteMQU9zMWvAeJKODBbZPM98ZCJfuSqz1hwtkE4YAe7lTWclX8zYaAdihBghVlpzLhyTVLHzBThQV3RCDxZH6vDdxI344Lwkl8MuUnDD5O2Vo+9t8GBiZJPp0dJRN2auLZovVH7BRXFRO9rQZ80idml4TfNBkqmd1Vr1qvIjR2grJ51Dr9YI89eZJyobOBi20oxiuG7qSZaO7eKCqGsKPaxM4c+Xi4Pc57/bZ0gcLUfjgvojxGl8NG3ZYnck0gKknueShuF0HuDnGHq45559ls6dPdtk6gVTmEChRRGKeyVoeoFuYrP/4x//mHZ3d82c7mbU0CbnGBTQG4Om87hT6H0f/Qs0ZNOt4fQfYvTyoFGriyyerCzPqNXpUJxVfA2kDrDNNXRqzoRrgu/DUZa/19AUjcbX3UfRVGbLI9w76lrcaneo0+0y3a8KmaEaCaONHsNxV5GldierL53bqJ+5fNb86M33zXC0+6lfRNyLiDhBwyMM3IdodgAx/5D76l/NwGlvt1r07JPn6be/9iK98MwTtNLtKM1R1j7QGweDKY1Gk+ZeIZqjXqwHJ5F5EDX+JkybHk2mNJxOaaVqURTNPXiaB9OKcw5uhPG0pJ3DId26t0u37+3ScDIlpSZpGKZVGqd4AcwZMguSi7pmWmS7t+w6vVWXpC3i7BMspM6VOkfB0RW33oAxO+OXvESt9dVNNlbIEwsujIFwIOhaw6wnVt8FlOKVcmM8BfND8ZeovIBrajgr8dkBvyVjgjkAQ+pKrcX7oCuXGdaR+kMGsth7gB1tp3rSMyb0oICMEgcGQ5KmxlXqyrvAIqKwmjmOewOV3pgoo5rSelpF0NPVsTVxbNzRhtKc9huYuyI3bVZUng8g+ek4ia8EuIqcmUj6tVZZQvc/Jc0eIKyxYWqxgvvqboFbB4tMKKeurIJaSkSJV1QSzQXsVkOD3bJsAC5KUkwmiRwP7/MSee6TTOVBluUAJsKfiCtT5f/q9WrmPX/ktv2UBz7MQE8Q3Gnj6aQ0e/0xcoc5q50Nd6IG009Zgubtbefpuq+KXeM+LfMd7Oq7lCRG5cdfeGXJRxqPi83PZmC/8ZT39Ten9ehVY+jr1pir1iBDiWY+bPm9bRPos4Y7di0tWCq1Wp6I+B1oJms0WbrID6zMe9K9MzRF5In31DGWOKCWi0QW4EvX0Aq9xTLdRKgmsXT4uIbi9csYSfvnAlcegkQKV37rpK6K5HD3nt+7e7ueDPtRXVUmsgnUhuykB1Rrc6VHS3mrEYjzPMfZStbDQTHPM9NCd248Y1v1sjpK52S+MXfEZC6FtTYME+qq8miKYVOPMHEWn1uidpayI22S7FM14xzgR3p+rbqCqg4UbqweyGZggXhprWrspl9cdhcKPNLYLiX9NHxZ7Sn6OUk04HeLYEX43DqtM2mo9nPODV9G04S366+5BXOio+BMSKmZdzt1EZsXw0Y1Io1zXRUMWpTH9LC6Ttk/cVPi6g/jtKF5MAOqYg3H3YAuQ0r3agrOBuwzahxTN4viI1yyBXD6M+l+fuzBCH1k2IWy1+3wsyCeGB9jGEERHkCE4wU6yzIDPdwLL7xAKysr8x9QoxpETODv6+vrYi09mdD29jYXkqAu7uzs2EVUKWwqYRYEl9oPPviAkScY/HxkROdzOppg+hPyID/KYK0SN8ocnC4ob7UowQZ8QWu1qJlL5C/Em2+292eDrKA9e8gD92gCZcntE41ku9djZBtRKOGZcrpJsmoqRDZCAWiXe934yvkz5sq507S1e8iUzE9zYMOJgjOYU+GehCHQd7/7XdZsfl6o1ovIkFXmxQnF/yMNfF5QHUEZBL0adEeMTqdFL1x9kr7x5Wc56qSVZ41WVlB2zy63If+V52N2YY9EmtBq8RrYHw51UxwxVCesIcNyFBPZOXsk2Ngoql2xjnRCN+/t0rXbO3y9UeByCWul5CMf/BJkko0UfQpxK6GhyrnR7Y7rLa/VnaWesXEc6zMVmNvidSMSMlifs5JEvm/6jGh6v6bLUSm/43N10qqVCRzpUgVkcuZlH5KqY07Fhav1HY88EeHURiL7FAkNOZb/REwH5T0PTIi40IzZpcaYqWfOrOZ7GhP7+T4YmDMbLyZJGuXttk3S1M8mSg1a0NJTMLtTN2yTpD5OM+dsVpdODf3mdJTgdW8aOqaV4k1przjLbFvvnMvYEdXadeP8MpxiuckOdlMtfCHWW2qEkh6Q3k/zhoQPhauZL9ihwCV/zE5aPTPQCOPisUlp02nIslGdjyQ2xQjtn7WVLo4jbzkGCkCwbXwCTCBShxcy7AxYsDyADZ4+o4f6/oF7rKPHgVwxM53CjGtKk2klmnZsbKywhOvaW8eFsx15R7enQ9+XyJtH33YcIVzjV1NL6WpGWRnTF4PP8dmMx8Xmpz+WnPcvk3d/ZDz9G2vMBUyOaB5q8VfpTiNSh7W2ka5babwfib6O65ORFiTLnkxmyO8ZIJred7l7KB2+2EBfIJVrxf5jC3kXRvn/OrEAPJuJJo8L2JoMP/z4uxSm4vIWaTQKByxzAWvITqcT39/bNaODfVNXJW+LYZ0O6+Xlbkab64g76bBRDJzyFAaUSQ16pSwnOC02wd0udE1909FttzKm4cIdtc/OfCUdjqc0KyoDh7+gS2GL6shSp92itV7XAC0t61qpgQ+fFNiizs4RSjHI4QXTqQ0OL3UsQ5GV1kqigG4oA52kAeu0JRfCpXVNYXFn8Do/hmiSCkwWXeWo4boendcaoDIgrIvyg8WPqj9o1d1U9BnuWAGqXU+FJnFjIPIEVOJ6AQI/aQR03Mz9TrzRPL9aLbuhP+20ckqzRDQd4TAVD3Va6AYHv/rD4qvmVb5v1srP1ZDsORsjW3KZnr64ieYHO31+nKKGz0/D+LrvTLBn4MWLF+nVV1+lp556qjGjIbWhr7XwQfGxmM+JYhMOoNeuXTuSqxnGeDwGtRZRKAYUWqCgj3r891G5ft3jfmkVD0FmPuFx+hCxIq8TnIQXH9jFcxHC20kNaWVKFffvB7zDvAp5EHxx/JD0GrTyFpW9LlMdZ9PZnAIZMvkcu5DClch02q3o6UtnzasvPUvbh0P65fU74kD6KQy8L5ogaHZozipTs//sz/7s4aZAjzx0JjTBFPRTuPcWtO4f9xVxLyx3OmwMdK9GTMuUi4ILm+v01S89Q1efvES9TkcbsSGfkTj2BBmbeSvn90UhCLFLEslzDGfOKRvURJTG0shjA6Iso9Veh5barYZNEqQKHFPhpKF40B/S2x9s0VvX79DWTp/XZm4EG2oyRgOphecy1hMn6mZdBx0Jvx7Q+eW1U2Z1Y5PyTpdTdrxjfhAjb1LQmdoIlXVJwvH5qBphc7BlgHQIexcX0EZvZtDnG9kXWdkTMcoZKzWX3WLJaLwZN9Thxs+1N1bfGdNmpcmOrxMjRabVvRb2R3ueaArDOe/Z5b/Sy50o9Fcz7of1P44MGuTEEZdHqdQNHhfWVGs0nqm0rq7SxLooj8nG7MRueemycg4l9YSLMnGGlYaDQycKxzV1Yl7UYcSVKME1gPFPVaoRoM5hwIpZYhgWxdC7ko3XfC8xVwXxlWzmLlBck4jXbHCIQGtl+UW6wL4wcrnwXmCShUKSvSVkP+RNZAq+9kguCNzdI2cqULg0hu+kPdFnO/AROs2DjkOs4f8h973VWJ95N5vPHPxRdkxEcOUtXX0/A++kEbZ+uEQAQiAvKm1FphXrs/b/LyTz+HhcbH5KQzwg4qec979be/dN8vQlQ/QMmoHe09izwB0ehGz6IPpJLTTdohAvxCUZ9c2XJwR6RNDKEXnS0cyImT6xudZ06IwBhoyVXgvaRcoOcGIFHqafWOcjmA5pdBOXoBq1YlLV6wGqStDmKorptL+/Q4e7251iNv3/2HvvXsuu605wrb1PuPmlyolREikNLUpq0WrJcWS3bYyBng8wH9Aw/I8NB8AwPDDgMB7bA9putdyWxMyKL9580l6DFfa5572qIquKVSIpcktFFqvevffcE/ZKv5AmSULcUa3qWnZL5mOe2xoKlDYAbaZ1LX8QMOfuGMvfVirPHaWxO+4istmJX+B6rd3fqoZ1WQu/kUtlTRg1efKSYHHAHcD2ZEiLVYkrs1N4kCdlXMHguxFOC9rNArGKVNXWxhyp4nFhhJxIR7D1zIpdRNx4Z7ZTRDLlOiWUtAVqd4ulWMG1I9T2CsUuJLXRozNNbX/TSWqx20Dc8HyITg8EN53QeDLUGaI2Zq7DU6EgjlAbSSpa2SPtVDuHVZomPHl3qXfpeNB3XGj1Whgttsk5dgSMWhU+5krUqkiLD/FrxjaMRoTRZ2vSFicT/O+dyQhefv4qXD63A7fuHijh/7Hf76Nfw8XlN77xDXjjjTeErxknENHEmydKbDHBiSIXjPz3nNyznyZPLbnovE+lkojm8znxFKqua3wciwnsNEg+zYWtOI9ajpwtnJ7WfSPehFUtoizQ4TE9bBJGLfxd+jJO0XMfk7aQIe8fccVEmPfO0XAMFcPCqkbOg/CjOnYdodHENe/33ZXLF+H733oF5mudPv7s/btS1HzSxfchC01duXJFPDUZMsuwbC40uZHxya8FidBSnmlTsyyfvEhuj6UjtvYoR8eFXyoWNwqrFsQNe+TWNWRN0mbce1sT+N63vgHf/darcGFvW+JEfYarmiTcZO3BztZQ0BHBlEb53uHGEMOPxQ6FlfsGGTBdgZuwbGezMxqIArgzWytnNhllTcC2JtP5WpRmf/r+LXj3w3swXxVyfRLvrMl75tt2lMzbzIMFS5tGtuzReKfZvXANJnsXIOv1LNTL+Dy0TZTNQC1n5BXTcpyI9NDEaBfeop039kdCKpLrbHTYKLpcuJX84DDdqIRosEFcYKqHF0qT3HGByjYqK3VnFNZKo/BcGEg5pNYjfGIlXomIokJ7i1ZxHaFm1gGnJaphh02SpT7r9VDUFuv6jNIvtWrhvIdyc3Uxn7rF9DB3jXO5D85bQ3fjYKYf5CIKCk0LSYHDeQiyoaaajxl8rUYoilpU6NtQLk3kDfWlpfFschDapCiEJnCFbTUk+ZPed/1RX62VrIh02okHGX7oOWQxndSg+Hpvo9tAX0UNjVybsDz8AfJW+MOnUHW5OKHlTy6KRqeaohuyUeU3ChJrndyBQG/XFO7hwNdZ76O3YzSSseSwIQHX1JAadbnruvBFX18Wm59waQKNV0KgX/bOfzdBeAMQXgSinkBfSR5aTQk2npaNcRtS2xsqPQrtxJFupD0FdssjIM0Se0Iz0gKysL0r00pM52tm6RSwrY8EntJQhMWy1DeKd5U1faRydTrRFE9OJdWrlKrUUU1ZNrOTQ784OfRVWbg0S0OW5yHPUkwwuL3xEMb9TP0wKZzeS2QE5iHPEMaDDPpZpjDZZiP/HTuyLHhwPFX5d96k2WtMVNRsShCkI1WL8AZvkFnKKrcD3jQxFq5dgdOHLakDghV3kgAArqvgWcwxSXVHbi0gN8ZR7V66ad+dVu/rKqyaEVWn8D1zQG1Jh6rotOlDnlZpbakNdCrAxA5m+09Tfw3xe1HH3wvb+7Q9L9SBE7fES3OxMcEpUA9FURIESyTaD2aIt0NYpd75Qa+X7G2NeMosBuAsx99antl0RcggrYUHtJ39j4SsdVG2+NmCz8qKJ4zU+Pzc9hh2t0ZS+NTNk0WXTpPj1OIkkv0vv/Od74iYD3MsvcrXn/I4Ey/I9VqUPzlZ/cd//EeBL7711lsPFPvhXYkT2qqqnuwcfAYaALxZcgIn956YHzwbsBJf0+VqJV6K90EQHr7wTJz9qBdhTNgft4Tny5D2ejCcTJSjO5+r1U5sCFijLDAaJUlkkvbSC1fhV9cFLFYFHJ0s4OZ+2QodbayKHm/xPcmCU6xEy4t5wn/9138tU/Wn5ZVpcD7wwp9tPvYoH2YH0l2P4+sq3V+vzLfY2ODnbrlaC6yVLYi2x0P41tdfht/+wbfhlReuSVGoXLi2AdHu6VnmZeq5PRm31BGecK6KwpqhwYzdA6waVUBm/8I8zURoCmx/Z15ntS7heLGE927dgw/uHsH+4QzuHZ9IIyGiSh4IY451dxCjf9J+NalyHCvxTLbD+WvPrfYuXU36gxGPuSpqQk0b/l2J2gOORllBhWglCrKN27aqw8qEkmFKQ+RGJmFUj40YVdGXU9SsaFesrWwaWDWzkAGd+o9vzGFEYJELVBkolnadDNKLPM7lcDfmNMMUaEEmnjr6a1TZnz9VdDEa75OyNxjng+EYZmlGjeyP2HHkoA1PW/2EYTWbuaP9e3R8kmOxPaCcqT/UNnEl6nm38aJsDLdsTdgMVZ3RKdo+NnupbpqapeyTNPGO97hNjtAq/21gr+YjJx7qCqtIoP0MrbqkN+FQIN/DUR88o8buuyVkOlwpH5YV4c8Ai6jlWab6+0faDD+9GG6yh2xFcDJfw3xRSIWIRidqBwqKHV40iPdKlxwFxPrjjprP/xZD4dlSWhoJjZ69z1Zv/FNfXxabT7g04PhRWVZfaQh/CIC/gwDXQWEQY0uN2Tl7iYg5EuRa8FFsIDrxPwSbKOqmHaW/+YZnw/zSikouABmKm1tByZt8SsqvZO3vXCAsmuGLpqlNNHmTra1wcqZLWjBkQ708WwI9w3ETKyzWRFzAYs/r/lE3VZms53O2O3GclDLbOyeiXpbApJfAue0B9PKNPHRiYiW6GzmBz7L9Sd7rQZ4nkhRqkMbWToDPCCdxi3mjxHOvfsssFMTS7A2YE7Qlxuw9lXiEyagH53cmcHAyEx5oWcZB2MN3iFMCbupeDMuygVXV+Jwnc6DEkw7FoVXe6f7TmapbHEwj0ZlyQc7nCbIRH9GYCO2UbqB4Z49yM+C27T0i8WL9Ca1WoE5UVUTAuBfxfSMsLHY+N+/VBilsQS5W9EYc1qnczdls1tl3V89WlOm8r6o6o0BuNEBUflEmcBzmIKEJI2g3QzuxrfpdoFPT3Idepy4/87O0cXcvsWU0xNk5yzLGi/KEgbXzNSurmniPTnZ2duBXf/VX4fvf/75MjkTl1CaaYPcNTzGZmwmW9DM09s///M/h7//+74WT+QjfqHsIH3v8n5lJsyRuNbAt++MUDY+7OOlnP8QlTwM/fkbZWYiPeFj4kRvXRy6SKQuLBVUTFYVar5Yi/ILSlIjOQwHED5AnZb0cvvbiNbh1cAz/8fZtmC5WMp3TQw1t8+5JrjM3PPj+Y0/Nv/3bv5XmR/slO42yuM5+hrNGDpiiq+zTjXIaecrDwgVsXv9p3IG8n1VV3e77YOGhKEpYOITxaAhffekG/P4Pvwffee1r0hBVRE4no49xoAkGkaQ4paZYPbZQElS7qKIOwMb7YJ/PrxGenXgphnA0X8Kdg2P3wZ1DePvmXbh9cCzIIJVl4Ynm/Q/1BgGzQRFSaFpOPXtNDrd34dLV58Kl68+H0WSnQYcu6FiUd/RESTGM3pIwMpB8BVkBlHKxfNM0PzFLjwpZcFBUxnEhtCK2uJJupJxLp6gqiTXRxiz+qihyM9l0HzFBVQliQZ3c9ktScUN5L3GWkokmYkqqRsu/X6nvNA4ksDkbQCrRMSDLy6KnLO81eX9AadYLVVEwd9LOGZ15LrRHyzSexWIVDqcFzdc5pr2IK9Wfiwio+NqgehbGaZH5YCmtZ0JPFIS3iohTFiEqi2qL0b0sZIOAJsDYwc5idBbFNl+h9vNIBalJGLrGqKLW9zkqzLbeLPqdEtETIfCtWtCpRondLXTfn302V4TzLUs4OF6IT61PEPIsVXTCBvUlOTkXnCnRXAWr2ifftmd9MPmmrtnvnAcfEHiKo7DEU3nglyuuL4vNJ1tJVYdLq6OTHzhwvwuIrzvnriGBD2IxYrLdQXgCzEXIjGcJhlzzrceTpuPmXyCQEVIis9ihlLBRZIuKayu7bmPmdTKSR7p8ahFhRoYiclPr5otKsNdVdQz63QZ1SXHQZUGDuznsp8gUhLJeTI97q9lJTqFxHNh4vwuhYY0xnAx6sDXus9+lwolAfaPUm0nFUxj6sygb2D9ZwmylU0vXZmooyYQQztGstsy7iiFrbIFyPFvCxd2xwHTFb7NWlUf+jNGwBzeunCNW15vOlliWbfe8u/s99MmPM8miCrAoAg5ygFyDcrB93LXDNXY61vqHul7NXbOT9h8UOGhxVvo+6bUaSIA0n6v78Yf4oANr/6MdaGIsGjcTz3gMm7eLt8xmqkFIpz6Qz2NlfM3WI0uX66RETvi9EdbXVt3SsU7KUCdl3eBWCJgkLAHfyVi634NcO4zVQNkIZzNESNlpJdzP/OoMh1vxKJ4GlGXJPrBRd+EJo02ERBP3QCpFJjrxLfze977XGuVTZ0IRxYE4oeeiUrleKQv90L/927/R3bt30axOnvr6rFy1plEe8LOM8QJPXhXyC2z08FlaJPttAqPxGKqygKauIFTR29bQD16Tz/VqLfvu1mQMr750A77z9dswWyykSFmuS+EKpsIJ1gJn0xr76CvO73379m2BzfI9+Jd/+ZciCnS2mOwWnA8qZp0J32Q+EeEdTghFsZXVlvkXozcpDsM+GW/4cbcfhc3eP6Xlyfd8uYbnr12G3/m178Lv/dp34fzutvInz04TI5qnKOHweAr7R8cwF0VakuLRvJ1l3/YIbWOxbhQiLSJE67UWFSGE6WI9++DOgf/x2x/237+974+mS2m+cjmeuUTeMypWt8dizT7xeEVsTf9lWsdcNu9hvL0LV57/Sjh3+SoOh1s80ayD1HqiKuv00BguS1Zrikd4o4guVpmVUMlTzzJseqWNqn5qAWmxNoGoqy49XKxlzkg0lFPA00o9BT1DfqVkriFqoMm8QVpLc58kH0p40Gu+ZVbRiZcmWwBFtBiZwq0z2UUuTGtUQiIDbSvnfeKdF9gWNsHqctpgD1o4laIrWHppWRIs1gG3coWSqf+p6RZ00DyioSPCTBuqCZ0q+oTzWjO0t6obZOiVAA5E5HcD45c/0iAf0ZquM/bUe1a7GI0NPx03OaRhUQfwgTrfo13+4yyOPo9rtWb03BoWhdjSQ1+GH44RBTz64S4cA/NXSOE4AVpgB/+KqtUMja8BQyo+8P1g3n6PxLD/Yq8vi83HW3y/XSCi1wLR68W6+PUkzX41TRwXdPt8L/JQTmEaZOL22Gc8vghqA4rwDuPgzUufk0mG9udqhRIWqogm0JKlQ1gGkkIFxdh4Y06LNgXtOxYNUnEg6+qh64AsnRWhAi0hnYrmqMVsBKKjHYvmJEH2wAocTzhDsppN/eHdW8lqfuI5efBZbl0gcQmRh5X5IwzzISk2Q5SQUc8/74Rb887NQ/jJe3dg/2gmUNiYHHAxKtCjLBGRFYYhMpT2/TsHErwXLNl+vIBLu2vo55l4lOlOr4Epz3Pa3ho1O1sjhvUmAkN5jJQzFnDcWC6q0JR1cHnqOCqVQeN8JhwL1VmS+F8HDHVDrmHZc5NB8hi99uykiviDeI4MAoTMAipTFXFTNLoWAuNsuiuiOS1ixaaStFHusVxAOwPBJpJugyZSDkmACN+JAce6z41ZeSY8zq4F/tMBdj14t+wSFtLIQOVD1tcqdacLz20Hf8YnMV5LNHGTxYkNw8GkIfG0ns6f34oddM51RB25rCsBkKXeG17nyZe9OImwSobQMnT25ZdfFuGVCJc9JUaDKN6GnEjyhJMLzX/6p39qbt68WdRs+rh5v1/s9QwjPt+rDPVntEXLm/osLVUCgizPYbK1LR6K89lcinBJZo2rJ40KhmSy/QU4uHJhF777v70EBydTmK1WcOfwBDyqeIhMReL+8ghORPxMv/vuuzJRj79/EGzzjK6SrMiUjzYLjGRhqB/HFm4wnsJBkI2ibG/5LCyGSF7Y24Vffv1V+G8/+C/w4vUrQik4y9ME8xkUteiCIcxTOJnPoWxqiYf8d4HIilqSwgK5kcJsMq6WnJcyT0QcihKKsmCl2epn798N79zaz2fzhWyp3ABuGoM1OXffuWrVa034hYJOitMsh/HOLgzGWzTZ2aOL154PvfGEKyUMOt4T8UCVl3C1xbI+C93IDUWiHjuy+pijlyC0HGBuDOLo852aXie0N5faY3j1xIQoH11pI17CztD6wOKJSbJ8yHMAACAASURBVM5soRw3RSVSrkDBv4OOyxapoJB8Xg6ab9UtpDcARPddAIvP3JZBXydJJvCsEBrRdDCw0CYgY8cW1xoORdmI8F7b5LGI2bVEU4VXD5xIBBWYYnHHQdMEJwg1sSThIQRNGDHOjWqZjge16eA0h3UBVGNPiZihDfrxyKD11DY0kY8S9TV7i8uvBrLweGqrn9uFBEVdw3xZwnJVSbGY2vPmHVaB2HsVa0/NEgiPQPJ1jAYAsndyoVm7Sm7dWV1Dz2dfzjAfcX1ZbD762gGgrwKFH4S6+QECPj/o9y4Ggi3W2CCCOWAkqvFESza28wIjUaW2eE9ywVcadzOzmVitXaxNjRBJd7j5j6WBOnOBnsjmi6lxF5hrWZn6aW1kcIY/pAZJKWPlQvq5uani1kbU55DUSJAA5jXgyjlXV+t1erx/d3CyfyevqwLT1MvR15U4pYDHDAa9FFgpVuCT6t8hlau1PFklFm4fzeGn796EuwdHUBRVmxG20E5uk3oHu1tjuHJxx2TgqRXk2J8u4N7xDLZHPVH5itLefHK4SM2zlLIsC1mW1c4hw138R6oE6drURcp15CFA0agAbZJ4DC6IertUTdLgD42vanIFt75KoqJu5O+50EydFppSa9g4NE3cIMvoudyzNJ5gUjlY+RDPQPTWpBgRN2I/0F6wjadnC4Nti08C6FTWbXc0tuqDKuXZz4mdjlW4A0beRvlzL8lNOIXIgfuKTz1iUymuLcL5KGboWp+2zQFtEGF2459JOtnIPNQVQPY524ZaVXsN3I16N2KeuWTU7yHLxxdP6n6yuWuTuD8PBgP46le/ChcvXmwFYcT2wm14sDy14Ikni7Kwn+G//Mu/8K9wdHRUqdjGox/Ckx35J1vYuek+qx1iyXqbRvYkzc/wWQ/lHxnSvHmFvoRtWbZ2dnTiPZvKNEzR/JKUEttRhRAcC0OxKuXz1y7A919/VbiI//Nn78PNu8cwna9a31iFtRLQ/XXjmY8ngXKzMFDzgCILTu0r1L5GOLde/fl4x+PP5WNnygQXrQXz+LuFEmzQMR93+n8exWiWeHj++iX4jV/+Fvyfv/0DeOXF67I71OWDOaVo+6HAlttnWabTHHjsuEPrw1sKD16fdYY/T8YDGPQyiacHs7n78ds3h+/dPsD5YsU1r0JrW60Bbj42GyqzidqJ72z7R8hFpgS63mBA5y5dp73LV+u8N4C8P+QSsNHhmHALY39QbEJI5uWiQdGIfyVKsTlhZJYJyzjDclZ2xVaGstqWxjup4IDN9CrZ91QtlhX5Tzj0BYDUaU7lzFKj2gzsGIprCC4SlJiKFqGAsKLyv043RQCIctPDKEkVawEjZUHQXqxpweK7WToYT1ya83lpGM0lYkEbAA92dBQQQl3DermA1XKJdTMmdKlajm5sv0jDM7lIf2G+iZdi37OQUcpD2IZijc3Hxt7o2hyS+6JVHXbBOapDCKyvkWweKdOJaP2to0iNVLty7iIqSvUv6s0N+dHrVB+5y/TpvCp+00cWOHvERWf2wsePUfYqLrC50FwtK5lylnxTeW/zZvSywwVaEiQrc4gDZ0qyknm70Kb57otQoD/F9WWx+TFLlLhcch0QfrMJ9W8BhF+CQFdJJbgZTzXljQ9RbEwMIEnR52kg5GoTbyHdCAPpFJLVaPlXbZBaxozk2lmTkJ4Rb9gC4WciPUTcTmKfyyP/Eak/Z2dqQYVtgQrbBJwaVGUoU1TmclJryx/aDULxAp5489fxXL1czNzhvTvpcjZ1yPCbBqisVO2R+ZJ5ysIGPciTCM/ZdPOcJGZB8PFv3z6Cg5O5TLOE4G7+X6TvCexsxFOy49mcJ0Rw73Cq8A7r3jKU9mC6gMuLEYwHuXlbkXT4fOJxPOz58ztj3NkaVwfH02a5YuVqSj6i3mw3yyjjUzfEO/eKef6oQS1vQsjXFVVFFagKROuyoWXVOEbqVg0AF5t83N6h/VLyq0UUHiT4Xur8uOdhlLoqS5hf4xrRDVBoj3duw+G4L80m7EBzuxBY2BR1ZLzMrm0Jdb7kBr4qNUkclAZTyiOzglEWB1oV9ZEOy6T3qygI5l7drKPgXqs11AolwSYbP1VQ8FRV7DrqjWruU88JkZ6FL+fGU86Mb6xjPcgyZDsCTgLni/WTv3snIedzdvnyZXj11VfFUiKK/DgTSTl7jzOUlsWA3nzzTXzrrbd8Xdf9z9VU85ncBx/xcY9TLKJO8tleRPYy/7RzqvuWmtRTpFl8/JKvIhQDB/3RSAq12pJg1YqTEMW8OU7Qe3XTuDrUsDUawBuvvQwXdiewOxnDX//j/4D945mJmOApBe6PW2TWSx+96Mx530Bi7VQLSoYhvDrJD6fuC2o5I5+8mHzcl/Nzx1wvnv6uyrVMh7a3RvDrb3wT/q///kP49mtfE1Vuhsg++K11EiaWUXWkOzBwXhsZzqYt0Lk/bSYm8YULSbY+4c/g3Y2VZ+8dTfvHEmMbEc6zXqN9tyDCWVLQpywGo6gIL3GbZB9Gn4rCLN8Xw8kWjnf2aGv3fI3OOwqNb+omsKm28S2DqZUSaWEIscDjPEVlJKBHClqxKShEVVmjGbAojxxdZipCZPjUYDrOxt3EFWnxOhDBIhSNC7Ww1b1CdRC0sOKcaWTHpkchfE4WIOK2K4sNiQ2RM3SZgM81xmIsnoIUoUA9nyT5YDyp+sNR5ZIkbZq6U19EzqRxJLme5qlmscbFaskQVZO+0ORP5VAxIM9jFcKEDcOjnAuJorWc1ombGIluMwFnlAJPQtuJhcLL6s3c7cH7O5mwIZM9uU7aKBCTTN2rStFFqkL7UOhCMOScN+W/2or9zKxp4hFs3vypxhsZxswMGDyyIcpjvoVA0mC9rtlKrx1okHnCO8eGQ4wyDHeD9+9h0RxgQw1xu6JixfwEmt6XxeUnWV8Wmw9f/FDtYJJ+3Tn3WwHx95q6etWJYBhG4ho/6VPdBMXLJ1G6FY1VgS1StlsTDDSp7ch7C9iKUavwimhe6xQqtw1xaR0889bESmxL2EqFoCCgvvLqpGixzUeOrOJWtBqKm5YqqeK52abkxC2aIAVnagWLZLLe+STUVbKYHvn58T6Feq1TyqpB7rClzouy7LnJGLaGPQliVVsAepnucfHJghPv3DqE2/emYnfCcFlJQThacCFZmyIbOklObt87ko2Af1Y6rcwZokaV3soKlmUlyQdvkiTJnpfTOOrn/vrlXfjw3rnl7XuHsFwWDEX56GLTxoRyLYmQrU+KmtZFTe+UdeAJ58V50VyYrxq/WDd5UVO/rslVTbDGohaczHkE49X4qPoqMFH9iNQ5mC4rGKQ+HeSeRv10PcpclXvsMek1cY4aMfYOWvbaiLM1lmy7913WFLUdTsLoXWnga8A2Chu33Ybn4jwzbMF2pLYnAopiAkyqCU9R3z+9sK4vmVaFzdqJvHeBodV5mkjsq0W0hlFf1IHf2fEypDrRyUplsu5gBe+zKi7oESXynmCZFzaZAnAj928/z+HczgQGeQ8Apk92zGfOxXA4hFdeeUV8NXlqGe+HaHvSXTylYpuJP/3TP4V///d/Z/uTzxHv5tOZZz5OoYEG/16tS0nSEp8/y8kmaQOQQqSyPfILbZrFD+twPJb/PmbIJgsbMUbPOcZkcjbMYilO0RkO9rbG3DDh/RPe+fAei83A8Xxpz2nodOee/heWKSY3oGQPcW3zzZngnE5kz1jaALTIj2d99/D0Ca2iGvZyuHRuV/wt7xwcwmyxhOeuXob/8tor8NqrL8FwewzFfHm/tYit7niI4xnTRqbzuTRgI2Q50EYIPX6+aQpKjGRjelaYbYTDqh67GtrNygvDabUpYh5mAmnWh6bh+7cMCszgiV1A4Tv7hIaTrWrvwiUabW8ncXRjiJfEkIQo00fENSksNRNVWeE7MvJFLt7KdNO8Hb+hNKjQS+oG3GQnJaV686QOEAtXVCisfS4LDtnr5WGrTWk1tTjndaKpOZFpY6girVi7MXJLGiUNKaeTc5w+irukYmBN9M6LGJHGYEfISrhQplne9IZj6A/H3nsvlAkWdJLGjfVIveQ1rZI8sjUNN6bVVBMj4ohEOlkSPGnd8+WKgpCt5sJGhhY21iom5hOC+ZNJr1a+N49OvRXh9/l2QxQiqgGcmL2YDYDdl8LbLFXdnxWqH9rpQ+PKtBLzMrCohO/aVcc3+9hTOoqfdOn786DlWBSHZbjymMWmPQTVqoLD4yWsVpXqbyQuHjRag5zD+T1I/b+7ZfkWNqHS8+qeQcv6i7e+LDYfsJxzWUbuhgP3KzXC7wSCN6AJ55xzpai1Ks+SOyElKUyVn/8BEvZaLqQm+pUqEcaHFCL53NpIAmOVYgc2cgeRG9jowy1v7hAZKsGbLE80uagVGyc0SLkzxQrhiJpX1Vr/jkaieqsZP0dM3mR1PCLTPzsyEq4pK99yoMiqquwvTk6yYj0XKg0Xkbwp8ebHxtLnt0dw+ZyK9rSrFTN1UojdO17AB3eOYDovbFqm3VznE+jlOWCPeUOFFJeldO4Uipn61vNITqeXrqH2O0miGJnf5UYtcTwc1Bf3do4no0HYP5wyv4HrPP+QLltr8BmhJkIkKak8WTe3GoDD5bqmk3U9XleU1VXwLfUFFSrrY+R1EC2x5KSjWSpF4mhZB1rXgLNVIxPgwbKGySBxO4PUjZxEuhr1hkjCJl50oLF2eUIkDrTXy2rmzc5OtEkELfqQBVdOKjMWJDTyyKZbbr8XqYc4JT1zwqyTXZspsxcoNwcf5zxL+qepD44V/IAV/6hVUZQd3ISinME9N338uMLmiz7lJPYZxofavgo/P8h+oVx8ML/s3PYEJuJd5p7Y/iSuONX89re/LbYnzMkEu2ZnGyk8vWL1TxZk+bM/+zO4devWJ/2OH7n0WVVVxAdaKTzmIupMvj8mS/k5Dz/bxcfIPB8WgeE9a9B/pkeDZudw/8V+hBWs2cUFBgsGcTHSHAWoS9ZqgaxpKKEgdAP5uUrM/klgtC9cu0Df+6WXiaGrP33vjmNUChdUXUGyZ7Fik45LOmc2xfIMUSW2HvcVuT8nyDUfC08yU++AkT38nA8GuTR8tscjEQT6ze+9Dl9/+Tnl4a0Ksel62CKz0fayRzQiEHQynctr0ijiQ6e/r/o5a4OT1dkPT+Zwa/9IXn88W8hUjZWHG9yIzUj05CJVnEdirAjys6CiC0GEAkHmN6HfHzbnLl+jc5evYX84cjr50RE1IqOkeB4nUFjBpijaCiNrkMQ6TcfnCyCxE9m2vqTEH6P0mI6EvB7EO1y8GiUEeEW6YiECiUBjZIEgmWyJJVwK6idn4vTkrVB0ZurMxYGzTnJMORj3uEC1dKvl+ES/gjHlUkVEh2suZrXgFGqRFNNrdC4djLZwsrNLxbJH69USm3oOLc2Rx71JptfGrhlPq9nrtIkGu+KAZ6CDJjRV4C/gMcUgVniS15jfo/BmY24SNM+JYllBfGnQ8kLWClDhx67P9ubZgHaCDmbt4ww+HSxA89sxlDYIlDZ76P1qDQHcFJAUxZ3STcxuuxu4ATg9haWBgW9g7polsIFwP/oyMS4Wj9w/nMPdo7lM+tl5gIXGOg1vPvJpWtb3KE9mIfOAzIepvzTJfBrry2Lz9PIhhK2t8dZrdRV+Y1WW368Rvu6d2wLAKQFMme/ItY1MBxV/wdKQra8lCEQylDoNQgMo6nlG4o3O+JsU1NqAp5lxs1QEo7fBVGHvlxlVz9m0k3V50iBqAVJ8BoM0gNHHlIPgRAGON/qx6qhzkUpRXVRaaCYHrhuzGCZj33lX8PGvFif5Yn7kQ1V4NUz3mliyYW3iYHcyBIYMcsAUiruTXV67sg2rsZWiPnsyX4kJdfwU3vz6aQaj0VCguBSmUJRz4EYrm3WzCiLDe5jLIjDSoOo9nMzXwYR0IofB6mtVvfXsGbXu9XvMNx3UTTOETcHhPyYpEZXeKoTx0aK+crKsh6uivlrWtOcE6hx9ROwkNzp67qiNy9IQ3lGltW5fxOyWTPxchN5i3cB03eBukdVb/aQcZsgcUQlYIXIy4wwzJlSRerkhZyiTdyOs0Errd3g4ZLY5FfM6zODSPPc2/EzebGvz1Yv4pe4inuYr7BetovX6rBAn3ww/xjRJMMtShgn56GQdN3kn5vJ0StRGxjasrNg0nUToma6nBu1BfU6DcLFJmknm/erg/M4ILuxuwf965+YnLjbzPIevfe1rokJ7/fr1h/4cFwoffvgh/MM//IOogLL657NcfK36vR4M+gwXXsqk76lcu0dpNkRPUXq2NicPW3yu2ZeyaWvAZ1r64icZzpPtRQydHG9N5DhnJzMs1qsELA/vWjrxlC3HDC6d26IffOurRZYlNBn+JP8fP33fM3JltS5+bmI8sbAV3v6nnOs5EbpLIM9T6dNyg/SD2/tiin/l4h787q+9Af/9h9+Hl25clj28LD/es1aKEJOD98KxTM2ft9Gp5pm7ioSztxEF5wLzJ+/chjv7J1KIF2wl5nCDdN4UAerFGRqoq9I4eqQW2tx9TB2lPqPJ3vn15RdfLnYvXk3TtJeGqE6q06rGwkbd1i1gIoeseeekCcmig2un9iSGqJAIxZFFkFSo1KI1yXRSBIAK5U3CEFEsVLjxGpwefhMNc0m1opxZcalbogoRJYLcYoQYtRV2Za8hiVP6+CysqO1LQ1ShQY1oDkTHE61MA8TQSMbdJEp7vR5meT+sl8v2/GH7z03zQKpgfk6Khk6WEFaVh3HO01KhCQmVVkxSAvuZC8/GK0pe0UQqiGvNWIS26cC9dnSePUeDUawS750z6lL7AJ/Wo9+YZZMVrhvB2WDFZkQM3KdEe2aRuhe078bXQ5rO8R7bSPK2P/cUl9IIzn+iCK6wQJgtSziZrgUdwE0kbtDws2WaTzzaHDVN85xDZK/8O8b95Yu+jvfWpwbD+ZyvL4vNzcocwEt1g78+r4r/BohfB+8GfP8FEKJ63YIdtBvMuwR7Z05QBXu4m7cyFOPKgJoDk2tpjJWfGMaBO32NchgEItDXGZl04ezvYWl8nW0dmrHZMQ2kmGU/piA43NwMi9muJLfaodbNh5gnsQaB2rKJsoCTgnFleNOT6SYpNKFCnYKmzrtBWazWJwd3aXp0gOInJrKqDLX0NtUcw6W9CQwHOuASxTszA+dQsFrXcO9oCSfLtWqLp4li5FkYPEmBjZLz4QiaYt3yL6P0OpVamOqwRKFb3H2bLgvhbi6LGnqVCR7Y4sTPO8x3Jn23NeyfJB4HRRF2iFxm/FNDPJ/aJOIMkc8rT3M9NTRZNPUroOdt7J1Ao6MOSKes03V2fz7FlKcNxcnZm5iXF6yZOzAtYbFu/Mkw6e31E9wdpCwwEwWTyBqEuAlpHVEgiP6VekQi3hFhtlEyX6KXuJGI6l/gwGxKtpzcJJ7Ed03hWY1NFB680IrL9vRZXBcLmiZgUwWXpgmlqWssAe/waJRfKMWlcYWi/yZDt+w97Di6JxMjfFccyOiTwkFPKzl/oqUy/e2Qtr1miQNWRkbmD/P0o3iEpPOjFkNoeaLJhSbDlTen5vRXYE9DNs9nBdB//dd/fdg7PrVim/nJeS+F8Wgg6qxQVE9lKv0o7xAHfa0I1s95kU1yozj1Z33F4jDLchEM4r3i5DhAsS60mIuTFCvi1cfR0+7WZPXtV16sB1mOu1uj/r//5H346ft3YLpYiihLXTcutIS1p78+Kwqz0d9zsV4Ll5KpAFW9llj4yss34Dfe+Cb89g++A6++fAPyPBOe5qNM+tGgw5zo93t9eZZm8wUUxsk+qw7aFUwVf78QYH86h8PZAhhdorBLs8ToPOoaB4yzJvdtLYryznsW0sPBcAR7F69U567cWG2dO1+lWc+FpnEUiGGoqb3XipDYxSMTSKP216KarPA1jD7E+3wKyA1uUVStVPeHcxRWfpXGu/xeAErsIym0INHPA5mGyutogNpHjZ6H3grPxjiKTgd5Ut+I3xGjhFFeD3PSMZ04AKjykExuk1iOMewXxFpFOIcGCxWOJ6l1Chep4gMamqapyrJwq+U8mR4fhHK9FgEm57xMLdnhrC7WgvhC55AbO6uipltHK7w134FRL4EM6qgUyweR5WoIQ0r5xpa2wiJ7MrMUC5qOSjwjzJuw1hSOMkbKodnFqYLwGcSTNTK6VJyWfmP3SWQS8XGxaI7CsT52T+3EkPbnYoXshP6FreTD0y86n7jvpijZsgywWFXI6BQpNj02TAVyHhgJ53TSDTeCc79HDq/5mm4HxOlqmN7y4N9JQrgHDmZGbbvf++jL9ZHry2JT85fdZll80zv4/RDgNwDxGgv6OYEgCmzWJoQ0tueWqyxTUZPunvaRWGWWKLBHlAr8qDiQPZmJwADU7kQ4dEZej3D9nvyZktgjzJUMpsIVmciFE/GWJpwGftVIOQwCmeWEvI+tvyaZAi4uUY2V86BYiEJhMzYBVWK8sNbRsbAZ9JazGR7v3/Wr2RQF08+FSSBRgr28N4GXrp+D87tjYeYzHEGDjCqW88Y2W5fw7p1DuHs4Vx6nKLUmAukajLdg68JlETE4WS7EFgW63EQ6IxxhqrYsfDBdrmA6X8J42BcPkbbusqA6GfTD+b3JwWDQTxar4koIYZg4ZwELjXJDFJV64wasvpcM+8EeBtiTruemiUxtvXhfdvmA2hUe/BOxUx/7qAEcLMsGq7rxVRUEdjMZJNDPEDIXzaC7GyydfteOv03odLHbvzfORiCbqtttKOr0jJNOPOQ+KK42juXPdEc/9sGxyQ6X/nVVF3VNlXfMqRB/NSt6zTxaOr8cK5v2SLkLXInKJLuTndmKdCxTyrOAAtt9gCX5Yyx6qsHPxe8QOxiBn8NAxfZgkF46t5WN+hnOmO/2CdbW1pYo0HKxHkz0Rc8lnio4j4+PpdhkBVq2PXnIemqVETeFFouF2C6UBr/8eS2yz/+0lsL8P18EntZmKklhaB6tc7+AYrmAqq7NXH4jGlrVjfOJS3cmI3j9lefx2oUdePnaZfiP927SwdE0HM+W4c7BCR7PZshJG/PpOYHjq+NNkfps88o73ExYPqXzcGpF0b6POZp4awfSKdPe1gheun4ZXnvlBfjtX/0uvPFLr8DFczvSKmYO5aM8C3Egrr6cKziezdgjWqgk3Wf74bBlVZ8W8mTdyOfytVOOqAXFOIfieNqEtsEnwY99N3sZ5PmQdi9cKi5ef6Eeb+8hF6AUxGPGGdUiaLeTSikWSTQlrEku+3Fue3Qtitdi+8bENzKmhOQW3uQ757YHe220C5qIPTN7Ko7T1jEUPcXZhtSeNBb+0byGi0Augon5l/K3zv4bxAKOpBAQupAWlRJHhlKgATBNKAnirUnRrtpZrkSGWIlyHHKc6NyK8zhqQk5N0LapgtOwe60Fgu4c8TPGQkJ3Dxbw/n5Ju8Menu+JnoI8ZSSeKnKdsNVGcNhmGtgOlMHU3UE1JepGdCgcymQWW1XyrjJDewapRVttUE6bQjQuEQdpVKQvmCJ0m/Hcf8vBBkbb/mGQ66BkrpxhzfZAfbJY2wE/2ab0CTdceUM2wObmgVuXVVJUDaSQhCxxBXrwoaFeUAmPLST4OgDcaJTGxT5CN9FVN+uAt1w1/M9e5n+0qpdvswvFl0Xno68vbLGpinmMmcdfGvTT/70q6+9BgO8gwGUVIBf4h8M4DUHqKddAHjB1/gCcIW+iiCNRS4MwV6VYZMhibtMdeYz1NaqOaaqgvJWnpkxr3EtyhqBgYL0q2YLwHTS5RZgrVl9e40RqnIQQ70QiW9AODCkhhZawkBDSQpVu5TM5ca+IJ3aBcik8defMhIqOSKGp3WJ21FtMj11dVer3xO7IrArY87A76sGFnQn081Snkh3eIO8ORUWwP13B3YOZmFQzB0Wk35jzkuUw2NnhYlMkzNbzGfjkiNvpmyAd4ZQWfHgDHPUz2B73Rck2Gk9L0sKJt0E0eTrWy5P82uXd1YW9rQ8Oj2eX6qbJPRF/xyWgFPqZkNoJWVhpZcbS2wA45AG2zRKJ4nOhYzjoeI2cWWhVDMWy9ZSO69k921HNbsoQxARY4zEjLecrVvglmBc17Aw8jPsJ9lMngkqmsdPReVU4snA5OucLI2dDFRyCALR46wzBfsy1nUxeCeODE+XdOdWIV7ftsDlyfrlRLru8Y20koxO9daGseI/iE6b3oYvYYRG7aC1KqU2k4jRFmghsfxJht3Cqs2oG4fKlkk+NqPcIS7BNDQ/YsWLP2cu7u9nOeAi390+euBDjROPKlSvwla98pS04l8ulPIcMr+VfKKJdlYgB/c3f/A188MEH8fO61dhT7zDzR3CRwX5yn2dE0ePeUuJlOujBcChkTZtgfT6+f4TUpkxh2N4Gl6Yw58kIC9OI0EyQKMUoeN5g6jr0PWJv3M/d4Mp52Nud0Gtfu0FVVeF0vvLv3TnAD24fyB7/4b0jePM/34WDY2108L3anBHz4T9zQpwLn26zAHQixPQPEbarVRX7vrahIHUc9PJMxIDO7U7g0vkd+OoL1+GVl27A11++AV997jpsT4atRdejLuXxOWnU3Dk8grc/uA1394+kYRuFv2LoedD2YbmLeSiaUq8xFcFgTRD1CHXiZL6wSGmaYz4Y0GT3XNjeO19v7V4oBuPt4JxjDi+/U2EKo7ns55IBETdhOZdobJrJfebE1GTZq3JpIj1cDJWiMBFoZKX80oSofKuGrjZvJE1w5D52BMEKJ4+hq5XAc6UoFQQW5zILFg5iyK1ROY4QkKeXV4Cop6KKQhEaoSO2TWGNC3P8Fjgt91lZ7YpfP0TxsJCpaU+EeoAKFYPFsZV9Bddx3qeY9QfkJVgiJL1+k/qUIcmuqkq7VfTEy6RaJtMIi+UajqYlLIsMoK/iUkwvYnxXYmrBTRPkezP9InrftvcgmUen0vcIdQAAIABJREFUnEtMApEXiSPFDFkM7aiWQ6eQjJYnhi7S6acXAUG0XEDhvI0qIgu9JcQa/8F72sMKPvY53VS2+Mjq71F0SG1v3KlmaEQn6zT7FIXpiZbAEwIdnSzre4eLdLGqTIk2uDqEBKmrlSE5FDc/WNtkF0XjBF5hQWcEWhDV79VF/S+pc//P3rD3I1yF90AdH55Ufv4Ls75wxaY8pHVwvX7/xu65C99arKvfRIRfIYQLQDQSfpoWZ4UqmqkNBpK2s4j0cUctEqNEHAhMQ6XknHTslLxdSS3C5HqU33P+L+ecQMjspLQ6txYPRJLpp1f/TClOE2z1VMQPqlRPTHnvxnijI4HhUkskyFUcSHw4GeKQoElWKxSRN1Fi/yvhPogCl6LSuPFWruZzmh0e5uVykaJtMChKol5UNoeDDDJ+N96gGtVgkzOGDoo6wJ2juSQfc4Fp2dZjMFnvU+jlQ8jzvmyk/dEY+oMBlDYhaT9LurYkPELtGKLAd5nQLQk2Qz86puqyKzVsZg57l89tbV2/tHPzw5v77x/P54MA1Pd6n/M5GypsmE4cwKoDPhFOiPA6NFHPjZfQbnIdGZ4zfI1Iazm77kfttsUoOsOZRCgLwLIIkvjwd+Yp584ogXGOG3XaTjG+af0BnFKwxXhstFIvV+yhTsxRZ+946khiaSg+kbTZ1bvMD4lQZwONDU6disRQL02wn6cZx+OIniVtv24aoqFpg6GqLCoUkWG0/Cs1PXo6/TnJ52GPEvaTYx41uV4vc89dvYAvXr0I79y8B8v1kxlusrcm8zW/8Y1viAot71sMl2XrE4byxVuTPTUZPvvmm2+etZsIz1KJ9lmLxfw81uMePU/tGO64NR5JsdKOID4v39f2EOYHjoZjyJIM0iyF+fEJLJcrnXAmHh1TkZsmCfasiofryDOiRB575u2/cPW88PJ533335j3m/sHfv/mfsFit4UF1F0/gInzv01woFlWuRXKgqex2FxfFly7swe72BK5c2IX/+p1vwC997UW4uLcDF/a2YHdnCwa9nnpLN09SPOtnN2L/1AAZD5A61+jjmlSqDuyMvqLFgzNqhURj3phJ91fk6ms4avrjrXowGuN4+1y1tXduMRhv+TTJcvQub6ICXWgpE6boJvnDEBBZr4IpOWPSSSDnIiYaJ0ivRhNuKhh9xdYnTF9FgH0QcSDa0eahTEk58KXKeed8ipv7rfkIe0EvSYXsMpu1yYSLVHH2ALWQZPJOavkYw3JL9QzHAQUhGdXaWDY2C192EmGbNHCRqlNQ4Yiq6jOurW884kkuEs6IBwwO+1Kg94cuyzKzCfGAtbsv5jdNg9oAF5YFsaT9qmaRK8CcbTFjnLZrB6Y0S8aZVEtOa8omHRi0NidECTcKT6FRZ6Ly0CYXaB/201go6VBHIaF4r2kBzOq6fB/69HFDrcDocwv6j1MQ8gNzAgBHgMQiUlunY1WL0PKfeKppr26q2h9OF3g0XblS3A70/euqcXF6bLlPdJYYyHCCoKvozg2UPQJ40QV43Tn4ccjcjwOW/wmAPwKA93XY9OV60PpCFZs8iZtsbU/Q43cWx7PfznzyX8uqfh4dbhs/rDAxHi4ED1HbPedVblmKy4VJcKM9MEPN8ZnwLZyEzBSzMoXIko3EhDMQi8MgMEMD2ptRH28hK3HQQN4IBf7RWIHLPlQnXCSiKMZSqg8KVgTRvkleZ7ZalBCap5XCVrYNssvfi6ElnBtzccsbep83ZFS1WfZ9wsXJESynR66qKtH76eWpdHfZU/PC7hj2JkNIUOF8UbuCkxEOePOigqPpCubzQgogZzLuXBiyemNvlMNwawf6gxHW1doebysujRtD0n33ap5VlFJ8FczZnK9lSsqTo61xAdsmB9dFfdR1M9nq97efv3L+J++8f/cni9V6q27CK877AUSIjGMIELf22MOUJ9I6sTNNOn+6nAM7bfpnHpqI7WjLPX+q+EQ4Uwp22TNAmEDjvIlDcCVVy+fW4JWF3jiYLZXXmCSaFPXBWQfbclsT8unCKFt6JBkjhbkmctiNXOxoV2BtXu1qgqqgy/dyqgIZzuTOEv8C36tqPm1DPOHLcP+AeUO5Zzn9lPI0U5PwGBzUA8X6JE5PuV0zmXw0ejzBJtSqTHRfXPlc4BUNHujrpvbcELlyfgdeuHoBxj/qf6Ji8/nnn4dLly61dhO9Xk+4m/xvXj/72c/gD/7gD0R99uDg4Oxwxn9ezt/nZXGhwrw8FoYRL8PP6+kl3bP7w4EkmCx+g+6ImxkItq875ZirQInKbWOIlkZm/zEZ9uW128OBKKRuDYfwzs27cHiygNlqbRA9hZeu12UrYPZpriaosNyDajlu4jB0+MXnLsM3X3kRnr96Ga5cPBe+/Y0Xm5eeu4b9LPMmqKcicaJk+ZjFsyX78rpA0Msy8egcHfVgsSxaX9NHoafF/bTLgweD2SdJ2urMZfkAzl2+Wp67cmM+mmy7NO9D2usvnXM5UBhZk5uZe7mJCbJSbKVaD0L14SJRBFeFdkMSCOfa0IYhKn9zQWKLTdzc5IZ3iurHzEfIHctMQ4NMMZ3pUSykOS60Fla5JYbjrkVQiEQ1PzNNCz5lWwbPOVLoLe6Zp6bTohdDVLMVESItZId2zlfC3xCYL89qJYp67frqoFAHB3KAtcoike2hmKW9Hg5GE0jTHixmx67mw9Py+MwFiTA4bR2sK6Bl6aEKNQx9Le39YGgmtttkv1T1Wm0k1nd56M4QZdKjdeIpLjKULMYnavjszcn3IH+tM83oGC9aZXodJrR6ZmrRGaexKr7YREXax4N7oNHD4DFjTVS3X0fO7ZkTGd/vSVA5dCp9syeuDuSWq9qxnkjTbAYV4f5ilu+noZ2GQBs2a1zc6d0LABMK9BXn4LtI8JMV0o+cg791Af45EB1uzMO/XJsT9wVYNkmZoPdfyfq97xGFXynL8nXv3GWGASovMwRUxbOlWZo40mmielOqZHZjHVExDEaRppfZ5BxU3rsHthlbAPBKYVAEi0wVQbgGqXEmvdUnkaXRmHx4bQbJguxHQsbjcda6J/AVfUjJ4LWlTi2lMK2lK6hmv4KOJFWw5c26ND6nym2roVOO4CLJH9erRa9cnIAPpWMLopphRBwMR32YMDxwbyzm37JpRvU7IzdyUrIqGxHxWRWVbJaDfi4FIitWcmHSn2zTZPc8DMYTLBa8iyei4JmIF5Uqo9VGWOcSMBptcfd4tlhJIO7nGVzYGbeWJR2WIhe0yaCX7167tNdcOr/1P9/58F5erKsbifMTh/g+IRwA0DkAOh906pefufsf9Dy0HiJxJwoW/ZT4WVu041LUMK9mj9ewqrmghzodfXQb4r6RNILFtsQF5cE2AMcL5jMCTAYOtvoJZImTCN/lZtCZo5RGozYv9Lvp9d9MF2MgE1ElEL5taNpGe4u97G6RtBFWmut9JTCmXIKcXOOsGQ/7tU+SJpBAdjNprJuPaXsG0RmdpNOZtSDZTgZOwYCe6qJTXwsxjg+eSjTACG3j+9aDPCMXz23DaNiHO4cnT/SeDJ1l25MIqxOT/n7fDh9hf38f/uiP/gj+8A//EN555x1oJQa/LDIfuLqc8CddZMJfDqIo1+dzthvNjdCEg8Zb2yLiNp/NYbVYQFmq0LpXRwpotZAwci4VmSCTuYabIBl85+svwrWLe3D38AT2j2dwOF2YJzKI8M3hyQxu3j2CmwfHcDJfKvTTpsWsaswfxCq/D1MYxo7C1yf+/iaqlmaJNDq3xkO4cG4XXrh6Eb7+1efhGy8/B89dvQjndrZg2O/TztYw5FkmvEJW5Q0PsmF5rKUtaEn061o9qjfsu8d6eKNPdLTMEFsTZIhtLwy2tinr92kw3ILdC1fC9vmLq7zX45/oQ6BeCEItYcoFe16yDG5ijczSepO55BSEa+HjMXpLp5m1eXXHgjS3Y+acYwA6QSS1WYM9pX9KMYgQ8xEuXBldpN1IU6YVb0z1IpeGuTTebWrKAYS2TOyQ97gdUIX1SvMZaaRrFYHy+RiVRO1zGDebGZK0Fvsu3SujfBJ/Dx4YlGpphSOSqS66LMtZjZacFIc1KvvS3SdY0zaAA6vOL3ExneFyNWS5XmpIPDqx3Td0dCtRsSYwq5vOZLshi5v61hFW3YbVrhXaGe5oVDTsMFI2OUz7hyoVIs+wTDZj+Hhs9MGTxBq+l8btVPzBReWTxjCy6WJhQ6Kc81TOKQ9PVnAyW5sHOkYb2VMD4U5qFR7Kn9JzzBS4bQrUQ8KdxtHLBOE5QHgJ0/RDILzb1PW7ROHAoSACvvDrF77Y5JuqKKvnnU9/M83y36ib+nUK4ZIY9Aaa2zNYiUEuuIENHJPOQ8DwjIVxGMQ2hDZJ/8o2RuoYEjfWjes+QJoEUiwUxSA5ekBW1jnhLhzDVEqDpcROG9ciqW2McRNlSMkA1cqktgcrE4EiFHgCkwJTO44CIuwWWElLJKtHoKKkESvfVGWBs6P9ZH5y4Jqq4KKHeXlMTJeNaNBLYXfUhyzz5ndp+Yc9sFxgsgH4yWIlEFrekLnTPernEkxdNoTJ+YvQH2+B9wnlWY6TyTYst7ZgvV5Cycq1JgMPEtTJ7AXAeAUg2kqzxRoW60qTFXVNbk+yQEwQr+zujPu7O5P/lSR+2TQNbwIZgD9kLRUCOG9F5ikloi714dR0D8xU2X6fQw25yPsEw1fUVoCiFpvihdpUBI2ryKcVxzSX2N+FznvyhddeQ7BMjm3PskQ7AtM1C28Eie/M3+RiU9TnrCu+6WZb8gRSkejfAaW+0wQIZMPF+OXsNVUj3CzZfLvV2IO4QtYNbnGaPNlmBdpRv0fjfo8Sj2YY28E2qydqUPYol5tOOrEbtUblAtdRpMQ/fdSn2bI+++LLYE3cGOEklu1P9rbH8NYHdx47MeWp5quvvioqtK6j0ts+a6sV/NVf/ZUUmz/96U/bv+7wXODLovPpL7HAsB43bz/p5xpGbP663PVMU0i2tiHv9WCe5TCbz4THCQbzFLXvVO2veCClarzi+yfTS0Y0TIYD2BoO4KVrF6Goa7HDCYZUYX7vdLGEm3f24cdv34K3PrwDh7MlzBdrKUh5wqMen178PPWBdTaBaY/2od/EqzT8A3tUThtdUhDvbo9hMh5CP8vg3M427GyPREvg4t4uvPTcZfjq89fg+euXxSs3Zai6Cqa4QJSWZWVApwc8Uo8zDaLNnIS/+/HJDPaPpjBfFMLn84+huInOE++ZMmhsIhQ3SDzBlMX4tsN4e5fy3sCleU9ymsCiJoJSwYF3AkVlHYhct+imspyAFe+dIK1ImtfMSeP8pCdAJa1l+gAhQftQUh/NaPcGFnr4M8cd9U7218zIhq+RhqSNfUZwSZyJgXJubvoT++8jgTYG2BZiklxcaDaCNOJDXqvehVhlNJqLiZp5ol6Nbf7EsTJ0OrCbskyKYPQQBRQBggprbYXBcBJmR4dp1RQP4s1sLjFTHhZzON6/B8fHQ1pe2nGDrAcOCpH0ZbQRD0Y59or9hnpoKpzWiskG9CQ543PGeO9NRKEJLS331MFDW3CaCjyFNrZLw6bDlyGb3YlTwM+NEqCwPJ1cy/T66bfssN0w2u4Ne2wuFoXkooJg7rh9P2BviQZ0+AAnuPgTYkUDsblPeDUQ8TDodXTuIAD9FAK8ieDfghrvpUC3AeFuK/75BVy/sMWm2i3QsCmrr1Ad/o807f1u4v1L1IQRZ/ABAhPMbzvCHmEYGWUskQ1N78/Y4qnJqSiQuSNHRgrfZkshPDLpnDdbfbL5ffqGsSgN1uANQ8Qpf18FeeQmXVKQaSdDTzKmw4CCx3vK0TTVN6RdlZVGZ0/IQPw41d+zIBQ3ymAdsNSwi94U39am5Mkz3LUdB/ta8fsz14EBjfVqMYXje7cHB3fuuGI5Z6UgcUxaVzXMV2tgq1GG04p2ediIvRjxEw5mS/jw7jEcTZfSrRX4bFVDwXYpgyFsXbgBW7sX2fMNm6Yhci70R1sw3t7F6XSK61VhFEGDiFgMkGq7gdbkelWWMFuuBbbFXbnEaYESN9O6qieDfjq4evHcnb2tybtHx7Pdpg5Ln/lLQOF5AHcOonRCvI6b1R3ubegPLaYjQAol9LBSviUozx26+YZMtlnNNkAdaqyYWQ4ZlJAY/DYy47Ez2NYAwOZhWmQH8ZbjviprsCxLFo4I0M+8mp2TBhHnooVI2LRAW6Gg098OO9/KbFhkulmFKAoU9eTdRqFXBxnxXAwtriVdCE7gtIQwddKsUQav+muTBbtODYoeYsEJdr2Y/1VX1bMUW3lQZ/IZFGF67Gzv4tHR7mQEL1w+B2+9fwsPjueP9c24yHzttdfgwoULLVeT+ZgRPvvP//zPMtFkYaDOio2jutOifjbFZsdA/LNiUfHzWL00g/M7zNkb63P4KQrdPK21mYQ46vUGyLz6rN+H9WoF5Wop/2Y7Koz+f9zQapTz5H2izSxuWvF9byiVJMlg1FPQiL23tCe/cuMSvvridZl+nixXcOdgCh/e3pcGJeeAy/UapvOF8iBZWWa1hvmqaBVzYxNMYH+BTdlT2J4MYNzPRdWcPfT4OZG4kKZw3ho+3qc0mQzglRev40vXr8B4NBQ+5vZ4CEniYNTvyX9PRkOxEQmmoC37m8CHgz1HD32cwikuxccsvnf4M5jfenQyhdlsIU031kV4XHuHYEV6MNSeNAQ80yHTUBYFzk+O3XI+h+V85tar+SDna5wkLklTnyRphc7V6HzqnHfee4axssdmFmlFpuDOR7XWWyAwQosRMykqWoq/e2K7gKmHcx+GpemFm8L1c24q/Ja8y5dMlfsp35cDocI2WNDPinx7wIIV52bmGydi0mmuO/ud6Vy0F8qZZkVudKPgZLophSjZqDBuXo31IxOb/ynMVlukARMfhpPtMNraocO7N31VrjuIodNLbZkYnVXC9OSIbh/sVfuLHbc1SJIeM6FI2an8f4Wou9ZztYkK7vo/Yqhu22yMfE4TFrSWgrwuiKxCaBQTjC4q+mMM+qRylk6EgWgTHRr19hSlZRMcegYumadu1+jpbgrAH7mINr7feD/F5mHL2aAoaQc+IcgwhPMkUalqlfcfuvARmrXO7luwe5BFNncAiF0QCkB6Bb1/3RHepAoOvIN3CMP/B4hvAvg7Hc/OL8z6RS023WK5uui9/0Eoq9/K0vwHiHhNNCMdrUA5BgGRVc+kuNzmbpZIXGv3i1tXc6sNchXYke4bmqdUuakZMFiTKKLidQIpWwVudLpUCZa7IT0TGiPZkHXzQ+OE8m+TOHxS+oTsJ6nxMCtDXkTeJuFmQso7T4YkHTw2Ti6tGe/teZUuIwKxytrKgEAiRsrxdL2ac2AK89kUyvWaFdNYCl3UUFmcZ9jPiSc2BtPUI7Xaal0GuHu8gA/uHYvZdExET6Yz4e7sXbwOF67coOF4oi1PjiHOk88HkPcHmKWpwKkarXwBeVPgzdFvTIe5o8cbIZteM/9nXVQ6RUo7GwcGFhtqtse9cOn81nJrMvhZaPDPmzqMMKPfJ4KLNp3u4vBjr891pHiioYqwKROJmA1kUEEP1pB2xD43kkEtgdZr4wBc6vi1JWvGCz+zQYbaJhKRtYGr48YY8rhoJ2WMmIQP81UDHK8YWluzZi70c6cFfhcVukn6xWSb7xlSZb+N807057b/5kvAkGX5TDoNRyYr+iWfbii+Q3bqS/ON3xCWVS3tPe/86SJXlRAkJ928p55l1f3tqNOy9HponongSvQqfZYrNlGjaAOFEMaDnK5f2sPzOxN3eLLARy3K+D3YW/OVV14RYaD5fA4ffvghHB4ewvnz56EoCvjjP/5j+Lu/+zv5/QNWCw17Vl+ZjzF1muDWp0WJPrPryVWBsR2ATEYDeOHGFXjh2mVgH1UW1vj8FNtk/7i/miHD7/GjkmQZjtIU+r0eFIMBrFdLWC2WTV2V6vnQNPL6INKjDlgJpmpKqQv4b4JxysFpHUHioYvokzT0ejlevbSHVy/uypY3na/g9v4xHHOBWTXCzed9nc/5uqrEDoTRF6zsuX88lXuNOaJlXcvElRs6Vy7twXjQg0VRwOHJkj0OZe/a296GF1+4DNcunueGJPH09vrl8/Dc5QuY9zKJN0lEUlgCHkRluTLxncc4seIbKZoMmViAfMSKgktcMB9NZ1J4r9Zr2RvdIxeaZnvBRXDgOJpRmudCqOfvlOQ9lYGdnkBVrsknSVhOD8PRvVuZT9I8TZOQ9QaY5blL8l7TG4w86yhkec+5JE1QJYmFU0chZCzCDIhMFWKRw8yQu1yg6XfVZCU42+hJTZK9QlxlltazpCAI01BgsFSb1kVC+hBx9cTChlOhGSFNRGFhgwjblsklUhE0B+IY16AixZg+lEvTXj++UmuSaDNjM+oIs6XgqGXDRGKUFqz8vVgo2LHCErqUj75erXC9mKWhqRy6NjV4+A6LqnZUrte4fzidHZ2s5niuN84T2K0bmZSHWHFvhnASc8lgy6TSfXJu2qxEGriWM3SPQQf7WJoecd4itDo2cuq5ApsMp7OkWVSb33k0eXs2+xq1qD+1n/vIHw2k3Gj+yv5xeqdkwp1xBbYYKmEpiLjGPEzxoXs3bc5uaFPdM7Ns01VZokLEh6akDHZfpQDuEiHnm/QyEsz4EBCbXwbEfwiE/y8g/QeATDq/MCq2v2jFJke4CQG9fOve/m/mWf5DIHoViPa0k4ILkIaSADnYe5JvBoaHjIwUzvzISFw+sYd5F9SnEmKsUGitmBH3rZ0ZFO+vG1pMudEcj5RLIDVHZUE/NQT+0IZMpRoYU26ze4XTIk8gTatBhMcwMcHRxuqGjaZLVGKhiNfk7goLAOEgggWAYIeAbUDYQkXEiXakE9bUVC1XUK1XTrH7qAII1MCwr96aF3aG4i/X2lhYaVU2BMcLTgaWMJuvRQK+lyVy0KxAyK8Z7ezBaHuX5cPRXk8OE3C9Pqb9keyCAqXkgzfxIR3EmoekRQ0+JhYakg3DsJHthmEyfCyj7jx/pQrX64IL0irN/IgQzmmRT0u773NjPsTE/FRPD03AhwvMTGCzNaTMwrT+Y1wP+D1GCI7W/9zyquW9SLqXHhJKoIIEGsyg5u8LUaAIjSig0YQ5lTNqYF0RFFZkepdCYkGjK05h56FSxUBRQ279NWUKI9ujbqN8TquKBQyCfBtpqBNSbZ18MNfYj9S+sPPNyQ03I0TOna+gMWAgiotwb5ibFiZaoOmuCRZREF5YBLw8s/j2jBcDDhyaBU8AadSMhj3aHg+lkfI4i5PiaHmyu7vLwj9w7949ePvtt2WSyfzMv/iLvxDO5gMWPuhefth60Gj/UV/39Bh0n92lENNEIZ1NEBjm89cvwcVzuzol+JxYrHXcpAgeOL/ASPiOpG9Is4wLT+YJ02A4qor1KjR1k9V1lbCQTlGV0mDi84AMA2Q0i9NgJVm+TdsaFmGngCJ7HkQnwHPuz0XjuZ0UtkZDnUY2NRRi8QgCYeXYwc0U/rnVuoC7R1ONLb1c4fYEYjtybmcH0jyFsmYf5kI8P/t5Dy5f2IOL53dEN4BBoYGxsCyulSVWWNr0Uk5MO8V80lNsvVvs5KkPuxZOjn81W8CHt+/Bzbv7sGaDeXc/B/BhKxiGlO2npCnoHKW9AdtxMPUl8FRXm3meBsNx4Hh4sn/XzY4Os8A+GmlGPskoz7I0GQzCeLJDw/EWZr2B+GCPt3fIM0dCdWV8CHVi8PzE7p016lRRYamBanOdzNVXg8NKO2XkgjCxZq5NIiWx4hxHvDblZ1CS7pWJDnobCHid1CljBZUnKgkVKOOGqUMNWpPfqEmN5lsU5WJLpUWiihwR1BupHEmC0DQ0SHv7wMTKQuJvWSYsCHSyf8/NTw787GjfNXUtdJCPWgp39awuT7Ojo6PFycF/OLpwPsuTbzbrZohNwKg6K/dfYxxCJzG9RIH/Yg8ixdKeSbF505pd/WxDy/W08E8bZlNbVG4QKBsfVivi7BX81En+1Yga9ZM+A4+yHMQGxccuU7podRwe4j5334qS/K30PgS2lVsUgdWzqzpEkMZDGwbtgEi4liKMmGI7FGrXEoHetobLy7GWUk4d4ebN5a7i+575xm9AgJcqqr6Pjv4J0P3fQPCmCY8G6OTzv4j8l1+UYpOvTR8dvgBIv1YF+hVAeA0Ar7CGiUBLCW0c1fpRDYn5AGjevig87crmVArLkGkj1qJkxqR3bLs+iO24Xp6CyhJ9bko1pmCVSIMrYGTH5MZpqNT0SGEfhs5MrIB01vlR9RWt6FDVcUWtgaeelZAiKHCxxBtnISAJ5nXqNLPSQlQ2K7KMKGkjmXJBPXcXneNA0iSzk0M8vncb1osZV5OsfKacgIZEGOjq+QlsD/uSiDRmtcF8kYqIDk+W+PatA7yzP5VCkLvdXi0x5KsNxhNg+EmSZZsBkG4ciD6FtD+ktD+QZipy5zppjcVPkWF4o+Q6eLYuYV6UktRzgOZg2xhRkd87TdJeWYW9xbK42IQwTRL3HXD4OvG9oMExZohWaFLkF6puhCghBNaNhRwKyLGU6WaEzT4u/c+2dMNkkBSrCdYiKFRBAyWlUIkXsgNq8VgxQDCvJ0h3n9GqqWebEQfjvoeY03XuZzBYR2bBWrqr0YuNOgbmYqbWBChqEviaZ69pxCbUgKEOPnZJopyaWXuaWTfGMa5ZeAXiacCqKNywn0I3EMeUKx5D6AgBUey8ArQ2KEo+dc+q6oyLb5Za0E4MVdfu+ROt1izfe/BZag0ReXRxMhomVy5siyctw/1W5aNRNdg/c29vTwpNFgRi/uaNGzdgNpvBn/zJnwhX87333gt2Lh8Jtnd2dY3jZdGDBU82ORmcUhLlLkQVwqM6qn1uF9l35dCR91K4eumciMgMB/0W1v+ZXxLXlN6BaoAfhak7GfMmv4m5v5VzAAAgAElEQVTQYDIqik8S6HnveepFoUH2xa2GIyjqUgeYAoVXGK2pmmsiDAr1L6tCxOSiTy9P4wQSK9UHN6rAhHEijM9iR2ifJTma558LEg5ZDZjtWtDxazMpPtlqgsz+ioMvi9L1eXpp9jSWkKMeTy3fMTy96Q3KRFMLTveg/NVFqxUOQEkKVVHBux/chp++8wEcHk+VEvGIOTjHH59mDHUOeZqHEGrBRiZpj+1qsCrZG7sIWa9HWd6Ti7JerZLVapmwLyTJHrVCB2uss5zSpnRU1816OReM5mA0Dk19ox7tnHPcQHSYlojeBwp9jdyChEkMtEWGyOKOghdrE0V2sod1LDIbtUOJvpe4RGx5maJmhC5wk79SDp/YS9Ta0HcDg8dqAUmstI8Sjo03mrLzpExcJaZI/93SfDvj4usmvp3ejNwK2/oyy+N0ee+1XqakLotsNZvhydGBP7x7Mzk5uAcNUz0aUW1FfISmgHAtm8atlotmNT++HeqducPe8wDNkCKKF6x5b+1uJ3JEwNPUJNptyL7LZ5l9YT1abW8ius7Kk8BuB9w875hxt6nT6Xtc97TNjasPJQkiQbvLH90s+YQLTc31kZ49zu2SmA48Yh9G7h0mxOp0XlLVJlAzXawXx7O1r6rQE1obnq5fY1HfTpnRUXw/3CADTwHA2CdfmgJOStxGrip1R7CWWKqOF3/rXgC8DoDXgOhrAegKAl0igA8IYB8RbyUOpjp5cI9eX39O1ue/2GRfyhAugfNvUJL/AB29gXXzFYdua3PzCNagEoUz8bGUTc3gILoZokA0UDqvajci/15JAUewks/SXaZSuAWxC2JCDj0qnThIU4Xs1lMIvj4nujlxwdsXpVkUwATfS/a+UkTyTbmy6qGn96cozXojfAdsu1ayLTBXs7JOnkw/xX6FxD8qt2J3aqbIEzYFRoCl03t/Yp3GqqwKOrx3Oz28d8sXq7kdOhPWG+jnCZzfHsHu9kj4JKK2KZMwB5iwTUcFt9hX884xnMzmCq3yG2nTrNeDnQuXm+FkW617eexoGwDvssI49T0WMoDxZALT6XHLHbCLC3Z+WigbF8Hsk1Q1EXZJLXiTTwojcpumvrFYl79cN81LzuMbDuGK9rskMGrPVf6Jvt2YDc3qICBPM7nIzEELzTNH82S3afs7xY94MzzhfyeYQEmZTDs3OeCm8yhFZ0OwLBqaFwh5io4Fgzry3bEw4IZDKi7JuKFr8DkrygArFvswCAn7eq4L9fXku9Fh2wgl2LToI4RWojgibq4Iib9qFZDW67L0y6LoNWHIDjpahmniqLYrIUord8el9v2aRjlYTdNSST7iPBPEXuETJIqo9xB3wo/s2dyB+xWJH+89zcqFzfKTJKnrqsKqqT1PZ168ehneeO1luLl/Aj9+64NTgicPW1xcjkYjKTqZo8lTzul0CsfHx+Kl+fbbb4eO4MZwM0V/9NURCW6L/oetljIUNvkBRejXZ6jW6hbQdMbawvqJj3+40uypZXO9dukcvP7qV+Dl567J/scNlp/7ws2pf8TptUA8SdE6OTiZFBk5mj729bGhwd7QoorsnUzesx7BoJ0EtsgSS63ONCm4sFMepWt/LlCr6d2eaL1ShsIwhETcbBhi6lOZckoBLBAPNL6bzmRVFD4mdmpxUscY0fmUDhLm6V092lyLzkjDhOpBFEj5T1Iuiongg5t34J//9cfwHz99F1irILEJ8aOsNE1ptDWhnd1zTZ75sFoshe/J33xZBaybEstl5UNTh6oocT47ck1ZYVOU6NNU0ozQNC0ctC5rXIWZr9arUNUNTo/2ab2aw9bOeewNxzjc3m6Gw23wiU8ChYVhDNnuxCvsVRKWnn332NjWfFmk6sXIm39OLN2cii1yftXnopM0L+K5d1+hiHYZWUVWxAkkn7ImOonAEKA1isnEXVlrQwvSJt7fxjlNBC2m3FLzNJPOLmmeJ5ep5PjHfp1cjK8WM39ycG98ePeWnx3eTRbTmeeJsbquqF/1Y5QAPMwfrdZlf10Uy9CkxxTCZbNCjdwr8ylXUAHToFAhpC5sHguLwtY8Nuaj+FxbQyyQPgzxR+3+ayENmgKzeCG6BhqMDZDY+BEPz+5D9yzXYz57j0ljjlK8jaoAYVLWdXl8UhTHsyIvmtDGiW67FfWkY7DIxiBHtiFEJzmVb993c0WGhPCCeoGKpU5lisqZiXVG+cYBCRwalY6LoqHCHjPXQ4DfBYCvrkPzPgK8mSX+bwc99yNy5awuB1AnqtItU5ZfgPW5LjYR3bBZF98kCj/MsvTX69C8KFLEiANr2qxko0GZQCpQVa0ZmADPnpoLioqvWhwyt4AJD167crJxFWpFgrU182oF5cv71xFLRQp15T9LjfwH1ukL9rNmsm7CnNqpk4irvSvz+UQJtyMLjEsd4UvhWVHcJB2uFJKLDGwVMT6ZcqoBM9pbluYtheZtRRbn+ed7MskiKteLJS5PDl1VLEX91otRtSaSrCR7YWdEo16vMeI799fEcWW9LvHweAHTxUoe0DTjyeT/z96bPcl1nPeC35d5ltq6ekVjJUiKpCgukihSliXqWtaV5WuP7fswL/NnzpMfxjHzMDHhebDjXnmVKErigoVAo9faq86SmRPfkqeqGwCBboCbYlJBkUB3VZ3KkyfzW34LKgw28MS0Wx1Y29gJeadXIzdf2W+UORreS7PMGDQ5dXDW1qBYzGEyE4sT0wgQid8kBw8kwZulzNGh5FdUWTWJIFNSropbsl/Z2D8avjtdFLSnvm4s7lLHmeeY8114JGYptpLJzoRUZxM4F3fniePse0myWQo8V3lDJe9Tq9ekCSP5jVKltLKhqgNk6elcHMUITYsbfJNYbGhaOJgWAeb0b07SZQHQNFQqgkEr1GNIzpwBZ+dHAN6cRwaj6urEtTKLokLqavPLLdnisAmZHIasvCfFGBVBMF694FClYomPejYwfMTgCrpME0vbn1vKIDTd6SZBO/0dRdzpXEdbY5SfpiHLskVVlq6u6441JiVF2j9561V/d/8I9o6OSSjoie9Nc0Jqs5Rg7uzsMFfzn/7pn7ir+eGHHza/9swl6KdIvpacn0crfX6dxllLnfN+10cNbp0k0tH8i/ffhb94/wdwfXeLAzTnv3yuKkr1SLWjAZ8iDKOXpDojYckFOyc6I0LrNXjlfTkxcBpwp0AL6RmeerRCbKcYo0CSh24QrL5GVDIBIntKRYbiry5fhbDSYabTR0U7HlNACWeSwcfMWASjXLSpEBNN2YRJoW9RwHA2Q+KvDkcT+PcPPoJ/++APcHgyFB6efZpbIigesvrc6KR+p1NjFhZ2ls5NTaGBL6GVLjDPKyR6RFGUOJ9LYMBdFZSOsVhzypf03mGgAmQFSGxET/DOELAsFmZ0eEBenGF9exevvfQq9Lcueepye+NNcN7q9DpeT150EATmyh3Olt5msV9DShZDpbhWFQYK2LiXBdWXFZoR8hHM6rFcwHdB1myKIkxEf1dLtYJfU1CaZcSmzmksZcUcls8MKz6gfCtIPDGhrwrapqWEty5LnI5HZnx8iMOjB2ZwdJjNRgOsqCtPyYmJm8r5Hh1urLvQm87c5qxwx9z6Ru5KKK2E7PaQfOZIlUrPX3G/5qIsJZBmpXjT6ADiKs9z5SSIjc2HLdL0iSq892TrkibWciG48tAEhi7O3jd3GE0KQ6PKHsBPZ1U5HC+S+aLM69pbES9UnBpimdpkbKypSF05BKRGki2ruuW9p665FZFE0+xOmqwmZ/InbD5fh05lEvT3CMKNgoIkg3XSgrkGALsew4sYXALGTskDHz18IF6zX6+C7rOOb2ayKYHqNVdXv/RV8Zc2Tb5rjL0ZQuhSFS2QyioDcbBEMHM94KgSkUZfSQxhSpLeaLjLl2iplZS86CmUKoVkl61lT5xdHIzuU04qdVLZi0IoWlqS5Y6xWcMiQk6VyFLeZYJW5GQXK3UrSdU0mxa+ZHVyNhpRy+Xs1AsngmW+qbpoNNEM2uUg4vJIG3WZ0u9qZckT1IIsgvn0Cd6l8+kgmQ6PDSnXebUeocCfqsdbax3Y6jNXE50TwD9hb8kzk5QEP/rsEMeLCoiXttZpQ0niCoG4gA7SpBV6/Y2qzZyRhGtxGgCYoGJrNI10gCVZi5ExlXMMsTLRAwlXwmrFE9N1JQzz9SwUkbFtlz7pxoR5WYZbnx30b3929MZiXuTWmisGzUYQX6oSA2+25iw0w4LzBJ3NoDY5m5J8cXys08mkcEO5JqHdP1GutSvuntK1JWXaisEa0rEgDrLKB2woN2ZMRZKiDt155e20qGEwdzBbOChrUXYjXibFfDSHkaMUlvyaeFERY8ILPnY5vSj+NJUWH0IynRV4cDzEwWiK13c3ha9rhLnFOG2DEY7IH8NFa40cG+6mk+7HKR/ks5usvE0ZN/ALGj4Dk4WBxScgBqbLH54fGBr08EmTDPNWC8tFYRfFgsVOWlnqr+9uzd759svh97futf55/ElaKzTwcZ1B6vBSF/POnTvM1fzHf/xH+Id/+Af41a9+xVDaFbW9x/mTPeV1f/5o7s15Teu/whEeA2296DegYsnmeg/eeOUm/OQHb8Lbr78EHVJqLcuvJAhQl4YY8T7dWhUl8nwlGLrwjKzO7dl5XsUWypLx8e/jLzTFpIcv/DSaA8WbYImpWLmvTaK6UuxXjtQTpVxRiq6fg4rgc73SJDddRVk+7ZDOq+WHez5f4Ml4Eu49OKxvfbaXDEcTs38wgDv3H8DB8YBFjgh2/LQIQSqmtkwNXX+C+eTYoK8wJ10EJqBWkJuA3XaAIg0wKy0uagMFK5wbbrs4R4U/iEbhwrMFhpAyylhCHId1sbATWuPjIczHYyLfVq6qq7Wt7TxtkYgQHx5EwclUBjcIhEWqkQpTJEoSnbqJ9ioJVZYFxJ5AuQXXpJNuUYr2M0Xn9FTMp1RlWOr2Jcq/QPkz1x8q8FgQCjkY5tSxdh7bqkjMN2UaFDUfAHMjsq9U3zbUyGNdh9kknxCN6GjfDA72YXR8YMrFTJRiUezGIkSokcp72sEd9tAaz8qNybSak7ARi0N5Lsx6Y8wYEIfBUffVrwUROEJuMjbuj1KURYUquXqp8cV2KbDqqxnLUFoJlpBLHk32IJOYUtsd0NSg2CPaEdx9qRb4zU1yUM9xrT1RTcXbonZ5WXqCTus9Vc0LckI3vmql6WSt16u31/vQaaf9yXxhD49GOBjPcV5U6NlfNyI3oJlzWBaxGiufM03Tps8scQtrxFD8bnQvJ+/XS4jh+z7U/dLblxLI/y+A8P8qgumPZnzjkk3anIvp7M26KP83a+1/RzAvgqIy+SYy6w4W2pRqBwwdBORupu4aFUt6C2qeftYi8m9gvoUgcEDgrHPtdPbUDWJJqQ7Ci2xwEJJAJQqxdUpWxyaf8dxVq5mnCadK73YJiuDR0cpWYWQjJiEhLxszWatwl7TUGndGlRhORJGEg0KqxA+jDxv9rKPwElVsY+VdJupTfFyXs3R8cmSK2cx22zlfxYwsRYKDtVYbdja6sNZu0VNhmReJ0nedzGq4dzSEO3snQIlnK182ivjX6IO6Pd/fvrRo93qVNSYL0jSUJGMZVYBNc0hbPfAmYWgaCQanScb8BJb7VSsQr2qBC0qgRnM2CqfkFqKqKXtMOrh/OMTff/pgc/9omNchOGNNviwNRpydStkH1CZ18C0sqjZUJLdIgOEY6nzhI4qdizFKIRZooQ1FwxJV7qUgx2i+w6T0s9Sau1kCn4GBDUTzNpHQXQjjRek7g2ndGcxrmBROeJmC2+Hkj+5frcbkp0Ahy1Hx2peb1MZYlVtCa73GlnSzsazKZDRbwHCyYKGL9Z4lT1B0LiDZEdA/9LIsSWyaGMamqCWKPADWsiAIBVJPCLqsJFj4VBDAzxn4XPc9XST0PbIsb6MVVXziqVGk1G6l+OYrN/xP9r8Dh4Mp3L5/xGJKVLwpqyo45099F4IpkljD73//e7h16xb8/d//Pfz2t79lqNxKkPws1x/3mgvPYUxEv0lDVECRH32v1jxPHBTVWsuFtK31PvR6beEqrybgAhJwcRmcB2O38iYR2/nkTuX53x8fKqp8AaOZzzM996dbJw93OqFB+4gYjMbUDjX5WF2/Gh0/u2kDLiPH87zIGOHoiSI3sN7AeDzjpPL3n96FT+7eh9t399jmZDKZyzkHS2P5p5mjoMFxcJRBzkLwVBwWmQerWqoUQ6cJQMsgdNMApUOghHNSWZjWlr1L3MoX9IpkZhhn5Mrz+xgwJNJUlTCbjvDBZ59gWRZma3Il2djZ9d21DWfTnHibbQhuTnVQ1sUAbEvtm7uPsS4QlGNZy2yFLEQtDENJoXQ5uWsZ9QZEhIjufaUCLYaL5GIHoAmRaKvSa6h67r0Aq4WjLLlcEB2NCXVJjTFUcCnqqgiLsshc7W25mMPg8IE5Odwz8+kEF9MpFPOF0GlMfGRiscScOyAglFgVfD6elVsn48WUHN/SJHGm5j3focEJOeD42g8DhmPAcB0COQagxdjt1g5/lEN10mll31sarq6bvY1+dqogdOZy2B9BJS69j5RZycpJe8FzsT8mY9/4lhpqP5fWUyehArjGfSziI2avHtFMSWirlaed65e2pt955UZxeXsdZ8UC794/hE8/O8K9gxNWxCbFXirW15p4Mn6eocx8pug+q4r0QRStSJyLiwSe1X5ROva8f4WVSSYY+ssG4UYI8IaD5KpC3f4fhZD/UYxvUrJJCupbw9H4LTuf/682sX+HFl/Spgx5C9RSCcNICKEyTYZo8qYaKkDaSkV0WgyrCIKpkBtOuzcL5wU1acDYXgqC1WSnYxFHjY5K/KwWUt0ztMlWuh3m0l1t8Es2soeUs4kCOWEwRyIbSkhM9OQUhrINkT6ODEElzH0ppASfo7htEDczVXlEkG4mrivwjVGM0d+I9AARTcLwiarC8dGRGRzumVaKeOPa1TCbVvjJZw/4AFxf68ClzR608mRJlCPPF1exR9rxZA7TRcHwzlqNGmMXJM/bsLa9CxuXr5p2dy1GUEY6iiE2xniW6FDL2h1odXpobMJE/JSECazhZHK5eUpiSH5Jh6MZHA1nLEiEqmBLm/NiVuDdewfw0a291vFwkvgQKmtl50QBeiUaUCi4WLyq0lBjC0qTMa034MURVBcbS+sUDy2uQUuiWUhjmr8jGVlSsknKtMfTurCIw62OXVjLlj2zRRWqSeHMcFqn47nDRelYHtmr/iSdTZSPg5dOZa1wKnz4UKEcf8bBgoe8cUVBLeIQPJuUnAPD1RtaCPmeTucVlrX4aJ+MJrB3cMwJFkHudjZ6cHmzb6KXnWPlSV7mkLdy5veiKuw9csgkRfuVi9+gzw/szh1qxkHrNc1bJm21OUjzdUXfkdZ0fuXSBvzwzVeSew9OYFGUMJotQmIMl1XnrjwVJFMQQFYnBKWlJPODDz7gRPM5ji93cX9lA1fAZsCwe7Iuoc7xmKD69ZODKboX5PVIvE0b0Rax+758OUUuJFEJp8UhvqjxDbh9TBFh6S17cRUpnusaRS9UIuuAxMOKUMtHKOo+8cLwCY8/KodQ3/AxLR79a0bgaKLGRby6ZruWB0cnePfePtz5bA8+/OSuuffgIBmMxob3BQH3qM3JOQJ6pVIsygAjOu4cQp4QJjTCKa1gqLhzI1CtPAnQTjy0kwC92sOsRpjXBuY+gdpzrVC2e9Lnkaq29r+FuIPGcit0MZ2YI38nnc/GZjoa4PbuNdff3g2tTg+QVMidc96T/yadr5xl1nreB3GLZA9CI6on/NapJosMMWSuJePFoMuJqOFzhiUgtM7K7dIg9nNBYhqk5ClhT3J2EZCMVYvtFcf67F9iJGxxriyLxXx4uB/GwyPm2TlX+eHBPo6ODw2pIXMeRgclJnwukSBQPC+kQBqF0p7uvqH6rSzK2o6mFZaVL1uZnRvAjKbFCyeVBIPGwCg8rBUdFxGbS5RRiDUpVf5rFrxp8paHznKV14UlwsCwILQyWhskHqoA4zfKyunpBu3dZe0YNSsCS8Lnpi+cZ8nBxnrv41Yrzzut7IWruxtrL13fSXY2+y1S3t5Z7+HlnQ28t38Sjk+mUBQVUjPmwckQjk/GrH9hlWfOXvBU8WCimZGfWQO9Ts4rezJbkJYDrMA7wnL26bFl9AnxmrvkkIEB2hACiZ383xjg6I8h+//aJ5sKK90MIbwLSfLDBODHAcK7EMJVVhaDMBdRPN4hlG0IHSTHQq2ENjiI5Q2zsY29ZA5gotIx9JoWJ3pMUmfSbw5iYuyV9Z+vQuf1d5yI+URdVNEGUD6oZf9FTiK5wofyZ15oqUbSpciJYxaQW+0g1xtq5YByxU4aXHTdRDoWAcAl9CFUuhO1VXxoqt8p0wR7Tqrz6EO+mE7d4d59HJ2c4GYvZ8XMhamkHZzl4dJGFzbWqFEjpFPLHEkHo8kCBuM5G3qTYfZ0VnAll9QIPcNvbWh3e37z0tW6v76TkmJMcF4ALMu7uvwXnTZpCt1+H3rkK8jm1oY7ReVKkM1fFAUGOpoWcDSacrIbFQwJiknXtX80hMPBGKfzBbVbqaLpG8KDmkiHKA1IyrCBvDMLTKFOtFX9lURzy5Takwqud8ypNWSaIlCjIGq8yqtoFc7cBExesIjteeXLwbR2B+OqO5nXLKCQJ2jogZjVHnytVXFkaw7lFEm1g8p9WrCJXRmrHXZQZ654fV5hrGVTmeM4y1AQFE5GU7x1bx/GkxnDDGlD3jsYwNFgyoIqN65sQf3SNcipuJAYTkDJbYzWUd5qs9UCrnQ8HzOeyTMzhCcB7S74vro+szyDbqcDdbGABfFhg8Oq9kme2nDzynb17ndeCrcfHCS/+u0tZL7UI96LoLJkcUIH18nJyfNONOFps5XPuw9f94CET280Ddw+imBQxyb4p+fB0K8tyor3vrVeh5VEnQunz/1YxL5Y1zG+yR9PASBC/OWca7HP88WXiw1RAkiGeRYI/RPHaZRvE4uvgPshctETVkInfYMKRpMJHBzRfncEe/sncH//APYPTmC2KODB4QmcDIaGoJPU6Yq0gnPfcF1g1K08KhLm2m3kDvLEcwE8TYwij33TKGcBCUo8rYd26mHNI0xLA8MKwqxOQ2XTULiA3hEV3jWyTmwJ4ytGAIjiu7GuKsP05AirYkbQUywWi3r76o26s7ZmDKk38RHjSUJQnwUmwDtVfY/3rAwiIqTWXCFmSk7jJMqRaxFKJK9N/j3qTi5Ev55jnqAUjyDMjqCgp2AgpmGM3LKkgkOwyc58OvGj4WE9OTlJTw4ewHR0klK+T3l5MZtgNV+IGEZiBdas0GYK26hz2GhK4dP7c4V43gAep4n5Q5aZBwChR1m5fgHSLuiLhi6SNRvl+u1YGV/ZYzmNpjNWAVwcCHltS6uYkIhuhdVPX13IYenI7htskqj+8ex5pCIce21SfJF8MY/XVzIChMl04U5GcyxrcgZifxkKtL1J7KzXyUeddqvbaqWw2e+0e21ODjOKWzf6a9jttMKVnU229aNC8WS+gE/vHcIfbu3BcDxbKthSAF96RuFlKr4kiAfLQonGnJrT1UTz1E1TBNklCOGXAG4NMOzWifk/sYKPGx7qN3R8bZNNuVm4liTJG5X37xjw7wcI75nEvMTwVpl4wviTzDarm4EYKhOvLFty2sKcKwYh2vZgxHxOlHC+1lgeCUcSG65kUPgrYCbJEj+2RmAdIOpnajcoyikoZrVyQtUSGqtId2gWV1QDRPGOwojzput13AElNVpZwzWK4SS7gbC8OBOLiQNKNUdI9F2jlH2tWP9EEnEWQCKfzR4EnGDAKWleuLrKZ6NBGJwchqpY4MwifHpnH2fcqaxhp99lFdpuhMfSByWG1VlG8xqOJwV0223odbt8mB4Pp2DJ4oWgRHkL+ru7VX97p0zSNHXBtxhlEJbJfqTfMNLZ1YHqj+21dfLihGIyaaAivDGgbKYGl3h56njSZx4OxmzqTckqddYeHA3heDRjBUSCv3mp2K4+5bi8hdxYgiQsIDUlY3C++q6Bknwx1C0oF+ThNIdWqwoWK8+lZy4XZwRXMthWvQ1X+zAra4K0hrwmPzmL3lp0BCFB6WRSokqMX5RozTNPVJwx0VWQBDEuAKs+WD090E2zMQrHJlWj6VS8ZVlFEIqyNPuDIUFq+RGiblBBxuvOM7SWDkKCjl6/tMX3lJIym1IzPvWJTUK73TGsPP8Vz/6FkwVFjJH/YLvThsW8xR6BDFojWwiEutdtjb798nX3xq17G7/95F52NCwa7urqmM1m3NVsbGK+JgO1bfAMXoRf6uBKNgXgwTSB2Xg6ZxTGeXioa70uvHzjKly/cglskkJZFGfjTS5Uhi8bDvF1HZFYggroOTUrGGCpM2s+P2nkAmqyKtQjNhdfPCQ4fpxQWngkkXRl9Dmg5LFcFDAeT+CjO/fgX3/9O/j9J3dhMJzQfsjHfJ63+Fmua+14aDHtIgslvqbmDZ+SjYQtMCiRtMZDy4aQ2cCylwZdbFCq7YpqHRhS3qGzMUDHoZubvBoVxkznPvOuRtuwTLyEz2pEnTAcMMHgaphPJmSvYuuqcmU5g82dK0l3faNq5Z05mqQKvu5Kd1G1iVigJ6gYD4gaPFvRgbg7i34wnW6lIrDoUCJRoZT1KAQpRthWinUyEjbU2Cnavi3Ylo7PJU5kgzHW2CTxtXd2MjjuHNy77Y737maz8SgviznWNan1CmRUQWQr+gFOi1XLuyQq+E5EZVTUDhoF+EfeLe4duuCp6HK3300/uLSZT/PMvF5VrsO6IFQEDyFn+C9Sc0QCSFiF08UuCXIbVGYwsfwronHAxuGiTum1TN7Ashv4liLRA++FEWxujP6ul04fiUNVTjzMk+iU903vcrK+hwnHg3l9+97AjvRuJ5wAACAASURBVCcLeQINLyzy3t0YjOavzhdVev3qVrvTaSXWmtQ5lzgu9ZOSUoJb6ymsr/WgritYVDW7M+xs9OHe4THD5Wnu50UZ9o+HBLfFSCUj14TJdM6NE/aGXy6spg6wQklZZTXV8qfwNgaXVwkM0IZ9RBh+VVP5PMbXL9mUKU/Kqnpxvqje73S6vwwBXg8h9AOEHVHXY8RHkP/mTotTfEO+tJNkCrQRaxOt58hBkcrPw0LPjx6KwA7F27kelbXCcTN9asVwVeQAOdET1II8ul46nSI2Ju9ZBsApd9K0C8rPu0BdbRBuQcxA6fpKTjSZpqg+m7LxOt2JYXnwcbepArG5M7DsGEp3FljBtgxyzTkJzEaeGte2vHdFsXCz0cDUiwVf7GQ2g5OR8Pg67QwubXZxe70LmU2kZihCvqxkOllUcDSaQZ5k0Okw35o7CAl3piy0+tth+8p11+p2ax+8CdxVCCuUP6G0CayXlMGIcWFClrax1e4h+RUu5lMxHUXRL22KcVHFjVRWqbs5mMJ0XrKJ92A8DbfvHeLB8Ygf7NSyKEKonSCZselmqrJicGB9EWwoiSCu5+PXY9B9TcEZHxaGSCsE8uWlJkJ1VM8gN/Jh5cJgWvr+ogrrtK8SX8dUhoqgMCsDk31dXGmMQfIhReczcCYXxXcoQ8JEZw+nkr3VIDA27hrOpJx6DCqnOqitqhppU63KkjvP3PrPUsgytgOR4CsESPMc1jc2YWNrHUxCVgYpafBXaZ6xQl64gEDPubaWL/DdOYikRJqM8DsdSRid6HbVzoc0teHK9ga8/epN+Lff3YKT0TQ8KnF7nMjNVzpIzjtJuTRHB6hz50uEzwZu537tGTVZq2IZj/O65FYJBZEO+aAnP0b6vTkV086RxBOX+PtvvAI/fe9tVqSlI6Z+tArt/59onh6pCuad7moK1LEMoj3wNIrSZ5EMX+o8y0mDIipLcT51MivH3Mu9oxMYDEYwHE7g4zv34F/+83dw696eYP3TDLrdHAzpxFLyYs5a8zz6GVj9WzzTgMeVmaCXFx5gVBo+F+gIpWQzNx5bSKJ5nmFYRCChnIaSJPqHUEoUBKxl5MGGOMcSKbGg9G1WLFElGG+M8oAYHsinOCGISigX8zA4fGAJXjs+Poadazfqnas3XXutT9pASH6fQVqsysHhQqVRAUMv3Ur+tISLwaYxyKxEx4I6mmz5pkr+DDE0yteVeEzUaIm+VEgRQs1axU02pWR4PDoOe7c/yQ7ufpJOBgOyg7EYrdmCdJ34WDsjfBXqWiMFCRmMiumwJZwK86DCZB6zIJ12caniOEusnWU2If2ItKYqgThWkwAwwVoT2pKoAbZ6j2MnLPpsGlX3YBFHj82h7jF228+uoVUSwbJt1lABImKa76vVpFP39mih+k0e0jLmiGI0XSTHo5mZL2qkWFVh71jV9cbhybjfbmfw4o3d0Gm1WFCCdZVInVdRj9FuyWQJnwm9Vg47G2twc7AFg9GEHVOm07n76O4+IetsWVSG0H6EiqmrmpShBeN9ZrHonuAVubb6U9WNoYcJXgDvv13a+ndFBZ+GAMeIjyUbfa3H1yrZ1E29W9b1O9Oy/u8+hJ+nNnuZ4NUQPIvcIOAJiK9TDuAtNMbBmo/IjhkTyW5ENFArW5ZQc9/blIjistIalbvY814OxkbFr1bjYCuWIvR5vu1RmWwQu5PBrYgEVaoQyjyzIMmsBO1LvrdeKy0sXte5GCTza7MgGyzo3t9Sq5X4/hiF/5A7qrw5RLhKIeJC/BqGP7JCnDHrzlXJbDqGKfEWXGWo4lqy+Ijn5K7faZHdCfTpsLSxuuVhvnBwNJ4zV3I4nkKxGDF0hzZegqdRJ7HdWwvru1d8f33bWmMzTjQFUgWw3MKWybHIKwDrS5NyqU0YysKJH71WuTDenT15Jf4sK+9npSMep31wOIQ79w/h8HjIwb1RQ1HQNE2osfpnX6HxJaT1HKxx8Ngj46sbdNC2SW0w8USLsVCbTCeP2fyLRenvn0yrB+OFe7moYYuqHBWLKQXLAbWeyvRECPLGY45VnYdykUFFEjV5IEtSMF6U/k7pujdskdOz3kCeYgGDwg9kInTl+MWkTNxrtyBNLG+2ZVlzsNJut2Brewt2Lu/CRr8frbcRtQz8RzGYL5VAp9slI3WoygIqxyJWia+x387S8PL1Xfv2azerjz/br4+Gk+yb8LUpICFkg/DSgpZ/voTPVdsjFtuUbgsHYXmecIS6qCqGfj1qcJJfi5BDwhY8ahNwjku/eW0X/urP3oMfv/sGtLtt7mRFM/Y/phFLgc9lYOMR8ahEUrEpUS3y+Q/lCDwPH1gUlIeId9I5SVc+GI/h3z/4A/z7hx/B/sExi/0QdeBwMGQER5omvPdRVEGwO6J3rELoHnVJ4ezPlGsfmaOrPzMrv1N6sTIhjbGiDiTGEDJMoQ0IbVOxci0FyajXbtQkP0stWOtsJ0yR9BHSloVjQJiUhICRT7AoPHQKlugRYzhpnFMqupSVHZcDWMymoaqKDK3p7SYvYpa3laHkjSaYKOVH/gZskWJW1IPUTisRDQ0t8rM0hoATKfFUE1KmC2mOV2tcQ/VSo3QpT3BfihnKxRxP9vfw8P7tMDjcT+aTceZVSjQIQRGSNIO82+Gdguzf6rKM8adMb5B0mfLRPE+Zg1cLsggEienjIRmWUkjaN5SqQqqWmP3xtOodDRejovKTVppMXV23ggjxsA4Rq9PSZ9G6ke4sUadYSIYjGY5f1XmMPDVRRXyoOeolokrUcs75aOuBTHtaXTQhJpdxjYG8j/iqe/ZRr71/uNLxTRxSkIBF4eyiZF/ZwBYSXrxwgnA3WR54Y60TNte7dZaSziYzjU89jkvvd9n5aS/o99rQbbfgys4Gx6KLsqoubfXN8XBqj0cT6mj68WxRHx6N4eBkkFS1NxTfyjN0qlBxFlUfNI7fYCAD4tx4/4Y1zgxO4D+Lwv6TBbj1Taxvfp2SzXZV1y8C2Hcd4i+rqv5FlqVXvXNk4zGDENbYxylwK9mjPIzKeSTvJBb6oazJBSRbEJbK5mBOu4dcGWPyOJXfADc0eYMVSA+plpTayYxJXaqFxqgz7cRrkzdGG5aQIGq7z4O8LlEoq1eum4lQRFFiY16D1yof7wgi183XrCIsyvOE4KRKjG1Ehox4TrYxcggak5Ugn2WyKMPFVUOJ5GvagQJiVhZFOjk+gsnwCJ0rURTxDFdBSbiFEk2C0LbSdJkOBGA+5K17J3D/cACLOYnBFLzRZXkK3RZ1sLKwtrFVb2xf8mneZpcSNZ6PHiJNycyImbMk6Sjez8aiYERNwhy/2OFJkhhshliQFDitQbaXGE/nJFMX7u0fk8w8q58yGiU0ptoaXrLMNO03npJN6xaG/o0Gv9iW1wUGNa29l6TR+goSKMEjuXIaIfG6gJOF6y5qtzMpXHdRQlMaDqoTEBE1xF0mOR7ipJLGbYaVs3w2Q1ZT7weDM8Gf3QdWJ8RIGhVPILFe0EJIEzTWTjZkUmSs6prvzWRGPAeCXOewvbkOly9tQXetB2meMdeJvqMIfXz9fRyfZgiQHiHLc+j2erhgCB13N40PISOJjM2N3uI7L18//p8ffFwdDSebhNT8unfG6MkjoYVoAv6lDXwY2d5s1+bJ+QrD3wj6t5AuTzjHtbfzDN5/9y34+Y++D5cvb9NDx8nrSqLpV2L/C9HwvqLx8LU+/2fPhyVtZDk4yCMWAP/No+brecyjHhMXkA89PagFQnEFr3lWZl0UrC77q//4EP79tx/B8WDU2CBQJ73dyskEm/9Mz73zpzvvPClnuvSr/91UYXGZllstmTov0KolvBLjHq9yDSzKgLWPDtGiHETdTqvgZVrD5LFGdcHEBvJcsO0UIMkTSMitxKcwKlFNxxU2xd0wSWpCbDSjoSCdhIGAYKknhw9ym6aZNUm9tXvNpa02a44H9dLWLxThr1bxnUafJSdJfehwnAcw0bi0pSDRyNONB1BUyvByFtGcGlKA8SZJbFVV5mT/Pt7+/W+S4cGeeHyokBMnDSQb6R0kmFGBXEqyKuTDcUjTnZA2BP2Tkam1kRS522oFKsIPJ1Ok8006rNzHWCr1SDGW6ucp1GF3PCt3Dk7mJ/NFtddtpUfGwDXnQ0Zrg9gjppZAJch1luD9IjBVLNh4q08pjmCkRi+fmKUVR+xaPqIoFpEg3BrzehtUmcFJJkYq6hcoqJXqUZ18GcrXTxixIE4MYns8mMFkUnhLfpoJ28ryGvRePOkvba3B669c99cubbFoH6vxYoCzE9D4TdNzXIt9GRVC6ZygY7/dyuxGr23KqsbRZA7j2cIdj6bzj27vYe1cdzCeqa+4b1SgNcaN9D4Goq3kHG1VBKAX/jgDeG8y8b/24DfSNPwDAN7+prWfv/JkUxQtcTME+GFd+b/xIfy0lecvWGva3vuFYXlovgk9EHlsIuIVynEU4R5sEsVad/9E/ZhCBKwyPJVRhZyFRXltshYp9fVE3Z9LAirCq5z0AfQCfTaKzpraoTjePDlRZEU0kN/FQsVqMxGYJrdl6saSKi4ffnUDA5HEOMjCMp3IVxAF3SWKRQn2ptnEEJMVlIvIsQrOTBYqqmF9gBEyp5U3cZoj8lmo55NROjo+SGazCdaUmDm5YPaUW2vDtZ112FjrMOQmqtMtCgcHJxO4sz+A49GUq7VUEfVxU6M2cbfnNrYvV531LQ82pVfWLGTE8F2eIMO+W6Ikh5GLE2JdHW0wSUKlNoxZeKpm3oIuDo1IEH3qoqrwaDC09/cOwiDPWYyGEp4sy+mgR7U3IGMpD9EDtUlpPBjnVHjVPkrH7SsdPsprO7lW9FUwhL4xudxwh7kr3TWo8SrZQtf0Xb1ydHAZgBBANoWqTqCeJ1i3MqhbBnxHdRQUWi3kGXg40otogbhHeA0KTFSgFegHNlVzuu7pfM6czcC8Js/l3Us7W/DKSzf436zUxtLhPibGfyxtojqEQNhZisRa7U7Hrq33oXY1VItFU9DotrL5i1d37r587dLgN3+4e632/mVFYDzTWFazL54MNr5hZx4GevbK+uK+sxeFBSsE+dQSoaCXOkbnSXwZHvaU04KqxP2TH7wFf/uLH8NrL9/gNlNJUKglTyus/BM7/n4VgfjMQ4K35/lshBWBiVWLFi/Cd0/iUD7t+NzKHT4nMaTH2vcsO5oYniVxjd6G1O0ZjEbcxSR9gg8/vQOf3LnPqrO0Rze8SFjy5QRmLloDp3h9UThiRacFQORYU6vKlvTfhosjXNlIUZQ0C0cWJoatTBqhM+0ZWrWwQHFrhgpSmHmV6YEKckItabGWrokKgbWjll3Kn03eIRtpDb4lIr8zhyJGR6JGiqZxMYdo4MChES2qFwsYHuxDQlB7iiMuXYUsb1VqQ5JKzExFdA7JKInKdJlI9opYyDHERfwiCB8z1aPMadxm5azRhEbshmgjoMZBykXYsqyGRw+yvTsfJ4OD+9YVJZiMrUDZ1kMcWUS51/s6zCcTvgY6j0QUKHpCi9WawI8Nd6q3t/qw2e/Bla0+F94+/PQ+FCQBLzcb45pp1uDypved99eK2h/UtcPARuJ86+ekIu899hCxy8kmfa48h9Zrl7Spy5ziieoa86jbBKiysG5GPjTJ84oTgz44TrUr5ebFDm2sWLiq5viOEWZPs23LlM31Ezq6t4SVcOLLPd8RhYpGdjhVjQdHY3hwOHFl7QprSHSRFYDz4ANpAJm1bh52t/u4ud61KUH1Grmf5vF85PWLYKOqLVI71Bhyd3PdNHXtLLPkz7zR67AOI92rz/aOYTIXFXSiORGasK5qcaKxxpFRg/OuErSlJMQUQhGfNwBc5sc5hUtZ4jYRw1rw5v+AgH9QFOM3Ynx1ySbTnn23rKoXDOJPrU3/JkuzPwP0O87VBC0doYiRtElUMwCOUZLEVA8sLfRp6ItCrqZiV6y/yXFDhT3iDBCXkROPlmjThDEJsCCG1aY28yVRoPALrtgYrjAQrDFIostiEJRgzhStYxRvXSoWiX6fuot1iNQ2SkxROkK6bdS05ysbPAsQSAK85E5cCDnrtyAvosCAeoaNkCw2e1LRGxaiQiuWKWKfhTHvLMRfCpW7yRPS4nOzLMJ0eOLHo2Mi+WOs9NFoZSlvpLsbXWiphxNXaCsPR8Mp3D0cwvF4wrDIhGE5AshgPkeWhfXt7WJtZ3dhW12sWXzWk9dpy5CHqcJoNOZ0ogpA51hQ6InnQqg1SWiEYlRCiapITgU92IuRJtY5xsEfnYzgk8RiniZwPJiIEI01vPFSWh8fWvHmNtqViwJ2TqvDeArT/FUPyoEJtkSxvdN2uKGEsbbOJ4n1NkkceOsc2uB4Ptxqkyf2PynsyKCCNpYmhSqLgbAoE8taR24yh1zWVFCEc4gbbSUcGT7OKCltAtTIL4gHGYv85Jl33pvZfEFcCIbnZNbAlUtb8IO3vg3ff/M12N3ZlOjBacz7R9DN5CFBH1kmTUXK32dJlllSWCahoHFZRZEal9pkfHlz/c5b37p5918+uDW8s3+0RjYyhGQwKl59kXwxcsLkTl50YjVgwRjUiB/Z2bd7FPfseelJLAsXq+9/uhd0HoGf84xepw03rl2Cn/3Jd+HvfvE+/PidN6DbaUMxL0W0LCYOsu+z6BkhLLVZ5VBoFs/lWoIcHs9vSDGy1s3OxsvUMyz6hD6PRFke7pXPOMd48lcOjcYBHUD5w/Md+4LPdh8Ifkp+00TL+MOnd1j8597eAeyfDOBkPObOUORySeEDlqVT/ey4XrD5JyZomlHw3kn2JA66SWDoamI8ZEnwZFVMTS8bWCUWS29gUFoYLBIoavkErkKbABnWvN9XnjArUgSuwOCccrqKdFpLyHEpsscIGBKQ7WyCz6zHagwW52atJRFGWgaYlrIPxQQ2Pt2oX4o7YHoC0F9UxTwMDw8Mu7o5H9a3d33e7tRoOPyiR2QqOhIoakEkMochUe/MGTVcgzi2UGxF33Cq1ihB7jcLDVnpOVFMxK9csOCj9+3FfOZGRwdu7/bHeLh3lz0503Y7kMVbXVco+g9cQCC/Nda7WywkxBLFXd3TvFReqJi+1mkxXJK6X6/cvArXr27DVr+Dnz0YwL2DAXc8QwVJXbkQlkj0ZZIoF5sgmssI8GIIgYjfPaJOocH7wYcj5+BF9oP33kj4qhQxPWMxcjd9bApg072EaPQedK/UDqfXPAl1rcV9NMSyXeyErgi+cXfeeaiKihNOmySwcts//4mUQsEK0oMpY4XOQsYNlS8LuiSrlJvyJOA7W1RwMp5DUXC8Z9lTn9DkEs244XgOD/YHdndz3Wyt9xgBI3alHLMH1Xh59L60RClQLgNFSc0U56xBn1Aiu9buvvbiFVzr5rDZ74a794+R9FHmiwoGtROCMkPbuSPixUhOWU/xE6joIp9DGW3f1+GnDsOuNeEFgPC/A+D/EKHUr//4SpJNMjZN0uRKXdfvnhyc/KLTav9ZnqXf9t61gZIw5MMvDxGDIU2taQBsYeDqCR+Y+nQrwZYXBS2mSso3uk+K94djnR3D+2zNFiMYCFFCgictFAy3Rc/wVDK0JxEDUpjN2PpQrjlohkKJXi6bJdfJghJ2a32Wc1FwYcSKWB1xtYKXT67S34um+SQrmf2VNMAgcZ+KeQnMZeAnXjqurCzL35rMgKmat45Rcowl5vkBGZHuCz/gyOdXxS1677P5bJKOBkc4H4+Cq2rk6pWVgLLbSmBzrQWdPNNVLtvHaDqDOw9O4N7RCIpS/OYCaEXNicpef32TrE7KVne9LMkvKBCXNiSBocFi0OwVZiEqvtypbog71CpL0sy02h0kZVnUfEf4KEH5pKwsxtdWa2hU1R72j0b8wFKlaFHU0hHkeTdhWUVvwEaAoYbEV+INjfG4/Hqg4Ohr0fWXLqwcEKDTV1XEj3Qr+1D0m4vfDLj64CCD0ltRnMUUXGZCyMOKIHKzBiVyIBVnIzVQv1IHRRsrnvqcPTxBEUNuMKRpGqz3fr4oLU19K8/g6qUteOftb8OfvvMmvHTjKnQ6La7eOvclHTzxKrH5/y9oiKc6gOkzJTh4FnYUoZAuFPM5zOekkhzmxpijjfXewfe/8+KDDz694weT6cvj2eIyPasZc3ZIftGBP+f1noJZXXSiVgRC6LkmHnapqsJnf+9RSoynnS2fZiwr86feBR/19xcfEeoosKWH34Z4dpe2NuCH330d/vrP/wT+/E+/D6/dvM5/T0WtejXRhKapUysnPyf0HXn+Pc9N5FxvFA0fPq8TyphEUWJfFaPQW5lghOo9y2jsKABWOpjP10dBKqp61mL2RWzckjwGFgL6ze8/YfGfew8OmJs5KxdQVXXDIWhUQB+6zNNcTEoKYxWPupdtsiOxIbQS7zqJg1YSSA2E9VsSw4FAYiAK09Aic5BZBy3rYVBYmNYJ26Ck1kHfluTYD+M6hZkzTaeq5OMh4xtrccHCQZoAgbUpYL4WQpYGR9oFaEKepbiRImSmpsAH57WFBcMrGeXEp2UDa2ngmqx+BFxonI4DoYrKxdzPp5N8+8qNpNtfJ6/rUq1JqK3kuN0qQUuibzcn1wAE2AoI1OkbaOevq5DCWo7H2BCQwqcx6KhTWM6n7eOD+8m9T3+XHN2/l1DHKGu3olP6kvuKTM/B4MV9zhjhKjKcNcifTUIczRy2N3rwwpVtuH5lC3a3+nBpsw/9tTZnKftHY0Z+tfNULoi44S6weqmi1qDRpjA0JXU6HhfZdFrP/RaMA4QtEgUO1GEji1DNFBtYbNP8CAL9x+W+GtWMl8rGK4W/mIOyTH2E2S4LAqiii7FJLUU77VR7pyKQNcdXTy0mwAooobM8XxuM75zvMxIiER7eG58/ciNekGUZEGzWJVvz6TIgD3qO5w11El0IRycT+OSzfbiyuwlXL22qMrFbMnHxqRESgX1mSb/JcYc+JMYmlGT2urknNdudzT5+encPbn92zLFst5XzEq1rT57wEeUV61BVEPRjqlQ8p9NNcf3rgZ8VT0UJaor9j5VO7Nd2fNnJJk1k3u51Xupt9P+2Lsq/8s59KwBsOjYF5iSp0OpWS+SwAyWHCXf3gnoiI6t0I98AOdqsYPPlJi3NmaOzEG90LdJQUUxmFNxJGnCsPDT0vrV6CeYmYE+4l4KAYe4k0tZLqxEp4SylA8rQVrpGF3RR6FaRcMMVQ8GwEH6GyQsTFlLBwhxFfbaSCgp3Pytt7WUSIXE2m0T3DzZMDlhp+FyJPHwDC6brmmsxa52+P6IZGsC6Kuad8eC4NTo5slVZYOREGk1COu0cup28gUPS9RPc5pi8K4dTrsawYIe1DRyHkrxOuw1r/W2Xdjacp8usqULHnRpWZCIaifI3qQtgQmRdLDMkDtSpktZZ24BufwumwwG4Qg427yN8D8FHCW9W2hPPPIZsBqkQiT9YcxSeIXzJ82q8CyY4BphgLEd+HXJNZA8TqB6yZuACRKKgF7uCTln2WUDlj3THM9zVJDsX2X/8qhqo/ltMZR5SnY09KlZponX3pAiUH0LncV6UJl4AqX+SXcSf/uAt+OH33oAXrlyCNEsZnnMhmOfj8J1PM1j5zwcU1/0vyECMV1ES9wpQpjAFBO1OD3prpILqqrosjwPCCXXvr13e6v35D9/IJrOq+tWHn9QUyKpvueM6Zwj2PKtyOa/PYSHH3pCHx6Iez3ZPQ/Qaf8pb1KhzrnBh4vs6d/Z3DXO3VaL/Al/n0d+BPr/bacFrL92A//Zf3oO//vmP4J03X4P1Xo+DPEo06TMfkUwQqmbBYm1U32H0iHleENHzjqfdwcxjCgFmJch94ojnxVmFYFDIiM4FnadO/Z8fD6VrLOfj5z/F/FFXK0iBGJ5TN33lzbX7h340mYbffXzX/OtvPsTffnQLpouFCDdQ96eqea9ukKBnxtm6T/xzlGPNjYd+Wod+7n0rCT5lWZIQl6nYdYUma28GqY1vmpq7n0kBMKksZJbhQ+C0nC2oFqMCDpY6nLAIHnJSXyeglrrR+LKA4mQffZYaQsAYm7JAH50dxlZoWwGSGsHNRSAoagWeUtBG6byhWojXVUGBBtTFHIvZLPN1Fdzl66GzvlmnWdY2zffT7s3S2LqlsilWFBfDin0hRt5iJExb6RZSYuTWZsMTHBzsmcH+np2cnFhXVbxCSfDH6REZu3ghrPpkBi0+eUbhUHF0rdtidMPO1hq8eH0Xrl/egt3NbiCFccOixGLWT2uALa2oEEfKtWJ9QBNLxadCq9wdWt8GzLys/fhgMN8/GRXlywF3EcwLtXfkZb2lt0yEb612IT2oOKJpfIKNejeyEi4IXzAolWC5ly5jOrE1Cac2hqApsFExIdCiIqrMr1cUi5wlT/tgNUnScrUzlYs6uGGmSIQq6qeceSS+mP1Se6w0d9N5BfN57Su3LKRHa4RoS5jYNJD/tzFxs+KnMW2+zdNdqNY1uHbARTDmehOVyxp/5dKa6baykKdsJAStdsoowelsAYPJnNaysZzlymV6AOr270GANUAWC8r0mdGLD5cA8M8DmtvOmwfBwK0ljuLrCRn7MpNN2jS20Ycfu7L8ZV2VfwYBXib1V83gF7IQcCGqrwybIDGfMSVeGLADAq+o5QFFUaJFlUtlfiT9nW9iJbaJYna7dhU1h2IOI3d0+JcZGtuEV5ToBtMOGFIvCy6R1aYwEmGBejlzGdo64feC0CLlKK7oBMJZhxbG9hqzzYWzqBEloVpY/EcS26DGx0jdzJJVcBHZ0FheH9YgcOO/VpBQT9XZjCa6VrHAicI0nHBGGcLRciH4yXSSHu/fx8ngmAzleTPzWumnbualjS5s9NqsMAui1AWzooQxq89YVmQrFtVSFZJOiE4LNjbWIe91nU8yV5N3ESVELIRAQRgn97VANPkh9HJvMQrDGIHFdnGgIgAAIABJREFUkeaahU5vDTYvX8PZeAjT431gmyqj3AkB7zQFJ9osSQzIaIWSqnE+kv3DIzYyUs11Jdi6APRuiVBWGNRX+XjSp1cMnZWuXyz5NWckJLXDNHhMHke+5+UoJnRBkEKwajDKolkOpahug8CTktgOU6ZKWKYr6tgVwuceCEYRVdT5ms0XrGzc6eTcxfzJD78HP3nnTbiyuy0Vxro+t0ejerMKqAXxkcmX3Dp0cqWaoK1+DJ+l5vO7Ps86HvHeeu0khoRr6xvBOVeNRwPy9tqxaLO1dmvx3huv5p28tdPr5vU//8cf/NFgbJyn+0cgC4cXg8M++0pmsZGKYD7n85B+3OWuTk5Y+UtjZGcQbvJK3nHWyBLFhsTw4V2fu2BB6446EszBI+RDkGLVC5cvwc9+/A78Lz//Efzkne/A1d1tLqaVZfEkexfqPVHwnIqYJhUcubP5hXTanjDwi1jbzRtqIIsR0hdkpyAshNF7pcEpu2SAZ89p26AvP28+LnLd8po0dm2e5+Cmj01cUZTu7t4h/vp3n+DvP76LBydDUTC1hhVJvRYgBKL46CcuhnxGuZS01NmaJPXQTQP0UwftxHPmFDg2gFNb9kPvGZbVwV5GhLM6JEVAon1NXcaczrmXjGx10tljI1iYhZQTyZbWqb0rwE8KTu+SDnEtE5K3FYuq1Abbyn1KVEaozZFxUJRGMNjaZlkGvdgoyFe1+FSWZYFueAR1XeJ0NsVrN192m7vXyKc29cHZyBnWYj2FR+R1TvobpeRH2NUDiGhUSUTacMZHGqzOuWI6NePhUf/4wT04enAPyvkcueNsrNqFiHJrnIjVPSXCSYmLmaY5CzuRd+L13Q1G4+xur8PW5hp0KclU81GmfgSvatgpf+dZUXPCkCVkH+NNTURQYGSeNhnDAA0OvAvzWVEWg1nxoKz9Z2liXnfOd0MI62gaoqciiHAJ+cLlQoqqs7F7Gdd/hNBGX3L0Dxd0w8p/BIioqQirlkw3Vv+piOIqF01/+GVxGs+F60fuINIbj1TUc13tjuCL2rOaodJgs3ntB6OZH08KO5uV7VUUAkt1QmDxwiuX1nF3pw/tViZilCu+lxfYoJougJfg1nHNDRE77QxfeekKbG/24P7BCXxy5wD+cPt+o40CMcYJjVGK4/wCkVHw3IBS1h/Nnk38zdrb9weD/ANn4NAaO22nGbsAfB3HF5Js0qKv5gsYwxBM3iZl061+f+OtyWT6w8l49Atj7PcJZRYwTNSeI1W+IiVkLZXDjrSESlO9Na6YINaiACb7hVYqqqD3ViphHHk6UePkMisbNKMoh1FVmpLESgnr7LyjJ1ep3kopBCy1UtsPAkedBUkb8yDgUxUJiqJFEj8pLyZoEmhUqzsJArP1KLgWq/YsolwbhOCuB7Ri3EFtd5nf6YUYj1RNLwB8Lt8do8Q8XWel20+XrU4EjkzVsp4ra5yNRmYyPDJ1MTdGX8ZBmDWws96F69vr0G9nWt1BJj9P5iWMpgUndVIJC8vggjbdJKFk06+tb1bGJqLHI3uamtJCGnGgEa8hRbQGrqe7nCjcmCw17W4Pumt9mI9OYD5fiAKkChhxFU5htUELwbTxE9yN+Beukvc1ij8RoKwQPekYZmVXr7nv1wA2GwfFtQT1qGP1LYoncbRC9iTZojKt4DjZxIcSTtRdPYcKWliQKBCYU4BGVGtM5hWj3pf47MdfQ/NwkPPESaIqdaWtqF53Dd587UX46bvfg3feeg12ttcFb6gKd+cauNK7Vfeaz70cDI+uGITPs1nAUylO/LTnBP2LjHLIWjl019ez2rnd+WS65T35oaDv99rJ9167iZXzpipr+Of/+APxom2I/eVnjKVjghD82R7JlzzOiKSAHvauKW98/tVRxZ0CSXxsj/LJwysvgP7dabfgR999Hf72v/4Y/utP34XXX7rOAacPorr7FGvV6DmQqTq4/4rhEaudgme6iMaOmKqVGvLQfND8FzMndgoqiiPiKanyyLjgaVZu0TM+Q1/ufBr9rrP5HPf2D81Ht+/aOw8e4IB0CoqKvaRrjLSO0zLEjxo+GkZaz+I/3cQTVLZuWV/n1pvEshqm9eGMeqdW/hrOw8pYQaWElnGhSgCLOoGpS6CoDCskPmo4b6EMKRSGupsOMqOcPirk2qWPJKGIAivDJz7r9DzRSI0fQuk8cPwa7ClIpiQuTkhAagKn88hBwnw6Br//mWm1WphnraK1tk7elimSSqusNFWrD4qpxJqDaiS6kKLSBKdr0XKtPlSLOYwGJ/54/549fnAP55MxVKT67RyE1U0XHrN8tLlJSAkS/Hnh6jbcuLIDOywA1IX1bs4wWmsNAYKM2FzKvHrtjHZaGUEiYbPfhsMTYUjqzFNHb00bIaQ+dAchnFADYF6H/qSo71fOjfLMVpbUapyg7GxT+A+N/UoQbjplPl4KqsFKqwIbSkDzHTUZjafk6o+W2Fp9lgldhOIJHRQCHEEFjKb1jihWdGYghxt++RbnehrlNGU6la5pfC6H2tMMoY1VZVVX02mVLuo6Q4tFK0/IZSat6zpz5AGPxNPP4PJWHwjmSvfhcVZa5/rq+m8tHhg57iQA65AP96Ut2Oj3uJs+W5SMGhzP5ihIRNROGTeNbjBSMTA1L1V9sWWhAbAD4N/yCfzQQPZvpoZPIP36xLVnxxfW2YxNkbquvzWazv/Ou+ovAOBFBLgC4NcAcJ/VUmUCiSxeSfsdO/rUlVI0Dv2V9nem/pMJRhw4Z3ocW5OAtBdYKfPNiKfJPpoo5r81C7eRf6H8bKxEbIa/aonS8u4pfM4FAuZeqQpRnCeIsisVEOfiqRvWiCtJqBdSnVVcOHIAwpAiRcI2kt/81BnlzdUieiTNBORWG+ZBPTtROoBUKiklyQ6E5VnoepNyYjSkitsCNoFHpU6/pirndjYZ+FAVdn2tzdv6cCqwIJJuvryzFrY318hoX76sAVhUNewPprB3PIaT0QyqUrqBzUZHOJF+P1y6eq1e274ELkmtQs6YE6vncd7IyMp3YZ9Svh/IsIr2aoBEk5WmGdhUptArs9MrfMecNV+mIKiWW8cV5yVChpYCv5L/I1jDS8NXcnsxboZfPdyAUuKyDuxfuPRWiwIQCdRpG7xppdwoFlFuHkvmjBz7KdSQYQkJVKJOeHqoyhoS7I8YOAoR4VmqBVRsiPP71AGi2GE47oBT1+n65R34/luvwp++8wa8/q0XYaO/JlUHV1/sfAkN/tF8XutZ0ArBrmCAn36sYo/lQHVSmXw+G3ZUxqSCfLvdSXzfEbwrm01nQTw5qdrZDm+9fA1u3zvE3966D/vDkb7m2a9B8DhfTqJJRZ9O3uJkjZ9V9uR06uGrxaWVbmZ8fp9q0Doi0SnlWF1kRLgZFUN++f578Nc/+xP4wZuvwvZmn7shtE69P881RUnHiNDAZ070nmFEOwglcZ3/jkcp/yhIQkllWVYwXRQwGk/g4GgA+4fHsFiU0F/rQquVU7c43Li26y9f2sTEWoKNmfM96w/1vKPlGD2EWQjPpejzxMHeiYjcpbr12b75l9/8znz4h1twf+8Q5osislmEXhIeD1SLcGSrKyO3nrqQ0Elr6KUeWwm4JISStQyEqvAwSq+pw55JNFHPdxVlJ9gXdUrpMygZrC0y/Hzps7a8RgqKFkQn4xq1sG+s8ZoY2hVpg1iXNHwv0XnMTc3fYValEAg6LKeqetssixGnbYX4QacPoM+wRbFoHT74LLQn43Rtcxtb/b63pBzkfRKIYiTuAhQTMRpsxa+SPELp8kNVFH4yODGjo/305OCBHRzuIavJsvCRWSZWjxlxv6FiQZLYsL21Dm986wa8+ep1vHZpk7172fpE2pKsle492YJh1HPid6fXkzDQ5Z0NTlBPhlOYzUtJ2OW8SjU5JW2RlyHgOgScWMR2avCqBdzWt5oQytY73wmiMnVKEE2T3CAFYr6KXOhikmySUrCgYtXWRRNho8lq4wOq6rURKhWTUZWAVJs4ifm4AcEcxyqwB2WQ9MDgRQ9wjvfWmwLulxhueaqPVb6aF1VG3aQ8pfQeWWuS5pu4qfRLiUm4o0kikwCPIRpcdCjsO8YmQc8hgm332m148eoOTGYFjKZzhtPS2UaxFB1DpIspVxpa0mx7qO6kKEHYNcG/icF8OwDuA8D0C5/cC44vJNnUZKQT6up7ZbH4S+/cX0GAt3kjMaZACGOlmWVB9kKazBYvSNM4MdTaUexyR0+EeYwmYmxFuGImZCR5EUMoarGhEpCjRpdCDXPtHI6ZP09/pk4bb3acRCYqvz3R5tLSD5PeX+P7oHLxygjg55a5m4GtWHKiSQAlVIGTKVaIFdNiVtLl5x3ZR4mtsdSOZXnA4FI3jM/BELhrKRLHSEkt+Y6ygFG6IgiXSNeTP4t2+YwY9FwMnE5gOjzOqnKBeSLVSVJbI/T41kaXqnQhyVrcr0zQGUrcBtM57B1N4Hg454AR9UCmA5cSPPLX3NjYcKQ6Z/NuUrOpi6+VPGhFvZR3xkoRBal6bFGHdyECPULW5vuqG22aZqHTW4e800MSVmF1OBJM8aeD1LgpUMJDVfdTrAGU047XSJB01XBHU22MNE7EC4Vlz2/QxZHIkcCPVpICZn9ZcGkL6qRjwSR5FJgDfXCwue1icZJhxR3NRySacRiQ9R4LFVCHBErMsAr0Dk8Z17HJtyQRJhiu8H7n1Rfhve+9AW+9/jJc393hip3XAOSZ5ncZeD3FVZ1vYEQIBW1CiDWRfY729vIVdF1TYN7pdKHsF5SEYVUUIk0fAq6vdeE737oBb9+6B9P5Ao6HE55jCS+f7XK+rPXNAl6JWOIRWoKCl8m8IIW+53IxQaH9Fx2Xdzbh/ffehr/5+Z/Cf3n3bXjx+hVo5SknmasJ8fmvCmL1+ksZj+0wXCDJDNEs14gYFC19QrFQgnVyMoLP7h/A3fsHcDIawWgyg4ODExjPZrC50Ye1bps7xFVdl5v9HuZrWcYiMuE8D21ozPkFLomgRBWnjMwvfEojPLCoK1Ge/eQO/McHf4BPb9+H+WIhNhBmSf9/HGQ2ejVSNzi3Dtpp4K5mJ3E+T33dsiFJkAPPXPbfZss6O1WPub2nutdcZEuCg64VzVbayCqviWPMHcPphFMcmzNIWfVQNj4nzbMIpNJExxlXzghLT7KzDPtdzw0nQCRA5FaS00cn3XTkslcXB3S+dtno5NjOJhNLzbzNxIZ2uyPfhcHWTLdJGO2mauicUBs+zer5eGyO9j4zh3u3Lb3PfDIxdbmQ5IpVU/H0l10285ZcTRQ1e0oqd7f6+O1vXQtvvXqDofTtPGXlYa/7bYiirRyGhZVjWU5din+qqmLIqdfOZ2hyzWZQbH2F+HaI8HGWkAii2Q0AN33tKJmfEYMJxWDx1I1fKXZipEiFJueLOL1mpldoqMu4CJtK6ooRa/PeDcha4zpVhpBuKorgkfptnoOHf2bFXhBpsXr+XhhqS69rVZWzju3wEr8o6qQoaiNzLZxVV0uxPMDjxeOew3goS2Rl88RAr9uBG1c24cHBNsew48kc5osSvKtprXofPHOAPTtMCBIClo0GoxdMfq3XA7gfUxJrbfgNINz5Qr7JM47nn2wikL/va6Gs3q+q+mch+HcA8YZhiw8S/wknQZQwOwFDSzs5hEPqgOAeaxGS8XkQfiVxGBOShg6NzLwK/QjvLG41iRKzF3qAEcRJbC+4m8miPiTwowx67kAmIcLvMNpXoUBsA7S1e1opRzOD6OcpX5QSp6n4SWKCAstlPmYIkrQqBJjKgSWzzoiDwNuEKZUGIzxOidVSpc1RV7Wl361UmxDl1xGIFDqUNQWG7/JG4BqsN1sw6JkHmBPodFHM3fBoH4dHh3Y2nZsiTSA1BlpJAt12SgR42Oi1aEKM0abOZLqAveMJkBEtw9dU/IMSPrpoqgRtrK9Db2PbY9bzFadzDjAmzSFEJVxJ9uWhmAOaKoAvBW+M2rGKGTxjZSHJ8tDtb2J3bRPm4xHUVSk/p9Z17fV3l8stRCUBqbM6/akNGA2hvcdQB/LVtMFj3GzjLmjwcwK4849VgO7nviXLF58SBFJgUhD1OW9TcKbFtFsGO2ETaJ8CyYgcredEM6aMn7NnWsEsE6zKwgJyLFjj8MmxclCuFvtDOQ/9bhe+dfM6/OCtV+Gdt16Fl1+8Bmu9Hqka8iYeO1DnUihdSpA+Dva6AnJ4tiG1jqCEziakeG7dKXkYAaLmMdUVbJL4fn+DUrJkOBpCrd6NZJD+6gu78LN3v8Pqr//+4S04PBmBcxf3tvyyB3U0Sbmagm2T5xC9AKhSS3DZWEy5uC3Lk8cpeJkO6sJ97zsvw1++/yfwi/d/AG+9Rl33LqMISADN+2eHTX0Zg75bGsXZSNH54cT7qdYtrzeGwArXnRW+PcKiLOHwZAi37+3BwdEJPDg8ho9v3+dkk+x7DFp+9ouygMPjAbSY67QFr7x43da1i/G2w2Vj7fOH/EbJGgdMnWGBLabnMp/8SxBb4v2f+Hbg2UbrX379O/jnf/0APr11H8bTqSpTmqWwTNwdztRPOCMw4ldJncYudzLJL9ORsmxpDcwMQMeLyOe5hL8IfCKNGj64bMwb4u1PQg0t9KwmwntvaI7UUx/CyFbmbyZQQMYXkYLs0yQu5J1hSK3AhEoIrmQfLl53hpJNx+9dFgboSI4WAA9lygzDtRxJEE+foLTG2DRrdxJXl+Ho/l3yw8TtK9dCp9dfiO40dBlSKyr1aoWZ8A5CGhN7dz62B3c/zZkGRJZtCjdFFc2BRz37emEU2FNCnaWWYbPXLm/DqzevwEsvXMLdzT6QAjjxMd1pi6cGKby6m8jJJOr9JB5WcpFbnssILVMUby2+7yGitzbmhVsfjctiUbqyleLEB0/xZTuKAGni18h8S6eUO8xpYDVsZR2hkii1n8wiukvsZcP/Y7Gh6LcZrz8m3arqvwSd6e8GEV8kwT9uKMBFE81mo69X/Hufcs0/43NvjFhH+CoZT+d2Mit9Ma+wqGqSLrZC53U+MbZqdzMSiEtsMyHP9MkPjZjrh1NfSz7Eq8rw+lonfOeVa9Bf6+BHtx/Ax7f3iMKGKQHHIzHchRRP0xJWShtsbXjVA/yE8iknWs8DhXF/9dC9lfHckk3DPgq47qr6+2DM3waE90MIL9DfcRcOYWEgLASqFlVhucBmhIvJ2HxK9BYKLW0jW3nAiB9YxLbyFGk79Wfk8JY3Ahu5axOWfOC4Jy0iv502gsBa2KiemNzyKoVriSq5HSJ8QhVomfxL4hAeTEPXJmhiCuKVWQfhYcZDJXYxUTiboYjXoVfL3EvNEZG9NQOL+xgVFZM9Hdm304hZLeaeDx1PfNFChSm8VgPpCnPe8jFB74OdnByZg8/uJKPBkWXTYPZyEq5mJ09ha60N3SzlGqJVJdrj0Qz2T0YsEBT071PtiFI34NL2Bly6chW6m7tYQupI/URJzVT+pLRIRcd5ZOqLNtNEmrDybVZzly3WNzlaEC5B3ulAt78Bg6M98ONRY479KIGZqAioP4le2bK5Rd8iXzOEFiOEdvX1z+sBWI5lcfFzfoWEgKoqNB6iqGmJ/CEFZ1vgTRbz5VXEFUYyv2FcMludQPaE3iTGhB0SLEIKc2Z4cg3HqYrzY6/XK2eLDu48S/yL13b8d994Fd/97uvm9W/dRFoPSWpFqbGuG+5Jo+j+eTYeq4XXpSCRFopX0WXR/1v/qK8Oz3AgRtTRF7MMlh+juS175WZZjr2NzSQYhMlgyFxjCtq2+j14+9Wb/OxRkWc8mzOU/VlHA6O6wBwhLm9BRBXAqS4mMrIA1GSc/qGfkeWLiC0I99urcqdU8yTJAQgNtBXOqlxedKJX3mO93w2v3rzuf/aj75qf//gd/MGb34YrO5u8j1Ve1ERJugG++Pv/zINEiygeCs43hv7MIXQrAeMTBseUmlwmnLQGtooajaf8z90Hh/Bg/xg+vXsP7u3tw+HJGE4GY5jOxa+dDPvltQkHx1QUabVyXOt1U3r2A0uKRCDp0ya+kCjtJFneh/A4EbTnPBiiyfNwMpzArz/8BP7pX38Dv//oDkxncy4Dk72FP03POAV6jcuNtVsCC/fARl5Dx/iQGErqeUe3UUlSEFirE/BU3yjCZ1cCdnl2ai1UUj6aomEvuFhsDI+4EbTYyRQLMRE/Zgpj6F7qf2dolL/pmy+nFCBoJR7WMsdcJPquZIlS+9O3OUI8UbvlVESrqhKm4yFDjBgGP50YV9eYtlp11uqELKUwIKRBBCLJH7xj5b6Uk+HA7N+91dq/+2k2On5ASrMCfjOxMSw87DRlVghUZcXnKd03TuJUByHPUtjdWoeXrl+GV1+6Ai9d34GNfodDuJjoPeZWnP6CKizWbbUCoVFIwT+xBusKm8K15HxkVYcn5AOLJmygwe58UV8mRdrJvBhvr2drpvbbZJcUVht5Mn9s3xv0cMLGBDwocN9wm0P040LULokZqggENWXZJRQvnscRABFWf6aZNRcegiDFCELfVW2OC47HLcMvZsTJpzixrGF0MoXP9k7wweHIDCcLsUQwTQwZdjZ71dXd7XBld9Ou99pLa6znn5+F1WmOEyHJJvnb53Dz6iXicAbyuieu7J29Q5jOS0IlJshIT2g9ggMfdYvpdm+iQLaPq7q+VnlSqmU47R9XssnVJcBsVi6+V3v3I+f9TwDde4AEIQhGeGAs6lMKDxF6XqSuUwRsBUkKa4Z/apIQpHNXK2SwUngb6M1SJdMG3oqSiOkxgFRNQrXoZEi6kUSRLVFqpRjoxs+K41YNqEu1XkE1cvXq75Sy6bVUq9p6cEh/FUNXlpJPVUq5Uq4lKlxX64+0O0a13Qb/T4ezx/h+oJ1aYLEgo1F47KQSJ9Tqvm8V/07fxatKJ0jHVESJiE+KxpR1Mc8n40GrmE0SluZmmWtEFgEyAbrUoey2udNp+HA1MC9qOBhO4XA4hUVRSdU2sSygwRrl7Qy2d7bC9uVrgL0NOq7IxLlmlTgpGtC3LFn8oFHyxQTE9oVFvZE5dsFEHRxc2RXpfchrs91f83mnZ2w6gMCGvPG5MSsB8OlSpBYHcPVQ5nOPVducejngQ/Cf57gTIj7F29FlsOqbWyr7graBvUnBJ21ONgWh/fg9mxLNHAr+J8Jnz+4uy3mjBZwAua0VnJ6SdbehroJB4ZfYh16LETJU88F3aWcT3njlZnj37dcp2TQ3rlxiyGxovMZWukSBiytj5kMD9PW5eWgq2D5CJi3lGok8c4FPVYWNLDl/UedPSjHxf0uFvXONCEe7wDhdpXzkPT5FCuYv2CjNES95rd9nnIL3Q6jLgn9ECeeb37rOiSYdRr/+6A6Mp4uLXODZO/nU585qhyD6U8KZZ40tXdotUUNzi1PdQTrMa1V8JV4RFR/ouzAfEA3D940KMdDPxFsUoKT18www2Ti2NsiA/Tr85AdvwH9577vhvbdfCzeu7mCa5eDrGhZFKW5ZQT2Y5Qk5R+X9OYym9fTkj6REk5JDMgO//+CIg0BSytzZ2uAgm+ae501rrzEZZQ6m7PfCquDyn+jgzecFHA1H8ODwBD7bO4Tb9+7DnXv7QCJV1NE7GY1hNi35uSIhiySxMC9LDuaZ52IT2Nnow6sv3oAbVy4hWR2RUJiCGZ4uOpUpyNQPuikhfRnREcLSHJ+gax9+dBv+53/+Du7e2+cOV5ImvIYpUfn8DWIJTSQLkrWsho3McUCz8irt1K5uGmq3JnvvExaBHAKrnY2g3EFWLtd3o2JjbhzUVFv1q7t+fI38Fzc+fKL+co65m3QWFUaM/DmRi6+UCoUXSw9vO4njzipVx4dFApNaeKIxuYz7jHM1GkqCrYU0zznOLmZTXiNc8MgymI+HuOhvJkk/q6UTyFQhws7SKWCno6E7vH87OXlwLy3nMyPXYaLQ3UoBTSr5ZK0lvFtR4iSERZKl1DmCy9vr8K2bV+DmtUtwebsP/V6b9qPgfJNcPPWzT/sgFdJ2L23Atd1NeHA4ZOgj1a0SNLHBqKc8eZybAgmY6v3aeFqE4bgIcLXfThObVJUrBWhgkkbWOO65UTAIoq2b/EwQCZJa0ka2YifXFPJOWYytfLNGXdZ7Va+VjibnPawZ+f9R96Y/dhxXvuA5kdvda6/ivomkNlqy5Xa/bne/HjTezBsM0N8GmG/zlw4GmAHeQ/frtttuy5ZkS7JWrrXfNdeIwVkiM2+xSFaRFK1JSCRrufdmRkZGnOW3IL+2LEpeC2h+hcY0Yezp89Od2o3EU9bUukDwrP3o/JWYtpEqNVOKPIf5eA4PHxzB/QeHcHA8x6ysuAABau8SBCH2ep1oZ2PFXbuwiaQOS2vs6yh4tg/XVMMrtSfEk5dIEstRHLg4CqEThey9SQ2dz756APNFxixiulU+SW7Htppo0vNJTasNZ2G7dNU6hG70Wi/kNR2vlGxSopQt0ovWVh/Os8V/Aev+HhAv0NrXsqjlFgw6NrGNtYvpakVZEVHLRB3T9UQymYPgUgV3+lJwYC0u264OukaVFuq1lD/WBkzXFIVgEGsV9j8iXmYkfpt1u6XQOxgpoLZsQWWtCqpkmswFkjTx5+TSmURxTaZzs7U3Xr4El5FiX+LEjYlOm16X10mmwHTpWjgJV2tEp++Ro0M6Rxob6nDmNX9TlHs1iEVP17BoDNn6FfPJcVTkCzMcjTDAyk3Gx0iWBmEcwEq/yypco17C2Y5jk1vAg/ECdo9mMJ3lvOESn4HkoUOCw1H3wjpYFHTDItcJYqefHwiLQB4vFA+6RAPskscJWaWNxBEWaKFs1XhRbR9rbKsJQoiTLnR7fRd3ukgYdnc2uNupVXHDSGYPclleAN+kE66ovZHNiYPyKf1KFJ6mIZ5mF8SNxzYvXDroYSEvzRISLPjfTyeZzb+YGwqRJJqxfTbgAAAgAElEQVSOwFMsHJlq9at76sly0iBwvUGvAzeuXIKf3XsbPrp319y8fsmQehtXkWwlSfPyQk1V14UD2FWLm/5p+7m4AEGmm0VAQQa/0tmKuCNsU0GdUllp1UpXGwyGBE0CtrQAswQebmfmP0QC0Q6Jn//+Td8MJbBuglDybhuurPAPp5Mx5HnOQS6JBtAzt6PqiP/2hz/DwfGk8V590d7/EocBaGK49uk/oy7NitQVRzrScXPL93+RZWoxYmuAO2fb2lWzXGmri/J1r/6lz98Id/j29Uvwtz+7B7/8+fvwwd2beOXilul0E47UcuZYLel14ulX92M5yFLCcCHqyf4hfPL5V/DJn75mHuHNa5fgp+/fhWuXd1iYiYNL/wwiNnZOTqwoWBKmosRqDk92D+D+o12GyR5Pp/Bo9xC+e/AIDsjeo6Vhbci0vtth6CElCPneAUzyHLpdAzvra/DBu7fho/fvwqXtDQiRUQ0v2wJ5o+PPSTiK0NR4Modvvn8Mv/34T/DlN/e5W9vtJpBlJZQL4Wu2KW71v12z3NCXZGFCMNNRWJFvWk21bi1C7WvEczzB9e1of9OK7hcoEo/fMKS9AEpIQSx+qMprnQhf2BO5AFXOid9ZMfyzYiYOJRaEho5C9bc+saE4MV2GQSTQ2rS0nGzSqQWqm1/VAluWAySauxHbhwRQ5TlSIkB7e54u3Gx8FPSGh1EYx1kSdyhmoQ2lZFPybIGHjx909+5/G07Hx4b567XwvoM2moXOk4tWcQidJOQ4gYSeoiSCi9trcOPKNly9sAE722uw0mtZu1X1FZ490VTeOD0blMRevbgJD3eP4GA8g3maaeuKnzvqRG2xDoBlnCOhuvB4Wqw92J2F1y+uDAf9GEzljonnHBizYp0Lac/jzc3I1sH2HKr55f0SPPiHh8NhUCNDrFME6XJp0eCyqJrvqAVi+0ZNoJILRUA9B8PkNFq78yyHkmNF7wr4UsXcpwOX5403tvbuF20JiHVcRGtckRaQpimkixTm04Ubj6eYLnJW+heYMMETfSUOzGyRxePpnAa86naSioocVqo0r7Xw6E4fh9bPqZNFcwBhfWXA6znNsclsgd89OnBVZa2tyGzehSfeB7HJMahRsOqou2nxYhS+pIreD3y8fLKJsFlW5c/T2ey/mjD4K3C4AwgbKpmZavSSIJPiyYOSeYSocE9SfOXuJTJ0goOdQJs8gkhg/SWMHcKIED9qVaNJJ8NVueOHIs8W6QTNtHOZqI5KhX5xVzsSkqtH5cUriTpVKG+Xu5zgZjrVCWYbqY6LwbpwLOxLPQcnCZXyKSS5og7uVBtqXSk48cOc6D6RaVc1YU6nw1TgRBjIc+C8krYBFdpxNWCfdw1akEldpK/JaY29ROdSNFgYxHixmCX7u4+j+eTYxZ3YOjvA6WSCFdl/BAFsr47gwsaQVbgo7yktutlsAQ/3xjidF1w9I9gUKa9RJS/LC6q0QAc6kJWGRGWqGEyJspdFLfpjpV2dwAvxNIsM0+Ss1nu8BwktmL7DbbXbS4kE0kZFXU6qttpavfI8kEAnnpr0rEq2Kar19S18M0rc0FolaCGhSltV+U3BG00jVMTTjLrgjDYBnVrVoWnpRznuYorNSc5iD6ekCfqbhiWDMubpRAS2ddY7gSBX3Ltq4bCk5E8PCkvJWwebG6tw7+4t+Nuf33Mf3L1FX5PyJBcimG96+q7gRAmaElkyYGf4e111gVasRgUloaBVyDDBqgrKoiDPNJOS32FOCjNO7I4QAzpMEIpxrTEQJwl0ej3utiifRifJDyn/VNeS/TesgoXx1M7OSR6qFw2KOjBcRT736XQM6WIB/W4Hbl5O3Nb6iLtHa8MB/svHf+Kgxnf/zgP1eVHH1+NFahjRScXn5qv6X2KWnWrHTCyJJNgR+wOywsmhdP1OB5IYkNaNUlsg1pEAAhVbpDaV6Yx7maoyrU0bqyN47/Z1+Lufvw9/87P34P3bN9kvk+yQiIhG3bgabniizk7z6S+CM3pBRxOlw8Nju79/BP/tX38H//rb38N3D3d5HO8/3mMvNUIcXNzZgCSMxCMSTa3Yu5jncHg8gePxhLlldA8ePN7jpOrBE/JALKATx/x+VLShDupslvJ9oLEj78BAIXQFWyEQEqYL169egL/+ybvwVx++y366FOSfUmz6UR6oBSoqOuwfHcOX3zyAjz/5Aj7+45ewd3jE3DvZygQGW7kWU54PXVddU0Oi3GUYlQyf7YTOO0WIEkSzJrTfAsRurfnqOYfVwnewVEhdgki6Wl2UOKIRFXlMyDxLLlBVyNCRtrCv1Q5n/T2UxLUsHJQhJZwtlwriuUksxb9MJ94NK+iFBiYFKyEKnsiKiB9ATTvkQiHtvEEcybiwwnwF6WyK0zCE3mDVxEk3MCNj4qTTR4O5szadHh26/cffJ4f7T4I8S6UYbgWldNqzY6WbygA4QuIQPPri9jr8/L2bcPvmBRgNehDR/qBiSNb5uycxyXnnKrlWJxGtPUPYWBlCHEYwrVKo0PpnJuKOtjwTlTFIHo/2aJpXXz4Ym6sXRvjWlVVqWVWGrC2kwz9Q5JqU7RHq7qaVE0VR8nbigymsI6eaJbKGWvHGdGY5tlmC0PobLozPij2C0YXcXaV6rjGO0BJFTntwDomLa6pDa9q1bsOZx881BqgAp1N3zrCGtGC90sksKLmE6XTOiaZvV3PBJQyQnukAjFKxJEilItJksoC9gwlOZym5veTcXWcnCy6+v04oP4rf/ekX5wsnXPtAhF4ngeuXNmD3YAen8zTcO5w6LkK0W2r1a/V7MlkCRHcBAS9zA+w1Cx2+juO8ySYq1+5qVZX/NSuy/8U5+4uqspcMBKTZf8T5BLI4T6QKqayXhgLxJJW5VCGgAaqIFyeAiJEOISnTdVAdFZkPKcVvo6t8pf4VFHzyv53wHNlWQ7iXbPrE80of+FDWOvTJXkX8dX5mKDqV74cKp1VVPFAYn7cvEe6lqNjy+3U0CKZHNPACPgwfZLlqLhB2VN3MOq8RLoJCXt5boRYSXziyghGcfsxuFkKe7/CDwMpGVJRkC5bIr7qC5PcuSVLyovHM0kUyOdyFoyePXRwZBhw7hcQOuglsrva4Y6mBHi6yAh7vH+Hu0ZRvElmi5EYgKVQp5EpXVUAQrUJvZd3FnQGNRinbKyfcRruYVFQgjm6oLOZQtdMy3YpafFa+B8a1iEfqLQWGFoooEe0jLuOep94kvBMSO0Tia/K/T292vSaXizMdFG8Tz6oxsW9tChQEBTHYwPvC25a9iS/2Se+BhIASTjSLJadIn7w6hswalgxKXcyJZqUsHazJHC4WYSuuijVbB/Ezq4on76WdTfjrn79vScXz7VtX7bDfYzvUvCyNh+m0W/c+cRERP+ZY90g0ERjq45Zo1paV4Ep01saUfORFzgFDmZdYFllIwlCZdKPoAZqjiIp1gjAchWSPYyRwMdQhpO7rYMjdd9l73Q+ZaOofvGYENSlS1gLjZfJf+C76NgSpHYwMBHEIwdERdzmrsoBRvws/ffsm9LsxrA678JvPvoIvvxcu3eu8sheX90/HNnrfXXpcqUFCMHtSJ6VA/mg6425G0ok5SCR1UwCxL3n6fc4n0EPn2enEsLW2Cu/dvQG//Oh9+OVH78H7d27A9sYqYfBBOpm5ClUtFcHP9gFwdhTX6z5oOoVsQYLcbfzVf3wG/+//+Hf44psHzNkkFfAHj/fcv3/8GcxnKV7a2YJhvwOrK0MuClLiR12Wx7sH8P3DJ5xEEQdxNs+YfzklmX0uFBiGbHX7CfR7Xb5HWS6CSTQEJBhEkutRHAI997euXoLN9RW4984t+ODdO3BlZ5OLDHn5o040lzopLIqQFyyA9Onn38DHn/4JPvniG9jdOwSyQqiCkOdoG0bengeu1hKUoJUSUuIyUrevGzpFrjRoyDPsV8/7DQfL8X39y0Z50z5QdWJOyZ3NThAB9WvyCrnbGbMkQgD50mCICGzOe4SBDgjvWlTGpRsWtEG7zltnyXuEaGGUlAwJO8wA0tKIUIJxTy0XtJZZ35nkLYBoGRWk8znMJ8fY7Q2TpNutojiuAoxsli9weriP46MDk6Yz6S4zVQtEMPzEiHlKY8VeFzlzyLc2VuCdty7Du7cvc7eIQ4qKO0it4rJ3Kjj/wWPAxXjtM/g2tm1KjQ20lWOjIcU9aVpO9w4Xk72jRXl9u78VJdGQYlKy0pRkuplwbAJJhSN6U13EeKuxXpWblZzJSSGUZr1pCg9ijiNKqyeKh7WPJ41JWdFrEy+SJxMEKVFD6mpmixx6A8vw6tewIHpQUqGJZiv3QK/AJQHaaeuJMfUpUJJZ5iUXZ6fTFOazOQs2kTYAcWijKCTzUig05miv/T6i8nSPvKxCSy4KCkk95/Kv9rBnmkZPdXlrlJbOF6KW0Jzq9zpw9dIG7B1N40VWuuPJvK6iN2+CVhGW2LwL5VHuhoPgLUDzO0VY/miO8ySbLLELgD+prP27rJj/ozF4E43pM6dZ+vNzSpQM4oqV7qK/E05VW8m0txB7DkoiiRhOnUbmTTLc1Um0aJTXMNYYtu8RmvKOpLWFNeHeSQJqxQ+TRz0T+XS+n2x34iRZXehdCYQLyoI1kfr0zSQxZop2qV6cRrt2sS4fVKgrVKynI4gUDEW2nR/+XCkyfW3CBQ3JkFYUfpgjUW2Voql4bvHDTuOyUP4p+2dSkZnZlbKK5JrIxbXYkVOIKqnsgqVMIqLFqyrLMp/Ng3Q2jWfTY5cy3I180Bx04hCIEL027LHfFAUXZVGReTU+Ojhm/02C1KDaS1EAQv/TqjQarcLa9k41WN+yUdK1XMEjSKxI2LOWkBRZdfEw5N3ISX+GQGJGrLjb0U6t5cKCLKPWTxXaUdh63Vq0YujF3RzrbFsS/BmHX7CcdjWFqyl8Tf4s59fsOmh4Q08iw33IpqW0rSXP78yUaCbggrhBiTcCDXVSGkClPE3x01xONJ1+TsDdzBwkySw55DBgagtW/TWEBfMIHNvRyDdJha6SBe/61R34h7/6Kfzyrz8or1/aKuIoCcuqCqxuYJ7/Ab5K7HHzTtRXXWVZxp4rsRWVUCtwRclKawz7KUtOCBi6AmLaX0pyiRScEHLEyr132s3PDWLIc4FEIAjWXRbgcu8IZKDb7ztSPxXB+h8oAPZz0DpBaLDysU6lMyaazSGzkLr3vTAQhGzlYDwRuHsSB3Dn+kUYdDuwtTqC/+fXf4DfFt+yWrR15+ep4jPQUOfyvDzltRRLhsrrns+FY0rrNsPuibP5GlA9lNjEUcRJzk/euQV/+9F78J8+JE/Xq7C5tgImCriak6epBlnyupcoJtUIyLPwr1/nwXw2I3i53YNj+M3v/gj/7d9+A9/cf8RJEF0/dR3p+h49OWDu5Seffw29bgyXd7ag1+/A4dGU1WKpI3o8nUFeCU+VoYVhwOs/cEGpgMmsZLGk+WLB9y0IjKPfofU+L0vs97tw7eI2vHPnBlzY3IDtzTUuQBGEnhZR9jh+imdbF2PgDOXB9qR7fWMtEYfTAJICBeLFQVZU8PjJPnz8xy/g33/3OXzx7bewf3jMCQ13hKlIkXvn/GecsJ5xGDgSzIFhVMEwZuUCp4nm67oOX4AO8OQ3vRBPjceVkj1txt2AinQBzKsISgw4MWxHtT5dppJCakOIIIAkLLnKLgmbhSiwtWLpyajbN1l6UcV7Cn2dU7JJFe5Anpzcc0Z9Gc467Q1ADc8t8hQmh3uYdLphf2VUYh+Oqa9WpItkPhnH2XxOI8qQVfALrKlFV/mgrytGLSAruxNagtAOVy9swltXtplvTK/lPdc9RTV56USz3i9ZpKlyVLJNkoihaZXahRg2fXeqco7ceLDWpfOsXMzmxbQo3SCM7BDQrFP8KHEv+htUaRYd+HtbW77V3D1HuX5OxgJa6EfTNkRDdQg8RaG34Xcz7auDRpApmiHz5KJxpfVlWJTcoAAhDr7q7Dai3+FME4E5r9vg1Xu1q4jeN75Wi3cqQJgTVHaewmK24HP0VAFRKEZXWmfStIDDcQqLvFB4s9I/nIhjEjVoNOxBnNBEdx10bCOIzldFznE0nd9zvc5JT0xgWH5S09pO431hcw3v3ixgkeVIcdIi5euoVHIRUDRunCLU/M0lFMJldPiBQ/fPAO67N4fde/HxomTT36ekqOyHaZH9E2LwS+fgqkHc1jZaBizmwxE9cbMSa/kBCFCkj7Wbp48dj69WZaS9jCogJKZzNMmke1cqp4wSDqobBkpp9povRosDbE8iHE3RWhGTYCz1s6QiIMUxTyPS68YFCDk9QREbCrWz6USW3flEsNIEMXC+qCjzRCXasV3eiJl7T8ksw7RqW5RUpeLoilPliiaqNAVSLMWe6lYF+txb7twyQZ/ns69AIQgPNde5HismhhLjsljMF+PDXZhPj8KqLAzlrqUuVJ0ohpVBDL1OwBdCPSzrSpilORxR9TvNGHamUlccTNDvDYYju3P1Oqxeulp1h6tsEugsc3CpbDnWa1hB0KKpCAKptwmNg+tqkanSIF1hwlCzETjJZuKB5QWEkwl++7qxfAYgv05bov+RpoC14P2wteX5WuOasxz0aSVzNYXzWidqyvonmxNSnrXo0eAtaKrsI8w7jaFgaR/iaRpodxZlPSkVLqsCQFCpj/KJXi49k6lyfyMR1Ko3ThL8cW/duOL+p7/5EP/+rz6EC9vrBDfP87IMqqpSrXmq+lj0HBJOAEuSFhdvTSpgVGVJM5cCAIbkVmQUXZSmUv8urhBVpVoxEIKWD0Nw2qUuthRqulx9RYgZblkLEfHT7ebTKXEbkBJYEt6JooiVqH6QhFM1G0Ds0byHbEy6Vs/epBBOtAiXOskMtQKDvcGA1bsIBjabTLlST4n/tYsbvEFSMNXtxvDN/X3YPRqzGTQlEWc+9Wec3mkiQKf80jPfhL5Fqrq0erNnpWUWGCzSRc0tO/PnnDgvCigHvS7cuHzBffjuLfibn76HH927C2/duMQQNhIhoS55Ns/F6PxVD47N/VJz5vnjrf5famHxwh6h2jDcf7wLv/qPT+Ff/v0T+Pq7+zyeFBxRk5+CauIXLbKcu5Ek7hKEyKJSVy9tw2Sawhdffwe7e8fQ7UTQH/YYHlqAdC2z0vsVWyZsUKeTElkWA4mIx2UcFSavb16AD969Bb/46Xtw++ZltjViaC1xdAm+Vp1eoJAiIlbq2xe9YAh/mIW4gUnyvDWB1NMm02P4/Ovv4ONPvoQ/f3vfHR5NuABLPzdeBfQ5rY2a48NJHSnPVjCKKxfTUqAX/po74ubUAcJTfsKBCdErLMSmgoUNoHQBCwZhC1Hnz5+7gc7AwsbQsSV0eVsA5riRgnSkRc9TnnY5MeaqOk60xzmJyiC0FFQbvrcHq+ry59cAKkJNJ0cQJgkM1zbscGWDLDjDPE2j+WxiCioiqnUSidABCDeT5h1IA44hq7Q+UoGkZBwwwIXNVbhzYwc214f8e3lVKfPqNW/8mq1FobGEKCBZI1tVSElBGIQ2jgObZ4VJi5IbB8bgtHLuOC9sOc+rsADMewb3q4qhBOvUQHEcqyCh7ohHyYV65lGSerTnw4ptHI0nSSnF9AuiD+ZqYTDvcV0jjTyXu0VB8gJidf1BkyX9NVWrziFdZBB3YhWAeuXJ7dGJLTNY36KHFpxA+8RMmwNDmh1FVsFisWDOerbIIM9KLlBbvU404MXQkOKso0kG+0cLSDNbwye1Gi5wO+ogdkWPJIqCVqH4XNfoJ3tbUeHMr3Un6TXQWFMNBz24tL0KT/ZWYHdvDGnOwPdK7Bu4iOGV9HlZ8u/lEIcG4C1TVdfBLB564a0fw/GiZHPVIFzLyvIeovsHW7n/DFDcIKUO8aJ1gqqQhSkB4/rc2XI2kKQDPdCjQuk3SJLoOBtH6QpgzswCQoFwl9BKh1DAE5EI8xDaw1EvIRAOqG+1MxSVO6OuCVGxhi84tilhfQQtAkYtrECI6DrS8CFtVgKn85SjxDZx2pYx/FqTqTVKTwJLLtDloEjdFh47VOn2JrvRsIX8siWK4QSWIIELnZw9aJJISk5LMRNmgwD23dTUo8OdKGSxICYMi9eIEXNCQ/xJ4jaUjuApx/tPXDqbOj8YFMSQmMqo34HNUY+5mtKFAoaezBY5zNOS1WgpCTC6kMdJAL3uwG3uXHDrF6+4wfpOZYK44o4Ta89gyp4+XjhJYMo5d3AtXTNDZSmJp7Gn78/VT7SjQXvQ0CfqhigEoQgLcGKm2b0513aBrRjf347lF3uhhR/6sE6tIVxzWqi0QoshQ2cZPovNgi7BWsNVR66CkKdawf1KUKAtaAM9h4AFgHJOSUO+EdhKRrWGWDJaQIod7HPrKhdUqjQxGvXh9o0r8A+/+BB+8dN37ebaCPMsCyrrulVZRUVeGBbrCdTTS7tW9H+ZFdyZZMK7tVjWRvnI6oRqZN3UM9tdWSRoDZKsaaL8lbq5yxqahqHXEX8yyu5EgTPNDwq0ibcxm4xpCTAUsJvB0NHP6k/6YZJOJnipVSzvcy/4GKm8cFX3KUSNjgN3Z72KI8zGY0dQIWcL2Fgd4C/ev82CLA93D+Dzbx/B7z7/Dv78/SPuRJ3naAoZssoalb1/1ji1RSeedYnEC8yLWf0elLxkbvmZO899oPVna20Ed25cgZ/euw1/de9t+ODtWyz4sUpKvgYZdkTm7tZ6S5BXjyWd10s6+0rjq1nmdPryC44W1IKq9Q+f7MG//e5T+Odf/x6++f4JJ++9TsxiPQQrZ9ELCqCpYkfXX5YsNTedzWF1OISrF3dgRhDFNGd1QwrUeXe2/FyK5ZK13EGN40AKi9xdp2TXYLfbwRuXLsDP770NP7t3F65ducDwaFqyabwFmvzC++jLC6crVb6ZQ5thHFg6KoY8fLIPX3z1HT54sgczKoQwBDwA9fcl9ETQVn1FTWpadtAQhxa6pORO0NmggthYqUa7802ap8aLzKDl8YpQ+ZGnHvoBBuUZsXo/0CuwooVOWMHCOZhX0ok0rYaUVZNx1PZZ7kJIbSSeoCh88EoFp4Lg9NNwfkehoC+wzOGclwFk1qgR6LNRFH5kaS0gn+HZ8SFMjw+ite2LgyiJyN/H2bK0VVVhVVqMOiGsrQwEVn48hSLN6zWd7guhtMgcnxJR2hduX78I1y9t8rxnVWxb44d8fPhKYCbBqDmGmBNNYGXYx36SQJEVhC3jlogJjAuD0JWm8uIJlkWAnXNFWXXmaRmW1hWBMQfWlgfO4bsAsFmLSnJMxGJ5HBl5HrZPNgVHZCKKj8VdQNbbVq+wfkZNi4/fVpej7xv0hSe//7RgXwSrLkqYTRYsmhV3kxfd1PMMYXPwaTHFLqxRgNKyRypO51mOlPCm2s0ktWFKpJ3114itZ0L2szwrYO84hYNJynOghqkoDJvy+7wsyMyVNRKI8w6vgu4501U/Vaw90QPA9g9ILdFFAbp+HJoOKbiLGqrT1NxInuX73vXBal8VuK3ImFsO8LeCmPxxHKckmwj7s0MYJL3rURD9UxCH/5hX1Z2qqnYMmqFI+HJmYHSQDHc1xSczQnmwypbimt94Arlwxmwn4Gws3Uu3kESFYLX0O1jUJ0ItfhFU8+/HnULlUpbqy9SGr1nlD8bsdyznSFYkEwcEG2RBFD0P7gKOLKve1pO90MnQZW9QQKs3k+1IlPqjXo6G4bSiWsRxFSnd9nXfmer06QIvSzyvIrAamFiIrBRhErVZMfUslJVBlaac04XKd3Dp+5n6dHb1f2+TQjDDeZ7nbjo+7Mynxx0SW2FVbe7plpCEBtaGXbZYiIOIOyUUgBzN5vD4YALTWaZ2HAoLsML3SLpd7K2suLg/KDCMcteo2nAHWe6tUy8xL/vKJ005z0L4p0DGVjmLPemqp2NZNdLwTjR10RiSTO8Nh6xKS9W9imirZ6LBtA7/FLrTv4Vw2hev+XCyEQh81hfy1KaETLiDRNRnTXTiJJZL7DRtKIWMFCzg4bUZ824EMltwOhpqn8XV+TtqpUdqJIycpsoNVQxYxKATx448CO/evAw/fecWvnPzMjlwFntPnhAkO6bCDHUuaW5xxRRFtdJqZxJU+EGSTwfeJ0wQBYF2IK1Wvk1tVdLUGBw0Fc8l6KPncQTiQtoqgXp4lmzu1DF1i0Xqjo/HjJ7udDoqLmVONft/DYeBxrborMezZplR7jWfa7fTJf6pi5PYTY8nkM5naMIcrm6L5cS7Ny67d29dJrVF/O+/iuGTr+4zIuE8j0XAHR9ZNq17MQDoLBd5cow9r/M8BwWJl7Y34b3bVznh+eje2/D2rctwYXsDh70uBxSsklxYSbq0uvh6udfnSJAaUajTXtMuZT0V5HLTwhiGo+7tS9ftPz79nJVniZtL3WwTxAxPvnxxGzpJAA8fHcCT/SNYZCknf/6Zmk4WzD0kNcPtjTVWnj08HhOvs1aV5i6f4b4jj50xMcSBcDK3N9eZk7mztQ5v37wKd9+6Blvrqyyow7ZGDGs/w8iBImxa2K6/1GHUK2k2m1dfff+o+vXHn5nPvvgmOjgeQ1EUZ9pMjHYCC6EtcpQXh5RsWk601Ci6bWt47kOTIU+rDJ6XEPn1nH1WA2CvZtBE1yoCJsESugFxKkmF+Om3IsSLKCSKDXVhg7IygTOEKHPOsLZA6Z5rsegDeLJ8obGYFgEsirMPASpcvCoLnBztx+PDPdPrxemwG9lhJwoOggDp+si1m+afVyKn54XRVmQJFEooSutMtxNXFzdXFndvXjRra0OGlkp+5s21PZf/1Q8uUlDwlSS4vjLEXieCJ/slF3Go8JVVBWkaMIpHtl5qm8OAYriysuM0L47LgmBb8YBOyVobyZAwuI86nEEtYGhdTUOo6aHCzcTKT76lPVOE2ui+UsPNCUTVsJppK51xvuDbCsHJqT8AACAASURBVDP8WiJ7svgjz4njPU1kLyWqgoXXkXBCfUHyd+BPwjEKqsIsLQLqYi5mKfMxKxJ6ozWowrqDa1qLPqpICo3XZFbAw705/01rH6l6k/UJqkcrXT3xIslrddjriP7Dq11Ju0DlTnFv9sONLwpitTNti6J0tiixGwcwGnRgdxyavLLcIaO1vCxtznO78eDUNcTlFtyaM+G9bjz6l8eH6ed54U5TkXzjx1PJJgX+lbPXyyL/3xGD/9NZvE2JlNGkkiGdBC+VFoMvJxNnMEcHc1l3UWsw4gepk5uhZ0a7KlpOnFAywl1RTki4A2q0iyeWssivSzWJrcTekisgVhMZevISOb9aaCyyDITGStXc6blcyI/IWoUFzGLvoamuG4F0K1kdN6GuDzD9gFsWieKlWZhIfEbozShlxEhFSQLpWtD1sa1JqDlXwYkvQg+JM+o4me7Kw+8CxcN4P81CRewi75HCSlZeWKihaVgQr2kUyDGfCL1+ni5mZjo+6hZ5xhs+9WzJ1LiXdGGl32EFNfLK5J48wd+ynO1OJtOMF0qB1sqJW68sSebyGLowjCuGIrtma3VoEwRcFX5qLXkWCCpWChBORJAySXho7khhVt/Dk5x9wsn5ahiE2O2PXG+wguP9XShspk6QZ3lqXCtP8cI6/t2x7riBF/L6gQ5fYcwVPutqjIpMZhskUIU99tY8/bqcnrtjnmYslpRckSb2pggARZxslvxbTHXmRKKuZLOsM9+zkigwlXMjktKmjSgIgulo2De3Lm/F79+5Grx9/RJurq2CLXLYffyIrEc8l5YFDBQC67fBelDbFKfKaifZ84rafD30a3DTyNbmsyACEFutXJXY0KSyNqGmWg2b5yrPhLW+yCYiZDXb+XTGiW/e6UCn34Nevw+BFlxeK32hLlSqguBz35t/Zk5BzSi5DJv30LeJwgiHwxHGUQxZOoAsTyGdTiFczJ2zkR0NLrv10cBsrQzN2q/68O+ffgV7x9Ozn7yRwaUxrJx7bpTozhRJnw66O+uxtjKEW1cuwQfv3ISP7t2BD997C25fuwTrqyvsoQdWkkt6lmzrfF734/sSSSvW1bnlw6lgWql7QSS7JdboK7oDRV7B7uER/MenX8B//9Xv4I9ffMO+ff1+DzbXBtw9WVtbgZ++ewc2V1fgi2++h4//+Gf47sFjTiaJ20vBVJbnnKR+/3iXESks8EGKtRSoacBKgXqSxDDsd/nze70ej/uVnQ24985bcO3yBdhYGcFw2INOkkj0UhQCVTufwtK5eU+v+/AwwaKs8P6Tffz17z6Df/3tp3j/8ROHznAQig2uG10ryWs6/01szRGkk72DWgpJaCEyHk35yvOQNqLIN2he9MuK+JDCARMn/QLs9PwqiJH+D6Wie+JW+C8Nx6DoKjRl4UwVOpOQcr5leXAKEAIhByy9Xp9zK+X3KDBulADTJY6pkszqt0aSoGcC0fXZDdgdGqfjY3e899jtrK/C9koH4eqmKRdH8GjfMepq/2jKhRcKsmltZ/6SSiSmbBkVwPb6oLp+ZXtxaXs97EVRIqKksCCuP0qTIX4tfNomQQLyRuyTwNagy1xqst8gRegKLGZFrrkt+8UzBUTDjxLRHRmwHWftNSdQc8P2I76L7guznHw2diVGPS+9DRYnnVYEnYwm707VgNnFxFJN2eZq+SedUvBIMdlXjYqRWSUc8+cSVL6UZ4hUq2fTOXNh+8O+EJ+elZmdZX9tryPNvy1lUHlRBOkixcUsg2y+gIzQUgTxtxI51jEFNAlnA8+WN6I5Mp1ncDTLuNvtiyKBvpZ8nmkukXUN0TB6nUg6Om1W0qscSht0Eh6FCpelo1Sq3LPQpEoVR1eWZTWfz7DMc7My7MLV7TU4nC7IYoewfkwL1jO2sLzz0mV2jXOXnHX3igzulhV+W1V28RysxBs7Tl74Zg/NzxOT/GOF+L9W4N4W+KNOCAkRg9Z6hZ5UrB23Y0kUyRNS7r2vmeiQRHWhUORgKvT258LvzPlvVrI1ltVlKTl0LiFxH4cszlPq+zlVkJVuoVTnrRpHKZQVF8apXYlYcehjYiOdvRa9T6UsDYEQk1kgKHaskMtbjJjL8/XwAxtrruYRLAMF0lMbM2BBI+QEM0bNMXzf0kjiGWCtyGoLGSc+t1KLZqhdw1SVu+iYS7eWkjt+K+vVX9mnE01greunkymQh1W+yFxhLUYmZFz6xfUhbK32YVsNbBl4R1CbRQ5HkwWrfQ36XTbvTrNSkK6suGdcksQlKcaFhLtCHpBK2ltUPvOepM403CUuFjGvQHmBOUOHifMqIMyQieLyuqLZmsiahu8VuT9jGMZsXO79387TbtD3bvlzVfVPmlRTE0J0+iC8XsoNlxULUcLjz9TESwDgMSeaZZhA7Qa7/Or6bKSjSRtZxUnlgr+KoGKVQeFmipA9o68hJjsKY5zIIzmXlVUhCSZ0ImOiOImjQS8+3lxbPbp+aTu+c31n48rOejjodvl2ztMUi6KIFIZmam8v9B7Feq7OUxbkc2lKGLBLxiBeNaMWaPcbYg1xBXUNOxXpXINEsF0VUE6J46sTcAXbb6CoH6bzCqqiUE6Hg35/wFVZhB+kw/kqh4OGv2ygDS8WLit0u11CFjB8cd7pwOT4EGaTCYTWuctb69DvdmE06DPH419//yXcf7z/Qpijv2elErNfLU30p3v+dyD7gO2tNbh74wp89P5t+Pn7d+G9OzfgysUtGI4GHBQQKIiChjPCN9/IscQ9Pe2UfI/agwtU1pQCO6MdZVpf5/MMDo8m8PX3j+DTL76Br759wIU9EjYJIgNREsLdG1fh7u3r8O7tG7DSH8D21jrsbG3C5199C19+/T082ttn1ARJ5tNBCrQEt6Wgl5ArHH10I+lqouHk8vLFLUa3DAZ92FxfheuXtuH6lYuwOiJFZyniEDfUQ/deBzz5zd8jeZZIeffho73gq+8emse7+46M0uMocqpiqlrugFg/f3L/Ai8zp9odlCmE1NEMHXTjipVoX2E6Lq102KAk4HwBL/dqGPOjqvfiP8bdMqwtWpZf0fqHktkKF4a5i4LYVYbhV7SOUmQWnfKRplGVpfcnq4weEaA4fDOwm0aQF/jindRJREmzjOxN0tnEzCcHyaXVDXzn1iVDEOWP/2Tg/pMDjkeKUs6XIJA0jzvGsDDW2nAAVy9uEHQ2uLSz2et2Eo8SduAbviKx89KTGE8mSC0hnjgMWNWZ+MypKXlNo++TBkZeqNGYvNxq93pujFkERoMSxJ7036v6WTOed9maYE2tFNtKxE5pZqDuBsav5oGW/nXqGo5ilbio5a4aJYQKQW1nLvxzI3srQViPjuT3u/3usjrtCSjrCw/TSHSwLkfB/4/TLCvn88VoMctigiSXVdkqcnk5cccdSk834g4uSiTulXgrtdWirSNgFWPLZnqszklaILmDUpP4KAyIbqMOR0/5WLZnKpz1udRJl6otQFe9lJqRfcG7UFZKCLLFbEYUJugnMWxv9GH1SdcdTeaQ5mWgYk5dzhc4X+L+QyTNM46rB9ZWNzIobya9qBO5aPFjWMJDUXAypBL6ti3t/1w4+1+MCe4BWhIAimQAsFA/g1AnqdVMnO5aIQRZpO5VqT1kIkV6CxBK4EJNTIijmbDwD9fzkWAEFddYjCjFSiGOobYVq5hKdyxwHkcnENhKq5EJM7O5q8cMUi/SkSu52HH3U4w/kprHKVxQVDuMRFoxLhceFkNwcxFRIfsGUqplmDDweDQiQTQWc0feniqyohBh5WH6ycvrHMkyZLpokd1EaWV8jNEGoRMvTk7C1PalQPLTdKzYa1StKwDhrFKSXaAqdglKwwWL+WQwPtp38+kkIJiQJ42TnQg1qYinOehG7HdBhV2q7h5PMzgcz6mD6aiaHUUhRiaFaZpyN4Gq35tbO+Vgdb3CICLjYfGX4PNgL8WZtvNJxjvUVj4yftIDgxmSDH4MQxVAkpCMB4DmEN/nUNt/lk37gxCDMGImfD1jz8QbWP55s1C71m848ILukli/5tKPh88qb6RJcgUpRbBZ8tVcSqJa54dOkk3DKoMlG5eUamWyYJF7T/M1tWSb34gYomQM4/5FsKcISBJ8NOrh2qgPW+sjd2lzDS7vrIdbayvxsN9hblNKvBMVWcEasYCNvq/gxqW/eAJOw11xny65ZtDrePype9aMCbTST43TGx5JC6Diq7b+83iiuKbK265ukoY/8Y6If0zf7g9HkpCeATL65g6vj3wyy0bvFMMSgxxFRBEMV1b5mbAWzWw8NtTJJVXpn71zg2FBl7bX4DeffA1//OYBCwg960L9xvwqB6oQiFkycz/bsTIYwM2rO3Dv7k346Ce34cO334I71y/DxtYa9DodfXYqyNIcvPrxqYWIv8CB4K0hZD7aGtbWzM1aLRQZERQ4UQTjrIXQcyS8QQqwj3cP4cn+Idx/tAeP9w64E0lFg7XVIUMHR4Mh3Ll1DT585zasro2YZ3kj2WGLlzs3LrFK7dffPoRHu4dQOeHb7e4fwMHhWKxnopihYtQdpkSXxnRtdQRv37oGVy5uw2jUgw4pIXelM4PKvRXRrx9VUeZchw/Ys6Jwj3b37FffPsDHu/smKwr2EfSoa9oapQEkNQD+DI3Andp4sKWIh82GytWMqjoZfbkTVE0H1xSZXmZ2y1oJ6At4PO8cKgRMYGBPP5Z+vfQFE8DSYZhjwFWvSETiRWdAYcNLnwcatTiBnJBqPCUAJJjUixDiAp3CabH9mqeEl1A3OXqLsjL5fB48fng/WElKeO/qOlM66EYRX5iUmakoDihwyCQMGKF1YXuN+ZmXttZhNOwGYRD0pEtVf1rHCQrvdKGlF42vJpbo4wVtY3OSxNdi2cebAh3qwFFxk5AoLbVgVBQXal3PkyZnyHBOc0T4JgygK2o4WHetuXelQZLR87DtPZF/z5D8UylqtaT+jq19V/p/bCnoFU/r7rfiD/U9Dbb2Xl9Ea/n+sFL8eMbUhUFWsOo1cZ19p5mSviiJnSShp8BEfVZL51WUglDJC4bHZovC5VmeZ1lWFHlRMeKgzsv8OmqaCKGGz56eu7F+RCXcdJ9Ui/q2nAghQYz6QpP4VMjiYcx6ffYUwXrgnj+NsC4tRtI39hQ3nknP1cfxenRZnuFiPg/zrCBbOK6VkABWNwmZFiBe28rllUL7VB34Jd9gi0OXo2HKYmQqJ4WG5574mznCLM96s8n4Xoju/4jD6H+z1t20DHVkoZq8hQdu4dt8n4LLWQtWpJWu4JAhsQ24xKh8dygmSZyJU0I24wQRYagd05ly3jtyUzFXoF3Mgjnibcl+wmK8KiWaWmtF1JkCTWSEByqpRKD9oUITwERHlXmdTjJn3TfIM5LPIZLPQ4V38oTJtRwTq64tJ34kJKSwyFxNNUpdVgPpdDts8fFlJ5B1J9cNJ9HI2jspqUoM80gNeyiJJ6kY8IstinBaDX1NtSdTsQx9lprD3YfmcPcR5IsF+6uHbP9pSamQsd+lXYGIBUGADeInC1LsmsF0kcEit6RkBd0o5KS0KCghMm4wGtnR5g7EvRXSVCORF9M8f6yoS0k0+T11NSAspDignqhydFQcJdVWVYTe9Nr5ijLDk6ta+tqQSErHdftDiDtdMLOJQpnOmmj6dUH8Kpt0tZW3KuAbpYS3/PJXPCpVn1WBgmbho48g+NAzobPLySe7Y7I2OPU3Q8gwgbwKxMRdoUUssq6Jly2EH0pVvSiKDDn9jnpds7k2hGsXN+Hy9hpcWF8l/7F+r5N0DYIpKhfmpVQSwcubiQOtbjuaoEny0xqjJoJwHo7pD+sXDWnrt4dVdfGW7pivFXvCg/aCZHKomfXJe9NKdbmSz5PeeB4isbEtVHMRuwqCkKGJPxB/8yUP9Rc6Ad4UrQCZ6U5ElZjPQ/e0P+izixJ1rKbTKfsDEnLhJ7evwrWddfjg9nX4zWdfw//4wxfw/aM9SNMcxvP0XNd8MuE/7ecsUZ7E0IsjmGeSQD3vM6jyT9YZ79y6Ch+++xb87L078P7b1+H6lQuwSlYagfjpkiiFcAS9YFGTGfygh6BjLDRIl9PHRVUxGfoSizqrDxTE2kGhyQo5p32LviZYHYn4HB5P4eh4AkfjCduXkA/meDqH3YNDue6YOdSwc2kd3rt9A967fRPWV0f8uSR2QUHe6rDPMLDLO5vw9s1r8ODJARzNpmDJvzDLOIij7TaOY1hbHXCyyVCZPIdOJ4ELWxskbAJxFKh1jQRojdDSDzzWP/DBz0poCHZefPP94/STL74OHjze69jKYRSGVjuJKJrlXv+8UfUT4TiBFHUCR4qzsNIpoRM6FgR61eHRx6RQ8cCwFoY5x6H5h9bU5fB0BqOel1i1ftaq1rQpZT6nIHSMZckM2bJZ+M1K4unPzHfohSYh5UcP+aSfdEKEYce4zIaQ5gX6bBPBQx5PICC8calzMJ2OIU2nYIopDEyBl3fW4d3bV7iARvObnhuyhCBBl34nIsE0t74yhNVRHwnOCvJ8nPCrdEa91Snm7IB7sdXfchdT/qoqFcayFSdXBXuHSia/mM1VNd8J+mSRawHONqwRb7kGENkKorJyUwc4gyC44IqKHu4u76tWhfcajR5xjPbQUYVJ1ymPwG4DSUQBfcHLFyqlKdCCmranmG9IOhAvU5/EtuMg/RmfQ1UxnJb81ueTmKHLzB0sREiqP+wVvUG3ioj/YZo11Ol7iJhZwSqyeZqLHVNRQJlXdN8GVgJLQVEaOVcOl01NgiRpXrY3pN678XYjPrbQPSKvHBxPcjg4zjjGVYPEOsGmzjityoSa6SQxhpxtOnhOInm67+fTR7vimugmfqZCkt/e6NzSxQLnsxkKdFr4/LSp9OKQuugilSnGGwSez5XEEdXCZsjT5xAA/mQcPibX5lcQLnutR5jNji5Alf99GMb/ZDC4Y5keDhn74Iu0DSUBRjmXTkU0QXrS1jdYQOGonXo5Qba+sLIWareKhHMsi+do7QnFC5OcisTjkpr/gdB8GcTRQ7RzS1xQRILmdrlDSsmNpwdba2unBFlqGEpL3UM0EDkmX2OBlDnRdZgayyhlGOQuIjCS0i8Ojj9HQAjIiXRRVyGFFudX7sT7IulkdixbTdxTVtzl1VvHjl/AyuKC6eBuKS1JnXqaNHF2ILYofBIzrY0knMxa8dtU2I0XYwrzxcwc7z3B8eEelnlmmBBN/AHmPxpWbVvtd7mSY9W0++H+MTw+mrIxNwVQ43EFeRJAN47ZINwEUdVbWS/iwYrDIDYq+MF3nNYZoSCyb2ZqwMwd2NDp/WZUBt0Pxw8FXQs1KOd6z0OtfFYghRdlKnAMh8rdgyBOYLC6DiubOzBfzCCbT9Q4+WxdSLrVhvNX23qkPVRReuiutba+jgO9pyZtSpXHKHrvX3JDDVkUiJNNnsGeALEcUfsNBpiVyCwcqJh7GEAUiP4zVejocS0rUIFfgpDI8MQmhG4Swdqoh5e31uDapS24uLkKq4MeBbPGBMj3M2dOhPXNRacq06j8Y17R2xQN3/GoN64Tle/6y6UKdtONxtZm9nRS43zpiDUDjLeo9SJCmtR6zqdktPp+5MWi/kZOPY74s6oK5tNpLd1OHE5TG2D/KJLOZ1Qcls/NQ4roGvuDIXGaWbF2OhmzRUpiArhIEMv1VXjr6gW4ff0C/P5P38Ij6pw9OYS94wkcj2eQUTJXPU95Vrp2lX4ewSrpKE8x76ffo+pwUDxbDZeSousXd+D9OzeUi3mbvTG3NzeEO06LYFlBwTYeVa0w6Dk5r/+oyyYnx97ju089jAqDzBcp7B+NYbpYsApmEsccAFMyzc99WcI8zVgRNuPEr2AvuIPxMTzeO4SDwwl/Lb6POeweHMFkOuPuAVtM9bpw++Y1+MVP3oa3rl+G0VAspmmM9Alg6XZKpnq9DpAQ1sbaCsxzGT9WETdi9UHrA3Gt6B45DZr5vgVSz8vzUpuYldc8Mf9/TzTJgSIQqzycTBb2weP98sGTPTeZz11EKudaNWbsFVpyXSJss/AaXauc7tRLMylhFFXQCyz7SPr1/bzwuhOHgUbfQTUczuV5Du7E3+3GIW29hIYhCk3lTC2rxmVX1wQbBgUX4+X0iZbhlGdvVb0YTlqgLKlEyU+sJjmUmK91LBc7aUmgvSjQ9t5yrVcdtOh1gSFbEEjTOQf8jwOAT6OAi060hr11bYcTztl8IUlNkkAYU0E8YKg4c4kqqRM5f5mSXLKWGDg4JDaJA1h/Dl+u3lNQJSBp/yBvaDoP4j3nmhyJB3QlXW+C3hFPkxPtkAuxxDFFXHqQvOsG14fpXGeL8mhRlAeBcRsAuGFt1TNoIt2WkDmKIBZpXvehJhN7Dqfz0SjwFBaQoai0ssI4imdzM1f8Ptnq3teTyReTsRYW9L9mdVCZqOPIezNlWC1bIBEVw4oFTbrIq/lsUXS7iQnIa0nvL1kz8RiWOXczy9yrWlu/3NJH9ozC/KG+hU3SyRhhNDkaLJ115PdPFSM0rW2Sxe5ImTOtYHecw2SRCyc1MDqOjlVX6LxF70HWakdmEgGejF5e4mh6r08Fcy86NHEuWG1c9gzmmQYBKIBALH5IlZb3Z4BeEo8dmMO0yLtFVQ5kHNnZg2ZAjtbNKJDqdIIuPyjumUzbN3aEga0GQRRtAmDfWvKGBK8y0dOkhnFoRhS0C/HTZHywYNAlaeto5Odad80Ptu9uoQjg8M+GjhJJEb0JmeMppTJ1bHCRX8daqo+BVzD182sJWSV8z6LFf8q1MESw1K5GF9Z30tQ7k5bZUt8vamUlXRUEWkDT3TXKkTw5M41HQHjUgV6DFzGiU45lC/AcVuVrasHGFzbE+5ATMaslRiPKvfW5tfldVq5PMBRFUYZ5OsciTZEM8R3KtkqTdGd9BG9d3oCt1QEvRFlZwMFkDt8/PuK/qbJNgSN5VZWWIZgcPMW9QdUbbSyCpI+sFkzUUGG5B3JvGD6MzKM1bqoonJGMJW2irj5XveZQ50/ZUnC0tYaP8D9lQ9BkoTtchY1L1+x8NsGDbIEEVzFnUC+X0a7Uk/H0RcS1qtmv46gnPHGeKld3CfxU5u4jhlCZDhA7BnXTX45wm1nNHoUmZIEUpMCBrx1ZNIQgLbT4U1EgJeNiZ5nfSlXeESut9WHU78L2xgpcu7DBNhIEx+CNlJJMVpIFFRioJU5s694ETw9JO6RZ/j53FVUNztaxfPs3lruZz7tzddW1nWi6Vl6uy3or0KIabqHiRZGApnxyK9y1yeS4Fl3o9Hq1ifuPIeF8qtO7FB1A6/vKyzIBdEj4KI64uzgZjxmWycb9QcBFhVH/Hbh9cRseHhzB0WQGjw+O4duH+7B7eAxP9sfw5GjsCPFAsKL2GLAKoZNAopNEsLk65ASVbAeqVvBC95mq08D8vuX3oDm5tbYCNy9fgJ+8e5MTpw/fvSNdTDJa53noWGGQPr/NDfwh+YEt9Wt8an67usP81Mw1WjCizuDj/UP48psH8O2DRzUUi5I98sGk2hL9DvlfjmczmEwmzMucc7I54Y4mJZphGMKFrXUYDHrg4fbEveXO71s34O/++kN459Y16R6ovUlzmlwkcbZwSONOwVOH5kEnrpEHQY09d3WnlRVMKen0PrhWCjdk7eXApaSMzjpkr2gN8Zc8JEAjnbCq2N8/xj/++Vvz7f1H3UWaYUDmpMbTaGXfsSLCwBupX1qiwNbdvn7kYKiwWS5Vv75rQ+VYtZX3z/kGTcewZSJYKyiS71hiSk42C4tqk7X8Ma5ln0XPPMn+W1VKsmpnTIPEPDk4jQpRV/n5nyFW0IPUDIIQJibk7rDfVBSsIS/x4i70vAfSXafuFxW2KBmj9SkOhZNJ4oYrBC0fiQW58cq71mHhu/HLZ0SrSYpNLFdq3PS0CW8r/6oTTKK9FGKNUXAnLocyL6GktYr2TA9X5udMEnM61zgS33JW4Xt6DTP6pymtxcPjxWTvcP6nmzvDYRQGO2UJvcq6YWBqzqWCgozWSytRGqYso5GfqAWraMel/VdEgaDmhdNiXtpqplfYU1V3vfcsViEqv17lllo4piaat9qbjB3Seye4cxojjp5ROpfz+SIiCOhsGgYiOqRqw5V0gyulE8nejgp113+3swalKUBrR+RcyWhp2qiqo2+Kq6o3cWcLC3ayKKu9cUpCiAEVZD1VSiNMCLkQZWA6T3kvvLyzxtQFrH0UXumo104fseByJPOM5weYZz+bzSHP87oQ4zQGrrTAEwSiO0F5fr+bkPjVvJyWcV7UdXsvSEekpjWDbrVjsjAwpz+6b/oIo6BbVEU2BmP3jDE9T+cRODcHQQWDN0UVlbIY69DLSGMrASLzUC4XJjzITltKyF3Gynl0B3L3sweiQFt6s3bnbO6Yo8gDR96ZpKC0TzEPwWedJIq5lilQp4ZPOB2L6QDMUbiSsYpA5SiyyrFyCak8zL6cqhJrvEleTabjBI+XtEqUdPmMvDRzRj6+IpqEOXLFzCTI6w8n6tTLi3UdCOX5cTmK8E+jsCJ/SPIlg+wTTRCvUTXdR5LLdiO10sx0D0j49kjugi2BU28hUROl6db0kgA2Rl3YXKGOVkAEYziepfBwbwwHx0w4lo2CgpZuzFDDOIwYvBn3V7E7WndB1MmlGWtVUZHvUShSoNz5VrEjRuIS/5fGP5NU2IaadFP5POaCAws9iUcq1Aq7rgFotv4kKNjK5hbMjy+6xfgQJuOjGi5y6uG87ZBrHu6WlxS0Ooa+kfxMoY+XOEpONG2jANh6X/bUJAkGpaZCTVz1647vXTTIDQwiiBJZDAkWQveVZXgIUVICb8jswxdHsLk2gs3VAWyujGBtpc+dTQr8ifdC1UeG6bFHX1vFrdVCYlU8CAXyg40inu8otjy7nGs2n6WVs9krlyCrpxn6S3zk6t/D9nh5ekb2DAAAIABJREFU6xRsOHGe5dmk47pDCWTFWVXzCOpuqq/yIsOA5rMZB9x0dLrdBov2l16JEXzEdIZAX4SRaEJEcQLD1QDCOIZwMnXzyRTyNEUqJBAU/saVTbJHkbmTl7B3PIMnB0fw9f1d9+X9J+7bh7vV4/0jPJrNg4z5ujIcLLJQlcwHvbS1CofHcxhP5jzarLpYSpAXYsCJzurGak1qIMjnrasX4Cdv34CP3r0D7929xYI0Yb8rSIKsgHy24A20/Ry/QREab0F7spjSbsAs1TG9IMgiy2Dv8BjuP96DL7++D0/2DviZpESROLPOq7dWlqvUlFgSsoSeobywkKYpfz+MSjieTGuFU7IquXX9Itx7+y24e/M6XL+8zRBdSubtSb8RDbWY5U/qELQmoOy4zRLams/P4Si3YH7VGStBP+rDCDsOD47H9je//9z9828+jr785vu4yHMkzrrIPvjFX/ATVgk5vPkTztHQAihrCIkBJYFz5Pjg+S9Ld+IljzpsJq9j79N/zsN3z0wLpuqBfDRFAiwhYhfugE2zJMxoIWfqPUYCCI5SrYGKOifI2YWKrVB36GyXyiu0LcgoFDpBAEXVkFM9nsD/Jk85ciexFYqInGWd/yLPITMIk1nK9j8EGac1hgpfpfp/qoD3qcG7KOBzbJKyBgS4Fb1diX+um/1G/qZOJSWUBD8nlAg9o+QTXSkNhqGkvDjaeg8URpTsPxQ7EcSXRfJOyWn9QcIIlbXD3aP51qdfHmZ3r6x9srbSuQUA21VZdjk+kiK+xNKOY0hRtWihNOsurC9SulYS56Ma+YOe62NwlqhWFFd2lwdr2QZXPLKVvELrhrGNkFAdPiEXH9zJPd65sCptWJW5tvi0J4la0XY+sdX/tSDs9xwfgHhRIueW4gh6SSyC/oKgZXEpJ115NrxmZAm6vHIV6ZCUJWtMSsBd+VPwxUyERbZgbjtZRPU7cR0tntNcr3U8FQnZulElhaVT35ghn9bCIl1wfEKQbBpfsnjxOhgKaOKGEJvvV86medkHdHFZ2R6K5zcd7JpBDXdrXObATctFsbDCH/2LH9RV7CNiX6dz4cSjkmb2nJNESgAlGSDvIuZMigdy3XG0vFZJgaLCpiyjfouCHhRHDecxtoXijCPW6VFFLXTMa7cKT6WblFJyp/CHSteHUGoyrpCXsGAP3ehY+c6s+KUeDVFrmatOVK4p2qGsMtJqAHVdY37G5T1IY5v5FE5SuxJbcl7MVZRHNW4aVCzg06lxC/I3iUgfoUy4kUJyKVFVRVYMNHI3ThaHBSXa7CNK4kCAfe0qezlVfRIZfUqrApZl4fLF3BVpZixJb+mDi6z4GEAviZhUT99bZAU82J/A/d1jhnnyxLYykbtxQqqGXBcoTYyd4apJeoMQDRausrW4YqNASzAVTB2TBtlSJpSWJP9SyAmligEhE9mZ6+q0ptZkF82TgF4aR9MI3iKTpAuj9S3or264+XyOtiyeTSyqi7D06VQRC8BZA89zUnJPRZcvd3Bb0Fq2Z3CtjdUjslkUyMTgsSOAzYYP0Cx2HPiEhgU+qDrHnmiFQOQIQkuV1H6ny5Uu6jytDfssBHJxaw02RkNOPqlK7DcC2kAo2VhWt/PdtFbqhiLBXsfdT/fW5JWIrcqnr/a4Gsrb5PitZB9PLncnYCu1FUrjDyrv1y4/N+fqr0X/ZBVo5sexkLSpDZwJ741hAEEYceA/m834vMo8h7jb5UTNnJIIv6lDz79dmnjhNFQyp9wwE0CvP4A47mC/14PJZAzzyUS86aiKF4eiWNZJYH11yIIad69fxL2jqXvw5KD85sETvL9/aGbzDCm4ottEBSiCgnbZtLwLnWTC84neQxSrSdyhcINOx17Z3ghuXt9hkRkqVF29uAO3r1+Cq5e3YYO4gnHCddaC/B7Vh7VxD3hjCaaMm+DEwnrCnj7WXjUSBaGhzwPKnKIkmQJiCkKJT7MgDtM8hem8w2NEySaZinsIGwWhvW4ESZSQTQRDZ5Fhgzkn7esrI7h76xr88ufvw/t3b8Gg3+fOIyW2zwkRsP34SC2NMXSae5x5HtNiS6rrQykYnvuGvI5l87Uc/iSoq/zNdw/h13/4zH3yxdd2NlsEBrxA04mltsZINJ3CsmKWE3Ri9o90cWBJ3K/EBl31eg4tLr3S4Plam88ZnCj1EwLYsLJsCYGL664n1n+230CDI2cgcyEQ3zIwohNAwS6PR2heeKI+ymKucCiiStPy9G5RvXbzst0sf8yFriyvV/RM0bNlVW+hqqTr2NqXTp17ElW4nryKRRjJzSDG9jai50DQdHpe03QBWZpxF5O8JS02ID2Bi+redEJRXTMjRo91IubUcTEX4PQnkBefwKzP0/In//Hn3d/dvbnyh//0/sU/AMIFJpeSqIbcSYqDjbNV7Y3ZTpBZzRqcbgHeLa5xgudmg9AejKD6JHlV3QyfqeoD8HSVyZ3clV5waLOGqGOMTFNtwwap4s9dw+9lGzJXP318XcKfbMSAoG6Lcx9V+kxu6fVWa7VREJo4jCLiZlO8g5yYSWJKa25b5ZfWZ9ojqbCQFwpe1Hv5OkjrGt86By01vqcwM4IE4wLLIrVlUTgVOjSe5Mw2P0aWnZIbBQCFdeZ4Oh/5aByNIiiREYcEl+micxOH7uEswwnYH8USDWGF5XUT4LvOwTUt8lUqcFOyahZZcMgPQq0UxeSKo8WLSJRbxeLCQN0RD3R4uaupUDhXQ0FVHcyxz6Qj9EYpEGxO1kpWsnU8aVeQRXg4waJzMi3hmUzen7tlJLQzdDTQDo4AcV+qCW6Ng2cm0qKHo4IiOlilTB/RQDqQDMVjfKKKibFHJAkgqWdyhI67rzOHBPWlxJz8C7mkrKRotF4USBwKIQQpEFWSQFKiSZYmrqMCGAVXoEA7oUjdWVruqABA6a0bO4cEOQ6d+HAWKioAov4LNpvP4Xj/iZtPxhygs3BMJcEo8S/JDJ0glhT/kCjQ470p250w50/LSlYx44vZnJvBUb9j40634k5QxQq0ynfAUgWN6Kmg6zgk0SIE7sImIgCFmoRzp9uoiHrOSbT0YU29hkpslPPXxg2YH9xkQbzsUECd9AbQ7a9AFD12WVngKVuNpm2iWcoLHIksm1CxFycSF90dGzL9y4dNdfWChUFcHWjWmzuKVhapUtP56FIk3c3WZ/pqs3JsHcGH6FfnpMpZIQzXhsy5pCSAOCKDQRfIF5PEf3pJyObvtFjKxmzrRdO2OXovWEgbHysPr/KVUl/N9IZU7sRrWib+LfK+kRK8bAcnNuz6uluLcX0vfPXbNpuV/yyoJ4dTWyGsGlsiMLauQgsknNoXFNawVLq1MJtOaXFnOO1gMOAuJ0N/tIP1WjqdiH7Hfu6A6zqo2+r5PretghonMXcdicfZ6XTZb5Sk07lSbyso8lKfdcfFCbIMuHFh09y7fQUms4UjP0L/vgSbnUznNs1ZP9EQT5G+R1DRftKBkrhMlatG/Z67sLNhrl7cxtFoCMPhiDmGQSRq79QdKGfzGq5c+7H+4Dk9Lpfsm+/jGURCWMBO60EDsg5w3oam02F7kJWVIdvNTOZztqYhVWAS3iEoHcHvjkmiPstrGBl1OOOYzMUR1roj5rES1zPpJNz1/dl7d+G9uzdgZTTkjnJZVc8ufDh4qmxTR4e1ytZ5hkoKtS+x+DmxGVMRvL900snwPnDH41n65+8fLr65/yicTObkt41hoLsMLsk+63arr1axncJRhRpZDGgYVS40VICvBQrrZFM8sME/uH+5a9fyrOZ0JQm7GXSB77FRl9KgJJPPfRMKvJyB1IWQkIszhX8oEOxaeOYMp0OdlySy0CsqGBNVwcES5NEfat/cKJ3rNVDdmva7o/EMprMFF6jQ1DkytmyzTh9zv+yyHzgXjUKmVmioQerkIvJDatcpc+TSxYIpKvTIBSaoLTXae2ENxdR3h3rfQqan9DoR9JNIuqSVXYov2qMcIgYVQLR/PA9/+8XuozuXV/7vYS8isahfALi7DmCLYl6Q4qk2wVvngCrSp60cnrhG9171yATl+Et8DRuoe5H3lXW+o2nqjVRwigpYPIk+8oUYd2KvbifCktKJZ0SjlgvtP+p3bHcPaz4l/TtARmARRQi4UKecVeuFIVud3PpzBXVFMU4UIo56nWDQSUQkMfDQDazF3Ojek+o3QXVDz2VXmyfUh9mE56JPn36ovphazpy2F/HnFexjOqM5SHS+jNbSqrIJB+GCvuBzy7PSZmmBZeldm/jSCxBxVVTvfspJqLmzjs6sEYMojGz+lNHuX+gIbWUXJsAFJ1yORQ5zVj6VFn4GSO1Y2ow4SvbxY6DoNpIcqwQ9ypDIQJsYfo4WKmATqcqqlY3NJqp9xXj6+vfZj55UYjmCpKRjUPvUaPGD2Yxy81Aq0KjJMcNbQ7VrCcVEnZW6BFsnfJRY12D6yIDhnJYVckktF1XbWFSoOSHiulzKwkZyvoFDR8lwSibMDlyClGix8A0kEjS6BSeDSJxUrjTQ3rVB3qKqqlvxxHBsuSK2HyAyxZ6OoLX/hPc+gDGIri3dk0yfsKFyRcuyKHA+HQeT40OTZnOk6jkpb1L3gSZpGCKZKzpqbJLvZpZXtXKk54CJ5x55Q6WMHe90e67b7dik2ysxjCw2ViW1rFkLJBFg3TbkISw4qZYFjvqmXCQQXQtXyFNiCCZMQNBNggWjFLQKKSkK6lEXMPSpCxrjiLtInagsm598Wpwv8rT6Jvojw/r2gUpfQy0vDk2l7RWdFfxGSpAja9v03UbEm7iXVgV+mv7liU/VTKvf71QXN0flxrBvkiiOgojEfhIOcEfDhBWF6R363Ri63Q5EYSRcLOKVkXWBlcqsV2Z19crfjMzyxv+MxchXzlHgqE6U6mHp7TSYWNqR6tfiUuegrk8+h/vjYbP1W3houP+1Jlmm7riVAJA31cqjKfxmJOon1gMHGJLNm1tVQZovJBnLUugNhtDt9ThR8+Ipr9zlfJFM+tKl4yuZ4DtNaujakk5iwzB03V4P8zQzWZ5y5dRDw4STKaWdOI7DrSSGzdWVJohDqTBUlp/hEoIgBmcDQUGReXhIwZXt9LqOEi1JsmLeuJE5gY7haPZEwvTmupjeS/klH2dxxKINqG7m+LWSApaL25ss5uP0+XIl+9LWQlvcDqU5RIWfMOAxF0uEENbXVuDqxU24fvki7GytwbDf58R1c22FC0UkKFSdhM2e7TC+S/FGj1pm+uSK8maOOhCWelZlrR3vHRw9/ub+o+rgYLxZllXHGFM4W4ldmmQOXfHxblrAfjlTI3GICD4bOoLUsg+8FfGkdleTZkiq79lVhNirHufegurejieZUqyEDdfPVI5dmAlIWz0DqYsqlmA1yKCEM3cGQh/M6Z52Fq8XfwEBikXMKEY4SkP1AT2ZrPpErLlko3sDcSOPJnP3/cMD2NlcZXrI+rAvFm8UF4qMr3bpTh4ibi9e3qS9gRG197hLW5aQkQALxTkqqOX/l4Ta1DxE4SZa4Ta3IARLA6f8zU4Swsqgw5SWSZrJ7t4qaC+9BGASGPONLe2Tz/58fPzVzf0vPnx76wG4gEb5Iji8zLEf77lB63Ob9dmRl51VfyzbEobQRKx2JWv2YgTr0Ueti1jaq1tnWmeWjc3KKQWu9p2Ep3wq64fr9EnjqbxiFaIv046mF0bycVKtwusraSJRX7+RYzEnSz43MEwiGPU7/I4MCVZOKvE+2WUBRVAqikVAkbrRfDK2qsWQaHHggukrrKc6N5/5PHO9wFWQZSks5gsW4DPCc7XWVqUqXwQU4lTOury0ZNsaOLCBOGLwWxN2YKo3gFCiseYQaRDgURLBASbBs3Hdb/gIjbG/Li10DZqeQfgpOLfK8FbkZIq6VrlYV9iu9podiMWIx2Rw40D5j1JnY/xkLQRDKMBIt4VC1V3IGoMTUe5cIsFFBcRP65QSxHLay2X/5my+RLmFWp5EabN4R3w5T2/OuuocUjeSkprYSWIXOc/JlKeTqwLqa9zXLhuL2qhVaCWKYHwjjT4ctM51RTMCPV3CuVqYiCC93BUFFe4p9NFIpAHDSSmNU0cT38ALTmtlyGg31ztVBarLyFxRrT/RuBaBMRFZjlRlZtLZcZBnCy7chUFgwiDA3JQ8NFEQcrWIT7KwXNFDIwtoWQl9ydtEOCvNoc5gzW1cuGqH65tBEARRZdlLtWBEPemYOmgco6k4IQuGYUsT4UZYZ7lLTVqW3DRVIag+dQ1I6ZwLPuD6KthEXU+ym8kJ3NJgaXnZ4WeXfOPCODasrG2CFoqiCc7aIY+mKxJV68rZ5Eh66rpAe6XTM1hRP/PwsIzKL/4AraTWSKJpPEQDT8CZmoCWTKFXe71q1E3ybhhEw0ESbm1sIImQ0OLoI+DAmHpB9wIikkj4axP1uLqb2IKyiOKeXdprYHn/UFQ41p8Daikg8Cqvrtt6IaLOs6aL2vA7XVPFtvapz1yGJ/mTagGfXTNaLfRsJZQb5l8HIlblwOvB+/vaVGLF49BYD3NyXOXmLidVuPt9VnmlpJOS+8bL6y8Dr32Zw1GCCHBsTLDodHurSTcZ9K12yzJJNjmAYu5ToXwprgpjpUm9CE0YUb+j5DsMagssQyor9ByGoQvjhO534KEcBfMxW/YnTtdA4wWnXFDfO8RzJePnOrC1Nr3cKNI19aQT1ngvWrUyILTBnRtXYXU4ZJXdxweH8O39J3B8dAxpWcAidaxSe2FzHXZ21gWmN8/Z3/La5W24vLMFWxuUaHYhJG/LmpNcvQ6Bijd5cKOm9n05OYrq+PjymDTFKiI+K2jz1HNyoqUCy+P9g6Pff/Knr8ef/enblfFs7tijNsCuQzc1gIdaiL5ioUY41RmY1XWS/CKHkYPEiMW9Y1X50z4bPbjiRMp0zqvUJVwbh2FLwPCFhw8+pEnFZ1C3ZDjAZv6p5Qq9dxl7eocTrB6LTLDVlpSdSn6E9LXOnn5aLWVxDipQ+DSBK7ETWliJK8gqA4tKkofSYq3OpdWR2jvyxOhy9+nx/pH97uE+3ri0Va6P+qnA1G0fmepRi1HWr/L5CbLzC6vkc/BJaqj5ImXxFfLupSIjJRfM56c9K3At31Er0Eu9M1YFf5b0BwzW055FSJIIhmTJ0o0hHBu2AxGGsKfmQlvICE2Iw7KCy4+fzAcO8X6UwJdVDl8iBDMOea1bUM0rMIbgv6HaKEkyIlPOeZ8S69dt6mgblOF19FLtClaulqlZTp5aXVdsTaa6w1nHUM8tJDX+o4rTxeXOZWuWtf9JDZqJ5AEwokEK1PuSA35NED1f3UdvctmBM3VcYdE3DQK2O4qcIWNJMnwziFbRXXRONJ8Kjs8EYbI66ML6aMBCmNSNLtj/UzjBYp0U/mA2aTznqCi7yGA+m0v8RnooAntjdKmtnBOwFaGx6LSsrVxlrKtLSwQ9yJ3DrloLdkCzd2twrwT4/WDuvjXuOSTiN3yExuCTwMH/labZYRhH/zkO4w+qyr7vAK6oH2KkvpQEa2XuIJGN5SY736apfA9EFgIsJHkUeA7FrCjwn0Qxyd5GBWXCoWkkbiiZ4oQq1SUg1OZWpf0uPR9OEDXidqxySl1pMZigpIesRfjhc8zzdOzjyDYhajnEkCo0tQAbQJ0AU7ItuBPHHVHeWFK5NRhze5s6tsj+m6hQXHoIiGPZ9YQ2RTxZIzU4I5Bf8hfl+RLUSbv0uQtNZLsCEWbBI5p8IwrjhPOKPVYOJGsaSfiiKi8wz9KAiMXs5lpUkJUC5SBvnhF1v5KI5bSPpwvYO5rAlGSh1Z6jvV4HhjoiK260uV0N1zcgThJ6XeUoC0W9DxwV8RMYKFO+p3LhnlMaNb0u/m9hJLuiqgv7hTqETV1FxPcUsWMp+Rafu4X4oXKn2nLEQDDBOIFub+CibhdxLPpQrYQNG8FuaKogXAfBuRNQf1c5xs0e6Zq/3TOAd887/BrNnMjCQl42y7PxPEYejAhKtjt5PuWHrzNiRb7wwf4Un5ip6R3O4MnRHNZGhwy/Gw2kwzns9blaRwFF6WqAiUraa4W2LeKDzaYRGKlxlNbzOGuLJqnr+AEyJxJB1xYJagm8YE0XUXl9n2wuj/M5x7ZWeEKzHHDK5ZhIHzJ6LqZc8SavV8VQuhqg1SS9lGwZDTDa10Vdv2o8Zs4OibhQxy5JEkq2+PlV+BGeXkn/UR2O0RPOzomHx9Uq9ucMIAliUSbVOWGtRWtL5xUC/f3V8SFfz9gENMZ4DAC7JNRmELZNgDEYwwpF3tvtqWBEutTin+zY8zip0RgeSvBDHa94j1RvAutmQuvwp00w9quXtmBrfQTH4ylc3tmGvYNDCbRoswsCuLD1/7H35kuaJdd92DmZd/uWWrqq19kBkCBkUpQEWiJF0YqQKFkOO+xnsN9Ej+CX8F8OL4qw/ZcVwRBFUqQgkwAhEASGM8Bsvdb6rXfJPI6z5L33q65udM/aIyGBnqnp+pa75M08y2+5ATePDkVtl4NFVjzkP6VwhM0/iwaY4tdyPNMoBuA6nsNL3omoVkzg6PrvQQtwY5b5S3T4w3c/+vjP/+wHf/XWBw8e3mC1sCLPtoBwAAQHEpxh9AYtkeEtIZO9kIMTR+KnyXYnhY9PQT9Hg6tqUxjAiJ/+LEfn8rIXDM3gPnV306MY9B9aDXARchehDgRP6bbaLexo+AynMhC6f3ByaILuFJ9RNnDeEi/WhIyYmjoc8JVZgAkrzEYv8OTxOeOordwfiiE9JVFmPtGmpo8fnXb3H5+vXrtzYzGdVJnvYhEIqlTS1ZQY6rrDbUfI9Pyy9KzfQcjQ2K0kl1uhTUhCES1UdbuFxCQS2Ncybfq6kc2aZTaQUARkkRuvr9zVPJiWEnPVTScwTk74fLLVGU6+QIK38sz9WtO2e//zv/ox/S+/+XvNjUn148Wm+7M2djNwcBMiVTEEp7FrWqMjaafOu0HToBcEYkGkFanI0P4ufPX5z2KPyhwlpGOH1vHHPLWnX136ceg4J95lesPA4YS1Q/cAHVYxRtaMcV74rpEkQTTbRQrUHwJYcdt5jOLrHkVJxXv04rOauxIuVzF++GhJjy9WTqSDvCr0gvnGdm0QdeM7xwfw9uu34OhwLtBatWJRZIoI8MRo4jyfvzB3SuS5AMzqsxvzwOY9l/dk24dV8EdIp8g+1m6xbiZNHTBB/dhmG4guTTy1GokLhgzxlADeV8HUV2ekTJoP+k82i+1PupzeqKbV3/Me/kno2r8lnTbEmXlFYi/hZYDpQVR4aFEk7PawFkjix7z7pHbrzK41WHsGLall468gfFGSxJHf23FSy0pTKRAQIryKCRUa1xvIjzmYDD3VCk0BfTefWkXJYkZWZVdFWpyCrpDWEgFv2k+ZclRJv1+SH1TojXKUMyEXaLeTRYUqqTZJXQ/759Z4Py2J96YsMpkp2AYDqPAxRfULYtiuRCsh6WYpnJjfFzmxbSX5V0jP2np+07Zr89VqQdvNysUYnFZ9RDCY5vOJOzqaw6TKpbJzcrmCJ5cr2Nad8AJb4/Lxz5wwZUVJ08PDdrJ/GHxWsE1lEOiuQn8ZV6DiS3qOpUBjtZRVJnNqubc6PVIYlaBWaGJSnBVzMlnqx7hOkAu6eq21U5p8RFnaJ0ape7FyvfemQxOkaoW/YDEgLmw6tyBwDcoDLOJXw+/Tv0k8Rkwh7eUAYWRdzdD7T8JocdcETyBKxtl89qKPxq2JcLlauy4QB/MwKdbw8PxSJv6kLODe7UN4685NuHV8IF1Q7nbyAtqIl5WqkFZlCU4k5dXeQyLo1N00MSjVVM+0Y2P8gQjY+2hCEp4A4zqOFX3TC2jYgKWkKtXbeG0uMVRRn+4W7vD5Rr9nTjKi6zyKT4yIfUXumRN1mXcbiPgEHP3MOe6Wu9djjN+iCAe8VMSkXjsSgukFk0LoE/DkuRlEfTBIt5NRAj4rxMNQEs+q4g4fpsUuQZJ2bx9S3w6Gz9Ai/2yDn78DFtIkDciGHr8zb9ZRbCyi2jtBVkIouRS9WuGONzNBLDhVpRZ5y2cfqP4qqBofFw5l8/xaWGpYAPzcY03zdTadyLN2cLgP2+1rfbLOliWsoJmbTxokDznbMQVJ8orwaL7A8VnjnFTsRS0a9g3E4XP1PqyzzH+8WtfvvvveR6cff/Lw7a7p8iLPJ4hcnNW9BIHuGgTK9wRA1A6mdNxAIU4ZqvjEdYI4RrPvDP3k8fr063njKpgk/fypSGJxlBzt9FcJdhIcj2SOGQjX7XB90RQYQuvVHiIJdQdVpeX9jcXpdt9J5ruZwjw9CJvywCq+Am3ihDYO9BWC0dueMdgonIuhT04vV+99+ODkrddvtW9XkyP0LsMYe84iWN7XiNuYiHAU3B1jrjrbQTFfumsbWdsleZHoU7n8su/z38crz2Pq7uHAlxROn1OPxkTP0GqEQmnnVQFH+zN4cLaCs1WtxusR+oLvcMm4E0XHHnE2n/jpSd3i//Qv/4D+5f/4u//x2988+F83rY+bbfgfCOgeUfRqg4pJ3UdOXWvW1JdVhenK/usuNo7pWDj2ZU5FCILh6u/mkJC6tVotNkFcO9yd9icLdgpHkNGI890twMpL1/A9E5hFirwqVFlRiLdRdUB8uo6AJOxJZDofiNG2Ho+hxVxS2yX1iBV+Mvi4qR188HjjfvLhmfvh3zyAjx+eYUpu+6IAC/G0WvR757XbxIrps2mFyZpFxOvsmnHymedDl/fTCHXTEPWOiuSaPnHBY71ew3q9EoqLs4IwGsoQtPspnudNiPFiWfvFauPa2Nk1latQmD9veaVNwlnzE+/zk3X+6nQ14coityGijyDCI4rdT0Ls/rwN8Lvo3D/2Dv8Oqn9iZslbmlOpLs4PQ9qg+ZKWKmIs7XJrCnAGpAs0DhJNThkyPfJJBD1N1XkuVRIrlSFKddwbl5nt6dKwAAAgAElEQVQrlmwpYjxM+yxij0qsUVRdaWpiNDVxoqi2G76vnXGXVnk56i1InNQKKTcjZCVV/pmTXRbwke+XRMWO2zMNmuuHqkvNyTirrdIaRTwHp5IIS5+fu6ucKLJtCCex7EcpOOstAs4tgMuNIsF2IRtUNd4Sibb8udIx1XZ4q3htmLBCLBCtQttOtsvltN1ukDcVDn6YP0Qdrfen1ebWwXxa5fn0ycUKzxdb2Gw7DXZGVbu+keWcq6qJm05mrc8KLvZ6oliQKqPzufC96dSejYWO4JL4mwhuAQi/llCVWdRTVCxdhOxOkqgKqkV2Mr5+CjRBC9+1POpN5KCltB6qdY7IhmtHymmry/xmUrGDrpRJQXc9J8k6Ky45t9usGFfxSNvbNKrcveiQRSxqAIkAO0bMohMl06iE5/hJj+4BSUW0M1PtIudEUpvfTy6XoiTMBYOLxQaOTs7lNayCx3CQlPQxPO9wPtNiQteK11lZ5hr8sjw7RFHEVBl5kMoed1NZ9Zahk8IKCNRvFdEuSs+l7C1JsD/wntdBtHMLyN7rejA0jjqKA7WODKLI8GgpHXGnmInzmybUddtykTFAzLd1R5vttgshLlye/ayp27/cbpsf7M0rPD6Y/1dlUbxV5B5ZFEvOVcoevIfEAbllXJx0bInbqh09lArnVjr+G9islrieTNxsNseqqrp8UnYZl2AdF56SzdAItTv4+H5VBUX+7okVvHam8rO7Z5jwIU/fO30PJ623zTP3xYNicRpnbrokqpV96qc5p1dyJEETMZr3hSj1JnQbomrDaYl/BOn+Oncxv/wxavpRGAqeikKSi+qw85mvwbv1oydPsvc/fPCtxbo+ZN8P6ZoQzZPwHkqh1nBPyQQvIjS2B3IluPTUW2ZfBx604jF3kC61AAOHL6lQmxwxCT+XNYLsf5iOr/93SgvRbKwHW61nYJLNgLaRqp4p8hh6qOMKX9So6ArXwurHT18tNJond1ZRy+owJlNft12PP5WvD8P019vG/fyTJ48+/PjhgzsH07ecx4O2DY5FR53EU/K8FdMM2cMSQtP4y3qDrYmjaRKhZv6cwnQmBJRgsOo7e/VAsC88u+QHGgeFdhyfg+1/DPEty0y42ZgwbdcFE9zbQKzAwTeQ/H93kLm7j86WP/zf/+Ddn/738M6fffud46P9efad1bq503bdHmIScUyFBGY8RMm3IKnRaiKaY6Q5Zjhx5KRojVZMDX0HNBVgB6uU8TkLZcYpUiukZH6Hdy/ovgsC3APCeb9pjCk1I5fRvrnai/oZmYyFJIlyBaozQkZafernJDEmmny0+OppQ8QKQGw1Hjs9/s02wNliQ0+WAT88WcFPf/4EP3l8JvQwFgdy6K0ZIL1+mM1KeOPOEbzz+m3k7iZfQE76eM/HHt6qXU7uPDrvdu/1Swy8giXuEWGRYMOJ5uVSvFvR8Fu9QJEVM0zMr93U9fbR+XK62DRi5+id+e8IajRNSkhlHP6LRwHx/ZrgydiZ/FUYTwcPnMQRnXax+x5A9vMsy34EFP9poPD7AO43nUE/U6sgqUSN5LqkS4lqVonG2eS/5IrgBkQhS6Jv7PEa+r3U8yL1uCakXp0rUzTNLKcPdicdDNC2aEfEiVspfEvEXLw0ydJ+xFRdVxdchf62atEh4XJjT1YGSihEVOuTTF6n0XFmAhRE6m3DiWSD4q3BCaW0rxEUEsp/V6sVEBX2VEZTQ+SurE9ImEFCTTIUthMpibBCiBvjlc7tGtVaVZKqRts223a1vKy22zWTN+XhYGgsGwdPyzzcOpjUh/OK4XCw2DTw+HwJ58uNLCYi1GlJBBPyI9cF+cbM93w12++yLN8QsZATW+NwzkEb47JWqgknEzy3R6oBEoscDymyQrE6qZGIeQnMWT0xnm5hCUtwumHzyW/MmqawgAKN7+t66B2aUXB6iB3Xv3yUzSCqryUliIzuJpb94ZxE0ZiKtCmPpmpv2UHknrMVPz0SKjVKi51GvprjDqeDkFXQ+anyNZ//ibrhh2gS6cwF8SJKAiIGVMlkWW628O5Hj2D65EzuIy+q00LFSCohvXPgqzYMvMhmopRXSfdzNi2Fo7DaNLDYbORQ+fXsv8oCJUf7e2KlwolpSi4DpQcmPl0XN/Od5JslKV2kkbKrcQC95V/RPtPul0ty5zb7OaZp2pbarsPFegsPnly4B0/OiuW6ZojJdts0tNzU1LKBm8NiW3fzelvfmc8m0zvH+3cP59PJVJLtCdw6mMONg3Qurrey3BVIUM6Q00o6JIJ2FMGICMRKrk2NzPfJizJMptMwmU1dyZYp/HrXo/9tSvfC8l/hMJfsl23Pj3/cTYiuiqO86Gd6q7zCywPUvx6jF8zCQaNDayhcWCX1fVVrq6/tOSYlS0rVoi99yBfb/MMUyfJC/9hn/n6el+vLi+XZX/zopzc++Oj+vdB1R7n3M3R4aMVoGeM1K/0sHjdBs9FJBrCXR5j5KPChZ1wN+wf3yETYLyLgSz0bn/vyMCqS7nLwdLV2kpNH+cOsp+edGb8mxyD/pqSW/kJH/PSnai4TBUY7KZgzpzYUY81tlAK2Rgu2HXSmsClxl/eO7ZgmT84W9bs/++RPXzucfP/W4Qw2Xfz1vJxlVVWAY1srnhddLFjsbb1cwma10i6V2c72mPiU9MRg2gFDHNAftYWyyUpgvGekbmYq7PYXn0M9TjjFXmOIITThcYOnMCQFetFDeRMQ/kXu8e8f7U1+8hd/8+gPt7H7o9//B/Vf/71fu/W/7c+K7Wrr/mHTdK/1hXSJoUVmt5/E/XNJUBpNzTOT3gs/jIbvHeWX182Bfl90VieUfs3V1wAr7xyCxFIw+Gn2l0/5NG40H6N1SRU9ZdBY5SJ6+0y+JOdEVFOEPXSu4iafPGEskxOHA+YOxGoTcbXu/GLd0Ccny/jxkyWeLlvXtAGbNtB0MsHNdtvDYMUP2mdiDff268fwq2/ehTtHM97khcPLRQkV0kz8eVSEU8vl7UFk8fNYw/kaM02H1Wc3Fn9xfNbvIU7nC8cfYLaOq1WTn1yu/bppGXHQQYZtCNISyJNNJaqrBn/8BQD+xIH7aRbb81c/2RwGd7HuO3RnELufOe/+0ufVPw+h+8cxdG/Lq4xzaVX9YMI5yc/MqUISI/jMbZOwUdCsVMmDNsSFm8dv2JoXp4j32EOUmdhOq7xYUZAl4/9l1un0yoOkOqUgoNiDRgHQ4jPkFeg+AA+kaKKWG/yiCpWcvyXt3pW2r3Dxo9DPSwJB8vaNKc2aWSRzQRliip11QNEkiRecKJroT23+bVyhqNTXrE/Oaku0+fymKrTDzyzmqIFsJ9Yt6iXK/tQuhlAsL87ys8cPiu1aMOqSGHC1hKt4t4/mxes3D/YmRVZqR2wDJxcrWG5r8e1x5juE5hLg8wyn+4dhfnjcFZOp9pxDwthhpgkuBU0ZuFvLXWS4gWoLw51mVuzdsxZ1a5ZOYttnqr5o1weMO+kgcS213MYPTAkqfMrk51oWULa8Ie0JsNoDr6Qo5sOR7YXQhAl2d1pzKBKvKrluWAwsUuqTTJ2paO35oZv3i0ZfdRORJerFQ8Z7PX9dcB6iK9Tr8zkNL0z4IjMwdXYRuOLK8u9sW3Pv+EBgTB8+PIcHJwtYbWqDwwKc0kpen5uVDXPGPOoGyFA+7vKxLcZ8VsrCu9k2ahhPKjQ0KTIoJ6XAgN68fQR3bx7C3qySzip/t5iHeyPNE/X+l+o9iP2aRtwZtY2VzKy4bZSUv23Yz6yV42WLCLDEOs9UrJgF9i6Wq/DkfBFX640km4/Olu7x6WW+3tQsQrXkIkrTdtMQI1vkvMm05BjjO+er9d7p5eLOrKomnJwzf+bWjX14884xvn7rCG8e7iEn1VEgtMlmfBhJiADNumWckPKms+3WvCllXVs75kdX0wlMpzMoJ5UsEQKBkVNierYa9T4FN7OZYZ9NX1DwHk0T48WgeUPtJZg+1qfryhpW5Zr3/ieZZI4HQeKzjeBjg+rP1+D804Fff6yGJDKQ6Vc2jJqIJl3Jz1iIoWtOTherd7/3g7/a/uG//8tfeXx6vvbOMcf4m6LsOZp/BE8H2WTKf6UXT03YKwJMswj+mdrQ8lxzgfTICtRfKTQcR0WOYSTkiYoBuagtsBcRv1ObhiHBpMEIFFNe9qITWq8twjRHOJjmohC4bYL0qFsYeIy2cqCh39bqn862b9qR9hkWddPs/fSDB8u9afmDv/0rbx6VVXlv227u8kLHdkx8dmxfsri8lCA+CeZxGsp7lor+jIQEKVqCPmgZ9Prxo41cqSVDl7IvUuOA0EF7HSOJWPCL/50go1WuupjsACBcAiexSuyEnCeUsnuA8GZZZm9sm3b/h++f7F+u6+8/Plt97x/+xuvN3eOZrzL3e3XTzVh/EB1mMVLFLJCxHY0oU0gzRWkykno6I4D1h58QRaN1inahoqGLxHQl0LKOBCO9W5meeimepSnGtyuQ1j6Ri3PYP2yY5lTqFhvzTrB/bqcDyk2aM0R4xzmYicUox1Ydw6EZ7QVQM+Koi7Bcd/DofAMfPDzHjx5fuMWakXqIea40I/aUZ1XvLg6+3/NpCd948zZ85507cPfGXIpJXJBoGqXOOEk8ohSc09ItcFapiF+PcniZkYoefFzLxULUZxMsm1KyPn6wFDIsuXYboo+BhfxiQkJ70aew+pBeUOr9IWIMJ7nDn+1N5uevGpToRWBRWwD6ceH9h5l3f7mqu+93If63Psu/4xDuhhhd0uE1iUoadxoVS9iDIp3hF3gqd9oxlHeK2qx1D1mllTtdZ6AdvYn5dTYmMOOlyynvcz1dGy2hSfBdFqYghqL2QkK91HKmsM2kngpJLdrbYxfNDJh62A5K0tzZ9eBD3sj8JNgTMSHnaobhEooIRiUCcLZoWZ/B0dDFzC1B1mZulPdZt1YSOm8Vvgur4E4tnHN2LGJk1LSbyfLiDNcX5466LjJR2qpesF8WcPd4Pzs+nDOkwD84uYAHJ+ew2jYGrbCOkhEnJtNJmMzmdHzvze3ewY2t997FEDnRq5HwkWDsJUGW67KRar1yWjNbSIJ1pgNQv9zk9udcurOAcxTLF1gadrYYScmmbmYfEzBigrSbLbYrUeS+CRPAQrqZyI3N2FMSBsgDQlL5FT09FLcxUuCU3MI+SEiV1hAHMjG8wFNKYkKtC2LaOPsPVaddiC43u5OrqwmYfFSAVBh3vAWLDpNCb9lXcxtb4dVOqg6ODqaQZ6Ukj8imMdECA1K+LVfq2H402jSXzMEWMsk8OOE898YUttyfVMCAobp8Cj/7GOC9Dx7A7ZuHcHQwE39WNuvPcy+JLG+meVLdxqROq1dclNx0sYNGzLKD2OjU21aS28VmLfMvd14SWX5vzXwFdOi9F6Gak4tL9+R8gWzmzb6Qm6bzTduxb6ZTCLqccCbiUUCFc670zh8T0Gy9qfNt3fa+nvefXMCDx+fw5PWF++Ybd+D20T7szyaSdMs161TqvO9yxDhYY9o/5fwsgWdaBcTg681KBCeazQYmsxlUkxlMpmyZkinoQwssPUSd+Rlj/oz4sEZD3D5HYOVTDjJ7qYyeZTX57OlMnyl4TpWb/4Sgsi80hkQbRg85Wmf3a5FsPz+J6GfSF3ku4zrd7jBFT4MSsoQy2/n4tqn3Y1Pf+OiTx8s/+d73Nz9594Oqbdu19/5BiPG4hzI858vsC6nMYtwrOpjm0eXXeuLtjEwoI1/8eOHcDs0PeTi54W6lphi9wGITZReSha4XYRAwVvQJMfvMztizBvuOzEuCTcd+hqCq1bGXj3Jx2LYtBrMGhp2BV+XO+emyfvvH9xebyY0uvHlrus3ixrNIPu+hvI0y/40hkV2I/QeCWbfITivcv4g7x5+4mXF4hPu671PXGHtbrOEvEYwFJh+aZxnMqhzK3Ms6r/SRBHOOnXO4FJeFKKIumTUfJHaqyux23YR//t7Hi19Z1d1fXCzb7W995+5fvH5z5qale8M5dwCAk4ixcORmIdKEUw7PcXGEmmHjfLkpWkGV3FOI5yv5TBKUIvW55yIl+WjXaOiPm1U4R15RYH3Dx6BSTzD5dIqKq4b7egzUVymG1DRFa5brsw12S3OScifmUdV4Q90ArtfBL9ctni1qOFnUUEtMEeDRxRruS8G9Qa8cUNjUnVqaeG8CP/pdeeHh9o09ePvODTjam0ihgZN/VihmwUC5h8Kt16YB3+Nh39/txH+agRZnCYdYBIE2phnhduCzMg2ZPxr7ZgiFLrYucx2jFJ3DqmmDojuH+wOjqc60tk88wE9zdD8v8nIzMBtfjfEyxHTGJvxF17Qft0A/8N79NhD8TlXkv9GF+FoIUf0wNQqO5pCoJH9FJov/pewAkiVIlUQtRtS6IB9BJ73yE2FNhMy9zKRRhBiiIkMQBhf0YMZGCl3VRBKEv6n2sklckDnqTGvM7G5KURPJOnW6tk5VzTGh+sXNKFhR12u6jKasSlsnIj4c27mtPm20Z4kTKVdJlHkz8xpN6jC1Xc/CZgvXeXzvDyoy1dwpjUunyrlzQwGCdi4cd/18XW+mq8uLLHRNm+fCCXdt17rcezzen+HtwzmVRUaX65run1zCycVSHqbCee18eWfZM1I5mYWDW/fao1v32moyawwGXCAhq1l9Qg4qIHqNtPezNGsAflqWotYr3CzhZG4UJiueP04sKRjWwXxgTfoLK0i0A/pHHq5MUQNSZIhyDdXUppF6GKJIwrNVQzSCf88h7Dnp1FcpHaSOs1aOiMXbAl9Zodc7q5H32wAoPQUCwYu1dpyJAgW1OumV+kYbfORb6QpToN196KWVK2RvPRyZVhQgC1uFb7pcaiLR3IS2DUjyxJ5Q603N/m/mDeMVuTLyQHMpPIQRF4BUUTBG8520WosXyfBcAhVO1FabLZxeLOHB6QVUZSEdSO1uZkOHVCiLxvPjzzBwLfMkeHIECiJWpIJF9nPdwVZ4oiQeoZOqIIeOONkUvQOnEOZNU/um0QVXGK9cVtFNjJ9/CfAUftPfoTwlOQnSKA2lAHApPpqtdFTPl2u4e3xA79y7ifduH8FMYMayIVJnkvQ9nj+5tFs3V2FQAbZtgG7TSAItEuqBRLGWbSym86m04nkas9pokRv/1SqkiYvBkutlXkpyz69NfKH+2D97duLMMzgq2kILcxYtWA49CqaTOy7DcNwVB/KXGZ+G7PwpxmeqMKO5OX/O3WS0YHFnL6VUzPo65Jq/8HogfEVdzfTcaJeDO5kMfdvG9WrVNdu6bustfvjx/er+wyfZtm7miHAXiG6hGOODey4Y1pAtvPzmLsYqi5i79Ig855ie9Qul5uiz9Bkgd9YIIvxFuaFp88QUyF/JLJKGwMBEGTIOrvTHK7edUpE2cgbkoZNGmVJl1W/SRGNeKIBN3UJNVnOoqXIOV+BgwwhAFW2w2r81fvSZmfRNigFBFL1zc3DuHyzq+Lc+Pq9vVNNQHeUQs3rrL5Kwm9laJNSR0Dr4pjBHc7Q2DVZYowSsZx4kZdehC+hGrWNnwmkUQp/cSwBq7cSC+dvTSgQZl4wealrTCND3h0CNE8cEmMs6DdhEoEchxtoDHlRF9g3E+HdOLrbf/bff/+S9Dx+uTr/z9o2ff/P1PaZD3ctyP6+KrJqWGRWZy7SgQichxlWIrJtBh6n3whmb6wUVIlw3J60zymlebbQjVr5FE1EptDlEIek+Q9pFktUOJiaJ6zMetGQoiJhfJGe+hdaEkGiO6wEx0W34ABo6bmq40UL0XQyw2gQ6X3awWDVwsdrC/bMlnF1sQDi5gaDuIhRFDm8dHgiw7exyCWeXaxHB7ExFmmfQtMjh5o05fOO1I7i5Vwp0UeILjrFMHDP5JSc1MmW6gaL/fO989alGEtFv2kYs1tbLlSS5aW6l7rRzvifhpKSXHSPOF7U/X2+bEGKnKr9XHj9MaAUpXp9Egj8t8vyPimr6YRQB01fLS+tTqKDhY6D4r4G6H4Qu/CF4912K8R/HSH838+6udMGIO5e4u431bCZ5rL1VdkRoRn8vyVfibU8Q4ZC7n1ET0Ik+nOK1Gc0aJYXVaH5f3EJqSD+Pn7KZLSmdgU2UTSOc1B7+muLK/nN61xJNeKN0dmUlxCk4XSnNdqUyODA317zR92LyZ1O1OjnHiJrH5KarxQIDwfUVPNFz5eyitIDNuKFW3RMZQ1EC5/NqHSdqofPbxWWxuDjN2rbFrChkjtZdi9M8w6O9KRzuVZ7Nllbb1p8ttvxvrfY4m+imwCZXxnucTPdiNWcp6Kw002tn8WljiXcjcGRQmLIlzWvbMczjR5JNlGtD0DiF2U+S+axwJ3lBA/HsbM0Swae8CCyPU6qgJOpJ8TfZMYgNBWcnzicfbRSop0R9/CBTSAmnvlEFhfQmawLrBu72UCKKyT/SPT9wTnuPVM/GXM1hoqs7mcuks3ldswgN3iHw0ZTchAg+tuC4WsuaVNGzJD3VWMYmejhfbp0aIEeFB+DQlVOjY5Fg1eUncU1kzxhSb+aBsvG++k2Jra3MhSZt1JbAr7cNbLZtLxMvcF2u+EGCImMPFU7HkgSWYvIUM4/PmKrqdrBbj7Buai5HkUCQcayTrqSsJIjgej7IMyFrPnXFsU8S7Zpw0NQFeHy24M5qfHJ6SSfnC/zmYu3evnsLDg+magtiH+tcsnRJAQjJ87LabuD0cgGnFyu4XG6FE81/GBLMyTgbjvOmdLbcgMMM5nszYIGiJMM8nVZiVaPQIYR7t4/hjbvHODHep3BreeNV+7Rk3jfI2b3cQKsiM6T9wgRMDp/zCWQFPjC3gU+vokvwJXQ1MQ5C36/U+Fp0MK+Mfvl4pQ6+t/Iwi4CuFfjZdrPBzWaTtWLG3y5CF/765Ozixz/5+f14vtq85ry7EWP8RhSzLHn08qc+WrCZERjnkqA4uYtYuNDvJqMxUlR5gaagFscx2Wt90SPVVxOGLCVOaX8HS/cM6KFJT68GO2CAdk/Y3hcdiHTiyE9SefbuJSZ6SnKCFFInzsXS57hsdS/OLCjqv3qABe4ek4pDHnnEvxvb1l8sVni63m8nB0VTdKHqwhZDQo64cR9K11DrFCEjexgf3SuRpwSbKKVDetSo9JAB3bLrUd0XBm3jF30XVvJnFWrmp4oGQi6RKRdasfffRx9i3CO19OGkOuSZW6B377P9R9OEbxHB7TJ30xDhjctV+9YP33vy7icny8uffDibHR9UeLBXtXeOZsXtG9OwN8kvityf5h6WziEHgMQcUS4Mo3UwnPEqJO2IyfIqzQ9TYpd4S4mnTgwqpcXpbCalGxkcN6E9+N4ne+QE4Bz2dk692KTMI7YjZCcAdDFSEZXRCV0LzLtVwXKKsN2E7HLdwarppKh7vqjzR2dr1mdA3sMv1lvRmeC4heHQfI77exO4cTiVa7xcb0R3IqHTWJeiKhBev7kP33ztGF67fSDd5mhFAv6MdB4Sr7FmiUviPDYRRSzJj4XyXngkISmR0w+ddN0ZPsser6nL21/HBM+OqhyVOXTs1/z4fInvf3JWXKxqxw2GEMQEUF0pNGTKR/kb5yBLAPixK4of+qo8v9a/9isen0py2xbXh7HrHp4tF9/fQ/fHWVb8Njj8J+jwu4Rwi81GNTTo1RqDwWRR/TlFl8obiT8ApgaVsJ+9JI+IK1OLZfi/2GpwkdPa/pBw/f3KYKI/nCyREpDRgimDz8oRNYaitnWXWwxi5L2xdbXCAaWeBItIaQYYLSLkJ6sgpFasTUD8MblitVFQBswSb7BvNvUChqQQcTB7RMFNgu/rZJKjy7HO1LhVILVoSXlnHUxcLy/b7XKRt80mw7zsO8lMGai4I8WrK0UvHau6ER9IMp9s7cglzlyGWV66yWyP8qKK6pMpBvlBPEOR7lkSPjV3DmdG1qI0awWX1vrHpOfF3Wrm0IqML1fxWLns1LY/TcBVCCCYOHHQyoHcM1JYcbLDEctl5OpPXlYxz0thyUkCaV2joqxYLAhqWGNXy+4brWrq5GqSuONoRywF8YZrTl3RIMRwYgcUyPwvCJtJu4Q9DSR1NBMUh/mWfVcTd9+Y/ikLvdN6hzSy9Q9XnXOMwD5BTRcpBKoD+dh5VnjGXCeIV8B4SpRIRN8FAEOg5bg4ciOiHsaSKmlK6uBJ1Yndhy5+mfBbxv017JNI/qGViuBgQrATeOD4R+yhRS5JnqcNPimk85FmTmSdB46Muy7yfTbEzpAy9ksyuXq0yyvHznYwi3Vkux86Xazw4eklPDm7hG+9eYe5qchQYX4fb2SqXhhl06q7FparLTw6vYCPHp3BybkkrZoEe+wFCN776BFs6xouVxutsrBNivfi6cWvu3l8CMeHBwIZ5qrmG6/fhm9/403kjujx4T5D2PlZparMY5HnDFZyUsgIpjRpfmovsXcEoxAsxQJlmJxXbpcWthjho65SUhx76XRxEJD5ovmJSGMwwkuPL+j4xBPta9LCHOeXKikPCo/46oeChrTFpaJr/DzWNazWKviy3WydICFC4ICei5U/+OD+6Z98/6cf/u3Ty/U3AGjfoWo4PD2H9W+EN+MiZBigpky901yEygUNu2mnk8FF4m4kkIXji9ijAtOwHJl2F9CXHqOO5vM/A0dwjB5BO5LANOUV3go9BkuyaSj3W6Ay9kNNxVu05nzslZnUb5PzA+9fBGGQFGL0v3j3nfhIszzAskXchkwex168nK7vnGvBVHarGRHNOhZXWS1p04ZFdFUXogaSHpNN+vApydLLGb9y8IfeURDvFcWHa6qoHSdWFGRANh0h2aSMDla7n7rPcqY2LTOYWsLZoRZcDT3DL5sG0FDNoW+nVb483J9d5pnPTi5W3fliw4wo/s68yN1BG+Lrp5ebuxeL7X6RZ9nhXrm+fTCBw/0JzKbZdlplj+/cmDb3jmb3ysKzBQbkebqVH0EAACAASURBVLTv9/IcsZlIwszwqXHBWcqaoOr+nGMxTUW0Br3LmUrp2fOOLQijxLoMZeJSfzTrVblxjIxLQa0UDyQ5M3AMR1+MI2sw1iFGVthvmkCd6DggNA3BghPLphPe5HLdwvlqA6ttJ1xLhseeXqyReZqMEvICa/UQGfoqTvwBLhYr2duF19kFSTBTsD2rChEJfPvuIbx+6xAmk8IUdlODIFixYCgkcFIoHF9G/TF6K/OjZ+LFh8Q6ps3QGHR2dbmCZtv0zZ1+upmGSAwCXUbv2B6Db0eEi+UW759cuvPltkwdfit+1Ubd4Mw4S+hI9sN2Dj/JHD6JDA9+BcenTTb7q+URz6lt/7SYzX+83jb/wXn8bZ/53wOg7zKsBbXSl7DejLFNQU3AHuNBqSlSpHod6cxBgztqn1STPCflMhPvtC5ngMFdPLc1JtjblDeq71di1mAGFEAtOHhjWUvyiqqQCtp54zkw1eORnTmYhksSvIliraLcUF5MWoDeTkUfVfmd1Esa8w0tVQwnATjBG0hWNzbS/gIqhJZLMa01bqIpAmSyOATmMEbibl4dxKpTQnUn7X/dSdabFs4vV7Bc171/T7+IW5kzKyuaHx5H/pNleafNWe3Imvruke05Ym1kVI6VfVQlyT3icvD8kfOoDZLBJ+Mt6I2I1GoCyAEBeitiB/XelLmQke6ZrakZZ2l+cJu1KEuspjN0WR6h3mjljXnsXaftUUvWQVFCPTc9idmnwueOat+oECoJuCm8Pm/I6xJkcxwVmDAO+2mSe5anpk5JHSatHTvw1OmmMPb44lfEWsiKDgIGV6i4JViiatVj0UGXnSGzTquceodik8pFG4XjckfOSUXNWcP8mu6Zwz4gcYw17Y10SaqkfFdMDuiKt8b4DPGZ4LxeaKQXFn4uG8wCu1GSPuClx58J6n8n9zpLL9P7yBwOctu2xdBEgQovlhs4X2zgN3/1LXj73k0JBu6fnMP5xVK8uNhrZbVeC/T85GwJF8sNtDEITEq7xqoWzIsUw3KatjWeCAFernv4LHfcN3UNDx+fwmZby+b48/uPjBd7ALePDuHGjQO4c/MI7hwfxv29mZtUJXEBKCnTgSXhdMXn9DlQPdlvLYCbID1jGqYbZarYz+IqvcD4ohOtAe1m3Pcv+Ptednwtupq2MsYxiuCVOPa0BJAWcBj2yEml2AOsVlBv1lIE4m6EUgDEoqFtmvb0wcnFyf3Hp3tt6N4o84w3wceAcPR0d3GQNGmj1yxSIS5QeYLC7W4FNgyegQOzQheiFnqNh54ag1d0B2LyXv8MV+VFLttTrxyom8oNsRUQSheS9roGYs5DHfxTcNqEB27Js0m1FD6hz8tGruq/MAofQfgkySE3yaOo/tZBOwbDNz7rJBPKRZz2oW1r2K4WoV4uPR7nkDntI6ii+VBLicnrmbsa3g24WOtchiQU0gf+V/Zd1A6hvNZ8GOVVyTJrJOwgC5J3PdqHk6P5pIR5VULtgqz5oY07fFfhlOWO9qYlvH7z4PD4xvzmw9NLfP+jJ9vTxSZvY4eenPPeH7E/eBfjrG6jO72oJ4tFU3u/qNATe2wffeetWye5n+GkQvfw9EKenbtHe3B8OJU9i/cnUdR0IBZnVe5kXyoynarcxImRcjm1EEg4LTrPaweu9hlOXeaLQIGVjUxXCXuRQGecV6bPxJa5kwHaJkJTs61IyC7WDS7WDTZd57jfUbeE620rYj+c/NVNB2zJx4KAHJ+g+uNJx5Lsmc293otETWEdi5OzBTw5WzASD2aTAvYnJUyrAg5mJRwfTOCI1ej3JipGSGo5EkRfY8w6JS1ws5AjaSGBk8WiKAZ+7otXeU0zhvfSKEq3y+USVpcLSaglnkrerinZHCljs6uo8y527Ku5ruHkcu02TSfyDk6bcuYmRLmyZHsUAOdRH4J3/y7z/scYQt126xc95i91fLZkMw3tnlx0FP8obOu/nJTlv8uL4vcB4Xcp0K8B4T2GiaJCGGvl7sm1DibbKzV2ReUJ5WEDEDlq4wia+ZHdgK6xpLMXiZK0KdpykmnzRjqQLSqU1felSKkG9BASZ9XyGtBtFTANOWFvoYbGgfK2PkRTSmvEUkWCeT4a5C5drVYmNOHk2hB0lAxrpUgo3hHC/wTtpKrRuc23xgmXFL1qgbPXpKyWuXQ3FWpYkeY4QbEQsbJknf0ULd52MMn54RPFd3p8sUImUnNHRqtY6SkjecDyoqT9G8ftjZt3tuVEGpeOuBSWrnRMiFRWAeYVnLmWzM0Uxd7Crmc03myZRH9E5ZdwytYxBs3jJHIPQTxE+TPE4sQqsZ31wZwFvoVVXp30P/kh9tJNRZ/lODu4QdP9fWrWS5W7ZX+VeouurbVyBOYDTslDF3dyk0SapnEUa8kRLwSsOs2w0ewpA2u7ckLAJ1U7G7VZ+tdKiy3ThPKpQH+3aK2ZcBTtNfmTqBHcaSPFIAPFzMcN+tg651nZthB4LvncIkdSASQ1IE1f0LjYnrnY8KJ0I2JecfIrpTHB18S++ubEC407sbpho0lXxTAk0gYDkkqMzzXJ6sKVIGF0ni+2PD/Vw7wuLbqSzl4fg5EAv4xypJ4T0nBjnilzTttW1HDlzjN09+HZhWzEbL6/3tawrht4/+NHsnklbtKmqWHJ8JU2yEPHHBHezDjoZXXnWuaodiD5e1kcQjhU4ukVBVLLgQ6biq9XxjchtgA4g8XlCj76uBTO58H+HL7zzTfx5vENXxQZTqoS2bLl6GAP9vZmMK0mwvFkVcUsWaRZkYPiNXYuat20h0hTi6evn8d6OZ0UiOxheLneHybR1S8pYaHrH8hfjpcYwzM3YAC+ijGyS0CtATdtDfVmA2xfwUrZbGHB8Nme+yGIAg7BlVx8cbm6eXJx+Ssxhrcd4oEDfGhWZ7NR4pfO1VAaJBsVb738gipjUSAQA/fkqzi8XvZut2MhqJuUFLm1qWb+bYp+d1a6i0bV4dihSAnn4K/Z40Je9upfnf1JG3GkhEQ9fTrVE0wXHCpsoHDOUjEUTiZrA2xD3qeFA3iUgw4nCWFy0UxdQLWK6nFhL3y0fDyFAyp8sg+gIWfbfbRHrVbqYdVSxGVxl9XKXT55VF7uRZofZMgFwL5IS2qKr//N4E0fxYOAFe1DxJRIJksMgIGI9RRAmNQyWZLN/rUDvcNOahBewgEHV1YZ8zaJ7TiC0ZVUeEbnIO8Lh/OJu7E3PZhOinBjb3J7VmZTvj4Pzpb1YtVky/W23DZhlnkfDwsX2hA5SZvXkWbQtD5QcHUd3emiy09WUNCyxR+/dw5Pzi/h1sEl7M1L2ctCJPPjdrA3YRu0DA7mEzial1QVGZt2sr1MHoLsoaILIUqxjjDLMJZVRtzp4/1zu+36NjAH5pyX5k4LoQx3Zd/LxbqFxaqF9ZY7l9Et684xsk6SbuZb1lGgsnmGEqfWdQeni7XsqZFSWwB7G5Cu1SJDuu6acMZeiXJaZHCDrc4Op3DrcA7H+xNRKGZvcY7jhBoVgkUJ0SzdrWdlc1tlRr3s4RULIhalTYEX4zwauiewuFLbNK5rGseqyCxYVddNf/9ljYlqDaOwdo01pHOLIoSFl5cb+PmDc7j/5BK7NsbMie9oMTReYGqVFP0YoMcA9AeI/l+h939lx/NCx/1lj88n2dy98JcA8Mcx0s+6EP+MuvDdLMv/ETr8LZILxfYiEidPjecAMHhVNYaN3IgaqxiXyp/OXuOtXNfHODsWIgAJcB8gNWCMJy5NPzGaoiQcYFlnbBHcxjpzmcHcOYnKrdvaWBJkmEXZ0MZCRuMMYm6TQnw0zU7FrL5lf2tMzbVUqCq4xPWwFXbLPEfTqjGfOlF9DZQ8PokWXV139Xo16drGW0VPlHsKD7A/Z5/BqWDqz1d1PFvWQuUr81wmd7AqHS+40729cHDzTj3dO2jQu5J0vVFRI+3SpgA/U2gs8H0JqLDY0qo5FQx2SpyPzFTBmDmdCpll1WfBT6jJ/tbgzwkzGc0B3du99tbtbATGxJ3hKAUHbve4ajbH/YMjWl2cQ7tZ2y3s1DtUmtgJP5pmBIr1SGDBg3AFprmDeFLeIS/QbOPIH3O1w4mJgBvVLyqOJtLwohdH1UmzmlrwYj8a+65rTOIP0q/nxYZ/0ZZZ6IBiDdzhZAtU9R31UqUmypKoQgexuXBhe9+HZgUIdwLGtyMXQXze8zT7RNmsTBQKgwIhFnnkaL5Glm0GU93j13RWw0scoJdd2l709UMINbRiniUWyL7PZo+EKbSTAIcr1FxhZinEYBb7EUVo6acf3BeYLHczT8+XsFhv1BsUbZOyjYgrLUzCZhuXqLY76ofaq/2OOueoXWdMtX2VxVODaKtCtV0Dl8sOaMHCBwto2xbKqnT1tpajv3l4INzO1+4dw5v3bsPNwxsw35sSw6NEIUlU/3yy2Ojn9Gg+OuPDB1v/rlf1GAeEn25/GnTGv7gxqkP/clx7F3vfhmerCWtAnGbKl3gldZ+OhibSNlPiYnPRqu1gu93AcrmQBFMEXXjzaRoJEr3aAxjwDynLfBsi4fli+c3FejNziDelAMx+0KDUjasuHeN1xABTUGYRDsoA8yxIse2a+Ozpv9QPSnoK1t9L+hE7S6Ghrp4an7XjuXMokeApzYCrmA8a/eQxiJhGB9ohyhLHjjgwEisxS+pc/1mp8bcDB3rZ6WN3wzuKleOdilwbNL1/fjiPlj+nPJ2ga2p3cvLE/bzowHeHdPvGVCGUaGgaSsmKeT0zeYviYL2hJiv9Gp8Sm1R8FR/oMNy6xPGkUeI7gtjIEF6oepOy+1fHXc2DaeU32w65c5cXDkr2A3W6j/I6fvdoLzvam951RAV1oTqclVX5xq329o29+pJFcU4Xk4cni7ztYixyz00vTugyTZod+Ch0jvn5YpP96P372AXITy/WsN2ywM4Cmu6s5zByEpcznzRH6fTx8d08mMK0ytlACDux1gDHqvNsHZKpFQjXJnxe+MyxSX5QFVf2smT8Mp8Hn0/uNORm9fltE0XrYbFpYS36GXyd1Uey6Vrpsgbjt/L3l2UmSSZfyFyZUUZP0lnhDOLYGRSYhZdUuDCTe8HHeTiv4N7xniSanGAzNzNV0Ti5NcbXYPNi9zvBeqSzFDj5zWFSTaGaVFIMeLGRehkoyXTTNLRdb6DebiXJjJ0V9THFW8mraBQ4ktncETLiCn92/9T97P4pni9q1Sj0mBprNoNN10EmIDtZ4HuA7g99hP9gGiqv7Pjck81+mUD8pGvDw7Bt/3yyn30Pi+y/7gL9FoV4CyjeMT9JRni0qGQtnnVb5WSbIpaslTFDcpycseuCI2K1bInOg8E8vZk0SYdNnmflb5k/o+YHBrHLwYltinbkUPCsnigW3K0jVUNlcaJURehMcCgDcJXp1/DPBfeAWOTGCmoVmE1HaqUbAcRBmh0qtNNZANiJUi6JIpmABYS3KLYgImGcPDUF5oCqrcQEYX6086Ztus162TX1tqMYcwkrI3dfMtiflzCZ5CIxzph4vgRsjizqbN0INiIeVjmW06lzmfRMeFVlP8rcDKXIYmx2r1eSBXILX1V7be6z5UuRcBdk4Ejz49wYLLgE7Wbzp2qyLfxJTsSpSE/9CJzCCeaWu8W2yzUEbgbKi0WN2z36LKMaLCszUn/Px9xJKJXjKLBWmVKhTwZ2ukIpYGduYqcwnJycJF/jHTym9nKkawGdkb+Lj8X94pBCFWg52WxGbkHD4ZhZKPt51MKejKwCHMDHjcRX8j2YQWBFWel65rIAuVivMLQnQOGRI6rB4W0WARfI1BXsa7RuLeckzGVSLXpn8vQjHpV5TjWdeaSNlHs/j9A1qUFdGaloDWopasf7dAN0R5I9BQcMy2GIayaG26rIy1TmSV7JJnZ2sYaz87VsxDH2gnsStHChIXV0wYKiTZO0zEig6jutITQxiZjUB9MOgb3PVuoVk52Hk02qhQ/vP5JEtWk64dB+UDyA+fsTuHvrCL7x5mtw+/gG7c1nYTotsMxKf8AQocM5TCcVTCv1Q1V4To/PSYfZWQLyhWigxy/fR3Ic2L9auu5f7RiRAZ676owJBF/WIDBRPESsGKGonn6dqEVvWDzj8lK6APwsSIHLgj101msjUSrr9NEWgYwcEN9xDifO+4dRbZe+5bWrqemTcT5G6Mp+8MdXeYB5ztB4hT89aw27cqFQRfKM96hPnEt4lnR9HUEerySVqVtKg6Dny47dt/TZJvSr8GAER2aBpcAkhsTWVMj6XnLCaQ1WtG6mEcBEFqjr1QB2ZYSkYxhd33l6mTU/vTZHclUeInfZ6uilIn/NtdjByHAY5z07cgTjFjKyZAOPTj1UGWJZeLgxT2I3Vm1B07Bn3+No1GQGbaWIUoINVn7QrnDvFx0H1dYYr1Fw5b/DQWwak6icqbw7h6EqsvpwNsWmDRM+GobUMqollYL5E+dVATcP525W5XMWzum6sAEo2v1pSfOq8Ef783x/Ps2mVQkPTy/ddts4Tva8JUmNXbUYwD06u5w+Pr+QBI5RMsc39kVrgGM/0HabHCcnveuN2rRlbov3T1fEyvLyN8w9cQ75v8tcLT+6GDm+9Yz3472RQ0gOQOs6SIIYTILfw+Aj6UCD75Bgh6OHL0jSqMVa8LnEWJtNK8kpX7uSFe8zB02IIlCox602JLwXZ+LXOhV48PH+FPi+8z2cTwo42p9KkgxJ6JE0LgwxwaV1SqmPptrmJQ5vKiaUVQnTvSkUZWF83ufPcIPNKVqia3G73mar9Qq26w2yzyv18aHRYSjxiCGFNUzU5M4l/7e/XG3xvfsn9NOPnuDJ+VplYhAzop0czSKvhH+LK3L+rwD996lpz3iOdM884q9+fGHJpg3eJB4j4L8pvPsbtkoBh7/TBfgvui78qnN4B0H8gQKo2M7WLEAYojnr4RGSZJKV8oUfyVOmNief3LpkZDFhMGVvl9RRTO4yWBE+szUpopa8uMBXqRekwGQrmwwMeY3m3RJVtEb46JVNBC6ZJf6mN25qR5bYSncV+9SHSb1rg6pV0insjRYkjk6qNZlNcYbAlaZqwfp6HpMnJdIkEOOy27rrOrUWYaEqr8bCAgGsWxaXwaXg4JVLph2sBK/RLIkdJbIsL1mNmiGutjN7TeRR7Vu0Xeil86iqMl1aP6xQ5BISgbjSIhBgvtaSvK4ogdhVkTYa78rZvXTSsZT9S7qg/L0taYmxsMpmw1xPRFc4I2C0TYtd24jRLVpQ0stAP7VIpADca9cpdn0fWRxlcFgQ0y4XbWEUM2hCE7jRj+YFjB/qZ3yLbszcaURv2k/PX7QcBHDcdYPQY1bRRGh8kM1VodRqGWqoHRJ3HHHd4S3bNRSZjc8YV8LCUzcBCjMI4QgQbqCjPJHRDevTdzhtTgnCmwNAIdA77gZm0uLvRqVz5arGPglLC+fu1Xu5Mf4MtAqSBTnEghbmdWVY7ev3gGsTXqfdWeZLcA6eoBO8KXNFlaul/JzIBkiuF06C50Ts6XpdC1MxsrF+ToQxAqd/X0qEnVXK7doyv0OjVCdzjY/5tG5gu6nh7GIJk7Ikng/i4zat6NbNG/jarWN4/bVb8K23Xofjw0NRQswzL+W1kIzsBMBv+vKf+0giwl/O0OWFud3yBOTP7Nb+ZzgY8gbwIhpIv7g4YPva53dbJcbnvdDJasU+vNvtFrarNWw3a0ky+b9jF0y2D9WjDxJiQHKhlv+ahVNijNzQKLz388xnZ4h4RqIKT2+zl3PUU3DemPoBXL84WE3WHA7MUP0aOaEXHdqjSEZTaIq0XANl7RUWtdm9tPAFPS/WnZSfCYa0N3kctpTBljLwIiehmhUdORT+qpmsSItW+D7O6BxxEBmy2EFFVnYhxy88LJQrHHnuKuctSkl65Iq5u45TAqqlyB4HDAWZn3PD4m8NVHlOk0KRJWQVBtIkwoAn4gQSOIYRQ3yFrJgaOeyqyo8Wf1NrhdQHUxsxLS/Qlf0g6ctnrOxf5QyRxUmViWo50zSaoPBNpudMSu7Q5fr9XmlXHcWli47zyemk8MXtgykWmaO9SYGPTpdwsdqIEA77TDK9o2aOJPMeOUYBFoDyMKlbKTxyKCzJGHceOYEsMlgsmRdZm1gRcysbXG315HhP5G7hNkS4WEdNqqXogxIsBbZxF+tJ9bBkyGyXaDb2Dz6XzLir4sude0UtNK1aylnSxcfWUYCTxUpEgcigu7NpIV1S2jT948JooEnhRWyJBX9uHs7FK5O7mbyHy/WzP3wztJNLqsGSDC5NdResm80/BxPd4/vKHc2iKmG2N5d/913sZz5rw1ztupZq8QRvcLvZslp2b20yDlTQejDUwy0NcOdcxz7jy7p2Hz85564mPDlfSnzpfS/ta7NV3s8WgidM16MY9xzij7zP/4DQ/3UPEn2FxxedbKaLxZ2tnwHgo7Isf5gBfHO5WPx9IPyn6PDbpMpKLDRTsjMSEJ4DSrJRDaI7sgtm1i3MVHAnPeOiQCuzlPqoWvEgNjkG4SE9JgPw94YsnOSVvXA4SccOIBlhSGdJNLxKi3mbtOxRbwvcu7Sn3nrou91AayRgSONMrUAI+2VTIbnWpYU9QqS+2Z5gimh+pOC6GEJbbzYX9XbDiVPJB5c2BH7YWPjko0fnEuxeLrbIgatMXjSjWq+7a5aXVE2mUM32Ql4UKZbfqiWqdDTR4K56nigdT/6eGrVSLd6opF6avMJ5RNzafeB7x7xc9iJdRKQpr8Omy8TVbW9ggE78V0m2fA1K9DUTlLkArVPrIXYBid4xd82HAJ3g4pNH0iBlfnV3T3gXmw4uqb9CsguBwcJ0aPpFqxjEThNLRsd7k45XX80RT2PnC510ULU5/DIijwQDS9gwkB4EEsLFia5jD1tK264FlaluLs/LyscmR2onzFRxwCIZVKoAtJtipJJFiBwMk4qSGu4IpipdWXmG/PjIdnDiGvZHM+kYITw+5UhhZYpffJJlR8IqI5j4KEkuS+aHVFGmXjQwlfqeGglTzKIAwRJrrWSS8kOiEZLd0A1IZ/Gsk3keFyLBrxATqHYcOPUfYN6degU70TnjZzEbFNSc3hseTdfB45MzWYFI6jSILK3/848ewN5sDm+/cRsWl2v49W9/A+7dvSnK0opn17PnwPyL5FS+wAePb81nOg7SMHKYsL8c/XW9ItX1mcbnPV9Q7eAnHBi3bePX6w0sFxfqOde1vTeuFPTMJLgvD+tpbTjIsr2T+cVUt+3F5Xr7qG5ahhF8BwmPgXCfcLA7iZRUVfW/RSxzVNQaI+4/y2zqETI6KTsp2gpdR7qsn9dIhfRerCgpKspIqIkx97FPPvXfvHZy0tkQA6ECdMSczEzwNE75B2YYiz3gWLrDVvAjMSqXHaWHfb2Mi1/aUj2SLzC40unxtjEzo/SrF9aOn4ILnWo99puEokUEscXBfV23VOa+80ru9SZFT9bgVPkJShmldS7NK0BprCqDcWUzt04ADsb7brDGSn+f/CK9ah5wN3BSFg7nlGO+5W6uE+hqExQtxckmuwVoNYSoKgpXFuK8s6fWmOStmdEdzis3nRR482AuFA+BqK4beGw2XCyiw8I8NUdELQn3cbWtJekjkfJnbuiUvwO2m1Z0fcUjkPmjSYneTlwF7gw2LCJc6oEqkGMnhnPqtxp0H/VWMEXzG+tVf20vbCXuJNnvmNfJ72MqS4Iz897GWgpllcPerJJuL5pnqRNLMIX0Hs4KONyfwY35FFhQSTuaYBok+jhEuX/SUmkEWqowsdI5RiNasmmd6hhHxXLvYTKdil0Zq8jjuAB/3bOe+omCyqgFKrtZrVn8SNcyo9YMz120qG2EuxbYLAozoOsinm9q/+Dkwr13/xROztcSALOruPlqcpzNWWQliApkfia8z7aOrBbhffl/ZUX1B3XTLD+1R/aXOL6MZDMNntVLRPyPCPDTyXTy513d/QiIftchfSMQvAUAb2rmBJ+QqrS+IegQUa0xG5Bh0c0hdckUetqJLYrCWTNRvUVUxIQmPtzwDNaRAzt3LjW0Vgncg959xZJR6N3jYlKnBRJIKG+AnJxyK4mD/VbhQaamqhYlCaitzgKIExbGQZNmJ0wVXxztiZrQsWiRAbNzUXE1xWneJZp6c7K+vHjQbTf7QHAHXPoAFO7Z4/MV1J1yXZZ1bQtilPgzA1EioyzPo88KV1STtprOl1mWB+rjcwxmsMxH3RjUmSc6K8zy5N8oLEoOecqdad7kUbOrKvmXWod6AQhnGCV7met1lg5mloqpZmvjbMOM+jtMnDNvXYxgsijofUZ5XkSfZS5E9bECg0bsbhZktQTUJ5ttpDGX3hnbFVGfOg2xxnWPq3LKxQ/SKhVPiwKlLT7yYiXiQP45nzg+ROs00hCu9IJ/KOBlzjqZkJRxJ25wWqHRD9gh4YKrXZ6oFGFaQIZg55g6+8wLbeMQqfDl5s2R4bdGlhcrFrnrw7GrnpXbCanQWtgZS9fxhBRItOuP/2UHDbUAhRhkUWSPefOaOIJpHuQB5TqkJP8mma9VIN35U2C5k+HZ7/ksNh31ZQHeYLtt6MNzP6rSX71b13ZMn3s/00P/rNutN1fVoocwt/As1sWVUpKgJDk3k4ldSJCAGSeP0oldrjZwuVzD2cWFQKY6qTZ3MJtOYDIp4WA+04DAMwPBnv/nnwj1NZeXq5I8b3QqpCbr7DU2hp9m8JrSo0V+OUbz7lUbPUwtRgxNlzUsmLFawWqxlI6mCK+MzTpw0INx/fyXjIGVyWsi4iIlb+pd04SLy+Xm0bZubwHit1hB3qgvtmtjX7vrlwNSaRAiNYfKnMJnP480fVSjYojwzIrW14IjPtXc1dNpldICeV9ajdSbgcKVYmuP4U3KRKxaJDX9BH4chlPQDERKRick6kg+uY9Dsgz7VN6/aXAW02RARYWhyMTDIU9db0oLGAAAIABJREFUgfGutvsm63I7KwzbEsro2NiG6DrFfXGsElSoRRwkElrZTkdcsfJxLyChlThFif0mNKo4j5FgaV1P/qWUIMbY/33f8SJwgp4pFD2DXZSfeYLKHh6jcaNET4AmZR5KVjmKIhop+xWLWzJKzDnMpkWeT7IcWVmVO5us5Hp8YwoXixq2bSsJDzcYzlc1bJpOtQYsqGERvPW2QS6uchJqnqPgM91zWL+5btRzmzmHFroJjJ0T1i4GH5PpZqZoK+kMMroLRjBjE0qSRLrI5dleblopErMgEXdXa7Z3Y/FFdAKZxSKDOgtQFWoVw9/Jgn5700rUZRl+zN3fSZmJlQl/BiYsgal4ivgS7vhgcqJ5aU2oY1WadeaV6tX6hCHNfO5VCWVVwXw67Tuaz5vcyQ+T7ZiYl7kWbmaNgoCLwdYuvOIo4Ib5AdD7uTIDZ9sG9+h05T58dIH3Ty/h7HINdcuSkLi1vgY3yFpEWtMo5ETAQ4hw7hH/BD38Pxi7Tzj4xknxym+LX2aymQYnM5ss8z8PTXcSY/zTssq+3bXhn3V1+G/QuzuRobIKweRkZ5960B6xJ9yaIeaIsKc0APllISgQ6aDyykKlddDN+EcCHzKoS0LpqU0C++YDm6VKYsfqccGUYsE6so15+eXmdSOWAkTIliaq0IKGJtDSiEima4dSi7TEojkkiq7coWs0ge7hvN682rSaMWiOGJWYTO2Vy5CR6s06rzerCUWaIAqzDOy9gpVnJT9ePHhRaCUt526gle3kCSXyVR4n83ksZ7OQZXlyK068US7PBOsoN6SbW44q+b61wLQzUQaxhWSIsFSftSM5Neg8nzwLHK1tswwmFtrDhknTG0G7UL8N9PC4rbEJMwtdhKvD9ShWCyuqCYTQqQ/W2CurH7YCWi2uY6o1ZX3HK5HDdaSfk/T5+FNShfUa5dmrAxOE9lkPfXqnG5I5g/WieVrGSH2VQtXXuEfsey/IkHiiPTJT5tGeqfsaZLJf8vT/MRhAdQR5FZZOK6ApSAsjr3Jc7xibt/EizbBaSp1cFM+2wmo0LDbRAVen/QsHIn38wr5tXKXwyqnkLua86CB3GmVy8llmqtmW+4iT1uG69VBHl/JzST69UWR1eg9wskRNGt87U1Dtw9znHTONjhV2kTFPn0syTKfxL57+Atnw2rgTWeniwcl+MJTzsIlz4F156ejL/e/EFFW7qKfnC3j3/Q/lIfzZRw/gYG8G77x5D9557S7MZ1M42J/14heYgDhE150HoVZQXXomX+Q+PnugNqigBwJ9Trvgl84R/eV4yTHu/nNHig3NV8sVNBtVZuy6xroLpupJuwmMwtAFrqeSC4iVE+maeAqIj8njrIsxr+t2v+26fREGShZaQ+2wx5+lkZZ37YpoIsWcPfxFi8CLD7QCeP6MshvtLsAvMVRkIEsqFmgVd02UepGf4ZN7Vc8UAGNvceKSonXPvBzKrklASWCRmIqT1gXmMCcRVe3rRgyUFxm8aOfOo8tzB75BCJ30VPH5zXSUREQ7a6E3yeNlcLFpaV42UtbwTuMpx46QmaCbMYaAJvbjesEg0sSV1UGlCBds9RsdAtl+TwYZ7q0+RnFDMJsM9viU14YehiafwQknq5erImrU4ycV2eF9npVg2f+x8K7zzM1z6KNKjaOJHmfc6VTwlhOaBCeAnKjtTQtojwKwD+Vy3cApdzrXmmwytLjpIicuwn28XDUS0vX0F9TiuWOP61xhr+s6CodSC0RR9lC0LiZ7QHLnJIsoIke5XDcGizdC0VIpDxOcZC7qwVT4oQJnbBW5E8XPnfmgufAyuYt5uD8VJXc+L04oyyKHSVHAHiea0wImfO16YSYDf5ngkSacwx+FvAjdqwSHU9t6SsDRnox6z/m1ys+cifJslihY12yKBq/QxSVGgftzsWyzWovgEZ+XE/t2E+qDIRZImx9aYiwtAIeR1VPatoNHZ0v3k4+ewMePzrkQwBBgNBHOFaq9Ils4TawJVOpdw1tRLpn/ARH9v5Hij7jLIlu7e/VZJV9FsinDbu6SgN51mfuwbOFjF9r7wdHvBpffMfEYHoWqnGqixwkQjtTgjI/MRUoW9mG+RGstgST+JFrJiLg0bZepidx0iT8o3EvErUqDYPLyjCYO1CpPVDp43HEUL06pPIhYgSjkeqMKBLMEiFJd0eSTE8vkJypJGhe1TG02FQ8dDfBcqV5amtwIXQ3Vrz6ELt+uV8fNZsOQixKdKygZNacHEtWWwxnfEKw6x617BwZmz0qa7x+3+4fH4H1eWgFgaKlY8VCuEamSoBWGtR7IMT0oBlDrTGL9Uhs6ZV87zdxxk2riDPV3Har3X9J4UaitrQdoKZ9SA6UosLH+TylZmXBBoeBzzrKSuMOpQJQ+ubIxbJ36o9Pyg8gFeMGn6sf1JzoIwezuF/p7HH3usxak/sXw9Afs/PLKe8ksYmEQp0nEcxhJF2Yekycn8QIf4pBRoULH9mlEahm4JONC+mBkrfT0CPpIjQ+doSm1WdOmL+DKYG6oab0ekmz66EUhnZW2yEmy+dznfbgrVqHgCiMLA5B4r5WeoPJB/jtTAVXS4JBqRxRmWSz2M8zXRYB154H1D1hkou0Md8xVx9BzZ3au+NW78TLS4KgqiqbqhzvQsat3lJ75H6PX0NMqxg13LFsTNuj31sEaoBP/Qegrs0m+nxHx680GfvLeh/D+R/dhbzaFjz6+D39z+xbcvXUT3n7jDuzvqZgQdz1ToDh23NWpImipTHbGl1X/uP4swZAOLy7N/MvxtR9oQm2cFLAiIxuar1cL6WiyH7RCEVWZWZVANZiXFTym+ZjWL0rVkTJy19LhIwA479qYbZvuxmqzKdd1M42RVD0e+hbHU8WS1OVztgFhQo58/njscW3qut99WtCu2IeNl5iUEMVIO5+YEsz0SrE5Ad9DVTkuIGudpHeE/rHXD/LjL9EVGzDLjOEDn/Y0dD3gwhkUkLG+fSPGcTB0AJ7ucArNwSCTAmcF8WfEzbZBpobsFZ5a3gSoTBXcqHId0gq1BMUQOdw7DOOi4EjCadTRHA8CtaaIJvgix2FdPbTjM66g7cdoFh5Kg+HkKnQGY3HsjanaB0Uhv2MBIxd37pn8IzOOuvnwhSE+cdo9ZN4nJ5CTsoT5pOIupFB+2OZuxTzRbS3dzKbTfYT3DvZd54SUz7EqvHEZoyT+nLGIR7sTGwUS6waXQYfBdeapnWRQOWgVixQOyoIKLDFUmI9pal1JtjVhmG8bNMnlRFJtVQhmVQmvHR9AXojNilqqMb+0yESV1o/CUG0XGDLHOMRpcruRjR0q0pcbH/vsfEA9R4gEZ8d8U/bPZDVbTja5m+nNt/NZ0NkUJ3D3kvmYy8VS1jIWAOpDCLKChLsSH+KVoi5i8B5j21F4dL7Cdz8+cR88Onfrda2lf+yfv8w0YDJjvBuFSuSOH8YA/95N3f8dO/j/2M6z//xXEN1ydXxlyeZ4sP1cEeD76/35J/XM/fHsyeq7tXO/7hF/g7SbdWuwGkGy7htoNZNMnEssQiaWukg/WmJph+OSAxnNkgTFQUnNVprxpjo7MoSWQF6DeIvTMvPfKlGF2aKtjXxcnUFtx1G3N/nTCxOu8vZ3ifaQHhRnirmN0dYmAgmhGJQaz2fiWoaNNttNvtks521ba7Z1ZafpoZiYVESDVuiiSvSyqldZTejg+E43P7wZvc9ycbQVsVvm+amgF6v5qgKsJN3O7FpAz1GuSaUmszrNnUKIOwKWSu2N1JiUX5qGQivXWqwYKAEN0gZqbTd5oNp0TVmxd2DVyKYXfV5QUZYoC4Us8uGZWCiSGjYYcwPk9rFHJQbVL4JR5zJdv2s+xJrkL1KWxtFv4zWvHn4Whx/mJxj8N6bE8BmV9oyrYoXrsg5d3UGW/LP6LPHKtyRkUJLm3t1cR+W3oUVqj9PAaUjyCLypR5vwQrR33HXwkbLSSyf3BQIPL+pr0cLLSFPXwjxvcV6BSLLnkjNGBb1rj5iSUDMqQlmgb2XGKpIE2xZhzTwQhvvwgxNUsj9aVeRKDPbCo78k49jD7kcSGE4tFGcw62s7nsOX72w51x2HCFR0Y+bdoH5DlmyOFYdS8MS8Fv6ZDbHdmmC7XsHFxTn89XsfwM3jI3j77m14++178J1feQduHd+QqjQHKuzZSbCzGaKqaX+u45cCPv+ZjHHxhj3xNitWmF2I+E/X1tKFJ0DrVKRCc1p3TBncdsPEq7IVzMkaz40r7245xFldd5Ozy2V+vljv1U2bc+IhwnipznTNYpSmuaoGAuQuQumjwaK+1PFZn4lUSYXkDd9jNMjyxqHcKa/tIvMinfqL7oi6DZcpjlwmvcKu7O9T5i4SNrpt02iPe7musH5qNJ8qFrXhAmFfj6KRfK+pjqfvkjU9hQhgHcKad8S43lZCIbDD6EKItSA6HZWeFU7zArrQSRyknMso165XBsUevLpzXQbsCwyop5GiuHNu+B1qEkim55DuABm3E2yd5rfk3nZVMwHiWDAVDnYv58hnxQRtkhdNoAE6yglcOe+ZLKLA3iVPyzYIGgasC77asHd0G9m6JM8Q+Xdny42IA/H3c9eU/SnVj1pYrtjRQMPgRFQoH94pr6yLsWYBPu/dtMyQhXuO9mZQln5kJaMK8KI/YefJXc599sLMVbRxQBvp60XEKA7xU0J79ZPISkrK88Zxh1MQ4GOxJxSKCgsyldLJrCTJ1LCT4i4w/GoRmpPcrmlhbUJm9WYr1xbkvvpdUDnqTU0PXzof+ZVGsW69bensctN98PDcffz4Apfrmpz4ZqAzqB8/DHv2ga63ydUp9xER/OtJ5v7PLMN/Wwc4/zokmOPxSiSbPLhtv51PH29uFI+PH13+iIrsWzHSf0mA/4iAfgchvmOG5YVBW827V7zvBe5pVhtBYLGp+xnhgqllpOtoZUlUKqZ5S4wKZIjuDiqc+zZM8id2xq/t6a5IcSnBWt2NPj8C6+17X5TSPp0k/JpTW8huOu2KJl9QMghuHEkVEAxCOc6yKZF+F08Y51wMHYSuSU/TzkMSU/JiVU8yuYUYte2TFRGrqsLp/j5DUQOFjpuaXA0M+rypWbUleokMJw0eg8YGgSuwqq5eg5UtwOKEiyS+pKwm7BN82bTC0WSRnPzRtl1Sak4lTU6fEy+3spZnKygOe2R9ltXVfF6WE7ZYO4EQWuNtwuij4OoyoNfGZeJRyW0iF8Pw29GL+w0RR13NK78fL0njeH1oYHNfrpP1Ivb8Tdz5N38/q9BiivhTljMqHg8VWOirX5gpFoxhL1zNDN0Is3Fd1pMyzqs/9+c9TpDH7yeTiNcOrFyvtPmJ1QBScD42mLmwS8kb79pSDmHgzdR1ofCdeL7yb/fyBvaLCJMMjY+hwUW/UaDpjrIPmXb6fUrwGG4rnBfu8hYIXUDYBoRNcLDpHNTBa5dzBDl72UGpvIjDpVXYnco1p0p8TF0CCy5S8tmXHNK9GSX1/RUfwW+vhshXM9T+slzZEPtKL3OyWGG3WQPFDSxWKzh9cgKPzs7EQ/TGwb7AbO/dOhJ47YRtUyaVetTxeYRr4bW/HF//MXj2fwEjcTN53+FgbLW4gMXlpXhnjlVllR836lCY5UTcEdMi5WmSzexx94DgIPNufx271ZOzy+bx2YItI3IBVwwL57Utt1THS0iRAgNUPqqt1ctBQV+JMUBoh+sDql23RRX3KzyQj/A00kMSy+tw9PbP3sRsNITPGToU097oRcn30zG7HUjswkb3Hf9XvmOlkqwBnN3OMCR7HKKYeFXiiWhHsA4B6zZaeopRX6qJK1Mhi7wA1zoRdEl8xghjnt84obxu9lCv5g+QupbYJ6m9KIwt4jRK3ccFPedS0qzFXN5OlTeqFZgEtXXO9cmTswQm9r6Tzkq1sXdKw6QmqUBS6U4y3NWl/Uj2aj3mECl2rUQMrGfiGWrL8NvlthEYMCd/02kpnVzmetZtN3oGNWkSixIv/EdqAxF3L0VfwbiVAh32BldGU/z1qQMZU9Iu/XXxwcTQW40kWLg576XQk+wi9bxISPsgjrjhLHZk4kboFHqduQyKiSaZZVlCxh6czug+5qP9LGX5pm1gu97CdrORtYyhr71lSnpWbA7QFYXmPsk0qgCfNXto3n984e6fL7OTiw1u645rOhwfd1GFT1MDyoH67KteiopoXMRIf5RN8P+Ykv83EeBiY7oV1zyur+x4ZZJNsISTCdXo3NmkyL632tbvAcW/dC77CSD+C4rxGwA0FT9IrcSPZDbUOsUEfJTCqEWgUyRis9MZOPj/2XuzZkmO7EzsHPeIyOVudatQKGy9sEmKbDZpIjUkrTmj0Wg4MhuT9KZHves36I/oP8xozGR6EGWysTGjRg8cqiVNc9VQTTabbAAEqgpVd8ubSyzuR3YW9/DMu9StQgEE0AiyGvfmzYyMxcP9LN9yKKObVH1WRQXEGHXCyADLMlNVAS1himoWbcU2RaK2pLI/qXzBLaEaMCeRzoxygtWlNFhGgd7WxlhMhcPBktY00EjEjpQPSspbZRcIqryr4mQ297O9Q1xdnhNXXYCoWJPJEs1oQSybv9fyjtCL1SXWkwnM9vZd3Uwqo1aCwnblHJlk34B0OKkVj0dSH1NA2mjyyB1QwZDXZnxtEGBtMUqir0loreuHwIC9iTlxA68idHLdWfiJO9IqDmtcMQ0eeiRs2cO7YMPpvXBumEz36r3DYzyfPsF+2aWc/ZaRRTkZlM4mcxBjr7cAi8TO3ppc8JFw5KbscIt2Rq4uPt4bBPWKtF1xHDCmDxQMLonbgT5e/VD2c0NwlVfIiw9MKraEs3wzjlejlGdPjf0RcbtzjHm9xXy+Y4pUxnFO0j0h96rkUvnXBAhKV5dht7jnW9zznXQXOIyc1QS1eHrhtrJi2V3UzacX89xqUB4RUAIxz4F55MAjwiY4uOwIzlvmduKdaH5WKBX4buU1w+bvqB0LFmkA1gbdF3dXheZhB+iSbLSY2HBlmWGxXqBSHCP0oP3cjArMkD7ljvXh6vFd1ym+6SzG6jwoHE54RAChD3C6uITlTz6EDz56JpyYB/cP4d1Hb8Lbjx7At955C7753ttwdDjPsvQGO7hVke/r7Qu1bUfM1w3tMVi8NhH7NNsIM+thzYnm+TlcnJ1B26qtMqbuT+7OjLNJen30niuP2aW1kYN0l2DjIdLFxXKzeHpyMTs5v2yGEDzPg8ZlrIpHefsZMa5mFGNrgtqTPMevadsqrn0ug0MzM+N3Y3b2Ts4KmN9CkK7u2NG8fn5JW7T0zxWJDKQ1weyvKAtev9ymdbHIvoRAQVVFvcENCyVGG1euSOK271Wm4hGhcOmGIMXXSrMaRmdxBU34mh20uege49gdTXcM87lhuf+R0brF3bR1KKrhTTq+EUE0clq3YCPGm888Qyg6VqXwkDKVMqQ2H2aydbvSScZcFM7NjQS9xRGinpJUTgIntRd4mbh8U4P3DmZqaROUpcZQVlb57boemROa1GO9iSpx8oja9ePCsSf1tUT2i2SEAXNBKXErk9q/XX/1rkYB27Egkti2iNq/g+zVHkfHbFs1tUfBAo/JQsW7PHdoY0Cvr3BbfQX1pIamqWEi2h4NeC44pLko+7IXyaEluiJaxgX8XmGza0FmqHL9VlxVQm9x3EcOfBKP1L6T7W+enFwOP/7oOT05W/phiJxoMlSZqX2sMLsPAG/CSO9r1c5JUJbLCPDDyrl/M526f08DnLNHRsWxE6/31ZZA0hd6+0Ilm+VmUJsT6ts/aOb13wyE/4GIfs0RfQ+AvkeI7xFz+ES8RpRAWgBsDVMyN9ZvZ0q0ooxIAOdsCYEiKoC1+WSylQebLU/QCHSmapEMujhpmpvihVl7Zox2BSZ1ZgXUZKxINl+pryTisYX6AvuVhE7lgMD4jwOpt6U3jqmCZ8VXTm1RTMk7zPcPw8P3vhVcVdHq7DlsVisMQ4chhqTsbtwvhGbCyrMTJYeLpHQFe0f34vzoXmD0LMWokGGdG2rum8pEjZTizo3+LjPBRpI94VqKqFFv5pWNJduDXZtek2dibuxebliqNSkXtDpT4eW3O9Jz9aYkJnA+FSthvzKZcvl4WJGQH8IpC6E678P84AD27h1XfRxg6FpFXd7oxTvGZNFX4qpDoQdHXXo5J+rbxeLr4zO65jfR70tKtFydjNclDPrenAONuCFI65r5RGEahJAsPXGbiySQHGvMdxgENnPl3HdEjdKClfzXrm2plVVeCwhxnEUN2Sqqu26QNV0n1DT4yER5UPN0ZnNiDQN6GCrxFDVNfXY4E0V1//IUwZ2UXb7bGcx2VumiuxwcbKJ74b5TksuG50dVD/NGVfVEaT8STSpC74g42QzkRLTIF4FW5UYumCSbkY2pgySnDO3tIv+XgzQ1MR9gjHe2ihxXAom7b7lLmq6PVL+dVKs3lys4u7gUIYbTi3P4u48/gXv3DsSf8+npGbzz5gN49OAY7t8/EgEHtFWT4h2y9K+3v6ctVThUpZxuVhAuJpjXcz9zkGZm9l23gSXDZs/PRWW273sd197ljufW58phTlfSHl5Lo+M6Fjg2/eXAt6mqipOK8PTkYvGjn368+eDxs722H2rErRLjtcneOMWpIFDDENrKlFZfw6a5A5rO6Wf4zIzaPWkyxyyeoi8p7QWszSVzvJN5QOaCW3ab7kKeV4XwFoXficXIGZeFV61b0Bi0Y6qu01ax2NQHLYemFJuUljwJ8IPMF2SeoqiydjFMGz0wHlacPPTsN260Nn1aotWzIRdCtk4Mx8Qu0xhyIjga56XiSbLCGDuYtCU2k7puo+JylivHMVFVcaGcboYwqpFnVACOSqeUUFcpWx7tRvLtSZ7gQ8y2NRJdCiOFqkE+pZQVTt6YV8p5ugYgUYrX0q2s6yzgJRRY0x8MWkZGRfpg9vfNdJDUmwxkqGlNiAzlw6+bPa35A7MYkZ1nUrDKo4vSJSi6ApCfAVNrd5JgVjUnmA1MZhP5uaoq6ShrElvENKmLKiJIQRLKIPYlg8xf3MVkjrlmxZARrXGnWJboRtuKxObnyZDLEKVzfHG5pvNlGzd9iG03cBGEmXPB3B6WkoehNMnYcrFXVwviYPISCP8fh/Cv5nP/bwHp4zjRY5jVlWEwwys9iX8f2xc22cybEmc/6tbL/6mezH4fCH4xxPBPgfzvgoPvItBhkt20ZKUmtT7ZoCim0hGxXDDBGhNxzcFaumecDJEIALV5UBcNHicqJWJVMgWFc/Y23dfIYA2W25a3yUc2hrmuEEvKFZsJC1YgmjqtGpFoJRYVkirdw6V4VBLOpF6hgjgDBxLq3ySJHkz3D9aPmu+s53uH9bOP3p+dPP7QXy7OPcXBCmw69HxVUT2ZRueElezqegL1ZEbNZNY751vrbtUmntTa4YpPGXJHk0/QsV8YUkK46ETO5yHiPdy15YQ9mu1MZ0W0FQI8JUL2RfuGeXDyfDRxaj9+ART5fPYUvrzVjUNT/uVaWMcPoEE+OoMtc2Jbeef7ZjKF+eGR79o1chbctxub9K5bVrNunp4MekuYiuy0KFSVvp27D3KubsL4fnmv+GtWCqVNOlNbRe9UUS6ygkKwhxvEiDJzMAzKY7qZo37C9pkZxLWpnCRaXRdhQK30RRqPPUNUiisBxVFtXbGdoGIreU+qv/ImhiNPYMBGyJSOAlQkiOkhYNUF6R/AFKRSEGgiaal4konOFThcB13E9uz5eaWNxksBYBVQJ47qlCOSm8KitGzwgVYuwn4d4N50gMOGOx9g0HPFILAR3VSA8nGrPoDjLcy/i9WQJZ4M7xXPJnJiR8TCRht5zZSHSavE0dr+cEOxpLwPZWq+Ozp3u9mqwKfQLA4o+KOL5SWcLS7h6Sdn8OO//Tv4hW+/C7/xvV+E7wCJ2ERd+Tjfm/HnfIKPfd3n/KJtqRVkEmCYg+yr22v0zcxBLyvhcTdzqZ6Zi8VCBIFEdEwNLa92x69R8y4D8VzUkmKrIg05ZxgGhuu18fxyvfr//ubvqj//8fv3njw/m3NQnD1ybzreYr6OYEiFmoA9Hu+unX3tFoulwrToX4ew1s1bxhYbuDLiKJaSi4HIhVoarysHKOTVLmqLrzlupQVYWg1Mv3YbrwLbcf7uvXzRtvUtorIYYII9DMj+n+PdGOm8KbVKk66ILaIWpRkxRnUkcps+wGLZxnXXxX0QBVOh6AhCI6T0NhbCC/qKM47FKOyjB4a4UxZJSSWNHSvtGJIJFtkzYbYio3rteMUTtQlGxf38vgwb3Vp76cpzke5pNLJu9oSkbSg7lJ3QgnkqnE8LsrIojkB1lVWWRZRsP1nB3bJV7e5qQhkSnzIda54XpJxveSUjDSPfqNrZaVtS7csKx5hEK+qHhXy0mxzA4mOLoimL8HBCLsJCVSX/uHs5mTTALgXMz+TuZrreMY4waP4/6XZ3HYQuJZf8r7NzjtLhjTGLP8mAi1Z4TbFSovBuFSlM2Ayt88rf8/RkAX/z+Aw2LGaFWPnKU9PUsW1psI8eW1eTP7wAwAsAnALQPSQ8QcT/Gxz8q3mNv48gdpB5Mc5Px5do++Inm7ZJK5/o9Kzy/xcM8cM9ovc9uv8iRvoNAnhH+JSOa/qwwQh844aoBcdgMfIGSXiFzDXcIHHCKVBcTppWankCBxbjR623yBjLo17ndvZAUps/68Y5RcpxF1WStal1/CTx1DYorVCFTmamUZB8PL1AdTmZ1A4mgFY0PKkQT1COuT72wmNEt55Op6dwfOw36wt/cfrJjM4N6mG11bxwezcSsG2ycCw/PZ1Gj+yzINBUUb1FFSbiXbRRbEeEHbifHi5UuGuQhFj7Hl7WPD13Du+90GG0jXmKgE+IgG1q3kSgPcyuiAyjRWcVWr5jHkcsCWqyJU/tzGKFqT1U6nPqHLEiBPuEso+hBfmoAAAgAElEQVQgZxZaKPQ2ocINeJPdGviVVOvarCShz8qi7nbfT9w/BZ7L/26t+OaFhLTTTONSzwundALVVjEdGo/NoVBYxCu7M5l1dnz1LATBPMYhQijgPGjVj7jL39zpMuihlNeklCY1F2yeVL1yX0XdNyoinM+ggrByGJ9FcE0E/5aDWLEFWuUiiwOlsnLvEBe1c9w+T9Ler20LAmFw23iyazaTE4IaA8yqAY8nAY6mzPPBDItihF4+6hQlXI2ht+4Hgj52lfky6agOEOoIa+54Dh4Ggdryg+Ziy1DoUPFrLl1+2vme7e+4PcAuN1vNc7LamSR9NwTYtOew6ToTDWrgk5MzmM1m8O5bD+E733wbjvYVXpt5NC8D1RmHTxLBxGvqNl9vn35zu+PjdW1XuMGW8HRsX8IiQKsVXC6XsFossmgGZETnCEHErYIa5uIXpMk+rU+oHZMwBO5Y+TBE1w4DnS1WcHKxCI+fncX3P/5k/vjkfN4NscIXPAfp8JOGmvDHGJbj+BmPULlPEattZ1qf69gmA7ukALr84rEbpKvWAA56gfe7KxPX7tSYVgPDpUDMWr1p/TO+Yyo+vWS2aT0ss5Xm+x6gwR5arETdvKfUzbv2Uqa1LxpqqnfKTXVd18PicgPLVeeOD4LzTWUJlTEVc+eXxpIzWdK2vZmXt9qvjt1J6bbnZTClb6XHJtjzIvQldgLILKztTmjahxPYqNt6bZwmKT8vUDyHu3SY0vOzTBQpFnN1StrTtU8vl38vbESujAYEcMV5Bq6F03jutjNIF0qc7GOGzGcOiwlN73YrU/0/C+qkxF0xAkWdwY5TKCKMTHMeahEzUmVZz11M7wvRJlZvjyIMFaxTK1zhwHDrHoa2hyg2cppcsoWewnx9Pp7URy2jpN1Gw1Yhxn7k2K0LAzw7W8JffngCP318Isl5XXsMQyA+pqJ3wKKce0SCuuSLpfkJ0Mfo8A+8c/9zVU//AGJ7+hJ1nS/s9qVJNtPGFYg3D/c/2hvC752cX/xo1YffqurJ9+vK/6YDfEcgRUhc/TpgfqSFW73yNKW1wv9/ICbJ2n7iuJw7ecrr1I4MGRkygHIQUbtx8lRw+5vbUI0MkCh+lMrvtHDU6CGcX3I2VBvcRTWYdaqLOu2idUulk1VbpMlOlGJDohY67HHNHkyKl0LgRjo23lVDXU+DryrtQUYxmxJIH4GaaW9WKy+cuKD+VNODg3h0/43h4PA+P7gMnudgkDuntXlNoCE4e8NHMkTZG0xEMzrMORJ3NJcClkC5ZvsCr1W8MUNoPwaE59xRtt+TaNNUrp+ufwOm6yUZLBnU3QXtKJN30i6kqSb/tGQho8pX0z3uanaP4nJxgeuLhY4OzaotyLambLyyqEC6SVopw1xVK6uJkFOtbUz8bkcz/RZFDMgVyzjlzlmCvOC4L6XIS2Pc9qs79piT4CJbSISWWzbmQzh1tpaWOI/qcI1U6hZENie/xbnRCEdOKK1UiER7IaRztVZ0FPd+LyruGAWy7gnR9OH4gRycMC64q8+lAoR7jIxz/jp2/qttiV7Ep8yx7xAU8lNb3zTEbFvCXkRUYVhPoO9m2DX7Ps4OaufqZO8CxfqSapovLiSOQ6PoqGZ6rGcPIBJhEllGo2O8TN8Nbrno4vSiq2dtQIwv+JKdWsGdt0BZsEw7Qvx8xAhPn53CxWIhXJt7h0fwK7/0bc/Fi2+/+7ZK07MBtvMWrMT83bcmnxqtMWR+w6g4An5+eSH9+8s3hbSQIk3dyIqE+LVyrm43WQLxMtF2HazXSxHM6NYb6LtOkky2BdjuBSWHX7R4cwStiN2xE4Eabiw5Ds47CiyWQuwPvbhcIXM/GX52uW7x+fkFPH1+zj6C7mK1nrYtQ9AiYuLL3Qxc2HYh4IWMn786wF49QOOTIP0rbvT5jxdM2qWg4jLSgUkem3rGY021SDYHuF4tnG64eF7gWXGrGIsWtaTnPiWcpWL2TUed/5q+zD4kirQuilhTB1FQIHHnstq4KQsqvIdGtDZI7UHY5m2xbt3ZcgP3N/NYV75XSwyu25KZIDpI9ezd5M0yC9JaLBoZhxHXOdVWpcTEnRD0pcip8SzqVYBRgw6ufENMcEtbQJOHZ8HnS13RdAzOuTGVzvP7mMyVpd8SUqvns42gGrvcY3qeoKZgeh4J/4ZFQSh1JksVVXk/KodSktjhuutnP4dQDi5nKDksRwmlDqAGx3ZhdWxJywEUFkoWIaOtVaxm66oaam6UsIVKoz9zVzPZl7C3/NAPMMRBupYMhx0MGitq2HZfgvlUl8WCrH9vAkD5+bDF2BWv5dCsWIg1aNVruu4CPD1fwU8/PoEPn5zBct0BBz8hhNAHHjokkgqI8BQIFxHgbfWnJ3Z7mEbCZ5Wrfq/C+C/PTi7/9P6b0/bGx+tLtn3pks20xRAWja/+dDKb/QTQ/Z+btvvHYYB/TIA/7z3eZ4gsW4gQctdSABX7o5+CeHIahFZ8mfaL7pG3yXVQKw/tqCmvk4S8KxxQnuyUlsLB9ZSk/S3j0RNaMkYYJJFDScTWpMaKlanAc/xnWqLyqHlzLemA5HtqgY1iNqUP6ujA+/MPwFet877mHqMgW90I95CNH74u5ImmaWYw2zsM0/l+8DUnqDwZ4KAlKImyLblmGDJzP2RuGCB5nJJon0d12hQMB6QcmmG/+sDkmlBF6hW1z+uBBeBrQNnvoT2pnaJlteatKsJ5UkbUBZ2n5F7yJ4XuXgLFCp27P5vPD+LxG3jx/Jm/ePYJxHZjvdNR3w5LtAdAur3EnciAFbJiLFIsVl3a/sAtnbHxDbYemjnw7jI+AjBSWpdtsgELlbuUiMbkM0epm32jyf6VWIEPg/2qfMXiQQT9wEqtaiJdzpSY6nU32oOU52Grvn0bOTtX5gVq1cVMUpzrHe45gLe0yx0rxYoT6xeKy6kRZZJI20p9cWF++5P+EpvVFmLRbtgWbxAQGk7cEPZde9FAf1HBcFA7ZLp9Ay+63bdtWFQp7LLa8BuQ8Iy5GQ7hyDu6p5FaxAmQi1WsZhW6piK46Dxseiew23BjR1a3lz3OYhjKxU+V+IvlEs4WUYbb2cVSkorl5Rq+/e5bcHS0Dw+O7wmvc29/rk9j1AozFtykcUvRr7CDpHlvhvSZcvOZb5hY91eKLLtDPSp7iBxsVXh+traSgymwOlPD7Lte4GUctLHgT7tawWazEiuAGEbYnUswuHJkWq5jvH8uorLpfgyRVm3fxb4b5qu2bVbrFjZ9oLbtgDuYz08v6XK1kamGx+HZYhUXqzVD0KWSxTOmQ0rJwZ3uWX4gyXzNjNv9BdpuTJivOxlKAjVJrjB3u0aBmtR/I0seazPqTxeOinlx91ol8CxkkTsN/PP3RygEYHYu8o1Xf/uVcWVh9YSAXlhGbqzx5uUY2WVgJSgqpLl591apKjqZVME5R+0w+Mt1S30fuEjOxS3WuKisb2dT0q3VQh6pXtnuymgYr61206q6yofGxf0hDMjPBseEELFla0pwotLvwCw1cum6mHq2Op5p/nSw3S2EvJ7kGCtxQ3P9uehgZtBs0f0EO3ZwMReOZd4HZ/Zmo/rTdgF6Z94s7v2tNzuHCqkrmeq02+deWpXkamyBdICkGN0oRJZhsQ3TOzjRrCuBqKYGQd92sOZi1aCJJt8P4WByp1KekWgeqHELCg1lTyA9LVmX4pqnMcMktvnnUjtxPif/jFx6frmG9x+fwPtPTuFiuZbdV8JIkWKvMx99RhKyONDSkJNqDxjpx5XHfz2ZuH8ZuvDDVxTS/8JuX9pk0zbWZj4DpEsP9Liq6z/Huvmtvu//QYzDryPgd5CDWSKjUSGYNaUqoCItHSs/ESvcig43d/LYSBXF7gSptSIQS7rWVlDtjALS2/PB/MpKrVdIhUwFJ8oWILSyCtwM1UOyVZ0hEaiUriZB6ioatFabQYktVinGXSQ8gwmPVRBp6tkys54Mk9lsaKYz17UtR49YsPPShM2atDCdzWFv/wCruqmCAISZiUYVo2xQWmzqZkEMNSbhqLK6MqvCNtbSWtmjOLP/Buvmyj8RtuGqDUopjFV4Ds2fcwkqysnlpRbQdbL+MSPOEu0ES0aloiTYR1odrMyKU1RYKVcVe1alrSfNZDKdIvsG5oqTSpQa5v9qMCzTFFaOHIf6Q67I2TJik904Caeq1y53jdL/oliAGFcz1y9HBrGtyKp0bOyFqEmuFPbiuIhI35oQCn3G64KQwFdak3G8IgySIjCGg3pOqj2YTUqy5Ygl1aA4l/IqFYsPjDAWMKqY8F3B5SYRmmcpoa9ZhiodCj9FTsxuqajvpvorVvCKAvo3b6qjGKMDU9xSyFYKh8XKZIA93Pg5dhO+OszrFjsxsVi5Rhn4Zb56N/kff1sAieocP0n38t+5b4iwN6tZhCjgvIqw6p3+MwuXkEZQPq6tfPZTXCnIle7kh8YG0+9/9AROzs7hL//mA7h3uAdvvvEAfvFb78Hbb70Bs+kEDvb26MHxIXnvReigHCssYEZRyLuqzC3dbC4qSXZbaVXiMwn0+TItRCsLcJ+Q5ndIbJ15Z91UzPlKbmVyifa7rBjMZerVUqvjBJMN4ddt9slkEY3B4GZgHZVEkc9zpO5Y/1cb6T41Ldiyq9209UefnMRnpxfcjaLlcoOX6w0b9NNy08Fqsw5dF8zpglw/DF75YSZJO37R3e+XBdpcseyNK403TXx/P9vdzyXFvBkg4zLfrkwgt3fIc1qUfuVgBUdOPptK14FWhMvGz4wpZkqVYk6bcn5SvvllNu0fgppxcIzDGV7AKQbo0aMKGRVdJC2wbzCJNG6dFhMzoq1J3GoMrO7pvPMTguio6D6P6/iYTThD5uRV3Zm9KJbqsNp54G6ad5XWlJkBFKNnmUO12+CaCtuwMJdGAGNjIrMLgU3/G7Vj6DB3SzMigGg8RkrV0jROkx93mbAl6MCWwAJsw4ZBcWHRPodJ/b3onpbj68Zi+zUUj/yxAt4rBStIXb+xsJ0SzNTZ5aiTk0fPXRuOVxgK6xUiy9e8rmp1wTc1YOFbsoBP2+t8xFDYqN7g2lxV3qV2KDP9S65z4rrKodJ4X6i4viPkOo0ZE5La4sNqRULCONPCaPsBnp8v4XzdwfliA09OL2Gx7hSay0W2IOU5RnNU9hWMrXiLFWcR8QAILpyjH5Jz/yug+9cI8BdftUQTvgLJZtp4Hn3iHZ55j38duvgDQPdrzsE/Q4DfiREeGqYnSEAkT7J00hjux4nVhDuQ2eUirZ8aiBgP07wi9dFi8LoTbiWpWqvOC0lbU7JClpxao4OaIkwiUI0ofE8cEyrmB+SEoVfIt0A/WGG8RbVS0VBddEqFR8lT1ZqlwOYHh/HojbdoeXHhY3heDV0/MsJtQyMg8ENX1Q01k0l0yAim4FT5Vt48RAHhqhULpuxBq20B1VUh6LpFznptAoRQvqYq1+qxU5pQG+3ostCP+JTOZMoQnIRcU/HQtP/OUCqW4mk6WBF2MKs1mxuF18rw540mCBCc866ZzoDtXPylV/WwwqepvAh5HrWInXgW4EJkDOBF9JeuzLmAV+banXk2dTbxykp8tUA24naUr9mp9QnC1gIrK55MjP7GMEQrCngjlCsdo8yTbJEitgFRFGsHseWgzFmhcsCbpUdxpCMnJAcczvxESfDUso8seafd2AzbEiPoKGroKjzA2kzceUV0XtSfX3u8Z4V/bskjZXyUflPlQphh76bI6riBFaHXBMgm4PMQaU63Fr9f7WAsIjw2cau9nUHk0v8wvJaVFFhNd94EWHacdCqXKUQnRGp5KOl2HZKXvaaZR2ef4y4TCyis1ht4ftrAx09P4KPHz+DB8RE8fHAEv/rL34n3j/eHuhKnqCZX2HNDS3jmKHMFIydQxcS4EfG6+bnjln1lBuv53OnUryvWfJW2orMQBaXi1amXbZUl+IxRDPEZEht6vu9sYaJy/2RWAInPtJXrXan8p+lP/8ARlHPshuCk0MXd88Wqg5PzxeTDJ8/D09MLvFxuiL38uLvZDQEjkygkaMSeeB1A4gE2R3RiZWuT0UuPn5xbW2DuzMfxy0ghTnM1GJQ1d7zKZCZD+LkCrn1KLr5Fm8H53Gd+gHkdoY8eum4MGVxaf9KlwZxmFkfxaiCAFPzH3B1VHJSTNDgIdNdloL4JX2kh/ghGc5etwwghut4KhEyFC4bbdphBTSNccgu1lKHeW5CLoh+XR4jaafgMV41mzeHM1iOogv8kVfajZjXBQpCk5D9+daHQdx3vcUx+0jGmt4xJG9LV6CLvLyeKBuctFoMtxf0S1pQuARW3d6fLhwX3P32mLDKV1i7p/WUclpJNUaGvvXhgsjhRxR1M76UwzhGo5+6lHQBzRMNaBX2kO8koZeZdJnQFQB4xyTMwJZhbBSUbxzF1MLPEcTE+Coh1WUDT9yUkB/O6eD5yFGJ0q3Zwl32AT06W8NGzM1isNtCxbYr4libDBeJsMxc07fI1iNQYSuEjoPh7MeL/Qh7/fVXh06QT81XbvirJZtoY3/wBAj1Gwv8Qu/BnA9Bf+sr/IyL4BlF8gN7vqx+VWARq0gPmwM+dvqT8RVSLEqz6uleY5j/zcNLPifIsJ1gsqLNGhaCi2ZWgwFEV1rEQngFyhxMaRRcKpj1k7GVSsBXvDIPnKq9zSHmI3S+WR17w99SzeXP04OFwefoM1quL2dB1fgQkoCBf5ZN1Dc1sbzM/OGqbycxmU+zB5UImF8wmPN+jdGMFXDoxXMPGeAd16rpK9K5PY63kFxYzEopIo+uZZiekhOfanvWJEaDRfDe9FWl7EXcSJSPbD/F+BN7mLD4gFRUiZ7lDK1BU52Ay34fpfJ9W52cYGTYsFahYzJhpAcGUdVimzH2tCVQsBM6GvcWsXIgD5InpxgBeBotZpFKxmIwyGdYNzOw/XaJC6KXhTgqVzjNblja/cYwjaFfwTiu+VufUl5PhY6LyJHxON/JvCoPwvP4koSkoX2fobC2qu3alLKxJifE4wwv1GCB6za4xsWKdEGIEVvd6oz0j/ga6OlOraR/LPg/9HLtVjcpqVVQCcRMce5bNI80Id4rEr7LRGFTI3g4BpFBybaaYJxaBQavZ/NQTHNZRupvL4OFs4413WsBXd7oYV2VAXury6RNjnS7ucrG1wEqqtZfw/keP4c2Hx8IN3ps08PajN9x8PgdfjTY/opotKtsJ7Cf32BHdCAV/TZtkQvuW0Def3fd8gbe8jKSiEBbJJlEINFA3uBCGSvhMXSuwM+4UdJuNSP8Hgc+y4X5QsYxshyD0iZabTxAiz9W+DFkdjqIaPJ8MgpzxAvo4v1zBjz94Sh89fV6dL5h72Q6X67Zdbza+7YZmYIisYQWdKnKD0kxUOI4A1+KhnXm/dx9HeSZG5W4zb1Ooa9k34jPfPu33XPm88tOyX2O+xyN5xNaQnMSJ/Lc5OCaaMvsBswosbimScceTBdP4vxWGjLZJAi6aMJSV7J0LfdczyqJrch96JOm5OUHCaOyBJtB9K/ol8X65G7buelhtWhjiTETPcOv+K0kFLROhIjFTGYrEf8UtVFN5nmwdJcUZTnSse2YnlPxPyusxWABQXYUNbd/Lrbu7u+jQFsN8fDlFl+X7tuOXaAmLuHDQTrv4umNJ57m7bYMJUJ4fuEYwLnUOR4jsaDEjPppoIl1VA820Vr9O0s6mZYI6d/D1ZQoY8y250CVCP8EEfLTLGOx3RFcUxFNJwjqhJWx251RTVAYpUrYFn4owvMjK5fBq8Sn1guS53PRwcrnB5aZnhAZ8+PgUThdLO+aYC0Kgp7Uj7ih/6ImQRUP/rvLufyPX/I/DEP4cKGyu3oGvzvZVSzbTxknLMwr077owfFzT8INJM/2lxk++P4T+P+tCOEb0DLVaSTLFM7B083A/IUlIhHMg4wGMrE5j5y07BEbFNgh303p+qiDKKqyAuAYUCKpCQbR1ZIkj8nzfWW9oYhMhoSa4/HuweSQmZybSDiu38YgTiPn+ETx4+73Qd108h8dutVpCCD1qJ2vgJIMODg77B2+9tzp68OZQVc00knAqO4HxKqfKC3zYJk2bJ2pQtSBR3EViT1OByLUFZNCskxiSK3BiysJiTjqhAoFRgSBZtnzy7TQfyQHH2SA9osGYFhMtUurEiZL4y12oJHnh7M37drq3V88P77nF80+o71oNlQ0ZRzQK5KTK1lhhUpGb4KeAsQcX2ry2XxXKuH411b3hKD5cVEm3ZnfK+T8IhDb0LI8WMeFgkvuyXdYXrN2v1B1KSSdqsqeQWpUpV55jmrAz7IWDxjxRk6YhqB6iKbke8UJb35N4Mh7C4AUBIOMNjLIBKRSgUfP90wd+ybMy2XWlKnrevQRazA8aHDInmuHh+KaoFhHNohlbg/cp/vp0gSIVVYc73LN86VHlEGt22vIkCV/dmxx7701IQ+1UokURrujAbwdZ2jmI13RDy993AzOXoHpBxFsk8VziRhZTVkteLdf1r/z8N/Fb774lnp2VmWZr1uFq1h5jhJAWm0zufucaUCp7pzj5021o8+udizBf5u06IR8NlOUSZ/HHYRjQVBhd1/Z1t2mx6zfcKSAxKw/SUZQuQbITSNNQ7kqYWwJTQGy3DcddXKJy6AZRRUGo2j5IN5w5llyg2AwhDCG2p+eX7qcfPcWnZxd9u24rQheHSHEYegpDlGRC5bdzmW2i34c+k7MUJh0sgPYvm3DyWTEneurBBPSu0BU/k+01fE/+eNoRT1G9BbTlMz/C7EdeZg+VICOSZ6U3PaxNrGATS8isBeCkliRz7GHiovDxYScHSjBq01p4yeIWFUG+dVG5ICn8JOLmpvVir7RSxx3s3Hs59kiw6QaBLi67AAezWjELIpqfs0dJMhPMlXJyATlBGTuc21BaTnrETsOM+lnplJ+ZYFLl6QKmSna0YvC1jeHrLthOdbNUcIaU8MHu72N6uZW06rmxTsAGnSDhZuVnd5LirUOAouGZeoE2GYAJIUlopbqLcVzQ0z5YLdb4/Hz9q6oRcZ+xgM3XT70v1SIsQuiCdDCFX8mw2GDzUSGgNFKYMHuEYU4UtfMYlZyWA1AyOfpkL2Pvl1qNfix51I0ec6kDnpsTaf6zuIkL0her1n34yQV9/HyBG+G193ByscrvTc/R7h0erzi9D+R+COh+VFXxr6rK/WCA6kcQ4vDKleIvyfZVTTZ1E59K+qsQwgdNU/8hRvfH/RAeI+LvIsU3QRMpHlVTbYxw3CkD05mID5DyBEHJ6ZQeS7RFTztx6sXZ2EMQzSusMugtmCQXJ0/8vgHHgCFR/b3ZJoJ1CivjQ/bmB8p/54Srt4T2QDTRgGLdTNzxm++Sr5p2Mp0Pzx5/2FyenVQhtuB9DXuHx8Ob735z8/Dd9+J8/8CwICmhwBZQ9Fv4eDfS/CGG/EqHeG1cCZ9sAG0OGvQ55dej5ZBsvCHXarB5wNtMtbFOA/MtuELNsMWpTEU6fzSFloEhaOx7dGwKrJcTf7n+1ha0+IHTza5pZn7v8MhN9vZps75k2w/kCpjy9G2ul5Q17Kgi6JcGxwvTRJNAGraGz3UiCtvDCwT8E5lGewMij3Z/4zZa5CEQa8uAdnZ6zaLwGrZyd5IIyuG6saGaPMkM1tvzzBpDOhRRCpZFInUyr++jkYofaTu6xuAqFRxOOkgp9SIyZ6rXzdt0MGY2uHWEsgxUpjgdDdVQW9NTDamtw+s//XW/KVi6dSty/WzVyrCAfQ5fcIBpxbBHBx0HjAPBmgWgSE3QnfFwyuHkLBF9kcJtuV2XlMqtiwSXl2v4659+hBeXS1ycL+D0+Ql9+xtvwfHxPZxMJqYa6J2va1e5hhVua4dYqRKiOoLnfpig3rLMyOsaA1/ZRLNMMEcBkDG4UTiz0IOWIYZ+6If9rm2btttg6Af+V7ds8Db0/CZufoiXL7lR5AcNkmiTAyRlMavfTLTO5LxC4h13Jfjj3brt+k9OF+7x81P//OwSTs4WdLZY0qodsOs6XLVsaD5c0hBqdG5miu9anDCSiHW5Cq5djnonO4/0nTYqnifvtavJ6qfJxPMzD+w+g+8Qn8AYeJ4KWj8Sv69RVsDex7jjnipYxQZa8pJk1qil1o5zOtN/F2sLE7GIYpOi1WLubvpcmh0pEjkpjSn5eLnHbavANaZhlcv+5i5PYy+z32hB2rqPsGwJmomLkwqZJOe24/703EDxFfp13tRmy26uWmMEsfuhEAUdJGigMCZGW+iSsahcp5PNcNIrh5EKoLg1aZcw1e2PFPtJUeUV3k/KfdGE+HLz4Hr18ERQKs6cyvWn6AAbjFTiKr4ODMORbqUJ5Iifs1cBHys8CjpC+unSqQySoIbQyfVT/rd+eYjJ9c8KvoqXGJXc0Y2nmEppNBYFUlihNZCYx0QqJGxfeEpIwSavO1qFsMYoitdnRrUxlHAgVsem82UHp5drfPzsHJmXycU1Z+H0pK5yoloWK4oxwId0FgL9cQjDv6hq+sGm704P5weX6JtPIY395dm+2slm2hA3iP5x23e/3wX6cDZtfhj79lcJ8NuI7pcA8Js28JIJjrMEMYpqDUPxFUCiaLyxBdWr+I38zIlTa+v/1Dp4IFYfAI3ARUmSOGflIFJinuyPd9eZjqJ8f0wwdP0Ob93SIB0ZqVYRi3EEctFNprPu/qN31r6eVL6ufdU0rl2vaDLfoweP3ukevvPNsHdwxOqzancCqUotwkCJld8n+pjNbSuFs4pSVjRb3WgJRUIqCgzZqs7eOKZcOWLlECS1GwhZaVcVaWUyNkqBxcGCa1Smtzangt2LytqUjZH7e4jMAzOGYSQRB5rvH8L+vWPYrC5hvVyY4pgS8NE7I41vjYice3EMPEiyGcGFNbjEZSz+9/qNtJaAlfhN7jZnyLqnY8kTjW8nLBoO1/y42qRxCqlX/mufqPEAACAASURBVNqlc246m9wZ85BXrT6wPocmAjILchImSrS1wGhvkCHMvzgbsBWEqhLArhnSGLg0dxtfc0CWsEPX5ekmacR55HyrY5Cr+moArYIYaTX/VNun+jwVrdXKExw49gNlqwgHPXtzDg4WnYMlCwlFZ+JSqZMzLr7xmktMBqnzqFJUgXmht2TIqAbXWuWPAVjg5c/aHs4uLuByuYL/6DvvwfG9AxvmTnzPJs0EqqbG2nv0VQ38nLoU0Ekgwcw+Bo/EK9fpVluVn5EtB0rXiH+kaxSNd6mw2I6hsLHv+67vu7bvuhkHeNyNViEVfS9F07q2LqjTudc5573SLtHuDyQTdmEwINBkCBGH0CllLIIbiKqu74dn5xcnP/3oef/k+fn8Yrmani0u67PzVd32gV10mFo/JConRIZLCi0FtNjLnGlC3O5+R5v/HZplbdl8f5kR4DI8fWw1fQ65JlmRGOzYt4/5FQ+AROSNuMzccrzg1Ac7x3BpdeFUsY0VrGMtz3ZjyaZ0Oe29STy9nGeSkjukrvbVjGdco2BMAO56Kmj1i7QUR10aszhB8W1x/LYru9h5BTOUsh9I1q6BBTLYuBq2Oa3ZYoTnPqf17qS2KoUy7y250bVaEnuBbII8Y+oIp+uENqKKwjBtH+TYZaaryaYFWK5Q9Tahdu3ohbi1xOrfVegmceS3i09uK6Fkqzym5qZuLpR/S2r3ON653CcsoPjOmweuU6fN5HGpSaUmnIztS4B0j2kNGsV5ggiOBYHByjyUBXwSDDzqfcgcntFnM+ts0NhtzN3XUiHX2FFjj+L6cSdnpgfmgHIDV8dFWuO8G+MSVMTXuhvg+WULj59fwLPzJWzaQbw6KQ5CQZBgtvKGNqDC632MIfk9k6ZufeV+3G7iDxzQDz88++j9/+af/Db8+G9P4K+fLEDMt2GAiq9XhaK0e7lq4cHnURj7nLafjWRTNpl4Oufcn4NzPyGEdyLC9zzQ7yLB9wnh2wRwgNJ5s4cc85M4CMdROZlNGtjsd2kPW9QES2gvtkAbRgwhyX/21tWcqXqr7KKx0c1J2MqenZnxGFvphJIoVqVpnqG+/LfKpgkx7uHn2vs6Ht5/QJX3YX54OPSbDTV7+3B470GY7+3z/NOwRxkBrZCTYgdMUJ6JWI8zcxPFwQpUClPXjYyRpaoKzrDxIQcGKhzEJZ7EqekdCTx3yl2NKPBi5tyIPmE0RD3vukcWgtAn24k3qiI1vPAMOLEUvisyd5S7xpzA9qj8WtCxq922+d4BHL/5NjLvCJ8iLJcXAEMPztUKm7mVdMnl+poGT1jzGsdwWhpZf7ZmXM2rEoyI1W+zAOtN6dz1r6bK4/ZaNUKTPo95ZqvjaAlXPwTUCmN5wB6im0D0TTaJ1uzRGAo6RLdyFAt+OLNm18s6WgpdyCkJgrcw43rlTXZGDrqgqpN6TiOHzeWEM+7UOtGw8mQLoReY8TX16PKSpZz5s+UiFoGgeCN5SeClMBAqgr2GYNmrgu06ONgEJD7/Unsx3kD+5ceiccrL2kTMXKwbRyu/v/bQ1BX0fYRPzhbQM7ys9jCfTxViyw+vq2E6baitpeKNHMAxzLZpGqjqWivHVc2/+6quk+3IyDHUnymVlU1Z8qu8aYFNA62tEpN2kaJB3FXKn8xrWhJMhsKywE/fiT3JwOqt/TAf+m4SWfDOqRK5jGvrWjrvXOJUOYe8Hp0BUAPgD5DIRw22yRtrkq3hGKK4WrfudHGJzMEMg7YXluvWnV+u3Oli2T47W5wvl5tuCOGNLgyTIUaO3XhQRFT/4EMddhLuDpa1aH2I10MoMiGlXjMvvzbqBRQ9tl2m242brSx6LVN3LnUePu2A0l0ES7789uQna/S5RRD3tx4rnd57u71l5e6FmyAvzDIwW8sAbHV30olpAclDkkNivs5QUKcRM6Q4d4Eqxejkk0vds5ySpETsquTtXa9Ztk0pU1mpDVsWgIUs0V02hwq66vuA/RBMcZzrCtGZ4wCkE07ObKMwkFJryBIRSdrMOqyqtL4R7YKnjib/zvBZVr3zZnGSqnwp2aAiONoWJtq+GFh2i43PhJRN2guxIhMl8k6VfEc54txKHRNFQwwQVNJXsCvtsi82jteDNHThxNGJ2q72ROUZZYUvVoWtVJZD3sMFw4y0VzXWYD6WPDZ5PmLxHjlehrmZJUmpBKuJo8sJPaTOn0Gz1XtTIwMqRqJyNpPNiJ3VaBQA5dVIFzU3kNPMIaeHtSWrmCxnvDPPwYE5uQE6FUWj1WaAk8Ua2c7k9GIlVib8WV4HOUidVJ6C7WsYQo4LpYgiybkTjYNpU8Mb9w/7h8cHH2yG+KdNH//um5sj+G//y38Ef/gnP4F3/uapydTquGJ+KBccnp8s4f99/wQa18m9p8Rm/pJuP0PJ5rghMFcTfhwAPuxj+NtmCH/s6ur7AP43AeEXAMWjMwU5aUxzZy/kRUJzDakgsXoeolSIK0qLkBXyEGCTAUH5WacMhscUFaublbOXWMKfrUf2pNOoWYm3cuKgdgLWOZXELro49HPvHe3fO+6n+/vEXB02Vqh9LT2EqJMg6gLHUqhiunFgMenGzgXMA4jPb4/USnGwSSytZ3VqoKTpW+RVAfoIAqeNKtQk1bWJofjWo/USGVpD8pPO9usMcS+dXk7oLaLYqA8qOhy1CUyZLsmSAE5mczp++LZKXA8DtJs1tH1n1qAFUnUMB3ZLtpE8A4ca5xniasnmOLHleu/25p0ikBNf8w5RsYQANJoH552mNfHmBPOVKvt32ahYDzuBuGTCoTWrWKe4Eshx3Opqbh1tXspTRc+Wj8TN04TPYHvm9fl682mu0kSUhDPmA9LjFLNyDIXaoi3kCJkaFiOMHfDbjowsIivyo9d2Dtef1tb4kHZPxfjCAPMKYVMHsUpZDY6WvY/rXsIDn6wp4k6tRU3x1f/tRYNqFMwYRUIAFGLGxvsfPzuDuvoAnjw7h+OjOTx645iqStSuUepUwnlp9b4b5IqDmMlkCpPZFCWoEwEJk8EX9WTOeXAwH4Em1THyMZqw1dZBfw4J6bXiIS+5SXelvKcp+VD7J0FrKJ9JA7mhVSEfFfOKAjNjLn4nCrJcYRcdVxSz8gguxjBP5uQaNKJS71OAhda9FLtc1XmVfJZnyyFwAYHn1VBVla8q51hZ8cnJRfzwyQk+eXaKJ4tLDiKlwHm53OBy1fohxKMQBy5QMQ+ai4x1XYm+j+RHqJ6HlV5D8brubR3gNUSpGlsa6iaGR1fwHanI4190B8quW7BujpdOyg1z+ctvQhOxxGhqEP20xTFb2320RKl0ZVOlA3pxspkC6iAdtegULSXf57eXnatPsQwuQqvNFvuzjD69R1VhoxSzUo3dEItWOJcuGdJWF+nlOJvBEtuyXFAmFCkVeak7IclZhI67Z5I8eWwqpWwqpoZyEiZiQlZ9EJEZGL1ixI9aoLJOOpoEKgrEcxXPW9q50+RTETCWiEcy+w0yeGgwX0dVTlVUQUrOKXNDUzgk814h7ORZrdWEucb5RqGpKtplx27qrLkr6cbgiBOWFLGmBqsz4qE++06UYAWBxJ7clXYsOZ5JHsyyV066nRarFOWgRSuBEA+qCjv0KuSj81KQOSsVP1ISXXbJU2I89k92R2sSFkIIO/wPsiS8jLVS0nk9TNh8e8YGrKFlrRTgtBTTR17Leji/bOH8cg0rsXkKcL5oJclkPnqAKHNsDARdr9dJ63Yq2JKKDEn7Yl57ONyfwXTSwN5sAu8+uu+/9c7DedX4hpad/5XvfBPDEOmf/vYvwT//ne9mUaVU3Km8g7PFGv6Hf/F/wKQy3fQvuSLBz2SyWWybnuKfVLH/MYL/IQD9QwD6fiT8VQB8CykyTGWSMEcgSapAUFlcKFhxZm5WHt6epR7JiGo6X/f2iIi9h4ntDAatT9OQJ+V0is2i+RKa6qosxo3VaCsi7MyexGsUIYq2XBmuIMYWneuaybQRJV0h4kmiKaw8xeBjLVYjkXoVRuKOIaxZHZcAK+QMjf029XhcNiWKRg0XXidNBELMSaaqxewlC0bxG0Wa5sl4fER69StVGK7ttbFkVPUL+DwylU+IPPzLyiC8fHzGjZUABsS7UyxQHENp5W5cnp/C+ckz6Nu1VscQCwW9G8IMNYKPIh+DFXrqc2ksLfTXfWjb7qQMb254u5iRBl2xKevY5cgzl+1TJ2Ncd8mq5WRBzWdi2aCGyAZzydSWtEL5LAyRi6Ng6n5XiadWQZAum4x5PhvFz+nruvi/vIBGypt2a+vZa061ftnLp+Bd6F+2E83tZIt/CAzDGiJUtbuVtzn2TT+/uZ+u/KxLJnPR+FjZN28/Oliz1UTrYS2Kw3q7NoPfgcnq3zi0HPWZb/luewC6LsIwtFo7QOl6wbPTS1mQD+cz+IVvPZLxwlYWvMDOJzXUzssC3Hdc4e7ks5xDrt1SDLu52ylVc5a/9447CrGq6uArT1VVc2uUFM2VVA0LiNXWQRfP9k7hpxS++pTbnQo+1wn47F5PMo6wUjCiD11ACXijesdxp1jsSNpWFDD1g1H+JsFmIiAY4MSxx5AXgW7HQbbxMNniRK4fwwT5OgzWnbGuaTUM4bgbYuyGwV0sN7DZcG0UhqqqudvaLJcb9+HT5/jTj5/R2eVlaNtevC9F9Vl5WN47d487l0atSvM93/Z6lO3IVw6T9VWW8L56a3hITyGVpIr7mHMUvHr30xDYqt/ZLzzv1L4oHY5owpd6hHN+hkIPSfyvygqlacXg8z66NtnMExiVBg0v/l5ToeXbyfY1nGym+SdDMW3op65m2uI1qIXU6U3XY5ROGJOhZGXkUOxoKvuHY8J490snyW2gXPgiKoXNxqLudZo6+kKhGlo825FEZ0+ajwN5jFihY1Eas1iJ0jEcyQQUR69H/tklH8WgHExJ4AYUVWZO7pq6yQkZiwSpCE7qXHlLyqOJsTnj/Uf16Jd9mupt8saUDD8mFXiGrfFz6/i+khWJtCKTFmHNvHJiykoZgczDUcWfYhyVdOU4FJ5LYhukqKPBi1iQysjLJGFzKRoktkQBRRNx4OITW2GxOnXXd4qkiIQ8BymyYpCWRWS0DV9rR1b9sKCOlcN8uiY0thul0+nGZDoLD2UjA0VeZL0BytcvFdLSMzG2NzGP2uwvqvQvvRrWGvHoyIoTGGQ9C8Dz3pPzJZwuNnDOc2DHKt2Rfaex7XrtlldaKFUhwV5sG5xLlM9o41l5qrNpA48eHMLxvX2oUKMgBzhdLDffvV/t/fN7j45oNQx/dhDxo+VqvV5idcXJTgpAroL//r/7r8al7HMoqH6W2896spmmtiUh/gkC/YRi/HdDoH/gnf9N7933CODnieAhqM1JNKpBZzBXiQIQRcW2KpsP5innSLuEnc2n3pbJklrmhDqjkBpOZDtusRHQzFRs04KV7lWxqEnntDbS81rXI4pOMZDenrmBFGOYJnAe2bx/LmG2KCqymbGXVihWkVVLEqk+S8QQYRSLUAEgSpBe3p/AaaMJ+cyZX6kYeZjpdaNTxVpK1jUYzG+PxvpWEhVScSKUffLbeGpdSDWXRFnNWXGVlAcrj2LjHdaT6Z7bP7qP+4dH0G2W0Ldt0dm8cWGUvhbGIIhPSiu2RnMZeHJdYpHaW7f5lGyPtIQfKogrhT7AlV5hcYyWrPcmtnRTsvlK+Q/KICGZYIdQ1pnTPI4yZV+dEK/EUfknrhhWSciNUME3HjPXpzjHV8g3r/8cYlH8ozGQUZNBhpFVEGgQxUUbh1uujGQ+anLr3S2HNeqjv+5c8873b4uPKaIeXLmJbuLITapIly3CJjjoooeeIU5BZevR7AuSQRDQ7V+4XTHmhTZ9SBfey9VGrC44mZxNJ7Batnh4uIfvvfUGvP3GPTicz6FpKvFWE9Ep9dzEfhhcP/QoRt5aoedqBCdNJN52NTfHaucbzUskEW0mUAv3Uz+TlCWlN5AEJdLFuUPS97IbaTtYFS7xho4UpsmJcreCTPI5GgRWOpcMOeslWeOw1A1tj33XynPIrcaUVAbrGrhkQWH7SP5/Evxz9sjJj+Ppi7lUgkjk4iOpyTpfM2YZBGq73i+Wa1hu2rhcrmm1aashRs/G5CdnS1quWpL1yKF0MNebHi+WK1isNzEwD0OpGyhkW8RY1RxOyppSlZdbfa0NHbYlgEJjAnb77bnur+42s/ObOpY8JzQcCLuiBvGqQ0PzoU7WaS3UBhpFrlIS7RML/pqj5L/t2YHerWioJxZNXN2V69n2hKHiCR1VIgQ0EN6h91h4UOfaYirQJaIADpr1p4R699Be8A2WNHDiRTvdKrQWdmX69Nftjzt9qROmkNbS3Cp17J1I5W+opg1MYYo9YtWDG+JW4Uk4hRY+OWcIG4OxxVT55SdgIIi9qqU6cyDnrqgzqK2gMLwmobwsciI6m81g1kw0KTGALoURB5YLnxFzgsIH4PTWjor0WnFG1QzZLmZLZ86y9cSnDAmCWtxARORGRadedY4RZHOnTY2keKFfpcJiMscwdDP2A/SRaTQKiW3bXpPmqJDYlDjLOhko82ApwfSNC8tzV0oaZZ216465sFEMAFDeqSDTuHOcbE3EMLWg88T0+G1NNEWNpxgROv/rLUZdK9A5GmIMy03LCrJ+sWpxvRmAqQGnyw100rUMsNl0QiXihL+qfFS7JPJxrMCDVatM4BihmVVwOJvDvcM9eOuNI3jv7QcwmdTw+OkZfPTkBE4uPvEfPDn5xsPjg//6e9/9xnsPH77xh7Ff/++xvfwjd/jWGif3tuhbdrpwXj4MX+KuJnydbF7ZFgD4Jx7iXxPEH7aBfscj/q53/rcQ8QFRPLTUhYuFnSpv8sIj3cYmw38YTivsbBHUaeQ9wk8U7so0+d9qy02ellZ8GbQazdNaTaIITktLaDU2JAoGtefncaUcH/Yf45kVWu1GEnPjUhLGhSZbELlrqMUiBcU46bACxYYQpwKXFbVzZ6gZ4VmykEOlnBSqDI7QS5IpCrHMp5QnJFmYSImPZzqMtihr5IVJv0ULPZjWMlugidJxoeEo1BdLF3FBAas5msB6NX+R6q4WSiP4AKFiNbSD4/t079E72LYbuDj5BMKgukc3x52UhIl0vUO19sCMhKIrK+DYb9T3lhzG3S1PrVppFeIqFWXEXCC2G0vZ6ybJ76Y9x3QtboofqOjnvFy13rp6yb9t+4/WomchJF9bI/v2zbFPmwvgRKTJ9pHl5j9dgW4r+9/6gw6mJHqzBZYTxVYVzZiAF7EMbwdWQqzJKrIxSdJef6C76+Tr2qgQ6brTvFx2btKhssToXqVOqOsB4LJH6KoRukn5cRyPH3f3ectmktl5XygdIw3Gnp6eweOnp3B0sAdcEeYE9J03j+HNB0fQVBU32UIMMQonELDirpjBc9UajjW6GJPPD8hqLfV87lIkaFXVNDCZNFD5WpUPXYJceahrFSHSk6AMF0udH616FN6Trny6cnE8AzluqB9hPv2s1mhfadxK+ZmDM0s2o/CWeg3OyCBocVCIbNfTwPKwnB/E0IRhaDQ5dBbQKU+zPId8bqrqk7qkAr3tQ1wj4DOD477htGcQ+iH4fjP0q00bzxfL5tnJBT05OeuenVxwwHWAjnyMyLxM2nQ976vmYx9Y23aIanLsxdB8jeA6RdgIwmJAh7ruRWTl91boYYCzMdm6dUTdGEph1o8pcqFi2KaQfPdZuDItJLU/g9TnpvcroCpsAESFwYrSeuJqBgAWQaIrFj/X7wLrF7xna9OFlRFSxLoPU772CeqYxlxaTAaooItseeIhJmzJzvXYFQbC4togFONaX/cUiS2MvOOtKIze6Xql70uWFkWymb7fuwBM0BALlKLjyh8Uw3+vVl0Kq1QGTilna+kTsmrooq/igubkfIsTDFpHHsRmIPXtbM1lxASpJAVpsiPcPTuz9Gxl/8gAwlFHU0pNRBNvHEPmNgoM90jnIlFthdIR22IA3JqwsfCsHYukqVCVJd/RmEdyDdH0VlmtbSwImJKrdfusfRq53lATL962Apv+hz0vWlwVSKyIifXia8mJYh/CCAPGomgm1X7DJqWEtYiRYlKwZjO9Im/22UUQ7OijzrWpexu1NYMp8pLvHe3HMcnjSgY+Jq76QrSxlDjZYHBguYkCDBdO6dDD6cU6fPTsjMV+3OW6x/WmB6YKMFqjYvy1CanxP1+5OJnULZ9tx57AIVZmma6UVdObOJjP8NHDI/jW22/Ae4/egEcP78HD40MpxJ4w8mexhsvVkukhfgjhndlP6wNP/htvHe/t7zfuBIF+pFn/jXW0r8T2dbJ57YaXA4U/6UL8aFbVP22w+vEwhN8CB78CiA+TvoJyOCkot1DQHKLZiWIFIvVBrjUn/7de/AvAVGVx7CISwVKKewj7ymGWAiVTEGrp/I/Ik2BxEC9snLxy5bixGZifTm9dx1bBmCLY03DBr6gImwORKEd4w/FW47RNWqlNKZ9NksU6xM+uCR3RhIA4wOkLw+2NpVJ8zpwAL3SCo7kd6xQT99TWUVLBiF4TKWqM9EDGT+Wz44R6KursABcWLR8YRLdSeC4Jf34638Oj+w9xvVi4drWE9eWiSMGuhBcJpOHJNUguCM/B+RBRosXoMMMlE88izatChDAw4w2R6ZXftSxIcukHQNxuK42dmR2VQN0mZp9zW4yENx7MDZtA64Yo8NHtua4080brVd5l92TZEgcQg0CYUqkhQ6U+HQD1xmI977Zyqt6aJThsaZVMjoMwrMTX64rqjP3KthAMNay9Swzjz2sLxp12BtN/adGknADygy/q9EFcLCZVhEtWre09dMxpHZTHBi95F1zxHIDVqL3XkptWhHudGCsHT56dSQDHnCeG1Pq9acazGsQLMN8ZjRusc2McG+GnUewDJkg+Q9vapbNuwiigwRVxgblVCnMja/8IzM05g5ZV0VeekyP+ZKXQMQu4wMQ5jCmQYlqwoHOsLpjXMaqVUoyJC6ZJoUr7j7wlPrvBBHzCUIiOWeIpQY2QLV2v9qbU2OsC0eJsm0YERA4UFSbntKOh99x13VCtNm3ohvBJF+RgRPa6G4b1etNNF8u1v1hu3HLTDpfLZXh6uticXiybrh+mXlDjpmYsjdWQov3IUDznUkwtXMtzRGSlywND6ngbcqcA+CHqPPVzALB/zfDcHXI3DT/aEUMth/atQ7bs0iWopndRbE8ShP86heY7beMjk0qEc6aUkDbXI+bh/Jo3KQbGIQThd9SQ4JB2PmOBkMXRvHQ1QyJs714f8/GNxtlMCYAiUYL820ICyRCPTVoPUucT7nANU6MwDABtL8JziaJhN3lsSnkz1Sr36zAVAVGh31poQVfVRHEwpVVxT5S/b8SeYhWPNvfayf6MS/GTynVOrNFDJ8ilXEWisTgkx5qyaCP7p+KUEHiMV8kK9xRH5ox6Q+q14qJS6pQ23N1kBAYo9BWKDH0L7WCnH8uiNF9t40Ra4KXNPRMfSuq4lH0orajGhYbQG+qB+xQ0iTHyfOIgGbjbFJISzVxglWSzL44JFf6LOo+mOSiJDOn1ghwbyvgx/mj2tLTFMwhtaBwL6fzzftIqQOohq+MmXaMs0mmxF2bQ2W53FPN/lQjNaJu1dWal298HWCw38PGzc3h6vpRd1F4pHvosEISug4QFEI0BkGpFZ19Uj0xbiVlbjzDsTZvqG4+O61/++Xf8z733lnv44Ahm0ylMmkr8qb3Xbm3bRWhqpPPFEv/qJ/HIk//l/dk3FvP9478GP3sOED/5DGaOL9T2dbJ588aP5yeV9//GUfyjzXr9682k+c/dpP4+xfgdAHxDLU1wYRVde3al4jFHhY+CJoy0DzpwHYwqKia5mDM6hFyjgpTAefUdG9MA0uJUBbrgoym7MvmqSXQIB8RYDhXbIeLupggViZhQbvMRcytr/R5JiqdCd5PuqfAjE7+UzK1pyH0iouRhEHTa4X3xeWKiHKIliMlCxRPiPirXsxJjNV4wE1HEvDnRzteo8Z1W8QX6UZtae5qmewTcCBxJOKjS5XREYeAAc+/gHtx/9Hbs2pVj6Ee/XoJNuds3W5NbTbRNOzWytkkcItcFhJJaeE9l+W3r9mln82YLj6Jeud353K7Xj3NvWpNG3+w00co9c8X7r9nwRYv/dcc3RBJxhT4UKUTy1Cr7Btlri27NdyUplaeAS+GUG5Hj/m5TBv6UGyovU0xZHWVVP7CYSbqbVEFDPTCL0SW9qqLkKjoPA1GsyHv/mnGYt216mZoXFBPusuVmOT9Qe3WAuef/OuiaCKvBw+nGwdBp8SB1nrPXQDEGdzetYFuAQ2biIz5qkE24eX/9MMD5ciU8l4YtBSDCw/tHeLg3c5OmihaseROvUZiVtgqFsM0EG1dXHGGidAvQvB+tei9BgaqpjmO0u5rDqCiRs2cbond+VQnxCufCIzZukiQl3ClFbw2BYIV1/bwk2SKSZWPJxDBS8SkKLjYiN20ld8RUWQf15NMOgdGYEFUkiJuP0qER8cHkeMUo4hRvid2M8vHkwUPFc2GaByJpR2K57uDk/BJOzhfT1aZ9Z9P1Q7vpq9Wme3a52lxu2vZw3XUP1m0/6fuhCiG6fggMKWOo3WaIInDjUDnylHJajnPHVhbDyGhuqrFyDc1iPTdeFYUjr1xnUB4x5TVXZ8yrdbmd5nvK8G4b/7Tzc/qdE03mNL9QmPxFG6Xjx30LkSvTtIy2nqMu0K8P9GDji5EnkyFQY02b9FeDVhsXEkX+VzibcCPOO2XE24fI82bjgyJSIMvpZ3hk5hhbUnHLsmdHZokmt+07DraHYo2xCpMJt6SgwSXcT5qLxJY6qLBQCKJqXTdTbolRt166KCAJW1hkXtjQerUKi24YJrRPA0z8BNa1c6vITtgu9C7zhKPqC6piM0DpjUYmAETekr8QtNB6UwAAIABJREFUt88sUa2t8ZiKkuv1RlAMIijU1JJoGOdyTKTToh5pLNthUhrVebUftGClFiRJmEi1LKLBSnf9HBNvV603xHPO63vNs7K481Rk+dsxhUFznXZCrm20FY3ZDInNfFFRGkl+A2BTei785C5mSjRx7ESmvY4qtYmGUApnGKLD4/gc23jhWzSYKm43BDi5XMPJ6RrO1xu5te0QYL3ZwGLV1nx9Gf3Pn5s0FTUTj5fLFpbrloASvFrAeVLEM3l0r37RZD5SxFSP7p03769/+VtvTr/18Lg5mk8mDtGxPRjjHPgbDg8YWjuHxeWa4bnEfztbrHAyqfw333vj248ePfhnl+3iAx8n//bBzLfTZnOjgvyXffs62bx9I4W24ceB4kmH9GdNdH/oAv0TcMTQ2p8XsQObi+352mgXU0KEqT0tU/0Wwa9uiUMi4JzUuzPoP5FwBJsCrNnHiaooyAp7TuE6yMp/LXKiq++vUHkPPDvV5tQUbG5B6Xpq0olq3cz7HGkSphEbbf5P/p98AL0u8lIVNKUK7pait9WmsvkumrVLZb6iXi1LYE/UdSmBRKXnhEbeS82SGpVjyke2JVZP0hmm2uZh7vhMpRusAZrBkUR5RKwyebZkK5SqcsS4vb7d4MmTjUzeSWktb1sTNdgE59lL0jlGiMnp2PqSK6J237SHXUyE129bDAXhlZHATCWLL/Z1l4X7dedoJPLkTJIfhX+K70iu3i7xLuDunT7DVI/Q2fFMCvjLHfZT/HznD3Eno3YMV/QjN9GgyWx23lMNU6ukb113hdE6Jj4PNIodfMot0SvhVqsU2vIZfJXVhgr0QlbrFGMAx+pkEWY+SpeTREuMOZ2Yu8y5ZXTLONMaUtI/1HEds1ANZMWPDVeTY4RuGGDNfmHrNaw2Hfzce2+6pt6vmOSSa8RWqZZ9ei/KN1yn8ILSHzWwuYiUuodg3T3xe4MsvsGQVA0gMXnWkSq2CoQ9SqPV+wqT4qQY8lkUpD57lX4HC2UlKFZqvApUWIPGaN6VzqC6USdUijFw1IjsR0eVNzEJJbmiiMFF81ZOU5yoVgpNgJvDMQg+bYoJf2bXWjqgdossEBIFyPPFCp6eLuCTk/N4crYIi9Wm2vTdo/Wmd5erzXK5XK/bduA7fgQO7kXuwkUuPgqni4+fk82ecEvBp5whfaRY8gpn5sOch1axHQPCd22g7HY1Lf7N4esV7t81W/n3W2bYW3ZgqteNU86mQ1ukP8UzbUF7k34mpYMHQQcRuVd6cm/6rtRsE/EbSfa3eI0x8fT0mKSj2Rme5CbsiGD/aHsG1gVY/5XEW83wy/WybO66Wy8jD/uBod292kmIjMR48JjWFMvQAYvJL/c37RnIOvqleF75ZUmpOQoWIvSBcBk8Rj8LHetdu4omhKF2m7riq2MQiqjGpTom3LiUJ3VYsoBEhy3lWY92KhbpR35ON60WMWmjCtyjh2U61FFlNlqCyVDh9Hu6M6mzyXYjXriMyuNOHcTyCqh4VBg5kbRzYATFlENbf6OccI9KqMnXE8rCRKp+p9dp9PschZuK/RpEhRPvRCHYumUwJqRjTJVrS/Y7jeMgDYmgWoVJXIrV87lrudr0IvTD3pSLVQvPLi7hYtWrkNGga39dV+i8E2xZtxlo6APRfEK+8i5qX4GpACJGCUIfI1baZsg6N2A2GmcKIGAmTSTA6mA27e/d249cwly3GymKzpxOCCws9fabx/SL334bN+sWPnxyisMg9oNwuljCJ8/P73/r3Qf/yWQ2/wtE/xdI9P7tT9WXe/s62bz7xpDQD4HohPr2L/qq+sNpXf/jSPH7QPgNBJrzALXo6UIXITxGXaAtuOIoi5xlUTGVakzVTo2shapHewKR1dIOBwf8AHS8bpJ8h/mVIRlgUBnVXPsmbeUwjoJnrp5SUVihr8G4pV66scwVQqzUGgWcta3qlEuBYoOlEkhJ4EgQeBL5eKtWJ3sLdvvmhLi2ClWwCWcfEVcClUWxQ3GqsSHWJR6MwGiTJ402W5iq4LU6von4EWgSzKq50KGDREZbmi3NAXuzcUtnOj+g+2++A8uLU1henMHqYgHXrcC5MgpkeBaO/GoMnjubvDAxwjcRN6zgilE4jBKwiQqbAWFubIQlyW4oFlEoKptlNQ+uKfy/3g0t6OBAwILhEueSDrKPJNDmKaJ0g3IVEnP6NtbOMfFMCgwWFbvVto5xp+5+encP3aza7AwmxkXIYdcqNJnjkSrW+pxPb98qDupY8U8Aa3dztHnRZpBx5rfdWrZ86VA13TGnMWTQcIDy1+Snyu7F1Ed4MOul+3u68bDsvRyctyqVVqduP4w0RncvS7rzEnR1aubNY4y3e/tzhrlBONhztUvKfjEPElEZlKaaF4awBTNSbUtwbhChoConiKLOyk9WEFc6FhbyyZNS4bPJIkVk6jlxmlmyesXfVl/XZyFmjgMKP0jkH1lgKXqVg0xqllayVzsF1IJclvZP8ZmOMM8ZNoM54qj6KX6u6NFXrgaR1o+ejcIZMajNXC8KtZt1iwGiBU0q9LRatyI88f7j53RysYhcNW977lYOvu3Yc3CoQwj3Ne7DmVOkjU/znUPcM5Pz2khZGz1lrIGyuE0xiEafrhtGRWOd+fRJyuuezg+5C3qnga2JjhRALRm+zsz4xn2la+8rEZ9RbbJPN61em78Z/HmXGvlatjTv9EHEdVKjB1OQn5Iu7U7xwz9CaMfbcP2JeBNESHZGV9j0OP4nJwq70r1YTvQ6b3CxhguYYqHFhcwA2egfi2oYZpx0ankl5kweMcU5WHIWB9hsQpKcwZycpIlH4eyOhsAek7FnJyCcd4MXrUQOIiK71KnT0BC7jmi9arkDKJFEUhSdT2uYMiQ/A7hU405svzjh23VqpeI403UgVWzGhANNFy57SabPRZl1OIlM1iuJexjNi4sqt/XguKTMmpK9Qngn52bSXnD5eia9Q8yHo463ImRkheYMEDXrkbxOY6GUvzVAysTbpIfM2ila9uZL6+DyJIqnKSWmufho+3KpCYC6TvA03DPfvVeY7KrVBJORHdzN5DXn7HIFn5wspLgpfN/Ky3uJ1NKnik4KoYPI/0JYrlu5Jfx8oUNRJ4oUNeeLkWPYC0L3ifNu5dSA5AAIjwjicR9Dc7FikbVusj+va+rB1Z2H0KhdTlV5Otqfx8P9ObnK+WCgET5XVvn+4MnJ9Nsnj9795tuz7wZyP9eF8HjOOidf0Xzz62Tz5bcVUfxRJPqgHbo/Ojo8+uFyufhPQ4y/UVH9TV5wo3Y71wDCxTTUCUcabC9im5TfDXgD2WDKW1mep5MmkyNE5IeY7F2BZZOjdplgJNZGO5obBH1ltSFv6EuvFRtJgAfLEhyhQXs1XdSmhc7gg1VxHCWakuJGa+VJotlwiYhQ0CgKTLDHLElYq0ATMC8eoQjMMWWsVpV9Cg2owSJGauPFlWFKbCz+eZL81iwnXRt8tjYSoScVMeJVprNuHMok7RxO9vbg3huPYHl+yl1O5nwh+4/qxOrSfcgDICeLjkE37GIYga+Wo17kfVHgunY3cRQHui2GKRynFCIkQXVafEsY57j2xoKwVM7NN37FSyYp0l4OpDYIUK6FY2ypgT7DlQUyV0FajXG7qpo2l3qI4glWiCrtrMm5+vlZ5NMIObBMmReUF0fSAcrJZrbsSpwU46Fox1ftQfzdmJO79yCvy7a+Vqm88OIdXQc4uvWUU7znIMeeVz9WBgtzT+Cm2v9guPHl4KEPmM0P7nrCNx6T4vAkOGAZea7iPn52Bg+ODuHe0T5Mm0Yq9hyJjnBUue4yJyrnSLzkUKGjHEOK5D0yClasN0yl1So1UZUQFbed+EylWpYo2GLlk+2HWBeYUX6q3kcV52ElFHnISf2HAkkQEmXaQkKujVcimZa6uty25I6miuwLzAsLKXzmdzK8ynPm40UQUWCp3NR06pLedH2sL9cbXC7Xxm3TyWy1aWG5amHTD5JgrtsehjjI6yfnl3R6vmK+lVesLQsPUxuDzNMM+5pzQY4FQnALfin3j22uDi2RZ7XvhVhiERxcVUnFVHjkXHdwSp3wO9JpVrGTNYE0aYVgjK+arhHOKZpy+WGxZhrP5WuliYjFR6X9jK2+K97F79SJIX1KPj9VPnj9h1+BW32nL7NvY+5cGAKGwo4qIXFKZU/jukAsxvxNZ5sEkyrR84viR1nanuQuXoGRzsflMCciCZYcyfiLUZEyfa/ehVQom6cDSkXX0ewExkTjBbOPwUStnFkO5qy25ymEwDzjqqq5vsNmJpsh1tS7aRNhECllD8ERelq1LT09W+JqtXG8wjMSg4/j3v4UHhzMYW8+EXsRpgKwEnYSHPNJaTbPMeVcg2N8UXQUt26sHbrPHU29FjoXjAmcMxF3oSeIB2ZSs7a6D+3AaJPAH+okPCpq7eBhMxey3I8dlh1vpGQxJeXZkUKUxHzSaMxF5lRE3hYZzMXs8hKUBwIj2gxh5M5G9ZSVriXTMoYBRLSobQdYth2cL9dwslhLF7PvBzjYn8E7D49FQG6x5OSToKlVMdjZYxplf70+IygTMrIwIs+nKqYheiGcbF5SpCdA9BMA+gsC+CmA30df/8cA+D2bDi9DJHdxueZ/8fhgJmWetuVeeieUjGpaw6SuIiedQyDP65UcD4rgEH709AQ++OhZff9w/729g/rXsR4eB9f/ZAjC4X+lLQUi0ybB0W2OsLXpdVTOX3X7Otl89W01xOGvqsY/ntPRD7pN+9sU+38YKf4aAL2D4O6pz5jARlNHERXuqrAvY0xx/R1NNp0JP5yUib8mW4pkgCeN0hSGrJGilJX3gnQxSZIzHmAr6/rtqWiRJISg/l+C3YimAtsIZIBbdcjlHLRam0xDgy3RKbkEzII8SRpMBI8SLNZ8MBnowNptTHqUNy4TLRGtswlotWALSx2/36UZkrJwoKyEIBxSNh3lEI7rpK1UlxDm0WqOADiz+TOKuBKHdKwm5jwd3X8EQ9fJ8nDy9DHE9TBmOuhGzkCR8UnIS418RnzN4qBsVEiiDGoDwv8XOAZCV/R7xm18rA2nph6bObragsMUSatAncJLCePf+Y2ydFj1OSajy1JZExMNglu3Yr/jdyvN1wouWlGS67M1xuAwMifYxcTpAD2xaP44r4A3K1PXGwNXTp4arw75ycGkSPfF/oRLh10cBGpblXfJurcSNEWVg/fuTrHk7smQPfMJNnubbefurkjDp5cOYtP35K8ytTwor1dKOieO4HgaREzJbRDOWTwpNRpfunyxvSUPNwlEQ4SLxQo+cCdwcLAHD44PoGFV2d2qt7XgTEk/B81mCq79LeMvCb49ECMdvJjbS2TnwezUVBxFgrISol7AungUJjjuWOrPl0cwI0kunH3w0PFUM0RR6I5TrkZ5kfiPWdhWjNctWKIEfQ3mtRcHQT/wqbT9MCzX69j1QyO4XRGvEFVEXG9aaDeSUCInk2wu3rWdii+1HSyWLay7VoLiEAIJ7TIorUIt5ORkohVNkp0Vl+/MEA6S52VGrthYG1DF6yC1xNPl54KTJJAZRS+0CoaQ7dk8NljxsNbvY2V2uQaVqpPL30tNThgRoFxhlFZK1PhcscYxxLNh6J9gRO+8O/KVPzThJynGCvj7BcKN6bY6HJOafIe/DJutjsLXC2LHJKfk8jOcU8//n703bbbsSq7DMvdwhju8+6YagAIabEoiKYuWOJlBKSRRVDjCX/xXHeEvDvubbIlBW7JJNge3xVGNRjdQqOENdzzDHhyZufc55756BVQB1d2AAjuiUIX37nCmvXeuzJVr8YbXs62TaH7qIZv7GnycVP4gydWrRKHVkBeNFGXE1KE6tf9SPA153tAUkuqlrJWeBWxEByDE4yVkrFcOd0iIVMM8P6Y3YwoCjryQ7wDfo3PikyFRLVKZVVFsSgijxwPN4g4rs1fgZzFEJZ1K7P3cdp16cbuD7aFjQRnaG2dlAatFBacLsjIxUJcFLOYV0TCh1BrKQgAoTnsxMxi+8/xNGbfDBJj8IIufZa9MSNVhEQ9MBTayEcliZzH1RCaKLjM5QCIhAafT4zkuqg4xxvSX+V7kaqISkOpTa4NKK0TuzYTkK5orocd6FnjU5gAJHGMWSsLj12Z68jStSq8nULnbdyzos97Tn46fM3q+us5RbyX/fNs4fvY4eWKkmnh+OucK5+cvb2FLPbRuTC5CzIyUIN6pJDdL23RAKbhopa2xjbHlfza2+KOynv+ZKcofHPbrp7cvn/1T1zW/pk15BqQ/QqKbIVKbCG62h9B1zhVKYdt0mhwLSbCuKCxaqzU/S8s5WHsVvZfyC6l839xu4cdPn7cfffDgYrWa/YaK9kce7af2wvUsO/oVB53vH39seH52pLJLlR7ya3b9ccbx5zy+A5tfY/BkD2FT2sUPwamPm7b7Y6XNb6DSf+B8988R4GGaqtJ2Adime23geC3Wk/WYhHFa4ZUIQI2ifm/kZ/GQFiw9CghBiTBs+kq8NwdCgk++mZACAhYyJHcKoc5GevzzUp+PQQ2eYVGsXKTaiSHVgZLPJr/f5nQecdzTMeSyHVF817IKxRPWGwfhpjDDTQ7KhzG4NtwnBAPtF5ISdic9MZwhp0+rE3h3UggM6ZpywNSJamvgXsN6sYCL9z5gRciuaeOBHI59zzLYCMdZuOng9UeVxLsA7HutuNAr94wSkrxgqNQbBseZ4THf/OrEHjenkUqb+yzwCHCmY1MZDL+TB5a/p3dhWITVRM0iZzfZzE5JYkEssXWyyrwf/U4UStnuBiE6jVJsevW1X22xS2FJ7md+LRCjh5+AVGkiHPyoaCcRtOQwWjBgooUietDoJubiY8DAgFxUab8a8EqY47UX7fVDfY27ffdIc7ORuvtDxea0EU5Q8jP0BN90GpoEOdTXTILmSkVIoj7kZfbjp89hXhV8XR9enMBqsWA1WzLXnvYyxUnAkqoHmOlZKVPOPseifIpa02ScKD5m3SGhTmqxEWHhnpRZJtP7KCeqkwdbgJzdFxwmc5E7qw1j2CQURACXMu1k6yLVVimJk/k7vToF3bFre9ztD9yzSsG3Vkje6FSN1C9vN6rtHC+6VLnqHBmm+3QDkW1j1vsdg8yqsHwNCGy2rYutd5j636g0GpViVwed1MqqtG7rwaVmFJ1sxeMUShJzG26UzPkiKSAXd56VmHSdd6mekdW/zaQYOV2eMPf6T9rwTP6klHHkBGVg+w6C25b2xNb3ru/7vgsx3mqtPymr2WdGqdoa832l9Ue9c9r1fdazkl7jL3k+82RQw8X49gw+xyC9j1kdeahgDjhBknY9KCAJnC4lPAwGtj75sunroigal+ihUPQn9TKGsdI0poTHa8kZDR+h7UVlMybKpJ8AzHsv99FGNn6Hnyrjpmq0SXupSyroX3T72KzfmlgUlTemIJaBpYxF6gXny+XAxAZnoDWzE5TBRte1h/NVi12ae7TmE4AhY//toYWXN3uoCgPGav6bgOestDCrS5hVhudmSb+zBRSWaJPqqLKcEwJZpCPbqQ3rXOqDTOzVpJA+rEBDZSqDNhiXuATiJth1erNTH6xOE5GPSeVSQhzJzHeKycNGMShYHus0xHj/fc3vyfci9YOnZE+m/Caf1dQ9JnGI42ueIwi6D2RDQtXK7aGHpuuYJnuza4YsaJ+SGywG5BNtUJE4Uws//uwlrPetAFCFwhBJ6r2Y6M1DFZ0YA0S8oGC5rGJZlrGczXE2XzbL1fnNfHXazU/OH5X1/B+vr5+bH/1/P5g/++S/rEKMF9z7kMKzfdPHz19u8PHFCc4qSzK+cDgcSDwIbGGhKq16cLaCX/7wYXz64ho+e3YjvslMikNH3tNd358FH76/O8CHUan5o4/Kjeu+4IG/bw6kRLskIBD+9k9LIFvUvfdQgILtxjO1+OepeXh3fAc238FItK1dDOEvTFH9tdH6/4V2/xeg1G87bb6PMTyBCCepquhj8iAL3LcomodpAaCCTDWoteV1ktVpI3vkR8TBuiQtAjoJMrgk9kDzYJYU/GmaJRt2+chERxK6E/XkSf9kEjQX+KckiVqKvyZ7qvGqR7CL1Wuz6mAysh4Up/PxyNQm6lY/6eLTICq2EXPiNS0gaW1UMDZsigc/i1uiITExxNARUw1AzcUZhQMglE7LuCd6m/SEilpYUpukfigoZwvu3ySRoNvyKaxvr8Ctb4eg9nWDAJbXBWhfUHVTyo1akcdKUEZhRynUSe9lHq+jI7LiXgy8qRxXUyGVHXCygUig/i4VJ+iidNRPw1nCOGSnp9nPOGX6UJkB5c9Abh76NY/BtLCa+ZqTCyK3Z+pMm8r6Q1qECNRXo7Pdj96nI3H1SARnZgNsegXOozQGC8OaEwOePTctNFTd1IGDMwb8YbyXgStOAYwOYM1bM+W+cmyLR3997fFa4BpSBZMqGqvSsQolqAJuDtT3hUyFFpXZtz8UmVajKARRwCi5QR6cJMpDz3VdWlgtF0PVW+IhTLERVzgVG4RzNVzotFH4tPQsUchopRVTwGJuMBqCOa2GLO7osZeLCWygIOs2JkXmjrXPkKoXWeVW9EioikbtoJT31kQMVSRC8ennL+Jm3/oUFerFvOb3ESjs2QamDbfbHVVL8nSPu6bDq5uN3ewOciz03eRvF0YaXWGlJ5UAJ+U5CmNI9kT1LgQieumgqJUsid2iHiNxHPyCp5yIlNqijgA/qXQPj1riWNhU9TRHv5R1mZJ4ZWqtIFZLnQTgIF3w/J4sbGnT6uBTbMsJTrKYSz33rE9ASp2z2dydXpzf1mX5sjkcbrfb3WeA8LcPHjy4ffLhh6Sk/uDm+vryxcuXeP3yufSSxoh0zd4kE5Kre1z3VqNi3jd+pJWOKX5u9KacupmogRJHdEPDHsIdi+O+Xi18mGcpEeNTywD5IZOskGFx+cG9evDUhRGecMWrdyRG1DPYdD6J/AzCMWPZfNxL8JVliKdE+lBKdfVRMfiNR3vKl9P65fhJZMd6rRQZw6L3TomkFlQ0d1l8AdB4UKYzM6SpjLHEwlbxYVHF1ckC1ustPn+5gc+u1nBNyqFtDy2Sgm6AdsMkLbazILsMYrsQyKwrA4u6hNP5jCuhZ6saamuTp6MAHmPEJxTTHgnJ05Or1pm+mlVcBxV4lWyIpIKpUq9H8EmxNoPVPHvDGCUMa24CfDTnXLoRGQAPgEsljpx493D2jQTBaIfWijU3kFkTKklLpu8UNW15+JTCwVmB1YRZgZcNTDEEiWGolWKz77h6TCAxR7d+8CaO/DsClvtDx6CITrUsNK+PYqkl50BAkg7dqOxvDimZGOHFzQae32yFNs/sLTfMk5BiTap8CycNkajR9XLlFqtTmC9OYjWb99V80cxPVg/r+fL3TVFdGG329Wz+J8G5dXDx8fXzn1rne6JqU+BJVlP++c22uL7d64vVTFnqEe0cHPYHKKsSCmthMZ/Bh48v8PH5Cl683EDbk2aC9taaXilV7PZduW/675XK//L2oM7c0/nT4L58feMWgTSJXFBw29Dy7PgaG7TUDcaaA5RB9Fbm2y8y6fYd2HzHg/x30Ps/KZz722D0fwhK/3c+hH8BAf9bpeCj5HtJUZUX0IY4elTynLXSIzdE9X16PrTQYJNlSKZAiT9H5tOmP5iVAj2KAq7K1gIUMEg1FDvJVrMgRGoxQpUlCGTVZ4IY/W/P2iHit+5T255mHnDqIBUtB65+OpDWOVrRSoS4IjGfKOJD7bCiinofVytTBSwmAaOWhCXl8LFKK7QXcwMG2iYyVSvuAWJFokkEgIPI5ZbpbmYKo6JKCF2a+eqU+raiLYvofIeH7S3IhH4dZzDl+dBA0BYwaEDx1uvlnqjscXrE/xn7JDJ9CyefFkYzwdHFdFSqG48m9Z6KNJHCu1v12w/OJHrqWw0wtLrdOe8xSJgGB6zCGUM2qBhW+PG6HZ8jVU6GzMFRgDSqhcDxJXsDcJU6Rr68Sojis1kSSFRCTypQlP1czKTZyIFNFyw4otJms+hJsESDsqgEODVTx968nzF9xc+kl+ttxpdRcfOFpzTt3DK8BosG1p2BzuOxVelbjTg4CeTeLspqk6DD1WYH1+td2O6bsNsfaHMmFEeN18nVjhJLipQAy4ESFoWOqvg+cFKjhBBKpsopESChmpjP5t5Mx000PzHCYxBntIlk+eHZjy5ySZDmxH7fwnq356BwSX6glOlyDsrCwsl8Rn038vrg2X6XwOLffPxZ+PjZVUfBrNFazaqCV3UKjEhxkHqDDl0vxsuKfeuw7R12nRiIE9iioI2Oj89aUo5BKx8TDZkaOqNQ3l30XKgJMSk75pB+KppDfT7UgmEJy0+nWMLaJRwnBcfHQERuyntMgzH5ZhZR1mAQFXFksbkYuZ9S3/eMpCJIyEJK1tre0vk4p21hq8X8BB8+erT78Jd+6ebB5cVNDH5zOByurbW37z95Ur3/5IN/cnN7++jPf/CDh9fXNyfOO8tJBQYkbiqMef+8jK/9n68yjstxP+ORv0SAnU+ehyPtJS+hgSn/CpogFNoRrI1Vw6GQkUVgMniMI01SPEh9VjFM+00cspDT3mc6pqZzkgTKSZxJhmyam3plh7lTCx+eT65sTo4/SfwevQ3uftB4T1mJ1vWKCuM+OE9910pZ9jtnL3DpHWYVsY5z4bVCSiLrGup6FlerBTw43+HFag2zeUWiW/DieiO2I1F8iWkQ0CHBsz5V1eh5rErDbI1FXcCDsyU8WM25ckXXiNaNurJQl4aTa0artJNFmfNBejEteQRbxZ8JKVnGa2a2OYEM5tUEbMZBawAnszZLCYllUfr3QHBJ9jLT6qpK/kXIZW2WM0BQB6KJkvo/iYulVgGecjF5vDoXCWdRybHyIRasYkHrs1bE+I7kMdl2PdJ12LI6LK02EBnRAAAgAElEQVT7jXhPKmDvZQLvnH7GAJ9fbeH5es+AGjPjCksRUeP1cxSZysefYyijkEEdrf673Z4Th8xgoc3BFlDPa/JXJllHpbSN1paxqGpcnJzF1eWDsDw55+omKjxorXvU+kEIcdEeditaUJUxD1cXD68u3/vgdLe59m5zy01dXCKXhRCp354ovoUxQsXrOugJNM8CJxzofCmpsVrOYMd2LAF77/Szq1vz8Wcv9OX58sn3Pzj/5W0Xzv72rzZibPyaocWpHXa+gEOsQGOAfafhs3VJvm3Q9D08vKx+oVXM+8Z3YPNnNALiGiP8P6Vrf3RQ6i8C4r+KoP8HBfhPQowrEIsQAnM+2atoaXQfSDLSQoGxxshSzFkBMfdl0XwkHsAuAa8SQJUJ+jmhq2YSP3XV8xTNmnYaUpEJxauTNHaMcM4wwKiOm5zOuUzhMErFMfnmFphpuCzuk+i6BCyBev04w03AsB4tX+g4+QjLwVqOhTiGxJ8XVVumBJeDnR8FUAglIivotik/l5uPCpAFseeKpsjPBynXZu8pkRCfrU7JniDsbq9wt77Vh+3mOJyebswTIOUYbBpQvs88y8EX/L7pjBNheD+0nyY+dPTHTf2TI8CsNiCq8RTgmhDZ/+5rP6S8UVLgErO16USANk6ChDhS1KQjFlnHL6YdbQwm7j+m7HOZzfGPItesADop30bxcX0nq+KIZSNXK0vlU7U4+0OO1WSffDf9RCU4Tj4or/V0zQzV03lj/Nph610OWZDe7aEd6+c+stYDTZplEaDQPZQK4KrRHACOFLCvOHIQlBQODRtph3Cz3pJWB54sZniynDMljZJJPjgChAz0+ZuD2HxEFj0wTE2SWygV585H2O4a2GyaeGh777zXlmQDjeGAj6haJBqxmFUcCDjvkOhZ3GOpEDb7PTx7uWYRo6IwsJpVXJncHQ5wdrKAX/mlD0h4IlBl1fuAZK9CVK/nt1v106cvaX4i9XhRcEPG6C75zAVPOeXIAkA6TbaYmHBaq4GqnYMt1DrWZdmW1jgXfNE7r6k6Qtl+EhjyXKFJacX7O9c02UQJG+X4KU1PuL37BtEG4p54zfoC00zL0QPLmNnIe7j/v6VeVrp/pP4J4+yarG1KLEVRbGOqeqYX8zldELtcztV7773fv//+k/X7Tz7YX1yeL8qyvIgxPtZK/XpZVUvU6sl+u1lE77BtDrprpUqRlTh5nUnCP/411XdLlj8mQIHyLH2FqmbiNY5F8+MLmNHu3eudQdpXXy2iT+JAPg4E79Q+KWsoIyjN1cwuWvDJpidM9FzVkPUV8JYTbZmtoFHWSc0CMPFYYzXTdSenkJ+6dEyJPjkmGuEIBg7KiDCeAY6AdFhX4pGJyhddsdHPUcXgPeY+Qvq7d52yoTJFUcWqnlHlnFg2KmlW5MU7UmGtZaMaI71+VoOyBHxm8HA2g3oxY2/EH3/2Akh8iz57PiugbRxXpPrgUxZFEpkk2EXrzMs12RDtucLJyUwXmFZLla5FVbDXIlFwQ1KvzoFNVVk4od7QWcmgTyW12fFqjLsaE6lizOmhmEUfcNy/ciH/CI0aI5MmsOAf2SyhWA/JxZHyAqW2I1LvNiXyn3vnm965y44YFQhdUeidUsp4H0+azlW7Q9ftm77p+t50LhRaqCiQEni0RmLTdNSjzuJmu6bj68SWUZxkC1wVDimQpeqnn7T2cBAUQhL2kSSHSr3CeR+BlPwg2xdK7rGNDAHJcsbZRtf3WC8W8eLRk2CsJVscY8uSmG6e7PHq5QmUdR2MMqLH5nkrLiAGLay3uJHe3rDQpZlXi7mxZYWwuSXGSykOUoY1RSgIpsQlrzLUTuGkv5TOiQA47UfzWYGzWQHktelc0JRQ3Gwb+PTzl/DR+xfFe48uVyb62Zm7YkG7V57/5Hm78bIG7rozeA4zZia4PsKyCkydzZX0b9r4Dmz+DEcyRH6xWMz/sOnd37RN91cA+K8B1G8D4K/ECBdprW5SX2T2MEsmHJgUaqVHkvw2Exgk8KcipMZmAXrUu0jrTJ+UZI3sNWjGPSBSf0wH7F3JaxvHPIgs7iMKHYAZ87oE6qgCSfs6SbWxY0aqaibcgH1O1qXzIDqvmIQDLV4EAnn1LDhjJxtxkahbwHV/Abd5ydQMgYXv0cuySsqHFEhF+nwST6qJlpwEmGwkTsy4v8IYFEy0YEXNEmaLk/jwyffJghOff/qJOux3kb0GZFNK1Ls4wZTIlyvoGpQOoEJTRAhKtlvMgqbDyJqe4W5mNvVnxAnSGMrT8QiAgWQFMKiY95avFuzn9/TcFxaY/oR3ZSnVZE/LF024LYNL2PGl/eLve+U4J9ED86fZD1mx0sckfnlnIwcwFnuYUe+SspwxF7+0ZNadjtIRkEENDvujkilvaoOEe+S+JLr3RZKg/xox5N1IPojoVTQ/p6LJ3THEiPkZK6mPs+ylIbuPsO8UtF4NNDqc/P1Gl2FIMIgYF9Gi1pu9+vHTF2iNVauTGk/ms7ia1TCfV7rvXfns6lZTzxRNfcpok6BUXRRcQWDkFkNkOpvWSHS3m9ttXO8Okd5DAWFBVUOtWUiHqLjzquIqJQUH9HvKrtPPSeRjvTvAZ8+vOWi0hYZlVfJ3rg8NzMoS9q0LIcTm0cUpxSPl7XZnn13fwu12R8Cz4L7S4EmdjYOLMGRP0tpD1SnMgffEb+VIBZuvDU92kuTveo+OO9kj+hAG1dVZXUNVlvw91BeUxThSskb8iKcXfnymptHH9J5z5B5C5Lo9KiVE8uMbyxNBcaBLB0m2ANGyaq8xhgFn7sMKWfBDgVaGBVXq2QzmJys4Pz8rLi8fwMlqBSfLJZyfn8L5xZmdzeZnxtiFUnpJcXgIvmibloA6tG2bKtp8ITlxoEVTKdEL40AHnx5yXjMLHWBReKiL+FUtjMaM41tM0NyP/1VGfiyCJCwmd29Ubs0JQp+cW/lvGHs089zMTI2UyRwUeUcwShxoDzblTjN189WLgHfOa2SCwCRxCtNkQ664TdfLzCeHsUI7fke8s4PcAfCYK3Y6edsGyD0gnA8PAse0sZaokqm9CHL6FIbDEh+zhspvvcY2RLRKx7kx8aQq4LJe4NnFObz3+CF88vSKE0gUvTx/ectWQ0SNp8CJevmzroJQ/QG2TcugKTck5YYeWqtmVQHGCBuVhNEI/1E1szQaTk/mXPmqLAHTkntDmWLKjAyVvot78VBkrDFqo6JPDb2aKZ3Bdz70xKAwSlkCuqI6jKitIieYNri4Vqg6pUFRlrPzrvcuqBChSpRXsvfYuRi3+6YNm223adqelqe2KPR1UdjQu/D8sG9PnA8PexdWV9u9oUoi75eRVCoDOqZXi60a7/cgkSInbbn3HVnEp/MHBqOYWETjvdY847pOurzChE6MHCgjlEa48UTlPhw6ZpRUszqeP36vO33wXheDr/brW10vV+H88XtQ2FJxRaWooi0K0MYGpegy0KXxh/TVteRQsEkPZBHFCSIg6KKcL8r5aqW2Ny+5JQtF8VdBSh1IjOXTehS4F79tu1hYG2fEklnMkBKezLZTihOg1OZF1+p6vQ/7w94vTI02XE78cydMK45hPPzNzsKzroDT0kBVu9TmFI9pBd/A8R3Y/HkMyRh/7kP/v7gu/kVpi3+mlfrXHuFfRsSPElVJpwa3KF6/Of5Lbd3S70gbvT9alhXWTItlFdchy5hop7ECJNVAqJIlClGtEkMlUmXSj3rV6Ebio5h0CD6IKh0MJhsSGTgoqOvcdYmoCs47CaZk1goibuTckAKhaiJaksgpg1qvyR6gIL1/zOvjCm1UNnD5lKl1Qbzh+LMot9QkwGvioIue/OcHzDn4iyA1bq8ePCLuW1TaxOc//Rh22zWnPDFmr6ipSXGUy64KiKaD2Dui3xm2RKD4ShwbJjVKOAI0w0ifhdPqQbyzt6YEZghMj9NiPRiP1ADfdOSP5X40XvjjnSBoQsuK4yFMvyYMkoRv5MKR3kPZxtzjeUzIHeKUcIRd3/kKiUmeXr7bcJ9mtj3Ij4eY0Go4BAOF0qCxT7gbj+4PB3XeQ9cqFpO5J+H4dUb23/nFUG7lsQrZMiUHhQQ4z2vPIktrpeG2Rdg7HCrVWUNbxAje9Ksk8Do0Dn767Fq93OypJyfOXpSR6K2reRUvTxeeRCJ++uwKSdpeJfGekznR3QQE7g8NBW9I6pDUL3Z1u4XdvkEfHNkXEEhjvyepJAaSncfCWqSqJS0p3FPpIgd7VCkgqteuaXjBsr2Bru35njddCze3O7Ibocgr/qMPHnHl8+nLG/i7nzyj4GDofer5IlD0IbocOBE7gkmwjlGJ2V8IR8+8tGRF1XRdhT2Unp0l46Dqy6JDpJI5m8HDB5ew3zfw7Nlz2O934+ffydqo5IATRvCQ+/fFnwgmvZZsD6KDLQvuQSWLqCTEhGLloqM1JgYW5qAe1mCRzOaNRRZaQy3VGl47DasNU4Xo/OwMHj16FC8ePMDT0zM4PTmB5emKATPF2krhQxKgjSLGFWQbEr1ZWpO0sRwYjlBjgpgDS+geMxLGE02WHHStVRIGGZDHuxvjvjLeyHcw+NiTCm2yPJnYIEvCLKAo63GvYxIFmZ5bPhI/ZJXHHSq7whJ2yatkplcmR2kG+HhU3ZwwVe5sRnjPhZ32Yt8HH8cwAl8xcL0vuZoFxLxkJEYUnfpF0UivYNLPENKp4twIwx3Z43IcI/tgExGakNQPqYocLewJGNgKlg8W+NHiPHonBN+Lixu/WC3g+Yu12u8OmoAQ0RX3pBZN/d5+XMVpeod0ZYkmSxwHEh2i/VQlL0quemrN8/rpzZ7ZJNZYODuZwbIumF3BSbZKCAkdJTyNistZBfPSYNt5FjGizyu0hpaEaSg24fWPQKrG2tpIfY90c3eHrnXeH6zWjdbKdb3rDn3Xe0diSnHR9d52zq8R8CrEWOzb/vzQ+hPnXIUIcwWkrgTEolurAPtZVdKaUd3sGmKLcQcWAS2q6PLdTEnscCd5YYjdpfPaJMyhzGKJac0j2ikDUAKtpJWBgzibPNPJFopYLvyZxpAQT1xdPnLv/9KvNJcffNhFF1Rz2ClrC2/rmpJfSohavM1ZttwMYurAa7b0/hiQ9oD8aJZcqgyxU6ixni1weXoBNy9fgGtbXvP6ruFWjKvNIa6ud1BZhYvKZlG60DQtlGWJtrD44HwFjx+cwtVmC9ttw/VJ8n/uOo9XV5vD9dVmbc4WvatXiEPLO7BKPMHaH8UFfPriCkLoYGZCUtr+9ozvwObPd7QY4e/23n1iyvIHJ1X9HzHEf3Nomt8K0X9PozrjB59TO4n1k5sY0piYWwdhQXEGr8KkFEtrD4q8bHNkwyWrs+U+yEyRVWjS2su4SUAlt18GzJBAUqkJhTLmsEJipaojOZLQMs/Cr5QpKlIFMx9j7iYYabnyZ9oOltVti/SehoEyZlcn3gVpeSKv0EKSvkyP8eKDFw9UHU19SrScuIhRT69b2vj4FLhNQhdhcX4ZHrE1g0f/04/xsFtzZl56MVLgNGRiWRcElKlFXkb1pNWvOdM/2BtmYHVfwMFi4rKtjtXdiUDAsf8kSjU30UeClLYRX9nQv2iktAVXknzqTVV4B7QOO/5RVnqg7aYkQgr0Xi88MUlnc4E7y+AziVrjQM/SOShWXwE9v/GJp6AsjNl/Mqidojk1xEvIJuidV0B7MiVLgwT+vNHnCgn9zdfRq6Hi+Y4G/kLX4DhcjlcGUQ9NEaFQEoj6gwHaHrOyp/sKWVR5hD2Qrk4rfTW4NnsKqGNRWHXyvGJRidvdHgkU0qirEmZ1Fdu2D59f3eD1eqNI/ZGk5ImOdHW7YwApLBAccFfK0CnxxjtwzyayQJZPwlvSa5MN5621HJwSHYvosiGJ9txst/qHP/qkJiVBozSu93vu2aQgaVL9JnZIm0gDRbzTW0zTw2gdi6IkkWts2z6BtMk15Ir/aDqXrQMQx8C9bVukniTnpH+VQB1XeXjdkl7PfKXpfEhQiCTvR687UV+SC5M8jkiUqCg8eYAS9Sx6NlylaifTfQnsmaJAbrcIffqugCpl542xDILLsiRwCFVVw8npCh48fAjvvfcYHjy4xOViyZ9DVDvD11nxuhtD0Kn99EjoEhOG40RBVUlQqZIW9eSaTf2I7z5nkMDYoUc4GGQVO6NewUlfbyS/5Ux3yBXerz2C0PBcOr0s/zLC5chaSWR3sg+WqbQjeLu7TuNY4cwb8KASKheb6LRq/IBkvjRh3WRKLMShjD7urdOqJhydf/7I/F3Tqun0PhBvgmpzVFhXIRyxgo7nEW+IKUOevl9JtZNAnbHS/50TW+Lbn7kux94qqZ2IIxnK7TekrtsA+wWUWkGpLbHSKXfiLfp1dbHYfDC/6B+8t6v7w34V+nbWt626IlGaq1u43uyZeu97UT+lCvysLrhfk/bwzbaFpmXZIo4YuPrXO0kmHDqGwfOq5COkz6L1MSZKLSXg6M/F6QJ/9ZdqQFPEz5+9xE9f3HJSgKqjzgd1aPuCmQAcxmGorInGKEWVs6btSx/Ditw9iLXa937uQmAbuxhQO+8NJZE4NkO4CRGbGCMJT35Gy3CM8X2y0hP8rMqm9yXtBi54kgNP1zQO4J8F5ngh1BGTUBGBUmJZ0R1UXtF6mp7tic8nq59xHhC43Z5zUEGSJtKLIWujLqIuZ35W1bGoZ8RY88uzS3dy8QCKoiqoXG9LqrUwSYKyiZqJxTG3ZzALJBmIJovAAEYEODnNQm1tg5syoT+jbVfXcz1fLFVrrfD8IqvJwk8+v2b1zpN5wdY54uHs0DkSjOuxqgs4WRJ7p+Z1rO8dm8OzSSC28NP5S/Ph1fmj1eL8V+tC/aWudi+4UhwA/v1/KfgZenRZc1Ki+RLbp2/q+A5s/vyHPPzG/n3ou8/V4fDn1tjfdNr8nuva3zWm+EcQ45zRiQAtk3sPeciM9BCzzIzE8HnpTWeDk3Wd1rdW6LX8MjOWL7kPhz6jT4Ajp9xRqLsZZEKWnA8CJmlFx16LQqlBofb6MdQfaleaqQkiuw/pNX36k1UTEwWM/z//Lqsq2slrkoAROI18Lh0pn0ZWPUt9Q2ICTqsHgc9MJWZViYm4niYooY1xy9OLEL9HIo8KXj79BLbrG+WcU0K0Gbtnkx4SBFpEiyqZp+psnjlUHr9ocBYZ1SuBwD20tYnaXPKFCW8kjTP5AMlCO1IN9KOQw+szzXcPINGaqGcvsZEGi5b7vg4BBo5q6jr2pKQUoy8i2fyRgUzSHX5NqIf4Sizy1YZYLPIGRTXNEjw4ake7JwgCBhwKeiaTea6GDq/IvZyJFkkm5SQOwfQk9ZWptGGSdBkk/d64bPxzHJnGUGkfFwZiY5nNrnzqC/u6g/vRguMeE0oAtUwNbTH5Kabnl1u4YbPdg7WasvPYdSTO0caQLFHI0Hs0V4fUsDS51z4kgaEw3Et6hZ+YjytRUCQGa3Q+aOc8Soad4q8Yt7tGkYhQsrpiiqMkpPK3oCSIqPIdxZZgWm9LvdmYe9CjDBzKTJOByew9G773fc91eFq8SOHwuZP+VeoFIqow/Qkt666l9rUUXrMSpmEQSuCQs5JGK8OiMkEFn61W2DyeW4dd0yrne8z+u7TcGm3BasPWUVRlIOCgC0vCKrBYLuFkeQKXjy5hPl/w621RwsXlJTx+9BDOzs+gqqq8yqUkkGe1xCxnFxK3E6VVJCbroei8x/12C33bwnK5hMViBZv1mmm1dG04gAV4hfY50DEHG0phMXDciOErT9y7I6Y1VRPI1gZc14Dr/Gh8/xXGUI3EIF6Vyetwuvdnb0qah44VXO2gYg53Fs7pOh8nhxWTBzEJixRIn+CDytcp67MMVdQRoOarGyer1n1VzeE7BwGhcd+YYNp7//8Nr/7kJGUCEeA0lHzRSrxt2dN6asqRXp4YY1EaVwOiyHMRWVTySxjbXqTUxaOZtF6VU2idKWpXVSfd6iI0NnqMvtMP9nt8cLMNnz5/GT9/dqU2660NvmeV7MJIplYUs4E7kyj5aVFzcoueY7JGisGnajZynyJVSvdNPyTFOj6fALO2INpoVI2PbM+yb3lP1SyEB6zSzAI7krwJt9SThFF5UWcs2DwcVRVj6DiHTT7uyHmnW1Tqp6xJ6MM5EutNaEkdgPoRqfoiwAWBTQQ4jxBtK0QOiuts4BXENxxjKULoUYWU4WABJJuqs66X9czJ2tMrz4mw3LrEFcvY8/lnv2S6p7QGKbKZKerOFKYlKmw9X5p6scLZbNlV83koyyqawirUluS76f0Nsiwuxc+xTaWZeZRCtsSIY6GjTMJpIRFSVdYESYYAljkACkNZL4DiRri9xsN2rdnTsA+qcz3ebHawJYE48rY0hvcnEaOjaLGEis6FqqG9Y2HY1XKGVVHierOD9WZXXl1v/6F70vyOseqPA8ALq74gwf8tHN+BzV/coAd9E/vuh9YWP64X53/aNof/6GP3bzDib7q+/QggnKZWwmlPRBT7FFaitShp4TjuIzEDNp1sTlgwiJUKmYlKymGgUznNiMiQ7D3CM2ERHtqWSylYysLFESHNRKV7QmdcQR1JNWYCMnMaPQfUNuOmOxXNOHlt/l1zB6jaibDFqIaY89xEzUWspJ8oDkqNgYMX3hG7BLwNjOcsoCfQumT96vIhGmPQ2uLw/PNP4mG7noUQC987cO0hiq6JWCnQRfemgigu7hD7qefZF4+YxHZCijjlbo2lXUx9XgN3Il1C9ikUn74v7TvKd4MDdcqGulEVdPT1nLwQhodmeH+c/L9UNpHVpSRSDimOG0lPKYzApFLANBktqrtIhvNUg+ZMZ35HmBD5JiPjha8DvHLgkwJYBpu1ctCGAG2yBbgLuEl2/xAKFtJQSLBT2N2DSFIGC1QVCx6M01BaNfEWfasRQPx2DY59kt/I3WTo0UTwlY1+SWA7KLt13OOXAdb4TL3FkNLdGAQLa10seaiaRhlvnmFc3Yvs0UlgjqhrkiUHVjmEVN26U22myR+UQlKHonqhUNITjzqncWQ6yo9YbEUqeuRhiXmtTT2R/D9s8ZD8N7M1yvCFMSpq/RKxSW6ggVG8FYbP6rpO7EEiByORgGKUzMgAsnMVkoFiAq1ME0T5/YGUDBNY09aSahrM53OxGGga9H3PATf1FBFdlz6PIXYCn9YWikSSqOcTeG3h49IUEBalReWEtivHIF5xdV3B6emK/82AknrQ5nM4P79gYHl2eQ6L+TyBEMXAc7lckKk5f473bjB3H2Ro2AZ5XAfTXyimVVKnpp7N7WbLdjkiCHIMWqZAMyYxHKMkUdGHpMEZkdt8FYZh7r+LMai6BjFTGnxivy6VNkJSUU5JwrwvxJEQ4tlUW+xC/BEFNY5lYVDDgjiwAuNIJBVzbhdnyoVC2nFpoc+K8EStNmGyJ0DO1k5AJ4zuJQOnJ83PsQ6b/v0KqExV4QhD7Wh43evTmvdcrmSfEVk5lkCiYUP9zE6aAOMEHmJmbOUTSUr9/P8q7cc+eGxJzS8AVKQzpRCtJtedWEaaYfR1qL2DxYmfF333aP4glOe7cn/Y6/6wV65vet+1gdpzPFFizE7pYqf7rmPxDbLFWK1W3BXlmgOutw0cSMGU523kv+mIaVEhVoQBTQAU//6TZ2hsCevDgYEMV8DTjRG1Pb7vnEyiRBkTXDEt11TyJBVvpQtTGDo7FbgPGrWx5Z6+tm0OTzDGRzT9mNKO8YOUac7tTgumoYo7gWeyhA+OBTFpzVU4A7LeQ7DBR+ydmClQQiYJpzGQZ1ufRBGX+0WWJF50M4wJtqr9craE+fJElbO5skWFZVUHW5UHY8rWlmVti8oabXfKmA4ZEIe51KsZ7zkOWGKg8iZpklCTpcHI4pU0sXxqtkIp/gujbyIH1nGth5aPwEw5T8QPqqJWs6Xa3lxhd9izYBxdZfrSXdPCdn8A70+k/YHWoc6Do3vuZQ1bLmo4Wy2gj57WY1zMS06+kULtZy9u8eXtFi8u56XTM/Xv/saELjT30sq/jeM7sPkLHxyV3CplfqiU+nHv3A9d6343RPjvC2t/FwDeyw3uE7U4HP6M6zhlqrwkfDnxb8TsmxcJl8hKqcVa1K5BKoB9mmBc+U9YSCeLFZqDvQLVK60pgIvJlDtOKo79aN0y9GPm3+VqZzGp6BgYLc9cBo4TIFpMPgPS71wCov2kCgrpM7KYUiRaHlU7VRRbg6xEy/YwY7uKFNlCUISCtDa4PL1EVDYWs3m/vb3qu6YxzX4D65fPsW0a7r8AzsABtEEnFcRksYdvAjoSwEwFM97twtgKiRFev7mmSgn1gbDK2BcBzpSx5oome7TFgYo3BbFTk+xXF7IxSGB9ZMHI4kR55F0yeT2/RSXA6bgorzhFTkF4TA/bWK29I4Y0XKShqv4Vx93LIn5yIafpj37PapapXZGOeB8tFNHJQ5eOLdMXMZkGUjWO+kg0B+4jzewtBisj5tDmmwo07wwks/e59arxiPT8t1ENVLuv9cHTfx/1yUp9i1T8SmvI9pOosgVV6CCBjJEamt81YF+aa2m9YMbFjBNvRz2UQ05l+KFz/khcbOjbyb0EavLLVwbLcZsw9Abef2GmdFZrScG28CGIvIOxhoEhg0DIJuwyUw1X8gRAeg7OrCgeesdtReeXD7nP6enTp9DplsEeVSJ32y1VdrMtlfivmYjWGHTGsDw/rS2t80hU2EePHjGw7ZNoB1FfqYJ5en4Glw8u4eTkhKmwdBWIwkvvWS4WUNc1vzaKoZf02hqd1q0EBkLulkv+q2n9E23d4wpXzGBOKei6FtbrWzjs98kyKmmX3idkI2UMAWNHIjoR1CTR9E5GehACVWtYPP1VJd+3HXmN5kDcj2I8cdhiJA4gObMmFlzVxC/wWMrXcUgipivMUvLKwRw7LMk6m7oJ4yB1HMYjOW+j3zUAACAASURBVO7dmO4heByPjPnLQWVivK94z/qbJ+ygoDvZGN78KmKimyMnV3uinrKgFNlemHFrS/tWTJN6IDjIUJm4kxOn8o/YcOVO1O2pdcg6VggGdyCkIFRkrZC0Eo3SswrP6nM4Cc77vouua7tOFK44De66FpvdrtxvbsvdYaeMUupkOVOLSgN0DTy/uYXnL29gs2tZ9IaTAYVmJW2e7xBjc2jws2fXBKhR7JScCB8q8fI8ojHHoDI7YUhjUznTGFVWM1pwoGsO0DpaA7COIX6PliitYB4Dqffy2knn/ausT8EBT5SiBLIeSGQdkRA7QOXZg1hAqU96HykiTMCZWBJFFcvZ0lkqPjoHXduQCjYUZQXVbAbamqiNdVW9CPV8QdRYrJcnaMqS6hsk/qS10XXyeuFujtyiJdoegXOW6dhpGdgF8Z9fEgBmAQd5UFVa1j1bAFKBBbMofMzexVlTRGwN2I5GB201827aw67s23ZQiOTkWO9gs2/47xMulkamSpOAUV2LHQspnH/43gVXeXd7sYKhmIJYU9t9G642bf1yH7/XGf2TvdMftw7ftU7EL2x8Bza/MYPSI3jrvf+Tptl/bGz91wD4pxHC7yHAPw0Q30/OQjjct+O+KTHgjdQzxD3iJmUoBRzGXOVMVCUu3cCBs1OIJftnxtgzhUDEfIBb2JTxClnuPvdVulQxnFYgcUIIU5NKZ5cA4Xw8xuH10x7OvBfaAfSOrXW56plsT4bXQfr8flpFpeNM/Za8GKZgxydVcXphi0hqvViRii1VSQikLs/Oq7Ku9enFQ911TWz2a7g9O8PN1RXs1jcsxsG9AxE52M47M97pXXtdQINpkx+DpBF8wYRPOUWBOASJycD3TkXl7ncyYvfScE/AKEuiZyrTsA9NQGYGU/nnGHEwi07BOo5aDK8KH02ywulmZT3e8bxSb5B00Y7gHO9cq3cGvKZA+pVjzWfAHkLCVO+D5n5Mn5wLj0CQyu9h0WLoiFis/SDeMj68nCKdPrf3DX0vvv8GD67pI0CtPa5KRFLwpcRH/zNQvsM7QDJZHfB6FcLR/Zz+U/IFrDVLKtuysAH3aw2JraODjfdc/3Cs6XPvsd3781w9mahNJ5GSsfr7yqqQq4cek3E7HqmP5qrq8Hkp4RHEs43oq1QJ3a7XfK2oWkIxGFFWC1aArWG/4z5ODnZgTDaxDji9jgKf7WbDx1/VM3jv/ffgyZMPoapL/mwKXouihOXihOmwp2enUM9nsop5P4iAaCWU1lxdjkKCScfP1ZWRLRH8pDKZqnVSyxh/OrkJ5D9I4PbQNCzYJHp26jWKqQJa2HA5XW+d+hE1fLUK/BuN5O2aHoav/DE5ichCNT4nH+XZT4xhzkkG3n8sewR7uNuEmm/0uKBPFWEjZPos+Yo5KFVHFV+qyrMvWVIY1Pm2yAKdbLtyYjWO9Bic5ronG8nRfnL3+c97RU4EpypsrkKP73yTiwYgPdlcKJf9hyqJYlgZxusQE+FhVF0cgLC8gD+EE+/AJq5eKmGszgqsfI/SwxeIJQGSfyanDa58MUMMiXvvrNGtLUodCTB5yv2zlkUXfB/265ugjCUZWm00YlkqO59pbSpSpK3hwwdn8PnVGp5diSUbWaLMZxVVK8nHEa6T3YqsiewuFA0DURJCIisYj7KGxJy1GxlNSV3fJjc83/fouhaoshm1LgHa97gWIZN1FxlsMYuFgCjVvtlaDrVujTE7Zot4v1AWgxJ3vFkUHrBVxtiiqqMty4DcyYhIvpaz5crP5iedsSZ27QGaw76PIfZFVavZclGWVe2MKVpb1GjLstIsLGycSo9nUoudJfHEPlJsK0w9Eo60bJQVj1jlLYtiApxESTqmOJJoThzPeq4ayM+lJhCINRiSGCcvODtuHQOoSErFGEOFF7bjCkFaGRjQq8DAcb1tGXCeLWcgXs/UL9vCoWugns1htajhdDWH+nkJN5s9rDcNz9G6KihJt2xC+I3nO1C7oN43KvxRB/GvIcLVu8yT/aLGd2Dzmzd89OEZWPg/IMa/jBH+EFT8XWTLFPh1DPA+bwjDOiqOvLlyF4V/PmXACDATDQapBMqugTD2YuaIOpOY6AscVzIlisj2KWPi+bivMgd0GQTm4/Fpwmc6bF4E9ARUwuTn+Tv0uGIOwLO6h1rbJ8DpJj/D1N8ZcrCKxCaToIA2Gy89qtyvapFlCeg0dVHO5kVR15wt9O4Szh68F/ebW3j52U/wxWefwH67FsofZ89pJZrmiycNLPcsC7map7JzerrycQg0xq07f1b2KGNzb6JhcEXtvjqk7Kf0mq4XoDmO3L8k1RnZd8O9dbUUWyRDF0g56RHhTiqiQ/ADSRUo5rhkGt2Qb4NSPsFmSoBQV3wLgRUtKvgZjEGwIqXNCVCSYbliguxxRdenekdMMNkFDb0WGvDgjZiFLdIlpeeodyi9m8cZxzDpRf6idfVbAzQng7wrYWE999XQn3VnoE/X5F1L6Q5gkKpufW9htITLA+MoOpbDa0zZbFoDdQyxTmTt1x3e3Uzd+I+32Nan/dUs6pVVj9WEqXcPOHKO+ig7Ja7gRHvvk8BMEvYiL9D0Wi9UPA5cDBvAW8qyU2UUKGC8fvkctkU5eB9ubtfQNAcB7axMaZn2OlvM4eRkxRVKWuN2ux1XKB88fASPHj1kW5KqmkFZUTVF83fVVQ2zqgZDAks6WTcTNVdoemJtkCqYw/2L4xYRslwx3sfdyH6+MHlNHACKThNMzt0fv/aewfLlSXWA6LQzG2BZeCh0HO7RO4/Y3qF3U0zqnFSBHuqKcWTo0kv6qNAFxQJdXI0c2hqGAzo+JPZ1HDd33lxJ/IvsnoSvAiHn32S+SGdoet8wHxTkKuvRuWcmdKb6DtgSYHoO+X8nR0nIOamQktAMu33qt7s/IjojqlxlDcVsFpEq6qwdGHJ1M6P2Mbc6Vm/zZpFTolQV26egSlZ+ZEZVPWyPotAYIua1JhaJn+tJ7oXlaCh7RKQpafxUSmtFAcihOej17bXab9ekjRrVvIAiGKaQffjeKTz88CGs9wf4/OWamTTk9zmf1bznvLjZ4Ocnt/D8ZkcVME6qFBVROuvABbS2xb47mKY5IFd3tYXgOmYvUMWPLW+pj7Qkwa2K6LQQfQXGstqzd33n+r5tYwidVsqjIiFJE621jTW6KAydjkYkMFjWLBEWQ9ybojS+6+x+fV1wm0DwrFI9Wy796vJRN1uuCHxjUVS6ns2iLWpHZcredQR4e1aG0MbYsgjWFOShQjJ0BYeg0vcVYmCJQ7onLcShtxtTccHmHDG3Vxq06UGl382QXRmAqpfCsuMeXb73LrkzCIdLajAqdaTlfUXawqSKb9kXwdhQzeZYzeZhv1mrSMwUFj4z3Ee/ObRwsznAw7Oe7WykAu2hbVpO6s3qEmaVBaMViz7yms/FiwDOdbPm0PzDXeMu9hh/NWr4raIs/1OI/oe+6/4eEX88AONv4fgObH4Th+wWXVIC+zxA/HML8H8BwD+PiP8ixPibEOL7Sao1s/1IZtGLXyfqLB1HLIVkFdLhRPOLJzMvokjdytT/SdGRUUjN8IooszohhwwMXQKPmCqcNv1dTPaRDBIz1RbTv7eJzlZMwORdgdA88iR3k20ug9pcIc1guk7HdJjYprgJVZeyap4ql3QuQei1NpFplPDyGXGUIXitsmk0931YKOtFnJ+cxtliAfOTk/Di6dPYbG+w71oV2g6D71MkQFdc5Lo5KHqVMJROKtEyYxy8t3HwsUsYMKdeh/djApzUM4egg34FrvgoJtLU8+bcWNGMw/uTr8VxFHJUXb0vZELxcr3z2xAHeztUOiWkh052AdOiahRYW5w3a51UkDmeGqudP9sqH6Y+rhJ76Em8hYKZwfhcJd8qeSXdhIZoaQFgpnIgNlEMhqywJwG2Z/CvR3plHExtvo1g8gtHvknklXRSOPYnJQVf7+6x+Hm334ukkvgaYl0KChCTR3F6mHnwhM/9XHrCQxqEk4+aLkUQRJyljsV97jmmIWIdhVtElSoL1rAyprH8Y5cEjPJ341D9SQpvCYxyb2VSh83gLWZV2ij9e5RFl6DGQdu1FJzwcdMx76lXKDhBJ5QNUAiLE+qdPIHL8wt48JA8Lk/Z93I2q5lqSGIdRIG9uLzgvk8GeMYyAFWCUMQCg4CND8nzcuwtxVTieeWaJp+5V6/c5OYNgCSpuKpRczUZ9zPoagl0c19u6m/98mdGVDAxsj3AwpI/bpAe4W9ybSDT9NM6nguA6beZtU4QCuSP6Jvf96hm1fOcI5sWFxV7EHuwKiQd9aM0y1EdcrplcI0s5E9Pyze7hOm0Tmb7ilcB532chDEdQbxLMhcxUql9q2xP5GcSRFEZ6tkCyqqO8uyG3BU+sE1SYh7iMaNavMPFR1yYWJSolgOZT46F9ZiTRjQddkP997LuM/RhU8jIwMXr6EWHncIq52Kx32/19csX8PLZZ7jb3NIkV4flHLfkv6kVnJ+UsXqwIiYBXqxmoE0Bs3nNiSKaaw9P5+zBGdULIB0qa6tw8ehJWJ0/CEqr0Hcttrtd2O82SNRuqqC6voP2sCPPSY9Kl5SksmUdCfzxSTg22Imu6/a77e1Pt+ubj/u+JZPljyCG76Mytq5Ld3G66C5PF7o0mmzbi1jMVlHbBpRqsZjj+nZT/+TvnQ5xkxNFfInr+TxevvfEVYuToMhRUp4JR+uzKSvafXsgSxW5P20Uz3dWxRWDP76yKipqPaHigTqgZkqv1j6WQHbdBE4RnOIQh3ozObzqScQSUJ0oiHuKfTVt76JhwjsD0W0jU2xp06eGIdaCo/tCW0fDYRUlEiTGJI/3joXalNZlWRt61qjvviMqLRm+2zJYQ8wKxPW+w0PTsS80JaVZzKvrwXc9ew+TbQwlGdmDlWxeCJC6AFe3G3j2/KpYnj+6rE4Xp53X3wfEX1cRfhK1+eveuf81xvh/k2DxG06Qb9T4Dmx+8wcthi8wxv8dEH7olfqjGPD3tYp/oDD+Y+fjaaR4OtmlJB57XupV6sSner4deeq8x6hkfByFWioOmZokIiRqyJTWqagPDOpdx7tCnAj4wMTqJIPKNoHBqZBQmLzPTHo08+c26Xf15Hf57y69Zp6Os5lUV3Pmp5h8F7VIaJGrjcaHWFAbBQDZpjACsVFMOsedl6kRbIkMy9VlLGYLVy3O/c3zz+L186dl716a2Ik9A1cx6M/rTAelxYNHApwUWdJ1LoaX3NfTMlwQUf1hQInSu5kbXanq6ZguFyGE6bumd0iaRTPhK38P3nn50S8HqCq3ScGELiZUxSGnnoPzQcY8xS2B5e0iV45FnoeEWmAWRwWIqTLrOx/IboMRCnBQKxLTCFwR6IKo7E7PlF02ad/xJD0XuQ6qsqvdGENnXMEBIQXiBQ7gX03+/Fc3csRGhfXaUg8nATnFlST/prHh244Ir+W2pgqmzQmb5GQAWWUVklLr0bu5EiJATjFVVA+gUSqRKrEC3uyE8nsHg6RspgAizx8xU2pDpgSnwxg/n5IWOKW6g4iexPT/g4MvqbBaC1VNAShVNLd8V+qaqpWnnIxq9gduoCAfS3rd+cUFvP/+E3j8+HFShq35nBV5/HHTMbLSLAXqMfeUYjLOR1GOZUaICK8lA3sPYrT+qiZx9gS+c5G+8Hpmnj5mtkVK/tBnUb8mAWKqsJKgCgdsx726rx05wWaY3fDWBeuf6+A1xXO1m5+HqVJa0geCmDSJHRi2PBGo+AWZreFkpaIZ0nU1ykPBbI8wXv9McTq6b5OPSnZQONzOxMEZeNAqiRGE5GA87mdfBvBpHXYMNvGtVa7pOc2Vc/p3SeIt1QyNMpMV+25jxPFJBt6LQwMpBkrxRO7XK7lnc8z+hjDN2bIIDbSjID1ZyWU2RcxbNCEb5/oWusO+6JtG+Y68uj3erjew3iimUN7c7GC96/zJokSqItYlqa/KrskCW5Rgalv22O29g6rSoSjrbr46jfV8bojq6shDuG3ARzfMITIujsGzX7m2NhpbekseMYo3LqKxehKp2K1vrzeb64+bwy40+93jZr0xIQZbWK1WqxP35PGlP6sNlYrLnmqxyP2T2GFhP+kbRQCfgyttky/1gZSkkXzmrC2o2MxiRLSCUBe5ksuqedNl2nJsg/B4TWrf6eSx5KdcyS4cDsHHBiKWSqmS9X27tveOXHq9iqIULqswU0yw5K8jWz6tgzaFYjEio0lhOEjrghNLBfq51kSFZgJBdjsgT3UyEiVtNp50JBaldBR2iTwJdL4VmmDK0sXQ682hs5t9B6cLz6JqbHXD16SFam64Wl1ZC2FifUdJpttNA89fXIcn71/D6uxCNVrPus7/mkL8NWPs7xya5sPe+w8R8Q8B4BMA2L/VhPkFj+/A5rdpIDwDgGeI6q+Uhv8E4P8VeP9bMar/BhGfgFAPAhNHMWbqbGazJDAW9SSMk/iZyppKdYytJPujJ8/GPoG+2QQwmrSQthMqbG7WzgAzg0k7ec80dekne10eOPk70xKLCTgNk+rpVPk2Cw7pyZ84ObYUy2HQnLUUCmyMw7aYRf2GiisHiRzYkAqAjlW1wIv3Su5D8CH2zWFP4kFa6Eay0fo7YHMIZFMpD8d+xgywhq6/Y6rdVJV28KDmQLiJHiiDppLqH/f4UEeDH8WAeO+H0bxzzFS/lod2JJYiByBFcybOJRXakOqWFJozZTaSyhIr0ab2ejlQtkuRAERHH2rh5qL493nqt0hn9+aig289hqebKbEpuArIgU1+BFONaaDXEZWrDeT27MFiID/V4fEZIAh7s1Jzj5c+WgL/YuCnvrHR7DsauTpPD+7MytRbE0vLq1fqWF9zDHMbxarp1YeW6+mc2R76JbnXsCjSryP3LpJy4+GwZ1qq+EKaVO8IR0UXtjPBMFBZ4U5lhxWVUaqO00oeW4oozRlrx6bmLvnb9kfohlsTtIjmSF/Z2JOpWGxEqpZ0PlS5xPS5mKhW9JyRYixRYFcnK2hOTmC328J8sYSHDx9DVZX8PXTOpAxblCVTZh88oIrmiahBDrX3tGZlRdDgIfR+ABDcT6rkGoZpsyyzFSYpptTL9/oxWc6/AHUkIganszjJx9fZJ2Ab+Z4RwKbsP3wJ2MwzOzBzQYS98M6GdN/ghEBMFJNfwGAFUi+9yTphNwjHKuWkPtsGA00wbKM1PZtxIUXIrjpxAiJVMuguVYBSuSSONjYuDr2VU8Xy/CEId14HbFs4JlDSppOi8Qwyh9Tm63Kw6T9+oiT4pnCTnhGtLefOOSGoDZREb5wvwBSSC49hPIYx78rOhtmqM8cJpA7Wc9KZAeMgJJgTqjGOzC5ISfq5vIYM0ZBbgyXNGtkjQwRrGNDRCrkVMORPtdaz5WpFe0bYbjaqOTTsxUmjdSEeOmoA9ejdTrUtK9fCzWYLP/r0Jfztpy/g2fWe6MZBmSaur1+EerGMJPhDyqZYqWirmqU2mGdKbVBREVgiWNWjIsYaiwsR0vIxBPLS9KYoFCr9wJTFr/RdSxZwj2+cN4f9Dtu2MfvdgYCir4qKVHix5wRZUxKpdt9tETcvlDtsI1VShfruwXchdocGXNsZ7xwFK0pEFflRZMVDyVNK5iJr8UWuYKZCQ8QiYLCh7wz5iPbNoeiaPdGjTTWfc5Zvu74x+/WNbduD9q5XQgZR2rMvtic++JyAsNJGFVUNtirRllUoCtIZ0r5r6H3OlHWtZotlTxRhcnkgrSECn0rrlhOH5KBGOUrEHfWS2rKqlDKl1LnFO5QEaFvn8HZ7iJt9g51bgjaJDUU0ur6HKnhYzWfw8HwFP3n6Eppux/r0nntxSYCwU32zD963HaiiRYXzGJgSvPLO/duI6peVVr+FgP8hxvhnMcKPQVFf6Tc/+PgObH4LBwI87YP/37wPf26h+A2l4r8NEH4fAX4lAi4Siyjv8EIJST6aqSqlU1WLxH9oclLlbyq+oya0WT/ZpzNYzJmnbvLzu/2XMFGbrSc9m/n9agJA9QQwZpBo74kNujtVzvzduUIYJ98TJxWzOAHDTijC2voQSw605KiSpODwWcOpcF+Sp9YCiydnF973fXA9Z9TIa0kxHSVHTHe3y5g34jhR2yPaRlAZa07ZePDKScfhggl2IssVwWlhAKl4XH3LFyDiccQMMIDeyenFxHlTKT9LH0vfwMJRUiMK7EcJuQZz7Os9fEFM1UTuekSdlGmjOgpYkn8cZTGTROi7XSVzPJ17xPJkiIH9NHv2Dj2mf46JaqlwNl5DSRYPaQcMcaze5qCYOATcVYeU1UxKwW91lN8+um1MTwBR8GZWTqChyjqzj97pV/Wc5BJcu7jPZTYKE2MIsyljvVgs4OT0lJ+v7fqWAVdJIIU8ItuWK50PHz+G1ekprNdr+JzUW+nnKBLRPlFCx2pqzjyLbQgFHY7VAx0HBhR5aa0jq7IuF0hUVuqDJKDEvWSgmAYXEx02g03HPZghVQ/lwrG663zBiYuOg0yhaXOgk4zxSRiIqpSXDx6It6MTGuzZ+TmcLJes6Dira1awpKQQvYcALPVfEg02K8KyEoqTjgjqw2Txq5irl1JZi1leJaDknSZrzZQLeZdyPJD3EwiJ8W4+Mb0uvY1bF1TSipU4PbXRSbWKBbz6npVG7wL9u2PkgUauaiqVfVeHw33tIAYLKriT9PvZD0xWVT0/FyFnfwFzRTEtzATjKFFGatAu+wZNRtboDcmce3pHVFL5KtHDTDmubGYvaRjuzrikj/cw5ysnnBZ6XtGgKmqmXEffUW+gGD0eqeXdfS6+eGRN3bdZGAMH8cIVItp6WVUEOLmn+c6zmR/8IH16TN9UE6k6adOJk11c9sF9FJqXzsoEcfBu5NjFy4+oOsdp1y59n4VxjyXzXte3DQE3KuDGelYjrQvNfh9Lq3E1r2A5L8Fqmg6S4Dl0PTSx4T3p2ctb+LtPnsHHn99wE0tRlpH6BZ1zdj5fupPVmVdY0xwmipWTPTeS9GMA9EYmhNoy+y34suu96rvW9e1B932LruvK9rBfRucfaWPpotakku36Nrq2VeubG93s5sqtighkAEOkLPIZBW18c4B+dw2hb8g/M3rdUyGQFLQxeE8WLFkVLGm6SqtTnEzXUVeEMwc9skMCiwKRCFHZ7newW9/C+uZqvru5nqFWcbE6I+Va3K6vi9sXz9R+t0WqnDLJTBuMLkTne759WmwUAj0XxlgK/ag9yixWp13b7GF7fWW01maxOmtnJyunSYUtYF+U5WG+WvlqsSwUGmK/tYh6V1QzPz+5gGKxtE2z19LxGXTXHorD/qCU02yD4lJrhBQuZB2jy0HiQd978hA+efoCNruG94LCiCgbPX37tod968AXWc4DtxDiLSo8s9r+ZozwfQjw252PfwzO/Xuj7P+JCD+5p3fhGzW+A5vf0iGbqv4UY/lU6ebv+6b7IYL6A2X0P0PA92OEkyRdnXsMUq0nmRrTuxlnsmnmXRCY1V9h0mcZE9izk8rDFHxiEvExExptmABIPQGucA/YzDtDxlVl+pOPaUqT9em78nHbyfsysMQJPXcKnJ3EqlhoBWWQFL6fSB/kvtSMhJIzSaTFk1rZ9dnD9/sQXGh323jYb6BvO5LtPkJgMFQSM1ykcKCHXFBI/bT394fFyV84ej5mlCfOATkwgVRlwEHt6cg3E6eyQzi5lZNDzUA1gUcW8hE1OjKBTo2IOgcEOZ7jqDykdPdQ/EhXLiRaFL8sjkEcVx2CWCGgGqq8bzKmefYvfA9ObmBIWofi/YlHQPPO6Q9Zjp6DOgul8VCwndhE5GJ4MVUiiHDj2aiZKlPqzfvCvulA8+6lGQY9Y6WmqpuUDWZecaDso3pHPXFDUqxPiRjyEi6iMDIwJhsNoidRNS8w3bLnnsOPPvoIHj95wpv8y+fP4PrlFew2O+mbTJVFAmq//A/+ATz97DO4uroib0qpLCafPik4CuCkauVwToPiLA6UUhpVXcXLy0uYz2eo1C1XN0nqX7HKa8VgcKijxyDHocTUnfp5YgJxZVHCYrFkcEnHRP2XBCjn8yWDZiqez2cLOD07g5OzU6jKioNq8rIkcEmVPzJAV1lVhv7yZDjnwbV+qB5Ke6Qa+1OJnotqoEoOfX5D2DJRtRTj5aPH5IvveTz+G48XGgG7WoSHfFqjYmYPeBbcCM5zP2pz2B9Rke8+MbI8yudSP0mlA8xNBHKWyIDuiw7TUB8V9Yn2LUTvfi5TNN8TYqww0LxHSYcq3FTqoTWJ7E66JKJz/yKIR3lAKRWxMFwsdAgz1eMcO0VSPKxBNKUXH2UpJxtgSrblg2WfRIugy5LBZugCudrjKG51/7r6uiG9p/qtjWno+0gAxzMDxfBcYfN/JUKjw5Ms19Qni7cg7jtRXIqS9ro8MmikRsX8cEqxtBGxYWZFZBEalh3FkRlGm7nLhd4odtQdrVepKZQyJ2QMTEmh5WG3Uc1uU7imjcTHbMno0TlYzEq4OF3Aal5hRZpfISVVUkme5sD+0MH19gD7Q8trAXv2tiTGE23XND54Qvu8FBp6eFM+tEgZI5o0HoOC3vfWNYeiaZoQQ9A+9NV+c4vrF8912zZ6cXJqZ8tV6Mm6xVGvdEjJcKLxduQvA3qQilTQoYaGkh/Uz0CCYsojJ/YIZNkCXXAEFgn8em7N5ZwVV3oP8lzEInVmFNJKyUqxGAf6cix978rDbutvXz6LVy8+t/vbG3LShc3tlSvKWQy+V22zx26/p15V6gPL7AjBaUrnVgZS36UYDrkdqTugsTr4ro+b6xemOWy1fVZV9WxBLVOkgm1tUejV5WO8eO9JMV+eotKWrkhH4kvL0zO4uHwQ3GETW/5cAuedJX92QsvbQwdN1/P95cqNF9YLzZ9yYeHB2QIuz0/g0xc30HcOTuail9h1Dq7Xu3jZewsWiWtLTHn6KwAAIABJREFUltNtsu3cRaL1Rlgi4m/6GH+t6bvfsjF+XyvzPwPg332TBYS+A5vf9oGsNf+fXXP4ERb1DzSo31be/Q4q9dsI6lcB4hxZeRXFF4l5HxhEIy25Z4/bzGgENz60U+XX/k4gmumx7T0g0k8+101sS+5WH/OYgsX7qp25utFMeipwckz59/l3fgJUp0Alf2aX6MO0OShRU2TCfxeRvRDtQP+KQhoVQ+gYtdXalpXRhUVq7vcZAX6BMuGYM43ZnCHthXGQlge4A1YyLTZt+FPhhzvFyiE6kMzo0UcMtzQrxU4Dr4R6NeQikWDDkoRv6Q1E1/KsFHjMeI3Z++DueSb37HzZYpZzy1A3yedLb8WdQ3z9iEnEgdTjCgEgXzIwW2fI17AoBnhoef0+fisOf8cEODU1bEDrNWh0yZ4h35uUtUnnQUEbKQATVccadceZ7ls5pP4sl+7ey4xJeKlQEeYmQNMjU/z6OzfzuL7ypiOm9SHWMeKBvNJ8JMMhVREjIQO9whZxtTrhbPE6bJHUVh+//x68/+QxVx5JCXC/2cJPb3/CmWOq7tEgu5CXL54LeEkAkipoVFnk3pleggLKMlcJgBwODQNatvggAqFWqe9Ts5w99UNSk1BRFDifz3gVoWonUVhPT0+TgnHPE4sqsGQ9QpXIuqoArYAHg1LFJJBF5xQSfXS1OmUlWVZW1pqDTQKW1L9JAR6/x4lSLQO0LF+aBHeknhMHpddB8Iqqhj6BOhVGyizAuOjEaSpMJaP8EZ3wx2ae5h3Umde1u6tDZh7kCt5k8okkB9eXpZeVLAOalozrpSKQe2zvPovZ0CLn5OglpEZbkrK0SiAog6isVJ0quHkxJUqm0oZ6uIDM8l99gt/9GKqaLkDfhfGaHTFPZW7tg4VDLGD0cs2/jYk2PF6J6ZwL6VYFHx3ZIZKIKsZ4fOFj6pqYVDrvflCucuYEqe8aCH0LGHxStzMEOkek+iVVSkxAswUDbShGFee3uMpZJdwUFmxdg7JF/gVMiMg+9eAd0iFVYnsdiXbDnRAMHKnahsn9hURqIBqpECJzUVOezUi6CRtphSVeKBlt0skz10Inar3PqvcxoHJ9Pyc6KlFUu66Bpt2zQKxltecZrGYl1JURj6mYLZAiq5U2ZPjftAxCELIFmmJfW5p9fd8SnqNYRnwn2aKEbx+dC7GTegJ1fduWu9vr8vrFU0WMiNX5RVwsz6A7HKA9HLhCvDg7D4vT89A2exU8CxswAqT1i74/+p40sMUTjy5JqlnSV7O3LiXsvCMfJ1Kahf5wwI6AGJVclQ78FIri7wG5iokn6dENMO6vJtmDkqJ4EULQjgGsC0R7NYVF5zrsexKZRW80BrKaCbXXfZtU/LkOY2mN9FpR26VXIsQWVNs1qAjqsxOTU5l6S2tk37YqiZ95W5ZEaa1Av1BlPYeqWrRaFxQD1tYEdX5Sonr/EYTdNXxGDJm+5eTkak4VZgcvb7fw7HoHp4saqoL6WAMnDigRSZRZsqia1wX3ztN3z2czTnK9uFnHm5sNtoetKetzOpEgBZ+wSoR6h/JMR61wEQB/r3PducLwsLbF/wQAfwYAu7facn9O4zuw+V/LEAWtP/He/2Xs3b+zRfUvEfF/jBB/DyI+5tIUB0tUhFFRJ/+jdPbqDuW0SlXFflKhnO4DevLaDPTaSUVxChKLKcCbgNLiTvUzg0M96QntJt890Wod1Gnhjmptfm05+T6c9I3GybHw8aL0JVLUxvFa2lFDsowcPfpoM5E2yUiL8WGzVp5pGyJE8uVjtBWNEY/WgqSOO2TuB6rY8AK8I+AQx2At04WiqAXeL4eKk/e9ivGGzHaq4orgCmn65B7NN5ekJ3oWMoUw8L+liqmGgAUS2PXhy6oir5yBS0GBEtrSm78Tk+WMVR4MZRhTdTPXAMY7g2MQFBVYb1hQg3qcBmqVYGkFg708CiUxJROIgfMO3RB+EQNHVt79I09CqyMUIUBlENpA3m14x/r3Kw2Z34gkf9+yoUAQHUH2hKwqBkW2tMg0PqaoIm/U7X4Ph9s1C2rsdzugnicJ4AU1E+D7+Ec/gk9/+hMx4aa2mFTxZLDJ99in/kbJuzFQS0CM1WCpkliVHCSQsuvp2Zk6Oz1ngELVytPTMwaEpPD6wQcfwPLkhEETBXTWFkz1o2okgVGyFSFGREwVDA7QvMwbjrY4qLRDn2lGjxL7QlpL4uBZGRI1lmkH2QszPeN+koGic+EaTgafPon6ZhbGYAYvDIqjwuSEr4/pdYJX4kAJvk8ICYZ00/S9OIifDRMsCXVT4JqvDRebEO6tauZPhqQyHYaJKn6fw8Oaz0GlntAsBJXW0L5vQXFV09/Z3n42IycQ6X67lCwYnuX83DGLGblP8xAMq5FP7UbgaGW//9qIQg2oLqLa856vRfkPY6q0pwTm0d4AWbV1uAZDoo2o4GRFc9jKQkFJC52ez+M20uGy33cV6XiprYH6T/uoprZabzxSrZ0r/CQORPYnNHezopK0gzL4iqxAKgwg6uCjBsq9AE5qFWFFHY/8MgaopElQRxaY4fiC7E2kREaiOjhIVpMgkJHlRWVkH5IIYyrWkTxQ733T6ODYV1dBKjgSuDSJ6SBAXw0evYn1A/uug5ttw8+JTgqmvEBaHRnA+F4JhqMkPBoStRFBRFY57XvXd+1hW6xfvKyef/aJvXr2Kdiy0mVVw+LkwpuihvpkRetat7p4GMu6jraeAxod/P/P3pv1WpIk6WFm7rGe9W65VdbS3dPTjZkBycGAkh4okJAoSCD5C/SkP6cnQnyQBEESIVCEKIxE8IEENRqIPRx1T09X5X7Xs8TmboKZm0f4OXkzK6u6qqenVNGdlTfPPSdOLB7uZvZ99n3DYLnPMDcILSdJgVIVBKfAQ0YO59ZBnQcNJqOq/ILUDz20zRaYquv5oNgCDceGeHHkBF1MMSTIEp6KHoSoxEJnTFD1rhdLSd7n81XXtru8a/fIHafhvjHf1rNtHQswedc7Q+D45EVfg9tTyQ9cNJCFhG8bX29rc2p3O2zbvTHW4Gy5Iu71NVnmM5v31Xzp87LOiqpk+u2QWxrmOdso+bwyHssZ0hnr1l8t8fLqDbTtACerBTw8XcH1Zid9tp+/uoJHZwsRgOJip1jY9YP05HOyuZyzrVQBrFx7fbuRez94wqZt8Or1a38+uxiwWPHUzSIY3B+qbWviE8pjbIes8k/4EwD3sHPtZ967/xoA/oUq1n6YmtpvaPs+2fzubayi9ef1on7WtP2/g879AzT2PyPE3zNAKy5BWSHOjlAb3LMepEboqY0JJAI8LqHLxugn+l6CCgthYklCSULpjh6EFPlcJNTXSOXFpP8zT5Y1lySZZgrH5DsHRUEh+UyfnENMZB0iDpIOGVN4r+powcSYL1RGSeOR9w65/6tt9qwSJGbsomCmgh/HmcZYP/YaRB5UpWEq88dXMKo6hn/H6dmYNIA77NGMG6UvpBym5L3pr5N/xxxBnT1xVKqNyesxDB3P46D3kTxY30JBLQvyEIsFhv6v0J9KymvlF9k126u1wgfEdIFaE4KBD1DwwPTKh6CEPYBwgMIMMDgrnCoYG1VBk87JCmAQMY4cCvSiTmtD2MbPVi9BSOztDecCrnMSCHDBuch5Mfstt1p4//ZB+TLfjCojmBdO1SQz6B2OyPGvefocMM2KIjNFUWbiE4cEi8WchXooeMYVOAwW5ouFIF+/+uVfwtWbNzJarm/u4PXrNyOS51X0hs249/v9KNjDZ8o0291uCwYzqOtSElr2qWzaTqh5wS5kBnlZSL/n2dk5nJ+dSQ8l03eZKguSzAZxIE4iz05OYHWyFgSS+w050eXKd16UiqYaSU45+BfUDpl6lgvsAhRxKhz9NTnRNWrIPvWrxvkg+I+Hn/1bZZTYNyT3TKdOT0liGFGlzI5on1wfkwUlRUas1EZmHBcIRxPLlJG+O8E4Glg0vRgOAxMB9UD3DfY1SbJ4DznzrfFGNF6/t45BFVMPNk7229Afx4loUBKOHnjf/EMcc3YWBGpaJ8WqaSHG8b8TpT+DjkWBxln4gK/y3ucszuuOMG/6HHp+bpDVuXuxhLLanItjg+bU754QZsalbaS76gti9eUCTT32msLBcR4XHOJibsbzcnT4qS+/etNx8sKQGSM09DwUjDi3aXxIONW+jVQDQlLJIaBrrFHByqa0RCmsYrBHCWgmBQH1QN1nCi2JABD0YYFkGy8O+GX5iLHNniQxlQ4+pyYAzg+u77pmYKpn33WVFHGyMA65P7vIMmEoiLqMd4FtoSykwRNs9w52TSvXWRgVRMCiQvz8Z3lOaDNOjDMZLYbXJFvyo8NgYt93dnd1Wb958UV++eJZfnv5Gtr9DrKqgme/+HPYXt8YFsZZrE+H1el5O5uvvPOurKoaF8sT8l1Hw004ZTMyrEg8PnlKyQ1hmRuY1YFlQcqVpTimmNIsxR2hgmZJnCjIMkJgKIn4kphkQqlRKVcJ99JnWZRudXLhFusz9p8fnOuzfbPLuqbx3rmO+XnSMTCwEm/HfqNlP7QMQTLiKbTptm2HvmmY4ssJpega5lnurbVDlue+Xq7ZGoY4sbV5SXledPPVopvXs2xWF3Y1r/3JwmTLosvn2YAFjypPtKmNPV3XcL5eQNd5KIsczs+Wcl+vbjZwebuDq7s9nK7n4pHOxUAuOnZdJW0Qjx6cwUcPrmC7aeD6bicDra5yGQO31zdQn91CccLhMPbBUldw9dASF+y+GgTc82pCCCeDo/8ky4q6yvMHntz/5BH+8quV9L/d7ftk87u5UYDS6V+BNf8ebPbHBvDvA7i/Y1GUa9d61phAbpQkhWliGSmtKQqaigalSaNPbEgoSSrjOCuTn+P3VQnjp00QUZN8V+y5jKgnJqirS747T47VJghnmltpRRLq9BqgWPMK0GFc8MGMsuWSKnJNlzxlLlA4xqAvWCk4CQSPNwwRc6CGuaD+5zEDy+0eipxgQvEa+VOU9AYiKLJC8h2TB3VyFUfvviPq4luNhhqsHcZrsrTGX6bBnMjGISpN63B7q5JOkSOnHicgGSbjNMYfBRMRTQnn9GH1bIzjMFwwdUK+/41wHGhS6Nvkla0yDnpiNdCIZfrxIYAR2Qx1dsaGWJTDh7K2ROocg3s9oalOEH5oBy14c18jmvHc/vrmnO/eRnQTPazYgJOYHoS4QysJZwKIfdiWVE9CkA82K7Pq4eMn+Nmnn/jVasEGAlgUoqpKTdtR03Tm7u6O+2VE8OfzL76QhC+LSeTQj8kmD3IW3JHiAJpR8CYoBYIgf1Wdi0gPJ5Z9v5QxxAnjer2GsigksWQxnrOzM0EsOSmVYGr07wsPL9MxGcWUZFFQ2EISTaG8ahJDEWE3oUghfGwZkmZMDOX5836M9qdJzAd0E+MYVnQqIoxR+XoMXFPGxGH4IbMexRKTCSq0jJAyDoTqf6kKrRjFfNIe7Ph9ScIyWqCkXqSQKJ8arQuq4A/32YEibTGHFXpv3wkKEI/TqNAae+AyMjeIuvTh/MG9rYVlHRMv7wOEg7nrXQJA8RxJp8uolnaE9739uXeguO/b4rzBdlVMoXWRkUIY+yWCWxmhqs/mknRSEq2P5/PW8STXOjn2QFm1gJzgYWiL4ApcxW0CYzHCgI9qvDSlvUYLGNHiJ0zBSZKb9OLSwbe+fU1wfAe3KnC/Xz6OYaPGKe+/ktNvmZKeZQVUixOoFktBjwhpwwKu2ls5R72y3OumK9zeqK2G1Dv5EgcF/kwndYPjYsZ+vaYM9UTuIZeV0miimenpqJiifCayuDhe4PVvGMg33W6XNc12xt+Y24J4THsMxLIs50TZCj2WnztSgSykoFDcDA72TO1Xj1MeFL1vha5b1DVVdW2yQPHIDGJOFi1rSGyu3pjr18/rq9cv4frNS7Ei4cp4VpYCWV+/+sJsb6/9o6ef0fnjp3598Yj4+er3LfM6udAnd1Vsh7gfU+ZUMxajWAvE88hh4SlraF4WpukGQfhYBdrajPKiIGtCC6sEUtw7yr6ZBDWN3vAYZmeZvqQHlvtfXWibEduazIiImSVTMCe+6svZzHkXuQDSXd5owOnc4AfvO+HDmKA8Zpquafv9nltbSzkMlqYFtvgD9gplJV8uNgzGGFagLasqh3Vt4bQ2sMwhqzPA0g6WUc3cRBVytkibwcePzuH15QbeXG/hbrOH6w1rBLiQQXNvKNuWytwehguvT13Tw2w2h8fnK3h4sYL/91fP5XUuUvN1ZlS6bRqz297YfHk+mKy6oZBkLkD6XJkJL4UPCGNUUM5b7vgg5/9GQ806z2yF1v5jFhP94InpW96+Tza/2xuv1K8A8Z8Z7/+tteZfEOA/BIC/DwA/ShK6lCabrt6p8mvss3QJ4hjXvvyI1hqFeXaafBbJfihBP6vk+2wiRhRRzEK/y+h7+wQBzWGKI2ySeGZJIhyps6ktik++Oya5oVFdytphoaCJ2uX1oTbS2UKkIhbBc47tOqVablLxjCT5M1JmlCDTY6QLxUDmCJl8C6Y83B8HzEKlibQxgQZBc7zpY9Nh4Bigvg2Bjnu+h79EyU9GjBfeBsGPPoSy+AB3RnrkxIMV67zlPolgFoi6Nqs9ylcHDWTtg6gJ+K53TQDLYV+rNuiU1EHPfagGYfBWkYG37wXfqQydnFGgeVnIgL1qhcKiYS+N3wWx6iuxsZNzqwojiDTSfWHXd2PjkVEg4TwnaJgS2AkZTa5xKlT1pVsyGGIAzwJms7qiTz/7xPz0d3/EctDEqoPX19f44sUr2Nw+w7u7W0El900jCBEjgELzYm+6iJpB7Ek0QrOrqtDraFVhlml486qGk/MzePzokfhTMlrJySQr3HJfFSOURZVDVXCfTSF0MaE8hh6fEUmL9iJCraeA9vvQ/KRFllD74ms0qEqLCPZov6aw9xVG8JSgTKSi0ZpQek3CQIEboqliBKEfYBr7ilROfeEYlFrj8eoEEXo2p3TBiR2IWjfFhCoxUBx5AMqHkE8pzIjHxo2JyNk4KMbqWCyKRS/PgDAytXm+mIsQjRMF4PBB6cfMPLSDZVBtfHb5K2vrhN4d2t+MorSRhfEhw9BLX64pVNij3Sul+u1RjGodA29ZV33ZdzBtl0JQ6mlEBGMSKuI3bN0CeRAFinnSB55FVGE4VoiO6SOLee19ERBdtFDSIMh6sIvJJYnndJSvc1AhF9UbZUH6e+V83veMH3JMYFTWHQKjdJxHvwylnc4PFXk3UM5msLq4gNlyxUhVLyp84akXj3BBG7kyg7r4MjNFJgScyeOJOAD4TgR1gk4Bt7SyqM2gDBYrleGgEyAenDrcWwjJasniZVLL4kse65VAewJsyTvfd23Rdz1TP0M4oc8qr99OntVYXAjPj/iLoCEWjuFecc/dpNp6IiuyKgFy6wCJOWW0C/Kw32/x6sUX+PrzX9qr18+x2WwCq6KqfF7XrIzLwkTGljWsLx4OFx990i9Pzjn/ysn10DV7url8RTcvn5lmv4HMoiB2XCwLwYyRiEDmPXYyM5UzRYnzWcWWKLg1nZSBnfPohh7brhWVWrSFUz0IG4IfqfiFOyNnQOIYJb2miLzOsh+3IKJ8iVhegwW6QyHGkLU4sPZImBRJXBVAVMNxAKy0UZu91E1e0qx3a78jT9wvusAgRCwTEmu0McOVW+crbguxYEpD2br0+SJ3WWHIqmtrOGAN6vjwS5NRVVXoyEmvP/faP3txrUEliYaDqC0ORGE5QLku7JfKcz2vJXVeQDQolAK3iBe1sG8y6Ju99Fdgzsm3TGg8vtVLX6qNM2AZYn6MSWLqHSExFPqTYfD/wHi6Iwv/DBB/+dtQ8/4+2fz/wab89y8I4J8CwL8DgD8BgP8CAP4WAHx81K+ZbuM6nlIgkiQNjui0Nkn0ck3oUmm/FKGM+xoS9DEmm/2Rn2ZEQI02P8fkOCaTEc1M7dR8sk9K0Nhju7UhSaS9eHGG6q4NQWSI0nhq598ZMcNj5W8p/XMDLIai07Shinnw/Oc0oBRLr5hwht7ZcMHTxC9hpaWvjcminlHM3SaHhujLqfVYkzabvk1PeitdxDRPVs6UGfePdBBcoJQpMXbDjRDihLLwZDxgJo1RNsAuKk6FSZ9pPJIPQzZ1m+Rw37NprEVHQK98uOT1yXbgHMJGQqoQTBkVX4gCHRJsoQ/eds4K9WxuO8yRJogGJiQnbqKO2gdaHBcGigzh3Znxd2Mb9WREaZjRPPOWcevX2bj38ubmBu9ubsXQ3Lk9fvHFF/Di+St89vw5Pnv2DO7uNsIs4BHPqOOjRw8lgPuLn/8cdrudxBSMaHLieHp+DuccmLKYiLWSNEq/V1XAcrEIFNnTM/GyLKoSQkXdqD2HnqTaUwRGw/QkSYKQWHNwscqqdUekjqeWPIKek7jKjQ+aOUAZYaStyrWViGsS+hn9L01MAP00HuPdOEqENKQLFFXtMw99niE5jF6TgvJJwBv8e32StUgvaNpXrhLM8VKIWEteCyV36BoxmSeaqBo0+qoEQREMF1NjRhr/jEk80ySthc7geD149nHJ1JEWioL9KgT003oFo75ipBWv+8gdvLcRfkSKv8reeZeiksJiIc4lnRcRgdeglIz0M7YuCyjkB+q1qnvHuL4IBVt6LXFC83X/e1cIjZXRTe5L5y9oXLCH4jQw42SIxz57DxspI0Im73Nyfc1YQEzWIDwSF0qSTKd9mpw8syjQQNnBCvChvg2kzwiPD35+68UKsqLqAMxef7tgZ63QFygrmVXNBReGT5RXl8HCYj8Nt4MDYC2nJfI9clTcTuMVp88kwJe+OWrD3xiJLPMgKgQtaixkgE2aqfPOsc9lLm48jlWa/Lie5jb0O3cqGpPbaQ3nTie+d23fi2AZaMnXR1o8W0713Be5g75pocEtbe9u6PLl5/j62a/s7u6WExpHarHG/+GYhcV1uLf1wdNP8cmnv+vWFw/7rCgy8s4659u+bTbb2+t522znFOR5VaWbtLWas6/gb+BC3kiF9JUjq91C2/VSBOC5j4vxfbOjYRhcUQtRQmjMqjSeAxhWUzMYTOFBJzG+U0Vgk0VqMtUkVGYfPJ1EIAd4YlF3BMp9mNhaAQrGVhvaAkrCydZ+FQT5JnEHsgZMZX1RWnKFIVNnLlsX/DfZHFkDUm644NyoEQL3ooqYV99B0ztonaPNZi+jhTPSpvXw+uo2+Dmr71LTD7DvB+69pyCQG+4rt25wr3NeWJhVJVSlAJbQxnmB2Qdtw8KGHHHMfejy4Rh0TwHAYd/75aivQDSX8YrYq2T77zkH/5Uhe2Ey/McA8BcjQJK2Q/wGt++Tze/4hnggLtNosvlKVav+UwBgpPMPmHJylJdQQlFN8xZMPDBjCpQqwsZ/R/ptrwgnaGJnE0RzUJTTwNt03XgcJvmsUWqsSZDKIflcnySq8XhTldqIfBpFSDE5z4i2Mr0lM6FpiIXUZHJCqXYK26MA70sxkOeeK1ZgG0CDgRhQgSIFpNXIGEwqDc2NLgrjiWI8XDyGOGFEDwDS6qaZApSExjXSuuCeyOQAajqA/ULcGsUpKJBT7q/WxzSR4JjUpdm6BBQoqCATcxxF/d1IaZSNr40j4HaVFDc92uFY1NAF5EPyNhpr5Bh7YKcj54QyB/aay6SaPySIK/N7OIgShFaCvVwXdwiVTK58MgXaT8Ia6kk9XVb9L4s3dO0gMU2WfSVblL9+2wR2hYCE6HB8vXVbI9J1P5IhcxZ7Wg4scX8Lf/azfw/XV9e42W7gzZtLuNtsxc9yv9+F3icR+CmBKbar1VL+/ebVa+j6Tl6/eHABHz15Ap989gP46OlTUf4TTrQJ6KQoCbMnJ4v3iOCXGZEG6UlTSqj827nxeSWF06THj3BEBgPVVN+rBYyRWgrxg6Eok1JP47lH1sKI+MhfPgT+MXRPYSvvx6fyrUdBK0lR+TUCjOSjn2h8Acd/R1pueC7UNiWZVyaqribGbBOT5ZCXNcyWa6jny1AoeP0sVOcpmdogit9QSJQh9KqN6mZh5pREggWcmF6mRMiJUuy5/ynQ29ORxtepHQtyLAhGY+3svVvar6hlOlZa9TofJhK8b21E70mR7unhl8VxcJJsDi7s02BkV4RJjueajnKh0Lqkp/99p0G6n/Eic6FLbB/imD0eISjIIhfTBvm+LJjKewODJBHRqzT46vaSfoa5MZiVDCGZh/BcDKmOvR7D9HSjWLZw8txQLgkuf4d/RyfEl22kPc/GZtKXV2QlhgIxRb/MSDwRI2TuGdQRx+u4k17NMCLZIpmRok6znYLUl4yI+b3yj5GOpIx7GoWdwxw2CEM5nGqvft0SMCjtPEdrK0lGyam8nBmfSadUS0koA0dzvFNGn02ngkFsUSQWGi6ygzztNxtz+fK5xCM3b17i9vYKh6FHU2RkqfBs+eHbPaNpiNSaar7AB08/8x//zk+Hk/OHDi3nmU4K6UwrJYOtMVlh85xvKoIPPcUsYiPtQ4VVfTLG/weWIcqXRQZVnuG+GeBmuxfRm5P1kurlSjxBiaXnvdcx7mGK06gOvYeCIsdmc07mmSKqIAY6kVtgUlK4NUJPhnCP6mi1R+EecLWPmx57H6jTDYnOAi6Z7Zsbosp6W2RgZxnZRe6zWjV3ywzsqvCGKfiMQHPBeNMN0Ig3ZhhvnXPw+vIW3ry5hdv9nouawnVv9tzbb6IGAOVZhoxJdJDT1Y78XeNwyZpLCn2TxgcMsBru280DddZJsdIFMShsoN1uEYYms7BkpKPV8mIu94ngRmjGBA+1vYhj8hqlECLyb6dEeEYOih6GrXPuv8Gyelmv11DlOfi/gmDk+2TzO7yhVrPvmdIvVbHqL/Xn/1JRzlmCZKYIZlxKzFEiBzqGigRJJE3sIqJpk8+7owylT5BLTD7jEmpuqYnhtf5hi2NUAAAgAElEQVRulvR+YoKA9ontiUn24xOLFp8ceyoWdNwTGs3UEYMaZqsrJxMonckLMtGYb6xuHwYVzqVOLjC9BxNfynRhTpDK44RxVOsnHANP7/1bwUfqpAnJN6cqsMcbJm/2iRJHxn1UeS6BCBJGqRGNSxHvwetIC5MckIgYkAlW2BGuGQcHatLMyUQu/SrvjuWSQkaRjL13b/HSYWxPxcNACwMXl/0z2Q6Fgyw/fhlKL2f04nTaJ2VFkpDkvQX3fsIk4AKaRMaMOARC4f6GHk4AjmhytaKAd57qX8+NdAwxdXGWO9gNoY+O/PsVjN91y0WlkVkBkmw6ocf+4hd/Ab/4i78IVFlBOL36YLLXZi0CQcvlUhDJLC8Exfzh7/wIHm0fwnq1hk8+/RQef/QRnJ9fwHK5kD4k5+MdD0Uh1F4pUusRiPdTzm9qEaZYqJEAMD6j07SCGIN+PEggjTnKevRBIEULp7A8LcrQ0TxB04QQd+PfXaWOHzOxH1GDVoPqdasqr3EP4bjDdB3RYrkmJlEmHa1TUJBhw6JHeQnlbCmJ5myxBswy2Fy/CejvuxBupfpKLD9el6m/NBxPUOOVXlcv0i3h2IUmfbhXprrN2BfXBj4jF4a+cioj18cKvZo3p4JPX3fDpK8x5owsJNJ2TpKMeH98FH3TW8yJWONy6WkUCI4LYDT5A9+3jXOLD2Omyi1crBdQlznc7hrpKeuEinzAwZH/MqWV57o4RGmSC4WS0Ux2yfEIDSeizmovL+dNPUN2FJBg0mJg9PEO58RMHkYwN2zdor2njtJV6utsJPdotlr71en5XTVbtoZNEMPUHq+EVWx6aoAWg6LQHin+F5KYSoLZUZCCtRhjEUkOVfZIpm5qlarIM3xgSLGYoCS4I4/Djuo4IRGQfXo35J58WA21dMN3X9DtwQlybw2O4lSonrdt72Df9tANYfXNpDJrZIxyMYbfs7u7ti+/YB0Bh+1uS0VZ+/nqxG83t+w9ad0wYBBHM1TNl/jo4x/Q0x/+7m599nBvbMa/56Rsj8YOPOnaLCvzssyYGzD0jdx7NLHw5mWu8yICN8ihWINsbiA2LagMEO8h2iIRsa9l11rnZg5DDxAn8XMgbDkp5EfXCw3U96KWxqV7z6CIAfWmYmYyU6H5K6w6qfV6yXOdordMa2bHKbV82fjApM8zQ6bMAKvM20XOcwRlVe5NnRPMc+ROypz7Yofe4X7fw8b1sN31sNl2cHW3gaubrfibhvYMDy/fXMPL19fQdgOPQWSlWS8emk58k/kURMytKAGLmd+4YrjakznzmC1Z1tf1o85HYQqYVyWUeSb3k712JWYSvQEHm80dbm9vzLxeW7CViljJeGMG3hfcpxno3HAWjKMkhmWRJevBFGCIKd5/OPgBs8w6Kqt/ki2Wr2ez+oCt8pvavk82v8NbQDXfO6n/AgD+W03guEr00yOBnSj0k9qOpH6alCCFwxElNdruVUf2KSbZV5aGUklPZuqzaZPXO/33PDlOTJRlbbLfeAzuaH/+6Oc07j1g9JgQJfKDyUt4JmrlbLJZFMKvDRP5/erSqWw8JDs1mvHgFEqGf6XU0mnBTNT/0sPT4C8mpwdLNyUUxqN7/z4eVoKwsNBCtZhDPluA23EV/gh9TXdN4z/JjFbhQWwB42799CbUz3it2MrKZN4bFn7VqCRm9IcaS8nhSjOwHaAkB434ZeKYzPskn8WkP4kRgP2QC502x8nrzmiwPvXMTSUGCS45UGgHoVFzD4f5yqfzzW8RKLlndHztjXlis9zDgoNo9gUcgoDLfVtAx979dV6FYkQKnhFiForpA67CgdlyNoP5fAGr9RrOzk7h4uFD7bOcSaLKKBtXjDlW4Z7Lk9NTqGYzvfakCq9R2CYcB9NJ38orksSHK/MCcyRCMhGV9EotfWuqRRXBGZ9QFSSKqCD3PqUz65h43HMc8fswFk9w7M0TVDI5l4g6TuFEUuZBox6cQbQsoshxVETxC6ZbCaoZGIiKPIJYEWCWszgJ1LOF2E2wfUJR1VCWlVDoGu452twKhTYdWW/hrj5QKCYUdZoP+ZiqWSXqw6z6y4lfEym5cIhoelEk9VAXAKc1B+4UZEOUbvj+oR0vYuoxakY7qa/DBw85OyfiuVDhyAd02AliMkDfJzYner+NJtrcUMhUVkYBB4o6AO8/AFmsbah89J4EZTlbzuB3PjqHs9UMnl/eiXDLq6aXNJBp5Yn8uM5V+NaaFaq1ZlSY7rwuqBggvBKQSnC9FbDJG9TzCNYYAbkUexPKg8WJN+88F0wO5t4iVHxOFS1mpeT5+oxOHnzUzJaraxZ2iShXUGPB4C8uvl0UCOtSMxWBFaOQPQU1WapEcTboxEbFz7DRaDHdyudJlGlBNfM8mbFdg4HgTNc+h8F/B7t2Xzb7rcD3TPl1jowgWBgKrlWZw+lyJv17471k+uXQw5vbLby62cCu6UIhJDdQ1ZVEI7vdzvihN/vthpWzhYtaVnPKy0qelXazMX3bSLpbLZZ+fXpBp4+f4sPHT2lxctajNb0L9FxOzrpgEYMLi3aW2SwzlpFBz3o+I7MhFs2CnZnXYgJKUtx7L8wSVlPlYuDNzQb3nbPcAzxbnbkZnepjLte+1qHCdcmOvybaopD2xYakUno3AUPXQReY0GGh9mODAtvXgTcikAQ5K+CzaG8B3uYGlnXu8mVOdp6DqQuxyLJD35p+5/wVn+EgPqZ4e7uD2+1OWDOM0DKCvNnu4fJ6C5vdXp9XA2wFwygvzyusfjwEb2Dy3ksJPhD3gYwn4qLlnfPmVWXw0dkcluLlTHJ/ejfAzBg4Xc5htZipOrWDOiugYM9VBL7HeHN5ifXZQ7RZmQdjHsF32Xj5PNib0CtJOgFnCLQU1X6GT8HE8V0Dwd/Oipz7Ya+xH/47z/3E9NX6zL+J7ftk8zu6RdrWu73Jxo2bh/8HAPgMAHgAXyTU1CxZjWMUnqKTmPxJtypJ9GyCIsb+ybTn0h+t+NEbc5YkqVYtUXbHsXyiXmuPjidNbCFBaKNqbXHk42mO/ibp0TS8hkDhnefKElfXKLPWcVWLKSD+KFh89xZOz4N6Vx4VmMM6P/atKyKhWb5SNQ0e0sIO8j18m5I4BhLj+82U7aWf1ePPxPrASnUtyy2UuRU6bAzK08r6Qa9pVHVFDdEoorATEhMmNxy/M4p2+HcEF8kpxD7NDwv54rlKkVVxzeTDo6IUeigl4exVWBAiiW1URWSGjQhnsJiFmI8DdM5CZY2IBwUlU5wsJuJ9OLIQ6Dq1b61AJOLte4Kq39wm9CRSROLr8dmOrjlHdovMSV7Ca+KW7Ihsva/Ocbir0CcIfpKztSxiMithuVrC+YMLePz4MZyencNquVIrklNBNzkRjYI3TG8XX8xETZWpakKLjX3DWgSRoN+ryf+oehrwmXH+jPMp4dhfGKmPU+/eFBCPVhKKCIwCQbGnEycY6XiOHhViRTnRjOqU8j4mVPiQRElLqFHPSPJTMho9G/VhlCYmqzpddNSDKb8LIyEwG4YpaVb12Vw8+4LHaFXNIa9nwL527G0YxZIOJvD9BprNtfiGfvn6Q1MDQuxZpNDzXtUVzBdLQaRl7nw9ABvjc66QdsljaFaC3WCg9Fb6sosPFJuJnzfirRq+m4+b7rWX/tAt+FGysTyz+0JBItBnRX026cUdj0FeCMI5DWVC449FrPj0GIm577fAjYgvJ52nyxo+fnACTy/WsFqEAsDtphE05q5pVfgJRzutaVY+vCag83zrcGJvIEDL6GtAKbEBZ7mf00TarWiVgCSZgZIbek6dPzTLOkZpUX0a/dHakh5RYDPLs0W2yP1seeLmq5M8Z6noMHACRdbEL8ClD6q0nfKdB63V2JAYUsRYK5ULNYoQiTxqoM5Sh4T9uJKgfCZXL07po9NMzCJSQYQF03KlzkS+bpvGNrsd+2F6Y7UBsA+ji+/ZrCikZ4/nLqaTi8YDEOxbRtZ2cLvZq8K2NKSwlKsX/YMoSmbYycOh1kSw7xrkBNT1nePnc7Y+8euzi+Hi8Se4PLvAsipZgYxNOitPIXPR84nzlQ2kAhRskBNj7jvkcRuLQNE2iXT9HjyrKlrpQS0MF2UdsDBQP3hTzTeu71vH/UicEnpPvTQeYfRqkv5LG2Y60piQiVEshoMZawcFG7ogh6D05Yq7WnKOxSTLhCUzkDl8qTLEOvPruaUsR1eB73LXN+JCv92xjQzbjGzh8maL17cNdGLrxAjyANe3W7i63QjrgKmtPBDY/qrrBjlnfl6CR+dUrJEeIZsTuR6lx5xjA768fU/tZsvHbirr4PZiAQ8XmXyep9++DayJxayC05MFzOpKknZ+fqsifEfb9nB3fWUft7vB1is+An5ynYxdhMcAcAIIXyDB5xLPEv4QgJZe1ZEZD5Ee2VAQ/wM7uH9o2+Znbov/2ksLU/aBq/I3s32fbH4HN3lsv4KtBAD8DAD+VwD4AQD8h5qIRZ/MWmmrqdM1Hq3EeESvjUlCn3hvusSaxCWU1/s+D0nvZ6+fW2sC2iXJ605/LhLFW4BD9NUkx5ImnCZJZo+fg/g7JwubMVbYauRFMCArcsPBVpYXYV2iD73WGgxqUKhr52hHEhPN1MrEYqCljL+CMfY4aAmaFvIps0ohY4go3CQ+mXwwGt/bQDwiD67ZQcdf7HJtW7k/7MIR3eNZ0Noole8TuxRZqFVpIA0+g+iABsP37H1so0qV9r9kbhzPlX3ILMJgMBFyUVR1bP514jfHrTQ9ZWOiaZQE5slo11pIPnh55wp9KUXDKKcb+cExcdCgKSZZGuB3vSJcFQbU7f2n8a1uSnWLsYK5J5E/Rvq/dCP1TK2KcJ6teAoY6Acck/0PTTgPjwAl0eG+yx//+Mfw45/+BD75+FNRKUWbBaplZg6Q+QPBJkbolH3gxwbn6DURfqYUgaWJnj562oZPh7RICz9BgToZoMlDRSq1jBB7JAOUf3DuUSH2QLU1iPrEZDOq0UbUFI2ZjhsDRi+0LeMDnVZ7wn2YqpL9+lBWiAmv9qFOViiT2yyodYsVu5YqiCgVJWRlKf2vjJwwbZapsumcl16z2Lcrr6k1zL1V9KTIFl+IFGI+56qsYTFfShFsiKbycXpMqB2RlbBpEfbOwyLzcFECzEo8FFh7z0AzNqizsugKq3xCTPrxXb3r795EOZT721TYRRK23ouoiHPJOcQJQ49BhIMgl0TtEJU+FIM7fiwluXOBasuB6vnJDJ6cL2E5r6TYcrKo4ccfX4iR/K9eX4v/HyeejKyZL6H3p78bCwKa3LOEGs9mbKcSlDr9aCXFqOzUhhCtU95XAIgp7/3viKI4vP+8qGl5cjasTk5cUZa1JH7iOSgDOY90wpF1xTp2NH5HEyi2QYxQH9NCXkNF1AQRxfDQyGssNBMEeSHk9FVA50S6NvRxjoubzApG5L9YQZVRuIFZBCQPtrVBHtD7SfAp6OZ4LVSGFhlGonf7HrpuUrpmtK3fbi2jaCy0ygKyVT0jzCxJ+sbIeNtQlmf9bPHIzVbr7PzRU1ifPRjqxdJYmzGdl/+P4foYT2PnOXJJYcuJa9c1p92+sYzM8Xw3ZMHKw2jPeCxqxStqMxtUfkTkyIDjNoVB+2oFjWPeeEtZkaMz0BlPrPfFKlSVYbgyC0mmDcqvhtm3GLyd8oGtu0kIuNIgORB2fJHrnPJFTqa0QDa4eBN631sasDRUFaJeO8B2u8dnb67h6noryWTT9dKHeXWzNTe3O9H+WtSlFNTu9nuhzDKLpjE4JpRBcyEb57TgzOWlOMC+zZmpZI1iBXQcehoCFQRd3yI/75vNnm42DbXdnJZVJpM7CwQxSlrkOZyfLOHsZAFN08u03qtgFB/nze2V2d6+savZGaGth3HiIBmvCyA4J4AXQPSXIAi1CGNd4Bjzcn8y31tgpP9v+7b5u13T/pwAr4tq9htFN79PNr9jmzz8Krv/FTZO4P65cr9PAOD39TVtpB97MiFBACOmdyx8CkeKr/E9EbGMNNsuQT5TwaF0TMa8wCZ03YiMRtpue0SLBThENNNji/uO/Z2UfDbGw8c+n0GhFpEGrsp5TybPC6asFFVFbDUfYpP3Ldl6AERjkpK0KY1kWaSp7gswybs7hNFgXCifmuBQEnuMi2mafKYBe3psCTIae9D42DigE7Nong5dD64LvRhsdo8qnfFupj8q7ooiihI4S3hA1owXNbaesmo7L6BZhgf9TeMeIz0piOJpte7D+jal9y9DsM6A7yeKZPod3LPJvpuMWLYqNhIFAUHDJi/s6eDTyVtLGezYV1XUGZ0K+RsVSolr8GHvbESbZIHhbymY6omCNv2VwJvSUStAZHy+0i0UZEKWnAp2vXOLQ89GlCrNiY4KH18p4YzJC3ioZrXQZR8+egJnFxcixc+VZKYksRjN6KdJh4H5+F04oWd6DcZi/hj0j+u4m5BqmHrrAOiw79knojZH/ZjiVRkHxKhfrJWIeC2i/2UQth4Fg3DsFdU0lxVFUZMCT9P0Kv1TU+EpkAVD4hnp/eP++N9KWRdfSxLDBrEVYTsYsYFh9JMRTO57nS+gmC0hz8rwO0HkTXqK9ycn3EtbVMCBTLO9m+xbRnZEWkVK5oaErhqxzqIsRTm42W/h5voauq7T6zAJ4aTjiYsJrmO1FwJXvM0Tf98Yc0p1jf23bEgvCAaPPue+YlDGlQK13jEI3cD9d4PYnQi/ZLyMeDBWGQlsvYXeWzhuzqAv6dnkeZSLBNyPbMalK6w3RY7w5IKTzxrOVjX84vkbePZqA5umDYU+xHfu+V2nrWWKMN9HfEzi31QWIT5BmDxP79hQja3feZ19sKXJMqgXSzh78BhWJ+dgMu4Wp5If2mCHTIWSqDPmgAYGjaCWhEE4iJVmSacrnbvkv31IWOVILdM7UZJY4uSmDW7UVKjr1hDFiFhMMDx6ojhrgwWizJusSDtkWZ5neW7zLGcVUvQM6HlGsLjPHaFgeyy+dC7U7nnM8f3gwoogitrPzc80a9mKQKAq0wqKVlR+tlhRWc2MJ0d+8P1sMW9n6zNXzWewWJ1kBUvBSp+zR1XUbQWJJfUFZb/JYO+yEcXVrl9zsQR90B8oWQCozKLv71hLi+JnhfHCNZBinhR5jXoM8/zc2pz6/KQc/KLuvee5VaZNuX5M8jG5IU7lssySLVgI2wopwyu1mcdXxQCuISp6MC0atLUlW2eeQwfoHNHtpvG3txvb7ndmcIMRES72J2VRn+sNff7sNb66ugvPOVPSTKD8lmUu5zeEgkCgCHPRiULUgYKYGtFc4JaOPtCHpZnXZLkADnlZDlk2p7bdZ/vNre3aVu1rwjPQ9gPtusE3DnBpzCi11fc9FWUFjy5O8fGDU7jh/tCmh33LbR6dTGCbzQ5evXhuy8UDn68L7pAVKq2i6gwGLwjgMSA9M0D/FgD2APD3QhxPdxg8S4N3G+IPHZr/nAj+JDf4z8Gg+03GH98nm9+xjSvj1pqvU7Fg7vc/VXTzTPsiB0UPU1SzSBJGSF73yc8xPosmx3mSsEZkM034MOnfNEcJHyboaJ5Qb1P6bPqdkMS1MSlNV8D4/an3Zvx8KmxUJdRcSSqNR8scFjZ/yot8yKsZGO4MgG5UPx3REA0kU9GdECNpffcYqYyiGBo0heCM7yXbGStVxdMYVIN+JDY6jClq/F2swqH6JcfYOE0ARglsFKltrwGt1BRVpdMq6mk0aDX0dsLp06Bez0paZTD07XA7xqS8G7xDgqw6SQWUiqPywPG9RO3NOETA373p9eAFwuVhARmGRFApSfCtKNA6trARBNMTjqHR1JlJEMsAHNGw0IXFIK6SoX9L6XFMqpKMH82EcA7eC0W5rCBIpL8nqPtWtomp/fa1DKfcq6Ljl1/r9KMq58C0u56TfH8YYH6tU6Rgdn59fQ3Pnz+Dh48ewcnJGgzOBLUU5PLo4t0vonyYwI2BZvLcUPIgxeckfc+9J0BvYzFj9d9oEUl7AMOzHpGqqZ9egrbpkwfjbUrOvPZOTv3a4Xd+VLYUSX0XquMRr2AKMieLYock3pUV2FycBSQ5yRm1tLkE8YxgcgBlJeHM5OeIe6XX4X33UZ67ggWDFmCur8B1TUBcubASfRTedZ9GinE4H6YWcjDI58hegZJ0C9X/3d+tlaEw3xwlo+/biJHIKIQU+29H6d6vM3LDbMXzDpvdc+92PK8pwZx6T50o6Vr5M3xolnx07gz/cJD8+m4P9ctrafE6W1QswoZcUFjPSyrzM5iVGS7qCn718hquN60E0e+6SpR+wdFbDivMR2P2YDL/8vP50kss1HhLVT2n1dkDWp0+YISzU948t48ugYVk5M+4Qx8SxrC6UOjhzKTJDmP9FAdRlAU2yZeeChsefOSlIdJumTvQo+jJcGKrsYVRAouEBGgCUUDEW5h2u5e0hXwunydOBZ1nBVPXExQ5ICPPjGjNiny8CNzCEhBBLcaIXdEw9nlH5XYSwaCM5ov1cP7kk36+XsdeEFdWtS2rmmFUZLVZZd/qkiZcCqeEhkp7J0mFZ+bKZMO8ZKG1Uu5uVTN9viRuHRq48p7oQHNthhO+Og/2mSyuIzRtE5pYaehN5ofiovL0aNl7ImeCtpEgcHKaORK3iBprGMLkIwyUAu5gMtNTLQXbtu8LFlbCntFfD9ddB1e3e3z+6sZe3dwKMnm3bWDftjB4EuSQbzNTkrdNK+svPxerRQ11NYfFrJYhxOJAjHqG3vzAjBKF7YyhaINOySE2yxhsIKagMk25mi/aer7cVdWM7m4u56/cULFCcOz95jWA7/m27c2mdXBSGxEd5sml7XtfOIDFYoYPzlbm80UlVHvW5h1EB4BpvD28fn1pzh6+sevlykA2czRJhIP6bvLY/4EHugKCP0XEjwDhj0DUejHTIcM9mzV4+kMg9/eI7P+NgM/9+9S0v+Ht+2TzO7RFJMzar31bfw4A/yMAPAWAvwsAp4l1SUze9johzY6C0bjOHKOKEZEskySiT35HCbqYJ/2e6T77xCKlSiizcZV0STJ6jG765E9MPKOCbkx6s2R/Q5LwjkgnLzR5XnDVzHnnWPyO/ap8UZRuaPcWYm+V2HRxXuZG4Zkx8eOOFsM9Tj0YFl9Tb7zobScxhk/mEQqiEinP8QChU3n5e+lJeBwMQJJl6GdY/IPXT27+MlZ7Fj0AC9NJGp6DG5SJBMn3jhc4IqzH3iWavJostAzEXtSwB4yDIFhlqAXKu7sGeb1P0ed0i+suJWvxeM6oVdae66NOxUKiDYQeDvde1jiIAuRO8UuTBFCR/hU36UnzBhrIIbeBSAZJgBrv3X1uCUHtFmQR5PWCD4djaalm24mGCx8SfH1bWzjZ7G3ThvdvESUnUqqCBSgyDjbUE/FrbtIj5Dxbn8Dnn38OTz/+BJ5+9BFQVU1i0GOqFp+bwy+MSd5o2TFSaBVti4kmTPdsorR/yIlPaHp8zUTKq1qYHPYuJpZFoIim3vQwbxx+NSkv0FDA22MPaLDRJEVGQ/TM6CPmmY6pDLK8Cr2VRQE2K/TvXOYbQSttFlRnNRk9Zhh8aJKZvlf8ZesKbJEHkSAMvU6ejgsDWoiJAkEJ4in/42KRILVOBKgYBzFJgQ3ecUyKuo3/m27Ulxx7srNQxJh6077OJglkGxRHx2Eae9cVRUelMkchHUY30zLXe485PgAm9CfH4ubNroX+2SVu9w19/HDlH56uzHJuUVQ5C0tPzlZQVyXMqxx+8fwaXl1tBXmFt8bpdG2OvTPfex3fs1llgHjlowJE5Nu/c8Yh7THOsgLr+dovz867erlqbW53goDJIsY5D7MsxYokj6idJprssOjGeRzJ6cFGq9DeUCyEM3QUYpEg9QIVAbEm3A40HxwL1F4KoNG/qI/2KAjYM2rk+97sNrd+v71FprdyodpJa6sTxkxZ5DBnj8pc7x0zNbRnhuO4iCiGPk48eHakxcbmuFiv8ezxE1/Plh3XwcW2LUhfc7bSQ+jxM2zBAkS9LrmMyApZ31MsruOSCJZMb8iLkurZihazkvquESHEfTeIB2Th87FXJD6jDE/OSnGlBH4f38vQhxowdup2pvANzPPKIDd3ZhQk7nlhj8wOcZzTUpuguKzw6tEP4fng+axpW3j25tq8enMr3qR8PZgK+/pqI4kifx8rPb94cx38PjMLCx7n8xpD77sJdWtW+m16uL7bBfTTe9jtGnnm1lycybl1IPf1fEHFbOEGb0yzZ0qsG/Ky6POqJmFvlNVQ1nU3X57cIZrSAeX5m+dZu91k5LyhkYBB1LQ9cbLbesNOBrIoDj2jxA7KzOCiKkJfbry3WqDkQtDmdoP7mzfZ+sETnxUzFhEjcYINAyLa+X2GgFsA+t8A6I8R4AkhfByWOFFblhtjEB70jv5oAP8Ta7MXTdfSfc/8t7F9n2x+h7aYbJr3q3t+2fYvFdlk/56/o4nlbZK4tfp3kcQg2TsQRkjQxNFnOvHazJJeypioppYm8XWbJJJDsr+0wzlVnXVJ0pj6dMZjj0hrk/zbJv2hKSoa/y3zZl5W5IaeVcNZ/ZLyqvKwNYZJPBy8CYLlWZ2Mkhok6g65qyQHshVwPwG6VtYEOgg2D5X3vJvEJHwaEY0m2rFSniiiRoVDVJGDSNNFLcfyRIYZkC3Am2L0/TJS9h2E4lNWOWTlHETQriGh9RwnDV5X1/ui8tC9o3UG9XFPtC7GciXTALkh32cm7QE73Bm9G2Fjj2SNCu0x1A6hEUSSHlbI86NNBI1IK0/v7J1Zio+WFT85SQr1MKZ9YhJ48/tYrSCDwg/iQ0eC/kZ/5QnFAEosZ7TYya8xotv4QY4rz5wEHJxwG4MH6MdfQdIZAq0vT7MOtul6E6wML6AeNpmB29bCrrOisvl1T4WfKaYg397ewfXlpVAqmS4oPrdHKrD3Xc6+UXkAACAASURBVC86+kVMOGOimYpuHX1z8tMYGR/sIyAPTisdZiyloNDhuHgRkpax85OmXl+vNFujc7bI3w+TQiwmfZXS+8eRqswxYYyxImdUi2VkstDeSkkobSHXx+ah11KQSqPWCYlw3HER6fjvr3e/DGR5CVnGbWG3MPa0i33AhG5OvqOxQKPN33pdmCLt2z2U0EItzN8QjN13cAn+IRQO1/bQsAFyjmot8SUJ831J1q/x8HFRiQNfQTQpeZ71fxJra+s0I5l7l0uyOYzL6Hs21doWW0Huk8st5FUt7xcbhn6A67ZRlVBE560sDGZRMlKDRZn588wySGWa3tmbbYO7rtNixf1f+01MQ5lQEq1SOmW10vbbCNXfXzrQfmWmHNJ8tR4Wi5OmKKqWWyDBe9aC6ikI9fiAlknfh5X+SRl6qAqy1Og0JLOsJv+hQiz0A3mYOEF1OhKFsIik6KiwF4mTiBiLuCQRl+Wd2yYRA7rq3NAPfXvXdQ12fV/Ld+r8DlpIjgJ5mQkoIafB/LrMA1K0Y2shrwraMFUyNVAwTNDNC2uLfPDOEwb2k5FVP3gPBSgPzWBMxhIGGJJNudWDaM7K+w3xY1JXhV0sl7bf33nnmfrd4X7XwOVVhpuTOazqgum/fEkg0H6NZOxc7MhzLe4OoQDGIrND3+HQ7VjYy3cevcEcS+zZyFwQv2gDEs9t27R0dbOB282OUUpsmk5Ot65LEev55eev4fX1XSxTwWbbwHbfwHxWwZOLU8jrHC6thZ0LVFbeuPe07TopglUMKWOIC0jtnzhRXi9OYDarYF7PfVnPB8yrAcqFx2pJrEXRtR1fyq3NbZdlRWktXwW8tZnBvKhy771ZLNdDPVsOu9sb23edVl851Btwt29o3w7kKWMNEO5vZbzUsoQWGAYuSikCdl0PbnCjeBcfLAsU3dzc4Nl2Q1W1JoOZyiVL9bGiQO/msfcpIjwioD8Dgn+NiBdM3CIRZGIkGdnxlJUtfwoE/0HTNf8XIl59A4/2B23fJ5vfkW1ENX+9RBM0IWMPzicA8AgA/gAAVopoRhTwuPexUOQybes7xnZSQaFU+TX14YQkcU0/H5HI1EfTJ7+LaCkliGt8LybJZ0xaTbIfSBJpSo4pTX6Vuhu8CjiwMz33JhWY54WcE1ftM8wCEsMrgTPKqEm7WILcokP2jisABd10ik683VdInt66iDDu64gqFqHCiaM1CbPw/vm4TCmqcQNm4CXpzRW1DJVlFqpnK6iT5RI++vgTKGYLeP7iJQyvb+FmP4z4ER0cx9t0KQqNrhOdSu1FEh2QBJkQX2RZbL56q7EcuPq73a+oanDquRBKywGEoTCe2JlYKIwbCUKxN3YKiyN3Kpwjq0ayFQqjm7nptV8xog3JrRhPaOKuKuAVBFdFUMUJ9aiX5zf0dIlJtKJjCPSbTjq/dqkzYzN9pV3zteSqMbu/DEPwtsSvmsXGuc0a6JoWXr96DW/evIHZjClMc8gztR/7isnBZHsy0WvHz4/3TSNBE6arlH4uiZve6PBWHS0xGRUBIRxnqnFcKK0exx7SEPDElsXjPkZBOpX/zkw4Y4z6kGZiN8K9kWypwH2WbEOSVxVT+wPCaVGr+YmFy9F1+HZIVHy/ckE2GdHk43fa3yp+dU7nvPRo9B7ieC3C8XK8aE3gKwbLiOkxV13v0cOCdDbL+JoN3B89gGF17TIkm7+JDbVdgfvfe9ZFUXuZ9FTHeVEZJiw6tvM5dN7q1XvPlUUjhYMir8Awctz1cub8LPRNA03TBGEilF5RvNywmOod0/Ho04crfHAy52eJo9xmMSvM47NF+fp6m293nSCwfmwz/nbQDmMzrpLQ0DmDHFQjHLSGpM9PGEeZCKoxxXt5cu5PLh74+XJtDJqC/Kj1sA3TCldQ5SnrFaXUUUGooCpXVQcgjV+QF2gIlVdxc/GDCP8QRnKB0f71dhQ4RGGNhsK0HDZlo4LeKKwsICY7pg2MhrnBzf3gkGmZNs9Cz6VSNa0KaDn10hyvh/eKXmpZQRRn1aZIa8hWmKjItW1+P/ctcJtPGH5dj91+W7iuKZAVxDDr87Kmsiz4i0rvesukXqbUVEXZ1aXpMusL0+PitsrMjQHa327MdrsTXQUWsxFYN6jsShE8FMRCIZcLTGerudiGsGVLy+JCfUiK9/uGLUXw9m6LUvTtdiIoxCJVrHLLU2nJnt7ew/NX1/iLL175N1d3uG9aSRT5Gs3rSvpXr252oT9ZRDBDAXlWl1CVBezaLlRK82xMJvk4hFngCRbzSsSAThYzWMwLKNjCqaqgqme+rmqyXLApa++x6Acqhx5zdDKksJ+FTlVO2nxoZjDat+rZy7Iy1rLnqzt98Nhvbq6hby8D0zp4RhMLeG0b7rkFASVEhCz6DRkLRb2gxWIJRf4Gd/0Q2nm0ACfnfXVHZ5eX3i7OvKkWXB0Q8WlP7EAko28PQAsP8BMkZP/8fwUAPyKAH2LoX85Uo85l1j4Yhv5vXF6++Wi+WHyfbH6/fbUNNQgx+I2sqpdqh3KhKrBPFQUkRTqtUmtNkvgde1amSGbcRoTwSPwnvs8dJYeYJKaYJKZ49BokqGWd9IpGmkH8riJBTNtkH03Sv4lJkmmPElMk55if1ltEl5eVKauaLTYw9CnYgCb4jHged9KQf5wuhnqux+wgzxbUDae3H9Dojm5OpIiC5pceJrXLKB4EKp3vRa3FgLM5eFMBt+IHZNME6h1NN4P3wbTOj548gt//m38LTFGD838Cb+5agL1TMYlItlWkBsw7KF+oiaRe1GNab6TfjsIQX2+gvivJHK82e/CxEENuQiXZ+dF2YhyoSKIwy+GpKBHFa6f04EMdxfATv68FDhJLKMXcqldJeBwDNaLDZBwVkYjkvDj4+H0DV4MhJBWihsVu01mgUwWP+b9StPMrbTQO+chtnjwhv04IG5LNTAIPTjRfv34NpxcXULJ35ohQvv+ihDE3CdRE7Z6xnIPRgEP3o96TqKIXY/g9KqKGXipSqu+YREIoEISK/RAq/0qJGidCSr4j2pG4gHhG70VSYRDUuZ0DFEYKR0XYKliPcMLJyaYR9M5OiO3R+f/mOnN0AjWZ0NH43J0qbEqirH1MwWpmIqhTrM5ET1HQhN5m0HEf8MAiOPxcjLOckN6tWM+ZA6p7LOgIQsQdZjlpkvrt+g3FdJ4D6Kb14nkZN598N4YMRwJKFhzb+gpaCn17ZpwdDkggML6oasHzkzWcP/0U/DDAmxfP5O/QbzEV+Vgdc7vfS7C6b3vsgo0FnSwqmFW5qXLjHp4s3Y+eDLbtBvPF6xt5b5HZkS0zVTPfM++8NTG9zXSBmIRLXmKlyBoKJ0aQHGb6xGsTFduJkTqb42y29rPV2q/OL/gPlbOFQvpibjjIEiPqpcGPiLSCGlg3yP3nSrHh8gsvuNLAaGBUo42DI9hEGII8kC0F/ZPOABIdcemBK1Vi2GknvrCiKLTxkCBMRvbDHMnSDY5pTEVUno70SP57OQ9UZmmhkSwERfmU70EzOPHXZPuLXui1Ghj5UFphG6KiLNmCrQ2ANgX5AeLr4dt2v4Orly+K3d01o26O+w6r2cxwUuddn/dtk0kbRz0fTk7WrlhVBeCQb65uzObypek2t7Tf7ZDpqIHO6QR1437Izb6TxI6VjzlhY+ps+HcGq1klx307ODnWpkd4dX0LP/v5r/DN1Z1luvZutxMaLCvt8lmVZQbcQ8wxEKOar65vzc12D+0+CFgxGs7fGyjGoQjbi71KB7MyhwcnSyjrEi6v7mC724v+AqOUXLidlQUsFjWcLEpYLWdwsphLnyz7Lg9QuA4LR6YgMgX0aLCjzPTOWPb+9NEGRx5DkR5nKrIVdBscFzMWUdsADZi8rH1WVFqIDHMaH3u9WPq8nMPNXYMvL29hWa+hiBzmgdWVAU5OlvD0yQVcXl7CF30negeBJRcE8nbbLd1dv3HLB4+oKmviJnhGpLXIEpsyFgD4UwT4BSH+70T0LwHwAQCdxcKLNpJUHuB3B/I/zaz92XZz2/8a9eUP3r5PNr8jGw/wX5M+m248cn8FAP+9+m/+I6W9RmoqT6xbTd5Okl7OIkkWzT1CQnHDaSU7SCZj8hjprVnyfZjsGxKxINDXY5UzFQ6KAkRZgmhGBJP0nFJqcMwAi+S4jo9bynqI1s3mS7dYn/GEzebKQgkJRsikbZjmnfQ8x8EzlJBxEdr1byVshzjEuyeCuEz6iCCGNZUtjUNiyegl+x3y2syabwldLPbhRFSFm/wvWDb/0Rk8fnACPRYiLsJxjEu7LmO/pgZD9wlApCjngXF94kUQe1GjnfOHCEl8nU0qxBys5z5UNPyEVcZzEWVaHELnPeWBbnxoCHFAwyPRxDewJykTg8VeE4OoRnEU10ZVX6ZWskS6FElxZER57e2LwSIv1GymXhhGhUJQFlHPCRX+7dvibeYkwOLU0ysU7fGpn5K+D7rnFKrpIsM/DCIW9PrlCyjZjuPsTPpmJ+/I+7gA02sGIflOA6FxyemB45gpR+VYk9DWRsVbOLzBktwhJAIwMVDwioJGaxEvyFF091NBEbU7IKXU6mPCSWpWBgGfPIO8rKFkj8uyEhRT7EgY1TVmRC3TM/2rGhvj/RdaXSV0YlHmpWgXY5ReDIEGOnIHRmnQkQ4rSibOSzA7OE3kxzNEdZ7043nDeO6B8CAJJ6u8EhP6zYHK3bd17kwh3IsK5jQeo/USHbwzFLNan0PrstFeKVbi3mUVw+Oc+2A58J4vV3By/gDK+RIun/8KcGOkSIGZVTQ7XB/uZ2O1TQ7w7zYNPDit7ccXq/LBydwt69J+9vAEJXgfBnh5tZHjFaVsCK0BRv0W7zsmPLr2cuyB5z1ZDOkmfSmDC1RP8bs1Sd81HLw3FOGkQNGXs7lfXzyAk7OHrp4tHGYMl4sP417X7FLbmjtWOUX+N2Eu4j8qLhceTp1hkZShhaGoHEw1DZBpVaUa9d2q+BkVAeS/XGEa9OJYPQmrOnxDUE1ns1FybhgWzg9L5I7JjCVbJySAEyamb7IdjbBA4rzBDA7x2NzD65sN3O0a9PpcTAC5nBRL6nA2znUqkcRlWmyZ+6FAj0XhTGd72DS31DQtzusya5rr7PVmi5xEejdgWRWwnC9sfzOv7wq2T3GG0cfruzto90z9daPOwWbXwi9f3sDVZg+32wZu9y33GgrNl98X+k6DPyhfClZ7HSj0oF4yGtk/h8+rN9C7AZrGQSctA059fFGS1lz2Z7UlNCjy8t+cgOd5DrUUQYz0bg5d6NnkCZM1Gk9XM0l2b++EKg51mQuSuVzO4Gy9gPViDllR8Jw6QF4OLZW2HSwrxHbcJ905zJynTJSjfChchDElHjiRPRUXAggINzJ9lSWyvdx+bp8KCAPFAoKg81ngPV3d3uHnLz2cr0t6uKyk5ZKtlipycLEs8HeensP15TncbXdCD45W09pT74d2R665G3A4RcrY4FgeklYfzAIQuf2lIiNuEv8GwfwpAv2RD8xEXiZ5vDLl3FuTPSkr/MNmt/s/gOjZb2LF+D7Z/I5sQjf55pJN0LXhTwHgnyjC+R8rytlqMthoYjfTdaZLPDk7/fw8EfNJDy7Noo7ptZAkjqnYUExgY7IZE9E2QS3jvuM+Y+KZ6897/V2ZIJkplRaT1+EoLknXVI/GuKKu3erioVu+eWG6rsnZcJwpkeKwJVZR736AOfFzcrkMZLyWuS6pvMdg/OhypRL1Y/E4/Y6YYBYiRMQ02VFl9mCHkec50Q9Z0OXByQJ+57OH8NGTh5LktPs7aJqWq+JChQRNGt5L85qySsYdxnZL8ilV6hD145iEbQECffSbR+6UqQJlYTTxdQcWFlGQgzumSj3uNvaaJkkzTjjYOGx6MZPPhDY4UzotHKB7MA1lRZ5GbD65alMYETYOKp2oXxjhyuTMPrPBU9KqvUh6fl99exdu/k1c8PAA1RlBR1ylZ9l4O9KpI80gGvh/WVVV5JTE2DyXBIvH5MsXr6CqmA61gGIxH8V/DgPiJHFUNVQpxxjUhC9sHDNK0jI+ZmakEqJWc8akaBQacvp7GHulMUmEOMAAbyEUoOKjSuNhCTKq9FijA1/aIKQXtYCcey/LGReyoGA6qi3l9Sipn9Ji6eBa/XZsfE7cO8q6JGz/NqH9froG6X1K0HACTAoxXimVXlV9YRwv4nZLx2seitToWDugoAjLSavJvj2rIYHFhuCP2Pdql3PUDJyOcq9UfPHUjA8zQTIug7cBHngpkyboYezwnLo6fwjL84ewWK7hzfPP4fLFF0B3NzKOGFnmNWm3uZN+NyfU3g7bgQ3ovdBHz1c1rBcl/vDpGQzeiTr29aYRNC3YQLw93x+eVjKPSCtPKGh61x+0Scc3S1FIsm/WJSFNYjUp5ecgJrVEwlxYrE786uy8PeHzPD23tsgiLUV7JinTqVnV38LELCmDeDOKTQnbmAQ1TrF8wl6IKZwsIMWC+BBjC31cneoYoeg3CZOLezGpRFLzjkBGUmJOSDwl2UVkxVrXd63p9jsahkEbdIVcK/Yl/FiIvka8MGYqUDH9es/oYdNLz29mQsLPb2bqrHwbWMI8w6LIs1mBNC+8z43zy4zMLPP5UBT2LDvB05yp1S2uFnPz+vUV/NnlCxkPoiDvCkLfm83tFTJq6T2N80kQ6VGUnUDQzF88vxTUm4sWexbgYfpnFhhD3DfM++RjD7k5iZK+WKn0DrZNJ2OKP8vPB69jjGj3voOduEQ1kiyuFzOw+pxyIsvzXV0W8MmTB3BysoCb2y08f30l38v9l4vFDB6cruDpgzUsZ5WoMfNhF0UB9bymjNFGm3eUlb711m4H9LvWuF2P1HoejzyHMIYuqZ0LEsXS5+oU/ZYghgIpCUT9NVBp2T4mC2MVwxKA6Mq6dvP1CfR9C/vdlud/bLYbi2bPlRO43lfuZufxbIHiYyC9uIODepbDk7MF/OCjB/Di9TXsdp1acKlqvrGwb5tsd3dH9WnnbTEDz2A2DZ0OoSLYm3Bsi58iwI8J4N8A0P+DBJ8Q0lrGHwbnU4tmBZZ+b7u7ZTDpZegW/na375PN78AWaGb228CFeCD/MYi0ssyL/1GSUJbaJ7lNEr0moaymHpgpcnmAcyXxUookFkcWJyahw8Zksk9+HxPdPElQ0zjM67Hd6X4XiTgRJtTa2NMJB0ym6ZzGS4zSnJ/hjBfD84thc3uVDdfX6LhXIWzvj2tkJcwDemK99Cox2nXwluTn+zL1aVcop+OySgR/XJYLTfcQCUg/kAjl8LLpHcxKC4/OFvDpp0/h8eOPwHL2ud2GnhIbZlsBTadPJhfpvoCKQrIpPVtaqU9lJJWKGvoR2XMKxHsSrHmPYMvX34yiRWUeEKUuCrXg6HnPrURQcI8hOqHSDqr4cJT5H10BhJ3PBCTnYGEOvZqYx4/pFRG636DXYqLKmXGHOAZZ48Dl0qqgXhy8oZj3D1r9zYL7dWR4prf2t2bLDcE69+rxCLAZjCTnXpmpUQPBv+eYY/LoKQSlXMzp+0F605r9jgUsgGAeDPgT9CWqwUrZPyaNLDmpga2gazHxkUYkM7KRjrViIlo2eqjGfkOc/OTiyJAkgFFdkwm1NSbKOIoDacuDCP3kMm8X5QwypsOyb1tWQFZVkqjJz2w/cnSTjye338ZNRIIYfS0KaJvtwdWk5OlJy1Pxfkd1WnleyyLQOlUwzR7NZ5NpzFhWUE5CYrcUYWe4fzr8JjYOoJvOCYU21k8icW0UrI7zJQVxKBYj61j0CI12gbnQWKiUE35WhMcn+i3h4eYiBCdejz75IawePIJ6tYb5YgnVbAHz9an0BLpf/pyFWcLCJcHsIEgiM1QaILjm7Knv2e4BP3tyCp88XMPFcg7mE4CqzOFXr27g6nYHt9tWUFERQzVhNTFHD8c0C0amwjjr+VgRYf9KvU2l0g6TZ3u8WBN7wHvupaOTB4/x/PFTPLl4NCzWZ1zcnRF5Vvp02vZcSme02D+g7l/Fe1CSTBE7R45RkHYqq7TUL/QJq6mVZDIINlVcJ+bHWuwWEQtdYZlP2kbLFATMVYjAqcc0Z4FF0P+xuRta2m1uzObm2veM9gGwRbf6PIa+Qu4zNJnuApQF7IICqUtEzCYRQO2/Zu/LsoSz1cJ8fD7Ln56Cq4tOtN9rQ7ZgS9yZxYeLNXx8WiAndYwMfr6qhFVzslyIeFTb99h0HTT7TqxCOAkuikzsQg5KZ1wf9STo5mgZZo0yTTxwPyKPE68cFqN3fVTcjkVAQf9w1CrgN3gWByTpTZV9y3jrwqxwup7L8SzqGn7vRx/B4wdr+OLlJZwsSygzK2jlfD6Duq5guaj8rK44w2XKPfYuowHzYe+t3w+0bzrTtIMpugGKwXn0QcKbEemC68UW/E5PONeLHUSm5D1SJk1njl7HTC40qCCy1Fqb8TjNV6c3tNvcUds2wvTuuI+aibr1nBNevxms4f7sMgt6DZyEl54kqX54vobzkwU8e3ElyTgoW6p3zl7fbkzx+g1Wp4/6RbloWSjISRldHqQ+Tp+G43WkTwno/wSiPwXAP8Jga4NJ/zJTux9X9ewj8hLAfJ9sfr99yBaEF76l7QYA/mcA2ADAG0U4Yy/nnfZuQkKzbRTtzJOHMlJjIxqZJm7mnuUfE9QxJquDJpU2QSn7hLo7HL0/RUzTvs0seS0mrEUiQvR2qTzlbiXIqTGmLMoS68UK6/nCtbuN9f2g0U80bH+7W+oAImW6ia3kcIzjFWkYEerotznaMECUzddeTA1CA0U2h8GWQKi+0xhLtB7uRVgxCuF4UQ49X9bw8GwJy9VKe+FA5PFP1gtpqudsUEq83GckDS14cC4TrdaMF8oYGuXRQaw+uBofFHvHKoMKh3BQxYts5k1sZftmEycVHWHFPF44pTfTuRGzkiuJbMHioIAhlLU9ijlVpNsSJMhyEuY6odPmEMT1SRDO1Es18N+UrhmvUxRBSZYwg4mi8EglU2TPhaSMK6Fs5ZK7oPwnSKfFr3G9vr1UJa0eFexpVjgZMzuX2KAogp8+XO/cn/gNBDbbdrOF+XwHFxfnMJvXgnQGqle4bibSSjUoHtkeSmeFKBLNwqykCBGTpg1OAd+o5BzouXqoCcYNWhxI/k0RtYvhthjITQidBGi5KA7yH6EAVzOhxRZCjy1Hdooo2SZJVDqD/DYnmHGLEy8nmqyyuNWknxGLEZElmqwvDkZ8mBOEzo4GWDRjtVrIWO+8H4Nd1KRsFBjTa8M6jSz0ZU1I7JkOyqjLWIwdA+Ff/0rGBJiN3lnspOtpLKRMpJPpeU9KlUKN77mg5S04MhOV3/RQYEDNeU7ZUSZzkPS8IsJ8eQKf/OQP4Ie//zdhfnImPbvcvrg6Cz+zPyLrBly/eA6b22to93uZMzjxtyLONEhP3XbXwvVtA/t+kPH58cVahFOYsn0yr+HV9R08v9rA5fUO7vYddG5Qiwo4oG1DPEUV2/JaeMGgjmdUBbbFiWFkI9Cfotlh0DB91/q8nuHy7MI/fPoZXXz0aV/Pl45NGIMyDdipR02SvighEPofwlLEKqsdSUIpSitW65qDKtZGOy3eESehl7FgjTQeX6+DLbbVLLk/k+VEIVhfF/o7k9YHAYwov/RtU25ur2yz2xhrDRlbCHrINNIyz2BeFcFmIwvWTnwkfH94fuSEK7CJKLRO6GAaxXiAZB+P17X54UVFj1ascOQiXQMjSMyFh1W1GtcR1i5gS5J90wky+eb6Dl6+voHr2y3c7RtBv+W55IIr6bxnpvstgoUEEg+sZqWgrox48nFyUYMdPbj/kj/IxYzY2sDn1PXDSKiyiujxfCnU2Yw9pwuouJ9U+nodrJcz+OTJGaw4oaxq+PTpBZwsamZ50JNHF8geoFXJatc5DWiG3tvuhgyDyFkzQL7rDbUDcl0SWyLrPCsTh4qlQRJmOAjlWjxSPar4JbHuEmHGtR8f0G0O6Jjpy5CA035dLddzL4z4tg4Afm9NZmfzhS1nMyEkiMFplgUuVduh6zsWWsJdx9zbzKMRti3KfKfz43JRwYOzE5jPXkHTOzAU1qaO7WeaFsG8NNXqwmWzdVvN1watKZm7SzG+jpY/XqizTKt9RghXgPiJeJUHbarMS9qPUM8Xs6xrfyPyad8nm3/NN6mpSVz0rSWboAnn/6IJZ6OWKE8U8bxOEkpI0MdcE8AI18WktEySu9Q/8bhYj8nfeIQ6+kQJN3pz7hLrkjSBjT8X+j5MPgsJulkfPQ/pinoMLA66vNqiKLKymvmiqIiDRderDYIGvS6urAgHieeEjqGI9xDWwX6K1zyjnZCaDJkRblEc0eT6mZBbexsE9bwUuaKq43Q5cQyBY3XfqL86QZkhPDqZwe9++hA+efoEam6cd04oLBwortcLkRV3zQaMd7AbDHRJQpleFDi66Ha0XFELEKnsci/fcZIRgjQOJjO2AlEa4ze5jcdr2XReFIOBBfwmVEqPW9RUg/ALK9E5OqTupcMjHr9VeLJxuVwZPu8S+2hyEM4lorrxFsRrAkE5Iwo+ST5EkcJLkyKuMgBZBZA9yJxDCagF6Swsy9EfoA7fBjr8VbdJB4egsMFLVRQ6pvzsvclTtACJARBfvM1mA3l5CZ98+gmsVmuY1fVYeQe1HwFrxmct0F6dLK2gXmsRWZafpQ7CfW4mEKiC4PR4nyhBv2NfNMBE8fQu3jtUgafgKcn0Ufa15MBfRIwY6RNbkioglkUBpYj7lEGx8KgIlVIy/rpuTOXMNIkenLK9+Npg9DtNqPUUC3ThNUF2WIVydQIXDx7Bcv4LaK832geoc9y9VcpAk2bqoSSeBfsVWgmEv+kHgnfHSKZYjAwhkjaaJCNqj+p4X81YimOzu95x8pwJgwLCLA6VGaZkU8cP0gy2rYOu3YPJCzh99ASe/vin8OjTTqIFdAAAIABJREFUH8r1YQRzGHrpW6vnc3j86Y9gtlzC5bMv4PM//5ko1MriV82C+EG3F2uUQdGoX764Cgti5+DJxQrmdQn1xUoEVVhd9Pq0gTd3O+nlvN5shRIZ6oP49vyPCTsD0SYUo4o9gO4TuVXVVm+NtaymPGchoLMLXJ8/opOLR5vZcr2x1pIbhortxVAoQJIw7r0kAljptxa6zvbyh6A1UhvFUi7iWB1ieuSBSCGhsrBE542xRiTG1nYE2hSMIgo017uYEVd0Fej1Ohh5CkIjlidbT75ru+ak2e9WfhgsJ8oGjcdhYHKKoILiocn9mhSYLqgFMF6Ntm0Pd7s2JGjJFpxMwt/keqj/P/betMeSJMsOu2bm21tjj4xcKitr6+nu4fRQwxlRBMQB9VXQB+k/6js/EIQISIQEAQQliLNwlubsXV1bbhEZEW/3xewKdzF/9l5GLb1ldw7TgazKjHiLu7m52b33nHtO1plp6c3AmZ6dEyKSGLTP3aiiOSJMR0OmmoKul/PFBl/dLMz1bAG3iyXcLNZwu6jx+mZubm5vYb0JPQuBGE5VXnCSfH5EBegCXs1X0lfJfioZyy/Rc9a1NBiG1bitFfXkAdFatUc14xYD6eekv1MCPBxWSCwO7nVGgKODIT64d2IOpxMSQENXFGYenIeqwnyYuc46PwumrVvAtQe/blnc1ndoClFAFwEopsCS9AFtPyz/z9UHr8WJTgV2KW6iDpoKIGy1PciVCCLszDeahSujEgEgP6g6LQI1T1mXFy6v+FpC5MQ750yQZ9XWy4VZb8j0zAZD2amCAVxMAlLdHcCD8yM4OZqwqi8DSaQ4RePlPcxub+HV1ct8cvagLQaTQGOeEKVI3pluSafPwwQtByJU7HHSg0req+zhszGAc3Lnyd7QLvMu2fxHcfySI/O7j0Y9OOmB+woA/hAAvqco5kKTvTLxwwxJcgj6MGw0UUu9LlOa7dcdMfzLE4RylCSruSaLqd2JSz7TJKim0fNt95LXfc7p150PJudLCaafTA9hOD4ws+IKmrrFrYujBLSRFhV2VDN382qyJWlzopwWECidI0ptCCqmovoF/H8HXVaKsqxSa3rRH7wLJN5+RzxEJCewTcXpuIRP3rsHn3z0Plzcv4BqMOCAzrOPn4XhoAKqIl5TH2MXoFEKzD7Gl5LitpQ4ywgFt8zLDicJhNnrOdT/UCDTGqtesXcN+S92xO8hJDDPLXt8ks21V7sF0CSNfDNJBb+FjqluIelUjdfJdDebJvHS60l+eRlt8i6wpYoxu+cex42Zc2FLIbUJfVDk5XvYZ3e66LjQjoKkOxiQaUyFIjhZtk3Uvw0xfBNH/H7yVaPCRkOKwCH2bX7zfc3YO7LgTbauRQeBqEk31zcwm81YYIISNbYbSkVY4t97HpcWe3pES+q6W4AEEi9EA2LdmmTLEbXEoEmmJC/csMXomViRGEYnckGYBgO2p6DkUtBMp//PJCHmpGu3ypbeq7cBwdw/0ikqRSYHpM7I1M62VuEZ+bkKNkqxJ0LHADuzliIiUtw9OjmDyWQC17dLKSo4eV3q+7uNDok/SXYZBQwLw71djGtFhBp+scG1alvEEV0b2EuTaaZA6KnnNU9sn7Zf0gN/+i9Roc35T+wXd8ZzckzHJpCVzRDK6Snk5RTw+pZ7ICfHJ/Dgw0/g5OKBFDK7tkfTvaoXE/36/OFjsgnhsSurCq5ePIXl7BZIoVQ8UMWfltArQtA+fyEm+Mu6gUdnh3A4GcBkOODk4vxgDKu6hS8vZ/CTp1eMeK7qTu6lovYxVO2XbF3IerVh5L2ZXb+A3aeE1xwCqenBOsvyZjielIen94rDs3vh4PjMDsZTzIqK4oXb4D01p1WMIsmTTCb1c93DQRFGo5U9YUAZUZ1Vumym8ULNiSioUIqE6U4SSROt0Sp+nYpHix6oCT3N0BB9lv0w4vqxo7av7TNkd+J90xmC2kLokBKuLLes0lzkDkoS1ImWViaK0anSeVCbH4/9o7HdDgQ6J4HZ4Bspjim6j6qyzUVMNLzExbvDZqNUhHGSFNLJF0UZDg4m5lHb2k1Tw3zdhZe3G/z8i+fu7/7hJ1DXt1r0AUkyDydw/2QCJwcDPpPRsISj6YhtW+h7CeV8cbOA2jfiHUz9llUB906O4OHFCRxNBlBkGRr221UBIIoxqjKUZeGDyZBQUmtNyPI8VGVlICu62li/bsGtW7DkeUkYaUt1b48LcmChP2QgidoYa9C01jAv3ZldXSWS0xDygTx4jWoW02tKVVfsVBgKtZ6Sa923ZeY7GJpD+ba6yENJ/y6995m1NlTDcT2aTM38+rJomyZHpyk7Bmg2a2zWG8EiXRTS0j0rkK6EqOsSlTb7zLCQEmrMRCKgRK1dr1euqdcVYthYyHwyb0MSpdA5TRFgplufU2rwJu5otIRB17Tz4fDnXxB/huNdsvmWHxHZfEMHIZj/AQA+BYC/A4D/GQB+XxPO6MEZxX1qXeAhAkD69y5BE3vGVZJ47h9JuLiTlEYkskn+3SWJbuqXiQltNiKqqV9o+hx8UwSMCV030xjCD8dTovtYEl8w5ststVwwrYS97iTTNLu0rci7Sb+GCB0Fe2B6qhJiq356oU9kUKmz5JFJf/o8ZidY26Ok9T9Nkk1C0nwL46GD+6cTeHhxCqPRSFTPICYrUqWnBGZMtNp8AKtaESSiO73mIrD9flTxDrZXQfbc5uRZ/PaIg2S33nPJEFANkXpAQh56v9i9kO0Xn+W6sxB9x5SGjadDE8RUOtqckAKeCVCZhuxSwVMBYE+IxLF6rWwEG1aTlBtBlNoF+YxbhAkheiCWMbJzmZ6OKQqE2CdGW2ra7jj2px0tN/bkrDjwJQGUNjC6UlSOk7qoiPqbkLVQbEP9KcMcYeMtn6tnGYbdORqP9FmhhDMm3oJwImzWa7i8fAkvX76E07Mzplo653TPVmd0aa3inpjo7ygWIz6hhXU9FTbwph7vA6r6re1tRATJEZorPRmM2mnQbklJkWiKNmM0qhyPoBgMILM5BwiRNr6PBN2VZP6mH99Uietz82SO58WAhY0QVhIYe993VjKjMRE6isG30ZWIqkGBzNhBKIaCnKW0lcjGVAaFMgUaYkg6oipTkSPwmvXLADUjHbFj2myAplaUz2pPZdieT/RItj03IWJECE2wUHuSI1OLEeVhkipmZyvoTAFFPoXh9AyqwVjodM7B40++D48/+QEMx1NGNEOP0KuiNQo1kc6lqEo4f/wExgeH8PLLz+Anf/Xn/H/yHaWeYrLlyZy0cK3rDr68mpEtCieSj+8fw/l0zL2yZBk1HhYwGORQVRmMnubw5csZI28+yHcZs6Wf7w6Y3GlnezseCrJrGhzvQ4eIi7IarA5OzrOjs4vx0b2L0WR67IrB0NvMUR8cqaYyEG7ICN9I2E6VIe6ZlPTBSdHVNPooW626svc3slUFT8kcDavTzpGok71yHZPwSclzqCVQWggcGhwYVbpl307DKp5ZZJxo6BXXc5HeRaZOjI33Gbat87SZAToSiaG9j+iilGCQEM64UjVpHSfa3724N7KlCL0mtuFg324g2hxUuBNquPjpOrL3McgiflZOjfV9BF7m69c1DVmwh/E6Fs+1HDwWZUGJIx4dZmEybbHrWvv5V1/1moKFc3B6MIYPHxzDw/MDPjdC9An55vlmpCf+i5c3nHCSZ2tA8YpmpO7+Gf7Wx+/j+fGUkm2/bAE33lrWRECDuWPB17ZFUnWlbiLTrYNpZm3o2g1yC2bwpmoCDANijkhVGkoKTcMlbcM0V+6fJEdxY8jh0yiggVqAMLXs/Tw3cl1G1vRami8oxtp1UjNhtrJ8Bn0PJWlYIzfL8MwuOXWVvl4SChroCoiDwaiZHB7hzcuBbTbrnMYiLwq0Gfd2wnq5gvV6bcOhQ0tGqlbEwKRdBKCqcjiajGBQ5bCeSXjL6t6ZFCjpRoeupQ0tk+vkFaCQehubxI6MMVMEJOo3ofR220eOtcSwpjQGB5nN8XY4xG8Ss/xlHe+Szbf4SPGSN3x8qbYotc6hf649nKDJWOyntEnyCQkyGXffLqHEgiKjX8cf36pwbFOsuKmEBMlsE4qMT75LFwx+/SixZYkJckx099Vp9/8etUR7/02XZXh0fuGttZ11rnz22d/n6+WMfTfZBAKx9+H7xvvFZTkSjBCRPFKUtVodps0oqCC77YO6CGHtoqSvf2yKQiLTYQcZwPm0gnsnUxYrWDBFKsBk2sJwNGQFTK4QoIGjgwkcHi5Z0p+bIEiRoUMOur7+uwwjgp7pvZkmFrrtIfZV3P6MNS+iwJCNntXInyX446Z7x/f9vAeXLDMDAwpMA0JDXm99Zi5V+9J1LMzTcgFgt+phucO+5WusqWcW4+iS/n4Gm4BQMXenid4uryXMksCoiKL+vfccNWardPU1lgMAWwN2eg2bV29In5GSIcN/2BLplzZqP9vRV4gMch9d6QyPCVXWOyAhJgvtN8gSkM0JebKFRGU2qsEuZgt4dXUF69UKRuMRXydXmJRqxlKRFuNDtTPf4rjJRwrVLI6nUFol6OPqPBV1lOJMySXRXwmWy8nzsqqkIJQVLMDB9FlCWl22s0j9Y0Eu4VtKPvtFTy7alCX/EQsU8lBqZSE3URk2kaWNSJi23NJ9b1YzWL56xv8H+KbCqgqqaHLng/Rc9ypU3+WwWhAK+4DVdm0iQRNieFBi5tuYSEVhmygctX1mbRSKC6iyphZqKESFNtEcof5NjwUjTxlZxuQjVqsl2LQajVgQ6KPf+X04PLuXFKKirHek9MssI5os/YCQzWo0hnI0FouJvISby+ewmN1KnyCoyBjxUdsAlzcraEmgRrE8QrAyJzS+cZnD++eHMCkLGFYFK5Nez9Yi2iR40mtkvCj2RPuLzSw7Q+QZNMZmGzT20mX5Z5PD4+zs0fvvH5CFy2DoiX7IrZ6BldRorz6Qj+Z+BlGMlQrFseyGREMg6iyKODH3pGHOdpmGVfJCD+8xsZPRKVrOj/TfG1W1zVn8G8wNaD8n94AaSxt4rYqkA/0dLzVolHIkKwl9BsmMka+la9uGc+uyHLosaw0GzwknPxOUcFYFC8LQ+KvrkQQwZNNjJVGj+UjoZsTe2IeT2CtEaSX/yOEABkXW93TqU8CTICj7ZqeYi9vXyVuC82rUbYM1zqErKR/3LXtqRsYPOc0Mq5z7NAd5Lg2MRa+UxMwFQmwJJT+YVEyv3WwaQWyNg3Vn/fXGNHk76ExWmHkbYNkZcn0jJUNkKjVKQZ5dcajELvW+NXWNgPK7rFxe4Hq74YRxEMEGEytYGCX9jFeyA72NTqYxQiCiJNEqj4V7NGgOoECg9NYBtZL2Hm30fRJqeh3bHhQx4u9K88khsHE60bq7vBqE0eGxrcYTmN3coHXWjCaHJCcfNqulXawW9urVtXl4OjCTQdU/u6h7Fe0lRKMlP9DFpuZnkwo7xNLIeI3y1BNsuq4tsrxs+dow9hYHkllkS0ILcMFNreQPa2JXNM/ZTl5jBp1zWbWu38h29C7ZfIsP6dX8tYUtC+3jjL2U/0JVXk1ii9IkojuYKNjG2CtL/p4mp3cdmCCW6fsiSpkmnM12g9np70yZhS5Rur1LGAj2UFVIrsEnyS9VDm1elOHo9F5DNInZ9UtTrxcZ+4Sh+F7SVsWqnHBHBXjn65RqacSMw5ut5I6chJVWAWZ6qDbjd4S2+R2IMCaZ7aMRPDydwsFkzME2oWNrv2KBB97gDmKAksF0XHGPCTDSJDTXBjtONr8RBlYpf0FktYqrypCxFQ4TRCTSEpsOWXmVxT2yHIzLoWtr8F0ds5fvdL13HcZshV0k4ST0y/EYEkUl0jEBxSuytGSH0kJLNgW4Bd7FY7NQRCF+5tayhvCYhSn48wq2RNF7r/SzmDf3qFeMsgP2QZr0eqZCKkkfpAzYVlBId1sSGqBE13kLWUfBCbB6rfsVWMr8TOPOfawdFzmI9UZJZmC6sunFprZ5wbYoQfTZ3rvSSHBM19g0Df+O0RX2MhQEGWJhB7eBf5xnGQ9G1lstkO8pmK2IFdFcKVF0jEaKUI9ju5WcvQvJ35L6mDmZNJkoyu5Vv+AtRC1/Fs7ANxUu0s8I+qyRf17BRud2i1D7jpFtodRLYaAXq04apGhutM0G6uU1OL9ieybuod7jh6dev/zIeIB1C7BpJQIzdm8Fv+O+yD208Yv7OyoeuCJeRkUwKsjR34M6R6H2ZgJsLXDSceX5h8pi4M9z4IMDn5yDPNeWDRwptHVUoAsB5rc3MBgN4fTB+/DBb/8u3HvvCY8joZqwN+e2ljzb9n5ObmhTPjiAJ7/9uzCYHMJP/uKPmYper9b8zLCQjBNKJz1LN/MVJ6IEyj1uDuD4YAjDIueecOp9JSYM/Z8KgZ+6axYbYkGZzuvaJjRp+iyxTpHn2vW1h2owOTiCweEJDCbH7WB6YKrxJMuLqhJbVOqjIzsTFuLJDTWvCmJDHZsrRSkJiSoJ6QSEBds6IuRUbzPiqVJwHCFCFtwGiUxjYB5YRTMQUeMF04sHVtJyixu+Y7IGs82bJhml0nSDRY4vLPbsKS33GfTCriC3KlHCy6sCXWtM06zYFodX6UHJCaXVhZ8zZFJuYQ/JFm6WG1g16tscixXq9sK9fdbCIC9gOMilGKt7qYsbrNQ/jFWxgC3eHpk7/Vs4tkBtTuzIDqRuoG1abgSkpLJBhE3bwnLdsAiWqOm6vrAhmWBgCvmoKvDsaGwur5ewWtd8XeQCdL3ynX3V3s6zUOclVk3ry4DceCvKr5yjm8yInbeXAiDkxD2SpJKLDuKAyf2WRFtlC5rUTa2PCaU/kRHM1hgqLCA342unRMbN+4JQV0yjZTsTHppKhZ+sLEeEkMq9piKD3G8srHi2srudCCugt6hxJ2LrssIOxweDwWhqiAHDiCXtH2UF3nehQTTEDNiQclHmjNWWGlFbl37zo4Mx04+v5yuYL9bsT0uNmFVmTdfVsLi5MavZzBVl1VprmkACFIzYisoy92ui+R1A0mcz58wB4OQexkSlRaEUV9D5smq7dwJB745vPuKq8ms8qNz8b1WVlvo4/yUAPFTkMCKLdYJu+gRtzJLkMF5ESMSG9hHG/XgI04Qv+RyzJ04UK5tKieHfdwkymn0NfdfsxwPJe/apwJbWHuscjg+PNkfnF7Cez0b1emlRO9BJKISaxMka5dtCUNuHXVExVi83CcbR7J/eNx9SefdMi3r/0UP44fc/gmlloauX4pdVlNRoQmbOfDMpqB9NRzCdDqC4yljBru2kby0YCbjSlT7FjTAdLuHTieUHkWViIIdmZ4RNCnCQ9LtPJNMpegwh9Qn5uY99lJCbNQrxMaupKYMM2aPYDDs3I4t2RNVIr99PqAN7R37NxKFQYeFzbvQggvKAhT9CL1QeEHcm9ba/cBsNxx/1aE/ya4jtHntToEdCg1CmCI3xOUIehH7V64i9oWUDNSrjh4+8TjOARUvepI5VDNJprbRz7kGV58ZycSOopUmka9Gb6nrNYgmrxVLGiLLpTkAP9mmLqA0nkII4kuorJY4Ud1KmQNRXUagFfh2L+WiiyYIyRJsldJ37rqOq8uuE9bcpsbzr+FXyYqhQJH2rWZ8kgSI4GDMRSCweIBYKLIsKLZe3AKGB0aCAqm5hsxbqOFf4NOQNyRUYdaIk9HFdexbeydyWGWcSpe4eVO1dmWSrCH0ztfRqE2OA0EKvAmdB+9gsmO0apeucuimqujFsvQs1CeTeKxOFvzBd0iVlof7kzQo637KA1NHZOdz/4CM4ffiIk0JS+Uy9VEzy3EdK8lbxV5JGel9ZDeH80WNYL+f8zPi6hfVqIXPWCmJCiy5dJxn3k1rp9WIJj8+n8OjsCA5HFRceaSipp/PDByfUfwcvb5YwW65httqAb4ia65jiycXLuhF0mn/OfdWucGEwHuTV+b3jweTs0Snkw7bzWFFypgkg5eVeO0+c9sPlMmd4ENmiVAbLEA+ZMiNhJ8VqgYkmyZQHsLJoadA0KAMzISSLlnp9RehzJul7qwQk5zu55r42wBGhaNL7xuJELUBodSPOo8eYEdMz7krv2qbarJb5cnZrxBOSHVSoRY5pqcNSil6MvmM8V2RvzavbldiMYBR/jMr2JKLX9ayfnITQrIFOE1KrQnvyXLHV6O52goLj+dgiqNWQYGxo0fouWCTLEJOXriqqgtc8tuYATjZJuIghSCfdrvJ25CIt3Y7hsIT3Lk7xdhVwUXsSoLLGFSYbHoZscNB5cB5bVGIWdo5prPwPkhrOtOyq6sJsJTOgBNAaKEhtFRFe6dTPWACH7ut2WxSdcRkLq8NBxYNc989WCMSGtmSaV07RwAFSZ4+o8mU6v1B0hFjuuk30RYL+Xvs0MFMYwFOLLBNcEZsMkcCHbDCehKqqcLmYmdtXz521GQkIhcn0yNuspOTdUUw1KCk2xJ6RQ3vOdDSAhxfHnGw2dQstqx8F6MjgZlPj/NULmJ+cUrzZFWXpxdodGiOLZ6f0798hT1CkNjfECOIMgclxSD1ft2VZ3hDW8CtY+l873iWbb/Pxq4wSvvtBwkD/Xqm1zwHgfwGAx4pyRsGeKMxTJ4me17+77dbfJ4Ze3xONlvNExTaN6/YrMvFzUnGgoO+NtitrPedC1XSLbxhJsxdbhuS8fXLOhnn/ZNo0GK0Ojk6b+fFN7hY3Vb1eYuhYlrwPdLif5w50Lp58wRYcovJeB0lwXnftNP17vu0wsW+GHAlLB+f378GT7/2QLU/mzz6DdrNiaiAldYwmbWqY2zn73OUDkcwnA+XjwxGbMy+XXGnrN8LtGeHelSidUUkw3MfpRbOBg3gb6aJbFIGGswuCTZPaqvbwp+SfX9qBCQ21yCU54c2s9ZyoRSSmor5LVwuaGfKos793J2K8qVspim2KV+sCqtOOhkR9KmG9XsByPt+il7r5x74KmwbPkSKnXf5xiPdZ2XHs4zhxUECcsU6Szo7UYDPDyRsrBNrdasqv8ggQPduRk3dQOXyjSrXMmOvRXXxtHjm1dQpKQ6NAa7Op4fmz5/Ds8hIef/wxnBydST+xVDTAUj8TURKzDBz1U5KHJdG9XN7TNRmhVKWamIN8F6bA26wQ+4sc6Vz5phFKF2dWhiVkMyvZ6jAmYjsoNk8OVOsfNlnnhG92+wpePnvKqpzVZALD9QJWNVktxOhe00wuAtm+coFqSVJTeBvEiU4of9ubLD3jRtYgVfiM8zSimWgld6EEjAp1Jto9mXTj2WZ7JrHz7JPHyETQBr8ofwr7m5eJyWmA0HZQ5CWcXDyEJz/8EVy8/xH3vrJ3ZpLIxoemL1xpws4iVNQPSJlS0xCawmIsJB708MnHXFicHB7CF3/3NzB7dcnrSqbPGAW0dA6rdQMvRQmJWwnw3hEcTyyYXApCB2RDkTs4PRzDi5s5fPb8hsWDpGdahMpsZ/mZ7MjY33dyX80abm+vzfF8PHCTqsjd6bpzla87tB07zgtVMhbaAIU9qqhNgUi0RZgbYzZgcEBNA8jip2Q0xD19Gb9b7CzoNpJeJ/VkKq2Skx0uOqMRahCrzwZhXBk0JaPVwoChpm763DElLWiixy5w755yEh0K/zbEzl1PNNTNGjbrudmsV8ZWFamwYqyrOLX7cE4WYauFY+S9z8N603Ifrdjd2N6Gib/MWLVNKoiiiqtOVGvINi1TuVTP9WDDHaSMcwZKGUmZyZBmUVgHG9pgCcBmTJ7qJ/RvYu6gtzCHifPFBNBdFZYsTaxlxeVXsxUcTwdwTJ6sJk5bGQXqqS0HpTkfH3aP7ai99YW5ur61o8kRTA+P/HAyLrMsyzCEEiXrJ1rrmnnQ2CdzBCn7aDSGgnYGVlc1pjOWL0PkxY30Pxihr7bKKsiYRibPGgEAXujPxokKLdwiodxyGyrhHNN95cvpILJZhW+TEaKKJBgkkGgltx9rJKoW4YIRKpa4ll3PwPgC0NZZlvnBeOKr8RSpqFNvNlzqcjm1Izt8/gKo2G9ODgeRTo3Rko28V0iU8fzkCI5evIKnL4ndVXMjLrfK0D5I82u1NF3TuKIa5EYKFlTa6phOjvQ8YBkw5PJUxdyZGACYmYCzLM+eDo4Ov6T2rzexf7xLNt/i4zcj1+SDFuofA8D/qggneXH+MwB4ktBbbZKF2ORnZi+BjNiL30MnUzXZNKFMPTRTwlR8Xao465PP9sl5fNuRoqt+7/3c70kVTfCBKPA4OTmDBz6ExXzWXr/4ws2uroy1LlBwSzZPPqnw7x7SKE4CJwV2xMlQj+r9O70lxqREGau2yokeLP+rcBKwUy2RkAbISnCDA5hOR4w6zS+/gma9YLTBqkQmURRvr2+gaMZMmz2YDlmogozLmUZjwg6bNX6vUXl/le5jP1DqeaT2lwIFoaRqu808FFYV+RIPwxhkEEWXzI6zYLhv6FeVGKVAJyXkLMxAPKmGzhP15wFK9DAI5L/puO8q9lfs35u+zzJS2dCwIMiaFIRJLKXMwaxX4rnJPYGuP5EUG8btX3dhS7PtA9tBRnYoddtSNqtWBhEEIQtYVuKluZBbTj5tRItfE3z6JR9abs4JKXYIaytein1JWn1W0+tgkS3nYDQeMzK5XC6hUVotBdDz5YK94arJMZw//lAFMETd1FoR2GA/TaNm6LAN6u+cC28xOvkmj++y72CyaDqyeylKLl5hJwUtpq3rqkGJDuEKG1J3JQEhXhcsI+AbyKCmgk1LImM1F8jY2NCzzr9Q2JNCF2phy2r7Ac+JFnbvrNkt6vRsAFRaO0b6jZfu3UiTjSwijPY9McnTH2vyZ7TYA9pHavU9DeYwCwUsQ95bnqSnBLBlPOTDIRxdPILTB4+575Kox9KGIfZHCLizeCl9U+1mBjAYTdmLkRgAC1aiXQpXdDyGR598D8ZHR4xucjITAAAgAElEQVTkf/rjP6P+L74/oV4DbGrxfGTLIWAl2ufXM7lf6OFkPGT7CkKWx1XBf6hPbzwawU+fXcFXL69huWxUfChu7tviI/X8z2YLuHrxnMwA7eRoZbPpWTD5JIDJNuJ7KYKBhmcFM1rJP0gSBqEuOtYRl2lEpYQ1CQEBospq2laHnRJNtauIcq18VoVSIMmPM6gelePeOx5aEhviJJNIKV6vpIsscKV05kK3jBRP7islzM9hoO5UT5rVaK0ztK4tlhtD9H9mTBhp9WRV6n6rlk5xYhk5K2kyFQiqIgu5oxwtmNa3lpHi1sNiE8Jl7dpiU2CZOZNzAhdcYBcV0xqLXWZYnc/QM9J0xtQB/LKzzSY4bD2pCjHa50N0jqIExeR5nY1aN5y4vChyyv1ov6G9+OmrGc/Pk+mKkdkyJwsnUtbNYUotB6YEnx/g8PjIP/hoasc381BUZTOaHnQuc8TyskJZpaeMB5MkIcS4hcecqc8kbFSz7o8UA7zkX9zDcsT3SdzFdFqh9qlSek1iUexRxbEiSt9mgwbLXqMjLhOiPhxkjrBw1Eq8sBgFpGnm9W47vTmx9Ou0z4mllnTFKVCUXifS2k2VKdtW5agbjadheXttOx9EqRg7s5jPXL1eY+nQPHlwzAq/ZI8iBS3t2ywyOJwMYTQccqF/ZR001CoSJIylwtOmXmHT1qZieXSeM7R6FSz/xGJaJtaoIxUi1lFN13kfsP2yzPPn3Ruqn75LNt/So8+ofr002vSgh/PvAeASAP4UAP4nAPgfAeA9VYqNKGCjC3axR6+NiVuaHKbenXWiJhvnrdlLMNMYxybFY9wikPzeKmn0xu8QP8Ukc5O8Pl5H/J7aiaraaDSamuJBPhstF7mzZtJ1PueqE6LpGmrskFJZrET3noAavBCCtgqZrplwB6p597GbaG69LqN7NWYiOnR5s4Qvn7+EcjiGfHIM2fIG2s2cA3qCASiQD2w3sZEAsJEet3VdQ7NpJCnLHSeEIexr6oImnTEhIjGYXPMy8tAM3CZDIQAvh3ZrB5NeJe2AtKlmKhJktwLmd98g5crsGO3/HAcNwcBlkLsANfnbdYGRaMIzB6Zl/hZNoaCWKPuG+wG3o2G0T5ZFn2wOG6xg2ZJ9DNVaswTNVRpVqsyZBLAcI+1kkndP2L5qg1siM2BfrpV+UKXXEtjOggNsl2J2HqJfbATvPuI1FVmAUdGxJ+m8sTv2Fa99p0nUPcIWvUH1bCV0nfb+YjCG3MmyEbKvP/93ieQvdnzbIpkupLEMw0JbZcF2MEuXS1+0vsbrc0+JF9HSlxRAkehOBpyAdoNDKM+eQDmfgbu9hgy9Jm4iDRP7PPdLPmbvZ9tHR9HDHX469DYefYEIouaabllpy8LXTKJIlY2rX//wKZrZYgbLUMDal9r7/fpoMqoZAlTDIZzefwAX770P4+kBF6aCWptATIy3mVzfrwlqrxC6jts1fDWEcjIQpw56XpoNx9OUkJJf5/s//BGv+fViwUWc559/Cl13LfuANFCyQAkhy9RjNluu4L3zI3hwMmE/TqPWQdNBCeNHQziaDvm+/ZefPOWew8za/pwL7RsHySzg6mYON4uVGb24Ks8frorTRx81+fAAiPkcOm/VN78RoReMCmwED5G4SUnKsBi3N0NoDgXZvEnUFnADaApNETbAaCh5ZfJWGCQx4LvdauIy6uuE7NGJGwUhS50OG3FvwoTRxIhqITRaFENsgflc0zRhvVpi27XsV81uiip3ToU+QoSZ2s/FkhDdmHv03bqtEyh3GmY6v4LaZ9QNzNceL9dZk62yLmdWEgsUZSFAyVknOaQZ8fIIaFzjwTZIiCbHH5SIEfK7kvGRvkg6e2ucN1lp8qLq8rwM6FtH86/tOrhdeJ4Ll7cLKIge7ywURWbIAoUQ7oMTC2Fc2iWMbDU+bIvBFJ2z3pFbici5W7UeFxhXKJ65IIRmzcqp1NovKrAhstuN0KbpQSS/1sxosii/50iCElTJlxl1hLwXCQbq86XEkosChc4fFIVhrlt1qpQQlEAlJWbON6nggFXgR4Hp2k6Fhhq1zGGbHRYqQqZxs1NMwDA0bIEyzEfTA6xmk9C2re0o+EOxPrJg/LoNRJfOWh8Ma1MIg5efZfJsIRbUw7MD+PyrIdzcLqEoDPmkYheCIculzXIVms2qwxAaay0VSpwUXbhPlfQkC8NqWzuLlkrim2WH4Wkzn9+EX6Lw4jcd75LNt/i4i4r5az5o1l4DwB8DwJUmn+TH+TsA8Ih8f6hZX3s9h6pgux+f4B3I51r/GE0Uo3ptlajRhuRz7mp4ThPNqD6bfYcYCpKEOHoalUkCHCkIXLpyjgSuLdrhuM7KQZNLY5i5vXxu1vMZBw3cx0MoGZXcUVQuifYUD89yMtGTePdicOfvu6ceU4ztgMqrW1XjJGoWUQpv5wv4i7/4MfsVvnf/FA4HB+wNuV7MoN6slearn+nJ3EpQSC6esQmxmtezgb3uHqyuEPg1HneJkKjWLcEVovJI7S66ycaiG2+3dnveXL/zQhthRcTc9BS3O2/QL5hkpgcLm5AaCVoWPmBKLfURQoCa7ht5gpJ4k4rd7dwFs/U6iwVR1ICaVChrirhIQK7z4OsFzz66L2zzwFYcJqHL7his9SPa9+Ek8W9EKE2fbGriiolITpLACr3WQ94h+7/RGHPSqYb0vxK0kyILizDKPGycgzXxvDSW92h2EvWIytLJktWJiFh1vfgPbc4k2c8NKl2XfsXO//+xH7h/4WmhIrn2r1vk4mPzmpLsdxzH/QJFpzaY0X+Ro3OXg60G0Nkc5t2G+5P4qVHjRSpaee6NtNCSHmhDZu8BOn7fELquld5yKluZDFYo9MyIVpsE40fly8X5hP16oj5ByeBExM2kFPU+IdVPM7D7DJrteMVf7Tyi6fuMtI2RFcvSZ7AOxd2JpiLynJyUBdx79D588qN/CveffMDUV+6Pj95M0NNA9JmOva6ggkuWk83V4gbaroZyOOHf52UlX8X9z4ETztP7j6AaDElfAF5+8RncXr0UhJN65gmBplYOEoNT9V0SblutW1isa3hycQTH06GqqdLzCHDveARtd8EiN58+veT+UurrBG3NtVHErAtwO19D3XVQzlac2Q6rAUxOw3hYjcvOZb5mRaCgGuY23gQWcxEBUZMZsqqQTLtCxMJsc36WXOFEwqhiEffk9YZb9JrGgFmKKLIZqvKtkHEomeUXBRKIaVHAwSBqo8xarYSKi2QfYVBAcs/UXkT0TWuaTS3rFe2LWgigwul4UMB0WMGQBsyqeJTuLcHq9KP9hxg2jNBb01DhwItqtrIC0BUlghvYjXew8dCh557UJvqNGSELeIPcYdkGQQ4tOY1wl47BtaJzbLlpePzYBoSeiIyo1kU1aDarReFJsMCILdnCe1iuhDVKSTEVFwYl9e4uYHq9geqoMaGcINqiy6hL0WXGFQWU5YC8M4uAKvYk6sIgiaAu9MgJsPSaclLItPoc5Yazb6qQepAozA0q9VXeg4lrcg9sINuDCNU3cCJG/qqGexszXQCcWOGYMjAdmwOcXPlWSrhHp/sxa5AoVbXSqlKnVC/LCSwhqohllllfDIdYjsbB2ZySQD5NyzZJlosIpN47W9W4pPWuUGYYo6oy0QnRpPjsyxev4NnLG2Ib4P17x0jiS89eXptmvcZ2s6kx+MZYO1DmviTOIqKVdKWbaBPNBY7BcNgUZT77z3/yJ02k0P+qj3fJ5lt7mK8NIH4DjlZ9OAnl/M8A8N8CwL8CgP9OUc4qQTBt0lMZY/NYsF4nCGJcSLoksSz0/5FKm+3RdSFJRGMSWiTD810TzU4Ty/R9qc9nG2m9RKeVPdWMrLHeHR77R3neTQ6OzPWLZy58iba+fAZtXbO65XBywMlmU685qOr7OvG73d10YLDnFsfox3LfEX2WRRG7oN41oqm9ePYMXjx7CjcfPoEffu8jOBkfQ+E7aOsV7Vzan29YkbXKDIyILkOQQwCoW8H32DE59v5FNA1fx2HlHKmvKgNvM6DWC6JkOWGS9jfdQBLM6c9I/TGznumf9mexMfhFDj2ZLDeQZxkrK3pv+HzRBTBEY/IZ8XPu+JItApuS3AgtJmSYCuy2oxaiEWQ5Jee+RxKoxSTExs2Ypps+bUyQyv3PT1GVeP67d+Eu+J6LBKSySfKJRLXuBFGixJOKCPHefAuo852PWDghkaBh4WHQkVIHCYdYqS1zAKOefUnxgL0xbRRi2do8UJDWsirtZuf6/mtEMNMo67v8PB5R6Xg/3Pi2sdxHDuOfYHResR8lR2b87NeugrUpYO4dBC4gxdcQzbVT3RXbB9QkEkXxKIuR8iIjxSyxBozPhRL3d55D6XsiEiMXvQzuXUu0VzE9WT1lAcQfpLa3JkKeJhZgdiTQXhuz+K/ABaYc1t7BKuTQYPZavzco0krrQ1lUcP/JR/D93/sD9tMk+mzTqY8hWfug0OxMEkJGpJBD4igkg4HFeohq19UNr/kUExPCRj15wTP7jwWIinsPoD445Gtbzm85EF3R/4MXdLQTf2SipVM/4bPrOdyualah/fjRKZwdjRnhotdSm8jF8QjCxw+5gPXycsbvpd5ZEgnq/PYp5f5N7kls4eXLK0qmstOba3N8fpGX07ONzcdNCCTl2dlt4A8RaeyUF8RpOCVP0SAXo3mvZeVa4ETUmAGw4RJTcqPKaCt7NyvIkg1EpQlN/B5CsFZaNihYxVT2NkJFq77HcIdFhU3oPHFoBy7PXZ7luNlsoGkD75XUMz4sCzgYVVAUbjv/FJnuM+HARHAoS6Ey120rtilW+u2pyFaSj2+eRYY28yaZmU5ekExXNUHbFOgB2lgD19y7ijgmlV0jqC4J7IxY8ocxN5MZERJEUexmwZvMe++sfjcXk7l3sJM03Bhcb5y5Xazh8nYF41kNo6MzU1SjAtEb5/JudHgMxWBkhEfvjXiWkkek0KGNqL+OSHBHb3Ujyq5MX8h64MGw3QwtFo0GCYVI/RvUflwdT6I+MxXaswAQar17K+4jViWGKz8WtfXUSLILfRyJhKTz6pJppZaEgPIoKsmTyZiBwShOxbTkNrLrs6LwRTXw1pos+JbvK/kw0yB2betIt+H6dhWWdfDDIZd5rYOYECLvwdS+9OTBGXz25SWJBZn1usa66fiCu44Q9IVvmzpkWc59yogYHSDKHSWxLQrB1SGXWVIsev7FF190RVHAmzjeJZtv6/EbnGkmxw0A/AkAfA4AfwsA/6AJ50eKapb60l3AbttfuVAkdKR/ukRUKFJwU5uTu2Krr2Mcpt/3TaOZJq3x8Mm/I/CYChDR78dGGvq78eSgHQxG7uD41E4Oj/Hy6edI/PvxyRmvcVdffgbr1YyDJxfN5+PCyYW2GBy9fjlbZEPSuZx75z0bhqMrxUPQCIZIlWaiwSIuYLVacjWdgggqmr/34AQeHAxgfHQK69kVU3V4fSLajzMwHWUwHebwMnOw2sTWF6vIgNBjOanipNbtqdNKX2lgbELC2iyYntZpXRqACQoR302RAClLui4w2tj3I37DDftlHFE9krXSjaiTEm+KCvVUlDaNh9BmVFXemXRGVTKDao2DMqKMInQ8rogwLCo4GuUQNguo2w1TBEP/KdsupyiPj0Kpem1m9r2IiFtxEu39tX3hQn5vUmER2PMHpF28A2hIxIcELByyD6lV9HobkacelT/7gYqIlzkVMYiiTTYJwj/agfDN3qoQRL3UahJMPnCL2RxePH0Kl8+fwubDj2BQVmATI9//Gg6TjtcdC9+3jUNs2d1ZEHFL14hiOPuLYNB5FHmFXtUtO/1ZxzR4WZxZDQQGsLEVNPR0EIqHCSrI2BDyuhXNxSX4dlCMD2D64GNobl5Cu15D1tCzQmln9poFSnIFso5ob5x69PW9wf34JHYh24w0hS31OUlUsPvPSN/Tq79ue+hDcLBBBxsooAlGWyJe32YouaNiynA8hUeffB++97v/DN77+GMYjCdME2eKG4uuSkQev9bpzZPkROEdpZ33vkpeqLOha6RdIa6dyWkQwkl+nCcPHnHRqxpN4PrFV7BZzuHm5QtY3t4Izciavv/71XzNPXwqLgOnByMonRSMiJr/6FT6RZ8d3sLz61v48vkNNNRvqmtJXBcdK0EbWJMY0ctLUsd1tzev7NHF4+7w4okvRoe2C1kWPJcvuiRZkASR0Eik/RY3VmDkPvkzqhprSABGetm0OsHCPl6tMcrAaCQlnsjNqoYnDScPouoUyGLFFFa0BaxKEhg1xrcCRkHQ2oVrmtrUy3neNXUmYk2iNkTraJY5ph+TuFLO1ZbEj9VKoYWYPGSRQjRJFP+SfjrSXHA5tQ0MQjUcdVlG5lY96EtnS+c0ZytXHR8B+bkATjdgIzbdagcCOFa/xZZgbOOIosqUYELvSu+7InSdY2p3VcHhwTQcTAYmeG+ubxa4XG8MzQPLBSISldqENrzqwBbOWmcJJaeCel6UpeWid4gy9KVWDYO0biMVDwYoyeTKEGUV2ffUq/KwZa8pGXsKYBoQ0aghKxIzeYup0kFmubGovlcq6qPkBuOlD5g9OjN9gTVRmVCep17o2rCVjRHfeEnkc0nO2Y91pfafQ+C+UyS13HVcfEIgRJX6Nof1YDg2eVE4UiKW+ynGtOvNxlzNVp4o0WfHXO816foUmHZt4f7pEXzw3j1Y/u1n8OLq1gapkJC0sVvNb8tmvQL6DmNICAGFFWgwi2wnRTj7krHLslfe+/94u5j/5cXFBbp3yOa749uOtyPf5IMQzv8bAH4CAH+uAkK/BwCfAMCpvib2c0KSbJqkjzO1LSkSz842sS9pkt9v9e63vZnfllimR5pDxL7MJvl3k6Cp6XMUf1crgovMJMsyN54emuF4ivc//C001YBU6sznf/Pn8MVf/xk0mxX70e1UzFEoq+K1ZbY/jyemUQ+Rbh2STbiHgZMko6aGclqP0KvZuIhWLJdzCPNbtvggKftXry5hPruBF89PoP7BJ/DevUOwgw4yvGUhCssUz8D0n3vHY7gm+lPbstADM3akTMhdjKiSGncPpRFV3eDAqcBOEOF5UYvErUVAHOFYvadYo27FX4wSoO8URf8yjpSXY6KBtSSclrTq1h5841h4J6W52t4ALAYKpqd7InYcjFpXQo0VdKEFbDZQWTHz3h+2iMb093xnRLcJLvRKia9rApu9SklMLqxKcUpsKt6BtIs1ZJliA7RqIu1IxTYinVy8uOscv+MhOzw31WR223eHZps07x/SU2K294EFZDpYr5fw7Ksv4MuffgrXv/UDKC8e/Ca2FryR4+uu+psqbUGLGWZvrtB0rrWIQnY1kSoSK2wxqWyD/CEFFfo/OxTFiqAH8VKNxQmKffMh++16Kr6zKrXt6Z+RHRETNjnRAOVoAgcPP4TN1VNYvvwC8sUSLD1DhBKigbtuN2rkiGh3mgriVafXCunTlbAqUibB7iBun8gt6yC+GrVH3cIGckUzC4imwhGJjQclmYRoUo/me9/7Afzwn//38ODJh0BIAyWanYopmV7+NiI3YtWE6tsrJ4D992j7IFXJlK6MKioUdE0S9gC3Aeh4E5327OFjqAYjuDo9hec//QeYv3rFarZUcPSKbpLBP61gm7qDZ5dzvsGLVQP3jycwHVUszUprxcXRGA4nFUwnA0Y0G6YJe7GL4eRV/D0BRbGYTn22WMFssTTXs3X5vkd7/vhDn5Vj00jf5hqEHmgkUdKInfdaHlMrFpu8OFDf35gEiXWKB8ULyVAw1qL4dfHmWxEREjVTzYWjUJHGD52C9pEpZaO1pSz1nAPkbVPbxc2rfP7qyrZtbUhplPrJ6doJzZyOBzJOViydoiAUTWRiDN0u10xTpj2W0S3nesaPoJs5lIMRDicHNXksgtAlc1XZpcyvk+SRlliM6C0V6kaGpqY8MKQnlDGSqMUKSa2JLms6l+VdUQ4r5/KCBIKty8JwNIL333sQPnx8j9Zte3l5bV7dzGGx6eiZ7tZN182XGzKV8nlZ2bIcmPHhkaHiRUGxDvtZ8hdlasOhFnisHih6HExFpR5SnqdDeQ33W4ojAfaxl6omMkHZs40HcLhD2Z9DpQdvO19jzRXT1qwOEpFJA6ZWaQTtFeYp6fSpo8+vtyR8LnrMhTmLYyWTEShCvb4MgpBIlHGuzquqGx8cZaPpSVFvlrzI+a6zlqumjixi7OVsHR6djuygIMndZKUJ8jzTnPnovXtwczsXC5r1hp9HDOjWi8Vgs1ri5Nh3xnDVPleZji1ZTGi5HFH54L117o98i/9utVx+dnZ2fvfG8Ss43iWbb+kRF6C36Og02XyuAkL/RPs5/1CRzpEuIjHRjP5GQ10U2sTGBPTfsY8yUnL9ngDRdzGr/bZRjHEWJOcWk8+74vkiofoa2JoN87kwDYZaAUU9DEtjw3A4sn6zMaL5HgwhjtgHPdITw4uLVuhjkENIJtFcC+uhtAiDasCBi7U5hLyCDZQwX9SwWMy5H1A2OA8dqbwSJZZKccsF9xHSrv2Xf53B7eyMBSCOBgdQql6kr9fce3M6GcHxdMkS6B2jooq4eQttj2ba1wSNjK7GrBDpqDWeAsWGq+Suv57o3WW2N00TD8o1KDnmnh8jNM83fmxbupDMrsdU9IaWMeMZOlacjcmy1wYfAW0kPAq4hRToPZtNA6/IloFtHAreNh23gmAfkqKKBUAco0SoxCR5WW+lkIgHYY8ObRPhnbiZoqnMMfWNPQQJ/dBfBrUrIIoj8c1sJ8kmJfrcyuo06YwftX870vPazx3V1oJ9B6zprWJskmx8LXSqvSxBfTgpWCOa2mx2C4vlgtHR7B9BsnmX6M3Pc6QLEyZU2Ugd6cIWvU9ppNogJecRkcqIWgbhigX2/ItqGvL/oAlq9PDbuYMkulWNwGUltJv1jq9uPExybvw7FtIyUI6mMDy9D4PjCygWS3AUiLNgjtOkazdhZo4HWUYZ9qNgb1+UXORrR9HA65vqTuEP9uZyRE25OdWrmjSpy2Qs7rZGQjRFSIzX7R21aSmgUNsEtVCcv/cEPvrR78F9tjgpoWkaXqfF51DWeSNwinwnVedozSYhtyhghJo4JkJjWoVSBE2uKNpoKPyiK7zaargMDk7PIa8qptdTD+difgOb1ZpVn7mX38pzS38o6P3p05a9AClBev/eIfdx0rpC+9K4zODxvSNe86mw+eLyFpbrGjaN4Obkb9yPMSmedsjMj+XmmbO8cIZucvaoM8WosVneyKbJ3ouGh9qYNYv/GMM9dkntgVROCfV00odnvXA3WPyHidhgqb8RO+VgR1XUAkRV1LPnosDRQcisJi7GyeOJsZYSl+YMgndt02BbbzBQUjEooSxzRjWrwsKgJB9Sq3MA+8IAvZ28NV/eLnmMQhR8on3fbHdUSvirakhIVnBZ1lMm1SaEXlkKR4QnJyF+DZ8X0UkDjQWyYql+XCfXy2qtFiWF9lme5+PJgT04Pgvr9dp0dR2q0dgcHB/DxcV5mA4yvH92ZGfLGhd1wFUompu138yWtfEE6bkCqsEgL0cjUrStwdiV9EvCGLS1Uu2SM0SyyOPkbUWemlqgj/Ef9vVaFL1ow/09TBFFQT25ACFKccBoaCFKtka4QCxPq9eK3PnCHBqDwvPRm0rJJCkTZ/q9G63lV0aotpmGY4Wo6bIKLXl2UoJbKy23FnQgFifo5lrr8jyrRhM7Gk9p7LFtatM21CbpDClOd2izZ5c3eHUywPHZmFk/IVkrqCZHVjkPzo/g9vF9uF3W8NOvnpuOKnkkPIXo2q5z3vvGZc5zsqk7L2rwoJOTp7F12afNpv7f6tX6/8sKFxoWDXsze+a7ZPPd8aYPUkD7KxUP+jOl2f4r7et8P1GujQleFONZ6s8HuhjUiUCQSVBG7Bcq2PENuZvH9PVHfK3f+zzQ35WJZygk52wTFDYirj6h2fJ5+IakTuv29OQeFH/wh/nVs6/MzfXLsJpdw3I2s957U5QV0v7aNBvTNTWvstzoYAI7EheZMZT4kBQ9+VkNj+8R7QytzdCVlUU3JJl5fPHsS5jfvCQlAu7BofV6MKw4raPAphqPoKoG8OLFS7i5vobNRx/A95/ch5NRBr4WyXziZJBoQZG53mPLMp8piNk5qRzeMboxFCTiW2GitUrBKJ8LtQRiNuNAhxUTo7w3+h3hDdqYm8arR5/t7T/e9BH7w4gddZCHXh3ztjFMl4uJJmwjkp2eshgQk+jHauMZzXQmp3sKDgMXDWxUzEyrnEkfKySTM8oiWNj6QxrY9nVt6bgJMo5pz5ntzwv3Plzo3JoAENrZGegykP5ZJx5w0ttp+ve9NgXMzv80Ad8mzFYRn63Ay93oZsz2pfKj4o9WbCmoZ7Nr6l9KgvbrPvbRtJ+FimEj+hhdzRMBZ9xCCX3iWAd9rb7OKx2zZ1vraxjF9PLaiGZCgnL2hQ/c/hyS8+b/ky7WYAB5NWCbJeobNMk82x5JCEiBFxW1XAGTiyfQrubQeoTFZ5/B8rZRZ/9I5Ue1YheJSdt3xOmJKV02sgx6heb0RPsSD5shSjjrfd/SAOkz3Z83apuChSY4VpxdYgltcH3hDXuajuIr3G/sRaTnwXvw4W//N5xoZkXBrQ7RwzgkK5xVRJO4jmAFsRUBJOajipex2q301EyOd5MGzyRrjuJbqJOl9wO1BkYHh/Dwo+9xUa+sKvjsb/4KFvNb2NS1IG3cQxuUVhvg5bWH5aaD5aaGjx+ewsXJlOmeJDJUZo4TTqKNkjDO50+v4OnVjAWHqF+TRHOAKaRB1hgmzHq4fnVFK0t2uNyE8ck9Nz46K7PxVPiQ2HmUJJMGKk/21pK1vyXBQLWnEhtMFv4RrEgvtTUsqIMVCwLJRIotxxESR5DXWEW5BPUUCmemYLKNDwA/SiL09t4AACAASURBVMS+KQpPyF7XtbZpO+NcI76a1KvX0dkHwvZ6STemVoLYzMyXDfe4xt/1D6JSfgz7DmfGUUYvKG58mQNRRQXZPrBluxgwpfZnevGUlPZEynuImwPsH8mek8zIYmDamkE5HJnj+4/YU3K9nNvRwdRBddAtQ9FkwTksJzZ3B/WgQ+xaZ4aVCcVBsBjVbQjLlHRug4hL6hyRpBJjdpNrl63T8V6RTTkQIg1mzVYkjDRL8qaPLvtIWuoxZeI9MtqsWwgl3IUg0qRMK7shSgtRQ96XhtBI4fHQuQz0frdCy0ZSFc6lhmC86ox126eHRYIsiIhSK/2t/FivyIMTlayDaAowKLUlxKGzOVTDkS2Hg7Ba3rquaTi2y/OcvV3q9dK8vLw2Ly4O8fx4jCNnTLr/xYIxK9NenMCzVzO4npGfLXm6D8NoMsWiKHJuekcMSjUWQrAxcS5YAS3C7Wgw+vfN+ur/eP70y9tHTx733tVv4niXbL47fl0HPeR/o/2cfwQA/4OinEStvdCezlTYx27DGZEwTxLAmBBGoSGfxAT7sT/8jDFcDKNs0ieaJrAhOZ9avztFVbNE/Tb9fhYWyovCHp/fh8nRqV0vZuH68rm/fPoFVUbzajgiygOuFrO8bdZFYS2arg3g17SXQ1UWwdnMZGVpRmfvQXV8gXkxQOsytEWJhCKMThscjKfw9PMSZq+em66bc68m7aWkfGhYECbjoH1Tr2E+b6CoKhiUJXTnU8hZjTZAYUVEZljmJL/NtiStIrBWxT7Ca4Pa683K7i2FYOi434r66zrIvfT1Z2XJogim3kDbbmBHjVsXQ0Kuag5SHJSs1vdm2LS7h0QvslsCTPIAvvQckPsm40A8st4iatfbXKVweFTr9KRmkEuNPKNAAqGyHRhPCoQAWtTtiXopUmmSgDnAloock0vAbdK682+1iGkJYTA+4S5DosoJfX8km50FQbV860U/0EofFxUhXFQSsrAN/3XPNA628VA89b4XltRp6Xuwv9btsb27KbqrJwnSA4SwXq3gxVdP4cWz5/D+Bx9DNhj+mubFL+dIxykWAL4pFjA7I7VdWGhCWLv9WdAGqFYTR6/IJM3XmI9QjBt65VB9ryaYIWz7FUOC7Zg7nvvXgG5NfrOiYlrsZnELoWu3dj9JJd8omhc/CAWHgMHRPTj7hGwkcmhNAfWnXzDtkgtT1vXvMb2VovYwp164Sm9PLVB2ijKRxss92iXPcWJ1gPq/9r3TsG1hoD+U9FJyucYSVj7XvnRJgJnRIW1k/XeSmA6d88nFI/j4R78Pj7/3Q6A1ulPEdte3U32UDSgTQPqx2fYnKdrA3roAMW2OFF5tlORrJmVTl0XQs/8cboMgJMU6mBwewugHP4Lh5BCcK+CLT/8O6sUcfFdzCwWtCkP2AMxgttzA5c1cqJ9B7unFyYTFxkhFlQqUD44mMCoKTroomaLXk28z9eLTs0x94l7VgilAJ7xqMb+1nW+LzWaVg7VdUQ1qlxcbspXAgJXSYNl1VZs1naK2Gxae0d61uPeK8b9BXcQpgSSlUlrGc2qpR96/Gcmizy5Q1GPYA6O/G/wBxvafKSMXTOwGyDJHNFLaz1zjeDxWm5bZrTRXV02Hnow0ehbwtheWiqkkpMQspmSeAmyhU7a2EacVsnbJGNUjJR5RPs/1hmvZhQ386bIXvO0CHLKHpNA9a0X76KPX2ki4YUMyhNLmOR6c3OvKcgCb9bLI8pJUZtcv1ll925KwkHMe7doHCE2AkiTcjM0YNiXxH0IyEZG3RV7qEfs2KOkhhErmnqEks+Px5ioGqMcP0j0pjTHZFqCjjxY9cumT5JRQ6MMSXHi9v17oxZT4cb5ZG/k9J4xm2/rUan8qiG8qN0c7tjFBEYuSu04FDaquoPQOIwksIW2LlNx3KN1DRSQfyOXxGFib2W44mW7GB8du9upy3DZ1JgV/Y8JCJElya/HVbI3LFmGQESgbZK+W8RH2dGbg5GgMH79/H9ZNB19cLRGKsT86v29G44PMWq28AnurSme09KQ7lZ6iSOuPyyL/N8eHB/9ldnTMfbh94ekNHO+SzXfHr/tYK8L5JQD8RwD4LQD4fQD4AwC4r0nnUBPJOunJHGuC2SXIZkz8XMIa8wlKmv0MCGeK85TJ31HPI/aXRjQ1IpmpcFD8mdn73Eiz1bjchKIoQn50AoPRBA+Pz+rNZtMY5lQEbBpaK2kbMxiaVeg2S3To0eWZt3mRu6Jy5fgE3GBMGzfSymSs48piORzi0b0HJCPP2teEKISwgPVqyWqFtLbW65UornZCnrt6+QL+2nt4dXMMk6qAoevgcOhgXDq4ODvgnj6iWF7dLriBPS8cb5QUbECaFCRum52qM8aisQUKGslzrmZlu1wDMPL0QrVF2dZ2t/1TJGJTs5ASWYaY10Vz3tARqxiUKE3KlndtQhpWreu9I7GnzWpsngaROj4xjSTjegwZ73KFXYMlGxRRsnjt2EGS9tDPmMimSFN83VZ9M55g0KL5HvwYa+hmey+NwK0SrOrFe+EegTPdDkPRqqgFYKRIq2pxTGCV1WPZCoUiphw6slAI2wcHEiatjQIGSp2Vv7JaInShY9Tl8sULmM9uYaTJ5tt87Jz/t1xMWlHzavpWk8pv2NJgafhoHjV+S4FFpcNiUtTpRVeTz49FpJ1T2eZufKRWJzsCPMn04+Q3y6GsuIcLfLNWnCl5KHTipr6R8QMoOSoPz+D0wx8B5CPo8j+H9u/+Fua3t2AoOFdPxx5JJBEh3KXY3jlwe49JT/32SlKJPGPYBQeNzlRKLNfBMXWW1GY9l8w02U2RKyv017Zt2Obq8OyCqbMf/PbvwPT4VFHGkH643gujKKamEIjbtVErExjhac38d+nrqRqv0jI54g58XlZbK/oB4YKUCDW5cgBnj58wC2N4cAC3z7+CVy/oz0ueOGTsP6xKWG4a3j/o/5+/uOUe+03TsidnVeasPkzw38Eggw8uDrkI9+nTAp5f3cJq3XBCFqLaat8uYJiFs5zPbN20lIw537ZhfHS6Howm1EdYYggBkbNzitDzvibHYjGUOBHSxTYWmTiGsLBLbfoKhCjcbedIT5P1olrbu1eFPobgCYZC2+T+OF4PgxWZGcTg83qzyZpmZSJHlop6xADKnRF+a2wbSWoENMaUaAqyHZH99GGTU1YwU2xYMAod8F3HPeZ6pl5SHRpU0hE7hJCiaqMzKNfkbsEoItcxjEfaqK0psjwPo8PjMJwekIAPX/+6hXzdRc07QgNZdAc14VaCLGqJkftgKQmj+G0gdSSxV8E4IWPbEysG8wRcCBWUWWs2yncZZewrqqzU2f7+uCTYYIRPFWathh61KNOasdxzZM8tltzinJz+HVFtpuJSby9bAyAi2eJUjGjqtcYqAYogvxWBI0o4TauFD92iCVB3vqxG7cHJPT9/ddXNr6/ylmnyHVsAUXFntVzhYkkWKACHQ6sBZVzN1O+HDFSLDN5/cMKiEaMXNc5DZYrRoSkGI5r/DtmUlbdV2XpRcVHhkH9qrfnfQwj/6d7Fhb93cfFGUU14l2y+O36Djiv985cA8P8CwO+qPyclnz9QtDNPUM5Mk7550teZ9lNGZDFqVrSapA7uuOS7kM4UvXR7MUnsDXUJuhltVWKUdJePp1cKcEyQTYKKevZ3LEvIitMwDqH13mchhIItSCzz8cGHtsOubtEH6gHIwObWuZwNPnl9oUWHtyMSRJPKtssze3R6j+XJjk9PzXpxCy+efgk3V5ecPLA3WxAfL+otIdXUly9fACnmHkzGLEA0zgDeP5/A8eEILs4Qnr2aw+X1nPsJyjxnClQdpSd3BjbSNJHVaBGN9m8CeAq+SNaITKObNe8myOIXOe97FATh3s2g5ZM2ZqLTZc7ppv1mTIl3LktaM+kglX84LFj+CK4twrJ10PjX6bSiu2/6n7H4DimrBkH2qO9z5YmPk0NJXbCm496nyKKKqrKxGs6BB2US7Nnqk0lseupf7OPk/qAk692exTbiTiMVoyRAUOlHSGi8fZBkJEiM+HXv6WmiJypuE87EC1A+NrCQCukHOioS45Y9hNoLZzQBKQn1thbqulZ/xYh0ErpfwXA0hrzI+Rrf9mN/EUpVztKrC8lCREkjCfqsFalkJVivqCXPsy0FNiSf9RpbGbdVu34eKMoZY18bqWm4nUHO4A7qB9tp1n9HUC/YYjRi78d6SZTYDaN+NkKwycNlVLzKxB7DIM9XMT2B849HgMWIr7n5q78k5CV5j0achl1/Od4OGrSb3qcpaOOWoqrWqIVKpGqT1VENJojKptGTCskZci87eX5CBYuQwSbY/vmTSFwZbUbQyKi6PZocwvjwCN775PvwwT/5Xe6R5GdMPRl3HsD4lCpCS2tsn8TKgqI8AoGke/5DfA72qgNbr87IFrAJih5RZukn5X5OZq3kcO/xE36+LicTHuPNugbfMhgGbSciN7QPECWWvAN/8uwK1k3DY/T4/AjKUpgz9LkHwwK+//geTMcj+LFz8MXzV5yccjYRgjKeETobePzpPet6DnX9KazmM3d870F1/ujDMDk58865uUcSl8GReF5KXM49iobtSyjJcsLpxKArWqfuOJyEGm0X1eSnJM9Jqf+aTtsEk31f+v/iUqbG+UFxbkKszGoxt8vZtW14rfKiEIyiKJ+TJ2WRmbzX/EnKAGz300HdShsJ7Q20vwUxFu2LMbKWumCpX1USsNyA9hUSgZiXfsxQEFvqLySk8sCQ4q50edDJZskZBIV7c01Q1rScsOMH+Zwaa22WeZ0/WQhiq0LIH1upyICsOM8W5Smrir4SE1nDrU50HhEtVAGeGf+cCQicFllOdEUQiDQEnQQB4K0kmApqkrgRz9rGoOko+QxJ+5Qi1nRBhFLS9UoybCyhmHQehFI2CGGFwIJIQ8PcLIjnQMNMSfLGMLVWyQVCnUWWVjCB9eiZto3sudqyorEhXxceVhITasQ8lcRenR9NJquDk7Nwe/Usa9o6j/GW45jOw7LucLUJoeP9n2nD2u4iJ8SMAQp0iwzeOz+GMLDm6TJDslai7ZJGX/xIjdcMU6BNXqvDbZYV/6dB/HcO4BnbGvk3HjO9Szbf1iMiPW99Gf/1Y6k2KUSv/X8A4AkA/FNFOykBfZhceaH2KButhMUKZJzX8d/RJxP1dWnR/ZtGEPf+H/OG2EcaYwuffG9MQHPYjRHjAhx9OU2itGth6yJgLW0azvLaHTDkIOYgLZsZO9eYolpSiITIokq0wDgMRFMJ2dYJTutvXkyCiRp7cu8BVoMPDLQ1fP73P4af/sPfQ73ZcLAmxvkx4BeT8Xq1hrlSza5ZHc9DNaxgNKjg+GAAVzc5bIj604rgAwsQ3dlvB30QyPYuakvgIYcgffxCH+NqesXJJgVfQZV04wjGSj4t1ISk0kJNojUmJVi/mYPvo5Eog1mHpUM4LESdn85z3kjCCVt7vt1kznB/PycCtO7Lbo7sq7cg03pTwoBEeSxxHjsJOHqcVzwJyT+PvNtIAjAiFqC8oWiXszMBjdkBaqBHMLdUxoCx1q8JZIjUwr0HI0FOws6jAluMADV41Tovs4NM7MVVOwhKNhgksL3oUKRw7tAzWYAm8PVOJxP+vOVqKXYNgyEcHB3DeDrp3/e2HOmwxr/7vYUptXLpYm+lPt6UQK4p0eykv9Lri6NIj9HX9MUEszcP9xao/f9bu0VOM6WGd3ter9EGJX309xfO+Pu8rKAaT2AzHzAlE1WVxGjCiXGeR9pgj+SLWjIJWpWTCZy//yHMbm/g5vIldM++6NGwiA7SOsPUVij4aSHl49xIfzEXXhKF4/glKQLI3WExsdM1MVJVuGKIGWy6AlZQwCY47V3tYSbGN0FxRfq+tmmhMBbOH53DBz/4ETz8+LdgcnLG3+27tr8326M3MO77tvo1PVJyKYG1sdqJfWmib10wph/XyAjgNUBVYGnNhb4ghMlcML04GXW/0T2bnt6Tr3YZVMMJzK+eQz2/hsViwf33ZHXidH2h5PEr9tg0sNl08PB8CtNRqb2JBsZ5zqgn9efT6S1XNQuSkeAQJashyLW1KIksJ59NDevby/yGnJoz1xlrV8PxdGOzfANIokG887SqMjsEsTTpZD2jx8Z22uE+0CTNRmEcWcut2KRs9/OVlcRH6Zy9+X+swwX5PuNZtwhC0bW1a9Yr7NqGCseO6Mhem49pnxpWOYyqghV9adpTfZhnDFvGOB7DjFsxDf+O/t4R+kV0Hp6PQdZ+S/2QWW7Y4YNXg5xlak2/RDDSF2K3g9ibUFLebuMT+jevBmQ54hCZPhph1I77IRFZlxwZmJZyjKr5dmhZIbZEFtUx0Y02kh3iEhYZZT7VJ6PEV9BDMpnGeGGNdnuYsAXv43LFirsmMsI4p4pUZsXxKfEUhNEqMmokAeT35+rJugZkhJXuGHURREEibVs3tv98MCXK1ayUnhvVaKsIdhgSXeJ/9xtoLnPL6MNINGKcBQxrl+WbyeHheHJyPlpv1nm9WSFdKFUV0KCdzef48tUsnE+OQzVQtFUH0fZwuzzDbKFTFVh2mSFymtCSZZEK2I99VEsmpbE/bZvm3zowPzZmW/x908e7ZPMtPt7+Ov7XHqj02i8A4CkA/BgA/hMA/AtNOM8A4B4APFZUs0vioVTeuknUaWEvKUyPu548TP7AnrJtmVBmu72kttQkeP/zMVkEIlqaChhlCQJLi9/Axl7+ELRYTdmX2aBlOfORdkt6FBdm6j9RbRmp88dcI67GljyvqiHmwzF8+P0fmffe/wiePf0c/vJP/wgW8xkLAJkYjFFAtdlAvVmzeA9RXUmmfzQawuPzQ3jy4JQv4tOvruBmsWKvRCuOz1t07Y5B2BJrZd/2LKQPjEAQ8kqdBZ6sDYJXulfcV+SWxGSWApy16vOSquFWUvPrju0Cu0PR+/kOk8yhnoZVZAiHJPRD+gIkGhQs73ZbhHM7zTBNGqLFJIoK7Bq1j5MCFNvyjoEqigOJGA+PUReppZhGyztJLsDWXmCLPqmwkJG+TFAVPBMFgThaCenHST9mJAjFYrvdwldRKGjrHZfcb9RQr09uJfBkCUSUBJ2CrcJZDrC6IFslzX1CCThi7BqosiFMJlMOmGlONpuaBLRIlAMG1ag/39+8I4bxu0tNmlBGcZ+oBOt0aJuISuIWuYzhBfVfRhQTw+6HJuzP7c/uGJevCzv6+5ac413j6xOUM01e70JOacUiddlyOIWGqPyUaMX5b6XjN8QHw0SF4t6vHcgcnaZlWQ3g7MEjePDhR5wobWa3UG9WELpOa2wOamIJojBiyX+YwrdSfYFtFBeNvo8hJGO0fZDMa/fIQhsyWGKh/ZlR3ir2f0pCGOJzgEGLBUj+iHD68H148OEncHh6LgokXbczRD3d0WwTx54TH9fBiCyj0snR7J51rCipRTyqTLBJJgXvLGq9Ee+N7Db6vSZ6a0pSSgq1R+f3YTQ9gvPHH8Crp1/AP/zZn8LV9V9Bt2kY+RQU15JwHaw2DXz24hXM1xu4WR7BRw/P4PxwxAioDx1UzsHjswPu2b66WcGibuDFpeFnumaROKuZhixk7ENJGdJqmb348jOzXq+rg9P7k+nx6aAshwNi76hzMyb7a612FKAFaVZlpUqABdMicsN6HiRxaJWeGtSmWAyrmWrKAitOVrCgrTtG2BjaXcDvtQZdlmOW5Ux4DdyaaVjrgISVSO9gWBVs3URop02aqmkOEuV4OChYrZbYOyGSY42SI5Ul4jKX2SwbgTEkqFMHwhKBKJ6QY6CkzHh9LBmdZTF0zgXVMlVmQoMi1UpSRRkrrAott1APS99bMQsavH26Td8I7XVsC07Ytz3EQeIYbEKIFAKTCcrIY0w6eFmsPaP0P0bqMr2a/r42LC7O52hUUEjPhfmthM66JMPNWamWvkPoo94IdVZsdjk+0koNwoCQTundpJ5GDZFk8tN4WbUCrw2YZXTgpDEywoQe6dNG72+sqNJmyP6eWOv4lQbNEhA/QwhLY+xRNZkeHJ3d88v5beja2ni2jhW6+M2rK/vsxRFcnE5xNCixithIFELUgi+yijj6tgvkYiL9w0mWD/3KQMUDJrf9eDSa/OvVfPYflst5vVmv4PmzZ3dr8P2Kj3fJ5tt6/ONENfcP1ATsSpNNUrD9vzTZ/BgA/iUAfKjvOdAEdJR8RhTlSXs170o2v+nYH2VMPtMnlFhIks30iL9rk6qjSzbEGMvHc2y12kaLp3dgu2ACafU5lg+Xqh39fWQgUOzUahO+Iqa8ivAGqWFFZEFmPnRmsVhikedw7+wBXJwfw+lPT+H5V1/BzfWN9BWpQTO1wng1RCc5epM5WNYt/PTZNScVD4/H8IjkuOdruJotGd0kGhWTeRH7Bpe7bmjctIhWWzMyQFYsUhOg72VrC2lD4Y05z0vhOrWbvpeKaUfd9lvyzPaUUbgjIAaABLn9uld85yMaQ/cX2SecNsA4a6HJqIc1B+9jHxcwHVkCaCuJQ4fbakBE89AwItpKEwhbyuRsRdayWi39oX4rdnXDTpIMTf62yZ7pg1RrU+uDLR3SJOq0u0OyHZedDUmTRZNCo4kg0V1HNHGxsQmKzt2R8nDOu6NvO7meIKq+RMcmU3hqs/EdciLaJX2aRtVJb2cztlcgQRURrEHIs5z7h3/dx9clc7j3d5+Y6zNMvmclwj6E6ne5+v/Ze+8ny47sTOxk5nXP1ivbXe3QQMMNMBjD4YhciqtVrFYR+jP39yVXXENRCim0WmnJHdHMiAQHMwPfANpVV1fVq+euy0zFMXnvfa8LGADdGBh2RjTQ1fXMtXnzO+czCCZrAaJuHfQRkHHNuuRxvu0Tji4z2ckK9omK4ri6TfvQG29DsTwHO5+SBM5Re0ULVVAon769xkC0v9wJRJOwCMbb+3Dj5dcBF/f3b78P7uguVAJGORARixk8ZdY2otbJIKqF2qIa6zLqDnqm44fuYXtlK57kPd+LlY+gINosGwERslGhgaM27iXhdSIFZbIF1196Ba688DIMt3epEoP6+Q7tgL5Oy9/9BtGSafOK+1XU5eLHGQHzoK3ezCldu7s7/+qcOMBqxlWcKbF22eigCeWELi4G9fqQZgPY2tmD0dYOYPwCZjQff/whlGUFJkqASI2iy84rBw/PFmQmh/TQ+uoeHOyMxNzNwyAxcHVvDMNeCueLAozS1O3EKBW8/xuKL94XwnPQdQmr4sTMZ+fZ9PQkmuxftluTPT3a23f93tBqrSuMMhQTGgRRFVNIfe754YYAKhIqZ44dMXQh9Y0Mj51NmwOomucym8RodkQV9ngsNFRytjWG3F2Uk2eYptRPTYVYij5JE0iTmFQCzoWoWTyvnjrAC3TVtqyfRZqjF7MlzQQlqVQrjMtROjao0au4soAUUXINQt5uJHRVS8VnBtTS0aQbq2rLQgEU0t/D+xPWITL0ZJkl8DFlenHM5jeE3EpF+lgXea+twNCYtJIgdUsi9iIoJLRUyBqlRxpNR5TdmIyO0LaATXzweK6kgzr2FIdC7rCLtczNhjqraqpTAznJOjI/8g0ZJJJqDB65njz+C8fGQQMlBhKhBh20nliNEpdbJ3cNO9Xi9nsqOvTl7lrJPpWtbEp1VgO03luglFJptxcn2d5oe1cNxlv1+enD2NaWUoywjpCvcjiZzt3RtHDDQap3U6MTjrQWR0KnUBaM18y81Oq8UKp0dD4YXCqxSELOmNLGESXY3s16vf8wHG79x2K5OmZttiyhvobxDGx+a4d/mmuKb8PACfNIgGck4BNjU14WoHkNAF6Xn/c6+kmzsQbsrkg/bdn0aQ62j7HDOq/Tn/I76Ljl1h2QGYCv7rwmaDwD5cSRyl1rWpN7cBFV5RxESiu24nYqpVBkpWbyzRltsGaShlj+K6n66roqKQJlmeewqh1cvn4TfvpH/xziOIb7d+/AcrGg7oF1BkycQBTxJiF9ETPhHk0XUOQFLGdbMOglnHupI8i9ZT1dJyW+G2mxfrB8A0XQmRbzQFMSQ1TimNTSxfwGuNFrn8eujEvP4nnscJo1B791uqeX+IOntBp/PHvFc3cQF5Gx8zAyXHtYupQX7Z6zRm24rC5oxjZdJI8gQ1OcilEoYUHzIAuJqiEF/hMYP5vUZVw8O6dEbyfVd6ENNk0PraQT1kYlNAZDG9sSmpfNsVNdzmHb1XSuQ/kLJi+iefOqBb0NKEJxFeXEOihRw4l0YcxHtMEESEoTTTfGkL7l7PREADNAvz+AJEZXZd2BB19vd3OzG+aaHpl0I0O30gYAB1BUEkUiQDMW4IEU2VyApuoA1M3vA9W6yrqvcP9DZWXzGtn8+0WjNXxSkI0m0F/NoSpz+sPdS93p4nVv3rYtS+fWoiOLJaOhvas3RBfHfosYHVWXFUBZ0TxGxxuYIags3/+O2AcGamFTxJEn5KAkq5IiOBw6zLKZWUWiPkO0XDTyQv8UJzxKaDqa4WfR1UURaY2RDYLU0+u3XoVbb/wEDq5dBx3HBCq8bYsownsFFUJbOlJBL68xIaNGMBG1lVx7Jpx3rYUt2ZNIAUoAvBMTIh2AJkoQQtRUWHEH0xCce53Yscq5QADFn2OgPxrDjZe/B/liBnVZwqOje+QwzDGIht4TRby6n+cFfHDvEazyEl5Y7cPVgy0Y92Nypu0lEekTB2mMQAzi2MDHD07hfLaiLicdp0AWRT6n4Ydjvix0vloly/Opm24dwf7yRrV/5bloON4qtda5szZ1zPYJ8WS4NVi7WjKV1nMhW6lKcTo/NL4vDE7xa1M5LtqzLtIRUOWcSnF+hZwW/BpqX/m6LMokz3OEk3o07EOcRDBf5hzXhR3PyIRqH11/aJMWoQl7XcPpLIezWU6Z0qpZUoTCB5+HKKbOqdNekZMqGQU5UWHI5Ri6/EHx8QAAIABJREFUmGh2w7RSzCNt1hcrxq1s7oNGON6zvlVAFka9RGyI07TNNdcMOdIjaEHlOyt58sn0RMVtzsJUwq5yGAkSQpS9GOeCGB0h9VVFFMEC5EpbBK9nRdmQmLRL248dQ6slbBNBasfFWupuKFxk/Sm6EHvq7nnsRib8evwMilXBTm0/kL6alO9woHGG4df0vPdD6XZDcw0AnKOmE48lbjv+u2InX08mROyQvPTcGLgpptQ72pgkG4zdeHu/On14Py5XK74fI46Xn8/mcOfeMWp6we1kbtwzlOmdGLxVtUZO7rTW/lFh1MwqdAMGBsTeNpMAWhZ6p71z96Io/Q+Jyf5no6IPm/LX10CfDeMZ2PwWj0bf8k9rBCpsIUZCb0o3cU9yOtFM6ArZfHMHdDd0CuV1I/n3zQ5kdzQF5c6/dQ923fk56vw/ANJ6I/NzBS07LlRKA6A18vrgshvyO3N5P3Y4I0O2gbhecLpJG+C50shfgkRLi899JSvcRJYoTgUellIwnU79crlUe3u78Mr3fwyT7R34h1/8Nbzzy7dgtZhD7WswEk+AQAHNCvBJs8wLmJ/PYLXMYbI1JDAzGGRUkUZLfOe7eoCLl58SI95QxhBwlj4G4y3E2nXKjQyRy7JoKHWbkyV3OHl94J2BJDakdwlpHOGM+UA5+4pGWBvjthSFpQ5chqec5FFo/MMdTu8vPibdS6wBKhKoTx0MMGx4YplSFem6CYNrPHtDq1VJ7uBmb0MFUBKOvOgtXbOcaWJKfKAENm9cP54KWp5uOCWyLuP3B9pj5zyBaOrQBRl8CDQHSIyFyFawKA2BTXKsFXOMi46Rl8VxcC1Fqvdifk5uyFkcf6Vg87Ov7LbbGEAfAkjyaBYLMVyJkYGP524liZckw7KWGjx1NBvKVIiOf3wbHvvuzsrrqxyCixqt5hcZYdJEWmZ/sg9lsQI/PWYXavzTXGShYtXOJ93/kpG/UdAbjmDvyg0CJQh6UMeM85dazOQ+bNscdJ2ja6x0i6ht5WTSjbgQFPqbmP6eWwaVVBTzpjEwM8S/Y905dy9Ddmco5GiIkhSG4y3KFR3vHcDzr74BB9eep5+9GPDQPhFV1bTzlFDMm3Y1+IYuiyCXvGVUq40PTAOlOrrOzgXhm050OxO0U2h7nFvfb/lNh+GixemXNJ74XRKdMtzahpvfe4McVN2bAGdHd+neNpASbTRWnGqI27RYFfBhid27Gs6WOVzf24JLOwOKTkFGgskwziqh+xezOVHzeTqdw2yRk9SCPWF4P7XkUiIFeYlutaulr4oC1+OQJEnaH4w8pSLamvC2p+6kithwhQ5JJTubgUKnUQKiRkyu+HktVEqxRldCow3TWfBaKOXApXJ8jEffPgJZbJy3OxnD7tYQn40+jtEMj6m1EsMT7N7IbXRV1BTtxVrccN0TRvNsHpip3nji47RnfZu9E9rj0lnzzfpCeS9sLrJeLflJjp1OVUGICMJuIV0GZPZjiVYSAmKRakzaCuizPpGqTEsAlct1GXHphwyYMqHVVszOpc/jtY1qszCFEaSEFRzL9mOHMFUCh7yCvnSmcbvJUFGcbnSHFcZhWfw6mlY7znGJmD85YfBEQumxvB7yQebMtzZ/rufvATF9orvCSPEeHciWspY7B1CPCBRT19VfUoo6sNCWfsjxt5C13A3qvrKltE/iFCa7+3a2e9mWqzyqkREWxx5b3ednJ+aO1r6uCjedDutxFvlhPzb9NFZZGrtap2paGzgvI1WKb5DY9wu7gQgAOJGdJmny5wp6/1qp6M1vio3BM7D5LR3/JGHm48OKGy3+ORaa7d+I42wmtNpLEpOC/zYWsHlFuqAvCBgNesmlTGSDDWDpOqASOlpNs0HR9R3dZaDIngkw7nVepzrr0WAM1DSSOr9rTjMW84zW2DzMcQlFmk2kzNASilYoIzHViNi4jVzygiuokgWb10ZzrDNya6oK7t9/AMvlCq5evaH+cDSGYX8Mf/93/y8cPXhAWjikPEZJTDNZWVWUEYcdqPPlisDTcJDhAx56vVronAF9+MaX/OLuXfsbfhJqMMiE8TWQEkoFkxnPLqNanoC+0+HodFjILt7WtDhPY8+UXtN28sJrv4qFeOiy4TYQXayWnDRwkEIJQ8OSn5WLyDyD9lg2nIyRKGcu5uPngsKrVfgjAAeSwyoC5WEdmukKImVp4aua7oU41wnYtK5dpDcNYhes5327BJX/SHOmWdxBA2JaYNuFxL57LpDyjFVaKgCwwVPzok6nhrZPqHxMHXTU4dR00criHhe27vFnpKcoD8mGRbMaa+F8NoWThw9hsZhDNtn+0vPi57k+NsGm37hRAx2WxEWeHWILx82oSFLCvcSPYGezdq2Vdegs20CzDZ3Az8kO9l/R9X3R93R8u77Em1l+iJmbo90r9M/5+SnnT3YKIS3wUe17O5/DXTg0rBngAg7O9w9gOZvCarUkTS9F5vlgGMSlFexW1riGJA0z07fx9kqcb65tkAJAFZiVXkFYmhuZ3gx1HCJamSJNFGOjgqaKwIJS0BuN4PJzL8Lh8y+hORuYJBU9apvxEyJRaDjX7iIHFgpYlHxFulAiWqljnAjfc7o5RgQQIfSFoLlfwwGjn9HBWmnRXVuG1r4tkYS5jNGAIpOf1qAImgITAj0EoeP9A3jhjR/R9r33DzVMH97jSC1IyDAOd406qkQFtxRKj4ZAZ9MFzJc7cGVvDONhRuAK9Zs7oxSMmkAaGzLNwe+czlcExkLBkSNGlWTtAn6fOj95qJMsM73eIEXvnDhL6whnRovOrE6MZdm/iOmYtLPY3ULAaYiq6dlER3ZRHFBJZ2hJlsLPtVLOnueMS6KOpkAuqF7hszIbDPzyfOoXy5W6frgLz1+/gmwgtZgtQFl+TuL+JoSgXOuwXjPnxZhgluXC8UZPIAIlab/v4hTxErnpOnJv5QskaqmyDQVZsD5Z/CBgY6dX71GLiGuSvifXXi+LBGBhsKJGvxYwjdJT7ZkaC4qzKmNRO1MUCXUymZrqmMoawne8fCdtV0IdQK5k1khmxlgUWQjlMqUEP4tMQGTh0XUWO6pADK5YYlVs0JQCn1kXiOS0t8pLF5dzPvkcMlVKeVz/8fZ5RY6yOW2X5z6/V2E76IJZKjaaWnlPAHOqwJ+hNx0oPwSlJkTz9bRtvebOU1TolxBV1MBilxW3w6Hc1veHYz/ZP6jnszODTDLcibosVFXMtLOVxyi6+/cSshnu91OVYvxcbwjRcAtUOtImzjD6By8Kq422UvvCrLsZgH83MtFfxkn6b1wV/VyKB9+I8QxsfosHr8XbLsazQSDvpHMY3pNuYSSVyET+3xfA+VP5gyD0obw3ANFDAZ0BCHbvFdMBid1qm+pU3QKDLjjPJp3PCDTaAEzDqjpEqrhOlEsV3oMaTpxcKPPZh6+gCTMC0H1gLUnRakxIBLXGRVt3c+efp1NcoK3g2tUr8NN/8S9hsrcLP//Zf4Xb779DrrSBihmMPIwsVooip+cYLgqQZYOUW6Y9uY6TKD+1/AbrNCyHlPB7SjHlpc4dOQO04eb6M9BDs5TyskCv+PvR8Q/NKNBwRmnfoWJeYFzyFAaCSFx0VpWTk8nFUwRSfV1DkjpYWAXnuSOQoZp1LEcj4LGjLm3FuXNddxdmwDLQxids7WLS06J+LNYlZMpCph3RL2Ej3iIsHpvOiQvH/vGDcNFh7gKwzQJX+HcnABa/H6nWbC5kaYHZGAVvIjnNxkRMDxU6LZVONIE0NHrhxfHFSCt0NZUcO1xA11V14Ws/a6wdp4196+5veF1zY4tQOpiaktmL6CuXFetiagGNVtaMBtYzEG2XVtw5V90b9mtkPX3meJJbyPmQu6mhv7UduH+wmp8RBd2vOR35jfeKRlEmBVczbugNh7B/eBVWsyks5+ewnFmi2PN1aYhmLbGKHPUknTEnjq15LXmPrWkmBFKrCx3LTueYzDsl/5W6baLWQgo4Aen+AHYOr8O1F1+D3cuHFCFiSaPpmhig5qrzrulQkpkPdUcNz9W2PRY8LziKC2quz6BvFfoupXwGgB0MhTx3a1vwqdorPNyca5RlOU64DZTdWfP9CkK994xfcK7H+3338KqYh2m4/es3YXp8H2rS+UdA3TzaDU8tRjwf54scMDtzgSyZVQE3D3dha5QRgI+Uhq0+g8+IwCZGatX0HureapYNNM8PxdUYayt19vABxoC52tbRzqVD3esP8bwTCnSe/LxTx8/LKtQF+CDoDvOcjmjI9fad579vdJusgQxHMUynJNc0UYSsDI3XwXxZwCovCHSjMZAtCio4QYgRCvFQFH3FJoDkQmtYStBUnzVTqxHEV/lKhblRjN0z2baw7mBWFSlOtPxMQJH3xzWsMCUF8KTxvnKNftLIDZAIWCF3XvQ8kmqMUayjpHWN5jXKkkx1uHupQTUdYoSpkRAW0KWLCNq+3QYu2DtRl7iGL5EEp2DJ9okbNhpHoOACoVZA+6iFGq1l31x4tLQFfXK8TSlPBwgg4+9Rxl0JoEbKcaIYnIau6oIkSqxrXcm24LVxyQPsKvDbsk2mc72suJ/fmESWbDJF24feG7WJY9sfT9xosu3LqlAV5pxj/rmtIF/WqiwK3B+NXWU03sKKTWQSlQ2Hary964ejMd1XcdaHwXhLp9kgkoLIB2D0n1rv/mOi1LsA3xygCc/A5rd7fB2OUt/CERxpN8e70gW9JZPuqUxUqP38icStvCb5nuMOWIWOOVDVAZxdWqzuNDyGMqmmHWpthyO11tH08pnh4QGd1/CaR6nEaK2s9UJS6jCw2HYbA4zjzmQtEocGDJOFNilBO1WKsizhw9sfwWQygRe//xPYmuzB3/zX/wtuv/sOLJdL1kGhJXscNQ6RHEBdQ2EMPVCbWGnXbnh3dOm1KsRjUDlUEW2tdqyjwi3t6xJiZSFwOruAJwxp+YpTWwvO0FzCoqs6upriIilSjYEQRe2FCJDOZ32Zeym8H59OGMVSlJJfJQYZweDSKAeZKiCOHOhUwxwptUgbxodIEtMxtLQwbjt54WIgw6CuUE/of6WLoEK9rIug0BXUqoIedjmVb/bNS887ZFaud6Ma4Yz83zcLYCcAN+C8iwCml0JFE7Eii14Ey61eU94fXGnFK1J3QCRbBTrqZkfK0Q2I/0odUgEXF1GPazFXGY0GcHj1Bly6cpUcSjcYp791hCPuOlLULvDsAuraCy1WwCNNLDVQR4bK8xXAshbnWLcOYkPlSQdwGs5zd2PV2v++29QVmdWwINQfbbMozESQL86gKlZClZYOD3YwQUSpnc4NdSctF3eStAcHV58jIhu6Fy9n51CsVhBlGUQmpmUed43YnIZyLXFSR1OqLANc8KFJh5fKAOpKjWJwFayEIJzDkOGJ3y1ZMwgyCLZEAOPtXbhy61W4/srrsHPpkEAXxZs4mRtEM+k444OvE6M7MzlA4MVx8wwaaENMiJBDq6CJJuIcT7lhtSjbgzmYa8tLHBTYmu9A9wnS7CNXQYgujHT2sDmk3Kglvkj0r7UlJ+j9KzcgSfsw3tmBj3/zj/Dw7idQLOfsmB0AaoddgQwQzGpGILkqa3j+cBt2twagMEJFa9gaZDRn43mazpawyMvGLXtz4H5jB7YqC3h07xNV5qtoNZ/B/pXrbjiZlJGJMU7MOPDchUOwyHRZ1B7mwBbu5EyqlEI3ulgcV4Um61PF1keJ1IxW3CFlqS6bFLNYtc6LqFoulMOmnndw58EJUWMn/RT2xmiwlLZzoVCf8RLC5wdSvpWco5bbEnAPnUdV5oWqWFdSShoJdgwDKBPVBbX0gcGUR8FoHxzun5srUDkdQi3FbE/7r5r1DV97lZfPZuYt61tVUzgnohTSchMkEmF6jQe/AK/GAH4Hv59ey0LNECuSKyqTEBMcf3cuT6AtnO5pH7zQW9nZF3+O+RCplefubCm26SBPfgSoqNMMtGYj4iLLulvO/cTMVQabXDoBZkip4INFFzQ/sgzHq3iRMCneJoXOstQ57hMzTsGBAP3gWhbqDlYCdiOJYKlFzsSVdIpcpYey7g+3YLJ36FfLhT7PV5hm6pMk5fWLs8oTZV0rlAZQAd95WC1nYDGqbn7mscgzHG9H+J5ef7Q0oN5x4P4dGPVvy7p8P/tqZu0nGs/A5rd4POtsPvHAatU/bnwI5nv+EgD+HwD4PYlauSV60OsCPFXHxCdUP6Hzs+msMwfNSmF9rV53JvmgAdWdCqXvANfwXWxdTt79uq4tPfFjcai1ohlIPFcm3YVicCmKO0eCfmWMaX6Fi5+TkxPKTjs42IM//h/+J4ijFH715v8Hi/mMqtj4eiPxFGFXSPzgrGiRgilQ27tU0j1QDeD0EAQrTgxqQtYkykDEn52eLfoCu5OWyumFergeKMFmPGzrX5GDoYYqclQ512QIgvbxXKmHDsAIJ2YTaGx8eagq08BTgIuJonAUW8I0NqGXyvFHuporlxCbCEbxAEyawtImZAzQHwyI/jk7mwqldl2jtrkNRjoppPHzGlC5WVl2ycxdDT00TtH4XObnnlYh5L1VZbWLPi+dNdVcGO1xDIIiiTuBtpvSBX98kaK6hRfmGEWiBNSHjrbqfHezY83xVKKBEwKW47SBNMtIt7lazulh23QYFes+8dxm/T4cXr0Oz996GS4dXoFer/fp5y1cG/L/RqQjUSO1XGbo32HUuoNXMPRBMLmoudkUR/zvaOxjZONqocV2QStsFDUeA5Rfw/jM6/spjc/FIBD7ERMb6CP9mQxUIvBnD6EqV+1dHWieXjdmNU2XTjfdEEh6fdi/9hzkxQrKfAVnvaPGOArBpCzgoCxXEjnCWnTSVh4cwvTkGGYnxwSQkNaOxalaaI8ueHp77hiSy2hdU8FNkS2phrTfh8n+Zbj+4mtw7aVXYOvgErnmWqFHUoGF/pi2LSbAsqGqd7qRjQYAwbYAbe+bO/ixs+h9oMKGNXCHihzs2oSSq4InjryvmZblIz11eWswiLtUpyeqRaMaTIOcJUZBnGQErDGHeby1DUeffAhHH38I9z7+EPLzc36/1jQHG83MmNJ6OD5fQVEds+a2drC91YdhlpL2fpglcLg9gvNLOVTWwtm8IHo+3v+GK1hNl9hEHPNS15U6O35ginyhitUiv3zjZrm1ewmiNI0VCd8JVA48+FIF2inp/WjPE2BAClL0zWUqkO4hdbnYzZazFz3rEemz4rosB6v5eVQWK43FClcrODo5h4enM7i6P4FdBJtpTEs2LhAAST1yW1OHd1VW5HvABUtN+JWNsyzny6Z9n/WHNkrT0iu9JA65cqniIGPshWqR1uBxcUyNpSdRxPEuXFFUACPKh+RLGkFk5BVFk+DLK44XwQxSj7wgufkUR4KwKy1zcnyT6IxfWKiWhRXqM7g2iYKig4EeVZG4W6m8E0OhSLQlyFJPQk1crlsLrVlyKQ+vsGjB2JJI1j/hrojYsZYMlIyUXLh4gGZRTLQSyTbdVUiBxrTKynOsCbbQZx58H7WYTZUFfOE9zEgPSsZT1O2sKG6GCxUlZSqJJ584Z5N6Qh4E0vCgm0ejudn2wRXrbK2MjnyxXDqdRMpoY4rVUuVYqMF1Ec0/lp6v2mhvq0LlC09Zt1pHykTRQmn1C63Nv1Ve/5kD++FmvNY3ZTwDm9/i8dlmI8/GEwx8Ov49ALwNAP9F8jx/AAB/KAZEuwI6h8EHpAMWg8azy/8Lnc4wGYd1fdCBht/FHfBpO+Czi4fod1gJQ+9z51zqwvdSRRMSxSbYHedbyRSAlsvqSAPkiCaLNETdoStil/POnXsw6Pfhez/+A9LLIODMlwte6GkD2vBCAxctRpLfyUxSqwZ0MpAQoLkGfNdBHkZf1BwqRgciR9NdXNRrDBtlXWJXwxXq9O0CTa0v6psVlkQlIIipHJWx8VDbADbRSEhKrUZcG33nczbBXlhAi3UBJYaX1kJV4kKp05gItg+N5tERMNGa89YinUCsYzIbwS6MLfnZ6zeBycYIANEFECh/R8BpXULdztLUkCoLRmHEQ0UOtkZrWX665mIL3ZOO0W9zUYZywUXhz12KY+iINrEUYd3R7Tq3lkNrXVXozF9o6qJMDEmlGcFx8bk5J12uaTA1xOt2MBzCjVu34OaLL8L29g4BB4BPB1Hdak+XWoBAciWOryJGYsAp7YtAjc2la0krCN9GkVQdBqLSF5/DrhD7uz5MaMohl81++j4HYMNoQUOK+amKwchqcQa2KhmoETW/pvuYDjOe5yAHIPBGLQOoqgKiJIKDqzfoM85PjqlYgfMWOj/WRQ7LxZLAZkXGN4ZyPuM0g8ObL8FwZxfuv29gMT0VR1jWBwMVdhgE0PYlMdPenefiRBxBfziEncMr8Nyr34frL74Og/GEzYZIV4pU3ogBWnMft93CptUt5kJeXGPbF/EbmNaqxBPEd6Zz5vi28yE02y+W1ALQ2/uZTWjEbdZ1CgPEyjBNzJDHyCXiCAeBvPCFG5mDHPu6pOJQ2hvC5Rdehu3DK7Bz+Rom0MOdd39DLueoyR5kPQKTaOZ1tsihrD0sixLunczo+F5ajmB/ZwTbox7l7o4GGbxwZZfcxj95OIXjszmcz/OGQk9xSsKGoVW+Mcrayi9nZ3BM3V7j8bE42t5F0yZ0H7PeWnxKDTgbkc345JC1wlVa4VN+JQo+EQSVMlWkWqmBPLsrVjYojCLTdVXQccDtytCJWNeQlxX9nKYIFg3oYJgQ5kTUa2I0jGVacu25022kc23FQZl0nrHxg9GWH4wn2iD1gzvGpNnkU6+I2ALKl3JRJaSV9B7Z4rmogCPK0kQ2l5d6G4E3r+W9kdQP+UQr2ncHvstbIkCeUB6HUhWb6Phtj11heiT4Ujq+pokI4y1EQFfLZbPNWk/ScXpyf+XCAW0vdlyFxGM9x5dwd5CiXNjUSBAtny1QdVPQ9Oxnz8bC9FgtpWsKbFhBT/BaMQDG74+lk+m5uIDdYnLQTTnqhGi4JTvW+pVid18xQELKhK8ZTNLxT9l0iL4tE6CK34+7lwC719Itmvb7av/qczbrD4vVYlaZOE5MnPRWs3M1mz6y5Wql8tVCF8sFnWJtUhXHkY/TTCEFd2tv/yxN+z+vq/pPVar+XRxFd131NeWafI7xDGx+y4e/yAv/2XhaAzuf78ifvxP32x+LphMptq8AwE6n8wgdam3SAZ+w0bUMHc3u+nOTwdctXdtOlxPC37WmbCvyrldADzxaHSgQhx2gRBGtWoPR0HhjlpUsbMqq8riQSeKoxWvew3yxIN3Mq7/3z2Brew/e+vu/gdNHx0T/pKp/zWYvWNnGaTpJU6IJYbUfKWnBmfGzhGeh4h8TnYiBKi3wnQGnEuipirtz4WA1K9R2eNF8dftt3TpMeH9YjCG2I0dF1RoyRJGmfSWjH1F7GNUu9KSl1+jsqGuKNF3UfTlo4gjazpWXHDvF3Ucdg4564HUfSnrGAriqgNly0ZiLGK3WTI8uAk0O2qxQFXL52lh5yv+rVET5gpXWMFQ1xQuQ+qkqWofXzikJYJ2KBGHx32mwrMPEdUQs7gxNJlzDBuyImTreQBvnXrqp6HSMnSQXEX0Rrys0eSlUoFCub6sTGi0e353dPbh2/SaMJ9vtadroUG8O36kIKTH+DLlEpQWJWQAMb6OBIJPAaIdaHEBnOA/wGd8XfheSK77uGuFX9fWebT0hi5BazIWQAmng7rO/M7jt4jHKhiOI0wT6q20oFjPI8yXYMgeHYKauOH6D5i3Lul7Pmmf8DEcaTA29/gCuvvAi7B1epW7l4nwq5mUWzo8fwsO7GcxOTsQkh2naSMEcDEYw2TugvyMNlw2IeBvbuCYGfUmSEUjNBmPYuXQVti9dJpOi/SuHSG8jkID3mjESIRIKN54nMSL/dg5Kwz1ouo2di0vei7pPYhBgB72qm9YodSmpU+naezW8r/P5ocAUZBBsXK7FATgUyLSYCLVQlvSegdaspaupdavzCAZGtXRM4xj6yS4cJhndJIkxcPvdt2E5m0GMneRBRqRKvcw5TzbSpK+9/2gG00UB02UO1w4mcGl7BP00hv3JwA17CdJQ9Qf3T+DDuydwvhRarZR1HRc4lXAkyV+mqsr47NHDgbfWFascJgeXyrQ/mGutKufVVnNL+nBrootoKDFSvzBqZhMEnl4osyEOhM9oJJo8z/nPeCgcZLEBnUZILqWH9ShLoUdUa0UiFmLwKvaUJUaE5SILeg1YVUMAaIG948htuaLlXhynsdE6lhjiSgp+sZBJSu9Ft0mGUVT2XYboM5lCc4pRU5Lnzd1DjBYh7SE3OFHX6nmtwA1M74XEovg2N1I8oRqdUipjkOpDF68CBrbhSaLFeaomaq33IzEjwo5hKXTYquPCX6o2LhdBXSYdUJyCrUyjVr6fcpNkPxyffbK4ihq9Kc/WMWs7cfswBsc50Y840XTi9yFV2nHv3ZfimEtZmmL27MQbY8wZo9pC976lQ0fU4YjkTLRPDP490mk9d5clRkUnvV45SS6Xo3rPaqVjLNhUkz23vXfZFcVC54uZn51PXV0Wnhyek8Qm2cBu7ew/nOzs/yV49aer5fI/O589igffbDj3DGx+i4cXsHlRJMSz8dQHusr+lUStXJEO538nf17qdDmj9eV6qAY2YLO7Dm7WvAIgu+ZATn4O3c5upzQSwyCnKTeKyn8JZ37ppdRMe6GA6oOdRkj/le3ywcUUH4DOqrL0gM5n4JvFjq9rq7DivH/jFvx0sgPvvPlzOH5whwyFHBmhWGaoGNbOGIms+DxAszuUxKs4KnNafoL4rlqKR7fR1azVNgDR+hugQT+h0O/DI1ieWGVtIXIozDC06iDNGB50Y6BjU88GIo7pT/xUE0KRb2BV85W4cEAdF2ZhIdVyYVE2MoA0HYKOInDLJZT5kh1pl92hAAAgAElEQVQxNcZ/8EIPF+bWrveCKMOy8z3NbntOFg/75FxrIoQetc7F4HVFDg19U/FVYGUbO2LBtsJBDg7S2ZPP8i3Ya9xRVQv66J/Ef1CHWBXpUMPGcWlxqm+MirAmYuuSFlsVxbsZevZTMHoDhJUsqGVDJHsviSPY2dmBnd2dZlXRkrtbqkF3P3XTjmCDH0+ddP63SgBl5dtcy24nWF/QPf+8s65XTQMKnnbtudNY/dLjSY2zwqSGK94s8hR7gUX2QqlPuzM/dScwNiROUgJy/bKAsuAsTouh91gwwRzgugRbFtx9a8oaTDl1ht2de0Mya4HBaEJ0WgR+xdUbFEEyn55CuVxAvlpS/Mr89CGd87zIOb9ATIi80LYRgPWyPgFdBJu4bXvXnoNrr34fbrz0Box39wgUF/Mp1LYQ5i/OvBHLzJxtHZ4VU8Zdw4AIhlfM8KB5k0CqoxWqke4t0Mo9Agy/MkbmJMnWJNBI1q+2rfRIbBUfnPWCGwRX6JDDGbqeVHyxnSKba54HrNFmsE8SxUb4zk7gjUy7tuC0g6yXwdVbr5B8AB9pH7/3ayiqHI7PZlDUgRXDNOiioDQOWBQVLIsCyqqm7TncHfssidywl7osiaMkjtBQHT68dwrTWQlRpul4OSc1V+ua6m5dVeb80XFvcTa1i8WsqG2xmuxeWvT7I6vjGOM1koDAZRblCDLfhq567AwybVNLHqQib10+kDg5VmKMQEUz71VU1zZC3JOkEeW4YpzLoJ9RVjXTKPk7tbgHL0tHcTCLVSXddE3utE3fWzrgit6BMYqkVTGgfCzbC9D2ttGKYeAVCWsXiqi/qmRzHHpcBDfehAyA+KYTOb/XmjWGlmijaP6j0Im2ke2mUoGoJUUm8wrLBlBI5hk+MONGy8/gL7j2K8/p35i/aRxnluZKEcjFVDXUnuJ0saTvQXBJWZe+KcyznpJAJGooLUWVcs6kUFWJ2ixKHW9ksWVpfxSl7NL9hNOwU0LHDWlHfM8H46i643ArcTB0J/VahjCB+og6wfR+phcr1n/qEAQg3IE+eIlFUZT1GnJLsZFdGx3FcUbXY4yblw37Lh30PbhdX9Wl285X3tY1mkP6yJiZ1vG7Wun/O+kP/9w59zewXOTfhvX/M7D5nRifVcd/Np7ywJiV38ifn4u+838UwDmRqJWBfKXdiEzRF5yornlo3nHLrYXOiw+E7Q4VN+58Nj5oI6LJEO2H9CMV+a00wJbsx3GSQ7ZqmwPAQykWveiw0EAhKIWjdHIMKcuwrCDuj6nL+eFv/gE+ePtXAKslgcvwGlyoIS3NOX8hBfPzDO62Ko4xQXqjjSGJuhFa6/jVdy79btNuk7ULHa2TbhsMzULVkbkNL1PqSsLdZT3lgja06WC2i2MWk3Q9HtvfY8UaVadWMysprx0U85woYUhVzivm3sZGQT9hsL2qPCyJVszrGaSYogkALiqxg+w6JkLhogndxfBvIDpMNFta2IhcbWqDK4uYKI6afAyCSY/vdFHWKyTQBK5tdpIDPbaNSgFZlzrl16/wjR+VgFiOAxPqMjkvVkTZw6xUH0Bm5z26S6kFpjdmWQZZlja5od2tDF1w27kWQkBvDW2+ZSkUWQL5rqUTd68n2LyevsRsG6JQnvZQ3JqQjvyXp+k+rW5roBbjhuWWO8Bf5OnUmGUr7njHvT5EWZ+7lhaprBi/VBAlFs0zbIVzTsFacZIF1DJHGPo5ixJIR+xYjOZAeMXuXnVQE6ApCMAW+QJW5+dwevwQju/fQYMOysOM5d5j4AMwmuxA1sPOawq7V27A9VfegEsvvAyjnV0CFavpGZw5C8vzsqGCN+UnAYYIeskcSCkBoyBBCxLxJNTg0PUnRqFi5Rze/7h/ZHJE62TbeJN4iUzifee72Ik0IQBJmvME9Abtc6PZDsWshsihhX4PTdQk/t8GAyUB3YwvNM+lAnSJrEkKQnbM3L72HLxQV5ClBu59+B6cT884kxkCpRenKNt0gBFoeTclV1acKy/vDFGjiWGOsD3uwy3YpULgxw/PxXQV4HyGBk8hckrmFe88Gtuhhd35iU+8tX5xdprvXL6WT/Yul0maoldS7rwrGpX92mRHNFoBDL4VjjNgkzKmYmMe61JblYn3NR3poiwpKgxfkSQRsWYCLUJzL1jOBxa5Ko7PcRZf6xOlkX6raicyxponzShBh/UIPb45WkNB7JDCys8kR/ROvodQj5l7708EVGIsWswGHzQFYoctpg6oog4ifgRlhaIptGKTGyP3s2gr2VeLrxBCgLUcgoXi7EnMYxl61nVq6QjiDhRyQiLtPcasYNvW8raiEohNfaQ1b5jWTIcQmVos5+dLt1CcBJS0xRTK9wzlk/YhINvuVWCRUde1KegzJZqAOnAEjGITH/4EI9nkYToTrSWKlwkEA3VtlZ/Tes9TV9spJVnp7O5U0X6zpjSh94nNkvdBpa0CXyKhnE/UEguNyIeQa61skqQ6SVLjiaLrCxOZD5TWf/L+r3/97wfL5QeXr1z95vJmN8YzsPktHz4UyqTi+Gz8TgeaCf0JAPxMDIReE+D5R9Lp7IJL25nwunEqAaeErmeXLhuMg1zn96GBE16baK2X3vvCeYc0kS1FWhN6KGiqWnoMIA5Rm002aKDqKCWLB+pkcnQJetVCEieKq8a8hKX/mxiee/VHkGYDuP3OW3A+PWU3VXyf9VDUFdvWs37Ob2zr51pzEh3TRETHPXMJRFDBIKoee/Om3hA2uMcQIJEL7ouqyQENoNMFbIhgE03+fLtArJ1v7OnDIlg1us5W16iNGDngQk60qghmVgVTaNN+ApMkhbNcwdliCUUODSzlxZGHQaIhw0dKnUNNkW8RCVzwuDqh8zV6rqZj+DiNvunohUewZ3ryykewxLh1hZHrNUWlaM22sy7k+zUFgmCEIleRbwF802HtLEzbzmcwHIK1U93Via2ZC0m3UFPOoAFL5jvusfepcO3JUUMqZRJj9liPS/W2Xtt33MbQnQxU2VrAnpffFXVLme2CtC6P/ZtcvwtHEVdjScSdBOvXadi/yxG+E8HlvFCNaeQXdQfu7mBzDyvWRZokghgwq3JAhj2kGsD7w5ZiTIYmXRXdg9TpE1063jcmTjnPUhpSBLQsG+9QijtS/xcLOL73MTy4/S4UqwXpLrP+kDSfeG8Pdw5gNNmFdDiC4WQXRpMJxFnSgLU47UPaH0O+OKeuqxLnKCc6Z3KhlXuYo+Y1GQixmZlM9cjVtGG9Lco+3FflxL0WGvdtZkUwG8ARQJP7oFs58KJQC8ZDgV4g9/lmFFbI6wTRRYv6TeaB9vVOTISw6aXF+IjpzHXDRkFqLO5PlvbgxvO3SIt5+WAX7rz7a3h4dAwn53PIS9uwF0J/EQHx2XwJH94HSNME7dejrUEqOb4a9iYjyJIYRoM+3DmewqPzJeRlDYXlbqjmeYxc2OlTEVWVRTQ7eWiW01OzmM+XVZHDZP+w7vWHc2OiqadWs0W/Nwz539UedrxWhYAHTmxmtaWheBDm8yBlEy8h/LfY1XWMxdY0ifzOaKKQeXG+WDWd55S0vlq0v0aMmTzKWiHRHlJ8jqCxEFe9vHTRPTsuexXHmc8GQxelCXXSnHNIXzVkcMMgDoRyarzoJUH5AXgYA1E+sRtHeZiRUj5STA+t5LWO66qq4t8jOcHXjgMvY3mUCrVYJexRp0oEQNyRJCcpS9EfRGmFBI8TUki1p+5gjCZEoBV29lYaK4sKhgR6sfuqCfQm3qmeAFrL8SnKKI+9fBS+0HSPB7wW8NbjRqXyraGQN1LbKTQy+NE2WmGUjUqks4h7hPtJ3hZKkfGPbossKsTJRGz4Q9vpG5o1v0oxeQuvMcryxC6nU8yhrdnzli49vAEK/gy67yLu2lK9Ac8h+SAKGd7K81axGTF1z5XQofGGfKSj+G/zPP/3Wuu/cN7fVmxI9K0Zz8Dmd2J4WqRFz8Dm73rg3DMFgLlkev5C3G3RXOi/FW3nlmzTRYZBgSISiv9Rx9k2dDRDhlM3R2vtM/ChqrWuvIPUeYfi/ZnyVJEbMqUDrbwJdFKWFFM46O/QWaMzF0eBaOIBwZ7HfJTI8DQRFjH40D98/hVacH34mzfh0YM7pCEKNt2BmtUZa9/xeQZOvEmSgK00nGKstprBwDweG6XCwskH2eQFhNoOfVNtAArdpeM2ep1Q7fcy37cLIZBFI1vOCR3XuWYRxoE0BlYQw9wlRGfFfcDFhCf3yw7ChWBs5OgxhpTAyJZwaWsHentXYFF5eHB0BPPzc+kkfP77O1QnIs0KodJpilBBwNnXlswrUty6JhOcW1BsUsmgPNCBCQwilwoXu1bWNLrVmHmhTYfOY3uiA3ANraq2M8lrS3YYdVpBYSMoUKcLqgOlWpMiJV3o4Ew7GW/B5cNDGAwHUFQFaD/k8jJ2SSx31awAX+KGOY4pAQFA5QXxJN+mEY4S7mNlVQOyv+798KFC9gVpxp/5mU2rGYR2ihFMYV2NoCdtLhnXRanQboPa6IyH19D9LwY6o9092Dq4DJdvvgx1XUKa9UmXibR61FXHvR7F66DzqQ4Ue99GHOk4gWy0DWWxgNX0EW0Nawldc++G+B8InrHBYEtxHnAAkt198CGbUwUmguMuKCcT07Zo0nu3Cm4v968Xk67wufQ6YJpGW1Rp/aJFV8eRJfK9OpyEwDAQJzP+HmYjkDGqAqHYMj2UCkcks/B0HPeuXIeDyRiuX96Bt9/6Fbz5j7+GvJgS8IoMsxGddFvxDzq0fnD3EazyEnWbsDsewt7WAHpZBNGwXxsdF1rrpCxsNNUrFaYaUsMhopBkMHw+klO2JY+fyB7f7dmqsLPpab69f8lu7e6btDeItKJoQ5xGBpi1iXJTz7UI5cMkx9jdB32gNHLx3JTGmMhZa1CSsjUaQr/fh8I64oiO+zH0Ek2FByt9Qyp+IL81MpR8g0gXayXszFsjrcUH8x+8fqM09Vl/EMVxogkvsQdOpUDNuDiBBW6iBkdi8B2RREYopvwST6Y84umgfDDwAW/lSkmQOhwuUAazpMXES8mQwY0nPaLYpKrAxAprlEKxS2uqQvakD9OuqxRm53BiqpErCCGbE/OgFDQ+EAgE14rNiJYEUj271HpN7K2IaMFsYmRl32gmUOyWi2yu0ssBkpAkSv8SJlfC3UvKpuwzGNWmU9xX7XqNYkFLDwTCvTgT9zVReH0wM8fYl8jRTIJgWhHFWVPEjpoLMYpMmfCY+HDzC9BkuRStJExDPea8UqQSQxLpj1f58n+ZTR/9iffRL3rD4an6FnrNPQOb34HB1uwWE9X/qR+Kr2sEht49AZ+/ETOh/x4A/kD0ncPOtnU7mb4DPDv2KpT9mXZeH8KYSwGi3ciUmIivWsfGCWjVSip9xK2qRY8SStxRpyUW1qjMmOLyNa8v8AlQczh6TI61pl2lKQWjnX14+Ud/CB+/+xbcef9tonmVkpGHkR4iDNRwMX34cw3saCiTQdnfgx6cgl6dCJ+1ezB969Coha7kOuzk1uwRoANl1hpwvqWfQtBiqjajLgDWBspil8KHrqBv4kEsxLD0KcxcDIU0rqsSIKocUUxVo2fsrCc9R4bU9QqGowncev0NuPnGTyB3Cn71y1/CW//4Jpw8fEi2/6gD7VJMmwLABbRlcrPAziYBxdBOx4UkPrE15XKmYMAAZtvVnGsKrqFCakrYURDrSLI3XeeYBUZFeHKudy03zo4skjvHXX5DOjkXw8Kje2PCKzfn1txncQGLdEakm/mygv6gDzdeeAGuPf88KOwYn8/AZwMwWY9ukFmF9GvuWBrVXg74s3eBGv0VgEzV6jr9E1Bav8DX0Si/gcuOrxr0rrPZWz2v/m1fHBpUup18hbdN10k2wO5kxu6fKkSUjHge0B3XaxciljrbYQDS4QCG7pAuuNUcaZ7MwmuBH0jkkhLTItZN0tyKc4nyzRzUbJwPsSEc04xAEETXTQ2QYEAUMaWVooGsCy0SBpYEZiP6v5U4F9UxEAqHMRyVEHcSjhmEDiaYpmPcaKmR1WEr1i3iwyViZgpuo3VIY3W0jT1jYG//AC5t9chJ+z52N8/OwboaUnTnFuM1ZhJrMgo7PlvAfFHA0ckMru5t0aZcScZIsUXToOL6wdjEkYrSxMDteydwhp1Ex8waT0ZBchB9I5rwxTJXvnoY1UUZV/lqtJrPBuPtPTUcb9u418MNyORtsQoEUq480nSCYaOeDHV8oFQgrXKpI6N7WdbH3OnT8yXMlkj5tjAeZr6XGO6D0vnDU47sIUPhG1qONToklyjhoDkY1X8OrK+U807HJsFsRadJ4+JjCg3hA+7FhMd4NvtBOisZ6yA49KAwEzTnzqKPOQaE+NcVuwHAQHrowsMOpkiwCiaDDE6DNx+xpTTqWBVrDHtkGYFrEee5l6+oa1exQaGSbHECXrkYD2FrtqcYDAZajfZs0mo5wliJWRGZ9FSKQW0pPNSe1DZLAfx4sjNc53jK5CQzH/AtGUfyUlnfKnYDC+9pG3Hfet5LAZ5PRS2aTMU7TB3KXHStuI+ZONGWwi3CigppSamgz/UBPPaVYmdapNNmEOJmuFNKFv3iKm3kXlfi8lXzvsOpVvoX82X+f+zvHvxvD4u7v8rLevW1O8x9yfEMbH5HBj5AXKeC+mx8bWMJAB8AwEPpcv4nAPiXAPD7ktO5v+FEazsU001MpDuvcZ3fG/kTgCqBzUippSDCrJMjDwJOVee9my65zZoDGUNNl45mYC1h/ZXXxvkkjsMDnN6T9vr++dd+BL3BUL3/y1+QJslKWLmMJ153Iq2sggHUu69CVh6Df/QB+LpYe42W2AsquqDGKo4p6LtdvLVAcV2XGNSgvlnYtR3HDUeYQNjxDG7DghcXTuwiGEFRRzB3McW3uMZFn57EDXutaa4INsZMOeTfIG3vhe/9EF77/T+EwxdeomfQ3t4e7B1cgnfeeguOju7BarmCPC8IeKFuEfMo8XOrUtwZNwBnLaZBwctDS+e3wPqwN1CQ5srRYm9oaup0JhTDYmUxHbK+VOPO2c0CdE3Gb2ch/hi9NtA7feNyyXpK7LbG7KDrDQXlk0VRB2iCAHzrMc+vJHbVwcElePmN78PhCy+A1RGczc5Aj7YgjXvkGosazMq3doWqE9XqN7btqY7Ocf5d4r9NCvnTGJuFmd82ut/9VS+FAqDvgr0v8uWBtWm7hrBNNi5f3w2bPBRIxCm6iwEbNmpnW3Ae6g2G4Oo91lgvqzXqu9KhG+sE8GlymSVSIq59GyMf3wJC3dQC5d5zbFcjcxrSXYMeVQmwxGgnBoGOl8MemnxN5dd3zq+ROrzc5y07hb5TtPThbY1xV3P85HtQdhES85WkVFoFEXJNUQdqFAz6A7h2/Qa8/torUBQruHPviPSNxDRQ7RymJPpjYSso65pyOIkSay1c3hmZLE36RsfxtYMthdpOXPq8f/eEOqL8DPJi6KQEo1DrEhPDNALcsiz09PRRupzN3OLs1O5cvlLtHBz6tD/EKarypCFklzoCcprge702gWNnEK2/QS3jOLU7O9uVXZ3p0+MTVVZzGPX7MOglCuf4IAUgfCW9OHwqLPMSVgXrNhFwGo4Ko+esqyv6f9SLfNobKBNT4onh2gU1XI3nuBIrbq74uSjUOPNcVI5kClYENIkyKs88Bo5YmDZeheuBqjBLBtLYBSVdp22mzQZ4S+eTu6hIkaWHBOd3emjExHyRmLbaGHIzfagB82PSk1WERfKwB58izVcAYMZdT6/lqNeKvS3ExAexIBXmibzKlw8bBinP6gmFazImHUFz0zAAJUNYBINMF6dzjVUZildWbMDEIJVLOpRXxlbCAso5YyWS7cKiQ9p5yGAHeAttGdhcqEm+UXJd4cZqyu2UNZ3nzucRgP9ro/VfW7D/qaztP8Rxej/SUd0u27594xnY/I4MXGTbZ2DzmzTm0uG8LSZCqOf8oeg5MT5lb0OfqTo5nCF303eAKXRApupoQLsnvJSf446jre7oREMntJbP19JBhYaSK7SjNdTSMQFiHQ9rYtpfR/7q86/A3pXnVG4trIocPvi7v4Tp0b2ncjrwe2enj+D89BE5kB7u34RecQR2MZVadbvC1MHR1krwpQ5TnF8DHC0Qevy7pA3BJyVwjV2gh7Keibp8nnWfzG9G6mwKcx/BymnoBnaozv83v49FtwC9pAfXbr0IL//eH8Dlm7cgSTP6/GvXr8N4vAVXDy/DRx99CPc+uQMfffwRPHz4kN6PVGMyA8LFGi5Uomhtx/zG94ezhpKSwoUDoaHQuKowMDAR9KIKsshSLh7OKRgZYZ2S6DQIvDGmH7rwXZKxo1V4oDfHrDkO3BagvyMZduU4CRR1raSl9fbC7ih2aQp0+syXMBgN4Orzz8PhCy9Db3uftHGL5RLU+RzSaAusMpx7GU5jYABunIvfNtbJvJ9/BJdd9wW/70nG0/6OL/N5v9Na+zoT9su9/6Kd7ILJi7/ysdFcJx38puIIstGEnHPRzAh17Dhvogu1lFEa2jrI3KLCFwfqfoNygyY8MAt8M5eFeczLfATA4jiKpjJcvGHQaYS+60jrqpqt6HR6fcO2ZdBMHTfWvXLIR+hoWmHEB3GZgH+SlclaXd6PpjjkkI2dSyyMxfwYwvfu7u7BD374A+ilMbz11q/h9sf3qcuJcVKmIxnQQuvH7UQQ+d7dR3C+KiG/UZvrlyamF8WQmggOJhibuQ+9NIKPjqZwdr7iZxXLAJBSq6uq5k+TKkFdlaa2taujsqK5h4xgY79/pV+piMC1k/iO8NwtsavJdFJPHFcEcwhK0akmikyRJOkqMlFcWmswS3QnMbA7GcHWcMDgubbN0WdWjCKguSrrNb26XCXihKwAMxkHWxOdZgOkos69t5rMZ7hPZrhTCUg5ldxvPyddIsDQk1BBhfUCUVK9cLi59ucl6UMUFEwISSVPi1QHAlZ147vGb83ZCIeIy33Q3Lkj4ER5mOT6KrTjxhmfErooH5TBa0UmO2iZzg9f/HvMLrOYbali/ggE0yr1/AAuPH43R9ME6ZFV3LbUAu4KdORVvvW6wCeWQEpQ7RoJga1cmLJ+ah1w8YZlCi2bRA3Yn1qtPPi+l99rzh317PpL5yKRqmOPqb5tQd93gpDacnfTVFhZ5x6A1j/rpemf2ar+a+vtHe6yevC/21n2qY9nYPM7MvAhgJOVkWDFZ+MbM3IBnR9KVuffAsC/kD+vdZxroQMQwxqmlnu0ayQUftftiIaYwLgDVKPO/d3p7TRr4aoDQsNnhu/pdlGFGSMWtY+vo0PJ3adZT2VKwc7eJehnffj5//5nkM/Pn955QH5MWcEn907h8PpNuP6Dl8DPH8LJL/8LxSGEwQsV0R0h6DRm7cCFDhR05JNqo5sWdi2YdDT/CVhKCqTcizOwtBHMLAJN0x6gxrJeuDNu42FBEQgOlLOwvXcVXvjej+Dw5i3SN6GLIq1+4gi2xiPovfQi7B/sw4Nr12BndxfefvttOD4+JldH1JAl2OEUzey6aytrrmxnId3lb4fdtRa5UAYqgyFsGJVioWccxAaJwRYig8DTsBsl0fpIjtUsnNhwKehapfAtoZte4hmIb+Q0dTGXNoaFS7mbqZREybiGytxGNATbYE/7ePm5m3DlpZch3dqF3GqosP/gaqimC+gnBSSD/uYh/kKjS8P8MmwlJ1eNkdVVdzceG6EA/ikA50nGp+Gpzzse254v2ur8CseXNh2S4UK3+6J9/IL71zhBr22QIsfa/mSPiiEY01JVBXczGxdYT2COrvW6YuOrhvbaGkyq5iYNDDu1ZoxGYAqjlr0UAYNrrJO7UgpASqis9B90kXXyqOlE0wRjr0ZTKiZPfE8GHagLatNgX0RAFOcpJNV4uVdtxQka1gpLUldQL3JY1isoMgOT8RAOrhySo/RkMoGD37wN77z7Pnz84BEsVyVpUo2RvE/CJ5pimKaLHBZFTZICBGg3DiYw7qfkTntpMoRBGsN40IOP75/B8fkCFnkpTH8V8I4AY0/FANCRMlFEVNUiX5n5fGpH+aLsjUYIdmJHgE4RZAaP8YtkLa6560nxYwhJKzDgtDFWa+OMMQoNjDAj9NrlHbh6eQdGwwyKfMUzY9DvMm3YR0YrI2ZSWnJOfRCUEwKOXX8wrgbDrTKO06XysPDYQQOYyKMMO6sJC2gRGFLjcBiY5cIUKTWCIUXupw5Yl2l8KFyj5y2b2eAJRj1jzN1deWSQwyx2HwPX2rMZEFNeh975BPWdYt4eeQVCOaZjToVujxmb3ClFw0LLHVmKZYm5i4hyH5eLltERnZU7sdh9ZPd9rsRgHAuaBqXKi+eFauoemiOQSCmC25/51uAolHGM7H8kkiFea7Gbb+ORIbrPmqnFXE8lMK7YUZfjZ1QhmZkZ05fZ+ktxpqn2EvOtmlZuEwjOtypxvWklUHpwbxmt/zztZX8RRdE/2qqaf7HZ6Js9noHN78gIuk2H5gVi8PJsfKMG8j4/AoD70un8uTjX/h4A3OjEmwRclHTApNpYzujOssh3KLMBbNpOR7MUIKo7nxn+Dh06rdqYDz5rvXrR9vCb8KmQL2F7dx+uvfIGvPfzv/oULd8XGJ0YFiDjCQ93P7oLpcrg1Z/+EUQf/QrKs/uPbyQaXdQlRSgEt8NAp/Uy26+BC2jwZLe5KYYkGf0dQW3oDijhAOU+grmNYFnrNdtgaDIptSwkNwc+rWro9/pw/dYrcO3FVygT0JJTJjtS2oqXA6iZ3dvdhcFgAOPxhADne++9B3fv3oUiLziexTkoq7JZxKqgIQx9cNnri9bZnuPxYOE0dWbnSlNe4iByZMyUoBhYi1smMijYcY/+DbukTsLYSHxCbqCeIxq0gaqyFOVCtFnUZ7oIcptA1eR9b3SIutslhiFpL4Pdy1fhxVjkePYAACAASURBVB/8PuxcfR4qr2G1KIhahx2WepVDlOcUldF1Gf7Clxp0OjxfYnjpxiSqdcW96KPWKzVPdzzpZ38T6ueBoOMuulifwjZ+2jlZk0t+juEu+kDhpSbZAPq7l+m5vDx/RHErCArbE9T03tnNOvQ3BJAomYAIeGjVKcIEsyOB3VqLGz3NaPwdlnMsvfRNtAj7uM0khSAvhaDO9nDHkjuYHGvCgjw2QXOc5k/wBotqhlA7QVnnKQsVXV6L1RLmZ49gNZ/SbIi5qQiI0TTJzs5gZ2sEN28+B1evXYbB1gSef+llAog7kyEM334f3r99D05nS+4Go/lRx7AMNwVjuD55OIWi4rno5uVtGPZSmiMnIwNJFMEgS+D2/TP45MEpzEgPb8WcQKKXKJ+VdOnKW8yHUk5HkXPe1mW5LHueLBZCZEYll0WMOk7XFmZJ+4cdReecN1GUjLdGiSn2IFE1FfpuXt2D/e0hYlF6EDemwIJ/rWhfdXjGNXmnrUlUFMU+zQbLOEkX7B6L7rONjCacv4RrXBh1RiByG7uGQkW15NvguXsncS2x6AiB1gBas47T+1pzKSaVbl+Y1qlD16jpPf4VASKB8US0kyU0DX66mo1qpyFhVpFRT8zlSrSWpu5kXyJakKqEHVkExqgxtVr7xHsl3hVE7a1kho5lDRN1pjr+w554qAeSNRC+33NuuZe0spYV5kR7WcnfDfOc1Qo0LMHRd+D343Fb8f6yW21HkSHxKp4fr4w4TWdd9hjdMKw/QACqos6r/jCNzV/0k/hnufs0D4Rv73gGNr9DwwnN0TTOdc/GN3CUoulEjumbQq39YzETut6ZQNOO8VCYSENRPwDF8G/hZ9tOfpL7xB3MSuiyWed9gZ4roXD0nfUFXdTN8Wn/3g5+msPzP/4DOH1wFx7d+fCJgvyUuKJCJwIDu2APb78H1WoFl/deBH/+CJRru5ukhYrjNk9uw0BHNCtrm7yxE6LRwmy8iBZM1FnFgo6vGlOH2mvq0q1c3Bz87pf45jG4/gWOEqEtjPop3Lz1ErzwvTdga3efwBlxfVCD1dFvYVUfQWQvS+Hy4RXYmmzD4ZUr8NFHH8Enn3wCd+7cgdn0jOi06OyYZinR2Ngh2EtO3uN5kd1jHEQ0aISIQLAkV1cNS62hFzmINeUC0LYnykFk+O9FzSVkcp5ylnI8DW6v1eTMW1gDpcX3Gih9BAUZDpo22mH9wPDTOhioyKKQFqWv/QCuv/R9SPoTKMqa9it8gC5KqIsFeLsFkJj2jvmCowGpT/Cc37wGPm1sZppuvvfLbgIlUcCGJvFbNHRnhfq7HBq+WKHhs14W7rVsMAZ9oGgOmZ8eUT6oFsdXek2IYlKkjm+o6ex2zWDQiX1LMGjjTiH3i8RgpAFRgWLLPwgLAcSrDbWLTgWqShub1uUlN1x7NkSKxLgIX4gUfdO5tqwtqYOJkVGL6TlMTx/C/PwU5mencH58BMvzKe132uvRfiynUygXcxgMenDnzl344Q/fgJvP34ThsAcHV6/CcDiA8fYO9Ie/gV+/cxtOTqccAyIMieCGi4evsg6Oz2bwjuFIpcPdMWyNepAlhoDmtf0IsjSmnz8+OoWj0znpP6HJDpW5laKfLCF7o7U1oAtPFE+fUMApub3i9BZspWgaluYanQ00sMFo5DQ2ur+zPU4uZYdu0gO1yAuljYZVUUICTIHGApyruWiHfcC6dGqxKsgYSOQpTk6Mom2TIkIUx94Y5K0ROIudV2ieE8sFUSrRlVIXzjObRuZRK6KYxHNrXJx0qU1bMulFhVa6Z1K1suIJjGRq0kWyRlHJ7A9ePIp7DOZIcCle7Mz9EV0wVSKCIZ5mcgtSZFNpmmdB8iMGRVIRpozKUOu18mwyIGVMgexcelFNkdxxd7AJn+2FwjqCTr4NSA8qhCTP5kEUbwNLmW9QL2mkA5xjbAs0uk1qmNbsKKv6HkgXisdmSzY6ESvCWrXrMseXCi8W6IprpCsgTV/+4xRd5ejEtEK2E/JysVtf1CUXn78DuPMZ2PwOjbAoRV1AFD8Dm9/wkYuB0Dvy/193nGuvCr22607bsJ02/t+lvq42qLW6Yw5Ud6izgWYbdyJVulipC2Y34cnnYrC5siDd4et//K/grb/6P+HRx+8/UaWOuouyUAjaFvw8BLNVsQ0727cgOnkXdDAIxOdqxSZClKVHVvJxs9ACyX1sKGmwvuYCKZGG7/G15DV727wCn6SlM5A7gxyh5tCsNTl88MfvHBsOqYNeBHD92lV47Ue/D5euPU9ukVY6EkRIkvzIoAWriXrnKKB+e3ub6GeHh1fg0qUPOVR+Poe6sswRFLCGAemYFxoZ00QndA7B2kOsa24jbFhaVM6shkWtwTSfqyjKpR9ZenYuXQw1BZqhXoxBNJuTAFFzkWaM66vK6YbvFEh4sAmwOqYjTvTB/dEYDm++CIe3XoH+ZIeFRa6jKyO6dAlVviSNnIn7T9bVe0KgiV3e0rXg/dO+x21cb+H9oanxpAsM/2UPwsZQT2Fbvuj4utZWT3M/vYjfsuFI7hsHy9MjsHXVSOQI/GndnHAClI0rbDdbt3NMghEYFdJYl8gJDZ6NQKVD5gTgcGZ8p5PqBexsPlSCKZEmN1mnjbZaGW2UNqwd5CIfMikW0ymcTx/B6vwcVrMZTB8dw+nDezA7O4F8tYQ6zzm+A1GKiZhCb9m0Z3o+JZ01FdziCF64+RzlNk8O+pANtiDuj4hJ8qtfvU2AE1wDxKjL6RXLA3CuPDo5p21elSXsLoewPejBuJdClkVwaWcI6FKLOn+8HR+eLujexPmTMlnLWtgUDFu8cwp3NoqiiBxE6SECJVXPOFOLJmfVPk8RwxjnCWikSD2NDDpna59liZ8ucvXJgxM4Oz2H7X4Kk2ECKWrsIz7O2GuzlYP5soSyttgZxe5Z7ZxDQJlQEw5hjolUHMcZdjiZ2ky/j5muCRT3QcwppXoEssRNlSda31wE3PFE3q/PhNFZyQUQiwssfg5SVuNAgPHeo0NrAmyCg25FJTeIpSzCxkD4/lQmdttpuLoggsCWO30WfQ5hWs8GPqS8waOxkJMxlkwttpTj7aYDxjGWXjfrHcwPF/9yuSxsuG0k49Nwt1V1/x2PBj6+jBynQilYEfWXzY0w23MlPPEhHUFFLriR8nRsI9GoGu7cqrip8DD0NZLPCex0G/gJcuvyo49PCcNy7KTio/1hUdX3MYZ1HPchtg7SQR8wtA6v3sg8iXDgmzGegc3v2HDiHhknyXeuDf8dHYXkcn4MAD8TA6EfAMA1ALgCAIcCPFUHSG6ObrfRdyi0gfKBDrnn8oBMO+A17uR82g1Krb6I/vF5BxU+ipzs7r//3/xz+GA0ho9/8yaDvi85lJhlAHTD/z1V0ssig61oH/rlEUTKrh8kZdiFFV1VdSRrLt+igYCsGmTPXBujE4iSCGxdQ4l6G6msI+jFrhpqDwsfE5j6NN1d66jaAXLeQ2YUXL60B6++8QN47qVXoTcYoeMvVdqxI0kdPu+gZfzwhxCdzFdkhxdHCUy2d+BWFKOzBQyGQ/jk44/hdDqF5XwO+XIliytDjRDUQFknMUkXbGyYLui1GNSmuY5dEnhSbPwnKJU83S1T9ArLngoqdGDAEPWO8wc9Oe46UXvBRRfwmsbUiOFRSYverd09uP7S63Dr+z+G7YPLoCPThPGHDhEI7Q+7RlWeQ5L1N7/hdzqCjk/9lun3oo6mE6HPkwznO75NTzh0pwDxtB4n3YnqouGCq69fd2/+qof7irSzODJ0qPWX0V0UlmePWGMueZTgQoSSo2sa/9A9SuY2Mf2MESLQRj02GZghUxOPqg7Zt4EnKNpqvj/E2kSqKV5YAdh5Wy84odgOvxcq71xeVlVaVaWuykIha6LIFzA7OYHTo3twenQfZqenkC8XUOZLyFcLqIqSacCkPeSOK4JCik7Bz43YjXe5XMEH738AuzvbsDPZhv39PfBxDL3xBK7fjCEvna+K0n90+zbM5nOFxkHW+fVpXSmonIOT2ZKO3/HZEsb9HuyOezAeJNDPUtBGYfQI7E+GZMSzyGssxFNjKli1JEnqojjxcZap3nAcJ2kfQV0kHGMrxEzDcjveBsV6RjQLQmC2JU+AerZYqeL0xDw6OlaPzubkNIvndzJM4bn9CVzeHUO/n9J8XZWkPXVlVdna2tp7t/QaDXMdTWBxluG5cGnWd1GSaWPiRHE/Di3hjWeXPi+OrI4MezyFcNSUdcnUV3KcYt0m9cqNRHRYMtph2ic9Hj1L51MCUR7Q/Aclkj0xSQq5k037kbWXnjuUGHFCFF0072nAHlVsuRetUuBsSsuMHwavoAhk4m7lXqHuFMZsdaAKH3SRIqWXY2/kgmVDH990EXn/OAsUL33J9+GlCMdiU/OVzYi4S1oLsyD1ImtWpC3FijUDdwU6V5Qg5tHsp892yYF4E+yZ2bOCrhG+Q8N6TPtQJQrUIY7LoRAu1Mh68KdKwR2j9N8u6+p/Tbz5ZDAYkiYZx3AwbO559dseKN/w8QxsfsdGcAz9WnhIz8aTjBPJ5kQjof8MAM8BwD8DgH8FAC92YlCSDqAMZ1l0DBBW2boDOANwDBoT13kvbEShdGm3/TVdyOMY4dP+vfMKrp5naQYvvf5jGE124d2//+snMg1SIeYEF2RitIGbUq5yOIkM5DCCsV5Apusm1ARXFRqr2+gK6WrQKqaOgVFG7pdaJnPJ1XMMVlEDhMAFjR3KsqAOQmRSWnusascRJxAjF4f1nxsdw247WocuqXOQGQ1X9rfh9R+8AS+89kPoj3fk8cRdB1eHYPOg8+wCYf5PXVekQ8LOwGDQh1svvQJbOzvw/NERnB6fwNGD+3D/3j3qItRVDcvlnPY5URpKpNZW1WMnMDy1KbUbO5Qau5Hta7piF1yr5lYJMKppvVMLUKqD52zjPhsi3NpvWwMRzYZwl0exwzEMt3fhxR/+BDBaZ+fgEkRxKiH5rVkydnacd3IeK46A6srhvuKxSXt94q98Ar1psx1PERR+6s3dsab8gh/qIkUUPiwJmdpdTJ/4Ii5AT+tUfxWXS0NdR8fpwRa4XXaNzmen7OwqesRwr7Pbqxhz4rWuu5mXvj0r8noyk5E5kI042ZAngNZEI63dU7Go9I1yU7I3Fc15Eg1CTRlPncsc9ZfaFrmen5/5+fTML2dTVaxWsJxNYT49gfn0FJazGRXhWv2jZo2ldF3xHo7ThJgcdV1yoREdXeKYOrjz+QJuf3gbLl86oDlsNBpSYdCkGRaW3HMv3KojrdTDB/fik+lMzZY5dQLJTMl6AsWGClMWzmY5TGc5HJkZ3D2OYZilRKdNKBJF45QOvSSmeBHaXjEgSvoD3x+OXa8/dPh8Gm7tmiTtGVI5UL6kgE02osEj5jyDO27PeQY4SqmlA2uXRRUfP5yaO58cET2WqeweZosVrHJ2nb16aQJbgx4eJ2+dXSil5s65tLK1UU4ZqF0UmRiS/hDNgXyUZl7HkUOHXCGFaAFgqMmsmfCsUplkaXs9kz504zDLzlO6CRzhqZsqt0oJVOLOHEedeHJhTUg6TElV4lqlIDjg1lzxoO/AJxda/c5ZU6o2vSHCGsRLBxYlrJl0Flesr0TQ7lO8iPmQeS0sD/p+KSsGKqxqwnW5xmIE9AYHK4sdWPD0MNF0A8itI1NKqMeJHpW6t05SL3EREHF9gW68RPYZu5oxPfrEeN2zcVSHEaas8G0bC3yJ3MELDrNCTwHUXefs1IBfaaVv51X1vtHqk9Fw8KtVFb1XO7/qRiVZOezfhZSJZ2DzOzic2GVTsPMzZ9pv23ggf94UQ6FK9Jw7UnXcxYLXxlIsLM16nRVJAIql/DzeOA7dzuVFQPKzGIBf+KKK4gSuPncLBoMRvP0PfwNnD+586c67Eg1noNSGxRZ2IBc6JfOYsV/CQJdM/RSQR6HmvqauQhz1eCGEGsy8E53WfAMHpiMtkxYnZLqjSaNZegNzr2DuYym8qrWD1e1S4e0XyQdXtPKwMNnqwyuvvAiv/egnsHNwSPerCxl45CwpqwHVBB10Fv7BCZIfSAgk8d/TNIFLB5dhZzKB6mZJC7mjoyM4Pn4Id+/chY9ufwBJnJD748nJCTnZ4veaCx5iXrq72A2xXjcX16YrFdCKx5L5BWfqhGAT37bEqSys1/ippCkUOm4IcVeNXYIDE8Wwfeky3Hz9h/Dij38K23uX6PxZWayGKnEDyJ0TsB5WWOvb+FUOowV8C2gSC0bhj33+26RbPXqSjM7f1jX8IsNvWFV3R1jtfhEzHeB6itUGasz993Y943dzPKnr7Ddl0PXteLWcjXdEDqlgtTjlaxrvdaMglGZ8R1yNGcPBLYmjhNyagUyglzpB/RT7JCI55P0hX5DST6yG2gkDQYeCj2KaLs4/1kK5nFG81PT0GIrlMsoXi/700RHMTx+p5eKcitgVZvyWSyp0BVfaOE5kG1yT9eulC0urcqS81qEKxzMZAs4oimGV5/Dg6AiuXb8Kw9GQ9qMsrbMqrrPJvt27apWJjFL6DmVezpa5WhV8s2GqlRbgzQZAHurSEy11GhcQxxEblWkDWRyRxAi3CzuKRO9NUoWAE7uMqMnP+kNIsh5yma23lg1s/n/23vRZjuPKFzuZWUvvd8dGECRIkRKlkTQzGj3FG79nOybC4Q8Of/E/60/2Bz971jejkTQSxQ37BXD3pddaMo/jLFlduABIENxAojPi4uJu3dVVXZn5O+e3GFpzCbg0t0H0NYiAiq4su6x6oqnSEyYJ1j6Y2bxg8J0mDqraw/7JObuFl1UN1y+vw/qgi2mWLLK8O3UuzTGUg4BBgscSAe0h1OrHlFhrqWGoFBLROPrmncVgGINGklhlaHNKthjgMDhLCQjxlAy8eA3EhZXLwgqmmSYL2FQ0msiUCNCitDmaDqYK7gpxk21AppcYEDpXwak4GaWWhjqRt6cWen5+c8xb1RQTtR1Ur9B53UJcBZABa/T4qxozRAGqQcjibEbkWU+qnX3RrFK9i6jIfO2CXU6/Vs1/ODWW3XtZeWq0ec3HGOLcrv7pTuOjo16mNsaM0eDYoh07lxwag/frqr5lQvjMOrNvrDs3dXjs6+rEJG6BSITZ73nr8gvGCmz+QAdP/uLN/Lqfiu/roMnz96rt/BQA3geA95Ri2211OsOFGJSLG7g4qed6v1ctd1po7XPz1v/tF3Q1X2rQe3Jj5zL8/Df/I9z+8Pew+9mHQhd7ycHVPqWcBUWLtOFZgIMqDKCwCxi5BVf3I2yjxZ/ollKUVfMMpWJGPVOsz5CzK32g7v5JDlnWCFN2VJV8SGdiFMFSVxVXUPqatY1qYkFaasrFJJ3mez/7BWy/8ZbqNOsmioUKseKHoYZI7QxL3YhKV9A2USoc8aKvI89yyPMODIZD2NrZhvlsBre3bkOnk7NeiDaMi/lCNKC0YUyfDGc3Wh6v+RyCOlE2IR5Lx0T+InAWJ0BcmYPow1pbiCfrCUs6sFGbXO4TBARLVOAkISMMWN+5DG9/8HO4+dNfwtrOZf5dOneROvgELUmHJROnvMOREy5K4L5mwGmg2eNHlc5Tw1m5cahj51t/9yIDWzfwVwGcX+t4Lj38JWmnBq1nPZyxPm4zYfn5Cb3qC5a1vg87tPh6ksRCd21DX6yF+fhEKLJ032uesXQ8FXRqHBBT4Ol9UYumkuivVEwO2maxUsGRJ6M9q0dJvw/kzuoAKeOzlsdzkRnia5hPzmExmUBRzJkW+/DWJww4Sb9YFAtHmsyCtdCVvLmDupooyKXHIuBIj0eMCWjJJGiuKYtCihbeK6i2/JHnKQxHazAY9JidYRUkz6oKT8az8nRWFAtKxBhspX0P5SZicC7pZKfn6cHJmA3COD7GySwuJmjLaaHyNTtgR73rLGH5HGvYue5Ibrr8+sfGV6VzLnXWGtJNxrcTd+AIq6IAS8/xL2LxQmTKhF1wLFFJ+XbthGAxSfJiNFq3o9Fafno+NTTHEpjlRboOcHI+47WAeKuJS9CmPWc65cCmeR9hlhDwty7hK1/MJ4xyBqM1yDsdQ25DuHR0oskwKMPSyiGxiypoRBm9cDHMId2inBinEC8osHFGuMFefaRSI4ZIXhyAuBgYWCorykmehI3QXwlcpkbBJlGIRUPKYDZZLoHY1lPWkgSGFUo+Z191lFbkzWRmhLo34X47PUiuGknqIasZAIO7RCXRJmo2qYHPTFpDGZgSF45qmMTHE5TdJYtZqrcOnRvqVrqGgyCvPWj3VyvPEeBjwyQzBjMMxsXaDyBOyEYCrbmHlDjg4dMA9SFCOEyT7IG1dj/U5Rm4ZB6nhQuff9BjBTZ/wAMj6X3V3fy+jkqNg6jDeUlzOSmf878qzXbQcq+Nlb2sZfwDLRAa83Da+/C4p23bh8cmWszavNj6Mhc+f+nRG4zgx3/1G+gOhnDrP/6VHVNfdjzvIGi1PPddWGAKQ7eAni0hVS2ntbLRKopCqGpBqt+0hBmloUdjH9Y28WrEghOYcdSJ2P43nb72UtGiz0bdWVQs9npduH7jLfjFr38D1955n7u9dBxO3Raj064jIyPeTHrJ4cSLrxMbySkzGAiA1h6sFb4XOziyZX7CJkI33ybHR9KBVLC/tw/9x3vQ7XYY5HK8gI1uj5FTZbiTKy6YsZNimxD35ihQsjmZt8Ql5qA5geZzV8/AelTfAEf63STPWZO5fe06XH3nXbj65k3ojzaY0lzXrYDQIEUC62RzLcHthim2eWcILssF7LbTar+mwUCSRT0KwmH5OdKGY0TDl+lqPjW+wnF/W7uWrwDkXe1N7E58o6XQixW373rolpdzcUmbSHOOS1OYnh9CxXORWe5so9Eyin5cnFidbMnVEIj3w0F+zvpII2wPMc5EzaIwXCRjUR5RzimTdjFnRsRiNoaD3Ttw+HCXNdJEjT3YvQ9ElyUKLANDa5Wiq3pPu4zo4DxP1k977WYuSyTR2IieR75hWtR35A4fzXtkdHbt2lXo9fuwqD3un47r/ZOpnyzKUHpy/MzBDLfrkUvqvDey/bWTJMn3zOHREZsCBZ0PIkOinTEeu7003/sgelh6TjoXzH/0AetyDnVZ2N5wnRma1lqnuYlkCjMlrLfU4UmyqZHMx6AmOl7WT6Jv2IXLe+VoczPZ2trIjo6PzflkxlnIoNeSIi3cvIT9sym4rAv9rUEa0mFu0jQzTUmPpR3c1+z2RjDa2PHdwcjTGfbMQG+WnkriQKgDi/L3KJ06w308zrFEpqcCT84OxcGWNJ1nQgslyiu775a6AmQaAxJpsw0DiunCsgTQtFc1ew/kr0mvWatYIlGiLkrupqUrUcjP6TKwHpIXfZ0EOHalCULVYFB9bu3gQ/R2d1obbWsjZX9jOP5FeQFS3jFsIoS1FvCsGvzoTSWZmlqgaK8avFcK7ISrbzBKU0UreUAQnApK+Hm8D1Sdue+S9M/O2Q/Rmj/UFX4Evn5oHIHy5n0SXpGp6DsZK7D5Ax58t9Gknqwu8/d40CQ11jyvhwDwsVJs/1ZjU95Vii1N3qcKNAfRUrx1j8f9YQSd7c7mxYbKs/aSz2KKtr//vH3jM9lwRKF6+8d/YagL9+d/+wfKZ8NnbLNfaC968UAjjYsFIiGBKvRhZjMYuIJBp+icPAO9oJV6cjeUarfn74lkSsUoSE4IDrxJIUs7YJIc5hQPUvpn0whbCLTwgZwGodftwo2bN+HXv/nP8MHPfwGdQV/cGXUJbVRZxjRfgxZUl4bzWr2nzZKa9cSUAtQuKG0xvXZDsCrpREN/OIROt8td2i7FEATPpkEPdnfh/PSUTT28uk1yh8FaiGnuDOTYgMhBxTEqQVl9Ao6rloVRaDnyPn2RUEPiUTeIZICRE22NXYvXL12BG+99ANduvg9r2zuQZjlTDKmjCaqlZYWMErzkZAU1wzesrSWNE3VI+dvfBAezqUNj0919onOLUuTwrZvqSz/8N43AvuOBuGxJP4u7f7GT+lJMe7P81GYZfJcjdm1BO8K0JnfX1hhsUld+eroP5WLRmP/Ijtk2oI2LKqAGXarnlJMor1Ioq5Yp6MBAq2IHbdKaE7uhmkl3krqUk9Nj1nCX0zEc7j6Ak71HfP8HamtRZi9RUl2ArNPhghjWHipiSOoFwpjPqfMN6b+jCFuyiy0bq9GxFvOCX3ye50zXpQJfWVYiEgQDl3a2YWd7h6QPeHY6rg5Op8XpZMG0U3YKpfxHl1s3uGT7vVHV3bi06K1vJsPdu3b3wa4bTxfAKm3VzcthxJZ5BO9SRMyzhKNQiqqGOWs/xTonSfPQ6Q+RKbTGBAxBUyiYblqLgypnexhJizEVxYYY6WimYhQjJjRgU5t01uz25cswHp9Ddf8h0DHGIh5dT9Jt7h1PYFyg3fL9LO2vQX+wVs/Oz5NygeKzFjzmvb7fuHzNb16+ZrJul+bNUmJoTF/l8azbXNa8sKuEtoVEoRgGYIYjPZA83ZxGkxQGkfYKHQRYQ6W3GgGtOaKh2I8FRaugaFVjn53riLLmaCCrmBBR/AkB87nQZ8X5tjFtUAIOOdcacYjtiNuuUHgFEBoCzh0WSoqWk/I1rZHfqaRri0m8wnGmVHueWhnbPe5UsvGO2jBrRio0vllskGSXLrGoulVZfTXHNugbvdIiYiavBUuKkmF1dECyQT61abJfF9W/W2v+xTr3RzBwV/dq5atEUHkVxgqF/MCHFw/vJyp+q/G9HDHahMDmYwWc/0lB51+pay2o8yy2qLbphU5nW6cZAWbV+hpbv9cms9Utei083c97YpvcBqDYqug9ASStdeba2+/Zuq7MR//6D5E+puHKDVj+whHBZftgaAMnFF1WOcEsZNzl7Lka+jiFri34vohSCwaYwbMLXM0W/RGaW6gggzkVTW0K63kHemkKYV5DCf45Dx6bmgAAIABJREFU2ZXLgI/AG68OXL1xA/7617+Bv/jFL2hjwQY/cdDmTnRUtukQMG2UtVSiMzIYdMOioC7mlLS4u9gCcqj3fqhqMTrKMv7o9XowGo1g59IluHPnDty5dQseP3wE0+kEPHU+as/PHahXSVomot9R9id1fkknG5SGx+dseZGX/1tuRM2SX/SEAQrHmQxGsHX1Ddi8chUGa5usXd2++gb0RutgiVqsOlbeKFrVpDU1a83fNHLt07wL3f4I8l6faXXYLpl8jcM3HU3zhXjwRWbbizdL8/9XpPb9rOP7psfX8lyfkyf7XY2L15W7cBST0e/DyFxlkDg9PYTF9BxC5cX0khL/uPAjmkr6f9SqczdP5wD2aOGYolo10gDFdAxzMvM5PYGzw304PTyAYjaG+XQK0/EpF45oTvFVDVUxF7kACi2WjosnfHpOAp+ec4pRiL8OXSK+M6GuTAjt/bSRBppB6YjqfCSgT1kRdO/3KCv4Cvzo3Xfgxls3OObk8fEUjsazMC9qX0uxr7KWMgcJ9GGOZIya9DBJO2Gj0816lGppbPpw95GbLuam9qJ5D+y8K43fxC4ZI4SkssTCsJvxeaxIu09WcVkvrG9dqrevvBF6g76RHEZue2Xi2CoQhF1OUXu6yzQLjbBkZ1paDLKqRuvSbEFyEePL3ny+MJP5I6gqzxEsVNSk4zmblHA2LczMJ+nlN5OqOxhVw7UNnNnTtFwUxjiHw7UNApp+sL5pHafPhEqPKZNOZszixoLcZxEIKHLvmy5WqREiOSJ1iKkHbkqOSRETnVRNj6wAf+5QOs7xZJ0qk1AT07xbdc4TYJ2oWQ5yHZapq3yVOaeSs0K1Q4gBrYJDG6dQ+XuqIKJuTVlTyoxwMUeQ865TfaWVBDHuCc1+gjvLhru0qN50JpW4J5yrlrQjF49dYh0KyS/6+JnmllRvBF1LS7B8PNE3ibq5yh/HqQGzh4iHxpiPjHN/6PZ7d21WfVzPZ3sMuDVWdDWeHiuw+RoMMQxaxhasxvd60Lx4BgD/rs61/wIAvwKAv1NdZ9ZygSu1ytbTj/gGsA0yWALCqPuMUrPsAshs/655BqCsW48fTQUihTdCt6UDnHzfG2vD9Xd+ki6mk/zOh7+j1cW3jqUNbJ/55m1MLloObrJfCBw3UBULpovyxodMfeoU5mYEvbBgem0u3neiKUJU6pVY3JG/QW0zKLALhU/4HloEA0kZWH8DrVWrvUmmDQWFobO/Y5bDtTfehL/59a/gZz//JQyGaxyCzp0K55oNGut4bKKdCwGZUhjGhmtEzy/SGbvMo3yix+xbjWT1gSXRCvsnej4eytvc3NqC4Ug0nVs7O3Dv3j04PjyC45Nj7nROxxOm3Do136HjrUBMllDnE2lBYfPiI20YsBU+Lxni0nXRSBPqWPZHa3Dlxjtw/f0PBGAORpB3umASYTh5duRU2l3MHGyK2ZKhSh1Eps72B9AfbkB3bQscdWHwm+tifd0PixduqldttN9f37cUrfbxviqHjhe+kLe5gbTbg4G7wi6s9uQAyvEZucEKcPJqsqNRIkxtVdoo7X85V7ssyNAH5rMJF2lqX5OpD3csJ6eShTk5P4VCo0k4Voi7oFSE6oDLM77Xfa3MyUh3rWsxNWPAFYxS2Y1LMkyzDClHs1jMOBKlaWIxYghMn0VlQZBRD80JWZ7A1vY2XLlyGd+++TbefOcmbGxtwumkxIOzGZzPCnVNN5Vl2j4Z7vAkVMgJM8YTSEn6kG1cq65C4pO8nx0f7qUnpyemKCuBXlY7vbBsKacugX4v53kvCwmkFTE4XOj2B/XGzuVqfWcHHWWihODEmTcWWw0ufWBEWYHimppLG7qhlLIZTcBQlsGNe91htXHljez6eJaejGdwdHSiNF+OlYGKmTEVnB4fWGuNHWxu173RGlJHk9xnSDva7Q4g73ap4IdC42SYSytALRRZBpbszqSgN2gXrisgTZx0Lat1TaC1VbuGJApdw6aga9THgbM26YQL6EQBjlLHRBtXOzk3xiodtUB5duqC5uLPyydL9JuSoVmLuy91Hk1hKLtLdKNWBSGcKYpCR04MclgsO+CiUHcTddwVhz2ZmGSxQ4zRlapi4G5mapCAJ3a0gmuVCd6kb2ntM+aJxKMmrFoZ6Wiq/hQLxHCGBk6NcR9ZhH8yBv4IWXLbBLjnnCOhbfnygW6vz1iBzddkhKYjsRo/kBE0LoU+PlEzof8BAH6sRkI/0vt70gKC8X7PLtz7yQUwGFq/H4Gka+lCTUsbehGwgn6O1cyLESxwAZRWLknMOz/7a8oQM3c+/F0aQmh3Yr9wcNfe2pYzrQAjyn7bun4TFtMxTI4OGk0RuaOOoQeVT2EDppDbSihr1GnQDh5hzmlwMC0zmGHCOj06C2PuzAnV1nHmJrANf60e8MgOrwnkvR5v5DY2N+EXv/wl/PKXv2SQRyYavlX8MdZGdQk0ceFqKEhrI20IxSdECVAMOM3ShVXdFzDEHGmzpOZasQfj6AHdDHql1SdZBmub2/BW2oHNa9fhfDyBvUePYX/3PhzuPWaTEKK8EdWXQugpA1TmkFQ6CBFwgmgnjb52+qDuqUTGLKBcTPn5CPh3+wMYjDa5o3n17R/B9vU3+XtWnSy9xj7Y2A1V4C2m9kEMglC0a0nehc5wAwYbl6Db64NJUzkXr1BduQFrn2Ok86qwZp/qYr7MQbXp399yRuazxrO0AF80vtVubiyMEOcwz6CfbMl9MxyzcRB1OX1dLFkFtLf2HubFAhbTKVNN57MxnO3vwfj0mM1+KjXkoTnv7PCAAejS41L039bYpvpX+4rp6VJguzDlKk02/j+yLoJo2pGMbIwxDr0Wh5xFMr0hky8SSrrEmOHaANc2tiDv5KQb99vb27ixtRVG65s+HQxxXBhzMq1wUdZOWlsNayBpTOqE/uiFXhqSgDYxrltlW29Ul7proTPaRH/30/Rw75ENVQUJ6zJljqL5jgpsG6M+XLp8KQy2LtWYj3CT5YwWszynHEuf5BmDPI47AYrfQJGhS/CGYUAmsSDcYeNcSS6MsgFOouCHO4feBzspbd11g/nm1ev27fmCbYzOz8es37Rq5MTeATUBzsPEZZnpdofG2oTIoz5Ge/m6Dj6EQPU7XEaYpXp7lZrTTT9dxo0YXZcRq2b9lS5mT+I5CEwaMZPmPErWWy6EFsudwK5qGpucy8idEcAXGVP8mmt1uO3q7aMGDJigWLiXauCTG6LPCtFooY+F2pHtAJie6kVTI5rRqU6bPTUDYqMjFMDojLwmPidR1azlzpydbY1KhjhT86kU7KCOgDU71QbUwjbTjnNR55qFBdgzJtypjP9jHeyfLZqPrDEfmyQcYWILKMOTRe7V+NyxApuvyYgUux9CXs9qPDUouPIfAeCWGgf9NQD8FwWcXV2E6haQhAs5nU2nUX/mW+Azdjfj94vW37erF4n+3rOE8BeBZtX6fy7JKBm++/O/pv8kn/7+vzeOjK3f+9zRaJtaEz9tik4e3Yf1K2/C7OyYM9rao8AUxtjlLiS7oWYZdPOENYrTkqhoNcyos6dZafSqSnIZcE++IMZbIfJyRG+V5h24cuUq/Oj99+GDn/2UQSe9JgJUtHnkTp/ei8FL+LqJ6KQl7pIcPAK/tWRuYeQXBS2sg35GpavF/ZkRyp2RrK6KwaoGrVNHZFFDUSPYTg82B2swugywdfka3HzzOpwePYb9x4/h+OSU8+Em0ynMJ1OgTgYBR3Z/pO5sqLnDa9W0hDRhBAw7/SH019ZhQCC3KoD0RsPNbY4w2di6DMPtbRisbUCW50L7I6OiWjRhNrHaFfXqxqmLOQP7HJIkg6w7YKDZGa1B1u1Hk8xXsj34IuDlVTjsZ4He5pR+iQN8Vbu0LzK+bdDfiOZ1K0zGQeloAJ3BAPrrmzA7P4bp6REU03Oo5nMoZlM4PzuG86MDmI3PObfy5GgfDnfvw3w6AQgVF76M5nQSq4Po+sSgIO1lonOOhjcIi6CuI8G1ycfU3XNDFm0b/zAl1c9pLrDOOSQ01u31jU3S4NKk7na6mHcHRHx0nTRzl65ehe3LV/he73Tyot/rzQhNnZfozo7GvvZoF0VlfAAOOJEnBO3gRRGmrkcYefqhCgT+bB6yUQfX876beXTTWWHq6sBYdcn1KO6zaZqG3tqaX7t8o+rtvDGDrEeNLHZSNRyuyIUyq8ZAoOBMRdkCdAODNEbl1F+umG6piIldXk2zNlIHb1AUWHhnx9lgC669ZbqEZ+7cvmVPT04cmQ8zXVmpxr4qTTmfJ4nNaL4Mck2Au5wMiNCEOOtHHwZEdrubASpziTqtgr5YV6iuryBgULMgxZiHPmgpqNUEqdYSnYsaSMPGOHK2EbEj1FcW7ToFaaX4Umk6JwNvqRlGh5/m8VjTyW3qVIx2KG4EahOYirtQ2U+iDXWv2tgo0DDRHoCOMSoqISC1ejMh7y6L3eojb7XZqY67WELgTirrVU2AWo6TrxM9rG2iS1imhCeA5p4x+AkY90ma9f/o6/kf/Xz+kM41ukgn/lamiB/UWIHN12iEEIOcV3TaH+jY149PlWb7K9Vz/ljv9Z4CwqiLzPU0tKmtsaJ8sUcUO5V5a6pdKLhM9ftZq4uK7f3UhZiW+Dud1jEY6pq99ZNfcD7zZ3/4V3q/fqk3qoCyJyuNBIqOHtxmPebFQb9FWk4XUhi4nMFmaQ3MvIWZR5jVCBX6Jd1RwWH0DPRBOFSyKw9QczHHQL/Xg+2dHQaaP/3Zz2Dn8mXe+JBOKqpEoeU22T7FjbdF3GGRW6xD1SJJhrYhc6MgYDOGt2sEtmg7NW+PNZshZt1Y8KztsnwV6ipAGcQFPgHPus71tSFcHqRQXdmEazfegtPJDE4mCzg+PYfZZKpAGmExm0E5nzLVt5jPJItUyUxZtwd5pw/dIdFbR5xdl3a63NkcjNah2x+yeYiYnnB4jOxFjKRTSrPAcBYq6naDY006Heh0R5CxNnPIHSAnIeeccfmqbgC+T3uSizTPL33s0S9yNT5/GNDWjNaUjHiZAgi1tqL7KdC9NOKYorMHt2HvzqdwuvcYjvcfweTslAEg3fJnRwcwGZ+J1aazcXuunw0bEAk+C1xw4vSIVkh8nH8EM9lG69owRPSeb+Yq8QBlckPe7YbecM30BkNMs7y2SVLnecfmeY8qQ6GTdcJocyPpDYYke8MKvT+f14uq9nZR1l0vuU6eqZNLc2KnoEI7ZNxtIvRVcV4j2JpU7kyk8DX7Q7tO125dvYFlUdVJkplqMRWup0eTdDz2ev26s3GlcmuXve0MKvLupjhj7hQjg0xxKRUqRanv4kQiRZguWqvFdq6nak6mNRL+z5mMBKKa7E3We5LvbzBzcHmRjC7B1nVjpqVPKo/5YjolhGuMZpwSFlyQZt5XUC4KqltCt9srB2sbBdF8XWKdGtvENbTQhKCgCUtECvbaaVyohsKJSy2fw1rlNCDZknzMYowjUSPUhMzVFClIDqWykgREL4wwpPJAYBUNme5Qd5L9IKx4thcKaJW4rNa98jv8GEHe6vTzLms7NT7GSEezRkPGRCTOZVfZTGuv0Yw9Ot8acYxTgMog1YIaBVXy+tnGKNVdCL2PSmxYWKZQ+m3XSC7qTACvnRgItwHNv0LA30ICn6KB/SQdHiVIYujZq0FB+R6PFdh8zUbsbq4A5w96nADAPwHAnwDgXwHg19rp/DkA9GEZzmxaADDRD9v6HEFipNO2dZhaCWw0ndj62+TCY8dOZ8z4TJ8R0cKDKr5v/+SXhjQ/ouF8cV5kzH2T6JKWYdAzgGYcxJyaegfWJ+BCAtPSw3heQIAARQwut1I3FfoTQvCxwKr0VaW4UstztLEO7958B97/4Cdw80c/gp1Ll9mVgOltbYOboNLU6InXFO1V5yhlZcnQRP0b0OgPs+Tf8TFpun6TNcpdW4AFmX+wr4XoN0svIfFNS1v5TQQ8gy/FYoG3Sn3obY/AbRro1wAb80I6JARgyclxOmbdF9X7ic43I8ORumba8GBjnbWUKZkRDUccb0O0Ygl7b10TLQowHdlJp1eTCoQ+plaLFLae9UbQGa5Bb7gBSXcAKgldUjS/ABhd7LZ9W923V8ns5/NGfAuF8NXtE5/QSn7fdJ5f8POvTLHVnXbi5AOi4RTK/UButOdHR7B391OYnp1xoYbmrke3b8GdP/0O9u5+BpOTY54fqIBDncpiMWett03a0yg207UR8Tk0Jl3Kv4xxRzS/alAgTXDoZJIzVBijYhJP+Knje5josUmeY5p1qJDkR1s71XBjGzrdXkhcypVstEygJTRaJc7V6CxMZyVpIYNHT12nnt5/TlcM9hPiqpM4m1rucCGvE7i0mZV1hE1rMGYhokVfm4DWdntDvPbWu1VvOMTJ2YmlIqM1zhFjJe926uHmjrd53/gQiC9LSkQjoZRLqi5KxmShqZIdATfRG5xBmhOLILR65CJ84DwQ0xEwShmSokNklFcHa2wS8rUduPQWaQBSf7z3kIp01K501NWk6K/5fAaLYkbHDL3+CLevvlnuXHu77g836Yw6jBIRS11H7jKmgKTXpKxKfkYvfkVQsaqUAJvhWBMGoUa+P1P6a186jphqVSJEm28j1UqaivsE+ATAcrWh5L4gO8lyJ5evTUCTk64RRJfZM2KZzMUBwyZEKPIbhHPDVGSiy5LmlemvQ3HO59dTMfAVbm1mkOi0XNlIooOs9jFjoBg2YdkcW8piiykaPCP9KKBZp061lRzPCEy9qkq8NWbfID7yPnwcMNx11h1CYj+2AX6PIew16zKGFU32axorsPmajTaddgU4f/BjrKDzE6XY/p0Czmst3WZodTub6mwLUCat3UvR0m/alolPNBa6aOwTH8O2KLoxmuW5AmKiar7zs79kY5oHn/7pWb/yXKlbW78ZN1sEsD5vBI0joKDwWeFhuqgUYJKjoVGjHQVIEDSGRPZwdB/RRo8iRba2d+Cdd96FH3/wAdx4602OHKFVmkAfg9UIdKy4JnIqmXPRs2CZcRm7DRprwHVjLRXTzqy5OFbcKimygClj6sRYBQMzNLCobWOQ4T0yIKTn5bKw3v9sRcwGSQEqizCtJWPTq1oLbQK9/kBjS8S4iDqWMgK71y6ou4kB0qwrZiAEHmnD6ZQSq5pL1npJonqTFRhBp3EJb2voWtGZSrIU8k4PuqNN6A7X2USFNo4GlMj1Jdb/J4DmBV3haijO0MidFT3s+cMZrZp9wTlqg9JmiVXM56gDScyEKkBVBygpGmlRMYg8eXwPdj/9EO59+FvuYFL3fn3rEscWEQYg0yCirlNcFN3oFUeOWHjaisE0hTdJSfHqfqvzS3S4bjJ1BW4REqTijkSlGX58eu7ucBg63SF2B0Ps9QaQ97rY7Y98dzD0Wd4tJA6CNYNdqh1hYHoCW/UECrMMnokW0qkyOWi7UGgaenaMkaxKmVsVBBoGUNJYRWekDWajz7WuVYh0UiwgdQHTbqde29oJvqpS65JOmqRonK2tTWuOlfLSoUQx2MkUKdYgLq1eOmT0TKzrq0WMjkKflWqZFaBGgEWTqVQo39iZkY8RrW8Bia8bOJrDWTdY36LXFPJOPyzmEyyLhZ2en6aL2cx4X7FKP8mzcvPS1XLn2lt+uLndSZLEYl2TC22hbyot5HJUCHUoByCUWumPI3cz69ba640KMUk/aRALvVaVGgNJ4dlQh9PwUqDv2a5MDZgGwzRdL/Ra9kxKESh+hemxhpvzfL5MkOvFBjtOv1cDqKWBDK99T6+GPwziWWfJ3WH+YUfNghTyGTn1T3riBTVmQgH/zXHUTcC1vmgOzcJwBsYcoYGZMebUOftRqPw/lfPiDwj+ocuTWci6pV15yX5jYwU2X8PRbPBWYPN1GWQi9H8r4Py1xqX8FACuA8CGLiztLVRoAbqLXc6qRbtt/6yCJ3WcFzuhSauT+YXCYXIt/dHPfwVlMYeDB3fa1UW80Jl9algFYWQO1N/cgYcf/QEWs4l0IVvDaDhXkkhxd14EBpwEqow6RWLMhqQdBoecJ1CBxINkac7Aa317E65evQJv3XgLbr71NmzvbEOquXI1xYmgaUxwWOSjlgUQ70WlzRp1e433pahfbKNfVKKsMonk75mT7EVLuiAKXo2sxYyCIsfF4QBqbstlXmCAKfw93j1ZiU2r0AKnxaiBBZ8HKmgrIJZIAo3tFnkM2MxCL1tfmhPpJWJAXmtzIoQWvVky7yDGmkR6XgxgJ31ZRhvcDeiONiDvj8QkKRrtrMDQ1z7YJeSHdG7bubXP6XpHrl94wdesdR/5u2Xa0FOP7fR+im2UCDhZ61jVMJvOWT8+OT/mbhbFiyzGY9i/+xnsfvYhHD28C+PjQ5jPp3x3LK5eh0tvvcP3CDk298i5tj+A8WQM52dnSycmHXGeTDTuiFgeZVHz9xObMhOixlpdt8UF2ygHPQRryfSnNxiFbrePJnFmtLEN1BXs9gZVnndCmqbGpmmgbh05myKGGj3nUSYoADGV04QpBmzF7vOsVpsILpFiL0yiXwHHbkgTsbBCdbT6cioFcblZXgw2eGEoIeZGBWAgC9PgbFJ2+iNvEAkgpeqkWmFgeiydCOrWddVW1WPDNzFCPZXJKeWoDEYwJkhAWGSF0uwYxBCYuq1GerOIWKIxmTCk2WgnYDQ44uBf4xKXZIO1zbrT7VW1r8L0/MyfHx3kxWyeBwhJmqa+1x9MR9uXZ8P1zZz86jBUhst10tZLqSsoNQ9cMNjCaB1MQDCQnXmOguXp7HNsB1LlD1lCA0on9WIuhIW8Mo5F8dIYhTgvV9yVNHo9A1FfGZA7zRZ1nMVp6FqpWUCMPFEaLF1LiQPh98XQMH0WaVnJxFWdqwpBQWaGYhLEyFfiSHhtpOMiYOqgIV/we5zo0IVB1aNaNinqGDTb2sBPuLvqzAFYuI/BfIoIlIN5zxk4sM7cr8A9AGtLg99EKPNqXBwrsPmajpiPtTIMem0GaTn+DAC3AeDvAeAv1L32b9W9dl1PRK2/exFs2gu010iVjV1R1J9frA22avtPgcwWEfJpANrp9eHHf/kbqBZzODl4/KWuE9E413Yuw8bOJfDTI5juP+LuAHF96srz+55CvknnNF8QjalgAxwunVvTvHrfAljdPINuJ2dtDX1/bW0Drr77I3jzvQ/g8s4lWBsNYdjJIDjLHYulLktooSRB5ZXYatfBRz2o1Q2pb4LcgwpQotNrIJkSCaWs+jUqvYf2QGRgNC8RFt5AFSzUlJOJ4nro4/bBOTYgcRhUvwXcjXRGQtOcddzhDOxYiep+iXyMFTMhHDQbAEQFy4YpxXQOmcZLiJayAKlLqdsdkEp/A/TZWZM7DNo7jcIcRzExXch7A+gN1iEfrLHJEuileBl+57PAwPPAx+s8XhRwvUrjcymtuPz0db00xGWeU/uxGywlpsxsVtVU2wJCUXqoZucwPT2F8dkhnDzahcd3b8Hh3i7MJmTyUwoIPTthR1kqrtEDkkkZzwN1CYcPbsN0POb7ZvvaNeh1OxxNdH5+/lSFIBaqaC4hN+kYO9SEC8aoKJC5JkscujQjpGNcmpnuYK3e2Nrxg9HIZr2BIYpsmudorasNkjEMdbPCDEJYaIesJzpLft4gOCsCN6ORIdytEuMXZFdXZs9gw3/XX+DuF7uTLhpGjVEtIABRP9OG1smgj0uCwUh3TSAGs0ap0wozemMHigaRFh0d/1R/1kXRYFI3j+uNSgMlLWQwQj+N9UCjTrxGgXxpwZRa+KLaXSmYiRjEDK1T8YjjmBGi/BJVlaJG2E82SVydpv3aewxp1i0Gaxt1qD1RNugaeCoQWJsRBZZiVFj9QJ1j6jAiA2WgLEmrxz1F4EzJnGI6tEoYYz4K7oKK2VxiEHPx6yFMziAQNUOr4igVebXWIJ8vK9mc0cyPgC0LelN9/CDnk51mjbjFNp1JNl5CNuThdFatONLXJtX6baIF1pq1q9y31DCv5kJG5SdIXAvbIzDo9HwcSOeW9aBJ1OtTtigiTgzAEVhz6j0+TJP0w7yTfDSfTm8Xs9ldk+bHSZ76508eq/FNjRXYfI3HyqH2tRyFAs77GpfyHwDwvyvw3FHQV6hBg2/Fm+QtE6DYsYzTdXsPVreotBelchfHFxL3+qN1+Mmv/hb++M//Dc+PD9tOd/iM55cvaDXLu7D74b/D9NEQdjY34P0PbsLmWo9BVFFUDOYIPJZlBZ/dfwwf3X4Ei4WH1ElwegRWoACJupqdPIXLWyO20e/mDnqDNVi//jasvXkDsv4am/RM0IOtA2TOQSfNwFkPpiq4a2nUMZet8Hh3KkCUFZUKJIPquAj41a1ObI0Wau5EiqCIJJ8epWhOtNmSJDxZDolLwFYlU+4g0le5U+uFXmykJB8wNOcqVpm5bBykLm5Vi4pLa0AGpJa7n7XSYQ0D5eCVlu/UYMSocWTMAlV6s+iOQkOfDZLozTEsncEQOgPqZK6xHo0B7PeU6ro0WfkKj/Gq7oWeg/K+sp7xqx/CE+OLjgNf4vo84XzSUOKBaaxZIrtewTsIBRn7nBzD4wd3YP/2J3C0e5fvyfH5GYNKcm4mU5jTo0OWCzBrIiXqeIclAJSHSc9Anc96OoViPuf79/Bgn+en+XT+3BcgpAL2jeEIECr+cIBgVXEBShyde9jpD7E3XPO93iBkeccRdT3t9HxvMCKHWfo7qlXVwWMSgriJSswkKBci6vK5OxV4NpJzo0Y2EUNSniKju7phpYihmNJkuWMWZG3hu6cmuieA6aAxHX1VRn8nqGaTO12kERUgxwXSKq5XGlGcKLANwuPgjqk4lEqER8WRIdiseXPu/GFTJ0RJ6jDS7RTG8RkKNXQgukkCg5xRaZWkUqvu0SJydzSJTW5k91coIZAPnUnyTjfr5B3pkELTo8wx0GOI8ip0AAAgAElEQVQF1JiQhFqPRs0LsMm5JPopWrWay43s5SsUwx860o666lqOOpGLNeNOIzL4HcrDoUGtEFCmJoqLbWakS03zNFUszvhrxDV92/P5Q44l4TWChRBGu54mxqpwBij/TXSfTzB2rBuHXF4GPKjVL19X7rRiHe9iFDfeGiIiDQy6iVM9BYMT5GvgTizA7dpXn4LBe8P+8J6z7v79ew8eXrl6aaKFCb+CmN/dWIHN13hEys0KcL52IyiYvKsU2111sP1LALgBAG8AwEgd7C4aBl3sUEY6q211RSuIBgvLfdnzxhf9HEabOwQ4zce/+2c43X8c14tn/40xkPX6sJhMoOTIgBlANYee2YGdzT5c2dmAYb8HWZKAcxYWZQWDQZc1ix/ffQSzWcGgizZ/oFHZzhkgpm2aWNhcH8D777wBG72MN2+1LSCUMyjTHniKBeFsS/GVr6wAySSwx77QVRWEhaiJpI0heqaoluQLSytvqPh44s89L/q0owmcBm6iiU4Ew2mHaaed0SZvWMlwwi/IJbbkHD3vC/CLhYBE1EuGQV3rDQmKGDB6DXDn9TwsdV02gkM2J7KROSa0Qiua0qD5nTHrmyPbdHcG2r1lvWtdMxXZZSlkSQZJ1oGcKLODDdroisNsNPn/iuO72lV83rFbozRLFBfd5zVsv8jw6It+5/PGywBZq927qB2O3dAIwF7KvfYlx9f9PO3z8azqFbZfp4BLzFMIZPJDSyfdoJn3WC8mMDk7NuenJ3Cytwf3bt+CWx/+AQ4f7sJ8egYuSRkEJpmDPumgQczLSKccTAq5SyDrdKCmeaWsuCBEWbWgLE6sazg/PQNou1Y/QwpjtNWKyvXkbE0qfnV70On2gcHkcL0erm/6wWjNpFnujXPeGmets84SbrAcuE9JTYRznPiksa4SdW4nYHYmawTlMkJXBQJewBzrIJ0VnCktR4mqiMZwwMY1ArGCIRqqBIHGjuUcl/p+y7pEmgWpYyZBwwxsYuYTx5agqQ2o5TbHcrDjGmfBGDQ9mYpZG+CsmN4E7aJS3mKl7wMjQAgJnBK116nO0DJoZDtvBm82kjYoPgMN0zi1fGiUBYSpEUchzqfWoErfrF8YMnLXRQwMxqTzywdRy4UNHeRsSuo+moUW7wKKZTc9d5dzIhEtqzLB1oZyLY3mapLbK2lTkYFghRGqIV8fMuvJLINhBndsPCRKR3GzRcPJNplhQE4/ow4oZXxayflk7abY82gHl0B6BN0iHZZzYdVmPPoa04kSrabhxxWDHxPNBLn4MNOGZ0e84pDqnGzuA2AOAPBRVRQf+YCfdfN010B4WPjwIB/099eHg8liNi2sSyqzApivzFiBzdd8rADnaz1or0sbhn9QPScBzV8CwP8KAH/TMhHq6YQ9V5AajYRsC4RCKzYlOvJ8IZB8kUHr7sbOFfjp3/xXuPvZh7B/5xPOkNPRmAUZa7G3tkn5ZGY2fkxRJoYcZQ+Pxkxb5SzIgPD2dQfDQQfyxEEnc/Dum1fAGcv0tE/uPoLD0zGvagIOBWTRTuhsPIfHh6ewtTGCgRvxLhOLMfiTx4wcbGcEmJKRjYOKTDjIwZUcGyjNWul11B0kMFsiqtOu5dCyWYlQOwlcr2sLvpbSvHQ6sYk2geBV5ylfJ2kCnf4ajLavQj4cCDhlzu46U1VrX7OTrF/MoSrm3FEhMFsXcwiUlUkU3yBh8Ux1NVaBhBoUUmCela4kOdE2bTvaOXkC0HIkbHhE7iCJUPQa8yRyytRgUj6XnMuZAGXxdQZrkOQdBpwJM8fM98bB9WVGfKPyDtmIEwe2QNsLj+iP+W2iTTXIsVKnaITb8Xp9Xy+Z1QJANAlL4rVBiXik+kksbaXawXQGgqtr9OOJmRyfwPHBPkyO93BydGgO9h7D0eEBdy/PTk/g8PFjmM8XfA/RvcrzirPczaQnovxLH5YsA+paNpMnmiYuSQeK/EUons6oyZYAD6IkplHbKfmzjj+ybhfzXt+QLKHXG0Le73Nhh2JK0jQjGFUH6Yilappj1dgm0VsyvmMSkeQxBTVITiUfGEVZ9BR81QwaWVDJM4hVE1GaQmsGQXK8KKapEjMi9E4Mar4zkM4lRhaNxnsQ+OOuGHfWLGCuHTK6hCkYAlZMjR1zp1KyIElLmSICuSz1jbJ2MLJzkLuUHkVNADxF8/QalzU5LtYNEuAiIyPEjgmYkhGQvE1sYiQ2RI9Tb/Olwpe6dHON7CADnySCRgSfKN0DBVAiKpBNSC8q0wQfmNO9GrJz61LXT0dQoGhFQYS4+uaNzgCCcisB0CIFBjAUZTJE7kQy5ReUFl2z1tOwwQ+BwQyN2TT0nGAIIObaEA0cAs3OwYzrk+UTSolRg1r1QKS0AtKpNFEnzG8ZLgqYGJsmJk0BvXFmrjTqIiDsgTUPOp16Dzzu1SG72+0MP7OID6u6GpdFUbKJkiXnXbMCl6/gWIHN1VgBztWYKbX2AQB8rB//k3Y63weAt1rU1UPdlGy2QGi7IZC2QOeXHfi8fTetSMP1Dfjgb/4LXP/gL+HeH38L+7c/Bl+XT8ioyJhwPhkz6Yr1gYChDujH00Xy6PDUsC5T66uXNtcgS1NYH/bg/bev8mfqXP72wzvw8PEJVJVn8yBeUesA5+Uc7j86YkA4G6/D1qgDg4GHxOxBqGZQ97ch9C8DdIZMKcUgxnvkAb+AwBt1Z0PTJWGukTVQ1A4WBFap/0cbUnaDrXiPFV1wG3mjdsNIk0W5pEQ9HWxdhXww5J0hcFQBSOGfNsteACv2RpxdSZ1F7naWC9bCEmj3NcWY1GocJsX12M2EGJNALiB1JaV/l3IHl35OFD3q2JCOlRxlRWsmIIrcY+n1MOCUOASxoHQW0qyDLu9wd5lr5K+B+U8EiDWaxuzoZSoxX1V3+jLnGXWriI0r8pPayFdlRG1ic5zPoPsaWP68eQ0mAkoDGXUs1T2Wm/VBvK6q6QTOx6dhfnYE5wcPzeHufXOw98gc7e3B6cmRrRYLqErSUp7BYjHjxyU6LJtJa9GFMExZFFB6zw7WaZYy44CC/MktGuYLvj/VLZ7cVMj8ho13rJEbSxxDzZRpjyFk1tjaAx5aZ05SlyyGo43zrSvXqu5gNLBJttPp9XeyvLNtnUsod9MlxJKkHEKj/mcE0jD2rrXE1UzG3CVUWqcR8SK7fdIcv6b5hom2sTgWRPWJzhIYEUNRenQnDqPGWWZucn9SGpss3KPTxCY11ArN5DKSiyivTUFgCcb1oWJwJ91IQu05Em2UunfcqWRQk+prq7lDKsJAx505JM2lvIvFRpZpmql20FjvwHEZPBVy0DIdb61GPUbQkRHasKRHqbMqdSQxdlk9XyfJ3SoEMBs27OF2q+GiLUvmyVhIacZCKSWwJziybO4w6eBGKi+DMnoqzWcV4qkaMwEHu3BnOjESbyMdX8Nrc08uFfUJJYsTY0SZaXK41E6cO5KqyQTN3hSjHsH8UlM00eVNT+fyZpPHowKCznWk9UyYEE3OuFK5yOM7jzqrVVnft2my3+1mB35R7lc+zDExR4mzn1alv2OS+sgmZhq8mTpnZw6wqv3K3Of7MFZgczV4tAFntERfjdduEMJ5pDmdRKv9CQD8lTrY/kbBpW0BzHDhM16IPvmy43PfdKzFNACXrrwBO9ffhrt//Df48P/7v6AuF7xeUfdtfHjIexeXpOxCKB4/aGazwnofHNFkz6cFdynfe+sqXL+yBf1eDoNeDlm2CXknBcrQ/p29Bw8ODlnTmTqEtWEPstTBoqjg3sMDGE8m8PbVDbh5zcKaRXDFDNBXvBZ7m0LoDHiDSQ2DghwhA7svSKi6EUMex2VrFC+NRCPP0DY7ZiFy2YZu16CTIOeCaHEDyrmjKBIyA/JiUBLd+rGlyQSmrgoLLvVi/BMGNQPImkGk7qmI0kemP9ZGxUz0jG0yRzilgF4bSJeSQC875pK5kXFMO5ZGrOPtlDExH1zVPNqFNuKowVaI3xVi+S4ooLWCtsah/1t67jhM6z8vCjxRWh5g/cvpHb+1od1XQDleA00vTtgA0d9K7FH4rUlv0zQBSFOAgQM/lHe6PTo+s0cH+7CYzaBezOH48UN4eO82nOztwunhgTk5PLST6TksZgum1LPmMc1gQaCzKvkep1BYG4WM6sisVTFmChgVgdJnegwCnWLi4yghaR48ngCS41bYCVSaIvdMY+44az4JVU3dzHVArDpp8kkw+Lt5WTxMqsWZ6/SKzWtvrWV59l5V+g+scz+l/wfES4acP0WTTQ0qh435G0a3cSfaOaV2ykxEgCrBSGSQk9kRUSZHaNQMKmLBUV6WU8tRmotzzj+UrqzVjCeQTpYp5GHYNTXEywQIC2HZojJsYgcwdjv5eAVMyk1MVJLaCM/fqtw8OqTTIjFlN1rOj+RObo0aZyIeQvyvFRoqw7iSnW7F1FvouWwKxEJ7w1Y7cjS1AMuGp+Cl08pdVq+a0Fx0pwwgowYRNBJEQT7RYoHospW+xo5GrQi9VMC2rg5Gm7wqVTEQ3XJzPXmx6Bt0L6cOu6wz7arvrGRim6a2YM1y42ditVFfViK/JIuAiTRiqUSgHp9TfWtTPGzch43G1Mj1JSOmQzRmHgIO0iytirI8wbr6JE3T35sQPptMF/t1nh6nIcwtwjxJ7ElV+KmeC5mTVmZv36uxApur0YwYicIzX56zqckqa+C1HAsFm9Tp/B0A/D8A8L8o4LxKTUFdCEFptXgBbH6ZSsVzu5nP/m2EanJGICfc+PHPQrWYu89++/dQzma0WTNi5c/dM9qMzILQivpFGQx1DmbzEo7PJ7B/dAYHx+fwk3ffgHeuX4at9QF0sgx2Nkfwyx+/Cb1OBv/xWQ637j2GoqxgY60P1y5twMP9E7h1b0+0nt0cNgZT5hv18xSy9Bz87AAK0kASeSzrq25qGbPCAW5s7GP4d7i4YyXqhTekTZfRK9ADJUvJ3owod/Q9yuAbbFyC7midcz4Ji0bilEGjHdHWeYtR2HxuZEee5AQSu2IWpMqn0BLkcSdSf8YAWQFSiPEr8kda2m428+LIqTXugEtdonnyMvIvBNQt1Hc1vuXnbigAuOzAfRfHoK0T+PwE2qf/zr8iy8HzdKuhNRPFn8W4HxTpM1JTPrFgujlAlspr4noLuVLXha+LaTk+eJR+8uc/5bc+/gTG4zFYDDAZn8Pe7q49PT7CRVmYuqqXeZXUifRiysM5EGTUpZm50WWTczHj8dM8VpQMDzR+hNqYfH87Ds40xyEE4vOfWbBjC+4OonfB+z1E/Kh0+LFJ0tLWIQdrpsYmt7CuPi3r6rSsysojBpumaa8/+G3ohjfKqr5Zl9UHAcJPnbFvJtasm8T262CGwYd1Q/RZqQolGOeKJd96KZVAhqixy2U107FWQKV/xfOc1QkI1aw1VUiD7Wun8nVgMMYdVlOLvpAJpamiMepaEtW2kresAkkTdZtM2VXzH+6UVUpndUITpWxOmBrAGSL0LUAPl29n1zImCvouQXmdoHmbYmxjpNQSi6xqkGQSguyNjFZSp0qwnGNJrylV7Sk9wALEcMfqcVrTZEGCgGqmjbKREQHiXIsAYhQuF8gy5VQ0qyAur3zWaysg3ukpbgF67m7apWEbmwcRzo7Z2DZWBCU4dGmOB2KsBJJlKUVlfd3tDqcTOShKk1qa1ewBYK2ZOJuMhdpal2DwwFk8Kspwz4C765zFupjPM5fuFRYe2DS570p/NC4Wi7qb1sl3VoZcja97rMDmajw1yOyDwKb1NdhF8USO12q8VmOhrrW7SrP9Z+1y/ieNTrmsoDPGn8Q3Sgyu+qI3zosuJG3dEBvLh7qucXxerq9vup1rN/Kj/cc2TMYQFnNe8MQHgvO/aTtHjn6WTXaY/BRgNi/gbDqFg5MxnJxN4efv34CrlzZ4o7e9OYJOtwOjYY8daG/fFwdIp/EhbCtY1nB0NqXoAJguSri8MYANm0BijiAhjWQ5BextAXbWhE5KHQ6rVWLO3iR0mEinz2jn0qpxUFCfBHECEq0jSWO8BJmknQ4MNi9Db+sSOAKpYbm5DvgcSV7cfLeom7Hy7IxS52N8Z0tHiK0mdWTYW3z6cZ+gJ8akkguANzz52Fbz0L7TyeW72snE8xzzG58HuL8pLPpD1sb62H2NhkypOMaidj3zhEIhwTqik4/ncHJ0Bsf7+zA+PoDJyaGdnRzYk0cPzP07t2Dv0WNhN9B66AwsKCdzNjNRk+yYHmv13iEHE6nUaIco1hKEAe9D09mXa+5Z99yE1RvrGW5SAH1d/gcC7FkwhwD4SYCwRw6tBDy9dccOzKRObWHr4G0Gs4AwCYilaZkGhRAqa+1Bp5Mf4HT252Iy/nvE8IZJ02veZlsQcAcR3kyd+zEY+54PfiMg5tbYHgMObiQ2t7ZVCi9GsoNZNrmp6+gicUFabGIwBE3n0Br1RAtCxo0gCRJroUtd0CC6Q/JBI0cfKnVnOmuw81iM2ViuBhTPwY9fqTERa04V+IJ0WFljScUC0h7Wlk1nwGuFjOMzVLcYxGeIIkViic2QMqGLEiHCHU0+dORoEECrxyMu7bjsFmLSdA3peKyJ97t4FYsJd47MxeXjcPENIK8BOwxURQdJsZPphZlbKMk2QncpCOh8bmMZS098rfW+BJbzSVwNtMPKlNlMdBvN9ebXGlOgTVSHSlMimgK2GEwxIsyQifo8zfJJ17lHVVV9VNf1PQN2DpgUweNjdHCYJLC/mM0eJt1sUZVl3elnRWVtwa60yqxb7Tp/WGMFNlfj6UGzU5pC6HYgmc1YvwUrLefrPGhRugcAjzUq5Z9V0/l3SrXtXsjYbGs4nzXa3czPW1Muboe9AlvZqlVlyLu9+uZf/Ao2j4/c3r1P4ejRPTubTNMQgrPWDCQtko0O2OwhYeGkIUotTKYLuDV/DItFyeHn9HRXL29D3u3zR5aKY+2o34fpbN7kcSYpbyrhbLyAsvJwfD6Hs8kCLk0L2FhbwLA/g3R2BmF2DDB6A2B0BaDTl1gzyssMUSaEILImotQVooPURTuW/cUFtoaAFVNUs+4A+huXYLB1BdJuP3oGNeNF4qmx9Z8G7Rl4qgX51MnX57lov4CtC9po5Z5zAdvHyQxGs+x+vk6bi8jvg28QUH7u83/PgebnHj4VUCiKhBgD1BbLyF2a7xPjKfZoNoPJyTEcPrwPBw934d6dW3B8sAeTyTmcHO676WTcCXUwxWKB8zlFSqJJigLSNBVuaJqqSzXPD2zaw07OFtBaG0IIlvt/+GSd1gi/VxuHTS3OMFAK4aEF/LO1bowGzgH9RwaSz8Ak90Ko7nqoTynUiNxdTdqBjjVmYsrQ0B+ec0Jk/uA5hbSMC0DqjKa/qxDyUIdeZmCr08tvViH8pA7+hgGzGUJ4ExDecdZsUFg+ImSk60RFHZZ6wzJTBTW8udBoFlVDo7BUYxgjnclgRDvYgt26dmBTj3KcvGvF1FqeB3WybE5aE2cKlvMsa23I0UnuksoA8IkZzYkWkb2frHYEjUajJjIFSSdVqa1G3WWzVmfXLJ1047EKtba5vABZCBJXTPpTMI3VEL2IzDRAj+NKmIQi+lMGk6GJGJPYTg075h65ZQ82aApUccYOURTLukqJEZHiQAjUOi84Z5OBbEgUlBdyFEzpJT1nBoGurxEXXzk+r2DyovN8fGPJiaX4EfK4C1hYa+47MPfJzMfX1aPEwt00dbd8qB96H2YWM7JYmBtnysHQVnMcq5fwqn35OowV2FyN547A7nmkG68B01x5I6t602s8SnWtpciUjwDgzwo6fwEAN8ndTk/Ny64dbRAaWo9jW1/Hj8S4JM+SdJG7wSLv9JMsTYk5Zg4e7VL0SVLXZUpAjt1QyWNQcyejZpJ0WmVVw8ODE0g/sTDs5rC+NoLBaJ1/p+srePuNHVjr9+BkPIXd/SM4PDmnYG7g/ST5/s8LmC4KGM8WcHg+he21AVy/vAlXtw10nAE/JQ0n5VteAp92+cUY7XIa1Ugjmx1KF7N99rh6rlmXzmWQd0fS0dzYgawrLGb0T53DL33yn+pyfcEDPO/H7cd5kWPgtoDRkDV88uK/DuOFuovfBRL9DsbzaLEvNKLZlhqNcPcyBWQHaDLEGs9htpjBYjo1Zwd75mD3jjl59BD2Hz+Ee7dvw+OHu1ATw886mE6nrCFeWxuFvNsNtfeOTMVo1Kqn1I9grQnG2trQRh2D076X4kkeylqUBA41dylU4yZOqWBOEewtsPjfHMA/pi47oy2+t3juvXmM3hyDMfjEfYH4FdQt/EjUT50ZipcAOHTGfjL3xT9BCGuJTYazYnbNY/ig3+m8C2hGBuwaGFhHoA8cBgwjE3WChguNSsHlbqZhNobgBwI4pPUsG5KpACqv2Zhe3VNr0SmijSpqNA3QqXgDYpgOS2CpVjwU9Y+lAkJqFBb6d9JpNFiwgy2AC6JyJMBGMSWOzXIweh7xk+rXsJRAyslufgGRjXxijSC0LgK2a0etnjaaKEDVdwKKcVH8S9vaUlkhFGu2FKCWJBpBpZgkxzdUw0ZWkyLBvrS41UyQlUzSiR7tegTNOs0SxbhGKdw2VFh+3uWNmKComltvO4xsmIUx5hARdgHhUzRwZMCcpGnysQn1pwDmaDw+H88NTIfro1mSpkgGclJcjVm1r9tsvxorsLkanztQ870oQ5C0L+LwuRqv+fDqWPtQY1MoLuV/VuD5ti5snzc+r6vZboq16bN44WcsYQmedEwhJEmKGztXjU0Sn/UG8+NHD7vnx/vpYjFX+mtC5hvGU9i5l4cgwJdyQSXAyekUbu8ewObWGtu5dvs9zo/s5Rn0LmVweWcN1oZdpt9OZgsGl1Rvpq4ixZzMixLKYw/n4zmUmEC2tgNXBjm4xRlgVUCoFmDW3wDTW+cCPVfBKZokVGphAg3o5EovP7ZE06U5ZeOtQXdtC7qjDYp0kZPwAwAhiE9e5B/yaHe5XtTc4ps4J41C7Es++Mv+3Ys9+JJa3GxDn0MvjtRjE6NY9O/YtYbaSnQvnk+hKsYwOT7CvQd364PHj2F6fupOD/bc3sMHUJYLcC6Hhw8fwPnpKXcpE+pYuhRzLgY53hlba41skJuLp0JEnHvvxwSaMPiRIWYu2anWtY2/Kw63nINIfa7KIEyNtacAeBgg7IPB+4D2MzTwJ0jMnwzY3YTMWMmcK0HjKcPoW7gp1Ov6xPIH56v8wfv67zHgZvBhkKTQc6ndgABbVcBNj/4Ng3bTJbZjAEeAZpOBKIY1ADO0xhD1NGN6KHIns9QwDgE8iFGnTbRbAosLw+Y+DUqLJF2CTbW+GzqGwB5HmzAoFddVMhESx25yXmW9J8WfaH4mdfV6ShGtUOywO0y/bfI5GhhsVLMa2R7iYGH0SkpbF3H5VrAY1Ytxn6TUVFHgMo9VOagcXCW3vOR/MsDT9rZd6hAkK1RyNMFa9fdtetdM4+XupbgJg3YzxTI2afSb4gxLxYxaX4FR8x9R5iMTgecGsQoS4zwzYEuOQqHsTwkHpaoAPV+phkN0EkoDVPywHxlj/hER/hB8fQd8OLM2naU2OfHBF7K2Wl5fWwZDq/GajxXYXI0XGDKRUkZhsBYy5yDU4qq5Ap6v9aDK6R8B4EMA+DcA+D0A/B8A8Neq5XTPmGPwacJmUxVGBbJwAZCapgq+1IrQrq5mUBZ86j0442yytnmJMhurNElra407PT6wlC0ZfC1KnPY7FlXTlVgkpdTu3rGpfvux2d07gWuXt+DK5gg2Rj2OP0lNAle31+FnP3qTY1DuPj6EyXQO8wIgV/5UXQcYlyU8PjqDnKJTag8bbkG7JEiospt3ALoDwCRvBE8mOvs0AER5Yrw7c6zPHK7vSDezN2DdGJPKAnzvRhusROpX2aCM16PO/aVB9Te0T3sZcP9NFwTaj61wrbHIghbIdDZyMGXvXi5qocKHGsqzEzh7+BmcPrpnpif7cHqwj48ePAiHB/tQLuauqihipMJOr4uj4RqZvkCn041ZlyFxbkFFpPlsTvRQyjZEIwVWZT2yiynVXcuAYRzqQPPcOnc7kTpPGD2KaNP9GIIhcDk1zp4hwtggHqRJ9mkN/uOqru5YgEcGcPIkIyDmHX13wxjOqhzH00/AISA6ToZJ3GYIuO6D7xlrOwB+uyyr64h4uZN1t4xJrgZfXQVrB0h0WCMdPgzYlUxOk6k5jVBZxYmMKKwLdTMlK3E6z3N9+i4KdNPup1pbqyZyeaIwMxi1kVE3ixIVgmbGkJFiTBFzyftsdfvEcEj/RB+N3W7EdFVQJgNfg6qxbN0P5smLtyyKLhORIaLVNiXXNO+qxsgWxR2OnX2jNtJolxejuD6ugUGbp9HSKQosLYfLyII3kagUk6pigf76xCDcQ4RTArYus3NrLQYf3sAKb4CFgVXTJQP4yCNMvMeq14e9rJPfKwrzYV3O/x2Me4ySBtPcrauxGs8bK7C5Gi88Yu3OOge+rLhkaVtulKvx2o6glNpdpdn+b0qrvaSf13UhjOaXVctQqHtBB+OfIWmz+n3fyvukv6cYAEolz9i0ILA2BXrdQYDLV2tjjXNpklFkAXU4Obsu8u1A/Coo2SxPksVg0JmbxKX3Hh327z06tNcvbcBP330T3rt5lbM3SfOVugRuXN1mU6DNjQF8dn8fHjw6hqIooPYIdfC8CZ6MJ/Dpx5/CyeMeXFvP4dpGHzayLnQWp4DzEVstYtIRNweKEAm8h2GnDLLFoA4N3WN5rwe90RYM17Yh7fWaTo+9sKP5MuOL6Ipfic74JUezO3qN9iivykt91WoVT0GrVgHC2CXQZMfW2kNZLGAxn0M9GcPp8SH44IkG7/fufGz+9M//r71/62OYzyZQFpVZzOdZWZaIIdgky3yv359bm/rT05N+VVaJdjAJnFDX7MwH4kqEDWOgb8SXJrR8g6RZFJ4AACAASURBVPXUmR4gblAGpmR72IkxPP+daBzIPQ/mvztr9hJjx3WNU3IaXSyqM9PL9ihLEOrq+1Yyovl3jjzPm12ePw3D67Sqq36eZ4M0TwfltLjc7WTXrbNrZV2v+eD7Hrx1NhkYMJd9XW+SQU6SOuqAdtAzyKOJcE66QWPMQK+2jz1sppAaBk80EWasVZQueN5YrgrwTNXIpquSdGtU2wiIU2tQYm6pgyiuRT1BamiizyouO+rWLN+bQUyDAvNcmXqCCief3P8stahLDIaKfDlZmYBk4/a7RK3BiLVx4HysmKAJT0zKpeRh2iRgqEygDm4Q6izAGQCcWsNBzjZgmIc67LosecR5rZ4iWX1FiTABw4FD/NiCOyQqeF3VcwxFmSbpZpal71R1vVPUlXUuXaRJ525lKrrB5nknnHbXkrNwlkyqYu5XnYbV+DJjBTZX48sP1aDUYssGOSdfK/EELvDFVuN1GlQN/z8B4A8A8CYA/FhNhP4zAIwUIOYKGM8UfF7STVyp85HR70cdjLug3wwt4GlUPyWp4wi8ozTO+t5wncI2E+oO+rJGOD4A7ytKQWdXSIiZdyFgmqbnly9t7a4Pe917u/tvP3h01K0pJp3aE3UF7928Bpc2RpClKeRpCtevbMOw34Vep8O14t29Y5jM5twd4XTyRQGz6RymZ6cwnwxgXtVwPRvAldEUev1zwCSDikCmE5da7tAQjY/osarTzLo96K1vQT5YhyRJm4B6Y5a7Gf8CVMxndaNsy0YSntlRevr7X8d4JiXyNbo5XhU22csex7d1/MZE/9KYEUuMc2RsVk7HcHq4B8cPH+DJ/gNz8ugBnB7vw5WrV8ufvP9+cfbodnr3kw/z3Xt3DXUkkzTl7TxTZGmCybOy0+ueOLB+Pp1k3vtEi1CEm+aAYSGIgrtsOWvDpZPJqT8BTUkdLhPI1wUW3tlzk7jTBHEXffijR/MpRS6hg/2AyZ8hxVkXTDVblF7Ijd/OOfyWB83rp/whrIU/D/q9xFrMJtPQm9d1t4aaQnj7CbjL1uCGQZOjt4NgcN0CZhiw54hCApBXVb1jLawnCZktEXGFXGchMxa6IRBd1wwMmboSNdlayqZMFfuRu6ykM8nM1+UuKQM47jBaCFScZH1iqWsIMt1Xm6CIDf1ZlJtLdaVjtupyzsUYkstlS1F2LmfU2OZTDKtfx8IFd1ONIcMm+Y8x9hjZu8ikBA0hYMkZy84QM5wyTKcew0ld1+fOBcyzvPbeV3nWSRCxKKtqFwIVAXARkF2AJ0mS7hlrDojCTQgTqxohSUPl6ynTpp1bKv614NLv5P9ycu67s7pKujapE8Bx/JW4Bq3i8FbjZcYKbK7GSw9xutOsYar31RUrzz1FQDBH0a6KX6/fQM3n3FVq7UdqKPSXALCjOZ2bSmEq9IMWvakC0a5uXhb6O52WG+DSgVB+xhQsFbZQw7BS3pQz1rlOdwBbl64FS0Cu04HJ6REUxcLUiwXRalXawjsRV3twVR1CDMGmHM17jw7gbDKH08kc3n/rCrxxeROG/R4k1sDaYADv3ZBu5GjQhXuPD2D/4Jyps+RgS0F+5AJ5PJ7DovYwXiAUtg9vDXZgOAjgoISQ5GCyLtNlkzSDNO9ojIKFpEOOuD3JxMQlbdZccEp63jCf8zsBnzRVgRZobWwnfsD7iW+ze/tDGl/1fD2hw4wOLNHxRLuXzQftyKmmGYItJgs8PTg04+NDnJ48hoe3PsL7H3+Mp0d79uxoH+piXk9v3pzlvihPjo6grKvUJqlzzqFNiI2IUp8hjR9NOGXZS1hIZ9NgmubiAg0csAMowLbujWoJvWehGxWzzsHAQwy4R5+ts7ctwj3j/cMsNQfBJgd1GR6Ttu0ZOvPXbdT6Mb/wFvgz2+8GMHXtM2NN3nGObEo7WaeXWKK4er8VELfrKiQBcAbWEQgbgMdRYszAZG7d2DSn+Kiqqke+8kNrXZIlSR8gDDxipS6uV40JO4hmbgyeG0uxKrYvBQSzUGJrHzHkIGY4qUHqlAoLB5nmazIjtOYZaRx125Nw8CrAQvm8PYsmCRDoa2+sM8JvZfIXZZ6Svrd2iN46VwRyFKZiCoZTsLYsy3JhrT201lWV9/3EudI5e+LBlyYg+ecWTH119sCBO+ykySLJs8oXBfaHg9SEUJ2fnp/VEM6Dx7LydUg6eaVuvM97/z3v+7UWjV8Ta7LV+LbGCmyuxtcyuADta+7ujB3AeqjB5xlk1JUJ30OB2Wp8lRE3WQuNSdkDgL9RwPkz7XhuR8CoH4mCzv+fvTeLsezK0vP+tfcZ7hRz5MxkciarWHN3V1eVelCr1JIs6EGAAQM2LNiPtmy9GNCr3/xiAzIsSJBfDfvBDx4ATZCldrdU3a0u18iZmcw55og7z2fYey9j7XtuMphFsjhkJpPk/ogAIyIjb9y4ce7N859/rf8vTjmX5anXqKIat01P7YJ6W5CIrO8XWwYpLGL6E9IatZVVu50kFMUJjVfWaDTsY9Q7wWw8gLicijTlZbm+s3+c+svdZZE6H4plMSwNJrMcg/EUnc4IX3/pMp576iLWVxqII4WNtTqS5AI21xpYa9bxFvbRG039ybJUIWR5gSw3mGclJtMCOSVQrU28UKv7ehRdj+DWzgH1TT82q30wyaKL23eznbpGj+UM30f45//03PH98Kmveb+b+jJctA5nUI+caihxMa1wL99H3Vtd9pcrvVNvDcos52w8ckU25flwSJ2Du+7w9nURm5SNB9w92UOv3YYpSufkiUY82b992w37fREgcTadIYoieV1gejcZVs7/M6mPKGbztcyXHfoLS666Z3JvCl4U6bcW4oNlZ3HAwFyRGhNwm9ndYfBtjqJXodOrMKUkgNkqZdU9hhPKnzWnn273P/VE2MxO/RuA5c6hK5X2zciK2UXyi9I6ykxUU4iSZqOmVlsJm0KfHBwmeZmnkYrTZpqswKHpSlM6LSXGfJGALSW/R6gRHNWI3Co7mdvVhbe+iVMH+b9vYWqBbBOWHDTNHTu58LmtlB9rHRhwzv4iiLScyOHKEyjKwapBYGnZmUoFi2LSi3FWvzYplm4fEU0kDt06V7KF07KIqeyMomg2HWU5wc7qjY2inJZarccmadVG88GszEelrkfKxTXOoXR535QP7ntsH+SxF14mAw+UIDYDDxaxlyRIqChRxBr1tAkzz8KD/OVFTihuAzgB8DMAzwL4bQB/terorFWvQ7XK5VyO2sb3hQW56sTEVSL1Xg8YLYZpNRNLWqHFIueBFuEeZNK0js1zF6LW2gZWBj0X65iMKaJsvJgQctZFs9l8xTlvgZBPr62K2iVQpDcce8fSb/Y44LmnLmB7o+XFYateR3Q29m5mksa4vdf2Y7XT+RTG2moUS9wUg277BHs3riG1UzxzYQNnIoNocxuucQk2UotWlmX7Cb03BOhj/8tfRUZ84GXtj5iGGggsn4DL7YiP09FZ/R1JCZ2Q8sEuDbXc0V4E/Zgys/mo31OjXrs26Xdp0D52vcNdHnWOMep3MOwc0Xw0tNaUbMtS53mhZahGaRilSUYGdVbk6ezoKFFapjWV9F3yfcFjmQjHqjZkjRczi10RlgQ6A7BcZGqSop7y0xiSRc0HzLjrNB2SVkfauJvWcBup6oKojYU7Fvj03O8ALy80njqQTh+H/kWaaLHWSIvEVV72Yt7fialPrV6oec5RGkmIuJHFRr1IHZYEYCKnkdiSk9iWllg5pXTslFolUY5MM8conFpM3UZOulhcLtcnZe66IKdSvjeWe/82gvV7mO/9HKqXebeowuEq2qiaNVGLwN5F9VU4wgKff4LYDDw0/L/2cQLO5rivKSzw5cJVoznjasT2zWq89g8rx/NSNWK7TJ2lSnya6u9G1f/np05MiurEIiW5Ki1Xkv2qCpbdPIVP9HM+UhBxnFKcpKbebE6TWl1TrBvtvdtxPptLG7eEJxCz8efGkdZotJqINGE4HHu3XqpNpBpFRKcIyHp6BasrNX+eksQa57dlxLaJ9ZWWPzmYZTmK0i4i4GWeiR1MUeDkYAezUR+j9ja+Xs5xQSXQKgavXfZjtffHkHySObylAxrOUQIPClru+n6Ug+rdUVhfTSJrzFKvUG1dyHxiq8wKnc8nMNnMDNuH87vXXld7169G4+6xngwH5Ww8cmU+N3k+T0yep1p2KpWSp6JvhJAqIyIuffYKU9354nrnvczFtMNCaDKk/sFPWGQE//VS3yAdmTEYPZBMXbDsGs4AftsxvRnHuh1HNCwtjmxu21FuRybKRtBJ5l+T6H2HBgKPEr5XivlhR+SvXAxYXijxqT2nD+ZlMBBOLye++2fLT9+bBT/VOP6gLcVA4ItIEJuBh4osuTN9uRInAx9KXu1z/otKcP6gqkr5DQBXloXc1dvp16f4VGCQOiVK/ftaq1zSZ52zvovNLaJm9cIFlYRXkmGpMkpis37uAukk5lqt4brHB36kNp9PJbMPrPS9oEFrF2cXkRzDDIynGfaOemjWa0hrMS5f2MJ6S0ZqNZI4QpomqCWRf2s0Ely7fYD+cAorNXu0KIUfjWb+bTgcYT6d4vn+GBeHI6w/803oradh6hsPJKUnPN2+mHxWi1RcBVK968m8179Z7lri1N7l4g+8Qx+V87I16nfKyXCAbDbNh+3DuHu4q8TJnPQ6SXv/tuoe7FGZz11VNyLdublf/ddadvsi33ihfVyp8SOui7HFVR8Mo2TzWaNSAcsm/FL6MCUgxfcyglbBOGLiG+RDBiiLCG878L5ldZww3TDgu+LC6kgVhkmqH5yydnHdS/+6+uBAIBAIvB9BbAYeKot64NObYoGARwKBXgWwA+DfVoLz9wB8G8C5SnQ2q5HZZTpteqr6hO6NKFVVKTJPq7SYmc5vYjm/fMm66ugunZWRK9skHdHq+hYlSY1ltPZk9xZODndR5oUPu7LWYjqZLkaY3KKbs5rL9W7lzmEHk3mGywebePHZJ3Dl4hbqaepnYJuNGl569hJq9cSnaV6/c4h5Xvq6hrwsfViQZC72R1O8dm0HByd9PH/UwcvjHs6+MILefhouXYNLmxKhuXikgnIMfIbcE7jvPQ6Z3i13WFaUSKiKtcaqbDqJxsM+zScTmCLXw/bxSnvvlh132242HNhh98gMusd6PplEzthYdjVtKfnmcDpSkexbk6K6ZhWLO7pY7JRdOJYQsBEcj3z/IN1LsH5vIvoCCfTZJdCQJAGU1VwhuhGndCPSPLUFqyIvryKS2iaaaKjpongI96YZF7cbEtYDgUDg0xDEZuDhIqX7xL69OfyDHXgf+tXb7Sq99geVyykhQl8BcL56nVqORC1Ha0/vccZViq3fzVGkZJ2SnKLCOZGcXJMGM2YkvoPalVYqRxorqypJExGSXBrLzu5RMZ/DWetdSDnjlBtaXiPRi9Y1HxjU7o/8bmZvPMM8u4IrF7fRatQQR5F3OZ84t4n8xStYX21hOJ7i4LiP3aMOjHVIosi7psNp7tNu+4Mx5tkc3ylyXHy6jWTzIszKJdjGWelqALR+7AUnfdQRy0fEQh8sKtXDztOnQiYPXRW+JTuXkqTJSi2eh2yRivko87H5PJtPh4PhqH0Unezd3jjeuRmNOseuzGZu1Ovqca8dF/lcgn1KU+Sl7GAyOxmBJx3Fi+qfRQeFpIlK12VCi07FRRUF+deAKfuRWJU4YJOIGouqwXv/tvhUFgIGpOiXxOrP4PgmFE2VdiNTFiezMY9qqZqupHU3nfIk0lQgXNMJBAKBh0YQm4GHzsLZtJVJFQi8L3Ii+XY1YvvzyumUnc7fBXBxUebt9zTL+9ZklqEQp7cdo4U2pFxpLWe02jmnrZMeTgn+QOyck8gGq6NEbZy9uJigJeKT/btUZHNSIizFUlHv2jq+ZEWSZo2D9HD2xlO8dX0Ps1mO4/bQC85zZ9axtlL3e59XLp3xH/cGE0T6LnrDEUaTOUojXZ/sa01MyWgPp3jrxh7SOII2c5y7PEC8MQCt9GFXLoAbZ2SRNCwGfQyWO4ZftN3VR/azvHtdUJKeZZk5qoJ+ZG9aGWNUMc9l/zGK0lTCfezu1VfNwc2rpr2343r7u8Wwd+zy6UQSZm1ZFlI0KbUSWnakpZsojlNdRfj4OnuuFC2wfN8/32SoVpJkre8LhG/V2gQtgsV4scedS2E/nOsT6VGk1YQV7rBz/56N/QuA9lSiM4pyV+QTnPRKNGoJWufq4fpnIBAIPAKC2Aw8EpgNiILYDPxaRgBeAXDj1Nv3ATxVhQi1qnPuZX9bq3I7i+pjdeqcPF2cLBO01lYpnjpHzrGTFB+SeT/ZCEvSmt6+cFH+iMsy172jA21N4Z2x5c35boZqKU3GY8W9FO0no7GSQNvpj7Bz3MbLz1zGi09fxMb6ihRko1VP/Q7nZJJhNJnizt4J+uMZnJHAfeVFqQzujScZrt088M7ns9MMly6OsHZ2CKmGM1EdLlq7lzCLx9CFYX68itkWIUnB1fzYVHuXelH3uqDawZSMq3yWz8fdXtQ73l8p8pnbfuLp8frZC9O9a2/gx//sf1/Zv/H2mSLLYEwpi8928aIvjYXRMCY188FfxImCSkH+WkBWJYmmpyowYmLSC9EpVyl5f5Eki3O0mHSQ37Dc1lvEdFUpfcLgjoM7JLgeqWhgtT4ypthDYQaRhAlhkf0puUHynJP06EAgEAg8GoLYDDwa5MI0v1saHwj8GiYAfiQl4AD+dbXL+VcAfA/Axn2VkUuxmS4F5n1WupxZSqderDVBKrUX07XSGe/3Mn1g0Mb2OSP9mlGc6sHJIYpstojPXIzwyQ6ov9lliqGMw8Zx7M/CB+MZRrMM2bz0X/+V5zQ2Vhq+QiVNUjz95FlEsUIjTfHm9V10ByM//RcnMWI5o3YOR90hRvMcJ/0RvtLv4/k8x0bcgmqcAdVbPrxIFO5nKaBOJzC+7x8+JuLu3b27wEdBnRKWWORjSYcll2Vpy0z64zPKJqO4f7jX2L36Oh3dvha1NraH3/rDlV8qwtWbr/703M1Xf/b92XhwOW20iHRklZJ4WEpYxmJBhZJkWLAE9cghLyHN8pyd8+I8JKFTR5CsX1c1JQZw7wB0F6CXCD4caMLEb2mmP4mIf6wp7tpY5Ua5KXiYzwqVm6LBEVM1+h4IBAKBz5IgNgOPiIXSlKvlHM4CAx+dk+pN3M7XAVyvBOfTAFYqoVlW/09OpdWeFqPLMduVRbCJdzrlC6zvSdHaj+3J8tn2hcs2bTQ4rdWpvb/Ds8mQ+L07YYuZvyodxd+4dGwmCeZZgTuHXehEQ0cKLz11AWtrLUkuwkqzhueePI9GmiCONV6/toPBaOqTbr1azkvk1qKw1k8M5nmO/rTA8y7FxXQb9fomVGvV/yTOVuqZH72g+rBvt3Q3l5Lh0961B3lbD4vHyc39yLxbTSKzqs7XBXl16cCGuczL2WTQnQ47J/Gge5wMO0cYdU/cbDSg6aCXdg929ah7ZLYuPX0tba38M5Pnb1/9yY++VRTFc7VGa1tHCVdjrnLhR54rDkwtP6nOaICkMMi3yMr3rfvR2mVVCfvnslwC8mO7jniPmPoEDAn0Z4D+lw7lHUXuZprEN11pu4siC9z7Tfg42qAxA4FA4LEhiM3AI4OqUUQbxGbg4yNVB39ajdb+RZVc+9VqtHYLwGoVEhTdZ8CdLnfHqc97mSmWu9QbePcySZWWRgRsOVwm5axBuV9ymc0XWTOiU/0ep/KnysZaWCdOaIIkiZFYh7wssLPXAUkDeF76ZNrtzRVEUIi1xsWLZ1FrrWF1YxuvvnkNRyc9lFKtICuipBAphcI47B4P0B3lmJYRjG7gAsVoXngR8eoKYr0YD2X7eAodr/QVFnLiU/K4i7llkzzz471Se7qKhFQlNh0zRfDufj4pMB711Hw8yfuHO8M7b786P7h5tTkZdGvZbBoVs3FpitLQIgwosqZ0vb27xc+6R5NyPk+LYn45TuLzpHSdIZUhWLYWOp8aC16nZTkhL8qFmOBAPjtO+oacv2jEGIK4B6ahYtwyWr+RxPoQuetYFLulmuyzo2ENdePjnwOBQCDw2BPEZuCRonw6rUxIhfOEwMdG3MtdQIrY8WMAfwnAf1aN1abvk0D1Qf7GqVNvEFvrD0altVa1miT38JoiduUVKo1xkqhZ5BnJPqdk0yZJzUugIpuztXYx2VfV/EjiyTzLcWe/jaJ0vvbk5eeewNmtVZ9U22y0sHbuCjYuXMHK+jp++ovXsbt3hKLIF8JFk8wv+rTaoihxfHSI5NVfYjiY4txzHVx48RtYPXsBVkTvshDmIbk4y2J+/hjO4lLdP4in97J94kGKzQ8aA/5cOpQV9/1Msqlgqk/7vUeRejKBrSr5J6FU+XSGsigoz2c0Gfa5d7hnBseHPO51TOdgJzq6805r2D5qmrKs0eLvScJr6V1Drf2OZZ5nL8znk78K5kzp6Ld1pDcZmDHLqOy9cwt5sVf3HmB6j1XdB+iY/LSsKlmrY028C1PeNM7dcmlypznI9ux6Y26VsnBcnNrNDgQCgcDnhCA2A48UcYbUcmQrGJyBT4accB4C+CWAv1pVpej7Rmc/KsuTcrA4jBJbq5Srraxig1icS1ev1d1o0NWjfo/E7ZSqFOaFvBQ30lkHAwOl1MI1coTCWBycdJFlGSbTGb754hU8dfks6jrier3Orc1zaK1vqlZrFT/5yU9x5+4eBpOZdzXlzDxNYjRrMco8w+2bt7G3f4StWzfwcr+DF7//QzTOXfStKGX+8ETS8vn5cW/fPcA79KB/tqWAvj+l9pN+H7kdtRxL/YzHfauuy1JrlNCIvGsoh7SBzaY5FbOZmo4H6B3t86h3AjbOdY52VPdgh6f9rsrnMyv9mNlkXM9mEzbWSnVtqRRJPUlc7VZWQVn+e13QSv+NxceqxUAP4PEixIdq/ov43VocB3aKMQPzyLr4rlL0aqTL63Bu5AwmZO2hda5tHI9UgiErmle71wjNJIFAIPD5JYjNwCPFn9nLvhybsLsZ+DTIwbMD4L+rQoT+JoArADYBNCuXU31s74/9ACgUK6o31+jMBUK90eLV7bPot0942DmmbDZGWRa+ZHBp1Egvp+xuyiEtolMESFFaHPdG/my71ayjVq9h0xBWKeGNRou2tzbw1WcugmdPIyWLt28foDecVOksEbK8xDwvYJyImgG63Q5MPgVFMZ793h9i9dyW/8Hy/N27v+yff1CC7wv3DH0IP5Dj6mrFKT40SOmBfNNqbLca4xX3Umx3OfSkT9Yfwg40n2Sus7tj92+9rfrH+2rS7WDQOaTZZMxRFGPYOaJRr63YlJpBMyu2uhx8QERas15EMsubvv9JJIFbgN6WsVjZ/QTLnqbPsG0tXtv97qTsWk404dAavm2hjlSE/VTn72iyN6B0p3S6IIdCMTJLPpzZBXEZCAQCXxyC2Aw8ciRoMIoIZenAIZ428MkpqrHafwTgn1YVKd8E8BKAZ7GoSVi573Xu9Eks3ff55cdqGQpUX1nTcVozzWy1bDZaNkkSfbx3m7LZRDljQUp7t16+1lnHy/eXE4OkiKZZgXfuHGE6L/DUpTP0PIjq9VTJyX7CGZ66sIWYrN/7fOfuoRecZW6Q5QVIE+Io9kI2n+e4/c47KAwwGM3wwm/9Ls5eeQ5JjWAKQIzZ++dBP+146CKkh6vb+fxXifB9/3+Qt/sgnNJfy7L+NYFT+l64j6+AtQY8G81V7+SIhu0jSVPmbDYpD29c5btXX9PDziHKLEdZ5GLgk9YRmaIgW/oR7hG06gOqRkptgCiuFp29s7iYpKVCVpVBHBN78Uk+VXZxvIshLyJzpkkdpLE+KYzbdYzbpPggs9hRFjsOahAlNFJUjAguYyj7KR6NQCAQCHwOCGIz8JkgCZ1KMWTlLRD4FHCVRnuzevu/ATwD4OsAfrt6ew7A+nKH7QO+1a9+XqoXrFFRHMdRumVqrXXWSep8nhCIp+OBNsaKnoSOo8XXO7cI21zcgMwf+t3LnYM2uv0hprO5b7XP53M06ikaqcZKI8VTT5xHo17H9voKbu4eY/eo60WnKS0iUtCR9m2Ew/EM0zdew2gwQDEd4ls//Fs4//zziJpAngG2XDhty0AYOmWxfVIBpFDFvTzmQvOjCOv7ReEn+R7LI+X04/FQNtCr35mIS78srCuhSTAMiJDrscVZa92FyWAU9U+OeXRymB/v3LI7V19LRt1jstba2aivRp22yrOZv9AnylRGX0vx4OX24lguIwxBEsKlG/D1JBC/vL+4zHBvJzoiX2PiLf246nwdOKkvcWwUcBRrumaBtzLrrsZaHbLhNpQdGqOmMSP3DmggEAgEvlQEsRn4TCBfHM4wBiGmPvAgmVYVKW8B+CMA3wXwtwH8PoAn3ydE6NfCzkVi7kVpjTcvPcny/+bqhukc7dpB5zjO5lNV1amQ7G+yc+xYXHsmY9jPBRrrUBqD23snmM8zbO2s4OKZDbzw9EWc2VxHs1HHk0mCtWYd586secF59eYB9o67yIrC34aIWgkOcqZA/2gPd1/791htxkA5x9qTzyJqNhEngCuA0lRiiBcF/fKOZfrYQut0X+WjEJufdPz0dOLTw7qby93DZbTxA15Npeo99tOni9Djxeukrq57OFCZlcgm40HveP/moHNy7IwrnbVb3cP9qL13m8piXswno/zw5jUado41iIzSOpEREh3F3oWnKhbWsb/gV1QefArwJjM3q8zYCQGHBNSZsc0Ld1skb87AiOFiJrJa6Wva4YCV64LUm5HWr5Hj/Ykp+kmqilPJz4FAIBD4khLEZuAzQ9aBXHX5XgfFGXiw2Kqf818BeBvANQD/YVWX0vq457/MTtt8Ls4mr5+7aFsb29za3LL7N96kw707STGbg9Vic89ZoxwvSjB9Ywopn0Qrbn5/NEVvP051IwAAIABJREFUMEKzXsPJhREKCzz/pMPWxgrqaYL1tRaazTq2NlbRqNVgmHF3/xhZVqCWLipWFCWSnIvu0SFe+dG/wdHuHp78xvdw6Wvfwfali0iTyP90Ijj9NDCqEJv73M2P6no6hA26e9CDfSiWTqnkTKmFgUzLVli/e2uBbJpj1O1i0DnBqNdB/3g3O7j5Vtze3dk2pWkqpXg2HvJs3GcdJzKeTfPpxLCvGJFBbCgVRahKju99b63IMlAwU0LgjXvO/6IsqMnMm5IUS4oGRDSD4z2WnlumI7BSTFSy5RuKsRen0YFRtAerhso3mgQCgUAgsCCIzcBnioz8Wccgzb5nMBB4wEgNxC0A/wTAKwD+IwB/AODccjTwIwtPZtgiVypmqq1t8LnGighLLorC9o4PVDabk1cH0lCvo3t/R07ftcyMM8HKB0qpwjIO2gNkuUFvMMYzl8/iifOb2FxbiM6LZza9wTQvSlhj0RtMEEXaCxBpahGXsj+e46R3A7s7+9i9cwdP3bmF5771XVx85nm0ts/6vWhTCc77x0dPP9OCjvx4PIjHa1FzUwn+xWTrwjn1/a2MYp5jPh5j0uuis7+DwzvX6GT3NkbdEzcd9eqTfufZyaCfOGNT0pHsbs5BHCmlkyiKHRSZuFZPiCgh5sVc969a03JBJgY4OfVTGfLOJjE7EaP8jia+HqnouoO7ZVy5R6xHmmuS1WzZZFOGmuhGlIlaZvPQHvZAIBAIfE4JYjPwmWP82bBFGiX3YvUDgQeInElPAPy/AN4A8DsAfgjgG9VorSTY1j5EdJ4ac2S4siAzGcVJa02fffJ5ZmtNrdZA9+RYz8dDb9LrOCE/PithLNbKZK0/tuuNJpqtlpR7YtDv4e5BG73RFO3+CLPsMl58irC52kKcRNjeaOFbL15BM41x93Cxw9nrj1GUZiE4nUNeOMznI0xnr6N9dIjD2+/gK7/1e3jhN3+ArfMXkKQpcr/HSe+Omp5y5x63Z9snFXKPSjDTpxknXlakVCE/cjPyO7SFhSlKKrM58tkUk+EQ/ZMjdA/von+wi/bubdc93qPJoEdShaOUbiitWlGcpKz91Y2BzEqTohZZjlnSf/wmsYru/zf+npktK5vMExBipahg5gk7dACaKhK3E22K1GuA+ynYXSOmI6XUTHpU4HtVIpSw9+xxvv9qRiAQCAQCFUFsBj5z5ESlcAaaNJI4DpUogYeFq/o5/49qvPYbVYLt96sQoa1KdDaqkcLl22mkgp5skVExYR3Xmrz9xFM2Seu2tX7G9duHPB0NVFnkyhUZnBFhaMjvdEaL5NooSWFNCWMtSuMwGE9RlhLySd7df+qJbWystJCmMS6c2cBqs44rF8/g1t4x3ri+h7I9gDXGJzlHUQIo2Qct0T46gITASEoQFRO4b/wGtp99GZTUQG7hmi3hU+88rs+2ZW/N43L/PsnL0iKReDHGjGoH0xpGNskwHQ0xHQ/cfDLmybBHo/aRGvc6mA376LePMGgfYDIY2HI+KYuiiJhd5HuKtSZfH6X8kLbfzmVxKFnqSqDAfi9ZdjDV+/x+RSHOiLFDwC6xK0npngPts8IuHMbGqjFUcZQk9hZYd2z+np9o8VgEZRkIBAKBj0gQm4HHAglUkRNonSZ+jCwQeIjIATYG8OcAfgzgfwFwGcAlABervs4nq1TbS6dE6LvCczFSK2FApKMkam1uU6254hqrq+5k7w51D/dR5jmMKcg5J0tzvgBRxGC/a8TthLidSRz54J+8LHHn4MQL0MFkhucun8WFsxto1mpYX21ibaWJtdUGoihCpHdweNJHMc/u9XpGKgFFcgcZvb3beH0+wrjfxksG2H7uZaSNmtcJUo+yFE10am/zcbu+s9wzdY/hfftQ6FR/jnp3XtlvpxuHsigxGQxxsnsXBzfeRO9o3+XziZsMh1rE5Xwy8sdJWZYun42lTsepKOY4Sdxi69b70pGYl0vHnYnl2ExlP7P65v44lTYeBomDmVXpshkzugS6BU2/ZLJXVYmBNa6rYt1WhKFTdl7msCpaHhiP18MbCAQCgc8fQWwGHhtk7FAr5UNRHlgrfSDw4cjeWhuQEUK8VonKZvW2BuAFAN+r3uT91XuvmzJSa0rp2tRKax0lCa9tbFl2hn0ULSk1Gfe5zOeimkhqUYosQzabVTt7EhqkWFbd2EFNpplPoe2PpxhNZvi2KN7z20gT8kt9Uovy9Ref9OJLUm+LIwNrCv+x1gq1RAKENMZTCSEaYDiZoqQIX6EE555+Bmm94UOKXOVwiuNGixrNX2nRp8eg6uTz0OxPp2Jw6dSbYIxDMct4PplSPplgMuph3O1iOurhePcG7r7xCwxODolI+4VeGaF1zsjvycVJvYziFJaMHCSpP358KbF/RJb/bssHmhj1dydkSdaC5+ycCMspEfaUUreYccLMPaV4j8ncNsS3SUXHylF5/yx1WGQIBAKBwIMkiM3AY4WMGy72NhcdAOHSeuARwVWY0KR6Q+VLvQPgzyqn868B+OsAXr6XaOu7Ne1CXzhHKorUxpkLttFY47X1be6c7LtB95in46EyZam8yIwiqUlZnNQzG+PEv+KYwVpqTsS1LI1FGkdeQJ4/s4GlZSXVKC89fcnf3TiOcHAywCzL/SClH9F07Hs9Zax2Nh5h99qbUqSI6fg3cen5r2FlcwPiWnHlcPIHGFif9bOOH3NHczkee+8Vyr8j7qWFJQn4KTA46eB45wa1d29hcHKEQecQ8/HIj1EX4nAf7WMy7Gs5HqJIfv2yWQsyolK1FLhqxa7U/pd1+ntXd6AaoS3vjXs7zJj5jlJ0UynVc8wdEL9ltXsNjqTGZEaKZmzwKwIzEAgEAoGHRRCbgccPOSGXTkEnLoyF0iGlNvCZ4KreTnk7rlJt/y2A/xjA3wRwwQtSye0U7UFETpoiojiur6zKSLhrrK3ZlfVN1znY073OMRXZ1HdRaBV5O9M6I6mzvgGTFzGg/mKLOJtv3dz37ue3lMK57VVoWnRinNlsoVl7xu9z3to59uO3Byd9jGcZXMGQ26/XG0jTOiaDDm78/M+kLgPZcIBnf+MHWD2zLQOXPjj3gyr2VRWJxI/xTuejZOlankbcZVsa5PM5ymKOYj7zY7CZhPz0ezi8+TZ2r7+B7uEe5uMhTFlyWqujtrJK8rpmTOniOHFeODqWihFFpDQbE5VupnwJytKv9KKWMi8uSdx3OVrQZ6ZeVVYiX32TmP8oUuqVNK2NLGg8yKYninkcef/aE36dgUAgEHikBLEZeHxhhTI30Inyu2qBwGeIqQRnH8AdADcA/KfVaK0EsjiS6kHnlLNOOko4SWtIklTXm6uUpDVnTGE7R7PIhwaJQvBVFy4CkV7qCRGaUaRJ9jmP2gMvDSKlkBfncHZzDY16igiE1ZU6Gs0UW+srWF9r+DHau/tdTOZzPyZrrcFoNILShDTSmE0GKPMCxlk8952/hI1zZ6ErwXm/g6juLQN++Q43Pr3LWtmW94QmLcS5GI+T8RCTXhuTQRe9g11MR33MRkMM2of+/bLIMZNR5s4Rimwuv2dxNIlI2Ww6KYs8c2zNWGndleOHmc+AqQkiqTFRjp3yj7+/fsGWvUuJW0SSHOu2JL6boG4p4I5jGsiIbJzQO7FOX7el6S/WXSUW6oMuJwQCgUAg8GgIZ/CBxxpTWpRcohFFoLBPFPjsKQDsVKFCsuf59wB81e/OEVkJ63HWkrNWLZND02aTN85coPl0REU+x7Tf846mN6OUWuziEawCZqIu2HHdOdZSb9LujvDKW7fRG47xwlMXfR/nWrPhI0flL22sNvDi05fQSFOfWvvO7QN0BxMftiX7f373Mk7A8wLZW69gPBxiNhziqz/4IbYuX/aCU0KDln6X8mX/Vf/tpxCb9yo2HsKv6/RrgHufz32aG45OOZgs9SQGyOc58vl48ZiWBuNeG4d3b2HUPTJlXtiD229H/ZND7coC2WSMbD7zo9KSLCy/Qz+ZEWn//7LIJ2D3BjPPZFmTieTixTkmPieqnxbBVct4ni4xjn0tieI34aLX5ddibLGt2U3iuHbVQe9Yy0OlzJxABRHlH/ozBgKBQCDwiAliM/BYw4tkRthqg1PG+1To4gx8tnAVKvR/ARhVgvM3ANRFcGqlSjEkwRxJ6qxSStWaLdq+eIXk4smRZUzGg2rP8lRPIVEhEofZpWkSaxmbncsOZ6ePSV76MdnJbI4Xn3kC5zZXfQqtiELZ46w9dQGtZg1pEuOtW/sYDicwsj/oHEq72PkrxyPMr77mRz9VFOPl9A+xdfEsYg2Uxbsjs+4BjM6Ka+uH3x/GGG5123J/P/VW96nC0UgDSeQrf5GXFvm8RD5f7FZKf+mw2wZbi+mwj+7Rnuzo2o0zF2bZZFoft4+VKQqS0Wh5c8zslPK/XvL7mBLwI3/FFAroKdCYiS4y+CIYZ8lnCXObgatgDHy6rKZjMuaXRifdNK7fYp7tMJUmiVdTZWu5K7Kh4zLncAkuEAgEAo8xQWwGPhf4k2D4MBaQjvzZYTjFCnzGiND8F9VO598H8F0ZiSSlSs2cWediBhJnbSQO5sr6lghMZ50lt3ML2XRMy75CAisdxdLvydaWSipRVpsNl2Q5icDM84LuHrQxmc6RFQbfeukpnNta8yO2MnJbS2NcvrCFWpqg2ajjtat3cdLpwzjn61SU3wdVEGe1s38LR7ffxuUXXsbq+gZqzRhpunA4jZGQoV9vFf5akccPryOTq/RciWGNxYV1QOm/38d4TaBFtYoP+amCflwJTCYT5Nkc4+EAg04b034X3aMd7F59Hd3jA6T1pr8Do34HSZLolfWNuFara6XFoXaso9iKo83Oaqm8WTivNCbyd3Ed4A1mfGtRRcItBQnf5j6T+tM8jm46Ta+vGHeLS0MWnJm6vgFDI0VJ5mhaSNhsnKQg20BRSj5QEeY9AoFAIPBYE8Rm4HODnLiKU6MXZeaIfGcEh5OtwGeJjC3+cXUQ/l0A3ydgVSllHXPJjkVkSNdmJAm0K+tbfP5JJ7KIBu0Dnk0nMIXoDlnXpJoXaQwy1uUOlNdrtZQIqRz3eV5i/7jnHUtxSL/2/BO4dG4LcRL5IK1Ia59cS6RQFOJqWpz0hpjnpXcrRXD68U6JLe0fY/fqL3yA0OrWWWxfvITV7S0k6cLllF3OTztA8DBXPpcJsMs+Tvp1DuqplwmlAbletfz55LGZjWY4vnUTRzs34KzxO5eDk0MZmeVxv0vt/buYDnuotVYR6RjZZIRSR9Gwe9KwppRSTaOjaCppxAA33KKqRL7vTMZhpeNSEcgRGsyIiTlzwKHT8S0d0U9ja1+rF+UBlO0V4C6pyLAzZOI4jwzeY8EubvfzUAwTCAQCgUAQm4HPIV50MiOVk2qxYtSiaz+M1wY+I8pKcIrNdALgLxHRBaVUndlqrjp8xOkSwbm6sY04StBaW8egcwwRMzKuaYqcTFn6Mcw8L8vBaJLHkdZEnIq28O6bY3QGY7zxzl1MZxm+/kKJpy+fQb2WeIdPxlc315r42gtPIEkU3rlziN2jDmbzwo/dxiqCOG/dg11k0xkoTryAeuor38BL3/09XHj6WSSp8g6nrJV+WP3I+7qblQB8FLUl3tE81T3yvs/+0/2XVfKRCNNsmmPSH2A86Pqk2EHnCHtvv4Gju9e9c6jjBPl0JNk8YCiWPlRpJpmPRySPo5XRfm3Q2b+rIB9bO4NSfXa26Zxbcc7JRYZdAu8qRQNSKmPnFCxyJjpEpE60M72Ii9usG+8Qq+P12dxozrAfKeg09r/ML2NIUyAQCAS+WASxGfjcIieQEoIiPpFli0QCWUjf24MLBB4hIjR/VO1y/gTAbxHRd4j0CwTXYucsExwUdBRFtLK+yWmzqVbWNzDsdzA4OeHe4T4VWVaZVy6ZZXM/7amVNlKJIYG3i0Ob0J/MMLuxh7w0PrD0mSfPo1ZLF3ugkcLFM2to1RdptSvXa76PM8tLFIVBXhRw/S6K6QhFWSIzFsc7tzDsHOOr3/sruPTCV9BcX/NbhPYDBOe91Nb7BOejeuapqiLGVSO1y+97Wliedi6lDkbuZzHP0W+f+B3Mg+tv4WTvFqaDHvJshnw2xXQ48M6w0osLWI2VVdRXVl0cx+I8e8eTxKKUb2CNuJ6OtMpJ6S7Ydk1pe2B9HOnoGMQ/NVy+49hmqnRTy2pgiYak1BBpPIlNkdXn2WyuY2cQw/oHVH16SzkQCAQCgceIIDYDn2vk5NqyQ2YKMDmkUQ1KxQufI5yzBR4tMvD4OoBrAP6EQD9Q5P4GA78JovMMqrPYZL68nzhOa1jfOsfNtU1Xb6xJD6MyroA1pWPHMVvZ+SSz8PC40lfsazQYGqU1OOkPcH3nyM+SXjy7hdVW3TuXMlK7tZWiXk/QqCe4u9fGnYMO7uy3/a6f7HiutGqwNkF/PEPv+BCv/OkfoX2whxd/6/fw/Le/j+0nLvkRXdnhdPbdPczTSbP3akKWbuaj6uU8XU1SfSwDrPdKZBZWsr/v+TTHfNBDNh3yqNfFzo03cefNX1D77m1Mhn2U+RxaR5y2WvKzSBeq/xnE4ZxNxphNx+TKwmtYLfuYUk+iaAZGzxEPNWgfznXBGBJwxOBjIuwR1F0mfVKUWaZnJjdJs0BdI65mpf19JIUQ8BMIBAKBLzJBbAa+EDgvOks4p2XsTQyk6oR0ERsS3M7AI0RczutEOFBE75TW/GXS0e87674O5m1a9HJqv5ypNJJa5Na2t1EWV0jHCuNej+fTsXNyMIMiUZjseOHYycYlM+pJhM21FtIkwv5xH/3hFE+c7+P5Kxdw7sw60lrDV22kicWV89tYqdXEUsVwmiErCxn79H2RSRKjntSQ51PvbEotyqB9glGvjW/+7l/DhWefQZwsXDcvOO9rbbx/Y/r9hOZ7xOgDYvl9lVrIcPm/cowiKzGfZphPR8jGQ0nfQT6Z8MHVV2XkFVk2437niDr7OyjmohfZu5Si352xFEUJorjmR5nl8XK2pGI+J9njhNIZK90l8AkxThzopgIOifkOiPtcRP1agw8LmnWKQs9SatrlHO/CDQ1PwUAgEAh8+QhiM/AFg73b6d+I4ciHsUBqJJbWi6TZ+jG56useyYJZ4MvIlIh+zErdZOKfaOB3LOh3md3XpSIT1kWsQCx9/0nizly84urNVXQP9m37aA+TUY9sUYoTSlVNiqQIQZJq11fq1Gwkvg6l25+gKErsHHbRG07wzZeexuWLZ1Gvp15MxbHGubPriNMEaZri7eu72D/uoDea+C5IZxmOrRSFwhRznNx5Bz70xjGc+yHOXnkWaT1BHCnfO2nce0Xl/U+f+0drH8TTS1V1KqhuO4oWVSXi9ErZiC0cht0+Dm7dwNHubRq1DzFoH/vndxRFrnNw13UP9+RvKPlckef+dpSKoCIl0xHKSaSTdk5HEVtrnSnN3LlyJvnABMqNiMpY/XlE9I4q7QzOHWlHbVPMRnGjNXFKZ0TWyu+IOExWBAKBQCCAIDYDX3TcUkySr0P3SZNRo4W40fJ7XNaRP2Etswxc5n70Dv4kVN0TrdVyqGzMQfmFOocojn0NSzijDHwoRKyVOrHgf5em8RtxWvvTeZb9wXw6+d04iV9wjHUi1AiKkqRu1rdSTtK60XHsDu86Ox716v56iVwbcbyo11Cyq6h4NMm9uBxNZpJ2i9JYXL9D3tfP8hxPXtjG2tqKF2kMg631Fr790pM4t7WCa7f2cPXmHo46fRSl9aIzShLImKgxJfpHd/HWj//Y7zI+9fJ3cOm5F7F9/gLiOPmVn5ZOhQKdqq28x4dpzQ/Nkj6l12REVrZWbfX5WL5ZlmE6GbnxaFqMB93i4NY7dPvt15Le4aGajft6OhoqW+RI6g2rk7RwbGvOWBXFqXd+y2ImTjGT1tCWJf11ZErXB6kJs5uwM4eG3V3WUTeJ9aRuzY7J8tdLhWPoVLZFDcmYs7UcLlgFAoFAIPD+BLEZ+NLg9zuNkfN26OaKT/A0VPPJj+PjY+S9Y1gfAKKQxKkvw5cT7yiKYPMMhYqRiqVSFkgadZjMeOGpqjTcQOD9EFfdSY8F0bGOdC9O05uG3S9cWX5PKXxdgV5k4AkGJzqOitbGJhtb2vlsIsefsSaPvRuXZfIxFUZhNJ0xqchZ+P1MZZl9cOlgMsfNvWPMiwLGML7aaKJZSyXLxodoyf7ms5fPYmO1gWa9jp+/eRN7xz1Y4/wYqjR3JEmKosjR3tvBfDLC8Z0beOYbv4mXvvf7uPTsC4gTjbJ8V0VS1Vnp+y8r13MpQN0HaLB7lSUQV5XeM5273MH06UgKkCEELYMJVkZ5mV2R2+HJLh3dfEsf3Lml+t1OMR7224P2CU+Gw3XH0Hk2q+ezac2UBsY6bm0kqdaJdmbuR1o1aRimwjl7CLhCAWOt6Q3HeNVZc0ikps7pgXF0hEgPlFYmspizc3mhlHv/nyoQCAQCgcD9BLEZ+HIiLqWM00YponodUjAoJ8YiLmWPLo5Tv9cmaZ3ytbYsUGiFSGkpTfSfEwFgigJJrR4OosAHQ2JM3vPvSiLajZKkm5fmdTA9Y539hgP/Fa3V15jVJhE1W6sbfO6Jp2c60tNRr1ObjocSNUsyG16UloydUxQnSpxTuThCWvnqH3E4ZbT2oN337nytnuKpJ86iFi/+XI5ZcUbPbq7i5RevIDcWhbFodwZeLTrrqnAtDWdKDNpHfo9TakDSRh3rZ85h++Km35O0+bsjso7f62x+pKAg/zVUjbNWDUbVw+RrShyQzUqU+RT5dIxJ95jnk5HNhv3i5M51dffaG3RyuKey+Sw1hU2sdbmOIxMnaWLKMpHHgiLlnLP5fDpKZBrWOpdRPh8zMCkj2okofp2L4rg05hiO3lE6uY1IjQGyAIuktqfM2WBfBgKBQCDwMQliMxD4xDDKIkMUJ/eqEgKB+6GqA/aUUpGrFRMAtwDsOravRnHjNWbzl8ui+F4U6a/pODmzun22GafpoLmynvVPDvVIt2k+m3FZ5JEpDLF1REot9jl9Us6ih7PIS4ycxe3SoDQGo/EYl85uYntzFY00rSJcCdsbLbz8/BMwZYFrkcZwMpd+T8zF3afKVpSAnXyG7tEu9q6+jq3zlwF8EytbW4jri+l05qoixeFeL+QH1aHcG7Oldx3QOK6EZnU74kbKbZki44Nb72Dv+ps06Z3wuNc281HPTQcDPep27WjUn+dZppzzSb1rpPRE9jeddYnE+TJRycwTZ+3dYjbb1Yr6itHNy7LLTB3t3L5js+OiuG+tHSlEsypROIjKQCAQCAQeEEFsBgKfAjlRFzc0CWIz8KHQ+0kYGcfMARwT0b/TFN9CxD+14N9WzL8TRenXWmtbq/VGyzVX11V/Zc12DvfdqN+mIrfasSOSmVHtk3K8Kyni0sr+piXkhYWxFvN5jv2jHp66fA5PXzqLjZWmdzdlD/L81iropaewvtLEzlEXOwcdZJ2Bvx0RyPKm/X6yw8md63jl//3nON65iee/8R1ceeEF6LQG68RZ1dCJJMIuUmulcuRX1hgJLKOz/uvUQpeKqMwzh/Ggi0m3DTOf+r3qPCswHXV4//qbfOeNV9V0PCBXlvJ8M6bIx9aakkFxFMUitsk5W2pSHSKcOLFkmUuttVPgiXN427D6WUzqIIKZ5aWbWafmMXFhrS2hYxuO3UAgEAgEHg5BbAYCnwIZ1fNiU0ZpQ0hI4ANYJLRWgVPvC+dE6gYRHYD55+zsTxTx39KR/p0oajyh4jiK0jRSSjullRsPe5TPpspZS0452UHkRRWIkllZv4NJkh7rGO3eCJ3+CO3+CL3+GM9cPodz22t+fzOJIlw8u4m1lQbOba9jrdVAPU1w2O7LXiisBWq1xDudw+4x+u1jHN65hvnJDvL2t5E010G1FlpnLqGxtolaLfUuK1WRtKeXG/WiooRdbmia5f7PyzzH3vW3ceetVzDttzlW/kFSg35PqljUdNDBqNtDWRZSURLrSI+1Uq9oFfes4zNQVI+U7jLbA2vNDjPf1opOnHNlLGPvhLIo3bGBOiJQdroj1BP6SAKBQCAQeKgEsRkIfBr8ntsildaPBgbBGfgg+CP1gMgo5444dMTmpjPmDUTx99I0eVrT+iV1WZ+tt1aTk4O7aB/s8nw0YCs2ZiwTtYq1dHIaQ17k+fHaxbeV/eN2d4TBaOYdzK88ewkvPXMRW2srvmtTQn/qaYrVRh3nt9ZwY+cQN3aO0RtM/D5mWZbSAII8y5DPp7jmSvQO7iKuNZCubGLj0hWsn38SWxcvY/PCZTRW1nxfiYT6iAqWOdeydFTMxr7vUsKHpF4lm05w67WfYO/GVaRpRBsbmzbPMj4+3FfT0cgXGIm7SqQKpdQIoNdB+v9hopvM5aY1pdi0B1GU3DZI2myKkSY1q5pSSFMVxhsIBAKBQOAzIYjNQOBTcq8eBQ+4uT7whY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"/>
-    </pattern>
-  </defs>
-  <g id="组_1" data-name="组 1" transform="translate(-288 -726)">
-    <rect id="_7" data-name="7" width="98" height="65" transform="translate(520 744)" fill="url(#pattern)"/>
-    <rect id="_9" data-name="9" width="102" height="61" transform="translate(736 744)" fill="url(#pattern-2)"/>
-    <rect id="_11" data-name="11" width="107" height="85" transform="translate(288 726)" fill="url(#pattern-3)"/>
-    <rect id="_4" data-name="4" width="74" height="60" transform="translate(421 744)" fill="url(#pattern-4)"/>
-    <rect id="_5" data-name="5" width="69" height="54" transform="translate(644 747)" fill="url(#pattern-5)"/>
-  </g>
-</svg>
diff --git a/docs/static/js/misc.js b/docs/static/js/misc.js
new file mode 100644
index 000000000..c78a3b176
--- /dev/null
+++ b/docs/static/js/misc.js
@@ -0,0 +1,78 @@
+// Fix the hero text effect on large screens
+function resetHeroHidden() {
+    const scrollAmount = window.scrollY;
+    if (window.matchMedia("only screen and (min-width: 76.25em)").matches) {
+        // only enable this on large screens
+        if (scrollAmount == 0) {
+            $(".md-hero").attr("data-md-state", "");
+        } else {
+            $(".md-hero").attr("data-md-state", "hidden");
+        }
+    }
+}
+
+// https://github.com/bashtage/sphinx-material/blob/6e0ef822e58df57d6a9de5a58dc40c17fc34f557/sphinx_material/sphinx_material/static/javascripts/application.js#L1384
+$(window).on("scroll", resetHeroHidden);
+$(window).on("resize", resetHeroHidden);
+$(window).on("orientationchange", resetHeroHidden);
+
+
+// Sidebar header
+$(document).ready(function() {
+    let language = "en";  // default english
+    try {
+        language = READTHEDOCS_DATA["language"];
+    } catch (e) {}
+
+    const title = $(".md-sidebar--secondary .md-nav--secondary label.md-nav__title");
+    if (language == "en") {
+        title.text("On this page");
+    } else if (language == "zh") {
+        title.text("本页内容");
+    }
+});
+
+
+// Hide navigation bar when it's too short
+// Hide TOC header when it coincides with page title
+function hide_nav() {
+    const d = $('nav.md-tabs[data-md-component="tabs"]');
+    if (d.find('li').length <= 1) {
+        d.addClass('hidden');
+    }
+}
+
+// Expand link
+function expand_link() {
+    // on load, collapse all links without active on the inside
+    $(".md-nav__expand").filter(function (index) {
+        return $(".md-nav__link--active", this).length >= 1;
+    }).addClass("md-nav__expand--active");
+
+    function toggleExpand(event) {
+        event.preventDefault();
+
+        $(event.target)
+            .closest(".md-nav__expand")
+            .toggleClass("md-nav__expand--active");
+
+        return false;
+    }
+
+    // bind click events
+    $(".md-nav__expand > a").click(toggleExpand);
+    $(".md-nav__expand > a > .md-nav__tocarrow").click(toggleExpand);
+}
+
+// Propagate card link from another element
+function propagate_card_link() {
+    $(".card-link-clickable").each(function() {
+        $(this).attr("href", $(this).next("a.reference").attr("href"));
+    });
+}
+
+$(document).ready(function() {
+    hide_nav();
+    expand_link();
+    propagate_card_link();
+});
diff --git a/docs/static/js/sphinx_gallery.js b/docs/static/js/sphinx_gallery.js
index 4cb3ecda2..54262dac3 100644
--- a/docs/static/js/sphinx_gallery.js
+++ b/docs/static/js/sphinx_gallery.js
@@ -12,18 +12,18 @@ $(document).ready(function() {
         // the image links are stored in layout.html
         // to leverage jinja engine
         downloadNote.html(`
-            <a class="notebook-action-link" href="${colabLink}">
-                <div class="notebook-action-div">
-                    <img src="${GALLERY_LINKS.colab}"/>
-                    <div>Run in Google Colab</div>
-                </div>
-            </a>
             <a class="notebook-action-link" href="${notebookLink}">
                 <div class="notebook-action-div">
                     <img src="${GALLERY_LINKS.notebook}"/>
                     <div>Download Notebook</div>
                 </div>
             </a>
+            <a class="notebook-action-link" href="${colabLink}">
+                <div class="notebook-action-div">
+                    <img src="${GALLERY_LINKS.colab}"/>
+                    <div>Run in Google Colab</div>
+                </div>
+            </a>
             <a class="notebook-action-link" href="${githubLink}">
                 <div class="notebook-action-div">
                     <img src="${GALLERY_LINKS.github}"/>
diff --git a/docs/static/js/version.js b/docs/static/js/version.js
index 73e1ec768..58c605f69 100644
--- a/docs/static/js/version.js
+++ b/docs/static/js/version.js
@@ -1,46 +1,51 @@
-// Uncomment the following for debug
-// READTHEDOCS_DATA = {
-//     "ad_free": false,
-//     "api_host": "https://readthedocs.org",
-//     "build_date": "2022-01-25T06:27:55Z",
-//     "builder": "sphinx",
-//     "canonical_url": null,
-//     "commit": "ca66e346",
-//     "docroot": "/docs/en_US/",
-//     "features": { "docsearch_disabled": false },
-//     "global_analytics_code": "UA-17997319-1",
-//     "language": "en",
-//     "page": "Tutorial",
-//     "programming_language": "words",
-//     "project": "nni",
-//     "proxied_api_host": "/_",
-//     "source_suffix": ".rst",
-//     "subprojects": { "nni-zh": "https://nni.readthedocs.io/zh/stable/" },
-//     "theme": "sphinx_material",
-//     "user_analytics_code": "UA-136029994-1",
-//     "version": "latest"
-// };
-
-// READTHEDOCS_VERSIONS = [
-//     ["latest", "/en/latest/"],
-//     ["stable", "/en/stable/"],
-//     ["v2.6", "/en/v2.6/"],
-//     ["v2.5", "/en/v2.5/"],
-//     ["v2.4", "/en/v2.4/"],
-//     ["v2.3", "/en/v2.3/"]
-// ];
-// The above code is injected by readthedocs in production.
+try {
+    READTHEDOCS_DATA;
+} catch (e) {
+    console.log('READTHEDOCS_DATA is undefined. In debug mode.');
 
+    // mock info
+    READTHEDOCS_DATA = {
+        "ad_free": false,
+        "api_host": "https://readthedocs.org",
+        "build_date": "2022-01-25T06:27:55Z",
+        "builder": "sphinx",
+        "canonical_url": null,
+        "commit": "ca66e346",
+        "docroot": "/docs/en_US/",
+        "features": { "docsearch_disabled": false },
+        "global_analytics_code": "UA-17997319-1",
+        "language": "en",
+        "page": "Tutorial",
+        "programming_language": "words",
+        "project": "nni",
+        "proxied_api_host": "/_",
+        "source_suffix": ".rst",
+        "subprojects": { "nni-zh": "https://nni.readthedocs.io/zh/stable/" },
+        "theme": "sphinx_material",
+        "user_analytics_code": "UA-136029994-1",
+        "version": "latest"
+    };
+    
+    READTHEDOCS_VERSIONS = [
+        ["latest", "/en/latest/"],
+        ["stable", "/en/stable/"],
+        ["v2.6", "/en/v2.6/"],
+        ["v2.5", "/en/v2.5/"],
+        ["v2.4", "/en/v2.4/"],
+        ["v2.3", "/en/v2.3/"],
+        ["test-version", "/en/test-version"]
+    ];
+    // The above code is injected by readthedocs in production.
+}
 
-function create_dropdown(button_text, items) {
-    const dropdown = document.createElement("div");
-    dropdown.className = "md-flex__cell md-flex__cell--shrink drop";
+function create_dropdown(selector, button_text, items) {
+    const dropdown = $(selector);
     const button = document.createElement("button");
     button.innerHTML = button_text;
     const content = document.createElement("ul");
     // content.className = "dropdown-content md-hero";
-    dropdown.appendChild(button);
-    dropdown.appendChild(content);
+    dropdown.append(button);
+    dropdown.append(content);
 
     for (const key in items) {
         if (items.hasOwnProperty(key)) {
@@ -58,8 +63,8 @@ function create_dropdown(button_text, items) {
     $(button).click(function (e) {
         // first close all others.
         $(".drop").find(".active").removeClass("active");
-        $(dropdown).find("ul").addClass("active");
-        $(dropdown).find("button").addClass("active");
+        dropdown.find("ul").addClass("active");
+        dropdown.find("button").addClass("active");
         e.stopPropagation();
     })
     $(document).click(function () {
@@ -68,15 +73,16 @@ function create_dropdown(button_text, items) {
     return dropdown;
 }
 
-function remove_version_dropdown() {
-    $(".navheader").children().last().remove();
-}
-
 function add_version_dropdown() {
-    const prev_versions = Object.assign({}, ...READTHEDOCS_VERSIONS.map(([k, v]) => ({ [k]: v })));
+    const prev_versions = Object.assign(
+        {},
+        ...READTHEDOCS_VERSIONS
+            .filter(([k, v]) => (k === 'stable' || k == 'latest' || k.startsWith('v')))
+            .map(([k, v]) => ({ [k]: v }))
+    );
 
     const current_version = 'v: ' + READTHEDOCS_DATA["version"];
-    $(".navheader").append(create_dropdown(current_version, prev_versions));
+    create_dropdown(".drop.version", current_version, prev_versions);
 }
 
 function add_language_dropdown() {
@@ -98,14 +104,13 @@ function add_language_dropdown() {
         return pathname.join('/');
     }
 
-    $(".navheader").append(create_dropdown(language_dropdown[current_language], {
+    create_dropdown(".drop.language", language_dropdown[current_language], {
         [language_dropdown['en']]: get_dropdown_href('en'),
         [language_dropdown['zh']]: get_dropdown_href('zh')
-    }))
+    });
 }
 
 $(document).ready(function () {
-    remove_version_dropdown();
     add_language_dropdown();
     add_version_dropdown();
 });
diff --git a/docs/templates/autosummary/module.rst b/docs/templates/autosummary/module.rst
new file mode 100644
index 000000000..82a7a639e
--- /dev/null
+++ b/docs/templates/autosummary/module.rst
@@ -0,0 +1,68 @@
+.. Modified from https://raw.githubusercontent.com/sphinx-doc/sphinx/4.x/sphinx/ext/autosummary/templates/autosummary/module.rst
+
+{% if fullname == 'nni' %}
+Python API Reference
+====================
+{% else %}
+{{ fullname | escape | underline }}
+{% endif %}
+
+.. automodule:: {{ fullname }}
+   :noindex:
+
+   {% block attributes %}
+   {% if attributes %}
+   .. rubric:: {{ _('Module Attributes') }}
+
+   .. autosummary::
+   {% for item in attributes %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block functions %}
+   {% if functions %}
+   .. rubric:: {{ _('Functions') }}
+
+   .. autosummary::
+   {% for item in functions %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block classes %}
+   {% if classes %}
+   .. rubric:: {{ _('Classes') }}
+
+   .. autosummary::
+   {% for item in classes %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block exceptions %}
+   {% if exceptions %}
+   .. rubric:: {{ _('Exceptions') }}
+
+   .. autosummary::
+   {% for item in exceptions %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+{% block modules %}
+{% if modules %}
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :recursive:
+{% for item in modules %}
+   {{ item }}
+{%- endfor %}
+{% endif %}
+{% endblock %}
diff --git a/docs/templates/globaltoc.html b/docs/templates/globaltoc.html
new file mode 100644
index 000000000..ab4b41a53
--- /dev/null
+++ b/docs/templates/globaltoc.html
@@ -0,0 +1,36 @@
+{% set toctree = toctree(maxdepth=theme_globaltoc_depth|toint, collapse=theme_globaltoc_collapse|tobool, includehidden=theme_globaltoc_includehidden|tobool, titles_only=True) %}
+{% if toctree and sidebars and 'globaltoc.html' in sidebars %}
+  {% set toctree_nodes = derender_toc(toctree, False) %}
+  <ul class="md-nav__list">
+    <li class="md-nav__item md-nav__overview">
+      <a href="{{ pathto('index')|e }}" class="md-nav__link{% if pagename == 'index' %} md-nav__link--active{% endif %}">{{ _('Overview') }}</a>
+    </li>
+  {%- for item in toctree_nodes recursive %}
+    <li class="md-nav__item{% if item.children %} md-nav__expand{% endif %}">
+    {% if "caption" in item %}
+      <span class="md-nav__link caption">{{ item.caption }}</span>
+    {% else %}
+    {% if item.current %}
+    <input class="md-toggle md-nav__toggle" data-md-toggle="toc" type="checkbox" id="__toc">
+    <label class="md-nav__link md-nav__link--active" for="__toc"> {{ item.contents }} </label>
+    {% endif %}
+      <a href="{{ item.href|e }}" class="md-nav__link{% if item.current %} md-nav__link--active{% endif %}">
+      {% if item.children %}
+        <span class="material-icons md-nav__tocarrow">&#xE5C5;</span>
+      {% endif %}
+      {{ item.contents }}
+      </a>
+      {% if item.current %}
+        {%- set sphinx_material_include_searchbox=False %}
+        {% include "localtoc.html" %}
+      {% endif %}
+      {%- set sphinx_material_include_searchbox=True %}
+      {%- if item.children -%}
+        <ul class="md-nav__list"> {{ loop(item.children) }}</ul>
+      {%- endif %}
+    {% endif %}
+    </li>
+  {%- endfor %}
+  </ul>
+  {# TODO: Fallback to toc? #}
+{% endif %}
diff --git a/docs/templates/header.html b/docs/templates/header.html
new file mode 100644
index 000000000..859716a19
--- /dev/null
+++ b/docs/templates/header.html
@@ -0,0 +1,41 @@
+<header class="md-header" data-md-component="header">
+  <nav class="md-header-nav md-grid">
+    <div class="md-flex navheader">
+      <div class="md-flex__cell md-flex__cell--shrink">
+        <a href="{{ pathto(master_doc)|e }}" title="{{ docstitle|e }}"
+            class="md-header-nav__button md-logo">
+          {% if theme_logo_icon|e %}
+            <i class="md-icon">{{ theme_logo_icon }}</i>
+          {% elif logo %}
+              <img src="{{ pathto('_static/' ~ logo, 1) }}" height="26"
+                    alt="{{ shorttitle|striptags|e }} logo">
+          {% else %}
+            &nbsp;
+          {% endif %}
+        </a>
+      </div>
+      <div class="md-flex__cell md-flex__cell--shrink">
+        <label class="md-icon md-icon--menu md-header-nav__button" for="__drawer"></label>
+      </div>
+      <div class="md-flex__cell md-flex__cell--stretch">
+        <div class="md-flex__ellipsis md-header-nav__title" data-md-component="title">
+          <span class="md-header-nav__topic">{{ theme_nav_title or shorttitle }}</span>
+          <span class="md-header-nav__topic"> {{ title|striptags|e }} </span>
+        </div>
+      </div>
+      <div class="md-flex__cell md-flex__cell--shrink">
+        <label class="md-icon md-icon--search md-header-nav__button" for="__search"></label>
+        {% include "searchbox.html" %}
+      </div>
+      <div class="md-flex__cell md-flex__cell--shrink drop version"></div>
+      <div class="md-flex__cell md-flex__cell--shrink drop language"></div>
+      <div class="md-flex__cell md-flex__cell--shrink">
+        {% if theme_repo_url %}
+        <div class="md-header-nav__source">
+          {% include "repo.html" %}
+        </div>
+        {% endif %}
+      </div>
+    </div>
+  </nav>
+</header>
diff --git a/docs/templates/hero.html b/docs/templates/hero.html
new file mode 100644
index 000000000..5d3272d27
--- /dev/null
+++ b/docs/templates/hero.html
@@ -0,0 +1,11 @@
+{% if pagename in theme_heroes %}
+{% set hero = theme_heroes[pagename] %}
+<div class="md-hero{% if nav_bar_tabs %} md-hero--expand{% endif %}" data-md-component="hero">
+  <div class="md-hero__inner md-grid">
+    <p>{{ hero }}</p>
+  </div>
+  <div class="md-hero__background">
+    <img src="{{ pathto('_static/img/hero-background.svg', 1) }}" />
+  </div>
+</div>
+{% endif %}
diff --git a/docs/templates/layout.html b/docs/templates/layout.html
index 4de124e35..090fd4d44 100644
--- a/docs/templates/layout.html
+++ b/docs/templates/layout.html
@@ -1,8 +1,23 @@
 {% extends "!layout.html" %}
 
+{#- SPECIFY PARTICULAR FONTS FOR HEADERS #}
+{% block font %}
+  <link href="https://fonts.gstatic.com/" rel="preconnect" crossorigin>
+  <link href="https://fonts.googleapis.com/css?family=Roboto+Mono:400,500,700|Roboto:400,400i,500,700|Google+Sans:400,500,600|Material+Icons&display=fallback" rel="stylesheet">
+{% endblock %}
+
 {#- TO INJECT INFORMATION FROM READTHEDOCS HERE #}
 {% block scripts %}
   {{ super() }}
+
+  {#- CUSTOM THEME #}
+  <style>
+    :root {
+      --custom-color-primary: #1381cf;
+      --custom-color-accent: #0e619d;
+    }
+  </style>
+
   {% if versions %}
   <script type="text/javascript">
     READTHEDOCS_VERSIONS = {{ versions | tojson }}
diff --git a/docs/templates/relbar.html b/docs/templates/relbar.html
new file mode 100644
index 000000000..a83229aeb
--- /dev/null
+++ b/docs/templates/relbar.html
@@ -0,0 +1,28 @@
+{%- if nav_bar_tabs and pagename != 'index' %}
+  <nav class="md-tabs" data-md-component="tabs">
+    <div class="md-tabs__inner md-grid">
+      <ul class="md-tabs__list">
+        {%- block rootrellink %}
+          {%- if theme_master_doc %}
+          <li class="md-tabs__item">
+            <a href="{{ pathto(master_doc)|e }}" class="md-tabs__link">
+              {{ shorttitle }}
+              <span class="material-icons md-tabs__arrow">&#xEAC9;</span>
+            </a>
+          </li>
+          {%- endif %}
+        {%- endblock %}
+        {#- DO NOT SUPPORT THEME NAV LINKS HERE #}
+        {%- for parent in parents %}
+          <li class="md-tabs__item">
+            <a href="{{ parent.link|e }}" class="md-tabs__link">
+              {{ parent.title }}
+              <span class="material-icons md-tabs__arrow">&#xEAC9;</span>
+            </a>
+          </li>
+        {%- endfor %}
+        <li class="md-tabs__item"><a href="#" class="md-tabs__link">{{ title|striptags|e }}</a></li>
+      </ul>
+    </div>
+  </nav>
+{%- endif %}
diff --git a/docs/tools/chineselink.py b/docs/tools/chineselink.py
index 5cf13b367..b2c77fe3a 100644
--- a/docs/tools/chineselink.py
+++ b/docs/tools/chineselink.py
@@ -78,7 +78,7 @@ for path in iterate_dir(Path('source')):
                 failed_files.append('(redundant) ' + source_path.as_posix())
                 if not pipeline_mode:
                     print(f'Deleting {source_path}')
-                    source_path.unlink()
+                    path.unlink()
 
 
 if pipeline_mode and failed_files:
diff --git a/docs/tools/md2rst.py b/docs/tools/md2rst.py
deleted file mode 100644
index 1136c7599..000000000
--- a/docs/tools/md2rst.py
+++ /dev/null
@@ -1,135 +0,0 @@
-import argparse
-import m2r
-import os
-import re
-import shutil
-from pathlib import Path
-
-
-def single_line_process(line):
-    if line == ' .. contents::':
-        return '.. contents::'
-    # https://github.com/sphinx-doc/sphinx/issues/3921
-    line = re.sub(r'(`.*? <.*?>`)_', r'\1__', line)
-    # inline emphasis
-    line = re.sub(r'\*\*\\ (.*?)\\ \*\*', r' **\1** ', line)
-    line = re.sub(r'\*(.*?)\\ \*', r'*\1*', line)
-    line = re.sub(r'\*\*(.*?) \*\*', r'**\1** ', line)
-    line = re.sub(r'\\\*\\\*(.*?)\*\*', r'**\1**', line)
-    line = re.sub(r'\\\*\\\*(.*?)\*\*\\ ', r'**\1**', line)
-    line = line.replace(r'\* - `\**', r'* - `**')
-    line = re.sub(r'\\\* \*\*(.*?)\*\* \(\\\*\s*(.*?)\s*\*\\ \)', r'* \1 (\2)', line)
-    line = re.sub(r'\<(.*)\.md(\>|#)', r'<\1.rst\2', line)
-    line = re.sub(r'`\*\*(.*?)\*\* <#(.*?)>`__', r'`\1 <#\2>`__', line)
-    line = re.sub(r'\*\* (classArgs|stop|FLOPS.*?|pruned.*?|large.*?|path|pythonPath|2D.*?|codeDirectory|ps|worker|Tuner|Assessor)\*\*',
-                  r' **\1**', line)
-
-    line = line.replace('.. code-block:::: bash', '.. code-block:: bash')
-    line = line.replace('raw-html-m2r', 'raw-html')
-    line = line.replace('[toc]', '.. toctree::')
-
-    # image
-    line = re.sub(r'\:raw\-html\:`\<img src\=\"(.*?)\" style\=\"zoom\: ?(\d+)\%\;\" \/\>`', r'\n.. image:: \1\n   :scale: \2%', line)
-
-    # special case (per line handling)
-    line = line.replace('Nb = |Db|', r'Nb = \|Db\|')
-    line = line.replace('  Here is just a small list of libraries ', '\nHere is just a small list of libraries ')
-    line = line.replace('  Find the data management region in job submission page.', 'Find the data management region in job submission page.')
-    line = line.replace('Tuner/InstallCustomizedTuner.md', 'Tuner/InstallCustomizedTuner')
-    line = line.replace('&#10003;', ':raw-html:`&#10003;`')
-    line = line.replace(' **builtinTunerName** and** classArgs**', '**builtinTunerName** and **classArgs**')
-    line = line.replace('`\ ``nnictl ss_gen`` <../Tutorial/Nnictl.rst>`__', '`nnictl ss_gen <../Tutorial/Nnictl.rst>`__')
-    line = line.replace('**Step 1. Install NNI, follow the install guide `here <../Tutorial/QuickStart.rst>`__.**',
-                        '**Step 1. Install NNI, follow the install guide** `here <../Tutorial/QuickStart.rst>`__.')
-    line = line.replace('*Please refer to `here ', 'Please refer to `here ')
-    # line = line.replace('\* **optimize_mode** ', '* **optimize_mode** ')
-    if line == '~' * len(line):
-        line = '^' * len(line)
-    return line
-
-
-def special_case_replace(full_text):
-    replace_pairs = {}
-    replace_pairs['PyTorch\n"""""""'] = '**PyTorch**'
-    replace_pairs['Search Space\n============'] = '.. role:: raw-html(raw)\n   :format: html\n\nSearch Space\n============'
-    for file in os.listdir(Path(__file__).parent / 'patches'):
-        with open(Path(__file__).parent / 'patches' / file) as f:
-            r, s = f.read().split('%%%%%%\n')
-        replace_pairs[r] = s
-    for r, s in replace_pairs.items():
-        full_text = full_text.replace(r, s)
-    return full_text
-
-
-def process_table(content):
-    content = content.replace('------ |', '------|')
-    lines = []
-    for line in content.split('\n'):
-        if line.startswith('  |'):
-            line = line[2:]
-        lines.append(line)
-    return '\n'.join(lines)
-
-
-def process_github_link(line):
-    line = re.sub(r'`(\\ ``)?([^`]*?)(``)? \<(.*?)(blob|tree)/v1.9/(.*?)\>`__', r':githublink:`\2 <\6>`', line)
-    if 'githublink' in line:
-        line = re.sub(r'\*Example: (.*)\*', r'*Example:* \1', line)
-    line = line.replace('https://nni.readthedocs.io/en/latest', '')
-    return line
-
-
-for root, dirs, files in os.walk('en_US'):
-    root = Path(root)
-    for file in files:
-        if not file.endswith('.md') or file == 'Release_v1.0.md':
-            continue
-
-        with open(root / file) as f:
-            md_content = f.read()
-
-        if file == 'Nnictl.md':
-            md_content = process_table(md_content)
-
-        out = m2r.convert(md_content)
-        lines = out.split('\n')
-        if lines[0] == '':
-            lines = lines[1:]
-
-        # remove code-block eval_rst
-        i = 0
-        while i < len(lines):
-            line = lines[i]
-            if line.strip() == '.. code-block:: eval_rst':
-                space_count = line.index('.')
-                lines[i] = lines[i + 1] = None
-                if i > 0 and lines[i - 1]:
-                    lines[i] = ''  # blank line
-                i += 2
-                while i < len(lines) and (lines[i].startswith(' ' * (space_count + 3)) or lines[i] == ''):
-                    lines[i] = lines[i][space_count + 3:]
-                    i += 1
-            elif line.strip() == '.. code-block' or line.strip() == '.. code-block::':
-                lines[i] += ':: bash'
-                i += 1
-            else:
-                i += 1
-
-        lines = [l for l in lines if l is not None]
-
-        lines = list(map(single_line_process, lines))
-
-        if file != 'Release.md':
-            # githublink
-            lines = list(map(process_github_link, lines))
-
-        out = '\n'.join(lines)
-        out = special_case_replace(out)
-
-        with open(root / (Path(file).stem + '.rst'), 'w') as f:
-            f.write(out)
-
-        # back it up and remove
-        moved_root = Path('archive_en_US') / root.relative_to('en_US')
-        moved_root.mkdir(exist_ok=True)
-        shutil.move(root / file, moved_root / file)
diff --git a/docs/tools/patches/1.txt b/docs/tools/patches/1.txt
deleted file mode 100644
index cbee52ede..000000000
--- a/docs/tools/patches/1.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-   * - GP Tuner
-     - :raw-html:`&#10003;`
-     - 
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - 
-     - 
-     - 
-%%%%%%
-   * - GP Tuner
-     - :raw-html:`&#10003;`
-     - 
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - :raw-html:`&#10003;`
-     - 
-     - 
-     - 
-     - 
diff --git a/docs/tools/patches/10.txt b/docs/tools/patches/10.txt
deleted file mode 100644
index ba95164f9..000000000
--- a/docs/tools/patches/10.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-An SSH server needs a port; you need to expose Docker's SSH port to NNI as the connection port. For example, if you set your container's SSH port as  **``A``** \ , you should map the container's port ** ``A``**  to your remote host machine's other port ** ``B``** \ , NNI will connect port ** ``B``**  as an SSH port, and your host machine will map the connection from port ** ``B``**  to port ** ``A``**  then NNI could connect to your Docker container.
-%%%%%%
-An SSH server needs a port; you need to expose Docker's SSH port to NNI as the connection port. For example, if you set your container's SSH port as ``A``, you should map the container's port ``A`` to your remote host machine's other port ``B``, NNI will connect port ``B`` as an SSH port, and your host machine will map the connection from port ``B`` to port ``A`` then NNI could connect to your Docker container.
diff --git a/docs/tools/patches/11.txt b/docs/tools/patches/11.txt
deleted file mode 100644
index df6e5d9c4..000000000
--- a/docs/tools/patches/11.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-If the id ends with *, nnictl will stop all experiments whose ids matchs the regular.
-%%%%%%
-If the id ends with \*, nnictl will stop all experiments whose ids matchs the regular.
diff --git a/docs/tools/patches/12.txt b/docs/tools/patches/12.txt
deleted file mode 100644
index f1d88c078..000000000
--- a/docs/tools/patches/12.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-..
-
-   make: *** [install-XXX] Segmentation fault (core dumped)
-%%%%%%
-.. code-block:: text
-
-   make: *** [install-XXX] Segmentation fault (core dumped)
diff --git a/docs/tools/patches/13.txt b/docs/tools/patches/13.txt
deleted file mode 100644
index c17cedbca..000000000
--- a/docs/tools/patches/13.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-  Click ``Submit job`` button in web portal.
-%%%%%%
-Click ``Submit job`` button in web portal.
diff --git a/docs/tools/patches/14.txt b/docs/tools/patches/14.txt
deleted file mode 100644
index 88866fa2b..000000000
--- a/docs/tools/patches/14.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-:raw-html:`<div >
-    <img src="https://github.com/microsoft/Cream/blob/main/demo/intro.jpg" width="800"/>
-</div>`
-%%%%%%
-:raw-html:`<div ><img src="https://github.com/microsoft/Cream/blob/main/demo/intro.jpg" width="800"/></div>`
\ No newline at end of file
diff --git a/docs/tools/patches/15.txt b/docs/tools/patches/15.txt
deleted file mode 100644
index 030074b45..000000000
--- a/docs/tools/patches/15.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-.. list-table::
-   :header-rows: 1
-
-%%%%%%
-.. list-table::
-   :header-rows: 1
-   :widths: auto
-
diff --git a/docs/tools/patches/16.txt b/docs/tools/patches/16.txt
deleted file mode 100644
index f1592a713..000000000
--- a/docs/tools/patches/16.txt
+++ /dev/null
@@ -1,9 +0,0 @@
-.. code-block:: bash
-
-   1.1 Declare NNI API
-       Include `import nni` in your trial code to use NNI APIs.
-%%%%%%
-..
-
-   1.1 Declare NNI API
-       Include `import nni` in your trial code to use NNI APIs.
diff --git a/docs/tools/patches/17.txt b/docs/tools/patches/17.txt
deleted file mode 100644
index 21b449b06..000000000
--- a/docs/tools/patches/17.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-.. code-block:: bash
-
-   from nni.compression.pytorch.utils.counter import count_flops_params
-%%%%%%
-.. code-block:: python
-
-   from nni.compression.pytorch.utils.counter import count_flops_params
diff --git a/docs/tools/patches/18.txt b/docs/tools/patches/18.txt
deleted file mode 100644
index 3938dc4cb..000000000
--- a/docs/tools/patches/18.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-.. code-block:: bash
-
-   NNI's official image msranni/nni does not support SSH servers for the time being; you should build your own Docker image with an SSH configuration or use other images as a remote server.
-%%%%%%
-.. code-block:: text
-
-   NNI's official image msranni/nni does not support SSH servers for the time being; you should build your own Docker image with an SSH configuration or use other images as a remote server.
diff --git a/docs/tools/patches/19.txt b/docs/tools/patches/19.txt
deleted file mode 100644
index 7fc65674b..000000000
--- a/docs/tools/patches/19.txt
+++ /dev/null
@@ -1,56 +0,0 @@
-Code Styles & Naming Conventions
---------------------------------
-
-
-* We follow `PEP8 <https://www.python.org/dev/peps/pep-0008/>`__ for Python code and naming conventions, do try to adhere to the same when making a pull request or making a change. One can also take the help of linters such as ``flake8`` or ``pylint``
-* We also follow `NumPy Docstring Style <https://www.sphinx-doc.org/en/master/usage/extensions/example_numpy.html#example-numpy>`__ for Python Docstring Conventions. During the `documentation building <Contributing.rst#documentation>`__\ , we use `sphinx.ext.napoleon <https://www.sphinx-doc.org/en/master/usage/extensions/napoleon.html>`__ to generate Python API documentation from Docstring.
-* For docstrings, please refer to `numpydoc docstring guide <https://numpydoc.readthedocs.io/en/latest/format.html>`__ and `pandas docstring guide <https://python-sprints.github.io/pandas/guide/pandas_docstring.html>`__
-
-  * For function docstring, **description** , **Parameters**\ , and** Returns**\ /** Yields** are mandatory.
-  * For class docstring, **description**\ ,** Attributes** are mandatory.
-  * For docstring to describe ``dict``\ , which is commonly used in our hyper-param format description, please refer to [RiboKit : Doc Standards
-
-    * Internal Guideline on Writing Standards](https://ribokit.github.io/docs/text/)
-
-Documentation
--------------
-
-Our documentation is built with :githublink:`sphinx <docs>`.
-
-
-* 
-  Before submitting the documentation change, please **build homepage locally**\ : ``cd docs/en_US && make html``\ , then you can see all the built documentation webpage under the folder ``docs/en_US/_build/html``. It's also highly recommended taking care of** every WARNING** during the build, which is very likely the signal of a** deadlink** and other annoying issues.
-
-* 
-  For links, please consider using **relative paths** first. However, if the documentation is written in Markdown format, and:
-
-
-  * It's an image link which needs to be formatted with embedded html grammar, please use global URL like ``https://user-images.githubusercontent.com/44491713/51381727-e3d0f780-1b4f-11e9-96ab-d26b9198ba65.png``\ , which can be automatically generated by dragging picture onto `Github Issue <https://github.com/Microsoft/nni/issues/new>`__ Box.
-  * It cannot be re-formatted by sphinx, such as source code, please use its global URL. For source code that links to our github repo, please use URLs rooted at ``https://github.com/Microsoft/nni/tree/v1.9/`` (\ :githublink:`mnist.py <examples/trials/mnist-tfv1/mnist.py>` for example).
-%%%%%%
-Code Styles & Naming Conventions
---------------------------------
-
-* We follow `PEP8 <https://www.python.org/dev/peps/pep-0008/>`__ for Python code and naming conventions, do try to adhere to the same when making a pull request or making a change. One can also take the help of linters such as ``flake8`` or ``pylint``
-* We also follow `NumPy Docstring Style <https://www.sphinx-doc.org/en/master/usage/extensions/example_numpy.html#example-numpy>`__ for Python Docstring Conventions. During the `documentation building <Contributing.rst#documentation>`__\ , we use `sphinx.ext.napoleon <https://www.sphinx-doc.org/en/master/usage/extensions/napoleon.html>`__ to generate Python API documentation from Docstring.
-* For docstrings, please refer to `numpydoc docstring guide <https://numpydoc.readthedocs.io/en/latest/format.html>`__ and `pandas docstring guide <https://python-sprints.github.io/pandas/guide/pandas_docstring.html>`__
-
-  * For function docstring, **description**, **Parameters**, and **Returns** **Yields** are mandatory.
-  * For class docstring, **description**, **Attributes** are mandatory.
-  * For docstring to describe ``dict``, which is commonly used in our hyper-param format description, please refer to RiboKit Doc Standards
-
-    * `Internal Guideline on Writing Standards <https://ribokit.github.io/docs/text/>`__
-
-Documentation
--------------
-
-Our documentation is built with :githublink:`sphinx <docs>`.
-
-* Before submitting the documentation change, please **build homepage locally**: ``cd docs/en_US && make html``, then you can see all the built documentation webpage under the folder ``docs/en_US/_build/html``. It's also highly recommended taking care of **every WARNING** during the build, which is very likely the signal of a **deadlink** and other annoying issues.
-
-* 
-  For links, please consider using **relative paths** first. However, if the documentation is written in Markdown format, and:
-
-
-  * It's an image link which needs to be formatted with embedded html grammar, please use global URL like ``https://user-images.githubusercontent.com/44491713/51381727-e3d0f780-1b4f-11e9-96ab-d26b9198ba65.png``, which can be automatically generated by dragging picture onto `Github Issue <https://github.com/Microsoft/nni/issues/new>`__ Box.
-  * It cannot be re-formatted by sphinx, such as source code, please use its global URL. For source code that links to our github repo, please use URLs rooted at ``https://github.com/Microsoft/nni/tree/v1.9/`` (:githublink:`mnist.py <examples/trials/mnist-tfv1/mnist.py>` for example).
diff --git a/docs/tools/patches/2.txt b/docs/tools/patches/2.txt
deleted file mode 100644
index 6a5c87d26..000000000
--- a/docs/tools/patches/2.txt
+++ /dev/null
@@ -1,45 +0,0 @@
-   * - 
-     - Recommended
-     - Minimum
-   * - **Operating System**
-     - Ubuntu 16.04 or above
-   * - **CPU**
-     - Intel® Core™ i5 or AMD Phenom™ II X3 or better
-     - Intel® Core™ i3 or AMD Phenom™ X3 8650
-   * - **GPU**
-     - NVIDIA® GeForce® GTX 660 or better
-     - NVIDIA® GeForce® GTX 460
-   * - **Memory**
-     - 6 GB RAM
-     - 4 GB RAM
-   * - **Storage**
-     - 30 GB available hare drive space
-   * - **Internet**
-     - Boardband internet connection
-   * - **Resolution**
-     - 1024 x 768 minimum display resolution
-%%%%%%
-   * - 
-     - Recommended
-     - Minimum
-   * - **Operating System**
-     - Ubuntu 16.04 or above
-     -
-   * - **CPU**
-     - Intel® Core™ i5 or AMD Phenom™ II X3 or better
-     - Intel® Core™ i3 or AMD Phenom™ X3 8650
-   * - **GPU**
-     - NVIDIA® GeForce® GTX 660 or better
-     - NVIDIA® GeForce® GTX 460
-   * - **Memory**
-     - 6 GB RAM
-     - 4 GB RAM
-   * - **Storage**
-     - 30 GB available hare drive space
-     -
-   * - **Internet**
-     - Boardband internet connection
-     -
-   * - **Resolution**
-     - 1024 x 768 minimum display resolution
-     -
diff --git a/docs/tools/patches/20.txt b/docs/tools/patches/20.txt
deleted file mode 100644
index dfb73a53e..000000000
--- a/docs/tools/patches/20.txt
+++ /dev/null
@@ -1,44 +0,0 @@
-..
-
-   1.1 Declare NNI API
-       Include `import nni` in your trial code to use NNI APIs.
-
-   1.2 Get predefined parameters
-       Use the following code snippet:
-
-           RECEIVED_PARAMS = nni.get_next_parameter()
-
-       to get hyper-parameters' values assigned by tuner. `RECEIVED_PARAMS` is an object, for example:
-
-           {"conv_size": 2, "hidden_size": 124, "learning_rate": 0.0307, "dropout_rate": 0.2029}
-
-   1.3 Report NNI results
-       Use the API:
-
-           `nni.report_intermediate_result(accuracy)`
-
-       to send `accuracy` to assessor.
-
-       Use the API:
-
-           `nni.report_final_result(accuracy)`
-
-       to send `accuracy` to tuner.
-%%%%%%
-* Declare NNI API: include ``import nni`` in your trial code to use NNI APIs.
-* Get predefined parameters
-
-Use the following code snippet:
-
-.. code-block:: python
-
-   RECEIVED_PARAMS = nni.get_next_parameter()
-
-to get hyper-parameters' values assigned by tuner. ``RECEIVED_PARAMS`` is an object, for example:
-
-.. code-block:: json
-
-   {"conv_size": 2, "hidden_size": 124, "learning_rate": 0.0307, "dropout_rate": 0.2029}
-
-* Report NNI results: Use the API: ``nni.report_intermediate_result(accuracy)`` to send ``accuracy`` to assessor.
-  Use the API: ``nni.report_final_result(accuracy)`` to send `accuracy` to tuner.
diff --git a/docs/tools/patches/3.txt b/docs/tools/patches/3.txt
deleted file mode 100644
index 49037f5ab..000000000
--- a/docs/tools/patches/3.txt
+++ /dev/null
@@ -1,46 +0,0 @@
-   * - 
-     - Recommended
-     - Minimum
-   * - **Operating System**
-     - macOS 10.14.1 or above
-   * - **CPU**
-     - Intel® Core™ i7-4770 or better
-     - Intel® Core™ i5-760 or better
-   * - **GPU**
-     - AMD Radeon™ R9 M395X or better
-     - NVIDIA® GeForce® GT 750M or AMD Radeon™ R9 M290 or better
-   * - **Memory**
-     - 8 GB RAM
-     - 4 GB RAM
-   * - **Storage**
-     - 70GB available space SSD
-     - 70GB available space 7200 RPM HDD
-   * - **Internet**
-     - Boardband internet connection
-   * - **Resolution**
-     - 1024 x 768 minimum display resolution
-%%%%%%
-   * -
-     - Recommended
-     - Minimum
-   * - **Operating System**
-     - macOS 10.14.1 or above
-     - 
-   * - **CPU**
-     - Intel® Core™ i7-4770 or better
-     - Intel® Core™ i5-760 or better
-   * - **GPU**
-     - AMD Radeon™ R9 M395X or better
-     - NVIDIA® GeForce® GT 750M or AMD Radeon™ R9 M290 or better
-   * - **Memory**
-     - 8 GB RAM
-     - 4 GB RAM
-   * - **Storage**
-     - 70GB available space SSD
-     - 70GB available space 7200 RPM HDD
-   * - **Internet**
-     - Boardband internet connection
-     - 
-   * - **Resolution**
-     - 1024 x 768 minimum display resolution
-     - 
diff --git a/docs/tools/patches/4.txt b/docs/tools/patches/4.txt
deleted file mode 100644
index c4fe7bfaa..000000000
--- a/docs/tools/patches/4.txt
+++ /dev/null
@@ -1,45 +0,0 @@
-   * - 
-     - Recommended
-     - Minimum
-   * - **Operating System**
-     - Windows 10 1809 or above
-   * - **CPU**
-     - Intel® Core™ i5 or AMD Phenom™ II X3 or better
-     - Intel® Core™ i3 or AMD Phenom™ X3 8650
-   * - **GPU**
-     - NVIDIA® GeForce® GTX 660 or better
-     - NVIDIA® GeForce® GTX 460
-   * - **Memory**
-     - 6 GB RAM
-     - 4 GB RAM
-   * - **Storage**
-     - 30 GB available hare drive space
-   * - **Internet**
-     - Boardband internet connection
-   * - **Resolution**
-     - 1024 x 768 minimum display resolution
-%%%%%%
-   * -
-     - Recommended
-     - Minimum
-   * - **Operating System**
-     - Windows 10 1809 or above
-     - 
-   * - **CPU**
-     - Intel® Core™ i5 or AMD Phenom™ II X3 or better
-     - Intel® Core™ i3 or AMD Phenom™ X3 8650
-   * - **GPU**
-     - NVIDIA® GeForce® GTX 660 or better
-     - NVIDIA® GeForce® GTX 460
-   * - **Memory**
-     - 6 GB RAM
-     - 4 GB RAM
-   * - **Storage**
-     - 30 GB available hare drive space
-     - 
-   * - **Internet**
-     - Boardband internet connection
-     - 
-   * - **Resolution**
-     - 1024 x 768 minimum display resolution
-     - 
diff --git a/docs/tools/patches/5.txt b/docs/tools/patches/5.txt
deleted file mode 100644
index 0a0ee1d4e..000000000
--- a/docs/tools/patches/5.txt
+++ /dev/null
@@ -1,84 +0,0 @@
-   * - 
-     - s=4
-     - s=3
-     - s=2
-     - s=1
-     - s=0
-   * - i
-     - n r
-     - n r
-     - n r
-     - n r
-     - n r
-   * - 0
-     - 81 1
-     - 27 3
-     - 9 9
-     - 6 27
-     - 5 81
-   * - 1
-     - 27 3
-     - 9 9
-     - 3 27
-     - 2 81
-     - 
-   * - 2
-     - 9 9
-     - 3 27
-     - 1 81
-     - 
-     - 
-   * - 3
-     - 3 27
-     - 1 81
-     - 
-     - 
-     - 
-   * - 4
-     - 1 81
-     - 
-     - 
-     - 
-%%%%%%
-   * -
-     - s=4
-     - s=3
-     - s=2
-     - s=1
-     - s=0
-   * - i
-     - n r
-     - n r
-     - n r
-     - n r
-     - n r
-   * - 0
-     - 81 1
-     - 27 3
-     - 9 9
-     - 6 27
-     - 5 81
-   * - 1
-     - 27 3
-     - 9 9
-     - 3 27
-     - 2 81
-     -
-   * - 2
-     - 9 9
-     - 3 27
-     - 1 81
-     -
-     -
-   * - 3
-     - 3 27
-     - 1 81
-     -
-     -
-     -
-   * - 4
-     - 1 81
-     -
-     -
-     -
-     -
diff --git a/docs/tools/patches/6.txt b/docs/tools/patches/6.txt
deleted file mode 100644
index abf445780..000000000
--- a/docs/tools/patches/6.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-*Please refer to `here <https://nni.readthedocs.io/en/latest/sdk_reference.html>`__ for more APIs (e.g., ``nni.get_sequence_id()``\ ) provided by NNI.
-%%%%%%
-*Please refer to `here <https://nni.readthedocs.io/en/latest/sdk_reference.html>`__ for more APIs (e.g., ``nni.get_sequence_id()``\ ) provided by NNI.*
diff --git a/docs/tools/patches/7.txt b/docs/tools/patches/7.txt
deleted file mode 100644
index 57393d542..000000000
--- a/docs/tools/patches/7.txt
+++ /dev/null
@@ -1,44 +0,0 @@
-   #. For each filter 
-      .. image:: http://latex.codecogs.com/gif.latex?F_{i,j}
-         :target: http://latex.codecogs.com/gif.latex?F_{i,j}
-         :alt: 
-      , calculate the sum of its absolute kernel weights
-      .. image:: http://latex.codecogs.com/gif.latex?s_j=\sum_{l=1}^{n_i}\sum|K_l|
-         :target: http://latex.codecogs.com/gif.latex?s_j=\sum_{l=1}^{n_i}\sum|K_l|
-         :alt: 
-
-   #. Sort the filters by 
-      .. image:: http://latex.codecogs.com/gif.latex?s_j
-         :target: http://latex.codecogs.com/gif.latex?s_j
-         :alt: 
-      .
-   #. Prune 
-      .. image:: http://latex.codecogs.com/gif.latex?m
-         :target: http://latex.codecogs.com/gif.latex?m
-         :alt: 
-       filters with the smallest sum values and their corresponding feature maps. The
-        kernels in the next convolutional layer corresponding to the pruned feature maps are also
-      .. code-block:: bash
-
-         removed.
-
-   #. A new kernel matrix is created for both the 
-      .. image:: http://latex.codecogs.com/gif.latex?i
-         :target: http://latex.codecogs.com/gif.latex?i
-         :alt: 
-      th and 
-      .. image:: http://latex.codecogs.com/gif.latex?i+1
-         :target: http://latex.codecogs.com/gif.latex?i+1
-         :alt: 
-      th layers, and the remaining kernel
-        weights are copied to the new model.
-%%%%%%
-   #. For each filter :math:`F_{i,j}`, calculate the sum of its absolute kernel weights :math:`s_j=\sum_{l=1}^{n_i}\sum|K_l|`.
-
-   #. Sort the filters by :math:`s_j`.
-
-   #. Prune :math:`m` filters with the smallest sum values and their corresponding feature maps. The
-      kernels in the next convolutional layer corresponding to the pruned feature maps are also removed.
-
-   #. A new kernel matrix is created for both the :math:`i`-th and :math:`i+1`-th layers, and the remaining kernel
-      weights are copied to the new model.
diff --git a/docs/tools/patches/8.txt b/docs/tools/patches/8.txt
deleted file mode 100644
index f92d650f6..000000000
--- a/docs/tools/patches/8.txt
+++ /dev/null
@@ -1,25 +0,0 @@
-#. A **Kubernetes** cluster using Kubernetes 1.8 or later. Follow this `guideline <https://kubernetes.io/docs/setup/>`__ to set up Kubernetes
-#. Prepare a **kubeconfig** file, which will be used by NNI to interact with your Kubernetes API server. By default, NNI manager will use $(HOME)/.kube/config as kubeconfig file's path. You can also specify other kubeconfig files by setting the** KUBECONFIG** environment variable. Refer this `guideline <https://kubernetes.io/docs/concepts/configuration/organize-cluster-access-kubeconfig>`__ to learn more about kubeconfig.
-#. If your NNI trial job needs GPU resource, you should follow this `guideline <https://github.com/NVIDIA/k8s-device-plugin>`__ to configure **Nvidia device plugin for Kubernetes**.
-#. Prepare a **NFS server** and export a general purpose mount (we recommend to map your NFS server path in ``root_squash option``\ , otherwise permission issue may raise when NNI copies files to NFS. Refer this `page <https://linux.die.net/man/5/exports>`__ to learn what root_squash option is), or** Azure File Storage**.
-#. 
-   Install **NFS client** on the machine where you install NNI and run nnictl to create experiment. Run this command to install NFSv4 client:
-
-   .. code-block:: bash
-
-       apt-get install nfs-common
-
-#. 
-   Install **NNI**\ , follow the install guide `here <../Tutorial/QuickStart.rst>`__.
-%%%%%%
-#. A **Kubernetes** cluster using Kubernetes 1.8 or later. Follow this `guideline <https://kubernetes.io/docs/setup/>`__ to set up Kubernetes
-#. Prepare a **kubeconfig** file, which will be used by NNI to interact with your Kubernetes API server. By default, NNI manager will use $(HOME)/.kube/config as kubeconfig file's path. You can also specify other kubeconfig files by setting the**KUBECONFIG** environment variable. Refer this `guideline <https://kubernetes.io/docs/concepts/configuration/organize-cluster-access-kubeconfig>`__ to learn more about kubeconfig.
-#. If your NNI trial job needs GPU resource, you should follow this `guideline <https://github.com/NVIDIA/k8s-device-plugin>`__ to configure **Nvidia device plugin for Kubernetes**.
-#. Prepare a **NFS server** and export a general purpose mount (we recommend to map your NFS server path in ``root_squash option``\ , otherwise permission issue may raise when NNI copies files to NFS. Refer this `page <https://linux.die.net/man/5/exports>`__ to learn what root_squash option is), or **Azure File Storage**.
-#. Install **NFS client** on the machine where you install NNI and run nnictl to create experiment. Run this command to install NFSv4 client:
-
-.. code-block:: bash
-
-    apt-get install nfs-common
-
-#. Install **NNI**\ , follow the install guide `here <../Tutorial/QuickStart>`__.
diff --git a/docs/tools/patches/9.txt b/docs/tools/patches/9.txt
deleted file mode 100644
index 17560dcea..000000000
--- a/docs/tools/patches/9.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-#. A **Kubernetes** cluster using Kubernetes 1.8 or later. Follow this `guideline <https://kubernetes.io/docs/setup/>`__ to set up Kubernetes
-#. Download, set up, and deploy **Kubeflow** to your Kubernetes cluster. Follow this `guideline <https://www.kubeflow.org/docs/started/getting-started/>`__ to setup Kubeflow.
-#. Prepare a **kubeconfig** file, which will be used by NNI to interact with your Kubernetes API server. By default, NNI manager will use $(HOME)/.kube/config as kubeconfig file's path. You can also specify other kubeconfig files by setting the** KUBECONFIG** environment variable. Refer this `guideline <https://kubernetes.io/docs/concepts/configuration/organize-cluster-access-kubeconfig>`__ to learn more about kubeconfig.
-#. If your NNI trial job needs GPU resource, you should follow this `guideline <https://github.com/NVIDIA/k8s-device-plugin>`__ to configure **Nvidia device plugin for Kubernetes**.
-#. Prepare a **NFS server** and export a general purpose mount (we recommend to map your NFS server path in ``root_squash option``\ , otherwise permission issue may raise when NNI copy files to NFS. Refer this `page <https://linux.die.net/man/5/exports>`__ to learn what root_squash option is), or** Azure File Storage**.
-#. 
-   Install **NFS client** on the machine where you install NNI and run nnictl to create experiment. Run this command to install NFSv4 client:
-
-   .. code-block:: bash
-
-       apt-get install nfs-common
-
-#. 
-   Install **NNI**\ , follow the install guide `here <../Tutorial/QuickStart.rst>`__.
-%%%%%%
-#. A **Kubernetes** cluster using Kubernetes 1.8 or later. Follow this `guideline <https://kubernetes.io/docs/setup/>`__ to set up Kubernetes
-#. Download, set up, and deploy **Kubeflow** to your Kubernetes cluster. Follow this `guideline <https://www.kubeflow.org/docs/started/getting-started/>`__ to setup Kubeflow.
-#. Prepare a **kubeconfig** file, which will be used by NNI to interact with your Kubernetes API server. By default, NNI manager will use $(HOME)/.kube/config as kubeconfig file's path. You can also specify other kubeconfig files by setting the**KUBECONFIG** environment variable. Refer this `guideline <https://kubernetes.io/docs/concepts/configuration/organize-cluster-access-kubeconfig>`__ to learn more about kubeconfig.
-#. If your NNI trial job needs GPU resource, you should follow this `guideline <https://github.com/NVIDIA/k8s-device-plugin>`__ to configure **Nvidia device plugin for Kubernetes**.
-#. Prepare a **NFS server** and export a general purpose mount (we recommend to map your NFS server path in ``root_squash option``\ , otherwise permission issue may raise when NNI copy files to NFS. Refer this `page <https://linux.die.net/man/5/exports>`__ to learn what root_squash option is), or**Azure File Storage**.
-#. Install **NFS client** on the machine where you install NNI and run nnictl to create experiment. Run this command to install NFSv4 client:
-
-.. code-block:: bash
-
-    apt-get install nfs-common
-
-#. Install **NNI**\ , follow the install guide `here <../Tutorial/QuickStart>`__.
diff --git a/docs/tools/translation_patch.py b/docs/tools/translation_patch.py
new file mode 100644
index 000000000..f47bbfe03
--- /dev/null
+++ b/docs/tools/translation_patch.py
@@ -0,0 +1,17 @@
+"""
+Fix a troublsome translation in sphinx.
+Related PR: https://github.com/sphinx-doc/sphinx/pull/10303
+"""
+
+import subprocess
+from pathlib import Path
+
+import sphinx
+
+sphinx_path = Path(sphinx.__path__[0]) / 'locale/zh_CN/LC_MESSAGES'
+po_content = (sphinx_path / 'sphinx.po').read_text()
+po_content = po_content.replace('%s的别名', '%s 的别名')
+(sphinx_path / 'sphinx.po').write_text(po_content)
+
+# build po -> mo
+subprocess.run(['msgfmt', '-c', str(sphinx_path / 'sphinx.po'), '-o', str(sphinx_path / 'sphinx.mo')], check=True)
diff --git a/examples/model_compress/end2end_compression.py b/examples/model_compress/end2end_compression.py
index 062d6351d..664beb7bb 100644
--- a/examples/model_compress/end2end_compression.py
+++ b/examples/model_compress/end2end_compression.py
@@ -15,7 +15,7 @@ import torch.optim as optim
 from torch.optim.lr_scheduler import StepLR
 from torchvision import datasets, transforms
 
-from nni.compression.pytorch.utils.counter import count_flops_params
+from nni.compression.pytorch.utils import count_flops_params
 from nni.compression.pytorch import ModelSpeedup
 
 from nni.algorithms.compression.pytorch.pruning import L1FilterPruner
@@ -277,7 +277,7 @@ if __name__ == '__main__':
     parser.add_argument('--sparsity', type=float, default=0.5,
                         help='target overall target sparsity')
     parser.add_argument('--dependency-aware', action='store_true', default=False,
-                        help='toggle dependency aware mode')
+                        help='toggle dependency-aware mode')
 
     # finetuning
     parser.add_argument('--finetune-epochs', type=int, default=5,
@@ -290,8 +290,8 @@ if __name__ == '__main__':
     #                     help='learning rate to finetune the model')
 
     # speedup
-    # parser.add_argument('--speed-up', action='store_true', default=False,
-    #                     help='whether to speed-up the pruned model')
+    # parser.add_argument('--speedup', action='store_true', default=False,
+    #                     help='whether to speedup the pruned model')
 
     # parser.add_argument('--nni', action='store_true', default=False,
     #                     help="whether to tune the pruners using NNi tuners")
diff --git a/examples/model_compress/pruning/v2/activation_pruning_torch.py b/examples/model_compress/pruning/activation_pruning_torch.py
similarity index 94%
rename from examples/model_compress/pruning/v2/activation_pruning_torch.py
rename to examples/model_compress/pruning/activation_pruning_torch.py
index 4e79bd510..9e81def6d 100644
--- a/examples/model_compress/pruning/v2/activation_pruning_torch.py
+++ b/examples/model_compress/pruning/activation_pruning_torch.py
@@ -4,7 +4,7 @@
 '''
 NNI example for supported ActivationAPoZRank and ActivationMeanRank pruning algorithms.
 In this example, we show the end-to-end pruning process: pre-training -> pruning -> fine-tuning.
-Note that pruners use masks to simulate the real pruning. In order to obtain a real compressed model, model speed up is required.
+Note that pruners use masks to simulate the real pruning. In order to obtain a real compressed model, model speedup is required.
 
 '''
 import argparse
@@ -16,11 +16,11 @@ from torch.optim.lr_scheduler import MultiStepLR
 
 import nni
 from nni.compression.pytorch import ModelSpeedup
-from nni.compression.pytorch.utils.counter import count_flops_params
-from nni.algorithms.compression.v2.pytorch.pruning.basic_pruner import ActivationAPoZRankPruner, ActivationMeanRankPruner
+from nni.compression.pytorch.utils import count_flops_params
+from nni.compression.pytorch.pruning import ActivationAPoZRankPruner, ActivationMeanRankPruner
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[1] / 'models'))
 from cifar10.vgg import VGG
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
diff --git a/examples/model_compress/pruning/v2/admm_pruning_torch.py b/examples/model_compress/pruning/admm_pruning_torch.py
similarity index 91%
rename from examples/model_compress/pruning/v2/admm_pruning_torch.py
rename to examples/model_compress/pruning/admm_pruning_torch.py
index f37bbf71d..461cde1cc 100644
--- a/examples/model_compress/pruning/v2/admm_pruning_torch.py
+++ b/examples/model_compress/pruning/admm_pruning_torch.py
@@ -4,7 +4,7 @@
 '''
 NNI example for supported ADMM pruning algorithms.
 In this example, we show the end-to-end pruning process: pre-training -> pruning -> fine-tuning.
-Note that pruners use masks to simulate the real pruning. In order to obtain a real compressed model, model speed up is required.
+Note that pruners use masks to simulate the real pruning. In order to obtain a real compressed model, model speedup is required.
 
 '''
 import argparse
@@ -15,11 +15,12 @@ from torchvision import datasets, transforms
 from torch.optim.lr_scheduler import MultiStepLR
 
 import nni
-from nni.compression.pytorch.utils.counter import count_flops_params
-from nni.algorithms.compression.v2.pytorch.pruning.basic_pruner import ADMMPruner
+from nni.compression.pytorch.speedup import ModelSpeedup
+from nni.compression.pytorch.utils import count_flops_params
+from nni.compression.pytorch.pruning import ADMMPruner
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[1] / 'models'))
 from cifar10.vgg import VGG
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -108,18 +109,17 @@ if __name__ == '__main__':
     config_list = [{
         'sparsity': 0.8,
         'op_types': ['Conv2d'],
-    }, {
-        'sparsity': 0.92,
-        'op_types': ['Conv2d'],
     }]
 
     # make sure you have used nni.trace to wrap the optimizer class before initialize
     traced_optimizer = nni.trace(torch.optim.SGD)(model.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)
-    pruner = ADMMPruner(model, config_list, trainer, traced_optimizer, criterion, iterations=2, training_epochs=2)
+    pruner = ADMMPruner(model, config_list, trainer, traced_optimizer, criterion, iterations=10, training_epochs=1, granularity='coarse-grained')
     _, masks = pruner.compress()
     pruner.show_pruned_weights()
 
-    # Fine-grained method does not need to speedup
+    pruner._unwrap_model()
+    ModelSpeedup(model, torch.randn([128, 3, 32, 32]).to(device), masks).speedup_model()
+
     print('\n' + '=' * 50 + ' EVALUATE THE MODEL AFTER PRUNING ' + '=' * 50)
     evaluator(model)
 
diff --git a/examples/model_compress/pruning/v2/amc_pruning_torch.py b/examples/model_compress/pruning/amc_pruning_torch.py
similarity index 95%
rename from examples/model_compress/pruning/v2/amc_pruning_torch.py
rename to examples/model_compress/pruning/amc_pruning_torch.py
index 94e50e554..7e8a0a372 100644
--- a/examples/model_compress/pruning/v2/amc_pruning_torch.py
+++ b/examples/model_compress/pruning/amc_pruning_torch.py
@@ -5,11 +5,11 @@ import torch
 from torchvision import datasets, transforms
 from torch.optim.lr_scheduler import MultiStepLR
 
-from nni.algorithms.compression.v2.pytorch.pruning import AMCPruner
-from nni.compression.pytorch.utils.counter import count_flops_params
+from nni.compression.pytorch.pruning import AMCPruner
+from nni.compression.pytorch.utils import count_flops_params
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[1] / 'models'))
 from cifar10.vgg import VGG
 
 
diff --git a/examples/model_compress/pruning/v2/auto_compress_pruner.py b/examples/model_compress/pruning/auto_compress_pruner.py
similarity index 95%
rename from examples/model_compress/pruning/v2/auto_compress_pruner.py
rename to examples/model_compress/pruning/auto_compress_pruner.py
index b19a137a2..a27f0dfe7 100644
--- a/examples/model_compress/pruning/v2/auto_compress_pruner.py
+++ b/examples/model_compress/pruning/auto_compress_pruner.py
@@ -5,10 +5,10 @@ import torch
 from torchvision import datasets, transforms
 
 import nni
-from nni.algorithms.compression.v2.pytorch.pruning import AutoCompressPruner
+from nni.compression.pytorch.pruning import AutoCompressPruner
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[1] / 'models'))
 from cifar10.vgg import VGG
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
diff --git a/examples/model_compress/pruning/v2/fpgm_pruning_torch.py b/examples/model_compress/pruning/fpgm_pruning_torch.py
similarity index 94%
rename from examples/model_compress/pruning/v2/fpgm_pruning_torch.py
rename to examples/model_compress/pruning/fpgm_pruning_torch.py
index 335ffb3e7..31959acbc 100644
--- a/examples/model_compress/pruning/v2/fpgm_pruning_torch.py
+++ b/examples/model_compress/pruning/fpgm_pruning_torch.py
@@ -4,7 +4,7 @@
 '''
 NNI example for supported fpgm pruning algorithms.
 In this example, we show the end-to-end pruning process: pre-training -> pruning -> fine-tuning.
-Note that pruners use masks to simulate the real pruning. In order to obtain a real compressed model, model speed up is required.
+Note that pruners use masks to simulate the real pruning. In order to obtain a real compressed model, model speedup is required.
 
 '''
 import argparse
@@ -15,11 +15,11 @@ from torchvision import datasets, transforms
 from torch.optim.lr_scheduler import MultiStepLR
 
 from nni.compression.pytorch import ModelSpeedup
-from nni.compression.pytorch.utils.counter import count_flops_params
-from nni.algorithms.compression.v2.pytorch.pruning.basic_pruner import FPGMPruner
+from nni.compression.pytorch.utils import count_flops_params
+from nni.compression.pytorch.pruning import FPGMPruner
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[1] / 'models'))
 from cifar10.vgg import VGG
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
diff --git a/examples/model_compress/pruning/v2/iterative_pruning_torch.py b/examples/model_compress/pruning/iterative_pruning_torch.py
similarity index 93%
rename from examples/model_compress/pruning/v2/iterative_pruning_torch.py
rename to examples/model_compress/pruning/iterative_pruning_torch.py
index bf0967859..81ca245a2 100644
--- a/examples/model_compress/pruning/v2/iterative_pruning_torch.py
+++ b/examples/model_compress/pruning/iterative_pruning_torch.py
@@ -13,14 +13,14 @@ from tqdm import tqdm
 import torch
 from torchvision import datasets, transforms
 
-from nni.algorithms.compression.v2.pytorch.pruning import (
+from nni.compression.pytorch.pruning import (
     LinearPruner,
     AGPPruner,
     LotteryTicketPruner
 )
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[1] / 'models'))
 from cifar10.vgg import VGG
 
 
@@ -94,8 +94,8 @@ if __name__ == '__main__':
                         choices=['level', 'l1', 'l2', 'fpgm', 'slim', 'apoz',
                                  'mean_activation', 'taylorfo', 'admm'],
                         help='algorithm to evaluate weights to prune')
-    parser.add_argument('--speed-up', type=bool, default=False,
-                        help='Whether to speed-up the pruned model')
+    parser.add_argument('--speedup', type=bool, default=False,
+                        help='Whether to speedup the pruned model')
     parser.add_argument('--reset-weight', type=bool, default=True,
                         help='Whether to reset weight during each iteration')
 
@@ -120,8 +120,8 @@ if __name__ == '__main__':
                'evaluator': None,
                'finetuner': finetuner}
 
-    if args.speed_up:
-        kw_args['speed_up'] = args.speed_up
+    if args.speedup:
+        kw_args['speedup'] = args.speedup
         kw_args['dummy_input'] = torch.rand(10, 3, 32, 32).to(device)
 
     if args.pruner == 'linear':
diff --git a/examples/model_compress/pruning/.gitignore b/examples/model_compress/pruning/legacy/.gitignore
similarity index 100%
rename from examples/model_compress/pruning/.gitignore
rename to examples/model_compress/pruning/legacy/.gitignore
diff --git a/examples/model_compress/pruning/amc/README.md b/examples/model_compress/pruning/legacy/amc/README.md
similarity index 100%
rename from examples/model_compress/pruning/amc/README.md
rename to examples/model_compress/pruning/legacy/amc/README.md
diff --git a/examples/model_compress/pruning/amc/README_zh_CN.md b/examples/model_compress/pruning/legacy/amc/README_zh_CN.md
similarity index 100%
rename from examples/model_compress/pruning/amc/README_zh_CN.md
rename to examples/model_compress/pruning/legacy/amc/README_zh_CN.md
diff --git a/examples/model_compress/pruning/amc/amc_search.py b/examples/model_compress/pruning/legacy/amc/amc_search.py
similarity index 100%
rename from examples/model_compress/pruning/amc/amc_search.py
rename to examples/model_compress/pruning/legacy/amc/amc_search.py
diff --git a/examples/model_compress/pruning/amc/amc_train.py b/examples/model_compress/pruning/legacy/amc/amc_train.py
similarity index 99%
rename from examples/model_compress/pruning/amc/amc_train.py
rename to examples/model_compress/pruning/legacy/amc/amc_train.py
index c70bfe785..dd43498f5 100644
--- a/examples/model_compress/pruning/amc/amc_train.py
+++ b/examples/model_compress/pruning/legacy/amc/amc_train.py
@@ -23,7 +23,7 @@ from data import get_dataset
 from utils import AverageMeter, accuracy, progress_bar
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[3] / 'models'))
 from mobilenet import MobileNet
 from mobilenet_v2 import MobileNetV2
 
diff --git a/examples/model_compress/pruning/amc/data.py b/examples/model_compress/pruning/legacy/amc/data.py
similarity index 100%
rename from examples/model_compress/pruning/amc/data.py
rename to examples/model_compress/pruning/legacy/amc/data.py
diff --git a/examples/model_compress/pruning/amc/utils.py b/examples/model_compress/pruning/legacy/amc/utils.py
similarity index 100%
rename from examples/model_compress/pruning/amc/utils.py
rename to examples/model_compress/pruning/legacy/amc/utils.py
diff --git a/examples/model_compress/pruning/auto_pruners_torch.py b/examples/model_compress/pruning/legacy/auto_pruners_torch.py
similarity index 97%
rename from examples/model_compress/pruning/auto_pruners_torch.py
rename to examples/model_compress/pruning/legacy/auto_pruners_torch.py
index e30154927..ce653088c 100644
--- a/examples/model_compress/pruning/auto_pruners_torch.py
+++ b/examples/model_compress/pruning/legacy/auto_pruners_torch.py
@@ -17,10 +17,10 @@ from torchvision import datasets, transforms
 from nni.algorithms.compression.pytorch.pruning import L1FilterPruner, L2FilterPruner, FPGMPruner
 from nni.algorithms.compression.pytorch.pruning import SimulatedAnnealingPruner, ADMMPruner, NetAdaptPruner, AutoCompressPruner
 from nni.compression.pytorch import ModelSpeedup
-from nni.compression.pytorch.utils.counter import count_flops_params
+from nni.compression.pytorch.utils import count_flops_params
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[1] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
 from mnist.lenet import LeNet
 from cifar10.vgg import VGG
 from cifar10.resnet import ResNet18, ResNet50
@@ -292,8 +292,8 @@ def main(args):
             os.path.join(args.experiment_data_dir, 'model_masked.pth'), os.path.join(args.experiment_data_dir, 'mask.pth'))
         print('Masked model saved to %s' % args.experiment_data_dir)
 
-    # model speed up
-    if args.speed_up:
+    # model speedup
+    if args.speedup:
         if args.pruner != 'AutoCompressPruner':
             if args.model == 'LeNet':
                 model = LeNet().to(device)
@@ -310,11 +310,11 @@ def main(args):
             m_speedup = ModelSpeedup(model, dummy_input, masks_file, device)
             m_speedup.speedup_model()
             evaluation_result = evaluator(model)
-            print('Evaluation result (speed up model): %s' % evaluation_result)
+            print('Evaluation result (speedup model): %s' % evaluation_result)
             result['performance']['speedup'] = evaluation_result
 
-            torch.save(model.state_dict(), os.path.join(args.experiment_data_dir, 'model_speed_up.pth'))
-            print('Speed up model saved to %s' % args.experiment_data_dir)
+            torch.save(model.state_dict(), os.path.join(args.experiment_data_dir, 'model_speedup.pth'))
+            print('Speedup model saved to %s' % args.experiment_data_dir)
         flops, params, _ = count_flops_params(model, get_input_size(args.dataset))
         result['flops']['speedup'] = flops
         result['params']['speedup'] = params
@@ -402,9 +402,9 @@ if __name__ == '__main__':
     parser.add_argument('--sparsity-per-iteration', type=float, default=0.05,
                         help='sparsity_per_iteration of NetAdaptPruner')
 
-    # speed-up
-    parser.add_argument('--speed-up', type=str2bool, default=False,
-                        help='Whether to speed-up the pruned model')
+    # speedup
+    parser.add_argument('--speedup', type=str2bool, default=False,
+                        help='Whether to speedup the pruned model')
 
     # others
     parser.add_argument('--log-interval', type=int, default=200,
diff --git a/examples/model_compress/pruning/basic_pruners_torch.py b/examples/model_compress/pruning/legacy/basic_pruners_torch.py
similarity index 97%
rename from examples/model_compress/pruning/basic_pruners_torch.py
rename to examples/model_compress/pruning/legacy/basic_pruners_torch.py
index cbfd8e320..737e5533c 100644
--- a/examples/model_compress/pruning/basic_pruners_torch.py
+++ b/examples/model_compress/pruning/legacy/basic_pruners_torch.py
@@ -4,7 +4,7 @@
 '''
 NNI example for supported basic pruning algorithms.
 In this example, we show the end-to-end pruning process: pre-training -> pruning -> fine-tuning.
-Note that pruners use masks to simulate the real pruning. In order to obtain a real compressed model, model speed up is required.
+Note that pruners use masks to simulate the real pruning. In order to obtain a real compressed model, model speedup is required.
 You can also try auto_pruners_torch.py to see the usage of some automatic pruning algorithms.
 
 '''
@@ -18,12 +18,12 @@ from torch.optim.lr_scheduler import StepLR, MultiStepLR
 from torchvision import datasets, transforms
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[1] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
 from mnist.lenet import LeNet
 from cifar10.vgg import VGG
 from cifar10.resnet import ResNet18
 
-from nni.compression.pytorch.utils.counter import count_flops_params
+from nni.compression.pytorch.utils import count_flops_params
 
 import nni
 from nni.compression.pytorch import ModelSpeedup
@@ -292,7 +292,7 @@ def main(args):
     if args.test_only:
         test(args, model, device, criterion, test_loader)
 
-    if args.speed_up:
+    if args.speedup:
         # Unwrap all modules to normal state
         pruner._unwrap_model()
         m_speedup = ModelSpeedup(model, dummy_input, mask_path, device)
@@ -356,7 +356,7 @@ if __name__ == '__main__':
     parser.add_argument('--sparsity', type=float, default=0.5,
                         help='target overall target sparsity')
     parser.add_argument('--dependency-aware', action='store_true', default=False,
-                        help='toggle dependency aware mode')
+                        help='toggle dependency-aware mode')
     parser.add_argument('--global-sort', action='store_true', default=False,
                         help='toggle global sort mode')
     parser.add_argument('--pruner', type=str, default='l1filter',
@@ -364,9 +364,9 @@ if __name__ == '__main__':
                                  'fpgm', 'mean_activation', 'apoz', 'taylorfo'],
                         help='pruner to use')
 
-    # speed-up
-    parser.add_argument('--speed-up', action='store_true', default=False,
-                        help='Whether to speed-up the pruned model')
+    # speedup
+    parser.add_argument('--speedup', action='store_true', default=False,
+                        help='Whether to speedup the pruned model')
 
     # fine-tuning
     parser.add_argument('--fine-tune-epochs', type=int, default=160,
diff --git a/examples/model_compress/pruning/comparison_of_pruners/analyze.py b/examples/model_compress/pruning/legacy/comparison_of_pruners/analyze.py
similarity index 100%
rename from examples/model_compress/pruning/comparison_of_pruners/analyze.py
rename to examples/model_compress/pruning/legacy/comparison_of_pruners/analyze.py
diff --git a/examples/model_compress/pruning/comparison_of_pruners/cifar10/comparison_result_resnet18.json b/examples/model_compress/pruning/legacy/comparison_of_pruners/cifar10/comparison_result_resnet18.json
similarity index 100%
rename from examples/model_compress/pruning/comparison_of_pruners/cifar10/comparison_result_resnet18.json
rename to examples/model_compress/pruning/legacy/comparison_of_pruners/cifar10/comparison_result_resnet18.json
diff --git a/examples/model_compress/pruning/comparison_of_pruners/cifar10/comparison_result_resnet50.json b/examples/model_compress/pruning/legacy/comparison_of_pruners/cifar10/comparison_result_resnet50.json
similarity index 100%
rename from examples/model_compress/pruning/comparison_of_pruners/cifar10/comparison_result_resnet50.json
rename to examples/model_compress/pruning/legacy/comparison_of_pruners/cifar10/comparison_result_resnet50.json
diff --git a/examples/model_compress/pruning/comparison_of_pruners/cifar10/comparison_result_vgg16.json b/examples/model_compress/pruning/legacy/comparison_of_pruners/cifar10/comparison_result_vgg16.json
similarity index 100%
rename from examples/model_compress/pruning/comparison_of_pruners/cifar10/comparison_result_vgg16.json
rename to examples/model_compress/pruning/legacy/comparison_of_pruners/cifar10/comparison_result_vgg16.json
diff --git a/examples/model_compress/pruning/comparison_of_pruners/img/performance_comparison_resnet18.png b/examples/model_compress/pruning/legacy/comparison_of_pruners/img/performance_comparison_resnet18.png
similarity index 100%
rename from examples/model_compress/pruning/comparison_of_pruners/img/performance_comparison_resnet18.png
rename to examples/model_compress/pruning/legacy/comparison_of_pruners/img/performance_comparison_resnet18.png
diff --git a/examples/model_compress/pruning/comparison_of_pruners/img/performance_comparison_resnet50.png b/examples/model_compress/pruning/legacy/comparison_of_pruners/img/performance_comparison_resnet50.png
similarity index 100%
rename from examples/model_compress/pruning/comparison_of_pruners/img/performance_comparison_resnet50.png
rename to examples/model_compress/pruning/legacy/comparison_of_pruners/img/performance_comparison_resnet50.png
diff --git a/examples/model_compress/pruning/comparison_of_pruners/img/performance_comparison_vgg16.png b/examples/model_compress/pruning/legacy/comparison_of_pruners/img/performance_comparison_vgg16.png
similarity index 100%
rename from examples/model_compress/pruning/comparison_of_pruners/img/performance_comparison_vgg16.png
rename to examples/model_compress/pruning/legacy/comparison_of_pruners/img/performance_comparison_vgg16.png
diff --git a/examples/model_compress/pruning/config.yml b/examples/model_compress/pruning/legacy/config.yml
similarity index 100%
rename from examples/model_compress/pruning/config.yml
rename to examples/model_compress/pruning/legacy/config.yml
diff --git a/examples/model_compress/pruning/finetune_kd_torch.py b/examples/model_compress/pruning/legacy/finetune_kd_torch.py
similarity index 99%
rename from examples/model_compress/pruning/finetune_kd_torch.py
rename to examples/model_compress/pruning/legacy/finetune_kd_torch.py
index f1137d0ed..5f56825d4 100644
--- a/examples/model_compress/pruning/finetune_kd_torch.py
+++ b/examples/model_compress/pruning/legacy/finetune_kd_torch.py
@@ -19,7 +19,7 @@ from torch.optim.lr_scheduler import MultiStepLR
 from basic_pruners_torch import get_data
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[1] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
 from mnist.lenet import LeNet
 from cifar10.vgg import VGG
 
diff --git a/examples/model_compress/pruning/lottery_torch_mnist_fc.py b/examples/model_compress/pruning/legacy/lottery_torch_mnist_fc.py
similarity index 100%
rename from examples/model_compress/pruning/lottery_torch_mnist_fc.py
rename to examples/model_compress/pruning/legacy/lottery_torch_mnist_fc.py
diff --git a/examples/model_compress/pruning/mobilenetv2_end2end/Compressing MobileNetV2 with NNI Pruners.ipynb b/examples/model_compress/pruning/legacy/mobilenetv2_end2end/Compressing MobileNetV2 with NNI Pruners.ipynb
similarity index 99%
rename from examples/model_compress/pruning/mobilenetv2_end2end/Compressing MobileNetV2 with NNI Pruners.ipynb
rename to examples/model_compress/pruning/legacy/mobilenetv2_end2end/Compressing MobileNetV2 with NNI Pruners.ipynb
index 4be5044f3..ee11f9696 100644
--- a/examples/model_compress/pruning/mobilenetv2_end2end/Compressing MobileNetV2 with NNI Pruners.ipynb	
+++ b/examples/model_compress/pruning/legacy/mobilenetv2_end2end/Compressing MobileNetV2 with NNI Pruners.ipynb	
@@ -34,7 +34,7 @@
     "import numpy as np\n",
     "\n",
     "from nni.compression.pytorch import ModelSpeedup\n",
-    "from nni.compression.pytorch.utils.counter import count_flops_params\n",
+    "from nni.compression.pytorch.utils import count_flops_params\n",
     "\n",
     "from utils import create_model, get_dataloader\n",
     "\n",
@@ -492,7 +492,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 720x720 with 9 Axes>"
       ]
@@ -1426,7 +1426,7 @@
     "            --sparsity $sparsity \\\n",
     "            --pruning_mode $pruning_mode \\\n",
     "            --pruner_name l1 \\\n",
-    "            --speed_up \\\n",
+    "            --speedup \\\n",
     "            --finetune_epochs 30\n",
     "    done\n",
     "done\n",
@@ -1476,7 +1476,7 @@
     "            --sparsity $sparsity \\\n",
     "            --pruning_mode $pruning_mode \\\n",
     "            --pruner_name l1 \\\n",
-    "            --speed_up \\\n",
+    "            --speedup \\\n",
     "            --finetune_epochs 30\n",
     "    done\n",
     "done\n",
@@ -1539,7 +1539,7 @@
     "            --pruning_mode 'conv1andconv2' \\\n",
     "            --pruner_name 'agp' \\\n",
     "            --agp_n_iters $n_iters \\\n",
-    "            --speed_up \\\n",
+    "            --speedup \\\n",
     "            --finetune_epochs 30 \\\n",
     "    done\n",
     "done\n",
@@ -1579,7 +1579,7 @@
     "        --sparsity $sparsity \\\n",
     "        --pruning_mode 'conv1andconv2' \\\n",
     "        --pruner_name 'agp' \\\n",
-    "        --speed_up \\\n",
+    "        --speedup \\\n",
     "        --finetune_epochs 80\n",
     "done\n",
     "\n",
@@ -1590,7 +1590,7 @@
     "        --sparsity $sparsity \\\n",
     "        --pruning_mode 'conv1andconv2' \\\n",
     "        --pruner_name 'agp' \\\n",
-    "        --speed_up \\\n",
+    "        --speedup \\\n",
     "        --finetune_epochs 80 \\\n",
     "        -- kd\n",
     "done\n",
diff --git a/examples/model_compress/pruning/mobilenetv2_end2end/final_performance.png b/examples/model_compress/pruning/legacy/mobilenetv2_end2end/final_performance.png
similarity index 100%
rename from examples/model_compress/pruning/mobilenetv2_end2end/final_performance.png
rename to examples/model_compress/pruning/legacy/mobilenetv2_end2end/final_performance.png
diff --git a/examples/model_compress/pruning/mobilenetv2_end2end/mobilenet.png b/examples/model_compress/pruning/legacy/mobilenetv2_end2end/mobilenet.png
similarity index 100%
rename from examples/model_compress/pruning/mobilenetv2_end2end/mobilenet.png
rename to examples/model_compress/pruning/legacy/mobilenetv2_end2end/mobilenet.png
diff --git a/examples/model_compress/pruning/mobilenetv2_end2end/prepare_data.sh b/examples/model_compress/pruning/legacy/mobilenetv2_end2end/prepare_data.sh
similarity index 100%
rename from examples/model_compress/pruning/mobilenetv2_end2end/prepare_data.sh
rename to examples/model_compress/pruning/legacy/mobilenetv2_end2end/prepare_data.sh
diff --git a/examples/model_compress/pruning/mobilenetv2_end2end/preprocess.py b/examples/model_compress/pruning/legacy/mobilenetv2_end2end/preprocess.py
similarity index 100%
rename from examples/model_compress/pruning/mobilenetv2_end2end/preprocess.py
rename to examples/model_compress/pruning/legacy/mobilenetv2_end2end/preprocess.py
diff --git a/examples/model_compress/pruning/mobilenetv2_end2end/pretrain.py b/examples/model_compress/pruning/legacy/mobilenetv2_end2end/pretrain.py
similarity index 100%
rename from examples/model_compress/pruning/mobilenetv2_end2end/pretrain.py
rename to examples/model_compress/pruning/legacy/mobilenetv2_end2end/pretrain.py
diff --git a/examples/model_compress/pruning/mobilenetv2_end2end/pruning_experiments.py b/examples/model_compress/pruning/legacy/mobilenetv2_end2end/pruning_experiments.py
similarity index 98%
rename from examples/model_compress/pruning/mobilenetv2_end2end/pruning_experiments.py
rename to examples/model_compress/pruning/legacy/mobilenetv2_end2end/pruning_experiments.py
index 193f1756c..ec7f5f508 100644
--- a/examples/model_compress/pruning/mobilenetv2_end2end/pruning_experiments.py
+++ b/examples/model_compress/pruning/legacy/mobilenetv2_end2end/pruning_experiments.py
@@ -217,9 +217,9 @@ def parse_args():
     parser.add_argument('--agp_n_epochs_per_iter', type=int, default=1,
                         help='number of epochs per iteration for agp')
 
-    # speed-up
-    parser.add_argument('--speed_up', action='store_true', default=False,
-                        help='Whether to speed-up the pruned model')
+    # speedup
+    parser.add_argument('--speedup', action='store_true', default=False,
+                        help='Whether to speedup the pruned model')
 
     # finetuning parameters
     parser.add_argument('--n_workers', type=int, default=16,
@@ -336,7 +336,7 @@ def run_pruning(args):
     
     # model speedup
     pruner._unwrap_model()
-    if args.speed_up:
+    if args.speedup:
         dummy_input = torch.rand(1,3,224,224).to(device)
         ms = ModelSpeedup(model, dummy_input, args.experiment_dir + './mask_temp.pth')
         ms.speedup_model()
diff --git a/examples/model_compress/pruning/mobilenetv2_end2end/step1.png b/examples/model_compress/pruning/legacy/mobilenetv2_end2end/step1.png
similarity index 100%
rename from examples/model_compress/pruning/mobilenetv2_end2end/step1.png
rename to examples/model_compress/pruning/legacy/mobilenetv2_end2end/step1.png
diff --git a/examples/model_compress/pruning/mobilenetv2_end2end/step2.png b/examples/model_compress/pruning/legacy/mobilenetv2_end2end/step2.png
similarity index 100%
rename from examples/model_compress/pruning/mobilenetv2_end2end/step2.png
rename to examples/model_compress/pruning/legacy/mobilenetv2_end2end/step2.png
diff --git a/examples/model_compress/pruning/mobilenetv2_end2end/step3-1.png b/examples/model_compress/pruning/legacy/mobilenetv2_end2end/step3-1.png
similarity index 100%
rename from examples/model_compress/pruning/mobilenetv2_end2end/step3-1.png
rename to examples/model_compress/pruning/legacy/mobilenetv2_end2end/step3-1.png
diff --git a/examples/model_compress/pruning/mobilenetv2_end2end/step3-2.png b/examples/model_compress/pruning/legacy/mobilenetv2_end2end/step3-2.png
similarity index 100%
rename from examples/model_compress/pruning/mobilenetv2_end2end/step3-2.png
rename to examples/model_compress/pruning/legacy/mobilenetv2_end2end/step3-2.png
diff --git a/examples/model_compress/pruning/mobilenetv2_end2end/step4.png b/examples/model_compress/pruning/legacy/mobilenetv2_end2end/step4.png
similarity index 100%
rename from examples/model_compress/pruning/mobilenetv2_end2end/step4.png
rename to examples/model_compress/pruning/legacy/mobilenetv2_end2end/step4.png
diff --git a/examples/model_compress/pruning/mobilenetv2_end2end/test.py b/examples/model_compress/pruning/legacy/mobilenetv2_end2end/test.py
similarity index 100%
rename from examples/model_compress/pruning/mobilenetv2_end2end/test.py
rename to examples/model_compress/pruning/legacy/mobilenetv2_end2end/test.py
diff --git a/examples/model_compress/pruning/mobilenetv2_end2end/utils.py b/examples/model_compress/pruning/legacy/mobilenetv2_end2end/utils.py
similarity index 96%
rename from examples/model_compress/pruning/mobilenetv2_end2end/utils.py
rename to examples/model_compress/pruning/legacy/mobilenetv2_end2end/utils.py
index 09ca2765d..1fff822c0 100644
--- a/examples/model_compress/pruning/mobilenetv2_end2end/utils.py
+++ b/examples/model_compress/pruning/legacy/mobilenetv2_end2end/utils.py
@@ -7,10 +7,10 @@ import torch
 from torch.utils.data import Dataset, DataLoader
 import torchvision.transforms as transforms
 import numpy as np
-from nni.compression.pytorch.utils.counter import count_flops_params
+from nni.compression.pytorch.utils import count_flops_params
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[3] / 'models'))
 from mobilenet import MobileNet
 from mobilenet_v2 import MobileNetV2
 
diff --git a/examples/model_compress/pruning/naive_prune_tf.py b/examples/model_compress/pruning/legacy/naive_prune_tf.py
similarity index 100%
rename from examples/model_compress/pruning/naive_prune_tf.py
rename to examples/model_compress/pruning/legacy/naive_prune_tf.py
diff --git a/examples/model_compress/pruning/naive_prune_torch.py b/examples/model_compress/pruning/legacy/naive_prune_torch.py
similarity index 100%
rename from examples/model_compress/pruning/naive_prune_torch.py
rename to examples/model_compress/pruning/legacy/naive_prune_torch.py
diff --git a/examples/model_compress/pruning/speedup/model_speedup.py b/examples/model_compress/pruning/legacy/speedup/model_speedup.py
similarity index 98%
rename from examples/model_compress/pruning/speedup/model_speedup.py
rename to examples/model_compress/pruning/legacy/speedup/model_speedup.py
index 61cdd6ad1..237f73e6f 100644
--- a/examples/model_compress/pruning/speedup/model_speedup.py
+++ b/examples/model_compress/pruning/legacy/speedup/model_speedup.py
@@ -5,7 +5,7 @@ import time
 import torch
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[3] / 'models'))
 from cifar10.vgg import VGG
 from mnist.lenet import LeNet
 
diff --git a/examples/model_compress/pruning/speedup/speedup_mobilnetv2.py b/examples/model_compress/pruning/legacy/speedup/speedup_mobilnetv2.py
similarity index 100%
rename from examples/model_compress/pruning/speedup/speedup_mobilnetv2.py
rename to examples/model_compress/pruning/legacy/speedup/speedup_mobilnetv2.py
diff --git a/examples/model_compress/pruning/speedup/speedup_nanodet.py b/examples/model_compress/pruning/legacy/speedup/speedup_nanodet.py
similarity index 100%
rename from examples/model_compress/pruning/speedup/speedup_nanodet.py
rename to examples/model_compress/pruning/legacy/speedup/speedup_nanodet.py
diff --git a/examples/model_compress/pruning/speedup/speedup_yolov3.py b/examples/model_compress/pruning/legacy/speedup/speedup_yolov3.py
similarity index 100%
rename from examples/model_compress/pruning/speedup/speedup_yolov3.py
rename to examples/model_compress/pruning/legacy/speedup/speedup_yolov3.py
diff --git a/examples/model_compress/pruning/transformers/run.sh b/examples/model_compress/pruning/legacy/transformers/run.sh
similarity index 98%
rename from examples/model_compress/pruning/transformers/run.sh
rename to examples/model_compress/pruning/legacy/transformers/run.sh
index af00f1cf8..f4f2df3e1 100755
--- a/examples/model_compress/pruning/transformers/run.sh
+++ b/examples/model_compress/pruning/legacy/transformers/run.sh
@@ -29,7 +29,7 @@ if [[ ${TASK_LIST[*]} =~ (^|[[:space:]])$TASK_NAME($|[[:space:]]) ]]; then
 	   --ranking_criterion $RANKING_CRITERION \
 	   --num_iterations $NUM_ITERATIONS \
 	   --epochs_per_iteration $EPOCHS_PER_ITERATION \
-	   --speed_up \
+	   --speedup \
 	   --model_name $PRETRAINED_MODEL \
 	   --task_name $TASK_NAME \
 	   --max_length $MAX_LENGTH \
diff --git a/examples/model_compress/pruning/transformers/transformer_pruning.py b/examples/model_compress/pruning/legacy/transformers/transformer_pruning.py
similarity index 98%
rename from examples/model_compress/pruning/transformers/transformer_pruning.py
rename to examples/model_compress/pruning/legacy/transformers/transformer_pruning.py
index e71906a9e..304050c55 100644
--- a/examples/model_compress/pruning/transformers/transformer_pruning.py
+++ b/examples/model_compress/pruning/legacy/transformers/transformer_pruning.py
@@ -9,7 +9,7 @@ import torch
 from torch.utils.data.dataloader import DataLoader
 from tqdm.auto import tqdm
 
-from nni.compression.pytorch.utils.counter import count_flops_params
+from nni.compression.pytorch.utils import count_flops_params
 from nni.algorithms.compression.pytorch.pruning import TransformerHeadPruner
 
 import datasets
@@ -53,8 +53,8 @@ def parse_args():
     parser.add_argument("--epochs_per_iteration", type=int, default=1,
                         help="Epochs to finetune before the next pruning iteration "
                              "(only effective if num_iterations > 1).")
-    parser.add_argument("--speed_up", action="store_true", default=False,
-                        help="Whether to speed-up the pruned model")
+    parser.add_argument("--speedup", action="store_true", default=False,
+                        help="Whether to speedup the pruned model")
 
     # parameters for model training; no need to change them for running examples
     parser.add_argument("--max_length", type=int, default=128,
@@ -338,8 +338,8 @@ def main():
     # Currently, speeding up Transformers through NNI ModelSpeedup is not supported because of shape inference issues.
     # However, if you are using the transformers library, you can use the following workaround:
     # The following code gets the head pruning decisions from the pruner and calls the _prune_heads() function
-    # implemented in models from the transformers library to speed up the model.
-    if args.speed_up:
+    # implemented in models from the transformers library to speedup the model.
+    if args.speedup:
         speedup_rules = {}
         for group_idx, group in enumerate(pruner.attention_name_groups):
             # get the layer index
diff --git a/examples/model_compress/pruning/v2/level_pruning_torch.py b/examples/model_compress/pruning/level_pruning_torch.py
similarity index 94%
rename from examples/model_compress/pruning/v2/level_pruning_torch.py
rename to examples/model_compress/pruning/level_pruning_torch.py
index 1df8f66ab..7ee9ab914 100644
--- a/examples/model_compress/pruning/v2/level_pruning_torch.py
+++ b/examples/model_compress/pruning/level_pruning_torch.py
@@ -4,7 +4,7 @@
 '''
 NNI example for supported level pruning algorithm.
 In this example, we show the end-to-end pruning process: pre-training -> pruning -> fine-tuning.
-Note that pruners use masks to simulate the real pruning. In order to obtain a real compressed model, model speed up is required.
+Note that pruners use masks to simulate the real pruning. In order to obtain a real compressed model, model speedup is required.
 
 '''
 import argparse
@@ -14,11 +14,11 @@ import torch
 from torchvision import datasets, transforms
 from torch.optim.lr_scheduler import MultiStepLR
 
-from nni.compression.pytorch.utils.counter import count_flops_params
-from nni.algorithms.compression.v2.pytorch.pruning.basic_pruner import LevelPruner
+from nni.compression.pytorch.utils import count_flops_params
+from nni.compression.pytorch.pruning import LevelPruner
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[1] / 'models'))
 from cifar10.vgg import VGG
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
diff --git a/examples/model_compress/pruning/v2/movement_pruning_glue.py b/examples/model_compress/pruning/movement_pruning_glue.py
similarity index 90%
rename from examples/model_compress/pruning/v2/movement_pruning_glue.py
rename to examples/model_compress/pruning/movement_pruning_glue.py
index ae0418d62..6af023ce6 100644
--- a/examples/model_compress/pruning/v2/movement_pruning_glue.py
+++ b/examples/model_compress/pruning/movement_pruning_glue.py
@@ -1,4 +1,5 @@
 import functools
+import time
 from tqdm import tqdm
 
 import torch
@@ -14,7 +15,7 @@ from transformers import (
 )
 
 import nni
-from nni.algorithms.compression.v2.pytorch.pruning import MovementPruner
+from nni.compression.pytorch.pruning import MovementPruner
 
 
 task_to_keys = {
@@ -31,7 +32,7 @@ task_to_keys = {
 
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
-gradient_accumulation_steps = 16
+gradient_accumulation_steps = 8
 
 # a fake criterion because huggingface output already has loss
 def criterion(input, target):
@@ -40,7 +41,7 @@ def criterion(input, target):
 def trainer(model, optimizer, criterion, train_dataloader):
     model.train()
     counter = 0
-    for batch in tqdm(train_dataloader):
+    for batch in (train_dataloader):
         counter += 1
         batch.to(device)
         optimizer.zero_grad()
@@ -51,12 +52,14 @@ def trainer(model, optimizer, criterion, train_dataloader):
         loss.backward()
         if counter % gradient_accumulation_steps == 0 or counter == len(train_dataloader):
             optimizer.step()
-        if counter % 16000 == 0:
+        if counter % 800 == 0:
+            print('[{}]: {}'.format(time.asctime(time.localtime(time.time())), counter))
+        if counter % 8000 == 0:
             print('Step {}: {}'.format(counter // gradient_accumulation_steps, evaluator(model, metric, is_regression, validate_dataloader)))
 
 def evaluator(model, metric, is_regression, eval_dataloader):
     model.eval()
-    for batch in tqdm(eval_dataloader):
+    for batch in (eval_dataloader):
         batch.to(device)
         outputs = model(**batch)
         predictions = outputs.logits.argmax(dim=-1) if not is_regression else outputs.logits.squeeze()
@@ -70,8 +73,8 @@ if __name__ == '__main__':
     task_name = 'mnli'
     is_regression = False
     num_labels = 1 if is_regression else (3 if task_name == 'mnli' else 2)
-    train_batch_size = 8
-    eval_batch_size = 8
+    train_batch_size = 4
+    eval_batch_size = 4
 
     set_seed(1024)
 
@@ -113,7 +116,7 @@ if __name__ == '__main__':
     # make sure you have used nni.trace to wrap the optimizer class before initialize
     traced_optimizer = nni.trace(Adam)(model.parameters(), lr=2e-5)
     pruner = MovementPruner(model, config_list, p_trainer, traced_optimizer, criterion, training_epochs=10,
-                            warm_up_step=3000, cool_down_beginning_step=27000)
+                            warm_up_step=12272, cool_down_beginning_step=110448)
 
     _, masks = pruner.compress()
     pruner.show_pruned_weights()
diff --git a/examples/model_compress/pruning/v2/norm_pruning_torch.py b/examples/model_compress/pruning/norm_pruning_torch.py
similarity index 94%
rename from examples/model_compress/pruning/v2/norm_pruning_torch.py
rename to examples/model_compress/pruning/norm_pruning_torch.py
index a87510e84..f876fd328 100644
--- a/examples/model_compress/pruning/v2/norm_pruning_torch.py
+++ b/examples/model_compress/pruning/norm_pruning_torch.py
@@ -4,7 +4,7 @@
 '''
 NNI example for supported l1norm and l2norm pruning algorithms.
 In this example, we show the end-to-end pruning process: pre-training -> pruning -> fine-tuning.
-Note that pruners use masks to simulate the real pruning. In order to obtain a real compressed model, model speed up is required.
+Note that pruners use masks to simulate the real pruning. In order to obtain a real compressed model, model speedup is required.
 
 '''
 import argparse
@@ -15,11 +15,11 @@ from torchvision import datasets, transforms
 from torch.optim.lr_scheduler import MultiStepLR
 
 from nni.compression.pytorch import ModelSpeedup
-from nni.compression.pytorch.utils.counter import count_flops_params
-from nni.algorithms.compression.v2.pytorch.pruning.basic_pruner import L1NormPruner, L2NormPruner
+from nni.compression.pytorch.utils import count_flops_params
+from nni.compression.pytorch.pruning import L1NormPruner, L2NormPruner
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[1] / 'models'))
 from cifar10.vgg import VGG
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
diff --git a/examples/model_compress/pruning/v2/scheduler_torch.py b/examples/model_compress/pruning/scheduler_torch.py
similarity index 92%
rename from examples/model_compress/pruning/v2/scheduler_torch.py
rename to examples/model_compress/pruning/scheduler_torch.py
index 711e651cb..5ef3a7484 100644
--- a/examples/model_compress/pruning/v2/scheduler_torch.py
+++ b/examples/model_compress/pruning/scheduler_torch.py
@@ -4,12 +4,12 @@ from tqdm import tqdm
 import torch
 from torchvision import datasets, transforms
 
-from nni.algorithms.compression.v2.pytorch.pruning import L1NormPruner
+from nni.compression.pytorch.pruning import L1NormPruner
 from nni.algorithms.compression.v2.pytorch.pruning.tools import AGPTaskGenerator
 from nni.algorithms.compression.v2.pytorch.pruning.basic_scheduler import PruningScheduler
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[1] / 'models'))
 from cifar10.vgg import VGG
 
 
@@ -92,8 +92,8 @@ if __name__ == '__main__':
     # if you just want to keep the final result as the best result, you can pass evaluator as None.
     # or the result with the highest score (given by evaluator) will be the best result.
 
-    # scheduler = PruningScheduler(pruner, task_generator, finetuner=finetuner, speed_up=True, dummy_input=dummy_input, evaluator=evaluator)
-    scheduler = PruningScheduler(pruner, task_generator, finetuner=finetuner, speed_up=True, dummy_input=dummy_input, evaluator=None, reset_weight=False)
+    # scheduler = PruningScheduler(pruner, task_generator, finetuner=finetuner, speedup=True, dummy_input=dummy_input, evaluator=evaluator)
+    scheduler = PruningScheduler(pruner, task_generator, finetuner=finetuner, speedup=True, dummy_input=dummy_input, evaluator=None, reset_weight=False)
 
     scheduler.compress()
 
diff --git a/examples/model_compress/pruning/v2/simple_pruning_torch.py b/examples/model_compress/pruning/simple_pruning_torch.py
similarity index 92%
rename from examples/model_compress/pruning/v2/simple_pruning_torch.py
rename to examples/model_compress/pruning/simple_pruning_torch.py
index 8871d9583..08dafe12e 100644
--- a/examples/model_compress/pruning/v2/simple_pruning_torch.py
+++ b/examples/model_compress/pruning/simple_pruning_torch.py
@@ -4,11 +4,11 @@ from tqdm import tqdm
 import torch
 from torchvision import datasets, transforms
 
-from nni.algorithms.compression.v2.pytorch.pruning import L1NormPruner
+from nni.compression.pytorch.pruning import L1NormPruner
 from nni.compression.pytorch.speedup import ModelSpeedup
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[1] / 'models'))
 from cifar10.vgg import VGG
 
 
@@ -77,12 +77,12 @@ if __name__ == '__main__':
     pruner._unwrap_model()
     ModelSpeedup(model, dummy_input=torch.rand(10, 3, 32, 32).to(device), masks_file=masks).speedup_model()
 
-    print('\nThe accuracy after speed up:')
+    print('\nThe accuracy after speedup:')
     evaluator(model)
 
     # Need a new optimizer due to the modules in model will be replaced during speedup.
     optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)
-    print('\nFinetune the model after speed up:')
+    print('\nFinetune the model after speedup:')
     for i in range(5):
         trainer(model, optimizer, criterion, i)
         evaluator(model)
diff --git a/examples/model_compress/pruning/v2/simulated_anealing_pruning_torch.py b/examples/model_compress/pruning/simulated_anealing_pruning_torch.py
similarity index 96%
rename from examples/model_compress/pruning/v2/simulated_anealing_pruning_torch.py
rename to examples/model_compress/pruning/simulated_anealing_pruning_torch.py
index 8b33736f9..121b0f024 100644
--- a/examples/model_compress/pruning/v2/simulated_anealing_pruning_torch.py
+++ b/examples/model_compress/pruning/simulated_anealing_pruning_torch.py
@@ -13,10 +13,10 @@ from tqdm import tqdm
 import torch
 from torchvision import datasets, transforms
 
-from nni.algorithms.compression.v2.pytorch.pruning import SimulatedAnnealingPruner
+from nni.compression.pytorch.pruning import SimulatedAnnealingPruner
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[1] / 'models'))
 from cifar10.vgg import VGG
 
 
diff --git a/examples/model_compress/pruning/v2/slim_pruning_torch.py b/examples/model_compress/pruning/slim_pruning_torch.py
similarity index 95%
rename from examples/model_compress/pruning/v2/slim_pruning_torch.py
rename to examples/model_compress/pruning/slim_pruning_torch.py
index 33437ac10..b37d070b7 100644
--- a/examples/model_compress/pruning/v2/slim_pruning_torch.py
+++ b/examples/model_compress/pruning/slim_pruning_torch.py
@@ -4,7 +4,7 @@
 '''
 NNI example for supported slim pruning algorithms.
 In this example, we show the end-to-end pruning process: pre-training -> pruning -> speedup -> fine-tuning.
-Note that pruners use masks to simulate the real pruning. In order to obtain a real compressed model, model speed up is required.
+Note that pruners use masks to simulate the real pruning. In order to obtain a real compressed model, model speedup is required.
 
 '''
 import argparse
@@ -16,11 +16,11 @@ from torch.optim.lr_scheduler import MultiStepLR
 
 import nni
 from nni.compression.pytorch import ModelSpeedup
-from nni.compression.pytorch.utils.counter import count_flops_params
-from nni.algorithms.compression.v2.pytorch.pruning.basic_pruner import SlimPruner
+from nni.compression.pytorch.utils import count_flops_params
+from nni.compression.pytorch.pruning import SlimPruner
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[1] / 'models'))
 from cifar10.vgg import VGG
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
diff --git a/examples/model_compress/pruning/v2/taylorfo_pruning_torch.py b/examples/model_compress/pruning/taylorfo_pruning_torch.py
similarity index 94%
rename from examples/model_compress/pruning/v2/taylorfo_pruning_torch.py
rename to examples/model_compress/pruning/taylorfo_pruning_torch.py
index 59d1b17ef..9e579dbc2 100644
--- a/examples/model_compress/pruning/v2/taylorfo_pruning_torch.py
+++ b/examples/model_compress/pruning/taylorfo_pruning_torch.py
@@ -4,7 +4,7 @@
 '''
 NNI example for supported TaylorFOWeight pruning algorithms.
 In this example, we show the end-to-end pruning process: pre-training -> pruning -> fine-tuning.
-Note that pruners use masks to simulate the real pruning. In order to obtain a real compressed model, model speed up is required.
+Note that pruners use masks to simulate the real pruning. In order to obtain a real compressed model, model speedup is required.
 
 '''
 import argparse
@@ -16,11 +16,11 @@ from torch.optim.lr_scheduler import MultiStepLR
 
 import nni
 from nni.compression.pytorch import ModelSpeedup
-from nni.compression.pytorch.utils.counter import count_flops_params
-from nni.algorithms.compression.v2.pytorch.pruning.basic_pruner import TaylorFOWeightPruner
+from nni.compression.pytorch.utils import count_flops_params
+from nni.compression.pytorch.pruning import TaylorFOWeightPruner
 
 from pathlib import Path
-sys.path.append(str(Path(__file__).absolute().parents[2] / 'models'))
+sys.path.append(str(Path(__file__).absolute().parents[1] / 'models'))
 from cifar10.vgg import VGG
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
diff --git a/examples/nas/.gitignore b/examples/nas/.gitignore
index f05559065..2140f310a 100644
--- a/examples/nas/.gitignore
+++ b/examples/nas/.gitignore
@@ -8,3 +8,4 @@ _generated_model_*.py
 _generated_model
 generated
 lightning_logs
+model.onnx
diff --git a/examples/nas/multi-trial/mnasnet/base_mnasnet.py b/examples/nas/multi-trial/mnasnet/base_mnasnet.py
index f431812e3..c4db4c776 100644
--- a/examples/nas/multi-trial/mnasnet/base_mnasnet.py
+++ b/examples/nas/multi-trial/mnasnet/base_mnasnet.py
@@ -4,8 +4,8 @@ import warnings
 
 import torch
 import torch.nn as torch_nn
-from torchvision.models.utils import load_state_dict_from_url
 import torch.nn.functional as F
+from nni.retiarii import model_wrapper
 
 import sys
 from pathlib import Path
@@ -111,7 +111,7 @@ def _get_depths(depths, alpha):
     rather than down. """
     return [_round_to_multiple_of(depth * alpha, 8) for depth in depths]
 
-
+@model_wrapper
 class MNASNet(nn.Module):
     """ MNASNet, as described in https://arxiv.org/pdf/1807.11626.pdf. This
     implements the B1 variant of the model.
@@ -180,7 +180,7 @@ class MNASNet(nn.Module):
             nn.ReLU(inplace=True),
         ]
         self.layers = nn.Sequential(*layers)
-        self.classifier = nn.Sequential(nn.Dropout(p=dropout, inplace=True),
+        self.classifier = nn.Sequential(nn.Dropout(p=dropout),
                                         nn.Linear(1280, num_classes))
         self._initialize_weights()
         #self.for_test = 10
diff --git a/examples/nas/multi-trial/mnist/search.py b/examples/nas/multi-trial/mnist/search.py
index 25fa657f2..52d100749 100644
--- a/examples/nas/multi-trial/mnist/search.py
+++ b/examples/nas/multi-trial/mnist/search.py
@@ -107,6 +107,7 @@ def evaluate_model(model_cls):
 
     device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
 
+    model.to(device)
     for epoch in range(3):
         # train the model for one epoch
         train_epoch(model, device, train_loader, optimizer, epoch)
@@ -138,7 +139,7 @@ if __name__ == '__main__':
     # exp_config.execution_engine = 'base'
     # export_formatter = 'code'
 
-    exp.run(exp_config, 8081 + random.randint(0, 100))
+    exp.run(exp_config, 8080)
     print('Final model:')
     for model_code in exp.export_top_models(formatter=export_formatter):
         print(model_code)
diff --git a/examples/nas/oneshot/pfld/train.py b/examples/nas/oneshot/pfld/train.py
index d2530faf8..173ef14c8 100644
--- a/examples/nas/oneshot/pfld/train.py
+++ b/examples/nas/oneshot/pfld/train.py
@@ -57,6 +57,8 @@ def main(args):
 
     # the configuration for training control
     nas_config = NASConfig(
+        perf_metric=args.perf_metric,
+        lut_load=args.lut_load,
         model_dir=args.snapshot,
         nas_lr=args.theta_lr,
         mode=args.mode,
@@ -150,6 +152,15 @@ def main(args):
 
 
 def parse_args():
+    def str2bool(s):
+        if isinstance(s, bool):
+            return s
+        if s.lower() in ('yes', 'true', 't', 'y', '1'):
+            return True
+        if s.lower() in ('no', 'false', 'f', 'n', '0'):
+            return False
+        raise argparse.ArgumentTypeError('Boolean value expected.')
+
     """ Parse the user arguments. """
     parser = argparse.ArgumentParser(description="FBNet for PFLD")
     parser.add_argument("--dev_id", dest="dev_id", default="0", type=str)
@@ -174,6 +185,16 @@ def parse_args():
     )
     parser.add_argument("--train_batchsize", default=256, type=int)
     parser.add_argument("--val_batchsize", default=128, type=int)
+    parser.add_argument(
+        "--perf_metric", default="flops", type=str, choices=["flops", "latency"]
+    )
+    parser.add_argument(
+        "--lut_load", type=str2bool, default=False
+    )
+    parser.add_argument(
+        "--lut_load_format", default="json", type=str, choices=["json", "numpy"]
+    )
+
     args = parser.parse_args()
     args.snapshot = os.path.join(args.snapshot, 'supernet')
     args.log_file = os.path.join(args.snapshot, "{}.log".format('supernet'))
diff --git a/examples/nas/oneshot/spos/network.py b/examples/nas/oneshot/spos/network.py
index 5ea3ffd2e..92e25b292 100644
--- a/examples/nas/oneshot/spos/network.py
+++ b/examples/nas/oneshot/spos/network.py
@@ -8,10 +8,12 @@ import re
 import torch
 import nni.retiarii.nn.pytorch as nn
 from nni.retiarii.nn.pytorch import LayerChoice
+from nni.retiarii.serializer import model_wrapper
 
 from blocks import ShuffleNetBlock, ShuffleXceptionBlock
 
 
+@model_wrapper
 class ShuffleNetV2OneShot(nn.Module):
     block_keys = [
         'shufflenet_3x3',
diff --git a/examples/nas/oneshot/spos/search.py b/examples/nas/oneshot/spos/search.py
index a6896a3cd..b1d94be35 100644
--- a/examples/nas/oneshot/spos/search.py
+++ b/examples/nas/oneshot/spos/search.py
@@ -1,23 +1,114 @@
-# This file is to demo the usage of multi-trial NAS in the usage of SPOS search space.
-
-import click
+import time
 import json
-import nni.retiarii.evaluator.pytorch as pl
+import random
+import logging
+import argparse
+import numpy as np
+import torch
+import torchvision.transforms as transforms
+import torchvision.datasets as datasets
+
+import nni
+from nn_meter import load_latency_predictor
+import nni.retiarii.nn.pytorch as nn
 import nni.retiarii.strategy as strategy
-from nni.retiarii import serialize
+from nni.retiarii.evaluator.functional import FunctionalEvaluator
+from nni.retiarii.utils import original_state_dict_hooks
+from nni.retiarii.oneshot.pytorch.utils import AverageMeterGroup
 from nni.retiarii.experiment.pytorch import RetiariiExeConfig, RetiariiExperiment
-from torchvision import transforms
-from torchvision.datasets import CIFAR10
-from nn_meter import load_latency_predictor
 
-from network import ShuffleNetV2OneShot
-from utils import get_archchoice_by_model
+from network import ShuffleNetV2OneShot, load_and_parse_state_dict
+from utils import CrossEntropyLabelSmooth, accuracy, ToBGRTensor, get_archchoice_by_model
+
+logger = logging.getLogger("nni.spos.search")
+
+
+def retrain_bn(model, criterion, max_iters, log_freq, loader):
+    with torch.no_grad():
+        logger.info("Clear BN statistics...")
+        for m in model.modules():
+            if isinstance(m, nn.BatchNorm2d):
+                m.running_mean = torch.zeros_like(m.running_mean)
+                m.running_var = torch.ones_like(m.running_var)
+
+        logger.info("Train BN with training set (BN sanitize)...")
+        model.train()
+        meters = AverageMeterGroup()
+        for step in range(max_iters):
+            inputs, targets = next(iter(loader))
+            inputs, targets = inputs.to('cuda'), targets.to('cuda')
+            logits = model(inputs)
+            loss = criterion(logits, targets)
+            metrics = accuracy(logits, targets)
+            metrics["loss"] = loss.item()
+            meters.update(metrics)
+            if step % log_freq == 0 or step + 1 == max_iters:
+                logger.info("Train Step [%d/%d] %s", step + 1, max_iters, meters)
+
+
+def test_acc(model, criterion, log_freq, loader):
+    logger.info("Start testing...")
+    model.eval()
+    meters = AverageMeterGroup()
+    start_time = time.time()
+    with torch.no_grad():
+        for step, (inputs, targets) in enumerate(loader):
+            inputs, targets = inputs.to('cuda'), targets.to('cuda')
+            logits = model(inputs)
+            loss = criterion(logits, targets)
+            metrics = accuracy(logits, targets)
+            metrics["loss"] = loss.item()
+            meters.update(metrics)
+            if step % log_freq == 0 or step + 1 == len(loader):
+                logger.info("Valid Step [%d/%d] time %.3fs acc1 %.4f acc5 %.4f loss %.4f",
+                            step + 1, len(loader), time.time() - start_time,
+                            meters.acc1.avg, meters.acc5.avg, meters.loss.avg)
+    return meters.acc1.avg
+
+
+def evaluate_acc(class_cls, criterion, args):
+    model = class_cls()
+    with original_state_dict_hooks(model):
+        model.load_state_dict(load_and_parse_state_dict(args.checkpoint), strict=False)
+    model.cuda()
+
+    if args.spos_preprocessing:
+        train_trans = transforms.Compose([
+            transforms.RandomResizedCrop(224),
+            transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4),
+            transforms.RandomHorizontalFlip(0.5),
+            ToBGRTensor()
+        ])
+    else:
+        train_trans = transforms.Compose([
+            transforms.RandomResizedCrop(224),
+            transforms.ToTensor()
+        ])
+    val_trans = transforms.Compose([
+        transforms.RandomResizedCrop(224),
+        ToBGRTensor()
+        ])
+    train_dataset = datasets.ImageNet(args.imagenet_dir, split='train', transform=train_trans)
+    val_dataset = datasets.ImageNet(args.imagenet_dir, split='val', transform=val_trans)
+
+    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.train_batch_size, num_workers=args.workers, shuffle=True)
+    test_loader = torch.utils.data.DataLoader(val_dataset, batch_size=args.test_batch_size, num_workers=args.workers, shuffle=True)
+
+    acc_before = test_acc(model, criterion, args.log_frequency, test_loader)
+    nni.report_intermediate_result(acc_before)
+
+    retrain_bn(model, criterion, args.train_iters, args.log_frequency, train_loader)
+    acc = test_acc(model, criterion, args.log_frequency, test_loader)
+    assert isinstance(acc, float)
+    nni.report_intermediate_result(acc)
+    nni.report_final_result(acc)
 
 
 class LatencyFilter:
     def __init__(self, threshold, predictor, predictor_version=None, reverse=False):
         """
-        Filter the models according to predicted latency.
+        Filter the models according to predicted latency. If the predicted latency of the ir model is larger than
+        the given threshold, the ir model will be filtered and will not be considered as a searched architecture.
 
         Parameters
         ----------
@@ -37,42 +128,62 @@ class LatencyFilter:
         return latency < self.threshold
 
 
-@click.command()
-@click.option('--port', default=8081, help='On which port the experiment is run.')
-def _main(port):
-    base_model = ShuffleNetV2OneShot(32)
-    base_predictor = 'cortexA76cpu_tflite21'
-    transf = [
-        transforms.RandomCrop(32, padding=4),
-        transforms.RandomHorizontalFlip()
-    ]
-    normalize = [
-        transforms.ToTensor(),
-        transforms.Normalize([0.49139968, 0.48215827, 0.44653124], [0.24703233, 0.24348505, 0.26158768])
-    ]
-    # FIXME
-    # CIFAR10 is used here temporarily.
-    # Actually we should load weight from supernet and evaluate on imagenet.
-    train_dataset = serialize(CIFAR10, 'data', train=True, download=True, transform=transforms.Compose(transf + normalize))
-    test_dataset = serialize(CIFAR10, 'data', train=False, transform=transforms.Compose(normalize))
-
-    trainer = pl.Classification(train_dataloader=pl.DataLoader(train_dataset, batch_size=64),
-                                val_dataloaders=pl.DataLoader(test_dataset, batch_size=64),
-                                max_epochs=2, gpus=1)
-
-    simple_strategy = strategy.RegularizedEvolution(model_filter=LatencyFilter(threshold=100, predictor=base_predictor),
-                                                    sample_size=1, population_size=2, cycles=2)
-    exp = RetiariiExperiment(base_model, trainer, strategy=simple_strategy)
+def _main():
+    parser = argparse.ArgumentParser("SPOS Evolutional Search")
+    parser.add_argument("--port", type=int, default=8084)
+    parser.add_argument("--imagenet-dir", type=str, default="./data/imagenet")
+    parser.add_argument("--checkpoint", type=str, default="./data/checkpoint-150000.pth.tar")
+    parser.add_argument("--spos-preprocessing", action="store_true", default=False,
+                        help="When true, image values will range from 0 to 255 and use BGR "
+                             "(as in original repo).")
+    parser.add_argument("--seed", type=int, default=42)
+    parser.add_argument("--workers", type=int, default=6)
+    parser.add_argument("--train-batch-size", type=int, default=128)
+    parser.add_argument("--train-iters", type=int, default=200)
+    parser.add_argument("--test-batch-size", type=int, default=512)
+    parser.add_argument("--log-frequency", type=int, default=10)
+    parser.add_argument("--label-smoothing", type=float, default=0.1)
+    parser.add_argument("--evolution-sample-size", type=int, default=10)
+    parser.add_argument("--evolution-population-size", type=int, default=50)
+    parser.add_argument("--evolution-cycles", type=int, default=10)
+    parser.add_argument("--latency-filter", type=str, default=None,
+                        help="Apply latency filter by calling the name of the applied hardware.")
+    parser.add_argument("--latency-threshold", type=float, default=100)
+
+    args = parser.parse_args()
+
+    # use a fixed set of image will improve the performance
+    torch.manual_seed(args.seed)
+    torch.cuda.manual_seed_all(args.seed)
+    np.random.seed(args.seed)
+    random.seed(args.seed)
+    torch.backends.cudnn.deterministic = True
+
+    assert torch.cuda.is_available()
+
+    base_model = ShuffleNetV2OneShot()
+    criterion = CrossEntropyLabelSmooth(1000, args.label_smoothing)
+
+    if args.latency_filter:
+        latency_filter = LatencyFilter(threshold=args.latency_threshold, predictor=args.latency_filter)
+    else:
+        latency_filter = None
+
+    evaluator = FunctionalEvaluator(evaluate_acc, criterion=criterion, args=args)
+    evolution_strategy = strategy.RegularizedEvolution(
+        model_filter=latency_filter,
+        sample_size=args.evolution_sample_size, population_size=args.evolution_population_size, cycles=args.evolution_cycles)
+    exp = RetiariiExperiment(base_model, evaluator, strategy=evolution_strategy)
 
     exp_config = RetiariiExeConfig('local')
     exp_config.trial_concurrency = 2
-    # exp_config.max_trial_number = 2
     exp_config.trial_gpu_number = 1
+    exp_config.max_trial_number = args.evolution_cycles
     exp_config.training_service.use_active_gpu = False
     exp_config.execution_engine = 'base'
-    exp_config.dummy_input = [1, 3, 32, 32]
+    exp_config.dummy_input = [1, 3, 224, 224]
 
-    exp.run(exp_config, port)
+    exp.run(exp_config, args.port)
 
     print('Exported models:')
     for i, model in enumerate(exp.export_top_models(formatter='dict')):
@@ -80,6 +191,5 @@ def _main(port):
         with open(f'architecture_final_{i}.json', 'w') as f: 
             json.dump(get_archchoice_by_model(model), f, indent=4)
 
-
-if __name__ == '__main__':
+if __name__ == "__main__":
     _main()
diff --git a/examples/nas/oneshot/spos/supernet.py b/examples/nas/oneshot/spos/supernet.py
index 902d2a490..8f449d8c9 100644
--- a/examples/nas/oneshot/spos/supernet.py
+++ b/examples/nas/oneshot/spos/supernet.py
@@ -47,10 +47,7 @@ if __name__ == "__main__":
     if args.load_checkpoint:
         if not args.spos_preprocessing:
             logger.warning("You might want to use SPOS preprocessing if you are loading their checkpoints.")
-        # load state_dict and 
-        model_dict = model.state_dict()
-        model_dict.update(load_and_parse_state_dict())
-        model.load_state_dict(model_dict)
+        model.load_state_dict(load_and_parse_state_dict(), strict=False)
         logger.info(f'Model loaded from ./data/checkpoint-150000.pth.tar')
     model.cuda()
     if torch.cuda.device_count() > 1:  # exclude last gpu, saving for data preprocessing on gpu
diff --git a/examples/nas/oneshot/spos/utils.py b/examples/nas/oneshot/spos/utils.py
index a5800aa5b..cd2149f75 100644
--- a/examples/nas/oneshot/spos/utils.py
+++ b/examples/nas/oneshot/spos/utils.py
@@ -57,8 +57,13 @@ class ToBGRTensor(object):
 
 
 def get_archchoice_by_model(model):
+    """
+    For ``exp_config.execution_engine = 'base'``, the top model exported by ``exp.export_top_models`` is a dict with format as
+    ``"LayerChoice1": "layerchoice_LayerChoice1_0"``. However when loading architecture from ``fixed_arch``, the dict value is needed 
+    to be converted to int. This function removed "layerchoice" string in choice value to meet the requirement of ``fixed_arch``.
+    The output will be ``"LayerChoice1": "0"``.
+    """
     result = {}
     for k, v in model.items():
-        assert k in v
         result[k] = model[k].split("_")[-1]
     return result
diff --git a/examples/trials/ga_squad/requirements.txt b/examples/trials/ga_squad/requirements.txt
index 58f286f85..c5c58d4e2 100644
--- a/examples/trials/ga_squad/requirements.txt
+++ b/examples/trials/ga_squad/requirements.txt
@@ -1 +1 @@
-tensorflow==1.15.4
+tensorflow  # tested version: 1.15.4
diff --git a/examples/trials/network_morphism/requirements.txt b/examples/trials/network_morphism/requirements.txt
index e07ad3d7b..85578b36b 100644
--- a/examples/trials/network_morphism/requirements.txt
+++ b/examples/trials/network_morphism/requirements.txt
@@ -1,5 +1,5 @@
-numpy==1.18.5
-tensorflow==1.15.4
-torchvision==0.2.1
-Keras==2.3.1
-torch==0.4.1
+numpy  # tested version: 1.18.5
+tensorflow  # tested version: 1.15.4
+torchvision  # tested version: 0.2.1
+Keras  # tested version: 2.3.1
+torch  # tested version: 0.4.1
diff --git a/examples/tutorials/.gitignore b/examples/tutorials/.gitignore
new file mode 100644
index 000000000..679857366
--- /dev/null
+++ b/examples/tutorials/.gitignore
@@ -0,0 +1,3 @@
+data/
+log/
+*.onnx
\ No newline at end of file
diff --git a/examples/tutorials/hello_nas.py b/examples/tutorials/hello_nas.py
new file mode 100644
index 000000000..ca0b580aa
--- /dev/null
+++ b/examples/tutorials/hello_nas.py
@@ -0,0 +1,364 @@
+"""
+Hello, NAS!
+===========
+
+This is the 101 tutorial of Neural Architecture Search (NAS) on NNI.
+In this tutorial, we will search for a neural architecture on MNIST dataset with the help of NAS framework of NNI, i.e., *Retiarii*.
+We use multi-trial NAS as an example to show how to construct and explore a model space.
+
+There are mainly three crucial components for a neural architecture search task, namely,
+
+* Model search space that defines a set of models to explore.
+* A proper strategy as the method to explore this model space.
+* A model evaluator that reports the performance of every model in the space.
+
+Currently, PyTorch is the only supported framework by Retiarii, and we have only tested **PyTorch 1.7 to 1.10**.
+This tutorial assumes PyTorch context but it should also apply to other frameworks, which is in our future plan.
+
+Define your Model Space
+-----------------------
+
+Model space is defined by users to express a set of models that users want to explore, which contains potentially good-performing models.
+In this framework, a model space is defined with two parts: a base model and possible mutations on the base model.
+"""
+
+# %%
+#
+# Define Base Model
+# ^^^^^^^^^^^^^^^^^
+#
+# Defining a base model is almost the same as defining a PyTorch (or TensorFlow) model.
+# Usually, you only need to replace the code ``import torch.nn as nn`` with
+# ``import nni.retiarii.nn.pytorch as nn`` to use our wrapped PyTorch modules.
+#
+# Below is a very simple example of defining a base model.
+
+import torch
+import torch.nn.functional as F
+import nni.retiarii.nn.pytorch as nn
+from nni.retiarii import model_wrapper
+
+
+@model_wrapper      # this decorator should be put on the out most
+class Net(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv1 = nn.Conv2d(1, 32, 3, 1)
+        self.conv2 = nn.Conv2d(32, 64, 3, 1)
+        self.dropout1 = nn.Dropout(0.25)
+        self.dropout2 = nn.Dropout(0.5)
+        self.fc1 = nn.Linear(9216, 128)
+        self.fc2 = nn.Linear(128, 10)
+
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = F.max_pool2d(self.conv2(x), 2)
+        x = torch.flatten(self.dropout1(x), 1)
+        x = self.fc2(self.dropout2(F.relu(self.fc1(x))))
+        output = F.log_softmax(x, dim=1)
+        return output
+
+# %%
+# .. tip:: Always keep in mind that you should use ``import nni.retiarii.nn.pytorch as nn`` and :meth:`nni.retiarii.model_wrapper`.
+#          Many mistakes are a result of forgetting one of those.
+#          Also, please use ``torch.nn`` for submodules of ``nn.init``, e.g., ``torch.nn.init`` instead of ``nn.init``.
+#
+# Define Model Mutations
+# ^^^^^^^^^^^^^^^^^^^^^^
+#
+# A base model is only one concrete model not a model space. We provide :doc:`API and Primitives </nas/construct_space>`
+# for users to express how the base model can be mutated. That is, to build a model space which includes many models.
+#
+# Based on the above base model, we can define a model space as below.
+#
+# .. code-block:: diff
+#
+#   @model_wrapper
+#   class Net(nn.Module):
+#     def __init__(self):
+#       super().__init__()
+#       self.conv1 = nn.Conv2d(1, 32, 3, 1)
+#   -   self.conv2 = nn.Conv2d(32, 64, 3, 1)
+#   +   self.conv2 = nn.LayerChoice([
+#   +       nn.Conv2d(32, 64, 3, 1),
+#   +       DepthwiseSeparableConv(32, 64)
+#   +   ])
+#   -   self.dropout1 = nn.Dropout(0.25)
+#   +   self.dropout1 = nn.Dropout(nn.ValueChoice([0.25, 0.5, 0.75]))
+#       self.dropout2 = nn.Dropout(0.5)
+#   -   self.fc1 = nn.Linear(9216, 128)
+#   -   self.fc2 = nn.Linear(128, 10)
+#   +   feature = nn.ValueChoice([64, 128, 256])
+#   +   self.fc1 = nn.Linear(9216, feature)
+#   +   self.fc2 = nn.Linear(feature, 10)
+#
+#     def forward(self, x):
+#       x = F.relu(self.conv1(x))
+#       x = F.max_pool2d(self.conv2(x), 2)
+#       x = torch.flatten(self.dropout1(x), 1)
+#       x = self.fc2(self.dropout2(F.relu(self.fc1(x))))
+#       output = F.log_softmax(x, dim=1)
+#       return output
+#
+# This results in the following code:
+
+
+class DepthwiseSeparableConv(nn.Module):
+    def __init__(self, in_ch, out_ch):
+        super().__init__()
+        self.depthwise = nn.Conv2d(in_ch, in_ch, kernel_size=3, groups=in_ch)
+        self.pointwise = nn.Conv2d(in_ch, out_ch, kernel_size=1)
+
+    def forward(self, x):
+        return self.pointwise(self.depthwise(x))
+
+
+@model_wrapper
+class ModelSpace(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv1 = nn.Conv2d(1, 32, 3, 1)
+        # LayerChoice is used to select a layer between Conv2d and DwConv.
+        self.conv2 = nn.LayerChoice([
+            nn.Conv2d(32, 64, 3, 1),
+            DepthwiseSeparableConv(32, 64)
+        ])
+        # ValueChoice is used to select a dropout rate.
+        # ValueChoice can be used as parameter of modules wrapped in `nni.retiarii.nn.pytorch`
+        # or customized modules wrapped with `@basic_unit`.
+        self.dropout1 = nn.Dropout(nn.ValueChoice([0.25, 0.5, 0.75]))  # choose dropout rate from 0.25, 0.5 and 0.75
+        self.dropout2 = nn.Dropout(0.5)
+        feature = nn.ValueChoice([64, 128, 256])
+        self.fc1 = nn.Linear(9216, feature)
+        self.fc2 = nn.Linear(feature, 10)
+
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = F.max_pool2d(self.conv2(x), 2)
+        x = torch.flatten(self.dropout1(x), 1)
+        x = self.fc2(self.dropout2(F.relu(self.fc1(x))))
+        output = F.log_softmax(x, dim=1)
+        return output
+
+
+model_space = ModelSpace()
+model_space
+
+# %%
+# This example uses two mutation APIs,
+# :class:`nn.LayerChoice <nni.retiarii.nn.pytorch.LayerChoice>` and
+# :class:`nn.InputChoice <nni.retiarii.nn.pytorch.ValueChoice>`.
+# :class:`nn.LayerChoice <nni.retiarii.nn.pytorch.LayerChoice>`
+# takes a list of candidate modules (two in this example), one will be chosen for each sampled model.
+# It can be used like normal PyTorch module.
+# :class:`nn.InputChoice <nni.retiarii.nn.pytorch.ValueChoice>` takes a list of candidate values,
+# one will be chosen to take effect for each sampled model.
+#
+# More detailed API description and usage can be found :doc:`here </nas/construct_space>`.
+#
+# .. note::
+#
+#     We are actively enriching the mutation APIs, to facilitate easy construction of model space.
+#     If the currently supported mutation APIs cannot express your model space,
+#     please refer to :doc:`this doc </nas/mutator>` for customizing mutators.
+#
+# Explore the Defined Model Space
+# -------------------------------
+#
+# There are basically two exploration approaches: (1) search by evaluating each sampled model independently,
+# which is the search approach in :ref:`multi-trial NAS <multi-trial-nas>`
+# and (2) one-shot weight-sharing based search, which is used in one-shot NAS.
+# We demonstrate the first approach in this tutorial. Users can refer to :ref:`here <one-shot-nas>` for the second approach.
+#
+# First, users need to pick a proper exploration strategy to explore the defined model space.
+# Second, users need to pick or customize a model evaluator to evaluate the performance of each explored model.
+#
+# Pick an exploration strategy
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# Retiarii supports many :doc:`exploration strategies </nas/exploration_strategy>`.
+#
+# Simply choosing (i.e., instantiate) an exploration strategy as below.
+
+import nni.retiarii.strategy as strategy
+search_strategy = strategy.Random(dedup=True)  # dedup=False if deduplication is not wanted
+
+# %%
+# Pick or customize a model evaluator
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# In the exploration process, the exploration strategy repeatedly generates new models. A model evaluator is for training
+# and validating each generated model to obtain the model's performance.
+# The performance is sent to the exploration strategy for the strategy to generate better models.
+#
+# Retiarii has provided :doc:`built-in model evaluators </nas/evaluator>`, but to start with,
+# it is recommended to use :class:`FunctionalEvaluator <nni.retiarii.evaluator.FunctionalEvaluator>`,
+# that is, to wrap your own training and evaluation code with one single function.
+# This function should receive one single model class and uses :func:`nni.report_final_result` to report the final score of this model.
+#
+# An example here creates a simple evaluator that runs on MNIST dataset, trains for 2 epochs, and reports its validation accuracy.
+
+import nni
+
+from torchvision import transforms
+from torchvision.datasets import MNIST
+from torch.utils.data import DataLoader
+
+
+def train_epoch(model, device, train_loader, optimizer, epoch):
+    loss_fn = torch.nn.CrossEntropyLoss()
+    model.train()
+    for batch_idx, (data, target) in enumerate(train_loader):
+        data, target = data.to(device), target.to(device)
+        optimizer.zero_grad()
+        output = model(data)
+        loss = loss_fn(output, target)
+        loss.backward()
+        optimizer.step()
+        if batch_idx % 10 == 0:
+            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+                epoch, batch_idx * len(data), len(train_loader.dataset),
+                100. * batch_idx / len(train_loader), loss.item()))
+
+
+def test_epoch(model, device, test_loader):
+    model.eval()
+    test_loss = 0
+    correct = 0
+    with torch.no_grad():
+        for data, target in test_loader:
+            data, target = data.to(device), target.to(device)
+            output = model(data)
+            pred = output.argmax(dim=1, keepdim=True)
+            correct += pred.eq(target.view_as(pred)).sum().item()
+
+    test_loss /= len(test_loader.dataset)
+    accuracy = 100. * correct / len(test_loader.dataset)
+
+    print('\nTest set: Accuracy: {}/{} ({:.0f}%)\n'.format(
+          correct, len(test_loader.dataset), accuracy))
+
+    return accuracy
+
+
+def evaluate_model(model_cls):
+    # "model_cls" is a class, need to instantiate
+    model = model_cls()
+
+    device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+    model.to(device)
+
+    optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
+    transf = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
+    train_loader = DataLoader(MNIST('data/mnist', download=True, transform=transf), batch_size=64, shuffle=True)
+    test_loader = DataLoader(MNIST('data/mnist', download=True, train=False, transform=transf), batch_size=64)
+
+    for epoch in range(3):
+        # train the model for one epoch
+        train_epoch(model, device, train_loader, optimizer, epoch)
+        # test the model for one epoch
+        accuracy = test_epoch(model, device, test_loader)
+        # call report intermediate result. Result can be float or dict
+        nni.report_intermediate_result(accuracy)
+
+    # report final test result
+    nni.report_final_result(accuracy)
+
+
+# %%
+# Create the evaluator
+
+from nni.retiarii.evaluator import FunctionalEvaluator
+evaluator = FunctionalEvaluator(evaluate_model)
+
+# %%
+#
+# The ``train_epoch`` and ``test_epoch`` here can be any customized function,
+# where users can write their own training recipe.
+#
+# It is recommended that the ``evaluate_model`` here accepts no additional arguments other than ``model_cls``.
+# However, in the :doc:`advanced tutorial </nas/evaluator>`, we will show how to use additional arguments in case you actually need those.
+# In future, we will support mutation on the arguments of evaluators, which is commonly called "Hyper-parmeter tuning".
+#
+# Launch an Experiment
+# --------------------
+#
+# After all the above are prepared, it is time to start an experiment to do the model search. An example is shown below.
+
+from nni.retiarii.experiment.pytorch import RetiariiExperiment, RetiariiExeConfig
+exp = RetiariiExperiment(model_space, evaluator, [], search_strategy)
+exp_config = RetiariiExeConfig('local')
+exp_config.experiment_name = 'mnist_search'
+
+# %%
+# The following configurations are useful to control how many trials to run at most / at the same time.
+
+exp_config.max_trial_number = 4   # spawn 4 trials at most
+exp_config.trial_concurrency = 2  # will run two trials concurrently
+
+# %%
+# Remember to set the following config if you want to GPU.
+# ``use_active_gpu`` should be set true if you wish to use an occupied GPU (possibly running a GUI).
+
+exp_config.trial_gpu_number = 1
+exp_config.training_service.use_active_gpu = True
+
+# %%
+# Launch the experiment. The experiment should take several minutes to finish on a workstation with 2 GPUs.
+
+exp.run(exp_config, 8081)
+
+# %%
+# Users can also run Retiarii Experiment with :doc:`different training services </experiment/training_service/overview>`
+# besides ``local`` training service.
+#
+# Visualize the Experiment
+# ------------------------
+#
+# Users can visualize their experiment in the same way as visualizing a normal hyper-parameter tuning experiment.
+# For example, open ``localhost:8081`` in your browser, 8081 is the port that you set in ``exp.run``.
+# Please refer to :doc:`here </experiment/web_portal/web_portal>` for details.
+#
+# We support visualizing models with 3rd-party visualization engines (like `Netron <https://netron.app/>`__).
+# This can be used by clicking ``Visualization`` in detail panel for each trial.
+# Note that current visualization is based on `onnx <https://onnx.ai/>`__ ,
+# thus visualization is not feasible if the model cannot be exported into onnx.
+#
+# Built-in evaluators (e.g., Classification) will automatically export the model into a file.
+# For your own evaluator, you need to save your file into ``$NNI_OUTPUT_DIR/model.onnx`` to make this work.
+# For instance,
+
+import os
+from pathlib import Path
+
+
+def evaluate_model_with_visualization(model_cls):
+    model = model_cls()
+    # dump the model into an onnx
+    if 'NNI_OUTPUT_DIR' in os.environ:
+        dummy_input = torch.zeros(1, 3, 32, 32)
+        torch.onnx.export(model, (dummy_input, ),
+                          Path(os.environ['NNI_OUTPUT_DIR']) / 'model.onnx')
+    evaluate_model(model_cls)
+
+# %%
+# Relaunch the experiment, and a button is shown on Web portal.
+#
+# .. image:: ../../img/netron_entrance_webui.png
+#
+# Export Top Models
+# -----------------
+#
+# Users can export top models after the exploration is done using ``export_top_models``.
+
+for model_dict in exp.export_top_models(formatter='dict'):
+    print(model_dict)
+
+# %%
+# The output is ``json`` object which records the mutation actions of the top model.
+# If users want to output source code of the top model,
+# they can use :ref:`graph-based execution engine <graph-based-execution-engine>` for the experiment,
+# by simply adding the following two lines.
+
+exp_config.execution_engine = 'base'
+export_formatter = 'code'
diff --git a/examples/tutorials/hpo_nnictl/config.yaml b/examples/tutorials/hpo_nnictl/config.yaml
new file mode 100644
index 000000000..2761e58e0
--- /dev/null
+++ b/examples/tutorials/hpo_nnictl/config.yaml
@@ -0,0 +1,24 @@
+search_space:
+  features:
+    _type: choice
+    _value: [ 128, 256, 512, 1024 ]
+  lr:
+    _type: loguniform
+    _value: [ 0.0001, 0.1 ]
+  momentum:
+    _type: uniform
+    _value: [ 0, 1 ]
+
+trial_command: python model.py
+trial_code_directory: .
+
+trial_concurrency: 2
+max_trial_number: 10
+
+tuner:
+  name: TPE
+  class_args:
+    optimize_mode: maximize
+
+training_service:
+  platform: local
diff --git a/examples/tutorials/hpo_nnictl/model.py b/examples/tutorials/hpo_nnictl/model.py
new file mode 100644
index 000000000..1bb07bf14
--- /dev/null
+++ b/examples/tutorials/hpo_nnictl/model.py
@@ -0,0 +1,125 @@
+"""
+Port PyTorch Quickstart to NNI
+==============================
+This is a modified version of `PyTorch quickstart`_.
+
+It can be run directly and will have the exact same result as original version.
+
+Furthermore, it enables the ability of auto tuning with an NNI *experiment*, which will be detailed later.
+
+It is recommended to run this script directly first to verify the environment.
+
+There are 2 key differences from the original version:
+
+1. In `Get optimized hyperparameters`_ part, it receives generated hyperparameters.
+2. In `Train model and report accuracy`_ part, it reports accuracy metrics to NNI.
+
+.. _PyTorch quickstart: https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
+"""
+
+# %%
+import nni
+import torch
+from torch import nn
+from torch.utils.data import DataLoader
+from torchvision import datasets
+from torchvision.transforms import ToTensor
+
+# %%
+# Hyperparameters to be tuned
+# ---------------------------
+# These are the hyperparameters that will be tuned.
+params = {
+    'features': 512,
+    'lr': 0.001,
+    'momentum': 0,
+}
+
+# %%
+# Get optimized hyperparameters
+# -----------------------------
+# If run directly, :func:`nni.get_next_parameter` is a no-op and returns an empty dict.
+# But with an NNI *experiment*, it will receive optimized hyperparameters from tuning algorithm.
+optimized_params = nni.get_next_parameter()
+params.update(optimized_params)
+print(params)
+
+# %%
+# Load dataset
+# ------------
+training_data = datasets.FashionMNIST(root="data", train=True, download=True, transform=ToTensor())
+test_data = datasets.FashionMNIST(root="data", train=False, download=True, transform=ToTensor())
+
+batch_size = 64
+
+train_dataloader = DataLoader(training_data, batch_size=batch_size)
+test_dataloader = DataLoader(test_data, batch_size=batch_size)
+
+# %%
+# Build model with hyperparameters
+# --------------------------------
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Using {device} device")
+
+class NeuralNetwork(nn.Module):
+    def __init__(self):
+        super(NeuralNetwork, self).__init__()
+        self.flatten = nn.Flatten()
+        self.linear_relu_stack = nn.Sequential(
+            nn.Linear(28*28, params['features']),
+            nn.ReLU(),
+            nn.Linear(params['features'], params['features']),
+            nn.ReLU(),
+            nn.Linear(params['features'], 10)
+        )
+
+    def forward(self, x):
+        x = self.flatten(x)
+        logits = self.linear_relu_stack(x)
+        return logits
+
+model = NeuralNetwork().to(device)
+
+loss_fn = nn.CrossEntropyLoss()
+optimizer = torch.optim.SGD(model.parameters(), lr=params['lr'], momentum=params['momentum'])
+
+# %%
+# Define train and test
+# ---------------------
+def train(dataloader, model, loss_fn, optimizer):
+    size = len(dataloader.dataset)
+    model.train()
+    for batch, (X, y) in enumerate(dataloader):
+        X, y = X.to(device), y.to(device)
+        pred = model(X)
+        loss = loss_fn(pred, y)
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+def test(dataloader, model, loss_fn):
+    size = len(dataloader.dataset)
+    num_batches = len(dataloader)
+    model.eval()
+    test_loss, correct = 0, 0
+    with torch.no_grad():
+        for X, y in dataloader:
+            X, y = X.to(device), y.to(device)
+            pred = model(X)
+            test_loss += loss_fn(pred, y).item()
+            correct += (pred.argmax(1) == y).type(torch.float).sum().item()
+    test_loss /= num_batches
+    correct /= size
+    return correct
+
+# %%
+# Train model and report accuracy
+# -------------------------------
+# Report accuracy metrics to NNI so the tuning algorithm can suggest better hyperparameters.
+epochs = 5
+for t in range(epochs):
+    print(f"Epoch {t+1}\n-------------------------------")
+    train(train_dataloader, model, loss_fn, optimizer)
+    accuracy = test(test_dataloader, model, loss_fn)
+    nni.report_intermediate_result(accuracy)
+nni.report_final_result(accuracy)
diff --git a/examples/tutorials/hpo_nnictl/nnictl.rst b/examples/tutorials/hpo_nnictl/nnictl.rst
new file mode 100644
index 000000000..47bbf9574
--- /dev/null
+++ b/examples/tutorials/hpo_nnictl/nnictl.rst
@@ -0,0 +1,207 @@
+Run HPO Experiment with nnictl
+==============================
+
+This tutorial has exactly the same effect as :doc:`PyTorch quickstart <../hpo_quickstart_pytorch/main>`.
+
+Both tutorials optimize the model in `official PyTorch quickstart
+<https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html>`__ with auto-tuning,
+while this one manages the experiment with command line tool and YAML config file, instead of pure Python code.
+
+The tutorial consists of 4 steps: 
+
+1. Modify the model for auto-tuning.
+2. Define hyperparameters' search space.
+3. Create config file.
+4. Run the experiment.
+
+The first two steps are identical to quickstart.
+
+Step 1: Prepare the model
+-------------------------
+In first step, we need to prepare the model to be tuned.
+
+The model should be put in a separate script.
+It will be evaluated many times concurrently,
+and possibly will be trained on distributed platforms.
+
+In this tutorial, the model is defined in :doc:`model.py <model>`.
+
+In short, it is a PyTorch model with 3 additional API calls:
+
+1. Use :func:`nni.get_next_parameter` to fetch the hyperparameters to be evalutated.
+2. Use :func:`nni.report_intermediate_result` to report per-epoch accuracy metrics.
+3. Use :func:`nni.report_final_result` to report final accuracy.
+
+Please understand the model code before continue to next step.
+
+Step 2: Define search space
+---------------------------
+In model code, we have prepared 3 hyperparameters to be tuned:
+*features*, *lr*, and *momentum*.
+
+Here we need to define their *search space* so the tuning algorithm can sample them in desired range.
+
+Assuming we have following prior knowledge for these hyperparameters:
+
+1. *features* should be one of 128, 256, 512, 1024.
+2. *lr* should be a float between 0.0001 and 0.1, and it follows exponential distribution.
+3. *momentum* should be a float between 0 and 1.
+
+In NNI, the space of *features* is called ``choice``;
+the space of *lr* is called ``loguniform``;
+and the space of *momentum* is called ``uniform``.
+You may have noticed, these names are derived from ``numpy.random``.
+
+For full specification of search space, check :doc:`the reference </hpo/search_space>`.
+
+Now we can define the search space as follow:
+
+.. code-block:: yaml
+
+    search_space:
+      features:
+        _type: choice
+        _value: [ 128, 256, 512, 1024 ]
+      lr:
+        _type: loguniform
+        _value: [ 0.0001, 0.1 ]
+      momentum:
+        _type: uniform
+        _value: [ 0, 1 ]
+
+Step 3: Configure the experiment
+--------------------------------
+NNI uses an *experiment* to manage the HPO process.
+The *experiment config* defines how to train the models and how to explore the search space.
+
+In this tutorial we use a YAML file ``config.yaml`` to define the experiment.
+
+Configure trial code
+^^^^^^^^^^^^^^^^^^^^
+In NNI evaluation of each hyperparameter set is called a *trial*.
+So the model script is called *trial code*.
+
+.. code-block:: yaml
+
+    trial_command: python model.py
+    trial_code_directory: .
+
+When ``trial_code_directory`` is a relative path, it relates to the config file.
+So in this case we need to put ``config.yaml`` and ``model.py`` in the same directory.
+
+.. attention::
+
+    The rules for resolving relative path are different in YAML config file and :doc:`Python experiment API </reference/experiment>`.
+    In Python experiment API relative paths are relative to current working directory.
+
+Configure how many trials to run
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Here we evaluate 10 sets of hyperparameters in total, and concurrently evaluate 2 sets at a time.
+
+.. code-block:: yaml
+
+    max_trial_number: 10
+    trial_concurrency: 2
+
+You may also set ``max_experiment_duration = '1h'`` to limit running time.
+
+If neither ``max_trial_number`` nor ``max_experiment_duration`` are set,
+the experiment will run forever until you stop it.
+
+.. note::
+
+    ``max_trial_number`` is set to 10 here for a fast example.
+    In real world it should be set to a larger number.
+    With default config TPE tuner requires 20 trials to warm up.
+
+
+Configure tuning algorithm
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+Here we use :doc:`TPE tuner </hpo/tuners>`.
+
+.. code-block:: yaml
+
+    name: TPE
+    class_args:
+      optimize_mode: maximize
+
+Configure training service
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+In this tutorial we use *local* mode,
+which means models will be trained on local machine, without using any special training platform.
+
+.. code-block:: yaml
+
+    training_service:
+      platform: local
+
+Wrap up
+^^^^^^^
+
+The full content of ``config.yaml`` is as follow:
+
+.. code-block:: yaml
+
+    search_space:
+      features:
+        _type: choice
+        _value: [ 128, 256, 512, 1024 ]
+      lr:
+        _type: loguniform
+        _value: [ 0.0001, 0.1 ]
+      momentum:
+        _type: uniform
+        _value: [ 0, 1 ]
+    
+    trial_command: python model.py
+    trial_code_directory: .
+
+    trial_concurrency: 2
+    max_trial_number: 10
+    
+    tuner:
+      name: TPE
+      class_args:
+        optimize_mode: maximize
+    
+    training_service:
+      platform: local
+
+Step 4: Run the experiment
+--------------------------
+Now the experiment is ready. Launch it with ``nnictl create`` command:
+
+.. code-block:: bash
+
+    $ nnictl create --config config.yaml --port 8080
+
+You can use the web portal to view experiment status: http://localhost:8080.
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    [2022-04-01 12:00:00] Creating experiment, Experiment ID: p43ny6ew
+    [2022-04-01 12:00:00] Starting web server...
+    [2022-04-01 12:00:01] Setting up...
+    [2022-04-01 12:00:01] Web portal URLs: http://127.0.0.1:8080 http://192.168.1.1:8080
+    [2022-04-01 12:00:01] To stop experiment run "nnictl stop p43ny6ew" or "nnictl stop --all"
+    [2022-04-01 12:00:01] Reference: https://nni.readthedocs.io/en/stable/reference/nnictl.html
+
+When the experiment is done, use ``nnictl stop`` command to stop it.
+
+.. code-block:: bash
+
+    $ nnictl stop p43ny6ew
+
+.. rst-class:: sphx-glr-script-out
+
+ Out:
+
+ .. code-block:: none
+
+    INFO:  Stopping experiment 7u8yg9zw
+    INFO:  Stop experiment success.
diff --git a/examples/tutorials/hpo_quickstart_pytorch/README.rst b/examples/tutorials/hpo_quickstart_pytorch/README.rst
new file mode 100644
index 000000000..e69de29bb
diff --git a/examples/tutorials/hpo_quickstart_pytorch/main.py b/examples/tutorials/hpo_quickstart_pytorch/main.py
new file mode 100644
index 000000000..648e03be8
--- /dev/null
+++ b/examples/tutorials/hpo_quickstart_pytorch/main.py
@@ -0,0 +1,155 @@
+"""
+HPO Quickstart with PyTorch
+===========================
+This tutorial optimizes the model in `official PyTorch quickstart`_ with auto-tuning.
+
+The tutorial consists of 4 steps: 
+
+1. Modify the model for auto-tuning.
+2. Define hyperparameters' search space.
+3. Configure the experiment.
+4. Run the experiment.
+
+.. _official PyTorch quickstart: https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
+"""
+
+# %%
+# Step 1: Prepare the model
+# -------------------------
+# In first step, we need to prepare the model to be tuned.
+#
+# The model should be put in a separate script.
+# It will be evaluated many times concurrently,
+# and possibly will be trained on distributed platforms.
+#
+# In this tutorial, the model is defined in :doc:`model.py <model>`.
+#
+# In short, it is a PyTorch model with 3 additional API calls:
+#
+# 1. Use :func:`nni.get_next_parameter` to fetch the hyperparameters to be evalutated.
+# 2. Use :func:`nni.report_intermediate_result` to report per-epoch accuracy metrics.
+# 3. Use :func:`nni.report_final_result` to report final accuracy.
+#
+# Please understand the model code before continue to next step.
+
+# %%
+# Step 2: Define search space
+# ---------------------------
+# In model code, we have prepared 3 hyperparameters to be tuned:
+# *features*, *lr*, and *momentum*.
+#
+# Here we need to define their *search space* so the tuning algorithm can sample them in desired range.
+#
+# Assuming we have following prior knowledge for these hyperparameters:
+#
+# 1. *features* should be one of 128, 256, 512, 1024.
+# 2. *lr* should be a float between 0.0001 and 0.1, and it follows exponential distribution.
+# 3. *momentum* should be a float between 0 and 1.
+#
+# In NNI, the space of *features* is called ``choice``;
+# the space of *lr* is called ``loguniform``;
+# and the space of *momentum* is called ``uniform``.
+# You may have noticed, these names are derived from ``numpy.random``.
+#
+# For full specification of search space, check :doc:`the reference </hpo/search_space>`.
+#
+# Now we can define the search space as follow:
+
+search_space = {
+    'features': {'_type': 'choice', '_value': [128, 256, 512, 1024]},
+    'lr': {'_type': 'loguniform', '_value': [0.0001, 0.1]},
+    'momentum': {'_type': 'uniform', '_value': [0, 1]},
+}
+
+# %%
+# Step 3: Configure the experiment
+# --------------------------------
+# NNI uses an *experiment* to manage the HPO process.
+# The *experiment config* defines how to train the models and how to explore the search space.
+# 
+# In this tutorial we use a *local* mode experiment,
+# which means models will be trained on local machine, without using any special training platform.
+from nni.experiment import Experiment
+experiment = Experiment('local')
+
+# %%
+# Now we start to configure the experiment.
+#
+# Configure trial code
+# ^^^^^^^^^^^^^^^^^^^^
+# In NNI evaluation of each hyperparameter set is called a *trial*.
+# So the model script is called *trial code*.
+experiment.config.trial_command = 'python model.py'
+experiment.config.trial_code_directory = '.'
+# %%
+# When ``trial_code_directory`` is a relative path, it relates to current working directory.
+# To run ``main.py`` in a different path, you can set trial code directory to ``Path(__file__).parent``.
+# (`__file__ <https://docs.python.org/3.10/reference/datamodel.html#index-43>`__
+# is only available in standard Python, not in Jupyter Notebook.)
+#
+# .. attention::
+#
+#     If you are using Linux system without Conda,
+#     you may need to change ``"python model.py"`` to ``"python3 model.py"``.
+
+# %%
+# Configure search space
+# ^^^^^^^^^^^^^^^^^^^^^^
+experiment.config.search_space = search_space
+
+# %%
+# Configure tuning algorithm
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^
+# Here we use :doc:`TPE tuner </hpo/tuners>`.
+experiment.config.tuner.name = 'TPE'
+experiment.config.tuner.class_args['optimize_mode'] = 'maximize'
+
+# %%
+# Configure how many trials to run
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# Here we evaluate 10 sets of hyperparameters in total, and concurrently evaluate 2 sets at a time.
+experiment.config.max_trial_number = 10
+experiment.config.trial_concurrency = 2
+# %%
+# You may also set ``max_experiment_duration = '1h'`` to limit running time.
+#
+# If neither ``max_trial_number`` nor ``max_experiment_duration`` are set,
+# the experiment will run forever until you press Ctrl-C.
+#
+# .. note::
+#
+#     ``max_trial_number`` is set to 10 here for a fast example.
+#     In real world it should be set to a larger number.
+#     With default config TPE tuner requires 20 trials to warm up.
+
+# %%
+# Step 4: Run the experiment
+# --------------------------
+# Now the experiment is ready. Choose a port and launch it. (Here we use port 8080.)
+#
+# You can use the web portal to view experiment status: http://localhost:8080.
+experiment.run(8080)
+
+# %%
+# After the experiment is done
+# ----------------------------
+# Everything is done and it is safe to exit now. The following are optional.
+#
+# If you are using standard Python instead of Jupyter Notebook,
+# you can add ``input()`` or ``signal.pause()`` to prevent Python from exiting,
+# allowing you to view the web portal after the experiment is done.
+
+# input('Press enter to quit')
+experiment.stop()
+
+# %%
+# :meth:`nni.experiment.Experiment.stop` is automatically invoked when Python exits,
+# so it can be omitted in your code.
+#
+# After the experiment is stopped, you can run :meth:`nni.experiment.Experiment.view` to restart web portal.
+#
+# .. tip::
+#
+#     This example uses :doc:`Python API </reference/experiment>` to create experiment.
+#
+#     You can also create and manage experiments with :doc:`command line tool <../hpo_nnictl/nnictl>`.
diff --git a/examples/tutorials/hpo_quickstart_pytorch/model.py b/examples/tutorials/hpo_quickstart_pytorch/model.py
new file mode 100644
index 000000000..1bb07bf14
--- /dev/null
+++ b/examples/tutorials/hpo_quickstart_pytorch/model.py
@@ -0,0 +1,125 @@
+"""
+Port PyTorch Quickstart to NNI
+==============================
+This is a modified version of `PyTorch quickstart`_.
+
+It can be run directly and will have the exact same result as original version.
+
+Furthermore, it enables the ability of auto tuning with an NNI *experiment*, which will be detailed later.
+
+It is recommended to run this script directly first to verify the environment.
+
+There are 2 key differences from the original version:
+
+1. In `Get optimized hyperparameters`_ part, it receives generated hyperparameters.
+2. In `Train model and report accuracy`_ part, it reports accuracy metrics to NNI.
+
+.. _PyTorch quickstart: https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
+"""
+
+# %%
+import nni
+import torch
+from torch import nn
+from torch.utils.data import DataLoader
+from torchvision import datasets
+from torchvision.transforms import ToTensor
+
+# %%
+# Hyperparameters to be tuned
+# ---------------------------
+# These are the hyperparameters that will be tuned.
+params = {
+    'features': 512,
+    'lr': 0.001,
+    'momentum': 0,
+}
+
+# %%
+# Get optimized hyperparameters
+# -----------------------------
+# If run directly, :func:`nni.get_next_parameter` is a no-op and returns an empty dict.
+# But with an NNI *experiment*, it will receive optimized hyperparameters from tuning algorithm.
+optimized_params = nni.get_next_parameter()
+params.update(optimized_params)
+print(params)
+
+# %%
+# Load dataset
+# ------------
+training_data = datasets.FashionMNIST(root="data", train=True, download=True, transform=ToTensor())
+test_data = datasets.FashionMNIST(root="data", train=False, download=True, transform=ToTensor())
+
+batch_size = 64
+
+train_dataloader = DataLoader(training_data, batch_size=batch_size)
+test_dataloader = DataLoader(test_data, batch_size=batch_size)
+
+# %%
+# Build model with hyperparameters
+# --------------------------------
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Using {device} device")
+
+class NeuralNetwork(nn.Module):
+    def __init__(self):
+        super(NeuralNetwork, self).__init__()
+        self.flatten = nn.Flatten()
+        self.linear_relu_stack = nn.Sequential(
+            nn.Linear(28*28, params['features']),
+            nn.ReLU(),
+            nn.Linear(params['features'], params['features']),
+            nn.ReLU(),
+            nn.Linear(params['features'], 10)
+        )
+
+    def forward(self, x):
+        x = self.flatten(x)
+        logits = self.linear_relu_stack(x)
+        return logits
+
+model = NeuralNetwork().to(device)
+
+loss_fn = nn.CrossEntropyLoss()
+optimizer = torch.optim.SGD(model.parameters(), lr=params['lr'], momentum=params['momentum'])
+
+# %%
+# Define train and test
+# ---------------------
+def train(dataloader, model, loss_fn, optimizer):
+    size = len(dataloader.dataset)
+    model.train()
+    for batch, (X, y) in enumerate(dataloader):
+        X, y = X.to(device), y.to(device)
+        pred = model(X)
+        loss = loss_fn(pred, y)
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+def test(dataloader, model, loss_fn):
+    size = len(dataloader.dataset)
+    num_batches = len(dataloader)
+    model.eval()
+    test_loss, correct = 0, 0
+    with torch.no_grad():
+        for X, y in dataloader:
+            X, y = X.to(device), y.to(device)
+            pred = model(X)
+            test_loss += loss_fn(pred, y).item()
+            correct += (pred.argmax(1) == y).type(torch.float).sum().item()
+    test_loss /= num_batches
+    correct /= size
+    return correct
+
+# %%
+# Train model and report accuracy
+# -------------------------------
+# Report accuracy metrics to NNI so the tuning algorithm can suggest better hyperparameters.
+epochs = 5
+for t in range(epochs):
+    print(f"Epoch {t+1}\n-------------------------------")
+    train(train_dataloader, model, loss_fn, optimizer)
+    accuracy = test(test_dataloader, model, loss_fn)
+    nni.report_intermediate_result(accuracy)
+nni.report_final_result(accuracy)
diff --git a/examples/tutorials/hpo_quickstart_tensorflow/README.rst b/examples/tutorials/hpo_quickstart_tensorflow/README.rst
new file mode 100644
index 000000000..e69de29bb
diff --git a/examples/tutorials/hpo_quickstart_tensorflow/main.py b/examples/tutorials/hpo_quickstart_tensorflow/main.py
new file mode 100644
index 000000000..9413ba5e3
--- /dev/null
+++ b/examples/tutorials/hpo_quickstart_tensorflow/main.py
@@ -0,0 +1,157 @@
+"""
+HPO Quickstart with TensorFlow
+==============================
+This tutorial optimizes the model in `official TensorFlow quickstart`_ with auto-tuning.
+
+The tutorial consists of 4 steps: 
+
+1. Modify the model for auto-tuning.
+2. Define hyperparameters' search space.
+3. Configure the experiment.
+4. Run the experiment.
+
+.. _official TensorFlow quickstart: https://www.tensorflow.org/tutorials/quickstart/beginner
+"""
+
+# %%
+# Step 1: Prepare the model
+# -------------------------
+# In first step, we need to prepare the model to be tuned.
+#
+# The model should be put in a separate script.
+# It will be evaluated many times concurrently,
+# and possibly will be trained on distributed platforms.
+#
+# In this tutorial, the model is defined in :doc:`model.py <model>`.
+#
+# In short, it is a TensorFlow model with 3 additional API calls:
+#
+# 1. Use :func:`nni.get_next_parameter` to fetch the hyperparameters to be evalutated.
+# 2. Use :func:`nni.report_intermediate_result` to report per-epoch accuracy metrics.
+# 3. Use :func:`nni.report_final_result` to report final accuracy.
+#
+# Please understand the model code before continue to next step.
+
+# %%
+# Step 2: Define search space
+# ---------------------------
+# In model code, we have prepared 4 hyperparameters to be tuned:
+# *dense_units*, *activation_type*, *dropout_rate*, and *learning_rate*.
+#
+# Here we need to define their *search space* so the tuning algorithm can sample them in desired range.
+#
+# Assuming we have following prior knowledge for these hyperparameters:
+#
+# 1. *dense_units* should be one of 64, 128, 256.
+# 2. *activation_type* should be one of 'relu', 'tanh', 'swish', or None.
+# 3. *dropout_rate* should be a float between 0.5 and 0.9.
+# 4. *learning_rate* should be a float between 0.0001 and 0.1, and it follows exponential distribution.
+#
+# In NNI, the space of *dense_units* and *activation_type* is called ``choice``;
+# the space of *dropout_rate* is called ``uniform``;
+# and the space of *learning_rate* is called ``loguniform``.
+# You may have noticed, these names are derived from ``numpy.random``.
+#
+# For full specification of search space, check :doc:`the reference </hpo/search_space>`.
+#
+# Now we can define the search space as follow:
+
+search_space = {
+    'dense_units': {'_type': 'choice', '_value': [64, 128, 256]},
+    'activation_type': {'_type': 'choice', '_value': ['relu', 'tanh', 'swish', None]},
+    'dropout_rate': {'_type': 'uniform', '_value': [0.5, 0.9]},
+    'learning_rate': {'_type': 'loguniform', '_value': [0.0001, 0.1]},
+}
+
+# %%
+# Step 3: Configure the experiment
+# --------------------------------
+# NNI uses an *experiment* to manage the HPO process.
+# The *experiment config* defines how to train the models and how to explore the search space.
+# 
+# In this tutorial we use a *local* mode experiment,
+# which means models will be trained on local machine, without using any special training platform.
+from nni.experiment import Experiment
+experiment = Experiment('local')
+
+# %%
+# Now we start to configure the experiment.
+#
+# Configure trial code
+# ^^^^^^^^^^^^^^^^^^^^
+# In NNI evaluation of each hyperparameter set is called a *trial*.
+# So the model script is called *trial code*.
+experiment.config.trial_command = 'python model.py'
+experiment.config.trial_code_directory = '.'
+# %%
+# When ``trial_code_directory`` is a relative path, it relates to current working directory.
+# To run ``main.py`` in a different path, you can set trial code directory to ``Path(__file__).parent``.
+# (`__file__ <https://docs.python.org/3.10/reference/datamodel.html#index-43>`__
+# is only available in standard Python, not in Jupyter Notebook.)
+#
+# .. attention::
+#
+#     If you are using Linux system without Conda,
+#     you may need to change ``"python model.py"`` to ``"python3 model.py"``.
+
+# %%
+# Configure search space
+# ^^^^^^^^^^^^^^^^^^^^^^
+experiment.config.search_space = search_space
+
+# %%
+# Configure tuning algorithm
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^
+# Here we use :doc:`TPE tuner </hpo/tuners>`.
+experiment.config.tuner.name = 'TPE'
+experiment.config.tuner.class_args['optimize_mode'] = 'maximize'
+
+# %%
+# Configure how many trials to run
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# Here we evaluate 10 sets of hyperparameters in total, and concurrently evaluate 2 sets at a time.
+experiment.config.max_trial_number = 10
+experiment.config.trial_concurrency = 2
+# %%
+# You may also set ``max_experiment_duration = '1h'`` to limit running time.
+#
+# If neither ``max_trial_number`` nor ``max_experiment_duration`` are set,
+# the experiment will run forever until you press Ctrl-C.
+#
+# .. note::
+#
+#     ``max_trial_number`` is set to 10 here for a fast example.
+#     In real world it should be set to a larger number.
+#     With default config TPE tuner requires 20 trials to warm up.
+
+# %%
+# Step 4: Run the experiment
+# --------------------------
+# Now the experiment is ready. Choose a port and launch it. (Here we use port 8080.)
+#
+# You can use the web portal to view experiment status: http://localhost:8080.
+experiment.run(8080)
+
+# %%
+# After the experiment is done
+# ----------------------------
+# Everything is done and it is safe to exit now. The following are optional.
+#
+# If you are using standard Python instead of Jupyter Notebook,
+# you can add ``input()`` or ``signal.pause()`` to prevent Python from exiting,
+# allowing you to view the web portal after the experiment is done.
+
+# input('Press enter to quit')
+experiment.stop()
+
+# %%
+# :meth:`nni.experiment.Experiment.stop` is automatically invoked when Python exits,
+# so it can be omitted in your code.
+#
+# After the experiment is stopped, you can run :meth:`nni.experiment.Experiment.view` to restart web portal.
+#
+# .. tip::
+#
+#     This example uses :doc:`Python API </reference/experiment>` to create experiment.
+#
+#     You can also create and manage experiments with :doc:`command line tool <../hpo_nnictl/nnictl>`.
diff --git a/examples/tutorials/hpo_quickstart_tensorflow/model.py b/examples/tutorials/hpo_quickstart_tensorflow/model.py
new file mode 100644
index 000000000..c81c280dd
--- /dev/null
+++ b/examples/tutorials/hpo_quickstart_tensorflow/model.py
@@ -0,0 +1,88 @@
+"""
+Port TensorFlow Quickstart to NNI
+=================================
+This is a modified version of `TensorFlow quickstart`_.
+
+It can be run directly and will have the exact same result as original version.
+
+Furthermore, it enables the ability of auto tuning with an NNI *experiment*, which will be detailed later.
+
+It is recommended to run this script directly first to verify the environment.
+
+There are 3 key differences from the original version:
+
+1. In `Get optimized hyperparameters`_ part, it receives generated hyperparameters.
+2. In `(Optional) Report intermediate results`_ part, it reports per-epoch accuracy metrics.
+3. In `Report final result`_ part, it reports final accuracy.
+
+.. _TensorFlow quickstart: https://www.tensorflow.org/tutorials/quickstart/beginner
+"""
+
+# %%
+import nni
+import tensorflow as tf
+
+# %%
+# Hyperparameters to be tuned
+# ---------------------------
+# These are the hyperparameters that will be tuned later.
+params = {
+    'dense_units': 128,
+    'activation_type': 'relu',
+    'dropout_rate': 0.2,
+    'learning_rate': 0.001,
+}
+
+# %%
+# Get optimized hyperparameters
+# -----------------------------
+# If run directly, :func:`nni.get_next_parameter` is a no-op and returns an empty dict.
+# But with an NNI *experiment*, it will receive optimized hyperparameters from tuning algorithm.
+optimized_params = nni.get_next_parameter()
+params.update(optimized_params)
+print(params)
+
+# %%
+# Load dataset
+# ------------
+mnist = tf.keras.datasets.mnist
+
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
+x_train, x_test = x_train / 255.0, x_test / 255.0
+
+# %%
+# Build model with hyperparameters
+# --------------------------------
+model = tf.keras.models.Sequential([
+    tf.keras.layers.Flatten(input_shape=(28, 28)),
+    tf.keras.layers.Dense(params['dense_units'], activation=params['activation_type']),
+    tf.keras.layers.Dropout(params['dropout_rate']),
+    tf.keras.layers.Dense(10)
+])
+
+adam = tf.keras.optimizers.Adam(learning_rate=params['learning_rate'])
+loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
+model.compile(optimizer=adam, loss=loss_fn, metrics=['accuracy'])
+
+# %%
+# (Optional) Report intermediate results
+# --------------------------------------
+# The callback reports per-epoch accuracy to show learning curve in the web portal.
+# You can also leverage the metrics for early stopping with :doc:`NNI assessors </hpo/assessors>`.
+#
+# This part can be safely skipped and the experiment will work fine.
+callback = tf.keras.callbacks.LambdaCallback(
+    on_epoch_end = lambda epoch, logs: nni.report_intermediate_result(logs['accuracy'])
+)
+
+# %%
+# Train and evluate the model
+# ---------------------------
+model.fit(x_train, y_train, epochs=5, verbose=2, callbacks=[callback])
+loss, accuracy = model.evaluate(x_test, y_test, verbose=2)
+
+# %%
+# Report final result
+# -------------------
+# Report final accuracy to NNI so the tuning algorithm can suggest better hyperparameters.
+nni.report_final_result(accuracy)
diff --git a/examples/tutorials/nas_quick_start_mnist.py b/examples/tutorials/nas_quick_start_mnist.py
deleted file mode 100644
index 7e5827823..000000000
--- a/examples/tutorials/nas_quick_start_mnist.py
+++ /dev/null
@@ -1,11 +0,0 @@
-"""
-Get started with NAS on MNIST
-=============================
-"""
-
-# %%
-a = (1, 2, 3)
-a
-
-# %%
-print('hello')
diff --git a/examples/tutorials/nasbench_as_dataset.py b/examples/tutorials/nasbench_as_dataset.py
new file mode 100644
index 000000000..91cb37757
--- /dev/null
+++ b/examples/tutorials/nasbench_as_dataset.py
@@ -0,0 +1,185 @@
+"""
+Use NAS Benchmarks as Datasets
+==============================
+
+In this tutorial, we show how to use NAS Benchmarks as datasets.
+For research purposes we sometimes desire to query the benchmarks for architecture accuracies,
+rather than train them one by one from scratch.
+NNI has provided query tools so that users can easily get the retrieve the data in NAS benchmarks.
+"""
+
+# %%
+# Prerequisites
+# -------------
+# This tutorial assumes that you have already prepared your NAS benchmarks under cache directory
+# (by default, ``~/.cache/nni/nasbenchmark``).
+# If you haven't, please follow the data preparation guide in :doc:`/nas/benchmarks`.
+#
+# As a result, the directory should look like:
+
+import os
+os.listdir(os.path.expanduser('~/.cache/nni/nasbenchmark'))
+
+# %%
+import pprint
+
+from nni.nas.benchmarks.nasbench101 import query_nb101_trial_stats
+from nni.nas.benchmarks.nasbench201 import query_nb201_trial_stats
+from nni.nas.benchmarks.nds import query_nds_trial_stats
+
+# %%
+# NAS-Bench-101
+# -------------
+#
+# Use the following architecture as an example:
+#
+# .. image:: ../../img/nas-bench-101-example.png
+
+arch = {
+    'op1': 'conv3x3-bn-relu',
+    'op2': 'maxpool3x3',
+    'op3': 'conv3x3-bn-relu',
+    'op4': 'conv3x3-bn-relu',
+    'op5': 'conv1x1-bn-relu',
+    'input1': [0],
+    'input2': [1],
+    'input3': [2],
+    'input4': [0],
+    'input5': [0, 3, 4],
+    'input6': [2, 5]
+}
+for t in query_nb101_trial_stats(arch, 108, include_intermediates=True):
+    pprint.pprint(t)
+
+# %%
+# An architecture of NAS-Bench-101 could be trained more than once.
+# Each element of the returned generator is a dict which contains one of the training results of this trial config
+# (architecture + hyper-parameters) including train/valid/test accuracy,
+# training time, number of epochs, etc. The results of NAS-Bench-201 and NDS follow similar formats.
+#
+# NAS-Bench-201
+# -------------
+#
+# Use the following architecture as an example:
+#
+# .. image:: ../../img/nas-bench-201-example.png
+
+arch = {
+    '0_1': 'avg_pool_3x3',
+    '0_2': 'conv_1x1',
+    '1_2': 'skip_connect',
+    '0_3': 'conv_1x1',
+    '1_3': 'skip_connect',
+    '2_3': 'skip_connect'
+}
+for t in query_nb201_trial_stats(arch, 200, 'cifar100'):
+    pprint.pprint(t)
+
+# %%
+# Intermediate results are also available.
+
+for t in query_nb201_trial_stats(arch, None, 'imagenet16-120', include_intermediates=True):
+    print(t['config'])
+    print('Intermediates:', len(t['intermediates']))
+
+# %%
+# NDS
+# ---
+#
+# Use the following architecture as an example:
+#
+# .. image:: ../../img/nas-bench-nds-example.png
+#
+# Here, ``bot_muls``, ``ds``, ``num_gs``, ``ss`` and ``ws`` stand for "bottleneck multipliers",
+# "depths", "number of groups", "strides" and "widths" respectively.
+
+# %%
+model_spec = {
+    'bot_muls': [0.0, 0.25, 0.25, 0.25],
+    'ds': [1, 16, 1, 4],
+    'num_gs': [1, 2, 1, 2],
+    'ss': [1, 1, 2, 2],
+    'ws': [16, 64, 128, 16]
+}
+
+# %%
+# Use none as a wildcard.
+for t in query_nds_trial_stats('residual_bottleneck', None, None, model_spec, None, 'cifar10'):
+    pprint.pprint(t)
+
+# %%
+model_spec = {
+    'bot_muls': [0.0, 0.25, 0.25, 0.25],
+    'ds': [1, 16, 1, 4],
+    'num_gs': [1, 2, 1, 2],
+    'ss': [1, 1, 2, 2],
+    'ws': [16, 64, 128, 16]
+}
+for t in query_nds_trial_stats('residual_bottleneck', None, None, model_spec, None, 'cifar10', include_intermediates=True):
+    pprint.pprint(t['intermediates'][:10])
+
+# %%
+model_spec = {'ds': [1, 12, 12, 12], 'ss': [1, 1, 2, 2], 'ws': [16, 24, 24, 40]}
+for t in query_nds_trial_stats('residual_basic', 'resnet', 'random', model_spec, {}, 'cifar10'):
+    pprint.pprint(t)
+
+# %%
+# Get the first one.
+pprint.pprint(next(query_nds_trial_stats('vanilla', None, None, None, None, None)))
+
+# %%
+# Count number.
+model_spec = {'num_nodes_normal': 5, 'num_nodes_reduce': 5, 'depth': 12, 'width': 32, 'aux': False, 'drop_prob': 0.0}
+cell_spec = {
+    'normal_0_op_x': 'avg_pool_3x3',
+    'normal_0_input_x': 0,
+    'normal_0_op_y': 'conv_7x1_1x7',
+    'normal_0_input_y': 1,
+    'normal_1_op_x': 'sep_conv_3x3',
+    'normal_1_input_x': 2,
+    'normal_1_op_y': 'sep_conv_5x5',
+    'normal_1_input_y': 0,
+    'normal_2_op_x': 'dil_sep_conv_3x3',
+    'normal_2_input_x': 2,
+    'normal_2_op_y': 'dil_sep_conv_3x3',
+    'normal_2_input_y': 2,
+    'normal_3_op_x': 'skip_connect',
+    'normal_3_input_x': 4,
+    'normal_3_op_y': 'dil_sep_conv_3x3',
+    'normal_3_input_y': 4,
+    'normal_4_op_x': 'conv_7x1_1x7',
+    'normal_4_input_x': 2,
+    'normal_4_op_y': 'sep_conv_3x3',
+    'normal_4_input_y': 4,
+    'normal_concat': [3, 5, 6],
+    'reduce_0_op_x': 'avg_pool_3x3',
+    'reduce_0_input_x': 0,
+    'reduce_0_op_y': 'dil_sep_conv_3x3',
+    'reduce_0_input_y': 1,
+    'reduce_1_op_x': 'sep_conv_3x3',
+    'reduce_1_input_x': 0,
+    'reduce_1_op_y': 'sep_conv_3x3',
+    'reduce_1_input_y': 0,
+    'reduce_2_op_x': 'skip_connect',
+    'reduce_2_input_x': 2,
+    'reduce_2_op_y': 'sep_conv_7x7',
+    'reduce_2_input_y': 0,
+    'reduce_3_op_x': 'conv_7x1_1x7',
+    'reduce_3_input_x': 4,
+    'reduce_3_op_y': 'skip_connect',
+    'reduce_3_input_y': 4,
+    'reduce_4_op_x': 'conv_7x1_1x7',
+    'reduce_4_input_x': 0,
+    'reduce_4_op_y': 'conv_7x1_1x7',
+    'reduce_4_input_y': 5,
+    'reduce_concat': [3, 6]
+}
+
+for t in query_nds_trial_stats('nas_cell', None, None, model_spec, cell_spec, 'cifar10'):
+    assert t['config']['model_spec'] == model_spec
+    assert t['config']['cell_spec'] == cell_spec
+    pprint.pprint(t)
+
+# %%
+# Count number.
+print('NDS (amoeba) count:', len(list(query_nds_trial_stats(None, 'amoeba', None, None, None, None, None))))
diff --git a/examples/tutorials/nni_experiment.py b/examples/tutorials/nni_experiment.py
deleted file mode 100644
index f6619dd31..000000000
--- a/examples/tutorials/nni_experiment.py
+++ /dev/null
@@ -1,67 +0,0 @@
-"""
-Start and Manage a New Experiment
-=================================
-"""
-
-# %%
-# Configure Search Space
-# ----------------------
-
-search_space = {
-    "C": {"_type": "quniform", "_value": [0.1, 1, 0.1]},
-    "kernel": {"_type": "choice", "_value": ["linear", "rbf", "poly", "sigmoid"]},
-    "degree": {"_type": "choice", "_value": [1, 2, 3, 4]},
-    "gamma": {"_type": "quniform", "_value": [0.01, 0.1, 0.01]},
-    "coef0": {"_type": "quniform", "_value": [0.01, 0.1, 0.01]}
-}
-
-# %%
-# Configure Experiment
-# --------------------
-
-from nni.experiment import Experiment
-experiment = Experiment('local')
-experiment.config.experiment_name = 'Example'
-experiment.config.trial_concurrency = 2
-experiment.config.max_trial_number = 10
-experiment.config.search_space = search_space
-experiment.config.trial_command = 'python scripts/trial_sklearn.py'
-experiment.config.trial_code_directory = './'
-experiment.config.tuner.name = 'TPE'
-experiment.config.tuner.class_args['optimize_mode'] = 'maximize'
-experiment.config.training_service.use_active_gpu = True
-
-# %%
-# Start Experiment
-# ----------------
-experiment.start(8080)
-
-# %%
-# Experiment View & Control
-# -------------------------
-#
-# View the status of experiment.
-experiment.get_status()
-
-# %%
-# Wait until at least one trial finishes.
-import time
-
-for _ in range(10):
-    stats = experiment.get_job_statistics()
-    if any(stat['trialJobStatus'] == 'SUCCEEDED' for stat in stats):
-        break
-    time.sleep(10)
-
-# %%
-# Export the experiment data.
-experiment.export_data()
-
-# %%
-# Get metric of jobs
-experiment.get_job_metrics()
-
-# %%
-# Stop Experiment
-# ---------------
-experiment.stop()
diff --git a/examples/tutorials/pruning_customize.py b/examples/tutorials/pruning_customize.py
new file mode 100644
index 000000000..278b6fce6
--- /dev/null
+++ b/examples/tutorials/pruning_customize.py
@@ -0,0 +1,174 @@
+"""
+Customize Basic Pruner
+======================
+
+Users can easily customize a basic pruner in NNI. A large number of basic modules have been provided and can be reused.
+Follow the NNI pruning interface, users only need to focus on their creative parts without worrying about other regular modules.
+
+In this tutorial, we show how to customize a basic pruner.
+
+Concepts
+--------
+
+NNI abstracts the basic pruning process into three steps, collecting data, calculating metrics, allocating sparsity.
+Most pruning algorithms rely on a metric to decide where should be pruned. Using L1 norm pruner as an example,
+the first step is collecting model weights, the second step is calculating L1 norm for weight per output channel,
+the third step is ranking L1 norm metric and masking the output channels that have small L1 norm.
+
+In NNI basic pruner, these three step is implement as ``DataCollector``, ``MetricsCalculator`` and ``SparsityAllocator``.
+
+-   ``DataCollector``: This module take pruner as initialize parameter.
+    It will get the relevant information of the model from the pruner,
+    and sometimes it will also hook the model to get input, output or gradient of a layer or a tensor.
+    It can also patch optimizer if some special steps need to be executed before or after ``optimizer.step()``.
+
+-   ``MetricsCalculator``: This module will take the data collected from the ``DataCollector``,
+    then calculate the metrics. The metric shape is usually reduced from the data shape.
+    The ``dim`` taken by ``MetricsCalculator`` means which dimension will be kept after calculate metrics.
+    i.e., the collected data shape is (10, 20, 30), and the ``dim`` is 1, then the dimension-1 will be kept,
+    the output metrics shape should be (20,).
+
+-   ``SparsityAllocator``: This module take the metrics and generate the masks.
+    Different ``SparsityAllocator`` has different masks generation strategies.
+    A common and simple strategy is sorting the metrics' values and calculating a threshold according to the configured sparsity,
+    mask the positions which metric value smaller than the threshold.
+    The ``dim`` taken by ``SparsityAllocator`` means the metrics are for which dimension, the mask will be expanded to weight shape.
+    i.e., the metric shape is (20,), the corresponding layer weight shape is (20, 40), and the ``dim`` is 0.
+    ``SparsityAllocator`` will first generate a mask with shape (20,), then expand this mask to shape (20, 40).
+
+Simple Example: Customize a Block-L1NormPruner
+----------------------------------------------
+
+NNI already have L1NormPruner, but for the reason of reproducing the paper and reducing user configuration items,
+it only support pruning layer output channels. In this example, we will customize a pruner that supports block granularity for Linear.
+
+Note that you don't need to implement all these three kinds of tools for each time,
+NNI supports many predefined tools, and you can directly use these to customize your own pruner.
+This is a tutorial so we show how to define all these three kinds of pruning tools.
+
+Customize the pruning tools used by the pruner at first.
+"""
+
+import torch
+from nni.algorithms.compression.v2.pytorch.pruning.basic_pruner import BasicPruner
+from nni.algorithms.compression.v2.pytorch.pruning.tools import (
+    DataCollector,
+    MetricsCalculator,
+    SparsityAllocator
+)
+
+
+# This data collector collects weight in wrapped module as data.
+# The wrapped module is the module configured in pruner's config_list.
+# This implementation is similar as nni.algorithms.compression.v2.pytorch.pruning.tools.WeightDataCollector
+class WeightDataCollector(DataCollector):
+    def collect(self):
+        data = {}
+        # get_modules_wrapper will get all the wrapper in the compressor (pruner),
+        # it returns a dict with format {wrapper_name: wrapper},
+        # use wrapper.module to get the wrapped module.
+        for _, wrapper in self.compressor.get_modules_wrapper().items():
+            data[wrapper.name] = wrapper.module.weight.data
+        # return {wrapper_name: weight_data}
+        return data
+
+
+class BlockNormMetricsCalculator(MetricsCalculator):
+    def __init__(self, block_sparse_size):
+        # Because we will keep all dimension with block granularity, so fix ``dim=None``,
+        # means all dimensions will be kept.
+        super().__init__(dim=None, block_sparse_size=block_sparse_size)
+
+    def calculate_metrics(self, data):
+        data_length = len(self.block_sparse_size)
+        reduce_unfold_dims = list(range(data_length, 2 * data_length))
+
+        metrics = {}
+        for name, t in data.items():
+            # Unfold t as block size, and calculate L1 Norm for each block.
+            for dim, size in enumerate(self.block_sparse_size):
+                t = t.unfold(dim, size, size)
+            metrics[name] = t.norm(dim=reduce_unfold_dims, p=1)
+        # return {wrapper_name: block_metric}
+        return metrics
+
+
+# This implementation is similar as nni.algorithms.compression.v2.pytorch.pruning.tools.NormalSparsityAllocator
+class BlockSparsityAllocator(SparsityAllocator):
+    def __init__(self, pruner, block_sparse_size):
+        super().__init__(pruner, dim=None, block_sparse_size=block_sparse_size, continuous_mask=True)
+
+    def generate_sparsity(self, metrics):
+        masks = {}
+        for name, wrapper in self.pruner.get_modules_wrapper().items():
+            # wrapper.config['total_sparsity'] can get the configured sparsity ratio for this wrapped module
+            sparsity_rate = wrapper.config['total_sparsity']
+            # get metric for this wrapped module
+            metric = metrics[name]
+            # mask the metric with old mask, if the masked position need never recover,
+            # just keep this is ok if you are new in NNI pruning
+            if self.continuous_mask:
+                metric *= self._compress_mask(wrapper.weight_mask)
+            # convert sparsity ratio to prune number
+            prune_num = int(sparsity_rate * metric.numel())
+            # calculate the metric threshold
+            threshold = torch.topk(metric.view(-1), prune_num, largest=False)[0].max()
+            # generate mask, keep the metric positions that metric values greater than the threshold
+            mask = torch.gt(metric, threshold).type_as(metric)
+            # expand the mask to weight size, if the block is masked, this block will be filled with zeros,
+            # otherwise filled with ones
+            masks[name] = self._expand_mask(name, mask)
+            # merge the new mask with old mask, if the masked position need never recover,
+            # just keep this is ok if you are new in NNI pruning
+            if self.continuous_mask:
+                masks[name]['weight'] *= wrapper.weight_mask
+        return masks
+
+
+# %%
+# Customize the pruner.
+
+class BlockL1NormPruner(BasicPruner):
+    def __init__(self, model, config_list, block_sparse_size):
+        self.block_sparse_size = block_sparse_size
+        super().__init__(model, config_list)
+
+    # Implement reset_tools is enough for this pruner.
+    def reset_tools(self):
+        if self.data_collector is None:
+            self.data_collector = WeightDataCollector(self)
+        else:
+            self.data_collector.reset()
+        if self.metrics_calculator is None:
+            self.metrics_calculator = BlockNormMetricsCalculator(self.block_sparse_size)
+        if self.sparsity_allocator is None:
+            self.sparsity_allocator = BlockSparsityAllocator(self, self.block_sparse_size)
+
+
+# %%
+# Try this pruner.
+
+# Define a simple model.
+class TestModel(torch.nn.Module):
+    def __init__(self) -> None:
+        super().__init__()
+        self.fc1 = torch.nn.Linear(4, 8)
+        self.fc2 = torch.nn.Linear(8, 4)
+
+    def forward(self, x):
+        return self.fc2(self.fc1(x))
+
+model = TestModel()
+config_list = [{'op_types': ['Linear'], 'total_sparsity': 0.5}]
+# use 2x2 block
+_, masks = BlockL1NormPruner(model, config_list, [2, 2]).compress()
+
+# show the generated masks
+print('fc1 masks:\n', masks['fc1']['weight'])
+print('fc2 masks:\n', masks['fc2']['weight'])
+
+
+# %%
+# This time we successfully define a new pruner with pruning block granularity!
+# Note that we don't put validation logic in this example, like ``_validate_config_before_canonical``,
+# but for a robust implementation, we suggest you involve the validation logic.
diff --git a/examples/tutorials/pruning_quick_start_mnist.py b/examples/tutorials/pruning_quick_start_mnist.py
new file mode 100644
index 000000000..a2cee7d99
--- /dev/null
+++ b/examples/tutorials/pruning_quick_start_mnist.py
@@ -0,0 +1,109 @@
+"""
+Pruning Quickstart
+==================
+
+Model pruning is a technique to reduce the model size and computation by reducing model weight size or intermediate state size.
+There are three common practices for pruning a DNN model:
+
+#. Pre-training a model -> Pruning the model -> Fine-tuning the pruned model
+#. Pruning a model during training (i.e., pruning aware training) -> Fine-tuning the pruned model
+#. Pruning a model -> Training the pruned model from scratch
+
+NNI supports all of the above pruning practices by working on the key pruning stage.
+Following this tutorial for a quick look at how to use NNI to prune a model in a common practice.
+"""
+
+# %%
+# Preparation
+# -----------
+#
+# In this tutorial, we use a simple model and pre-trained on MNIST dataset.
+# If you are familiar with defining a model and training in pytorch, you can skip directly to `Pruning Model`_.
+
+import torch
+import torch.nn.functional as F
+from torch.optim import SGD
+
+from scripts.compression_mnist_model import TorchModel, trainer, evaluator, device
+
+# define the model
+model = TorchModel().to(device)
+
+# show the model structure, note that pruner will wrap the model layer.
+print(model)
+
+# %%
+
+# define the optimizer and criterion for pre-training
+
+optimizer = SGD(model.parameters(), 1e-2)
+criterion = F.nll_loss
+
+# pre-train and evaluate the model on MNIST dataset
+for epoch in range(3):
+    trainer(model, optimizer, criterion)
+    evaluator(model)
+
+# %%
+# Pruning Model
+# -------------
+#
+# Using L1NormPruner to prune the model and generate the masks.
+# Usually, a pruner requires original model and ``config_list`` as its inputs.
+# Detailed about how to write ``config_list`` please refer :doc:`compression config specification <../compression/compression_config_list>`.
+#
+# The following `config_list` means all layers whose type is `Linear` or `Conv2d` will be pruned,
+# except the layer named `fc3`, because `fc3` is `exclude`.
+# The final sparsity ratio for each layer is 50%. The layer named `fc3` will not be pruned.
+
+config_list = [{
+    'sparsity_per_layer': 0.5,
+    'op_types': ['Linear', 'Conv2d']
+}, {
+    'exclude': True,
+    'op_names': ['fc3']
+}]
+
+# %%
+# Pruners usually require `model` and `config_list` as input arguments.
+
+from nni.compression.pytorch.pruning import L1NormPruner
+pruner = L1NormPruner(model, config_list)
+
+# show the wrapped model structure, `PrunerModuleWrapper` have wrapped the layers that configured in the config_list.
+print(model)
+
+# %%
+
+# compress the model and generate the masks
+_, masks = pruner.compress()
+# show the masks sparsity
+for name, mask in masks.items():
+    print(name, ' sparsity : ', '{:.2}'.format(mask['weight'].sum() / mask['weight'].numel()))
+
+# %%
+# Speedup the original model with masks, note that `ModelSpeedup` requires an unwrapped model.
+# The model becomes smaller after speedup,
+# and reaches a higher sparsity ratio because `ModelSpeedup` will propagate the masks across layers.
+
+# need to unwrap the model, if the model is wrapped before speedup
+pruner._unwrap_model()
+
+# speedup the model, for more information about speedup, please refer :doc:`pruning_speedup`.
+from nni.compression.pytorch.speedup import ModelSpeedup
+
+ModelSpeedup(model, torch.rand(3, 1, 28, 28).to(device), masks).speedup_model()
+
+# %%
+# the model will become real smaller after speedup
+print(model)
+
+# %%
+# Fine-tuning Compacted Model
+# ---------------------------
+# Note that if the model has been sped up, you need to re-initialize a new optimizer for fine-tuning.
+# Because speedup will replace the masked big layers with dense small ones.
+
+optimizer = SGD(model.parameters(), 1e-2)
+for epoch in range(3):
+    trainer(model, optimizer, criterion)
diff --git a/examples/tutorials/pruning_speedup.py b/examples/tutorials/pruning_speedup.py
new file mode 100644
index 000000000..7cb4fd456
--- /dev/null
+++ b/examples/tutorials/pruning_speedup.py
@@ -0,0 +1,239 @@
+"""
+Speedup Model with Mask
+========================
+
+Introduction
+------------
+
+Pruning algorithms usually use weight masks to simulate the real pruning. Masks can be used
+to check model performance of a specific pruning (or sparsity), but there is no real speedup.
+Since model speedup is the ultimate goal of model pruning, we try to provide a tool to users
+to convert a model to a smaller one based on user provided masks (the masks come from the
+pruning algorithms).
+
+There are two types of pruning. One is fine-grained pruning, it does not change the shape of weights,
+and input/output tensors. Sparse kernel is required to speedup a fine-grained pruned layer.
+The other is coarse-grained pruning (e.g., channels), shape of weights and input/output tensors usually change due to such pruning.
+To speedup this kind of pruning, there is no need to use sparse kernel, just replace the pruned layer with smaller one.
+Since the support of sparse kernels in community is limited,
+we only support the speedup of coarse-grained pruning and leave the support of fine-grained pruning in future.
+
+Design and Implementation
+-------------------------
+
+To speedup a model, the pruned layers should be replaced, either replaced with smaller layer for coarse-grained mask,
+or replaced with sparse kernel for fine-grained mask. Coarse-grained mask usually changes the shape of weights or input/output tensors,
+thus, we should do shape inference to check are there other unpruned layers should be replaced as well due to shape change.
+Therefore, in our design, there are two main steps: first, do shape inference to find out all the modules that should be replaced;
+second, replace the modules.
+
+The first step requires topology (i.e., connections) of the model, we use ``jit.trace`` to obtain the model graph for PyTorch.
+The new shape of module is auto-inference by NNI, the unchanged parts of outputs during forward and inputs during backward are prepared for reduct.
+For each type of module, we should prepare a function for module replacement.
+The module replacement function returns a newly created module which is smaller.
+
+Usage
+-----
+
+"""
+
+# %%
+# Generate a mask for the model at first.
+# We usually use a NNI pruner to generate the masks then use ``ModelSpeedup`` to compact the model.
+# But in fact ``ModelSpeedup`` is a relatively independent tool, so you can use it independently.
+
+import torch
+from scripts.compression_mnist_model import TorchModel, device
+
+model = TorchModel().to(device)
+# masks = {layer_name: {'weight': weight_mask, 'bias': bias_mask}}
+conv1_mask = torch.ones_like(model.conv1.weight.data)
+# mask the first three output channels in conv1
+conv1_mask[0: 3] = 0
+masks = {'conv1': {'weight': conv1_mask}}
+
+# %%
+# Show the original model structure.
+print(model)
+
+# %%
+# Roughly test the original model inference speed.
+import time
+start = time.time()
+model(torch.rand(128, 1, 28, 28).to(device))
+print('Original Model - Elapsed Time : ', time.time() - start)
+
+# %%
+# Speedup the model and show the model structure after speedup.
+from nni.compression.pytorch import ModelSpeedup
+ModelSpeedup(model, torch.rand(10, 1, 28, 28).to(device), masks).speedup_model()
+print(model)
+
+# %%
+# Roughly test the model after speedup inference speed.
+start = time.time()
+model(torch.rand(128, 1, 28, 28).to(device))
+print('Speedup Model - Elapsed Time : ', time.time() - start)
+
+# %%
+# For combining usage of ``Pruner`` masks generation with ``ModelSpeedup``,
+# please refer to :doc:`Pruning Quick Start <pruning_quick_start_mnist>`.
+#
+# NOTE: The current implementation supports PyTorch 1.3.1 or newer.
+#
+# Limitations
+# -----------
+#
+# For PyTorch we can only replace modules, if functions in ``forward`` should be replaced,
+# our current implementation does not work. One workaround is make the function a PyTorch module.
+#
+# If you want to speedup your own model which cannot supported by the current implementation,
+# you need implement the replace function for module replacement, welcome to contribute.
+#
+# Speedup Results of Examples
+# ---------------------------
+#
+# The code of these experiments can be found :githublink:`here <examples/model_compress/pruning/legacy/speedup/model_speedup.py>`.
+#
+# These result are tested on the `legacy pruning framework <https://nni.readthedocs.io/en/v2.6/Compression/pruning.html>`_, new results will coming soon.
+#
+# slim pruner example
+# ^^^^^^^^^^^^^^^^^^^
+#
+# on one V100 GPU,
+# input tensor: ``torch.randn(64, 3, 32, 32)``
+#
+# .. list-table::
+#    :header-rows: 1
+#    :widths: auto
+#
+#    * - Times
+#      - Mask Latency
+#      - Speedup Latency
+#    * - 1
+#      - 0.01197
+#      - 0.005107
+#    * - 2
+#      - 0.02019
+#      - 0.008769
+#    * - 4
+#      - 0.02733
+#      - 0.014809
+#    * - 8
+#      - 0.04310
+#      - 0.027441
+#    * - 16
+#      - 0.07731
+#      - 0.05008
+#    * - 32
+#      - 0.14464
+#      - 0.10027
+#
+# fpgm pruner example
+# ^^^^^^^^^^^^^^^^^^^
+#
+# on cpu,
+# input tensor: ``torch.randn(64, 1, 28, 28)``\ ,
+# too large variance
+#
+# .. list-table::
+#    :header-rows: 1
+#    :widths: auto
+#
+#    * - Times
+#      - Mask Latency
+#      - Speedup Latency
+#    * - 1
+#      - 0.01383
+#      - 0.01839
+#    * - 2
+#      - 0.01167
+#      - 0.003558
+#    * - 4
+#      - 0.01636
+#      - 0.01088
+#    * - 40
+#      - 0.14412
+#      - 0.08268
+#    * - 40
+#      - 1.29385
+#      - 0.14408
+#    * - 40
+#      - 0.41035
+#      - 0.46162
+#    * - 400
+#      - 6.29020
+#      - 5.82143
+#
+# l1filter pruner example
+# ^^^^^^^^^^^^^^^^^^^^^^^
+#
+# on one V100 GPU,
+# input tensor: ``torch.randn(64, 3, 32, 32)``
+#
+# .. list-table::
+#    :header-rows: 1
+#    :widths: auto
+#
+#    * - Times
+#      - Mask Latency
+#      - Speedup Latency
+#    * - 1
+#      - 0.01026
+#      - 0.003677
+#    * - 2
+#      - 0.01657
+#      - 0.008161
+#    * - 4
+#      - 0.02458
+#      - 0.020018
+#    * - 8
+#      - 0.03498
+#      - 0.025504
+#    * - 16
+#      - 0.06757
+#      - 0.047523
+#    * - 32
+#      - 0.10487
+#      - 0.086442
+#
+# APoZ pruner example
+# ^^^^^^^^^^^^^^^^^^^
+#
+# on one V100 GPU,
+# input tensor: ``torch.randn(64, 3, 32, 32)``
+#
+# .. list-table::
+#    :header-rows: 1
+#    :widths: auto
+#
+#    * - Times
+#      - Mask Latency
+#      - Speedup Latency
+#    * - 1
+#      - 0.01389
+#      - 0.004208
+#    * - 2
+#      - 0.01628
+#      - 0.008310
+#    * - 4
+#      - 0.02521
+#      - 0.014008
+#    * - 8
+#      - 0.03386
+#      - 0.023923
+#    * - 16
+#      - 0.06042
+#      - 0.046183
+#    * - 32
+#      - 0.12421
+#      - 0.087113
+#
+# SimulatedAnnealing pruner example
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# In this experiment, we use SimulatedAnnealing pruner to prune the resnet18 on the cifar10 dataset.
+# We measure the latencies and accuracies of the pruned model under different sparsity ratios, as shown in the following figure.
+# The latency is measured on one V100 GPU and the input tensor is  ``torch.randn(128, 3, 32, 32)``.
+#
+# .. image:: ../../img/SA_latency_accuracy.png
diff --git a/examples/tutorials/quantization_customize.py b/examples/tutorials/quantization_customize.py
new file mode 100644
index 000000000..9305e1978
--- /dev/null
+++ b/examples/tutorials/quantization_customize.py
@@ -0,0 +1,123 @@
+"""
+Customize a new quantization algorithm
+======================================
+
+To write a new quantization algorithm, you can write a class that inherits ``nni.compression.pytorch.Quantizer``.
+Then, override the member functions with the logic of your algorithm. The member function to override is ``quantize_weight``.
+``quantize_weight`` directly returns the quantized weights rather than mask, because for quantization the quantized weights cannot be obtained by applying mask.
+"""
+
+from nni.compression.pytorch import Quantizer
+
+class YourQuantizer(Quantizer):
+    def __init__(self, model, config_list):
+        """
+        Suggest you to use the NNI defined spec for config
+        """
+        super().__init__(model, config_list)
+
+    def quantize_weight(self, weight, config, **kwargs):
+        """
+        quantize should overload this method to quantize weight tensors.
+        This method is effectively hooked to :meth:`forward` of the model.
+
+        Parameters
+        ----------
+        weight : Tensor
+            weight that needs to be quantized
+        config : dict
+            the configuration for weight quantization
+        """
+
+        # Put your code to generate `new_weight` here
+        new_weight = ...
+        return new_weight
+
+    def quantize_output(self, output, config, **kwargs):
+        """
+        quantize should overload this method to quantize output.
+        This method is effectively hooked to `:meth:`forward` of the model.
+
+        Parameters
+        ----------
+        output : Tensor
+            output that needs to be quantized
+        config : dict
+            the configuration for output quantization
+        """
+
+        # Put your code to generate `new_output` here
+        new_output = ...
+        return new_output
+
+    def quantize_input(self, *inputs, config, **kwargs):
+        """
+        quantize should overload this method to quantize input.
+        This method is effectively hooked to :meth:`forward` of the model.
+
+        Parameters
+        ----------
+        inputs : Tensor
+            inputs that needs to be quantized
+        config : dict
+            the configuration for inputs quantization
+        """
+
+        # Put your code to generate `new_input` here
+        new_input = ...
+        return new_input
+
+    def update_epoch(self, epoch_num):
+        pass
+
+    def step(self):
+        """
+        Can do some processing based on the model or weights binded
+        in the func bind_model
+        """
+        pass
+
+# %%
+# Customize backward function
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# Sometimes it's necessary for a quantization operation to have a customized backward function,
+# such as `Straight-Through Estimator <https://stackoverflow.com/questions/38361314/the-concept-of-straight-through-estimator-ste>`__\ ,
+# user can customize a backward function as follow:
+
+from nni.compression.pytorch.compressor import Quantizer, QuantGrad, QuantType
+
+class ClipGrad(QuantGrad):
+    @staticmethod
+    def quant_backward(tensor, grad_output, quant_type):
+        """
+        This method should be overrided by subclass to provide customized backward function,
+        default implementation is Straight-Through Estimator
+        Parameters
+        ----------
+        tensor : Tensor
+            input of quantization operation
+        grad_output : Tensor
+            gradient of the output of quantization operation
+        quant_type : QuantType
+            the type of quantization, it can be `QuantType.INPUT`, `QuantType.WEIGHT`, `QuantType.OUTPUT`,
+            you can define different behavior for different types.
+        Returns
+        -------
+        tensor
+            gradient of the input of quantization operation
+        """
+
+        # for quant_output function, set grad to zero if the absolute value of tensor is larger than 1
+        if quant_type == QuantType.OUTPUT:
+            grad_output[tensor.abs() > 1] = 0
+        return grad_output
+
+class _YourQuantizer(Quantizer):
+    def __init__(self, model, config_list):
+        super().__init__(model, config_list)
+        # set your customized backward function to overwrite default backward function
+        self.quant_grad = ClipGrad
+
+# %%
+# If you do not customize ``QuantGrad``, the default backward is Straight-Through Estimator. 
diff --git a/examples/tutorials/quantization_quick_start_mnist.py b/examples/tutorials/quantization_quick_start_mnist.py
new file mode 100644
index 000000000..c126e6fb4
--- /dev/null
+++ b/examples/tutorials/quantization_quick_start_mnist.py
@@ -0,0 +1,89 @@
+"""
+Quantization Quickstart
+=======================
+
+Quantization reduces model size and speeds up inference time by reducing the number of bits required to represent weights or activations.
+
+In NNI, both post-training quantization algorithms and quantization-aware training algorithms are supported.
+Here we use `QAT_Quantizer` as an example to show the usage of quantization in NNI.
+"""
+
+# %%
+# Preparation
+# -----------
+#
+# In this tutorial, we use a simple model and pre-train on MNIST dataset.
+# If you are familiar with defining a model and training in pytorch, you can skip directly to `Quantizing Model`_.
+
+import torch
+import torch.nn.functional as F
+from torch.optim import SGD
+
+from scripts.compression_mnist_model import TorchModel, trainer, evaluator, device, test_trt
+
+# define the model
+model = TorchModel().to(device)
+
+# define the optimizer and criterion for pre-training
+
+optimizer = SGD(model.parameters(), 1e-2)
+criterion = F.nll_loss
+
+# pre-train and evaluate the model on MNIST dataset
+for epoch in range(3):
+    trainer(model, optimizer, criterion)
+    evaluator(model)
+
+# %%
+# Quantizing Model
+# ----------------
+#
+# Initialize a `config_list`.
+# Detailed about how to write ``config_list`` please refer :doc:`compression config specification <../compression/compression_config_list>`.
+
+config_list = [{
+    'quant_types': ['input', 'weight'],
+    'quant_bits': {'input': 8, 'weight': 8},
+    'op_types': ['Conv2d']
+}, {
+    'quant_types': ['output'],
+    'quant_bits': {'output': 8},
+    'op_types': ['ReLU']
+}, {
+    'quant_types': ['input', 'weight'],
+    'quant_bits': {'input': 8, 'weight': 8},
+    'op_names': ['fc1', 'fc2']
+}]
+
+# %%
+# finetuning the model by using QAT
+from nni.algorithms.compression.pytorch.quantization import QAT_Quantizer
+dummy_input = torch.rand(32, 1, 28, 28).to(device)
+quantizer = QAT_Quantizer(model, config_list, optimizer, dummy_input)
+quantizer.compress()
+
+# %%
+# The model has now been wrapped, and quantization targets ('quant_types' setting in `config_list`)
+# will be quantized & dequantized for simulated quantization in the wrapped layers.
+# QAT is a training-aware quantizer, it will update scale and zero point during training.
+
+for epoch in range(3):
+    trainer(model, optimizer, criterion)
+    evaluator(model)
+
+# %%
+# export model and get calibration_config
+model_path = "./log/mnist_model.pth"
+calibration_path = "./log/mnist_calibration.pth"
+calibration_config = quantizer.export_model(model_path, calibration_path)
+
+print("calibration_config: ", calibration_config)
+
+# %%
+# build tensorRT engine to make a real speedup, for more information about speedup, please refer :doc:`quantization_speedup`.
+
+from nni.compression.pytorch.quantization_speedup import ModelSpeedupTensorRT
+input_shape = (32, 1, 28, 28)
+engine = ModelSpeedupTensorRT(model, input_shape, config=calibration_config, batchsize=32)
+engine.compress()
+test_trt(engine)
diff --git a/examples/tutorials/quantization_speedup.py b/examples/tutorials/quantization_speedup.py
new file mode 100644
index 000000000..132fc6281
--- /dev/null
+++ b/examples/tutorials/quantization_speedup.py
@@ -0,0 +1,168 @@
+"""
+SpeedUp Model with Calibration Config
+======================================
+
+
+Introduction
+------------
+
+Deep learning network has been computational intensive and memory intensive 
+which increases the difficulty of deploying deep neural network model. Quantization is a 
+fundamental technology which is widely used to reduce memory footprint and speedup inference 
+process. Many frameworks begin to support quantization, but few of them support mixed precision 
+quantization and get real speedup. Frameworks like `HAQ: Hardware-Aware Automated Quantization with Mixed Precision <https://arxiv.org/pdf/1811.08886.pdf>`__\, only support simulated mixed precision quantization which will 
+not speedup the inference process. To get real speedup of mixed precision quantization and 
+help people get the real feedback from hardware, we design a general framework with simple interface to allow NNI quantization algorithms to connect different 
+DL model optimization backends (e.g., TensorRT, NNFusion), which gives users an end-to-end experience that after quantizing their model 
+with quantization algorithms, the quantized model can be directly speeded up with the connected optimization backend. NNI connects 
+TensorRT at this stage, and will support more backends in the future.
+
+
+Design and Implementation
+-------------------------
+
+To support speeding up mixed precision quantization, we divide framework into two part, frontend and backend.  
+Frontend could be popular training frameworks such as PyTorch, TensorFlow etc. Backend could be inference 
+framework for different hardwares, such as TensorRT. At present, we support PyTorch as frontend and 
+TensorRT as backend. To convert PyTorch model to TensorRT engine, we leverage onnx as intermediate graph 
+representation. In this way, we convert PyTorch model to onnx model, then TensorRT parse onnx 
+model to generate inference engine. 
+
+
+Quantization aware training combines NNI quantization algorithm 'QAT' and NNI quantization speedup tool.
+Users should set config to train quantized model using QAT algorithm(please refer to :doc:`NNI Quantization Algorithms <../compression/quantizer>`  ).
+After quantization aware training, users can get new config with calibration parameters and model with quantized weight. By passing new config and model to quantization speedup tool, users can get real mixed precision speedup engine to do inference.
+
+
+After getting mixed precision engine, users can do inference with input data.
+
+
+Note
+
+
+* Recommend using "cpu"(host) as data device(for both inference data and calibration data) since data should be on host initially and it will be transposed to device before inference. If data type is not "cpu"(host), this tool will transpose it to "cpu" which may increases unnecessary overhead.
+* User can also do post-training quantization leveraging TensorRT directly(need to provide calibration dataset).
+* Not all op types are supported right now. At present, NNI supports Conv, Linear, Relu and MaxPool. More op types will be supported in the following release.
+
+
+Prerequisite
+------------
+CUDA version >= 11.0
+
+TensorRT version >= 7.2
+
+Note
+
+* If you haven't installed TensorRT before or use the old version, please refer to `TensorRT Installation Guide <https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html>`__\  
+
+Usage
+-----
+
+"""
+
+# %%
+import torch
+import torch.nn.functional as F
+from torch.optim import SGD
+from scripts.compression_mnist_model import TorchModel, device, trainer, evaluator, test_trt
+
+config_list = [{
+    'quant_types': ['input', 'weight'],
+    'quant_bits': {'input': 8, 'weight': 8},
+    'op_types': ['Conv2d']
+}, {
+    'quant_types': ['output'],
+    'quant_bits': {'output': 8},
+    'op_types': ['ReLU']
+}, {
+    'quant_types': ['input', 'weight'],
+    'quant_bits': {'input': 8, 'weight': 8},
+    'op_names': ['fc1', 'fc2']
+}]
+
+model = TorchModel().to(device)
+optimizer = SGD(model.parameters(), lr=0.01, momentum=0.5)
+criterion = F.nll_loss
+dummy_input = torch.rand(32, 1, 28, 28).to(device)
+
+from nni.algorithms.compression.pytorch.quantization import QAT_Quantizer
+quantizer = QAT_Quantizer(model, config_list, optimizer, dummy_input)
+quantizer.compress()
+
+# %%
+# finetuning the model by using QAT
+for epoch in range(3):
+    trainer(model, optimizer, criterion)
+    evaluator(model)
+
+# %%
+# export model and get calibration_config
+import os
+os.makedirs('log', exist_ok=True)
+model_path = "./log/mnist_model.pth"
+calibration_path = "./log/mnist_calibration.pth"
+calibration_config = quantizer.export_model(model_path, calibration_path)
+
+print("calibration_config: ", calibration_config)
+
+# %%
+# build tensorRT engine to make a real speedup
+
+from nni.compression.pytorch.quantization_speedup import ModelSpeedupTensorRT
+input_shape = (32, 1, 28, 28)
+engine = ModelSpeedupTensorRT(model, input_shape, config=calibration_config, batchsize=32)
+engine.compress()
+test_trt(engine)
+
+# %%
+# Note that NNI also supports post-training quantization directly, please refer to complete examples for detail.
+#
+# For complete examples please refer to :githublink:`the code <examples/model_compress/quantization/mixed_precision_speedup_mnist.py>`.
+#
+# For more parameters about the class 'TensorRTModelSpeedUp', you can refer to :doc:`Model Compression API Reference <../reference/compression/quantization_speedup>`.
+#
+# Mnist test
+# ^^^^^^^^^^
+#
+# on one GTX2080 GPU,
+# input tensor: ``torch.randn(128, 1, 28, 28)``
+#
+# .. list-table::
+#    :header-rows: 1
+#    :widths: auto
+#
+#    * - quantization strategy
+#      - Latency
+#      - accuracy
+#    * - all in 32bit
+#      - 0.001199961
+#      - 96%
+#    * - mixed precision(average bit 20.4)
+#      - 0.000753688
+#      - 96%
+#    * - all in 8bit
+#      - 0.000229869
+#      - 93.7%
+#
+# Cifar10 resnet18 test (train one epoch)
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# on one GTX2080 GPU,
+# input tensor: ``torch.randn(128, 3, 32, 32)``
+#
+# .. list-table::
+#    :header-rows: 1
+#    :widths: auto
+#
+#    * - quantization strategy
+#      - Latency
+#      - accuracy
+#    * - all in 32bit
+#      - 0.003286268
+#      - 54.21%
+#    * - mixed precision(average bit 11.55)
+#      - 0.001358022
+#      - 54.78%
+#    * - all in 8bit
+#      - 0.000859139
+#      - 52.81%
diff --git a/examples/tutorials/scripts/compression_mnist_model.py b/examples/tutorials/scripts/compression_mnist_model.py
new file mode 100644
index 000000000..1f1b5775b
--- /dev/null
+++ b/examples/tutorials/scripts/compression_mnist_model.py
@@ -0,0 +1,97 @@
+from pathlib import Path
+
+root_path = Path(__file__).parent.parent
+
+# define the model
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+class TorchModel(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv1 = nn.Conv2d(1, 6, 5, 1)
+        self.conv2 = nn.Conv2d(6, 16, 5, 1)
+        self.fc1 = nn.Linear(16 * 4 * 4, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.fc3 = nn.Linear(84, 10)
+        self.relu1 = nn.ReLU()
+        self.relu2 = nn.ReLU()
+        self.relu3 = nn.ReLU()
+        self.relu4 = nn.ReLU()
+        self.pool1 = nn.MaxPool2d((2, 2))
+        self.pool2 = nn.MaxPool2d((2, 2))
+
+    def forward(self, x):
+        x = self.pool1(self.relu1(self.conv1(x)))
+        x = self.pool2(self.relu2(self.conv2(x)))
+        x = torch.flatten(x, 1)
+        x = self.relu3(self.fc1(x))
+        x = self.relu4(self.fc2(x))
+        x = self.fc3(x)
+        return F.log_softmax(x, dim=1)
+
+use_cuda = torch.cuda.is_available()
+device = torch.device("cuda" if use_cuda else "cpu")
+
+# load data
+from torchvision import datasets, transforms
+
+train_loader = torch.utils.data.DataLoader(
+    datasets.MNIST(root_path / 'data', train=True, download=True, transform=transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize((0.1307,), (0.3081,))
+    ])), batch_size=128, shuffle=True)
+
+test_loader = torch.utils.data.DataLoader(
+    datasets.MNIST(root_path / 'data', train=False, transform=transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize((0.1307,), (0.3081,))
+    ])), batch_size=1000, shuffle=True)
+
+# define the trainer and evaluator
+def trainer(model, optimizer, criterion):
+    # training the model
+    model.train()
+    for data, target in train_loader:
+        data, target = data.to(device), target.to(device)
+        optimizer.zero_grad()
+        output = model(data)
+        loss = criterion(output, target)
+        loss.backward()
+        optimizer.step()
+
+def evaluator(model):
+    # evaluating the model accuracy and average test loss
+    model.eval()
+    test_loss = 0
+    correct = 0
+    test_dataset_length = len(test_loader.dataset)
+    with torch.no_grad():
+        for data, target in test_loader:
+            data, target = data.to(device), target.to(device)
+            output = model(data)
+            # sum up batch loss
+            test_loss += F.nll_loss(output, target, reduction='sum').item()
+            # get the index of the max log-probability
+            pred = output.argmax(dim=1, keepdim=True)
+            correct += pred.eq(target.view_as(pred)).sum().item()
+    test_loss /= test_dataset_length
+    accuracy = 100. * correct / test_dataset_length
+    print('Average test loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'.format(test_loss, correct, test_dataset_length, accuracy))
+
+def test_trt(engine):
+    test_loss = 0
+    correct = 0
+    time_elasped = 0
+    for data, target in test_loader:
+        output, time = engine.inference(data)
+        test_loss += F.nll_loss(output, target, reduction='sum').item()
+        pred = output.argmax(dim=1, keepdim=True)
+        correct += pred.eq(target.view_as(pred)).sum().item()
+        time_elasped += time
+    test_loss /= len(test_loader.dataset)
+
+    print('Loss: {}  Accuracy: {}%'.format(
+        test_loss, 100 * correct / len(test_loader.dataset)))
+    print("Inference elapsed_time (whole dataset): {}s".format(time_elasped))
diff --git a/nni/__init__.py b/nni/__init__.py
index 26a19c7d0..5c9258834 100644
--- a/nni/__init__.py
+++ b/nni/__init__.py
@@ -10,6 +10,7 @@ from .runtime.log import init_logger
 init_logger()
 
 from .common.serializer import trace, dump, load
+from .experiment import Experiment
 from .runtime.env_vars import dispatcher_env_vars
 from .utils import ClassArgsValidator
 
@@ -19,7 +20,7 @@ if dispatcher_env_vars.SDK_PROCESS != 'dispatcher':
     from .common.nas_utils import training_update
 
 class NoMoreTrialError(Exception):
-    def __init__(self, ErrorInfo):
+    def __init__(self, ErrorInfo='Search space fully explored'):
         super().__init__(self)
         self.errorinfo = ErrorInfo
 
diff --git a/nni/algorithms/compression/pytorch/auto_compress/experiment.py b/nni/algorithms/compression/pytorch/auto_compress/experiment.py
index 07c5c4b3c..15ce28c4b 100644
--- a/nni/algorithms/compression/pytorch/auto_compress/experiment.py
+++ b/nni/algorithms/compression/pytorch/auto_compress/experiment.py
@@ -1,9 +1,10 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
 
+from __future__ import annotations
+
 import inspect
 from pathlib import Path, PurePath
-from typing import overload, Union, List
 
 from nni.experiment import Experiment, ExperimentConfig
 from nni.algorithms.compression.pytorch.auto_compress.interface import AbstractAutoCompressionModule
@@ -11,49 +12,19 @@ from nni.algorithms.compression.pytorch.auto_compress.interface import AbstractA
 
 class AutoCompressionExperiment(Experiment):
 
-    @overload
-    def __init__(self, auto_compress_module: AbstractAutoCompressionModule, config: ExperimentConfig) -> None:
-        """
-        Prepare an experiment.
-
-        Use `Experiment.run()` to launch it.
-
-        Parameters
-        ----------
-        auto_compress_module
-            The module provided by the user implements the `AbstractAutoCompressionModule` interfaces.
-            Remember put the module file under `trial_code_directory`.
-        config
-            Experiment configuration.
-        """
-        ...
-
-    @overload
-    def __init__(self, auto_compress_module: AbstractAutoCompressionModule, training_service: Union[str, List[str]]) -> None:
+    def __init__(self, auto_compress_module: AbstractAutoCompressionModule, config_or_platform: ExperimentConfig | str | list[str]) -> None:
         """
-        Prepare an experiment, leaving configuration fields to be set later.
-
-        Example usage::
-
-            experiment = Experiment(auto_compress_module, 'remote')
-            experiment.config.trial_command = 'python3 trial.py'
-            experiment.config.machines.append(RemoteMachineConfig(ip=..., user_name=...))
-            ...
-            experiment.run(8080)
+        Prepare an auto compression experiment.
 
         Parameters
         ----------
         auto_compress_module
             The module provided by the user implements the `AbstractAutoCompressionModule` interfaces.
             Remember put the module file under `trial_code_directory`.
-        training_service
-            Name of training service.
-            Supported value: "local", "remote", "openpai", "aml", "kubeflow", "frameworkcontroller", "adl" and hybrid training service.
+        config_or_platform
+            Experiment configuration or training service name.
         """
-        ...
-
-    def __init__(self, auto_compress_module: AbstractAutoCompressionModule, config=None, training_service=None):
-        super().__init__(config, training_service)
+        super().__init__(config_or_platform)
 
         self.module_file_path = str(PurePath(inspect.getfile(auto_compress_module)))
         self.module_name = auto_compress_module.__name__
diff --git a/nni/algorithms/compression/pytorch/pruning/auto_compress_pruner.py b/nni/algorithms/compression/pytorch/pruning/auto_compress_pruner.py
index 9b082ab50..e6723d90c 100644
--- a/nni/algorithms/compression/pytorch/pruning/auto_compress_pruner.py
+++ b/nni/algorithms/compression/pytorch/pruning/auto_compress_pruner.py
@@ -201,7 +201,7 @@ class AutoCompressPruner(Pruner):
             ADMMpruner.export_model(os.path.join(self._experiment_data_dir, 'model_admm_masked.pth'), os.path.join(
                 self._experiment_data_dir, 'mask.pth'))
 
-            # use speed up to prune the model before next iteration,
+            # use speedup to prune the model before next iteration,
             # because SimulatedAnnealingPruner & ADMMPruner don't take masked models
             self._model_to_prune.load_state_dict(torch.load(os.path.join(
                 self._experiment_data_dir, 'model_admm_masked.pth')))
diff --git a/nni/algorithms/compression/pytorch/pruning/dependency_aware_pruner.py b/nni/algorithms/compression/pytorch/pruning/dependency_aware_pruner.py
index 2fbfe9475..d22d1ceef 100644
--- a/nni/algorithms/compression/pytorch/pruning/dependency_aware_pruner.py
+++ b/nni/algorithms/compression/pytorch/pruning/dependency_aware_pruner.py
@@ -35,7 +35,7 @@ class DependencyAwarePruner(Pruner):
 
         if self.dependency_aware:
             if not self._supported_dependency_aware():
-                raise ValueError('This pruner does not support dependency aware!')
+                raise ValueError('This pruner does not support dependency-aware!')
 
             errmsg = "When dependency_aware is set, the dummy_input should not be None"
             assert self.dummy_input is not None, errmsg
diff --git a/nni/algorithms/compression/pytorch/pruning/sensitivity_pruner.py b/nni/algorithms/compression/pytorch/pruning/sensitivity_pruner.py
index a8193a464..7870a7d59 100644
--- a/nni/algorithms/compression/pytorch/pruning/sensitivity_pruner.py
+++ b/nni/algorithms/compression/pytorch/pruning/sensitivity_pruner.py
@@ -10,7 +10,7 @@ import torch
 from schema import And, Optional
 from nni.compression.pytorch.compressor import Pruner
 from nni.compression.pytorch.utils.config_validation import PrunerSchema
-from nni.compression.pytorch.utils.sensitivity_analysis import SensitivityAnalysis
+from nni.compression.pytorch.utils import SensitivityAnalysis
 
 from .constants_pruner import PRUNER_DICT
 
diff --git a/nni/algorithms/compression/pytorch/quantization/bnn_quantizer.py b/nni/algorithms/compression/pytorch/quantization/bnn_quantizer.py
index 7010536ac..13c1c2073 100644
--- a/nni/algorithms/compression/pytorch/quantization/bnn_quantizer.py
+++ b/nni/algorithms/compression/pytorch/quantization/bnn_quantizer.py
@@ -22,9 +22,74 @@ class ClipGrad(QuantGrad):
 
 
 class BNNQuantizer(Quantizer):
-    """Binarized Neural Networks, as defined in:
-    Binarized Neural Networks: Training Deep Neural Networks with Weights and Outputs Constrained to +1 or -1
-    (https://arxiv.org/abs/1602.02830)
+    r"""
+    Binarized Neural Networks, as defined in:
+    `Binarized Neural Networks: Training Deep Neural Networks with Weights and
+    Activations Constrained to +1 or -1 <https://arxiv.org/abs/1602.02830>`__,
+
+    ..
+
+        We introduce a method to train Binarized Neural Networks (BNNs) - neural networks with binary weights and activations at run-time.
+        At training-time the binary weights and activations are used for computing the parameters gradients. During the forward pass,
+        BNNs drastically reduce memory size and accesses, and replace most arithmetic operations with bit-wise operations,
+        which is expected to substantially improve power-efficiency.
+
+    Parameters
+    ----------
+    model : torch.nn.Module
+        Model to be quantized.
+    config_list : List[Dict]
+        List of configurations for quantization. Supported keys for dict:
+            - quant_types : List[str]
+                Type of quantization you want to apply, currently support 'weight', 'input', 'output'.
+            - quant_bits : Union[int, Dict[str, int]]
+                Bits length of quantization, key is the quantization type, value is the length, eg. {'weight': 8},
+                When the type is int, all quantization types share same bits length.
+            - op_types : List[str]
+                Types of nn.module you want to apply quantization, eg. 'Conv2d'.
+            - op_names : List[str]
+                Names of nn.module you want to apply quantization, eg. 'conv1'.
+            - exclude : bool
+                Set True then the layers setting by op_types and op_names will be excluded from quantization.
+    optimizer : torch.optim.Optimizer
+        Optimizer is required in `BNNQuantizer`, NNI will patch the optimizer and count the optimize step number.
+
+    Examples
+    --------
+        >>> from nni.algorithms.compression.pytorch.quantization import BNNQuantizer
+        >>> model = ...
+        >>> config_list = [{'quant_types': ['weight', 'input'], 'quant_bits': {'weight': 8, 'input': 8}, 'op_types': ['Conv2d']}]
+        >>> optimizer = ...
+        >>> quantizer = BNNQuantizer(model, config_list, optimizer)
+        >>> quantizer.compress()
+        >>> # Training Process...
+
+    For detailed example please refer to
+    :githublink:`examples/model_compress/quantization/BNN_quantizer_cifar10.py
+    <examples/model_compress/quantization/BNN_quantizer_cifar10.py>`.
+
+    Notes
+    -----
+
+    **Results**
+
+    We implemented one of the experiments in
+    `Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1
+    <https://arxiv.org/abs/1602.02830>`__,
+    we quantized the **VGGNet** for CIFAR-10 in the paper. Our experiments results are as follows:
+
+    .. list-table::
+        :header-rows: 1
+        :widths: auto
+
+        * - Model
+            - Accuracy
+        * - VGGNet
+            - 86.93%
+
+    The experiments code can be found at
+    :githublink:`examples/model_compress/quantization/BNN_quantizer_cifar10.py
+    <examples/model_compress/quantization/BNN_quantizer_cifar10.py>`
     """
 
     def __init__(self, model, config_list, optimizer):
diff --git a/nni/algorithms/compression/pytorch/quantization/dorefa_quantizer.py b/nni/algorithms/compression/pytorch/quantization/dorefa_quantizer.py
index 6e1f76e25..24550e2fd 100644
--- a/nni/algorithms/compression/pytorch/quantization/dorefa_quantizer.py
+++ b/nni/algorithms/compression/pytorch/quantization/dorefa_quantizer.py
@@ -13,9 +13,45 @@ logger = logging.getLogger(__name__)
 
 
 class DoReFaQuantizer(Quantizer):
-    """Quantizer using the DoReFa scheme, as defined in:
-    Zhou et al., DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients
-    (https://arxiv.org/abs/1606.06160)
+    r"""
+    Quantizer using the DoReFa scheme, as defined in:
+    `DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients <https://arxiv.org/abs/1606.06160>`__,
+    authors Shuchang Zhou and Yuxin Wu provide an algorithm named DoReFa to quantize the weight, activation and gradients with training.
+
+    Parameters
+    ----------
+    model : torch.nn.Module
+        Model to be quantized.
+    config_list : List[Dict]
+        List of configurations for quantization. Supported keys for dict:
+            - quant_types : List[str]
+                Type of quantization you want to apply, currently support 'weight', 'input', 'output'.
+            - quant_bits : Union[int, Dict[str, int]]
+                Bits length of quantization, key is the quantization type, value is the length, eg. {'weight': 8},
+                When the type is int, all quantization types share same bits length.
+            - op_types : List[str]
+                Types of nn.module you want to apply quantization, eg. 'Conv2d'.
+            - op_names : List[str]
+                Names of nn.module you want to apply quantization, eg. 'conv1'.
+            - exclude : bool
+                Set True then the layers setting by op_types and op_names will be excluded from quantization.
+    optimizer : torch.optim.Optimizer
+        Optimizer is required in `DoReFaQuantizer`, NNI will patch the optimizer and count the optimize step number.
+
+    Examples
+    --------
+        >>> from nni.algorithms.compression.pytorch.quantization import DoReFaQuantizer
+        >>> model = ...
+        >>> config_list = [{'quant_types': ['weight', 'input'], 'quant_bits': {'weight': 8, 'input': 8}, 'op_types': ['Conv2d']}]
+        >>> optimizer = ...
+        >>> quantizer = DoReFaQuantizer(model, config_list, optimizer)
+        >>> quantizer.compress()
+        >>> # Training Process...
+
+    For detailed example please refer to
+    :githublink:`examples/model_compress/quantization/DoReFaQuantizer_torch_mnist.py
+    <examples/model_compress/quantization/DoReFaQuantizer_torch_mnist.py>`.
+
     """
 
     def __init__(self, model, config_list, optimizer):
diff --git a/nni/algorithms/compression/pytorch/quantization/lsq_quantizer.py b/nni/algorithms/compression/pytorch/quantization/lsq_quantizer.py
index ac6f86eb7..5a78d65cd 100644
--- a/nni/algorithms/compression/pytorch/quantization/lsq_quantizer.py
+++ b/nni/algorithms/compression/pytorch/quantization/lsq_quantizer.py
@@ -11,35 +11,56 @@ logger = logging.getLogger(__name__)
 
 
 class LsqQuantizer(Quantizer):
-    """Quantizer defined in:
-       Learned Step Size Quantization (ICLR 2020)
-       https://arxiv.org/pdf/1902.08153.pdf
+    r"""
+    Quantizer defined in: `LEARNED STEP SIZE QUANTIZATION <https://arxiv.org/pdf/1902.08153.pdf>`__,
+    authors Steven K. Esser and Jeffrey L. McKinstry provide an algorithm to train the scales with gradients.
+
+    ..
+
+        The authors introduce a novel means to estimate and scale the task loss gradient at each weight and activation
+        layer's quantizer step size, such that it can be learned in conjunction with other network parameters.
+
+    Parameters
+    ----------
+    model : torch.nn.Module
+        The model to be quantized.
+    config_list : List[Dict]
+        List of configurations for quantization. Supported keys for dict:
+            - quant_types : List[str]
+                Type of quantization you want to apply, currently support 'weight', 'input', 'output'.
+            - quant_bits : Union[int, Dict[str, int]]
+                Bits length of quantization, key is the quantization type, value is the length, eg. {'weight': 8},
+                When the type is int, all quantization types share same bits length.
+            - op_types : List[str]
+                Types of nn.module you want to apply quantization, eg. 'Conv2d'.
+            - op_names : List[str]
+                Names of nn.module you want to apply quantization, eg. 'conv1'.
+            - exclude : bool
+                Set True then the layers setting by op_types and op_names will be excluded from quantization.
+    optimizer : torch.optim.Optimizer
+        Optimizer is required in `LsqQuantizer`, NNI will patch the optimizer and count the optimize step number.
+    dummy_input : Tuple[torch.Tensor]
+        Inputs to the model, which are used to get the graph of the module. The graph is used to find Conv-Bn patterns.
+        And then the batch normalization folding would be enabled. If dummy_input is not given,
+        the batch normalization folding would be disabled.
+
+    Examples
+    --------
+        >>> from nni.algorithms.compression.pytorch.quantization import LsqQuantizer
+        >>> model = ...
+        >>> config_list = [{'quant_types': ['weight', 'input'], 'quant_bits': {'weight': 8, 'input': 8}, 'op_types': ['Conv2d']}]
+        >>> optimizer = ...
+        >>> dummy_input = torch.rand(...)
+        >>> quantizer = LsqQuantizer(model, config_list, optimizer, dummy_input=dummy_input)
+        >>> quantizer.compress()
+        >>> # Training Process...
+
+    For detailed example please refer to
+    :githublink:`examples/model_compress/quantization/LSQ_torch_quantizer.py <examples/model_compress/quantization/LSQ_torch_quantizer.py>`.
+
     """
 
     def __init__(self, model, config_list, optimizer, dummy_input=None):
-        """
-        Parameters
-        ----------
-        model : torch.nn.Module
-            the model to be quantized
-        config_list : list of dict
-            list of configurations for quantization
-            supported keys for dict:
-                - quant_types : list of string
-                    type of quantization you want to apply, currently support 'weight', 'input', 'output'
-                - quant_bits : int or dict of {str : int}
-                    bits length of quantization, key is the quantization type, value is the length, eg. {'weight': 8},
-                    when the type is int, all quantization types share same bits length
-                - quant_start_step : int
-                    disable quantization until model are run by certain number of steps, this allows the network to enter a more stable
-                    state where output quantization ranges do not exclude a significant fraction of values, default value is 0
-                - op_types : list of string
-                    types of nn.module you want to apply quantization, eg. 'Conv2d'
-                - dummy_input : tuple of tensor
-                    inputs to the model, which are used to get the graph of the module. The graph is used to find
-                    Conv-Bn patterns. And then the batch normalization folding would be enabled. If dummy_input is not
-                    given, the batch normalization folding would be disabled.
-        """
         assert isinstance(optimizer, torch.optim.Optimizer), "unrecognized optimizer type"
         super().__init__(model, config_list, optimizer, dummy_input)
         device = next(model.parameters()).device
diff --git a/nni/algorithms/compression/pytorch/quantization/native_quantizer.py b/nni/algorithms/compression/pytorch/quantization/native_quantizer.py
index 44a41716d..dc02b5a35 100644
--- a/nni/algorithms/compression/pytorch/quantization/native_quantizer.py
+++ b/nni/algorithms/compression/pytorch/quantization/native_quantizer.py
@@ -12,7 +12,32 @@ logger = logging.getLogger(__name__)
 
 
 class NaiveQuantizer(Quantizer):
-    """quantize weight to 8 bits
+    r"""
+    Quantize weight to 8 bits directly.
+
+    Parameters
+    ----------
+    model : torch.nn.Module
+        Model to be quantized.
+    config_list : List[Dict]
+        List of configurations for quantization. Supported keys:
+            - quant_types : List[str]
+                Type of quantization you want to apply, currently support 'weight', 'input', 'output'.
+            - quant_bits : Union[int, Dict[str, int]]
+                Bits length of quantization, key is the quantization type, value is the length, eg. {'weight': 8},
+                when the type is int, all quantization types share same bits length.
+            - op_types : List[str]
+                Types of nn.module you want to apply quantization, eg. 'Conv2d'.
+            - op_names : List[str]
+                Names of nn.module you want to apply quantization, eg. 'conv1'.
+            - exclude : bool
+                Set True then the layers setting by op_types and op_names will be excluded from quantization.
+
+    Examples
+    --------
+        >>> from nni.algorithms.compression.pytorch.quantization import NaiveQuantizer
+        >>> model = ...
+        >>> NaiveQuantizer(model).compress()
     """
 
     def __init__(self, model, config_list, optimizer=None):
diff --git a/nni/algorithms/compression/pytorch/quantization/observer_quantizer.py b/nni/algorithms/compression/pytorch/quantization/observer_quantizer.py
index bb793f373..1b53323fc 100644
--- a/nni/algorithms/compression/pytorch/quantization/observer_quantizer.py
+++ b/nni/algorithms/compression/pytorch/quantization/observer_quantizer.py
@@ -14,7 +14,12 @@ logger = logging.getLogger(__name__)
 
 
 class ObserverQuantizer(Quantizer):
-    """This quantizer uses observers to record weight/output statistics to get quantization information.
+    r"""
+    Observer quantizer is a framework of post-training quantization.
+    It will insert observers into the place where the quantization will happen.
+    During quantization calibration, each observer will record all the tensors it 'sees'.
+    These tensors will be used to calculate the quantization statistics after calibration.
+
     The whole process can be divided into three steps:
 
     1. It will register observers to the place where quantization would happen (just like registering hooks).
@@ -23,6 +28,66 @@ class ObserverQuantizer(Quantizer):
 
     Note that the observer type, tensor dtype and quantization qscheme are hard coded for now. Their customization
     are under development and will be ready soon.
+
+    Parameters
+    ----------
+    model : torch.nn.Module
+        Model to be quantized.
+    config_list : List[Dict]
+        List of configurations for quantization. Supported keys:
+            - quant_types : List[str]
+                Type of quantization you want to apply, currently support 'weight', 'input', 'output'.
+            - quant_bits : Union[int, Dict[str, int]]
+                Bits length of quantization, key is the quantization type, value is the length, eg. {'weight': 8},
+                when the type is int, all quantization types share same bits length.
+            - op_types : List[str]
+                Types of nn.module you want to apply quantization, eg. 'Conv2d'.
+            - op_names : List[str]
+                Names of nn.module you want to apply quantization, eg. 'conv1'.
+            - exclude : bool
+                Set True then the layers setting by op_types and op_names will be excluded from quantization.
+    optimizer : torch.optim.Optimizer
+        Optimizer is optional in `ObserverQuantizer`.
+
+    Examples
+    --------
+        >>> from nni.algorithms.compression.pytorch.quantization import ObserverQuantizer
+        >>> model = ...
+        >>> config_list = [{'quant_types': ['weight', 'input'], 'quant_bits': {'weight': 8, 'input': 8}, 'op_types': ['Conv2d']}]
+        >>> quantizer = ObserverQuantizer(model, config_list)
+        >>> # define a calibration function
+        >>> def calibration(model, calib_loader):
+        >>>     model.eval()
+        >>>     with torch.no_grad():
+        >>>         for data, _ in calib_loader:
+        >>>             model(data)
+        >>> calibration(model, calib_loader)
+        >>> quantizer.compress()
+
+    For detailed example please refer to
+    :githublink:`examples/model_compress/quantization/observer_quantizer.py <examples/model_compress/quantization/observer_quantizer.py>`.
+
+    .. note::
+        This quantizer is still under development for now. Some quantizer settings are hard-coded:
+
+        - weight observer: per_tensor_symmetric, qint8
+        - output observer: per_tensor_affine, quint8, reduce_range=True
+
+        Other settings (such as quant_type and op_names) can be configured.
+
+    Notes
+    -----
+
+    **About the compress API**
+
+    Before the `compress` API is called, the model will only record tensors' statistics and no quantization process will be executed.
+    After the `compress` API is called, the model will NOT record tensors' statistics any more. The quantization scale and zero point will
+    be generated for each tensor and will be used to quantize each tensor during inference (we call it evaluation mode)
+
+    **About calibration**
+
+    Usually we pick up about 100 training/evaluation examples for calibration. If you found the accuracy is a bit low, try
+    to reduce the number of calibration examples.
     """
 
     def __init__(self, model, config_list, optimizer=None):
diff --git a/nni/algorithms/compression/pytorch/quantization/qat_quantizer.py b/nni/algorithms/compression/pytorch/quantization/qat_quantizer.py
index ebb04446a..161628a58 100644
--- a/nni/algorithms/compression/pytorch/quantization/qat_quantizer.py
+++ b/nni/algorithms/compression/pytorch/quantization/qat_quantizer.py
@@ -107,36 +107,151 @@ def update_ema(biased_ema, value, decay):
 
 
 class QAT_Quantizer(Quantizer):
-    """Quantizer defined in:
-    Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
-    http://openaccess.thecvf.com/content_cvpr_2018/papers/Jacob_Quantization_and_Training_CVPR_2018_paper.pdf
+    r"""
+    Quantizer defined in:
+    `Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
+    <http://openaccess.thecvf.com/content_cvpr_2018/papers/Jacob_Quantization_and_Training_CVPR_2018_paper.pdf>`__
+
+    Authors Benoit Jacob and Skirmantas Kligys provide an algorithm to quantize the model with training.
+
+    ..
+
+        We propose an approach that simulates quantization effects in the forward pass of training.
+        Backpropagation still happens as usual, and all weights and biases are stored in floating point
+        so that they can be easily nudged by small amounts.
+        The forward propagation pass however simulates quantized inference as it will happen in the inference engine,
+        by implementing in floating-point arithmetic the rounding behavior of the quantization scheme:
+
+        * Weights are quantized before they are convolved with the input. If batch normalization (see [17]) is used for the layer,
+          the batch normalization parameters are “folded into” the weights before quantization.
+
+        * Activations are quantized at points where they would be during inference,
+          e.g. after the activation function is applied to a convolutional or fully connected layer’s output,
+          or after a bypass connection adds or concatenates the outputs of several layers together such as in ResNets.
+
+    Parameters
+    ----------
+    model : torch.nn.Module
+        Model to be quantized.
+    config_list : List[Dict]
+        List of configurations for quantization. Supported keys for dict:
+            - quant_types : List[str]
+                Type of quantization you want to apply, currently support 'weight', 'input', 'output'.
+            - quant_bits : Union[int, Dict[str, int]]
+                Bits length of quantization, key is the quantization type, value is the length, eg. {'weight': 8},
+                When the type is int, all quantization types share same bits length.
+            - quant_start_step : int
+                Disable quantization until model are run by certain number of steps, this allows the network to enter a more stable.
+                State where output quantization ranges do not exclude a significant fraction of values, default value is 0.
+            - op_types : List[str]
+                Types of nn.module you want to apply quantization, eg. 'Conv2d'.
+            - op_names : List[str]
+                Names of nn.module you want to apply quantization, eg. 'conv1'.
+            - exclude : bool
+                Set True then the layers setting by op_types and op_names will be excluded from quantization.
+    optimizer : torch.optim.Optimizer
+        Optimizer is required in `QAT_Quantizer`, NNI will patch the optimizer and count the optimize step number.
+    dummy_input : Tuple[torch.Tensor]
+        Inputs to the model, which are used to get the graph of the module. The graph is used to find Conv-Bn patterns.
+        And then the batch normalization folding would be enabled. If dummy_input is not given,
+        the batch normalization folding would be disabled.
+
+    Examples
+    --------
+        >>> from nni.algorithms.compression.pytorch.quantization import QAT_Quantizer
+        >>> model = ...
+        >>> config_list = [{'quant_types': ['weight', 'input'], 'quant_bits': {'weight': 8, 'input': 8}, 'op_types': ['Conv2d']}]
+        >>> optimizer = ...
+        >>> dummy_input = torch.rand(...)
+        >>> quantizer = QAT_Quantizer(model, config_list, optimizer, dummy_input=dummy_input)
+        >>> quantizer.compress()
+        >>> # Training Process...
+
+    For detailed example please refer to
+    :githublink:`examples/model_compress/quantization/QAT_torch_quantizer.py <examples/model_compress/quantization/QAT_torch_quantizer.py>`.
+
+    Notes
+    -----
+
+    **Batch normalization folding**
+
+    Batch normalization folding is supported in QAT quantizer. It can be easily enabled by passing an argument `dummy_input` to
+    the quantizer, like:
+
+    .. code-block:: python
+
+        # assume your model takes an input of shape (1, 1, 28, 28)
+        # and dummy_input must be on the same device as the model
+        dummy_input = torch.randn(1, 1, 28, 28)
+
+        # pass the dummy_input to the quantizer
+        quantizer = QAT_Quantizer(model, config_list, optimizer, dummy_input=dummy_input)
+
+
+    The quantizer will automatically detect Conv-BN patterns and simulate batch normalization folding process in the training
+    graph. Note that when the quantization aware training process is finished, the folded weight/bias would be restored after calling
+    `quantizer.export_model`.
+
+    **Quantization dtype and scheme customization**
+
+    Different backends on different devices use different quantization strategies (i.e. dtype (int or uint) and
+    scheme (per-tensor or per-channel and symmetric or affine)). QAT quantizer supports customization of mainstream dtypes and schemes.
+    There are two ways to set them. One way is setting them globally through a function named `set_quant_scheme_dtype` like:
+
+    .. code-block:: python
+
+        from nni.compression.pytorch.quantization.settings import set_quant_scheme_dtype
+
+        # This will set all the quantization of 'input' in 'per_tensor_affine' and 'uint' manner
+        set_quant_scheme_dtype('input', 'per_tensor_affine', 'uint)
+        # This will set all the quantization of 'output' in 'per_tensor_symmetric' and 'int' manner
+        set_quant_scheme_dtype('output', 'per_tensor_symmetric', 'int')
+        # This will set all the quantization of 'weight' in 'per_channel_symmetric' and 'int' manner
+        set_quant_scheme_dtype('weight', 'per_channel_symmetric', 'int')
+
+
+    The other way is more detailed. You can customize the dtype and scheme in each quantization config list like:
+
+    .. code-block:: python
+
+        config_list = [{
+            'quant_types': ['weight'],
+            'quant_bits':  8,
+            'op_types':['Conv2d', 'Linear'],
+            'quant_dtype': 'int',
+            'quant_scheme': 'per_channel_symmetric'
+        }, {
+            'quant_types': ['output'],
+            'quant_bits': 8,
+            'quant_start_step': 7000,
+            'op_types':['ReLU6'],
+            'quant_dtype': 'uint',
+            'quant_scheme': 'per_tensor_affine'
+        }]
+
+    **Multi-GPU training**
+
+    QAT quantizer natively supports multi-gpu training (DataParallel and DistributedDataParallel). Note that the quantizer
+    instantiation should happen before you wrap your model with DataParallel or DistributedDataParallel. For example:
+
+    .. code-block:: python
+
+        from torch.nn.parallel import DistributedDataParallel as DDP
+        from nni.algorithms.compression.pytorch.quantization import QAT_Quantizer
+
+        model = define_your_model()
+
+        model = QAT_Quantizer(model, **other_params)  # <--- QAT_Quantizer instantiation
+
+        model = DDP(model)
+
+        for i in range(epochs):
+            train(model)
+            eval(model)
+
     """
 
     def __init__(self, model, config_list, optimizer, dummy_input=None):
-        """
-        Parameters
-        ----------
-        layer : LayerInfo
-            the layer to quantize
-        config_list : list of dict
-            list of configurations for quantization
-            supported keys for dict:
-                - quant_types : list of string
-                    type of quantization you want to apply, currently support 'weight', 'input', 'output'
-                - quant_bits : int or dict of {str : int}
-                    bits length of quantization, key is the quantization type, value is the length, eg. {'weight', 8},
-                    when the type is int, all quantization types share same bits length
-                - quant_start_step : int
-                    disable quantization until model are run by certain number of steps, this allows the network to enter a more stable
-                    state where output quantization ranges do not exclude a significant fraction of values, default value is 0
-                - op_types : list of string
-                    types of nn.module you want to apply quantization, eg. 'Conv2d'
-                - dummy_input : tuple of tensor
-                    inputs to the model, which are used to get the graph of the module. The graph is used to find
-                    Conv-Bn patterns. And then the batch normalization folding would be enabled. If dummy_input is not
-                    given, the batch normalization folding would be disabled.
-        """
-
         assert isinstance(optimizer, torch.optim.Optimizer), "unrecognized optimizer type"
         super().__init__(model, config_list, optimizer, dummy_input)
         self.quant_grad = QATGrad.apply
diff --git a/nni/algorithms/compression/tensorflow/pruning/__init__.py b/nni/algorithms/compression/tensorflow/pruning/__init__.py
index c535fd751..90cad691e 100644
--- a/nni/algorithms/compression/tensorflow/pruning/__init__.py
+++ b/nni/algorithms/compression/tensorflow/pruning/__init__.py
@@ -1 +1,4 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from .one_shot_pruner import *
diff --git a/nni/algorithms/compression/tensorflow/pruning/one_shot_pruner.py b/nni/algorithms/compression/tensorflow/pruning/one_shot_pruner.py
index 2c1e1e3e0..23f08f76c 100644
--- a/nni/algorithms/compression/tensorflow/pruning/one_shot_pruner.py
+++ b/nni/algorithms/compression/tensorflow/pruning/one_shot_pruner.py
@@ -1,3 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import tensorflow as tf
 
 from nni.compression.tensorflow import Pruner
diff --git a/nni/algorithms/compression/v2/pytorch/base/__init__.py b/nni/algorithms/compression/v2/pytorch/base/__init__.py
index 6efb20cab..0634c0b32 100644
--- a/nni/algorithms/compression/v2/pytorch/base/__init__.py
+++ b/nni/algorithms/compression/v2/pytorch/base/__init__.py
@@ -1,3 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from .compressor import Compressor, LayerInfo
 from .pruner import Pruner, PrunerModuleWrapper
 from .scheduler import BasePruningScheduler, Task, TaskResult
diff --git a/nni/algorithms/compression/v2/pytorch/base/compressor.py b/nni/algorithms/compression/v2/pytorch/base/compressor.py
index 50e530cc8..dba226af7 100644
--- a/nni/algorithms/compression/v2/pytorch/base/compressor.py
+++ b/nni/algorithms/compression/v2/pytorch/base/compressor.py
@@ -35,17 +35,16 @@ def _setattr(model: Module, name: str, module: Module):
 class Compressor:
     """
     The abstract base pytorch compressor.
+
+    Parameters
+    ----------
+    model
+        The model under compressed.
+    config_list
+        The config list used by compressor, usually specifies the 'op_types' or 'op_names' that want to compress.
     """
 
     def __init__(self, model: Optional[Module], config_list: Optional[List[Dict]]):
-        """
-        Parameters
-        ----------
-        model
-            The model under compressed.
-        config_list
-            The config list used by compressor, usually specifies the 'op_types' or 'op_names' that want to compress.
-        """
         self.is_wrapped = False
         if model is not None:
             self.reset(model=model, config_list=config_list)
@@ -258,14 +257,7 @@ class Compressor:
         Dict[str, str]
             Return a dict `{original_model_parameter_name: wrapped_model_parameter_name}`
         """
-        if self.is_wrapped:
-            wrapped_param_names = {id(param): name for name, param in self.bound_model.named_parameters()}
-            self._unwrap_model()
-            parameter_name_map = {name: wrapped_param_names[id(param)] for name, param in self.bound_model.named_parameters()}
-            self._wrap_model()
-            return parameter_name_map
-        else:
-            raise Exception('When only the model is wrapped can get the parameter_name_map.')
+        raise NotImplementedError()
 
     def _wrap_modules(self, layer: LayerInfo, config: Dict):
         """
diff --git a/nni/algorithms/compression/v2/pytorch/base/pruner.py b/nni/algorithms/compression/v2/pytorch/base/pruner.py
index 730b9c749..c10b5b193 100644
--- a/nni/algorithms/compression/v2/pytorch/base/pruner.py
+++ b/nni/algorithms/compression/v2/pytorch/base/pruner.py
@@ -6,9 +6,9 @@ from typing import Dict, List, Optional, Tuple
 
 import torch
 from torch import Tensor
-from torch.nn import Module
+from torch.nn import Module, Parameter
 
-from .compressor import Compressor, LayerInfo
+from .compressor import Compressor, LayerInfo, _setattr
 
 _logger = logging.getLogger(__name__)
 
@@ -16,41 +16,68 @@ __all__ = ['Pruner']
 
 
 class PrunerModuleWrapper(Module):
-    def __init__(self, module: Module, module_name: str, config: Dict, pruner: Compressor):
-        """
-        Wrap a module to enable data parallel, forward method customization and buffer registeration.
+    """
+    Wrap a module to enable data parallel, forward method customization and buffer registeration.
+
+    Parameters
+    ----------
+    module
+        The module user wants to compress.
+    config
+        The configurations that users specify for compression.
+    module_name
+        The name of the module to compress, wrapper module shares same name.
+    """
 
-        Parameters
-        ----------
-        module
-            The module user wants to compress.
-        config
-            The configurations that users specify for compression.
-        module_name
-            The name of the module to compress, wrapper module shares same name.
-        pruner
-            The pruner used to calculate mask.
-        """
+    def __init__(self, module: Module, module_name: str, config: Dict):
         super().__init__()
         # origin layer information
         self.module = module
         self.name = module_name
-        # config and pruner
+        # config information
         self.config = config
-        self.pruner = pruner
+
+        self.weight = Parameter(torch.empty(self.module.weight.size()))
 
         # register buffer for mask
         self.register_buffer("weight_mask", torch.ones(self.module.weight.shape))
         if hasattr(self.module, 'bias') and self.module.bias is not None:
             self.register_buffer("bias_mask", torch.ones(self.module.bias.shape))
+            self.bias = Parameter(torch.empty(self.module.bias.size()))
         else:
             self.register_buffer("bias_mask", None)
 
+    def _weight2buffer(self):
+        """
+        When using this wrapper to inference, call `_weight2buffer()` to make original weight untrainable.
+        The best place to call this function is in `Pruner._wrap_model()`.
+        """
+        self.weight.data = self.module.weight.data
+        delattr(self.module, 'weight')
+        self.module.register_buffer('weight', self.weight.data)
+        if hasattr(self.module, 'bias') and self.module.bias is not None:
+            self.bias.data = self.module.bias.data
+            delattr(self.module, 'bias')
+            self.module.register_buffer('bias', self.bias.data)
+
+    def _weight2parameter(self):
+        """
+        When don't need to record score or need to export the model, call `_weight2parameter()` to make the original weight trainable.
+        The best place to call this function is in `Pruner._unwrap_model()`.
+        """
+        delattr(self.module, 'weight')
+        self.module.weight = Parameter(torch.empty(self.weight.size()))
+        self.module.weight.data = torch.mul(self.weight, self.weight_mask)
+        if hasattr(self.module, 'bias') and self.module.bias is not None:
+            delattr(self.module, 'bias')
+            self.module.bias = Parameter(torch.empty(self.bias.size()))
+            self.module.bias.data = torch.mul(self.bias, self.bias_mask)
+
     def forward(self, *inputs):
         # apply mask to weight, bias
-        self.module.weight.data = self.module.weight.data.mul_(self.weight_mask)
+        self.module.weight = torch.mul(self.weight, self.weight_mask)
         if hasattr(self.module, 'bias') and self.module.bias is not None:
-            self.module.bias.data = self.module.bias.data.mul_(self.bias_mask)
+            self.module.bias = torch.mul(self.bias, self.bias_mask)
         return self.module(*inputs)
 
 
@@ -74,12 +101,58 @@ class Pruner(Compressor):
             The configuration for generating the mask.
         """
         _logger.debug("Module detected to compress : %s.", layer.name)
-        wrapper = PrunerModuleWrapper(layer.module, layer.name, config, self)
+        wrapper = PrunerModuleWrapper(layer.module, layer.name, config)
         assert hasattr(layer.module, 'weight'), "module %s does not have 'weight' attribute" % layer.name
         # move newly registered buffers to the same device of weight
         wrapper.to(layer.module.weight.device)
         return wrapper
 
+    # The following `_wrap_model`, `_unwrap_model`, `get_origin2wrapped_parameter_name_map` can merge to `Compressor`,
+    # if quantizer use the similar structure wrapper.
+    def _wrap_model(self):
+        """
+        Wrap all modules that needed to be compressed.
+        Different from the parent function, call `wrapper._weight2buffer()` after replace the origin module to wrapper.
+        """
+        if not self.is_wrapped:
+            for _, wrapper in reversed(self.get_modules_wrapper().items()):
+                _setattr(self.bound_model, wrapper.name, wrapper)
+                wrapper._weight2buffer()
+            self.is_wrapped = True
+
+    def _unwrap_model(self):
+        """
+        Unwrap all modules that needed to be compressed.
+        Different from the parent function, call `wrapper._weight2parameter()` after replace the wrapper to origin module.
+        """
+        if self.is_wrapped:
+            for _, wrapper in self.get_modules_wrapper().items():
+                _setattr(self.bound_model, wrapper.name, wrapper.module)
+                wrapper._weight2parameter()
+            self.is_wrapped = False
+
+    def get_origin2wrapped_parameter_name_map(self) -> Dict[str, str]:
+        """
+        Get the name mapping of parameters from original model to wrapped model.
+
+        Returns
+        -------
+        Dict[str, str]
+            Return a dict `{original_model_parameter_name: wrapped_model_parameter_name}`
+        """
+        if self.is_wrapped:
+            wrapped_param_names = {id(param): name for name, param in self.bound_model.named_parameters()}
+            self._unwrap_model()
+            parameter_name_map = {}
+            for name, param in self.bound_model.named_parameters():
+                # If the parameter name in under wrapped module is `xxx.weight` or `xxx.bias`, the name will not change after wrap.
+                # If the parameter name in under wrapped module is others, the name `xxx.param` will change to `xxx.module.param` after wrap.
+                parameter_name_map[name] = wrapped_param_names[id(param)] if id(param) in wrapped_param_names else name
+            self._wrap_model()
+            return parameter_name_map
+        else:
+            raise Exception('When only the model is wrapped can get the parameter_name_map.')
+
     def load_masks(self, masks: Dict[str, Dict[str, Tensor]]):
         """
         Load an exist masks on the wrapper. You can train the model with an exist masks after load the masks.
diff --git a/nni/algorithms/compression/v2/pytorch/base/scheduler.py b/nni/algorithms/compression/v2/pytorch/base/scheduler.py
index 0c22b3eef..de3490249 100644
--- a/nni/algorithms/compression/v2/pytorch/base/scheduler.py
+++ b/nni/algorithms/compression/v2/pytorch/base/scheduler.py
@@ -20,7 +20,7 @@ class Task:
     _reference_counter = {}
 
     def __init__(self, task_id: int, model_path: str, masks_path: str, config_list_path: str,
-                 speed_up: Optional[bool] = True, finetune: Optional[bool] = True, evaluate: Optional[bool] = True):
+                 speedup: Optional[bool] = True, finetune: Optional[bool] = True, evaluate: Optional[bool] = True):
         """
         Parameters
         ----------
@@ -32,8 +32,8 @@ class Task:
             The path of the masks that applied on the model before pruning.
         config_list_path
             The path of the config list that used in this task.
-        speed_up
-            Control if this task needs speed up, True means use scheduler default value, False means no speed up.
+        speedup
+            Control if this task needs speedup, True means use scheduler default value, False means no speedup.
         finetune
             Control if this task needs finetune, True means use scheduler default value, False means no finetune.
         evaluate
@@ -44,7 +44,7 @@ class Task:
         self.masks_path = masks_path
         self.config_list_path = config_list_path
 
-        self.speed_up = speed_up
+        self.speedup = speedup
         self.finetune = finetune
         self.evaluate = evaluate
 
@@ -65,7 +65,7 @@ class Task:
             'model_path': str(self.model_path),
             'masks_path': str(self.masks_path),
             'config_list_path': str(self.config_list_path),
-            'speed_up': self.speed_up,
+            'speedup': self.speedup,
             'finetune': self.finetune,
             'evaluate': self.evaluate,
             'status': self.status,
diff --git a/nni/algorithms/compression/v2/pytorch/pruning/__init__.py b/nni/algorithms/compression/v2/pytorch/pruning/__init__.py
index 90b49cc5c..de3ff4d39 100644
--- a/nni/algorithms/compression/v2/pytorch/pruning/__init__.py
+++ b/nni/algorithms/compression/v2/pytorch/pruning/__init__.py
@@ -1,3 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from .basic_pruner import *
 from .basic_scheduler import PruningScheduler
 from .iterative_pruner import *
diff --git a/nni/algorithms/compression/v2/pytorch/pruning/amc_pruner.py b/nni/algorithms/compression/v2/pytorch/pruning/amc_pruner.py
index 3b4ee20a3..0e60533f0 100644
--- a/nni/algorithms/compression/v2/pytorch/pruning/amc_pruner.py
+++ b/nni/algorithms/compression/v2/pytorch/pruning/amc_pruner.py
@@ -12,7 +12,7 @@ from torch.nn import Module
 
 from nni.algorithms.compression.v2.pytorch.base import Task, TaskResult
 from nni.algorithms.compression.v2.pytorch.utils import compute_sparsity, config_list_canonical
-from nni.compression.pytorch.utils.counter import count_flops_params
+from nni.compression.pytorch.utils import count_flops_params
 
 from .iterative_pruner import IterativePruner, PRUNER_DICT
 from .tools import TaskGenerator
@@ -160,9 +160,13 @@ class AMCTaskGenerator(TaskGenerator):
 
 
 class AMCPruner(IterativePruner):
-    """
-    A pytorch implementation of AMC: AutoML for Model Compression and Acceleration on Mobile Devices.
-    (https://arxiv.org/pdf/1802.03494.pdf)
+    r"""
+    AMC pruner leverages reinforcement learning to provide the model compression policy.
+    According to the author, this learning-based compression policy outperforms conventional rule-based compression policy by having a higher compression ratio,
+    better preserving the accuracy and freeing human labor.
+
+    For more details, please refer to `AMC: AutoML for Model Compression and Acceleration on Mobile Devices <https://arxiv.org/pdf/1802.03494.pdf>`__.
+
     Suggust config all `total_sparsity` in `config_list` a same value.
     AMC pruner will treat the first sparsity in `config_list` as the global sparsity.
 
@@ -181,7 +185,7 @@ class AMCPruner(IterativePruner):
             - op_partial_names: Operation partial names to be pruned, will be autocompleted by NNI.
             - exclude  : Set True then the layers setting by op_types and op_names will be excluded from pruning.
     dummy_input : torch.Tensor
-        `dummy_input` is required for speed-up and tracing the model in RL environment.
+        `dummy_input` is required for speedup and tracing the model in RL environment.
     evaluator : Callable[[Module], float]
         Evaluate the pruned model and give a score.
     pruning_algorithm : str
@@ -216,6 +220,18 @@ class AMCPruner(IterativePruner):
     target : str
         'flops' or 'params'. Note that the sparsity in other pruners always means the parameters sparse, but in AMC, you can choose flops sparse.
         This parameter is used to explain what the sparsity setting in config_list refers to.
+
+    Examples
+    --------
+        >>> from nni.compression.pytorch.pruning import AMCPruner
+        >>> config_list = [{'op_types': ['Conv2d'], 'total_sparsity': 0.5, 'max_sparsity_per_layer': 0.8}]
+        >>> dummy_input = torch.rand(...).to(device)
+        >>> evaluator = ...
+        >>> finetuner = ...
+        >>> pruner = AMCPruner(400, model, config_list, dummy_input, evaluator, finetuner=finetuner)
+        >>> pruner.compress()
+
+    The full script can be found :githublink:`here <examples/model_compress/pruning/amc_pruning_torch.py>`.
     """
 
     def __init__(self, total_episode: int, model: Module, config_list: List[Dict], dummy_input: Tensor,
@@ -233,5 +249,5 @@ class AMCPruner(IterativePruner):
                                           ddpg_params=ddpg_params,
                                           target=target)
         pruner = PRUNER_DICT[pruning_algorithm](None, None, **pruning_params)
-        super().__init__(pruner, task_generator, finetuner=finetuner, speed_up=True, dummy_input=dummy_input,
+        super().__init__(pruner, task_generator, finetuner=finetuner, speedup=True, dummy_input=dummy_input,
                          evaluator=evaluator, reset_weight=False)
diff --git a/nni/algorithms/compression/v2/pytorch/pruning/auto_compress_pruner.py b/nni/algorithms/compression/v2/pytorch/pruning/auto_compress_pruner.py
index 08f79b523..91497316f 100644
--- a/nni/algorithms/compression/v2/pytorch/pruning/auto_compress_pruner.py
+++ b/nni/algorithms/compression/v2/pytorch/pruning/auto_compress_pruner.py
@@ -51,7 +51,16 @@ class AutoCompressTaskGenerator(LotteryTicketTaskGenerator):
 
 
 class AutoCompressPruner(IterativePruner):
-    """
+    r"""
+    For total iteration number :math:`N`, AutoCompressPruner prune the model that survive the previous iteration for a fixed sparsity ratio (e.g., :math:`1-{(1-0.8)}^{(1/N)}`) to achieve the overall sparsity (e.g., :math:`0.8`):
+
+    .. code-block:: bash
+
+        1. Generate sparsities distribution using SimulatedAnnealingPruner
+        2. Perform ADMM-based pruning to generate pruning result for the next iteration.
+
+    For more details, please refer to `AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates <https://arxiv.org/abs/1907.03141>`__.
+
     Parameters
     ----------
     model : Module
@@ -70,7 +79,7 @@ class AutoCompressPruner(IterativePruner):
             The model will be trained or inferenced `training_epochs` epochs.
         - traced_optimizer : nni.common.serializer.Traceable(torch.optim.Optimizer)
             The traced optimizer instance which the optimizer class is wrapped by nni.trace.
-            E.g. traced_optimizer = nni.trace(torch.nn.Adam)(model.parameters()).
+            E.g. ``traced_optimizer = nni.trace(torch.nn.Adam)(model.parameters())``.
         - criterion : Callable[[Tensor, Tensor], Tensor].
             The criterion function used in trainer. Take model output and target value as input, and return the loss.
         - iterations : int.
@@ -103,15 +112,43 @@ class AutoCompressPruner(IterativePruner):
     finetuner : Optional[Callable[[Module], None]]
         The finetuner handles all finetune logic, takes a pytorch module as input.
         It will be called at the end of each iteration, usually for neutralizing the accuracy loss brought by the pruning in this iteration.
-    speed_up : bool
-        If set True, speed up the model at the end of each iteration to make the pruned model compact.
+    speedup : bool
+        If set True, speedup the model at the end of each iteration to make the pruned model compact.
     dummy_input : Optional[torch.Tensor]
-        If `speed_up` is True, `dummy_input` is required for tracing the model in speed up.
+        If `speedup` is True, `dummy_input` is required for tracing the model in speedup.
+
+    Examples
+    --------
+        >>> import nni
+        >>> from nni.compression.pytorch.pruning import AutoCompressPruner
+        >>> model = ...
+        >>> config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
+        >>> # make sure you have used nni.trace to wrap the optimizer class before initialize
+        >>> traced_optimizer = nni.trace(torch.optim.Adam)(model.parameters())
+        >>> trainer = ...
+        >>> criterion = ...
+        >>> evaluator = ...
+        >>> finetuner = ...
+        >>> admm_params = {
+        >>>     'trainer': trainer,
+        >>>     'traced_optimizer': traced_optimizer,
+        >>>     'criterion': criterion,
+        >>>     'iterations': 10,
+        >>>     'training_epochs': 1
+        >>> }
+        >>> sa_params = {
+        >>>     'evaluator': evaluator
+        >>> }
+        >>> pruner = AutoCompressPruner(model, config_list, 10, admm_params, sa_params, finetuner=finetuner)
+        >>> pruner.compress()
+        >>> _, model, masks, _, _ = pruner.get_best_result()
+
+    The full script can be found :githublink:`here <examples/model_compress/pruning/auto_compress_pruner.py>`.
     """
 
     def __init__(self, model: Module, config_list: List[Dict], total_iteration: int, admm_params: Dict,
                  sa_params: Dict, log_dir: str = '.', keep_intermediate_result: bool = False,
-                 finetuner: Optional[Callable[[Module], None]] = None, speed_up: bool = False,
+                 finetuner: Optional[Callable[[Module], None]] = None, speedup: bool = False,
                  dummy_input: Optional[Tensor] = None, evaluator: Callable[[Module], float] = None):
         task_generator = AutoCompressTaskGenerator(total_iteration=total_iteration,
                                                    origin_model=model,
@@ -121,6 +158,21 @@ class AutoCompressPruner(IterativePruner):
                                                    keep_intermediate_result=keep_intermediate_result)
         if 'traced_optimizer' in admm_params:
             admm_params['traced_optimizer'] = OptimizerConstructHelper.from_trace(model, admm_params['traced_optimizer'])
+        # granularity in ADMM stage will align with SA stage, if 'granularity' is not specify
+        if 'granularity' not in admm_params:
+            # only if level pruning and fine-grained admm pruning used in SA, fine-grained admm pruning will used in auto-compress
+            if 'pruning_algorithm' in sa_params:
+                sa_algo = sa_params['pruning_algorithm']
+                sa_algo_params = sa_params.get('pruning_params')
+                if sa_algo in ['level']:
+                    admm_params['granularity'] = 'fine-grained'
+                elif sa_algo in ['admm'] and (sa_algo_params is not None) and not (sa_algo_params.get('granularity') == 'coarse-grained'):
+                    admm_params['granularity'] = 'fine-grained'
+                else:
+                    admm_params['granularity'] = 'coarse-grained'
+            else:
+                admm_params['granularity'] = 'fine-grained'
+
         pruner = ADMMPruner(None, None, **admm_params)
-        super().__init__(pruner, task_generator, finetuner=finetuner, speed_up=speed_up, dummy_input=dummy_input,
+        super().__init__(pruner, task_generator, finetuner=finetuner, speedup=speedup, dummy_input=dummy_input,
                          evaluator=evaluator, reset_weight=False)
diff --git a/nni/algorithms/compression/v2/pytorch/pruning/basic_pruner.py b/nni/algorithms/compression/v2/pytorch/pruning/basic_pruner.py
index be12da4b2..ee7beb6a6 100644
--- a/nni/algorithms/compression/v2/pytorch/pruning/basic_pruner.py
+++ b/nni/algorithms/compression/v2/pytorch/pruning/basic_pruner.py
@@ -125,11 +125,14 @@ class BasicPruner(Pruner):
 
 
 class LevelPruner(BasicPruner):
-    """
+    r"""
+    This is a basic pruner, and in some papers called it magnitude pruning or fine-grained pruning.
+    It will mask the smallest magnitude weights in each specified layer by a saprsity ratio configured in the config list.
+
     Parameters
     ----------
     model : torch.nn.Module
-        Model to be pruned
+        Model to be pruned.
     config_list : List[Dict]
         Supported keys:
             - sparsity : This is to specify the sparsity for each layer in this config to be compressed.
@@ -145,18 +148,18 @@ class LevelPruner(BasicPruner):
         operation an example, weight tensor will be split into sub block whose shape is aligned to
         balance_gran. Then finegrained pruning will be applied internal of sub block. This sparsity
         pattern have more chance to achieve better trade-off between model performance and hardware
-        acceleration. Please refer to releated paper for further information 'Balanced Sparsity for 
-        Efficient DNN Inference on GPU'(https://arxiv.org/pdf/1811.00206.pdf).
+        acceleration. Please refer to releated paper for further information `Balanced Sparsity for
+        Efficient DNN Inference on GPU <https://arxiv.org/pdf/1811.00206.pdf>`__.
     balance_gran : list
-        Balance_gran is for special sparse pattern balanced sparsity, Default value is None which means pruning 
+        Balance_gran is for special sparse pattern balanced sparsity, Default value is None which means pruning
         without awaring balance, namely normal finegrained pruning.
         If passing list of int, LevelPruner will prune the model in the granularity of multi-dimension block.
         Attention that the length of balance_gran should be smaller than tensor dimension.
         For instance, in Linear operation, length of balance_gran should be equal or smaller than two since
-        dimension of pruning weight is two. If setting balbance_gran = [5, 5], sparsity = 0.6, pruner will 
-        divide pruning parameters into multiple block with tile size (5,5) and each bank has 5 * 5 values 
-        and 10 values would be kept after pruning. Finegrained pruning is applied in the granularity of block 
-        so that each block will kept same number of non-zero values after pruning. Such pruning method "balance" 
+        dimension of pruning weight is two. If setting balbance_gran = [5, 5], sparsity = 0.6, pruner will
+        divide pruning parameters into multiple block with tile size (5,5) and each bank has 5 * 5 values
+        and 10 values would be kept after pruning. Finegrained pruning is applied in the granularity of block
+        so that each block will kept same number of non-zero values after pruning. Such pruning method "balance"
         the non-zero value in tensor which create chance for better hardware acceleration.
 
         Note: If length of given balance_gran smaller than length of pruning tensor shape, it will be made up
@@ -181,7 +184,16 @@ class LevelPruner(BasicPruner):
 
                 pruning result: Weight tensor whose shape is [64, 64] will be split into 4 [32, 32] sub blocks.
                                 Each sub block will be pruned 256 values.
-                
+
+    Examples
+    --------
+        >>> model = ...
+        >>> from nni.compression.pytorch.pruning import LevelPruner
+        >>> config_list = [{ 'sparsity': 0.8, 'op_types': ['default'] }]
+        >>> pruner = LevelPruner(model, config_list)
+        >>> masked_model, masks = pruner.compress()
+
+    For detailed example please refer to :githublink:`examples/model_compress/pruning/level_pruning_torch.py <examples/model_compress/pruning/level_pruning_torch.py>`
     """
 
     def __init__(self, model: Module, config_list: List[Dict], mode: str = "normal", balance_gran: Optional[List] = None):
@@ -215,7 +227,7 @@ class NormPruner(BasicPruner):
     Parameters
     ----------
     model : torch.nn.Module
-        Model to be pruned
+        Model to be pruned.
     config_list : List[Dict]
         Supported keys:
             - sparsity : This is to specify the sparsity for each layer in this config to be compressed.
@@ -272,11 +284,20 @@ class NormPruner(BasicPruner):
 
 
 class L1NormPruner(NormPruner):
-    """
+    r"""
+    L1 norm pruner computes the l1 norm of the layer weight on the first dimension,
+    then prune the weight blocks on this dimension with smaller l1 norm values.
+    i.e., compute the l1 norm of the filters in convolution layer as metric values,
+    compute the l1 norm of the weight by rows in linear layer as metric values.
+
+    For more details, please refer to `PRUNING FILTERS FOR EFFICIENT CONVNETS <https://arxiv.org/abs/1608.08710>`__.
+
+    In addition, L1 norm pruner also supports dependency-aware mode.
+
     Parameters
     ----------
     model : torch.nn.Module
-        Model to be pruned
+        Model to be pruned.
     config_list : List[Dict]
         Supported keys:
             - sparsity : This is to specify the sparsity for each layer in this config to be compressed.
@@ -305,16 +326,21 @@ class L1NormPruner(NormPruner):
 
 
 class L2NormPruner(NormPruner):
-    """
+    r"""
+    L2 norm pruner is a variant of L1 norm pruner.
+    The only different between L2 norm pruner and L1 norm pruner is L2 norm pruner prunes the weight with the smallest L2 norm of the weights.
+
+    L2 norm pruner also supports dependency-aware mode.
+
     Parameters
     ----------
     model : torch.nn.Module
-        Model to be pruned
+        Model to be pruned.
     config_list : List[Dict]
         Supported keys:
             - sparsity : This is to specify the sparsity for each layer in this config to be compressed.
             - sparsity_per_layer : Equals to sparsity.
-            - op_types : Conv2d and Linear are supported in L1NormPruner.
+            - op_types : Conv2d and Linear are supported in L2NormPruner.
             - op_names : Operation names to be pruned.
             - op_partial_names: Operation partial names to be pruned, will be autocompleted by NNI.
             - exclude : Set True then the layers setting by op_types and op_names will be excluded from pruning.
@@ -330,6 +356,16 @@ class L2NormPruner(NormPruner):
     dummy_input : Optional[torch.Tensor]
         The dummy input to analyze the topology constraints. Note that, the dummy_input
         should on the same device with the model.
+
+    Examples
+    --------
+        >>> model = ...
+        >>> from nni.compression.pytorch.pruning import L2NormPruner
+        >>> config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
+        >>> pruner = L2NormPruner(model, config_list)
+        >>> masked_model, masks = pruner.compress()
+
+    For detailed example please refer to :githublink:`examples/model_compress/pruning/norm_pruning_torch.py <examples/model_compress/pruning/norm_pruning_torch.py>`
     """
 
     def __init__(self, model: Module, config_list: List[Dict],
@@ -338,11 +374,18 @@ class L2NormPruner(NormPruner):
 
 
 class FPGMPruner(BasicPruner):
-    """
+    r"""
+    FPGM pruner prunes the blocks of the weight on the first dimension with the smallest geometric median.
+    FPGM chooses the weight blocks with the most replaceable contribution.
+
+    For more details, please refer to `Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration <https://arxiv.org/abs/1811.00250>`__.
+
+    FPGM pruner also supports dependency-aware mode.
+
     Parameters
     ----------
     model : torch.nn.Module
-        Model to be pruned
+        Model to be pruned.
     config_list : List[Dict]
         Supported keys:
             - sparsity : This is to specify the sparsity for each layer in this config to be compressed.
@@ -363,6 +406,16 @@ class FPGMPruner(BasicPruner):
     dummy_input : Optional[torch.Tensor]
         The dummy input to analyze the topology constraints. Note that, the dummy_input
         should on the same device with the model.
+
+    Examples
+    --------
+        >>> model = ...
+        >>> from nni.compression.pytorch.pruning import FPGMPruner
+        >>> config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
+        >>> pruner = FPGMPruner(model, config_list)
+        >>> masked_model, masks = pruner.compress()
+
+    For detailed example please refer to :githublink:`examples/model_compress/pruning/fpgm_pruning_torch.py <examples/model_compress/pruning/fpgm_pruning_torch.py>`
     """
 
     def __init__(self, model: Module, config_list: List[Dict],
@@ -396,11 +449,16 @@ class FPGMPruner(BasicPruner):
 
 
 class SlimPruner(BasicPruner):
-    """
+    r"""
+    Slim pruner adds sparsity regularization on the scaling factors of batch normalization (BN) layers during training to identify unimportant channels.
+    The channels with small scaling factor values will be pruned.
+
+    For more details, please refer to `Learning Efficient Convolutional Networks through Network Slimming <https://arxiv.org/abs/1708.06519>`__\.
+
     Parameters
     ----------
     model : torch.nn.Module
-        Model to be pruned
+        Model to be pruned.
     config_list : List[Dict]
         Supported keys:
             - sparsity : This is to specify the sparsity for each layer in this config to be compressed.
@@ -432,7 +490,7 @@ class SlimPruner(BasicPruner):
                 model.train(mode=training)
     traced_optimizer : nni.common.serializer.Traceable(torch.optim.Optimizer)
         The traced optimizer instance which the optimizer class is wrapped by nni.trace.
-        E.g. traced_optimizer = nni.trace(torch.nn.Adam)(model.parameters()).
+        E.g. ``traced_optimizer = nni.trace(torch.nn.Adam)(model.parameters())``.
     criterion : Callable[[Tensor, Tensor], Tensor]
         The criterion function used in trainer. Take model output and target value as input, and return the loss.
     training_epochs : int
@@ -444,6 +502,21 @@ class SlimPruner(BasicPruner):
         If prune the model in a global way, all layer weights with same config will be considered uniformly.
         That means a single layer may not reach or exceed the sparsity setting in config,
         but the total pruned weights meet the sparsity setting.
+
+    Examples
+    --------
+        >>> import nni
+        >>> from nni.compression.pytorch.pruning import SlimPruner
+        >>> model = ...
+        >>> # make sure you have used nni.trace to wrap the optimizer class before initialize
+        >>> traced_optimizer = nni.trace(torch.optim.Adam)(model.parameters())
+        >>> trainer = ...
+        >>> criterion = ...
+        >>> config_list = [{ 'sparsity': 0.8, 'op_types': ['BatchNorm2d'] }]
+        >>> pruner = SlimPruner(model, config_list, trainer, traced_optimizer, criterion, training_epochs=1)
+        >>> masked_model, masks = pruner.compress()
+
+    For detailed example please refer to :githublink:`examples/model_compress/pruning/slim_pruning_torch.py <examples/model_compress/pruning/slim_pruning_torch.py>`
     """
 
     def __init__(self, model: Module, config_list: List[Dict], trainer: Callable[[Module, Optimizer, Callable], None],
@@ -507,7 +580,7 @@ class ActivationPruner(BasicPruner):
     Parameters
     ----------
     model : torch.nn.Module
-        Model to be pruned
+        Model to be pruned.
     config_list : List[Dict]
         Supported keys:
             - sparsity : This is to specify the sparsity for each layer in this config to be compressed.
@@ -537,7 +610,7 @@ class ActivationPruner(BasicPruner):
                 model.train(mode=training)
     traced_optimizer : nni.common.serializer.Traceable(torch.optim.Optimizer)
         The traced optimizer instance which the optimizer class is wrapped by nni.trace.
-        E.g. traced_optimizer = nni.trace(torch.nn.Adam)(model.parameters()).
+        E.g. ``traced_optimizer = nni.trace(torch.nn.Adam)(model.parameters())``.
     criterion : Callable[[Tensor, Tensor], Tensor]
         The criterion function used in trainer. Take model output and target value as input, and return the loss.
     training_batches
@@ -627,6 +700,82 @@ class ActivationPruner(BasicPruner):
 
 
 class ActivationAPoZRankPruner(ActivationPruner):
+    r"""
+    Activation APoZ rank pruner is a pruner which prunes on the first weight dimension,
+    with the smallest importance criterion ``APoZ`` calculated from the output activations of convolution layers to achieve a preset level of network sparsity.
+    The pruning criterion ``APoZ`` is explained in the paper `Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures <https://arxiv.org/abs/1607.03250>`__.
+
+    The APoZ is defined as:
+    :math:`APoZ_{c}^{(i)} = APoZ\left(O_{c}^{(i)}\right)=\frac{\sum_{k}^{N} \sum_{j}^{M} f\left(O_{c, j}^{(i)}(k)=0\right)}{N \times M}`
+
+    Activation APoZ rank pruner also supports dependency-aware mode.
+
+    Parameters
+    ----------
+    model : torch.nn.Module
+        Model to be pruned.
+    config_list : List[Dict]
+        Supported keys:
+            - sparsity : This is to specify the sparsity for each layer in this config to be compressed.
+            - sparsity_per_layer : Equals to sparsity.
+            - op_types : Conv2d and Linear are supported in ActivationAPoZRankPruner.
+            - op_names : Operation names to be pruned.
+            - op_partial_names: Operation partial names to be pruned, will be autocompleted by NNI.
+            - exclude : Set True then the layers setting by op_types and op_names will be excluded from pruning.
+    trainer : Callable[[Module, Optimizer, Callable], None]
+        A callable function used to train model or just inference. Take model, optimizer, criterion as input.
+        The model will be trained or inferenced `training_epochs` epochs.
+
+        Example::
+
+            def trainer(model: Module, optimizer: Optimizer, criterion: Callable[[Tensor, Tensor], Tensor]):
+                training = model.training
+                model.train(mode=True)
+                device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+                for batch_idx, (data, target) in enumerate(train_loader):
+                    data, target = data.to(device), target.to(device)
+                    optimizer.zero_grad()
+                    output = model(data)
+                    loss = criterion(output, target)
+                    loss.backward()
+                    # If you don't want to update the model, you can skip `optimizer.step()`, and set train mode False.
+                    optimizer.step()
+                model.train(mode=training)
+    traced_optimizer : nni.common.serializer.Traceable(torch.optim.Optimizer)
+        The traced optimizer instance which the optimizer class is wrapped by nni.trace.
+        E.g. ``traced_optimizer = nni.trace(torch.nn.Adam)(model.parameters())``..
+    criterion : Callable[[Tensor, Tensor], Tensor]
+        The criterion function used in trainer. Take model output and target value as input, and return the loss.
+    training_batches
+        The batch number used to collect activations.
+    mode : str
+        'normal' or 'dependency_aware'.
+        If prune the model in a dependency-aware way, this pruner will
+        prune the model according to the activation-based metrics and the channel-dependency or
+        group-dependency of the model. In this way, the pruner will force the conv layers
+        that have dependencies to prune the same channels, so the speedup module can better
+        harvest the speed benefit from the pruned model. Note that, if set 'dependency_aware'
+        , the dummy_input cannot be None, because the pruner needs a dummy input to trace the
+        dependency between the conv layers.
+    dummy_input : Optional[torch.Tensor]
+        The dummy input to analyze the topology constraints. Note that, the dummy_input
+        should on the same device with the model.
+
+    Examples
+    --------
+        >>> import nni
+        >>> from nni.compression.pytorch.pruning import ActivationAPoZRankPruner
+        >>> model = ...
+        >>> # make sure you have used nni.trace to wrap the optimizer class before initialize
+        >>> traced_optimizer = nni.trace(torch.optim.Adam)(model.parameters())
+        >>> trainer = ...
+        >>> criterion = ...
+        >>> config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
+        >>> pruner = ActivationAPoZRankPruner(model, config_list, trainer, traced_optimizer, criterion, training_batches=20)
+        >>> masked_model, masks = pruner.compress()
+
+    For detailed example please refer to :githublink:`examples/model_compress/pruning/activation_pruning_torch.py <examples/model_compress/pruning/activation_pruning_torch.py>`
+    """
     def _activation_trans(self, output: Tensor) -> Tensor:
         # return a matrix that the position of zero in `output` is one, others is zero.
         return torch.eq(self._activation(output.detach()), torch.zeros_like(output)).type_as(output)
@@ -636,6 +785,80 @@ class ActivationAPoZRankPruner(ActivationPruner):
 
 
 class ActivationMeanRankPruner(ActivationPruner):
+    r"""
+    Activation mean rank pruner is a pruner which prunes on the first weight dimension,
+    with the smallest importance criterion ``mean activation`` calculated from the output activations of convolution layers to achieve a preset level of network sparsity.
+
+    The pruning criterion ``mean activation`` is explained in section 2.2 of the paper `Pruning Convolutional Neural Networks for Resource Efficient Inference <https://arxiv.org/abs/1611.06440>`__.
+
+    Activation mean rank pruner also supports dependency-aware mode.
+
+    Parameters
+    ----------
+    model : torch.nn.Module
+        Model to be pruned.
+    config_list : List[Dict]
+        Supported keys:
+            - sparsity : This is to specify the sparsity for each layer in this config to be compressed.
+            - sparsity_per_layer : Equals to sparsity.
+            - op_types : Conv2d and Linear are supported in ActivationPruner.
+            - op_names : Operation names to be pruned.
+            - op_partial_names: Operation partial names to be pruned, will be autocompleted by NNI.
+            - exclude : Set True then the layers setting by op_types and op_names will be excluded from pruning.
+    trainer : Callable[[Module, Optimizer, Callable], None]
+        A callable function used to train model or just inference. Take model, optimizer, criterion as input.
+        The model will be trained or inferenced `training_epochs` epochs.
+
+        Example::
+
+            def trainer(model: Module, optimizer: Optimizer, criterion: Callable[[Tensor, Tensor], Tensor]):
+                training = model.training
+                model.train(mode=True)
+                device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+                for batch_idx, (data, target) in enumerate(train_loader):
+                    data, target = data.to(device), target.to(device)
+                    optimizer.zero_grad()
+                    output = model(data)
+                    loss = criterion(output, target)
+                    loss.backward()
+                    # If you don't want to update the model, you can skip `optimizer.step()`, and set train mode False.
+                    optimizer.step()
+                model.train(mode=training)
+    traced_optimizer : nni.common.serializer.Traceable(torch.optim.Optimizer)
+        The traced optimizer instance which the optimizer class is wrapped by nni.trace.
+        E.g. ``traced_optimizer = nni.trace(torch.nn.Adam)(model.parameters())``..
+    criterion : Callable[[Tensor, Tensor], Tensor]
+        The criterion function used in trainer. Take model output and target value as input, and return the loss.
+    training_batches
+        The batch number used to collect activations.
+    mode : str
+        'normal' or 'dependency_aware'.
+        If prune the model in a dependency-aware way, this pruner will
+        prune the model according to the activation-based metrics and the channel-dependency or
+        group-dependency of the model. In this way, the pruner will force the conv layers
+        that have dependencies to prune the same channels, so the speedup module can better
+        harvest the speed benefit from the pruned model. Note that, if set 'dependency_aware'
+        , the dummy_input cannot be None, because the pruner needs a dummy input to trace the
+        dependency between the conv layers.
+    dummy_input : Optional[torch.Tensor]
+        The dummy input to analyze the topology constraints. Note that, the dummy_input
+        should on the same device with the model.
+
+    Examples
+    --------
+        >>> import nni
+        >>> from nni.compression.pytorch.pruning import ActivationMeanRankPruner
+        >>> model = ...
+        >>> # make sure you have used nni.trace to wrap the optimizer class before initialize
+        >>> traced_optimizer = nni.trace(torch.optim.Adam)(model.parameters())
+        >>> trainer = ...
+        >>> criterion = ...
+        >>> config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
+        >>> pruner = ActivationMeanRankPruner(model, config_list, trainer, traced_optimizer, criterion, training_batches=20)
+        >>> masked_model, masks = pruner.compress()
+
+    For detailed example please refer to :githublink:`examples/model_compress/pruning/activation_pruning_torch.py <examples/model_compress/pruning/activation_pruning_torch.py>`
+    """
     def _activation_trans(self, output: Tensor) -> Tensor:
         # return the activation of `output` directly.
         return self._activation(output.detach())
@@ -645,11 +868,21 @@ class ActivationMeanRankPruner(ActivationPruner):
 
 
 class TaylorFOWeightPruner(BasicPruner):
-    """
+    r"""
+    Taylor FO weight pruner is a pruner which prunes on the first weight dimension,
+    based on estimated importance calculated from the first order taylor expansion on weights to achieve a preset level of network sparsity.
+    The estimated importance is defined as the paper `Importance Estimation for Neural Network Pruning <http://jankautz.com/publications/Importance4NNPruning_CVPR19.pdf>`__.
+
+    :math:`\widehat{\mathcal{I}}_{\mathcal{S}}^{(1)}(\mathbf{W}) \triangleq \sum_{s \in \mathcal{S}} \mathcal{I}_{s}^{(1)}(\mathbf{W})=\sum_{s \in \mathcal{S}}\left(g_{s} w_{s}\right)^{2}`
+
+    Taylor FO weight pruner also supports dependency-aware mode.
+
+    What's more, we provide a global-sort mode for this pruner which is aligned with paper implementation.
+
     Parameters
     ----------
     model : torch.nn.Module
-        Model to be pruned
+        Model to be pruned.
     config_list : List[Dict]
         Supported keys:
             - sparsity : This is to specify the sparsity for each layer in this config to be compressed.
@@ -681,7 +914,7 @@ class TaylorFOWeightPruner(BasicPruner):
                 model.train(mode=training)
     traced_optimizer : nni.common.serializer.Traceable(torch.optim.Optimizer)
         The traced optimizer instance which the optimizer class is wrapped by nni.trace.
-        E.g. traced_optimizer = nni.trace(torch.nn.Adam)(model.parameters()).
+        E.g. ``traced_optimizer = nni.trace(torch.nn.Adam)(model.parameters())``.
     criterion : Callable[[Tensor, Tensor], Tensor]
         The criterion function used in trainer. Take model output and target value as input, and return the loss.
     training_batches : int
@@ -703,6 +936,21 @@ class TaylorFOWeightPruner(BasicPruner):
     dummy_input : Optional[torch.Tensor]
         The dummy input to analyze the topology constraints. Note that, the dummy_input
         should on the same device with the model.
+
+    Examples
+    --------
+        >>> import nni
+        >>> from nni.compression.pytorch.pruning import TaylorFOWeightPruner
+        >>> model = ...
+        >>> # make sure you have used nni.trace to wrap the optimizer class before initialize
+        >>> traced_optimizer = nni.trace(torch.optim.Adam)(model.parameters())
+        >>> trainer = ...
+        >>> criterion = ...
+        >>> config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
+        >>> pruner = TaylorFOWeightPruner(model, config_list, trainer, traced_optimizer, criterion, training_batches=20)
+        >>> masked_model, masks = pruner.compress()
+
+    For detailed example please refer to :githublink:`examples/model_compress/pruning/taylorfo_pruning_torch.py <examples/model_compress/pruning/taylorfo_pruning_torch.py>`
     """
 
     def __init__(self, model: Module, config_list: List[Dict], trainer: Callable[[Module, Optimizer, Callable], None],
@@ -751,7 +999,7 @@ class TaylorFOWeightPruner(BasicPruner):
         return (weight_tensor.detach() * grad.detach()).data.pow(2)
 
     def reset_tools(self):
-        hook_targets = {layer_info.name: layer_info.module.weight for layer_info, _ in self._detect_modules_to_compress()}
+        hook_targets = {name: wrapper.weight for name, wrapper in self.get_modules_wrapper().items()}
         collector_info = HookCollectorInfo(hook_targets, 'tensor', self._collector)
         if self.data_collector is None:
             self.data_collector = SingleHookTrainerBasedDataCollector(self, self.trainer, self.optimizer_helper, self.criterion,
@@ -772,13 +1020,17 @@ class TaylorFOWeightPruner(BasicPruner):
 
 
 class ADMMPruner(BasicPruner):
-    """
-    ADMM (Alternating Direction Method of Multipliers) Pruner is a kind of mathematical optimization technique.
-    The metric used in this pruner is the absolute value of the weight.
-    In each iteration, the weight with small magnitudes will be set to zero.
-    Only in the final iteration, the mask will be generated and apply to model wrapper.
+    r"""
+    Alternating Direction Method of Multipliers (ADMM) is a mathematical optimization technique,
+    by decomposing the original nonconvex problem into two subproblems that can be solved iteratively.
+    In weight pruning problem, these two subproblems are solved via 1) gradient descent algorithm and 2) Euclidean projection respectively. 
+
+    During the process of solving these two subproblems, the weights of the original model will be changed.
+    Then a fine-grained pruning will be applied to prune the model according to the config list given.
 
-    The original paper refer to: https://arxiv.org/abs/1804.03294.
+    This solution framework applies both to non-structured and different variations of structured pruning schemes.
+
+    For more details, please refer to `A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers <https://arxiv.org/abs/1804.03294>`__.
 
     Parameters
     ----------
@@ -814,17 +1066,38 @@ class ADMMPruner(BasicPruner):
                 model.train(mode=training)
     traced_optimizer : nni.common.serializer.Traceable(torch.optim.Optimizer)
         The traced optimizer instance which the optimizer class is wrapped by nni.trace.
-        E.g. traced_optimizer = nni.trace(torch.nn.Adam)(model.parameters()).
+        E.g. ``traced_optimizer = nni.trace(torch.nn.Adam)(model.parameters())``.
     criterion : Callable[[Tensor, Tensor], Tensor]
         The criterion function used in trainer. Take model output and target value as input, and return the loss.
     iterations : int
         The total iteration number in admm pruning algorithm.
     training_epochs : int
         The epoch number for training model in each iteration.
+    granularity : str
+        'fine-grained' or 'coarse-grained'.
+        If 'coarse-grained' is set, ADMM pruner will generate masks on output channels wise.
+        In original admm pruning paper, author implemented a fine-grained admm pruning.
+        In auto-compress paper, author used coarse-grained admm pruning.
+
+    Examples
+    --------
+        >>> import nni
+        >>> from nni.compression.pytorch.pruning import ADMMPruner
+        >>> model = ...
+        >>> # make sure you have used nni.trace to wrap the optimizer class before initialize
+        >>> traced_optimizer = nni.trace(torch.optim.Adam)(model.parameters())
+        >>> trainer = ...
+        >>> criterion = ...
+        >>> config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
+        >>> pruner = ADMMPruner(model, config_list, trainer, traced_optimizer, criterion, iterations=10, training_epochs=1)
+        >>> masked_model, masks = pruner.compress()
+
+    For detailed example please refer to :githublink:`examples/model_compress/pruning/admm_pruning_torch.py <examples/model_compress/pruning/admm_pruning_torch.py>`
     """
 
     def __init__(self, model: Module, config_list: List[Dict], trainer: Callable[[Module, Optimizer, Callable], None],
-                 traced_optimizer: Traceable, criterion: Callable[[Tensor, Tensor], Tensor], iterations: int, training_epochs: int):
+                 traced_optimizer: Traceable, criterion: Callable[[Tensor, Tensor], Tensor], iterations: int,
+                 training_epochs: int, granularity: str = 'fine-grained'):
         self.trainer = trainer
         if isinstance(traced_optimizer, OptimizerConstructHelper):
             self.optimizer_helper = traced_optimizer
@@ -833,6 +1106,8 @@ class ADMMPruner(BasicPruner):
         self.criterion = criterion
         self.iterations = iterations
         self.training_epochs = training_epochs
+        assert granularity in ['fine-grained', 'coarse-grained']
+        self.granularity = granularity
         super().__init__(model, config_list)
 
     def reset(self, model: Optional[Module], config_list: Optional[List[Dict]]):
@@ -864,9 +1139,15 @@ class ADMMPruner(BasicPruner):
         else:
             self.data_collector.reset()
         if self.metrics_calculator is None:
-            self.metrics_calculator = NormMetricsCalculator()
+            if self.granularity == 'fine-grained':
+                self.metrics_calculator = NormMetricsCalculator(p=1)
+            elif self.granularity == 'coarse-grained':
+                self.metrics_calculator = NormMetricsCalculator(dim=0, p=1)
         if self.sparsity_allocator is None:
-            self.sparsity_allocator = NormalSparsityAllocator(self)
+            if self.granularity == 'fine-grained':
+                self.sparsity_allocator = NormalSparsityAllocator(self)
+            elif self.granularity == 'coarse-grained':
+                self.sparsity_allocator = NormalSparsityAllocator(self, dim=0)
 
     def compress(self) -> Tuple[Module, Dict]:
         """
diff --git a/nni/algorithms/compression/v2/pytorch/pruning/basic_scheduler.py b/nni/algorithms/compression/v2/pytorch/pruning/basic_scheduler.py
index a516877f3..66416bdb6 100644
--- a/nni/algorithms/compression/v2/pytorch/pruning/basic_scheduler.py
+++ b/nni/algorithms/compression/v2/pytorch/pruning/basic_scheduler.py
@@ -26,10 +26,10 @@ class PruningScheduler(BasePruningScheduler):
     finetuner
         The finetuner handled all finetune logic, use a pytorch module as input.
         It will be called at the end of each iteration if reset_weight is False, will be called at the beginning of each iteration otherwise.
-    speed_up
-        If set True, speed up the model at the end of each iteration to make the pruned model compact.
+    speedup
+        If set True, speedup the model at the end of each iteration to make the pruned model compact.
     dummy_input
-        If `speed_up` is True, `dummy_input` is required for tracing the model in speed up.
+        If `speedup` is True, `dummy_input` is required for tracing the model in speedup.
     evaluator
         Evaluate the pruned model and give a score.
         If evaluator is None, the best result refers to the latest result.
@@ -37,12 +37,12 @@ class PruningScheduler(BasePruningScheduler):
         If set True, the model weight will reset to the origin model weight at the end of each iteration step.
     """
     def __init__(self, pruner: Pruner, task_generator: TaskGenerator, finetuner: Callable[[Module], None] = None,
-                 speed_up: bool = False, dummy_input: Tensor = None, evaluator: Optional[Callable[[Module], float]] = None,
+                 speedup: bool = False, dummy_input: Tensor = None, evaluator: Optional[Callable[[Module], float]] = None,
                  reset_weight: bool = False):
         self.pruner = pruner
         self.task_generator = task_generator
         self.finetuner = finetuner
-        self.speed_up = speed_up
+        self.speedup = speedup
         self.dummy_input = dummy_input
         self.evaluator = evaluator
         self.reset_weight = reset_weight
@@ -58,7 +58,7 @@ class PruningScheduler(BasePruningScheduler):
 
     def pruning_one_step_normal(self, task: Task) -> TaskResult:
         """
-        generate masks -> speed up -> finetune -> evaluate
+        generate masks -> speedup -> finetune -> evaluate
         """
         model, masks, config_list = task.load_data()
         self.pruner.reset(model, config_list)
@@ -72,14 +72,14 @@ class PruningScheduler(BasePruningScheduler):
         self.pruner.show_pruned_weights()
         self.pruner._unwrap_model()
 
-        # speed up
-        if self.speed_up and task.speed_up:
+        # speedup
+        if self.speedup and task.speedup:
             ModelSpeedup(compact_model, self.dummy_input, pruner_generated_masks).speedup_model()
             compact_model_masks = {}
 
         # finetune
         if self.finetuner is not None and task.finetune:
-            if self.speed_up:
+            if self.speedup:
                 self.finetuner(compact_model)
             else:
                 self.pruner._wrap_model()
@@ -88,7 +88,7 @@ class PruningScheduler(BasePruningScheduler):
 
         # evaluate
         if self.evaluator is not None and task.evaluate:
-            if self.speed_up:
+            if self.speedup:
                 score = self.evaluator(compact_model)
             else:
                 self.pruner._wrap_model()
@@ -104,7 +104,7 @@ class PruningScheduler(BasePruningScheduler):
 
     def pruning_one_step_reset_weight(self, task: Task) -> TaskResult:
         """
-        finetune -> generate masks -> reset weight -> speed up -> evaluate
+        finetune -> generate masks -> reset weight -> speedup -> evaluate
         """
         model, masks, config_list = task.load_data()
         checkpoint = deepcopy(model.state_dict())
@@ -126,14 +126,14 @@ class PruningScheduler(BasePruningScheduler):
         # reset model weight
         compact_model.load_state_dict(checkpoint)
 
-        # speed up
-        if self.speed_up and task.speed_up:
+        # speedup
+        if self.speedup and task.speedup:
             ModelSpeedup(compact_model, self.dummy_input, pruner_generated_masks).speedup_model()
             compact_model_masks = {}
 
         # evaluate
         if self.evaluator is not None and task.evaluate:
-            if self.speed_up:
+            if self.speedup:
                 score = self.evaluator(compact_model)
             else:
                 self.pruner._wrap_model()
diff --git a/nni/algorithms/compression/v2/pytorch/pruning/iterative_pruner.py b/nni/algorithms/compression/v2/pytorch/pruning/iterative_pruner.py
index aa6e7e703..9e7bdc538 100644
--- a/nni/algorithms/compression/v2/pytorch/pruning/iterative_pruner.py
+++ b/nni/algorithms/compression/v2/pytorch/pruning/iterative_pruner.py
@@ -70,7 +70,11 @@ class IterativePruner(PruningScheduler):
 
 
 class LinearPruner(IterativePruner):
-    """
+    r"""
+    Linear pruner is an iterative pruner, it will increase sparsity evenly from scratch during each iteration.
+
+    For example, the final sparsity is set as 0.5, and the iteration number is 5, then the sparsity used in each iteration are ``[0, 0.1, 0.2, 0.3, 0.4, 0.5]``.
+
     Parameters
     ----------
     model : Module
@@ -89,20 +93,31 @@ class LinearPruner(IterativePruner):
     finetuner : Optional[Callable[[Module], None]]
         The finetuner handled all finetune logic, use a pytorch module as input.
         It will be called at the end of each iteration, usually for neutralizing the accuracy loss brought by the pruning in this iteration.
-    speed_up : bool
-        If set True, speed up the model at the end of each iteration to make the pruned model compact.
+    speedup : bool
+        If set True, speedup the model at the end of each iteration to make the pruned model compact.
     dummy_input : Optional[torch.Tensor]
-        If `speed_up` is True, `dummy_input` is required for tracing the model in speed up.
+        If `speedup` is True, `dummy_input` is required for tracing the model in speedup.
     evaluator : Optional[Callable[[Module], float]]
         Evaluate the pruned model and give a score.
         If evaluator is None, the best result refers to the latest result.
     pruning_params : Dict
         If the chosen pruning_algorithm has extra parameters, put them as a dict to pass in.
+
+    Examples
+    --------
+        >>> from nni.compression.pytorch.pruning import LinearPruner
+        >>> config_list = [{'sparsity': 0.8, 'op_types': ['Conv2d']}]
+        >>> finetuner = ...
+        >>> pruner = LinearPruner(model, config_list, pruning_algorithm='l1', total_iteration=10, finetuner=finetuner)
+        >>> pruner.compress()
+        >>> _, model, masks, _, _ = pruner.get_best_result()
+
+    For detailed example please refer to :githublink:`examples/model_compress/pruning/iterative_pruning_torch.py <examples/model_compress/pruning/iterative_pruning_torch.py>`
     """
 
     def __init__(self, model: Module, config_list: List[Dict], pruning_algorithm: str,
                  total_iteration: int, log_dir: str = '.', keep_intermediate_result: bool = False,
-                 finetuner: Optional[Callable[[Module], None]] = None, speed_up: bool = False, dummy_input: Optional[Tensor] = None,
+                 finetuner: Optional[Callable[[Module], None]] = None, speedup: bool = False, dummy_input: Optional[Tensor] = None,
                  evaluator: Optional[Callable[[Module], float]] = None, pruning_params: Dict = {}):
         task_generator = LinearTaskGenerator(total_iteration=total_iteration,
                                              origin_model=model,
@@ -112,12 +127,19 @@ class LinearPruner(IterativePruner):
         if 'traced_optimizer' in pruning_params:
             pruning_params['traced_optimizer'] = OptimizerConstructHelper.from_trace(model, pruning_params['traced_optimizer'])
         pruner = PRUNER_DICT[pruning_algorithm](None, None, **pruning_params)
-        super().__init__(pruner, task_generator, finetuner=finetuner, speed_up=speed_up, dummy_input=dummy_input,
+        super().__init__(pruner, task_generator, finetuner=finetuner, speedup=speedup, dummy_input=dummy_input,
                          evaluator=evaluator, reset_weight=False)
 
 
 class AGPPruner(IterativePruner):
-    """
+    r"""
+    This is an iterative pruner, which the sparsity is increased from an initial sparsity value :math:`s_{i}` (usually 0) to a final sparsity value :math:`s_{f}` over a span of :math:`n` pruning iterations,
+    starting at training step :math:`t_{0}` and with pruning frequency :math:`\Delta t`:
+
+    :math:`s_{t}=s_{f}+\left(s_{i}-s_{f}\right)\left(1-\frac{t-t_{0}}{n \Delta t}\right)^{3} \text { for } t \in\left\{t_{0}, t_{0}+\Delta t, \ldots, t_{0} + n \Delta t\right\}`
+
+    For more details please refer to `To prune, or not to prune: exploring the efficacy of pruning for model compression <https://arxiv.org/abs/1710.01878>`__\.
+
     Parameters
     ----------
     model : Module
@@ -136,20 +158,31 @@ class AGPPruner(IterativePruner):
     finetuner : Optional[Callable[[Module], None]]
         The finetuner handled all finetune logic, use a pytorch module as input.
         It will be called at the end of each iteration, usually for neutralizing the accuracy loss brought by the pruning in this iteration.
-    speed_up : bool
-        If set True, speed up the model at the end of each iteration to make the pruned model compact.
+    speedup : bool
+        If set True, speedup the model at the end of each iteration to make the pruned model compact.
     dummy_input : Optional[torch.Tensor]
-        If `speed_up` is True, `dummy_input` is required for tracing the model in speed up.
+        If `speedup` is True, `dummy_input` is required for tracing the model in speedup.
     evaluator : Optional[Callable[[Module], float]]
         Evaluate the pruned model and give a score.
         If evaluator is None, the best result refers to the latest result.
     pruning_params : Dict
         If the chosen pruning_algorithm has extra parameters, put them as a dict to pass in.
+
+    Examples
+    --------
+        >>> from nni.compression.pytorch.pruning import AGPPruner
+        >>> config_list = [{'sparsity': 0.8, 'op_types': ['Conv2d']}]
+        >>> finetuner = ...
+        >>> pruner = AGPPruner(model, config_list, pruning_algorithm='l1', total_iteration=10, finetuner=finetuner)
+        >>> pruner.compress()
+        >>> _, model, masks, _, _ = pruner.get_best_result()
+
+    For detailed example please refer to :githublink:`examples/model_compress/pruning/iterative_pruning_torch.py <examples/model_compress/pruning/iterative_pruning_torch.py>`
     """
 
     def __init__(self, model: Module, config_list: List[Dict], pruning_algorithm: str,
                  total_iteration: int, log_dir: str = '.', keep_intermediate_result: bool = False,
-                 finetuner: Optional[Callable[[Module], None]] = None, speed_up: bool = False, dummy_input: Optional[Tensor] = None,
+                 finetuner: Optional[Callable[[Module], None]] = None, speedup: bool = False, dummy_input: Optional[Tensor] = None,
                  evaluator: Optional[Callable[[Module], float]] = None, pruning_params: Dict = {}):
         task_generator = AGPTaskGenerator(total_iteration=total_iteration,
                                           origin_model=model,
@@ -159,12 +192,30 @@ class AGPPruner(IterativePruner):
         if 'traced_optimizer' in pruning_params:
             pruning_params['traced_optimizer'] = OptimizerConstructHelper.from_trace(model, pruning_params['traced_optimizer'])
         pruner = PRUNER_DICT[pruning_algorithm](None, None, **pruning_params)
-        super().__init__(pruner, task_generator, finetuner=finetuner, speed_up=speed_up, dummy_input=dummy_input,
+        super().__init__(pruner, task_generator, finetuner=finetuner, speedup=speedup, dummy_input=dummy_input,
                          evaluator=evaluator, reset_weight=False)
 
 
 class LotteryTicketPruner(IterativePruner):
-    """
+    r"""
+    `The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks <https://arxiv.org/abs/1803.03635>`__\ ,
+    authors Jonathan Frankle and Michael Carbin,provides comprehensive measurement and analysis,
+    and articulate the *lottery ticket hypothesis*\ : dense, randomly-initialized, feed-forward networks contain subnetworks (*winning tickets*\ ) that
+    -- when trained in isolation -- reach test accuracy comparable to the original network in a similar number of iterations.
+
+    In this paper, the authors use the following process to prune a model, called *iterative prunning*\ :
+
+    ..
+
+        #. Randomly initialize a neural network f(x;theta_0) (where theta\ *0 follows D*\ {theta}).
+        #. Train the network for j iterations, arriving at parameters theta_j.
+        #. Prune p% of the parameters in theta_j, creating a mask m.
+        #. Reset the remaining parameters to their values in theta_0, creating the winning ticket f(x;m*theta_0).
+        #. Repeat step 2, 3, and 4.
+
+    If the configured final sparsity is P (e.g., 0.8) and there are n times iterative pruning,
+    each iterative pruning prunes 1-(1-P)^(1/n) of the weights that survive the previous round.
+
     Parameters
     ----------
     model : Module
@@ -183,10 +234,10 @@ class LotteryTicketPruner(IterativePruner):
     finetuner : Optional[Callable[[Module], None]]
         The finetuner handled all finetune logic, use a pytorch module as input.
         It will be called at the end of each iteration if reset_weight is False, will be called at the beginning of each iteration otherwise.
-    speed_up : bool
-        If set True, speed up the model at the end of each iteration to make the pruned model compact.
+    speedup : bool
+        If set True, speedup the model at the end of each iteration to make the pruned model compact.
     dummy_input : Optional[torch.Tensor]
-        If `speed_up` is True, `dummy_input` is required for tracing the model in speed up.
+        If `speedup` is True, `dummy_input` is required for tracing the model in speedup.
     evaluator : Optional[Callable[[Module], float]]
         Evaluate the pruned model and give a score.
         If evaluator is None, the best result refers to the latest result.
@@ -194,11 +245,23 @@ class LotteryTicketPruner(IterativePruner):
         If set True, the model weight will reset to the original model weight at the end of each iteration step.
     pruning_params : Dict
         If the chosen pruning_algorithm has extra parameters, put them as a dict to pass in.
+
+    Examples
+    --------
+        >>> from nni.compression.pytorch.pruning import LotteryTicketPruner
+        >>> config_list = [{'sparsity': 0.8, 'op_types': ['Conv2d']}]
+        >>> finetuner = ...
+        >>> pruner = LotteryTicketPruner(model, config_list, pruning_algorithm='l1', total_iteration=10, finetuner=finetuner, reset_weight=True)
+        >>> pruner.compress()
+        >>> _, model, masks, _, _ = pruner.get_best_result()
+
+    For detailed example please refer to :githublink:`examples/model_compress/pruning/iterative_pruning_torch.py <examples/model_compress/pruning/iterative_pruning_torch.py>`
+
     """
 
     def __init__(self, model: Module, config_list: List[Dict], pruning_algorithm: str,
                  total_iteration: int, log_dir: str = '.', keep_intermediate_result: bool = False,
-                 finetuner: Optional[Callable[[Module], None]] = None, speed_up: bool = False, dummy_input: Optional[Tensor] = None,
+                 finetuner: Optional[Callable[[Module], None]] = None, speedup: bool = False, dummy_input: Optional[Tensor] = None,
                  evaluator: Optional[Callable[[Module], float]] = None, reset_weight: bool = True,
                  pruning_params: Dict = {}):
         task_generator = LotteryTicketTaskGenerator(total_iteration=total_iteration,
@@ -209,12 +272,25 @@ class LotteryTicketPruner(IterativePruner):
         if 'traced_optimizer' in pruning_params:
             pruning_params['traced_optimizer'] = OptimizerConstructHelper.from_trace(model, pruning_params['traced_optimizer'])
         pruner = PRUNER_DICT[pruning_algorithm](None, None, **pruning_params)
-        super().__init__(pruner, task_generator, finetuner=finetuner, speed_up=speed_up, dummy_input=dummy_input,
+        super().__init__(pruner, task_generator, finetuner=finetuner, speedup=speedup, dummy_input=dummy_input,
                          evaluator=evaluator, reset_weight=reset_weight)
 
 
 class SimulatedAnnealingPruner(IterativePruner):
     """
+    We implement a guided heuristic search method, Simulated Annealing (SA) algorithm. As mentioned in the paper, this method is enhanced on guided search based on prior experience.
+    The enhanced SA technique is based on the observation that a DNN layer with more number of weights often has a higher degree of model compression with less impact on overall accuracy.
+
+    * Randomly initialize a pruning rate distribution (sparsities).
+    * While current_temperature < stop_temperature:
+
+        #. generate a perturbation to current distribution
+        #. Perform fast evaluation on the perturbated distribution
+        #. accept the perturbation according to the performance and probability, if not accepted, return to step 1
+        #. cool down, current_temperature <- current_temperature * cool_down_rate
+
+    For more details, please refer to `AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates <https://arxiv.org/abs/1907.03141>`__.
+
     Parameters
     ----------
     model : Module
@@ -242,16 +318,29 @@ class SimulatedAnnealingPruner(IterativePruner):
         If keeping the intermediate result, including intermediate model and masks during each iteration.
     finetuner : Optional[Callable[[Module], None]]
         The finetuner handled all finetune logic, use a pytorch module as input, will be called in each iteration.
-    speed_up : bool
-        If set True, speed up the model at the end of each iteration to make the pruned model compact.
+    speedup : bool
+        If set True, speedup the model at the end of each iteration to make the pruned model compact.
     dummy_input : Optional[torch.Tensor]
-        If `speed_up` is True, `dummy_input` is required for tracing the model in speed up.
+        If `speedup` is True, `dummy_input` is required for tracing the model in speedup.
+
+    Examples
+    --------
+        >>> from nni.compression.pytorch.pruning import SimulatedAnnealingPruner
+        >>> model = ...
+        >>> config_list = [{'sparsity': 0.8, 'op_types': ['Conv2d']}]
+        >>> evaluator = ...
+        >>> finetuner = ...
+        >>> pruner = SimulatedAnnealingPruner(model, config_list, pruning_algorithm='l1', evaluator=evaluator, cool_down_rate=0.9, finetuner=finetuner)
+        >>> pruner.compress()
+        >>> _, model, masks, _, _ = pruner.get_best_result()
+
+    For detailed example please refer to :githublink:`examples/model_compress/pruning/simulated_anealing_pruning_torch.py <examples/model_compress/pruning/simulated_anealing_pruning_torch.py>`
     """
 
     def __init__(self, model: Module, config_list: List[Dict], evaluator: Callable[[Module], float], start_temperature: float = 100,
                  stop_temperature: float = 20, cool_down_rate: float = 0.9, perturbation_magnitude: float = 0.35,
                  pruning_algorithm: str = 'level', pruning_params: Dict = {}, log_dir: str = '.', keep_intermediate_result: bool = False,
-                 finetuner: Optional[Callable[[Module], None]] = None, speed_up: bool = False, dummy_input: Optional[Tensor] = None):
+                 finetuner: Optional[Callable[[Module], None]] = None, speedup: bool = False, dummy_input: Optional[Tensor] = None):
         task_generator = SimulatedAnnealingTaskGenerator(origin_model=model,
                                                          origin_config_list=config_list,
                                                          start_temperature=start_temperature,
@@ -263,5 +352,5 @@ class SimulatedAnnealingPruner(IterativePruner):
         if 'traced_optimizer' in pruning_params:
             pruning_params['traced_optimizer'] = OptimizerConstructHelper.from_trace(model, pruning_params['traced_optimizer'])
         pruner = PRUNER_DICT[pruning_algorithm](None, None, **pruning_params)
-        super().__init__(pruner, task_generator, finetuner=finetuner, speed_up=speed_up, dummy_input=dummy_input,
+        super().__init__(pruner, task_generator, finetuner=finetuner, speedup=speedup, dummy_input=dummy_input,
                          evaluator=evaluator, reset_weight=False)
diff --git a/nni/algorithms/compression/v2/pytorch/pruning/movement_pruner.py b/nni/algorithms/compression/v2/pytorch/pruning/movement_pruner.py
index 3cde6b255..25fc2252a 100644
--- a/nni/algorithms/compression/v2/pytorch/pruning/movement_pruner.py
+++ b/nni/algorithms/compression/v2/pytorch/pruning/movement_pruner.py
@@ -10,7 +10,7 @@ from torch import autograd, Tensor
 from torch.nn import Module, Parameter
 from torch.optim import Optimizer, Adam
 
-from nni.algorithms.compression.v2.pytorch.base.compressor import Compressor, _setattr, LayerInfo
+from nni.algorithms.compression.v2.pytorch.base import PrunerModuleWrapper, LayerInfo
 from nni.algorithms.compression.v2.pytorch.pruning.basic_pruner import BasicPruner, NORMAL_SCHEMA, EXCLUDE_SCHEMA, INTERNAL_SCHEMA
 from nni.algorithms.compression.v2.pytorch.utils import CompressorSchema, OptimizerConstructHelper
 from nni.common.serializer import Traceable
@@ -25,7 +25,7 @@ from .tools import (
 _logger = logging.getLogger(__name__)
 
 
-class PrunerScoredModuleWrapper(Module):
+class PrunerScoredModuleWrapper(PrunerModuleWrapper):
     """
     Wrap a module to enable data parallel, forward method customization and buffer registeration.
     Different from `PrunerModuleWrapper`, `PrunerScoredModuleWrapper` will record the gradient.
@@ -38,59 +38,16 @@ class PrunerScoredModuleWrapper(Module):
         The configurations that users specify for compression.
     module_name
         The name of the module to compress, wrapper module shares same name.
-    pruner
-        The pruner used to calculate mask.
     """
-    def __init__(self, module: Module, module_name: str, config: Dict, pruner: Compressor):
-        super().__init__()
-        # origin layer information
-        self.module = module
-        self.name = module_name
-        # config and pruner
-        self.config = config
-        self.pruner = pruner
-
-        self.weight = Parameter(torch.empty(self.module.weight.size()))
+    def __init__(self, module: Module, module_name: str, config: Dict):
+        super().__init__(module, module_name, config)
         self.weight_score = Parameter(torch.empty(self.weight.size()))
         torch.nn.init.constant_(self.weight_score, val=0.0)
 
-        # register buffer for mask
-        self.register_buffer("weight_mask", torch.ones(self.module.weight.shape))
-        if hasattr(self.module, 'bias') and self.module.bias is not None:
-            self.register_buffer("bias_mask", torch.ones(self.module.bias.shape))
-            self.bias = Parameter(torch.empty(self.module.bias.size()))
-        else:
-            self.register_buffer("bias_mask", None)
-
-    def _weight2buffer(self):
-        """
-        When using this wrapper to inference, call `_weight2buffer()` to make original weight untrainable.
-        The best place to call this function is in `Pruner._wrap_model()`.
-        """
-        self.weight.data = self.module.weight.data
-        delattr(self.module, 'weight')
-        self.module.register_buffer('weight', self.weight.data)
-        if hasattr(self.module, 'bias') and self.module.bias is not None:
-            self.bias.data = self.module.bias.data
-            delattr(self.module, 'bias')
-            self.module.register_buffer('bias', self.bias.data)
-
-    def _weight2parameter(self):
-        """
-        When don't need to record score or need to export the model, call `_weight2parameter()` to make the original weight trainable.
-        The best place to call this function is in `Pruner._unwrap_model()`.
-        """
-        delattr(self.module, 'weight')
-        self.module.weight = Parameter(torch.empty(self.weight.size()))
-        self.module.weight.data = torch.mul(self.weight, self.weight_mask)
-        if hasattr(self.module, 'bias') and self.module.bias is not None:
-            delattr(self.module, 'bias')
-            self.module.bias = Parameter(torch.empty(self.bias.size()))
-            self.module.bias.data = torch.mul(self.bias, self.bias_mask)
-
     def forward(self, *inputs):
         # apply mask to weight, bias
-        self.module.weight = torch.mul(self.weight, _StraightThrough.apply(self.weight_score, self.weight_mask))
+        # NOTE: I don't know why training getting slower and slower if only `self.weight_mask` without `detach_()`
+        self.module.weight = torch.mul(self.weight, _StraightThrough.apply(self.weight_score, self.weight_mask.detach_()))
         if hasattr(self.module, 'bias') and self.module.bias is not None:
             self.module.bias = torch.mul(self.bias, self.bias_mask)
         return self.module(*inputs)
@@ -119,12 +76,26 @@ class WeightScoreTrainerBasedDataCollector(TrainerBasedDataCollector):
 
         data = {}
         for _, wrapper in self.compressor.get_modules_wrapper().items():
-            data[wrapper.name] = wrapper.weight_score.data.clone().detach()
+            data[wrapper.name] = wrapper.weight_score.data
         return data
 
 
 class MovementPruner(BasicPruner):
-    """
+    r"""
+    Movement pruner is an implementation of movement pruning.
+    This is a "fine-pruning" algorithm, which means the masks may change during each fine-tuning step.
+    Each weight element will be scored by the opposite of the sum of the product of weight and its gradient during each step.
+    This means the weight elements moving towards zero will accumulate negative scores, the weight elements moving away from zero will accumulate positive scores.
+    The weight elements with low scores will be masked during inference.
+
+    The following figure from the paper shows the weight pruning by movement pruning.
+
+    .. image:: ../../../img/movement_pruning.png
+        :target: ../../../img/movement_pruning.png
+        :alt:
+
+    For more details, please refer to `Movement Pruning: Adaptive Sparsity by Fine-Tuning <https://arxiv.org/abs/2005.07683>`__.
+
     Parameters
     ----------
     model : torch.nn.Module
@@ -158,7 +129,7 @@ class MovementPruner(BasicPruner):
                 model.train(mode=training)
     traced_optimizer : nni.common.serializer.Traceable(torch.optim.Optimizer)
         The traced optimizer instance which the optimizer class is wrapped by nni.trace.
-        E.g. traced_optimizer = nni.trace(torch.nn.Adam)(model.parameters()).
+        E.g. ``traced_optimizer = nni.trace(torch.nn.Adam)(model.parameters())``.
     criterion : Callable[[Tensor, Tensor], Tensor]
         The criterion function used in trainer. Take model output and target value as input, and return the loss.
     training_epochs : int
@@ -171,6 +142,21 @@ class MovementPruner(BasicPruner):
         The number of steps at which sparsity stops growing, note that the sparsity stop growing doesn't mean masks not changed.
         The sparsity after each `optimizer.step()` is:
         total_sparsity * (1 - (1 - (current_step - warm_up_step) / (cool_down_beginning_step - warm_up_step)) ** 3).
+
+    Examples
+    --------
+        >>> import nni
+        >>> from nni.compression.pytorch.pruning import MovementPruner
+        >>> model = ...
+        >>> # make sure you have used nni.trace to wrap the optimizer class before initialize
+        >>> traced_optimizer = nni.trace(torch.optim.Adam)(model.parameters())
+        >>> trainer = ...
+        >>> criterion = ...
+        >>> config_list = [{ 'sparsity': 0.8, 'op_types': ['Conv2d'] }]
+        >>> pruner = MovementPruner(model, config_list, trainer, traced_optimizer, criterion, 10, 3000, 27000)
+        >>> masked_model, masks = pruner.compress()
+
+    For detailed example please refer to :githublink:`examples/model_compress/pruning/movement_pruning_glue.py <examples/model_compress/pruning/movement_pruning_glue.py>`
     """
     def __init__(self, model: Module, config_list: List[Dict], trainer: Callable[[Module, Optimizer, Callable], None],
                  traced_optimizer: Traceable, criterion: Callable[[Tensor, Tensor], Tensor], training_epochs: int, warm_up_step: int,
@@ -230,28 +216,6 @@ class MovementPruner(BasicPruner):
         else:
             self.data_collector.reset()
 
-    def _wrap_model(self):
-        """
-        Wrap all modules that needed to be compressed.
-        Different from the parent function, call `wrapper._weight2buffer()` after replace the origin module to wrapper.
-        """
-        if not self.is_wrapped:
-            for _, wrapper in reversed(self.get_modules_wrapper().items()):
-                _setattr(self.bound_model, wrapper.name, wrapper)
-                wrapper._weight2buffer()
-            self.is_wrapped = True
-
-    def _unwrap_model(self):
-        """
-        Unwrap all modules that needed to be compressed.
-        Different from the parent function, call `wrapper._weight2parameter()` after replace the wrapper to origin module.
-        """
-        if self.is_wrapped:
-            for _, wrapper in self.get_modules_wrapper().items():
-                _setattr(self.bound_model, wrapper.name, wrapper.module)
-                wrapper._weight2parameter()
-            self.is_wrapped = False
-
     def _wrap_modules(self, layer: LayerInfo, config: Dict):
         """
         Create a wrapper module to replace the original one.
@@ -265,21 +229,12 @@ class MovementPruner(BasicPruner):
             The configuration for generating the mask.
         """
         _logger.debug("Module detected to compress : %s.", layer.name)
-        wrapper = PrunerScoredModuleWrapper(layer.module, layer.name, config, self)
+        wrapper = PrunerScoredModuleWrapper(layer.module, layer.name, config)
         assert hasattr(layer.module, 'weight'), "module %s does not have 'weight' attribute" % layer.name
         # move newly registered buffers to the same device of weight
         wrapper.to(layer.module.weight.device)
         return wrapper
 
-    def get_origin2wrapped_parameter_name_map(self) -> Dict[str, str]:
-        if self.is_wrapped:
-            self._unwrap_model()
-            parameter_name_map = {name: name for name, _ in self.bound_model.named_parameters()}
-            self._wrap_model()
-            return parameter_name_map
-        else:
-            raise Exception('When only the model is wrapped can get the parameter_name_map.')
-
     def compress(self) -> Tuple[Module, Dict]:
         # sparsity grow from 0
         for _, wrapper in self.get_modules_wrapper().items():
diff --git a/nni/algorithms/compression/v2/pytorch/pruning/tools/__init__.py b/nni/algorithms/compression/v2/pytorch/pruning/tools/__init__.py
index 15ec15803..b6506a129 100644
--- a/nni/algorithms/compression/v2/pytorch/pruning/tools/__init__.py
+++ b/nni/algorithms/compression/v2/pytorch/pruning/tools/__init__.py
@@ -1,3 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from .base import (
     HookCollectorInfo,
     DataCollector,
diff --git a/nni/algorithms/compression/v2/pytorch/pruning/tools/base.py b/nni/algorithms/compression/v2/pytorch/pruning/tools/base.py
index e9baabb92..dcbffe7cf 100644
--- a/nni/algorithms/compression/v2/pytorch/pruning/tools/base.py
+++ b/nni/algorithms/compression/v2/pytorch/pruning/tools/base.py
@@ -22,15 +22,14 @@ _logger = logging.getLogger(__name__)
 class DataCollector:
     """
     An abstract class for collect the data needed by the compressor.
+
+    Parameters
+    ----------
+    compressor
+        The compressor binded with this DataCollector.
     """
 
     def __init__(self, compressor: Compressor):
-        """
-        Parameters
-        ----------
-        compressor
-            The compressor binded with this DataCollector.
-        """
         self.compressor = compressor
 
     def reset(self):
@@ -242,42 +241,43 @@ class TrainerBasedDataCollector(DataCollector):
 class MetricsCalculator:
     """
     An abstract class for calculate a kind of metrics of the given data.
-    """
-    def __init__(self, dim: Optional[Union[int, List[int]]] = None,
-                 block_sparse_size: Optional[Union[int, List[int]]] = None):
-        """
-        Parameters
-        ----------
-        dim
-            The dimensions that corresponding to the under pruning weight dimensions in collected data.
-            None means one-to-one correspondence between pruned dimensions and data, which equal to set `dim` as all data dimensions.
-            Only these `dim` will be kept and other dimensions of the data will be reduced.
 
-            Example:
+    Parameters
+    ----------
+    dim
+        The dimensions that corresponding to the under pruning weight dimensions in collected data.
+        None means one-to-one correspondence between pruned dimensions and data, which equal to set `dim` as all data dimensions.
+        Only these `dim` will be kept and other dimensions of the data will be reduced.
 
-            If you want to prune the Conv2d weight in filter level, and the weight size is (32, 16, 3, 3) [out-channel, in-channel, kernal-size-1, kernal-size-2].
-            Then the under pruning dimensions is [0], which means you want to prune the filter or out-channel.
+        Example:
 
-                Case 1: Directly collect the conv module weight as data to calculate the metric.
-                Then the data has size (32, 16, 3, 3).
-                Mention that the dimension 0 of the data is corresponding to the under pruning weight dimension 0.
-                So in this case, `dim=0` will set in `__init__`.
+        If you want to prune the Conv2d weight in filter level, and the weight size is (32, 16, 3, 3) [out-channel, in-channel, kernal-size-1, kernal-size-2].
+        Then the under pruning dimensions is [0], which means you want to prune the filter or out-channel.
 
-                Case 2: Use the output of the conv module as data to calculate the metric.
-                Then the data has size (batch_num, 32, feature_map_size_1, feature_map_size_2).
-                Mention that the dimension 1 of the data is corresponding to the under pruning weight dimension 0.
-                So in this case, `dim=1` will set in `__init__`.
+            Case 1: Directly collect the conv module weight as data to calculate the metric.
+            Then the data has size (32, 16, 3, 3).
+            Mention that the dimension 0 of the data is corresponding to the under pruning weight dimension 0.
+            So in this case, `dim=0` will set in `__init__`.
 
-            In both of these two case, the metric of this module has size (32,).
-        block_sparse_size
-            This used to describe the block size a metric value represented. By default, None means the block size is ones(len(dim)).
-            Make sure len(dim) == len(block_sparse_size), and the block_sparse_size dimension position is corresponding to dim.
+            Case 2: Use the output of the conv module as data to calculate the metric.
+            Then the data has size (batch_num, 32, feature_map_size_1, feature_map_size_2).
+            Mention that the dimension 1 of the data is corresponding to the under pruning weight dimension 0.
+            So in this case, `dim=1` will set in `__init__`.
 
-            Example:
+        In both of these two case, the metric of this module has size (32,).
 
-            The under pruning weight size is (768, 768), and you want to apply a block sparse on dim=[0] with block size [64, 768],
-            then you can set block_sparse_size=[64]. The final metric size is (12,).
-        """
+    block_sparse_size
+        This used to describe the block size a metric value represented. By default, None means the block size is ones(len(dim)).
+        Make sure len(dim) == len(block_sparse_size), and the block_sparse_size dimension position is corresponding to dim.
+
+        Example:
+
+        The under pruning weight size is (768, 768), and you want to apply a block sparse on dim=[0] with block size [64, 768],
+        then you can set block_sparse_size=[64]. The final metric size is (12,).
+    """
+
+    def __init__(self, dim: Optional[Union[int, List[int]]] = None,
+                 block_sparse_size: Optional[Union[int, List[int]]] = None):
         self.dim = dim if not isinstance(dim, int) else [dim]
         self.block_sparse_size = block_sparse_size if not isinstance(block_sparse_size, int) else [block_sparse_size]
         if self.block_sparse_size is not None:
@@ -307,36 +307,35 @@ class MetricsCalculator:
 class SparsityAllocator:
     """
     An abstract class for allocate mask based on metrics.
+
+    Parameters
+    ----------
+    pruner
+        The pruner that binded with this `SparsityAllocator`.
+    dim
+        The under pruning weight dimensions, which metric size should equal to the under pruning weight size on these dimensions.
+        None means one-to-one correspondence between pruned dimensions and metric, which equal to set `dim` as all under pruning weight dimensions.
+        The mask will expand to the weight size depend on `dim`.
+
+        Example:
+
+        The under pruning weight has size (2, 3, 4), and `dim=1` means the under pruning weight dimension is 1.
+        Then the metric should have a size (3,), i.e., `metric=[0.9, 0.1, 0.8]`.
+        Assuming by some kind of `SparsityAllocator` get the mask on weight dimension 1 `mask=[1, 0, 1]`,
+        then the dimension mask will expand to the final mask `[[[1, 1, 1, 1], [0, 0, 0, 0], [1, 1, 1, 1]], [[1, 1, 1, 1], [0, 0, 0, 0], [1, 1, 1, 1]]]`.
+    block_sparse_size
+        This used to describe the block size a metric value represented. By default, None means the block size is ones(len(dim)).
+        Make sure len(dim) == len(block_sparse_size), and the block_sparse_size dimension position is corresponding to dim.
+
+        Example:
+
+        The metric size is (12,), and block_sparse_size=[64], then the mask will expand to (768,) at first before expand with `dim`.
+    continuous_mask
+        Inherit the mask already in the wrapper if set True.
     """
 
     def __init__(self, pruner: Compressor, dim: Optional[Union[int, List[int]]] = None,
                  block_sparse_size: Optional[Union[int, List[int]]] = None, continuous_mask: bool = True):
-        """
-        Parameters
-        ----------
-        pruner
-            The pruner that binded with this `SparsityAllocator`.
-        dim
-            The under pruning weight dimensions, which metric size should equal to the under pruning weight size on these dimensions.
-            None means one-to-one correspondence between pruned dimensions and metric, which equal to set `dim` as all under pruning weight dimensions.
-            The mask will expand to the weight size depend on `dim`.
-
-            Example:
-
-            The under pruning weight has size (2, 3, 4), and `dim=1` means the under pruning weight dimension is 1.
-            Then the metric should have a size (3,), i.e., `metric=[0.9, 0.1, 0.8]`.
-            Assuming by some kind of `SparsityAllocator` get the mask on weight dimension 1 `mask=[1, 0, 1]`,
-            then the dimension mask will expand to the final mask `[[[1, 1, 1, 1], [0, 0, 0, 0], [1, 1, 1, 1]], [[1, 1, 1, 1], [0, 0, 0, 0], [1, 1, 1, 1]]]`.
-        block_sparse_size
-            This used to describe the block size a metric value represented. By default, None means the block size is ones(len(dim)).
-            Make sure len(dim) == len(block_sparse_size), and the block_sparse_size dimension position is corresponding to dim.
-
-            Example:
-
-            The metric size is (12,), and block_sparse_size=[64], then the mask will expand to (768,) at first before expand with `dim`.
-        continuous_mask
-            Inherit the mask already in the wrapper if set True.
-        """
         self.pruner = pruner
         self.dim = dim if not isinstance(dim, int) else [dim]
         self.block_sparse_size = block_sparse_size if not isinstance(block_sparse_size, int) else [block_sparse_size]
@@ -385,7 +384,7 @@ class SparsityAllocator:
             weight_mask = weight_mask.expand(expand_size).reshape(reshape_size)
 
         wrapper = self.pruner.get_modules_wrapper()[name]
-        weight_size = wrapper.module.weight.data.size()
+        weight_size = wrapper.weight.data.size()
 
         if self.dim is None:
             assert weight_mask.size() == weight_size
diff --git a/nni/algorithms/compression/v2/pytorch/pruning/tools/data_collector.py b/nni/algorithms/compression/v2/pytorch/pruning/tools/data_collector.py
index 3b2f52655..915a07b99 100644
--- a/nni/algorithms/compression/v2/pytorch/pruning/tools/data_collector.py
+++ b/nni/algorithms/compression/v2/pytorch/pruning/tools/data_collector.py
@@ -24,7 +24,7 @@ class WeightDataCollector(DataCollector):
     def collect(self) -> Dict[str, Tensor]:
         data = {}
         for _, wrapper in self.compressor.get_modules_wrapper().items():
-            data[wrapper.name] = wrapper.module.weight.data
+            data[wrapper.name] = wrapper.weight.data
         return data
 
 
@@ -39,7 +39,7 @@ class WeightTrainerBasedDataCollector(TrainerBasedDataCollector):
 
         data = {}
         for _, wrapper in self.compressor.get_modules_wrapper().items():
-            data[wrapper.name] = wrapper.module.weight.data
+            data[wrapper.name] = wrapper.weight.data
         return data
 
 
diff --git a/nni/algorithms/compression/v2/pytorch/pruning/tools/metrics_calculator.py b/nni/algorithms/compression/v2/pytorch/pruning/tools/metrics_calculator.py
index d229d2a61..9bc390dd6 100644
--- a/nni/algorithms/compression/v2/pytorch/pruning/tools/metrics_calculator.py
+++ b/nni/algorithms/compression/v2/pytorch/pruning/tools/metrics_calculator.py
@@ -19,7 +19,8 @@ class StraightMetricsCalculator(MetricsCalculator):
     def calculate_metrics(self, data: Dict[str, Tensor]) -> Dict[str, Tensor]:
         metrics = {}
         for name, tensor in data.items():
-            metrics[name] = tensor.clone().detach()
+            # use inplace detach `detach_` here to avoid creating a new tensor
+            metrics[name] = tensor.clone().detach_()
         return metrics
 
 
diff --git a/nni/algorithms/compression/v2/pytorch/pruning/tools/rl_env/__init__.py b/nni/algorithms/compression/v2/pytorch/pruning/tools/rl_env/__init__.py
index c09ce9533..64e95de64 100644
--- a/nni/algorithms/compression/v2/pytorch/pruning/tools/rl_env/__init__.py
+++ b/nni/algorithms/compression/v2/pytorch/pruning/tools/rl_env/__init__.py
@@ -1,2 +1,5 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from .agent import DDPG
 from .amc_env import AMCEnv
diff --git a/nni/algorithms/compression/v2/pytorch/pruning/tools/rl_env/amc_env.py b/nni/algorithms/compression/v2/pytorch/pruning/tools/rl_env/amc_env.py
index 900c5f72c..39c9a0fdd 100644
--- a/nni/algorithms/compression/v2/pytorch/pruning/tools/rl_env/amc_env.py
+++ b/nni/algorithms/compression/v2/pytorch/pruning/tools/rl_env/amc_env.py
@@ -11,7 +11,7 @@ from torch import Tensor
 from torch.nn import Module
 
 from nni.algorithms.compression.v2.pytorch.utils import config_list_canonical
-from nni.compression.pytorch.utils.counter import count_flops_params
+from nni.compression.pytorch.utils import count_flops_params
 
 _logger = logging.getLogger(__name__)
 
diff --git a/nni/algorithms/compression/v2/pytorch/pruning/tools/sparsity_allocator.py b/nni/algorithms/compression/v2/pytorch/pruning/tools/sparsity_allocator.py
index f6cce6e45..3e598c771 100644
--- a/nni/algorithms/compression/v2/pytorch/pruning/tools/sparsity_allocator.py
+++ b/nni/algorithms/compression/v2/pytorch/pruning/tools/sparsity_allocator.py
@@ -70,8 +70,7 @@ class BankSparsityAllocator(SparsityAllocator):
             # make up for balance_gran
             balance_gran = [1] * (n_dim - len(self.balance_gran)) + self.balance_gran
             for i, j in zip(metric.shape, balance_gran):
-                assert i % j == 0, 'Length of {} weight is not \
-                    aligned with balance granularity'.format(name)
+                assert i % j == 0, 'Length of {} weight is not aligned with balance granularity'.format(name)
 
             mask = torch.zeros(metric.shape).type_as(metric)
             loop_iters = [range(int(i / j)) for i, j in zip(metric.shape, balance_gran)]
@@ -132,7 +131,7 @@ class GlobalSparsityAllocator(SparsityAllocator):
             if self.continuous_mask:
                 metric = metric * self._compress_mask(wrapper.weight_mask)
 
-            layer_weight_num = wrapper.module.weight.data.numel()
+            layer_weight_num = wrapper.weight.data.numel()
             total_weight_num += layer_weight_num
             expend_times = int(layer_weight_num / metric.numel())
 
@@ -160,7 +159,7 @@ class GlobalSparsityAllocator(SparsityAllocator):
 
 class Conv2dDependencyAwareAllocator(SparsityAllocator):
     """
-    A specify allocator for Conv2d with dependency aware.
+    A specify allocator for Conv2d with dependency-aware.
     """
 
     def __init__(self, pruner: Pruner, dim: int, dummy_input: Any):
diff --git a/nni/algorithms/compression/v2/pytorch/pruning/tools/task_generator.py b/nni/algorithms/compression/v2/pytorch/pruning/tools/task_generator.py
index 7d29d14fa..cf05042f0 100644
--- a/nni/algorithms/compression/v2/pytorch/pruning/tools/task_generator.py
+++ b/nni/algorithms/compression/v2/pytorch/pruning/tools/task_generator.py
@@ -239,7 +239,7 @@ class SimulatedAnnealingTaskGenerator(TaskGenerator):
 
         low_limit = 0
         while True:
-            # This is to speed up finding the legal sparsity.
+            # This is to speedup finding the legal sparsity.
             low_limit = (1 - low_limit) * 0.05 + low_limit
             random_sparsity = sorted(np.random.uniform(low_limit, 1, len(op_names)))
             rescaled_sparsity = self._rescale_sparsity(random_sparsity, target_sparsity, op_names)
diff --git a/nni/algorithms/compression/v2/pytorch/utils/__init__.py b/nni/algorithms/compression/v2/pytorch/utils/__init__.py
index 6876b86da..ac183b28d 100644
--- a/nni/algorithms/compression/v2/pytorch/utils/__init__.py
+++ b/nni/algorithms/compression/v2/pytorch/utils/__init__.py
@@ -1,3 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from .config_validation import CompressorSchema
 from .pruning import (
     config_list_canonical,
diff --git a/nni/algorithms/compression/v2/pytorch/utils/constructor_helper.py b/nni/algorithms/compression/v2/pytorch/utils/constructor_helper.py
index 6e2cdd1b7..c5672b506 100644
--- a/nni/algorithms/compression/v2/pytorch/utils/constructor_helper.py
+++ b/nni/algorithms/compression/v2/pytorch/utils/constructor_helper.py
@@ -10,7 +10,7 @@ from torch.optim import Optimizer
 from torch.optim.lr_scheduler import _LRScheduler
 
 from nni.common.serializer import _trace_cls
-from nni.common.serializer import Traceable
+from nni.common.serializer import Traceable, is_traceable
 
 __all__ = ['OptimizerConstructHelper', 'LRSchedulerConstructHelper']
 
@@ -80,14 +80,14 @@ class OptimizerConstructHelper(ConstructHelper):
 
     @staticmethod
     def from_trace(model: Module, optimizer_trace: Traceable):
-        assert isinstance(optimizer_trace, Traceable), \
+        assert is_traceable(optimizer_trace), \
             'Please use nni.trace to wrap the optimizer class before initialize the optimizer.'
         assert isinstance(optimizer_trace, Optimizer), \
             'It is not an instance of torch.nn.Optimizer.'
         return OptimizerConstructHelper(model,
-                                        optimizer_trace._get_nni_attr('symbol'),
-                                        *optimizer_trace._get_nni_attr('args'),
-                                        **optimizer_trace._get_nni_attr('kwargs'))
+                                        optimizer_trace.trace_symbol,
+                                        *optimizer_trace.trace_args,
+                                        **optimizer_trace.trace_kwargs)
 
 
 class LRSchedulerConstructHelper(ConstructHelper):
@@ -112,7 +112,7 @@ class LRSchedulerConstructHelper(ConstructHelper):
 
     @staticmethod
     def from_trace(lr_scheduler_trace: Traceable):
-        assert isinstance(lr_scheduler_trace, Traceable), \
+        assert is_traceable(lr_scheduler_trace), \
             'Please use nni.trace to wrap the lr scheduler class before initialize the scheduler.'
         assert isinstance(lr_scheduler_trace, _LRScheduler), \
             'It is not an instance of torch.nn.lr_scheduler._LRScheduler.'
diff --git a/nni/algorithms/compression/v2/pytorch/utils/pruning.py b/nni/algorithms/compression/v2/pytorch/utils/pruning.py
index 59a4852a5..6485cb385 100644
--- a/nni/algorithms/compression/v2/pytorch/utils/pruning.py
+++ b/nni/algorithms/compression/v2/pytorch/utils/pruning.py
@@ -198,7 +198,18 @@ def compute_sparsity(origin_model: Module, compact_model: Module, compact_model_
     The current state means `compact_model` + `compact_model_masks`
     (i.e., `compact_model_masks` applied on `compact_model`).
     The compact model is the origin model after pruning,
-    and it may have different structure with origin_model cause of speed up.
+    and it may have different structure with origin_model cause of speedup.
+
+    Parameters
+    ----------
+    origin_model : torch.nn.Module
+        The original un-pruned model.
+    compact_model : torch.nn.Module
+        The model after speedup or original model.
+    compact_model_masks: Dict[str, Dict[str, Tensor]]
+        The masks applied on the compact model, if the original model have been speedup, this should be {}.
+    config_list : List[Dict]
+        The config_list used by pruning the original model.
 
     Returns
     -------
diff --git a/nni/algorithms/feature_engineering/gbdt_selector/__init__.py b/nni/algorithms/feature_engineering/gbdt_selector/__init__.py
index 72970ab85..9dc6989de 100644
--- a/nni/algorithms/feature_engineering/gbdt_selector/__init__.py
+++ b/nni/algorithms/feature_engineering/gbdt_selector/__init__.py
@@ -1 +1,4 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from .gbdt_selector import GBDTSelector
\ No newline at end of file
diff --git a/nni/algorithms/feature_engineering/gradient_selector/__init__.py b/nni/algorithms/feature_engineering/gradient_selector/__init__.py
index a43cb7578..d4de4d7a4 100644
--- a/nni/algorithms/feature_engineering/gradient_selector/__init__.py
+++ b/nni/algorithms/feature_engineering/gradient_selector/__init__.py
@@ -1 +1,4 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from .gradient_selector import FeatureGradientSelector
\ No newline at end of file
diff --git a/nni/algorithms/hpo/batch_tuner.py b/nni/algorithms/hpo/batch_tuner.py
index b2df2617c..758525193 100644
--- a/nni/algorithms/hpo/batch_tuner.py
+++ b/nni/algorithms/hpo/batch_tuner.py
@@ -20,27 +20,64 @@ LOGGER = logging.getLogger('batch_tuner_AutoML')
 
 class BatchTuner(Tuner):
     """
-    BatchTuner is tuner will running all the configure that user want to run batchly.
+    Batch tuner is a special tuner that allows users to simply provide several hyperparameter sets,
+    and it will evaluate each set.
+
+    Batch tuner does **not** support standard search space.
+
+    Search space of batch tuner looks like a single ``choice`` in standard search space,
+    but it has different meaning.
+
+    Consider following search space:
+
+    .. code-block::
+
+        'combine_params': {
+            '_type': 'choice',
+            '_value': [
+                {'x': 0, 'y': 1},
+                {'x': 1, 'y': 2},
+                {'x': 1, 'y': 3},
+            ]
+        }
+
+    Batch tuner will generate following 4 hyperparameter sets:
+
+    1. {'x': 0, 'y': 1}
+    2. {'x': 1, 'y': 2}
+    3. {'x': 1, 'y': 3}
+
+    If this search space was used with grid search tuner, it would instead generate:
+
+    1. {'combine_params': {'x': 0, 'y': 1 }}
+    2. {'combine_params': {'x': 1, 'y': 2 }}
+    3. {'combine_params': {'x': 1, 'y': 3 }}
 
     Examples
     --------
-    The search space only be accepted like:
 
-        ::
-
-            {'combine_params':
-                { '_type': 'choice',
-                            '_value': '[{...}, {...}, {...}]',
-                }
+    .. code-block::
+
+        config.search_space = {
+            'combine_params': {
+                '_type': 'choice',
+                '_value': [
+                    {'optimizer': 'Adam', 'learning_rate': 0.001},
+                    {'optimizer': 'Adam', 'learning_rate': 0.0001},
+                    {'optimizer': 'Adam', 'learning_rate': 0.00001},
+                    {'optimizer': 'SGD', 'learning_rate': 0.01},
+                    {'optimizer': 'SGD', 'learning_rate': 0.005},
+                ]
             }
-
+        }
+        config.tuner.name = 'BatchTuner'
     """
 
     def __init__(self):
         self._count = -1
         self._values = []
 
-    def is_valid(self, search_space):
+    def _is_valid(self, search_space):
         """
         Check the search space is valid: only contains 'choice' type
 
@@ -70,27 +107,10 @@ class BatchTuner(Tuner):
         return None
 
     def update_search_space(self, search_space):
-        """Update the search space
-
-        Parameters
-        ----------
-        search_space : dict
-        """
         validate_search_space(search_space, ['choice'])
-        self._values = self.is_valid(search_space)
+        self._values = self._is_valid(search_space)
 
     def generate_parameters(self, parameter_id, **kwargs):
-        """Returns a dict of trial (hyper-)parameters, as a serializable object.
-
-        Parameters
-        ----------
-        parameter_id : int
-
-        Returns
-        -------
-        dict
-            A candidate parameter group.
-        """
         self._count += 1
         if self._count > len(self._values) - 1:
             raise nni.NoMoreTrialError('no more parameters now.')
@@ -100,13 +120,6 @@ class BatchTuner(Tuner):
         pass
 
     def import_data(self, data):
-        """Import additional data for tuning
-
-        Parameters
-        ----------
-        data:
-            a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
-        """
         if not self._values:
             LOGGER.info("Search space has not been initialized, skip this data import")
             return
diff --git a/nni/algorithms/hpo/bohb_advisor/__init__.py b/nni/algorithms/hpo/bohb_advisor/__init__.py
index 0ebb442e5..95a7b1b99 100644
--- a/nni/algorithms/hpo/bohb_advisor/__init__.py
+++ b/nni/algorithms/hpo/bohb_advisor/__init__.py
@@ -1 +1,4 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from .bohb_advisor import BOHB, BOHBClassArgsValidator
diff --git a/nni/algorithms/hpo/bohb_advisor/bohb_advisor.py b/nni/algorithms/hpo/bohb_advisor/bohb_advisor.py
index 40dbbaeb7..1fe33b00b 100644
--- a/nni/algorithms/hpo/bohb_advisor/bohb_advisor.py
+++ b/nni/algorithms/hpo/bohb_advisor/bohb_advisor.py
@@ -249,20 +249,52 @@ class BOHBClassArgsValidator(ClassArgsValidator):
 
 class BOHB(MsgDispatcherBase):
     """
-    BOHB performs robust and efficient hyperparameter optimization
-    at scale by combining the speed of Hyperband searches with the
-    guidance and guarantees of convergence of Bayesian Optimization.
-    Instead of sampling new configurations at random, BOHB uses
-    kernel density estimators to select promising candidates.
+    `BOHB <https://arxiv.org/abs/1807.01774>`__ is a robust and efficient hyperparameter tuning algorithm at scale.
+    BO is an abbreviation for "Bayesian Optimization" and HB is an abbreviation for "Hyperband".
+
+    BOHB relies on HB (Hyperband) to determine how many configurations to evaluate with which budget,
+    but it replaces the random selection of configurations at the beginning of each HB iteration
+    by a model-based search (Bayesian Optimization).
+    Once the desired number of configurations for the iteration is reached,
+    the standard successive halving procedure is carried out using these configurations.
+    It keeps track of the performance of all function evaluations g(x, b) of configurations x
+    on all budgets b to use as a basis for our models in later iterations.
+    Please refer to the paper :footcite:t:`falkner2018bohb` for detailed algorithm.
+
+    Note that BOHB needs additional installation using the following command:
+
+    .. code-block:: bash
+
+        pip install nni[BOHB]
+
+    Examples
+    --------
+
+    .. code-block::
+
+        config.advisor.name = 'BOHB'
+        config.advisor.class_args = {
+            'optimize_mode': 'maximize',
+            'min_budget': 1,
+            'max_budget': 27,
+            'eta': 3,
+            'min_points_in_model': 7,
+            'top_n_percent': 15,
+            'num_samples': 64,
+            'random_fraction': 0.33,
+            'bandwidth_factor': 3.0,
+            'min_bandwidth': 0.001
+        }
 
     Parameters
     ----------
     optimize_mode: str
-        optimize mode, 'maximize' or 'minimize'
+        Optimize mode, 'maximize' or 'minimize'.
     min_budget: float
-        The smallest budget to consider. Needs to be positive!
+        The smallest budget to assign to a trial job, (budget can be the number of mini-batches or epochs).
+        Needs to be positive.
     max_budget: float
-        The largest budget to consider. Needs to be larger than min_budget!
+        The largest budget to assign to a trial job. Needs to be larger than min_budget.
         The budgets will be geometrically distributed
         :math:`a^2 + b^2 = c^2 \\sim \\eta^k` for :math:`k\\in [0, 1, ... , num\\_subsets - 1]`.
     eta: int
@@ -271,21 +303,102 @@ class BOHB(MsgDispatcherBase):
         1/eta of them 'advances' to the next round.
         Must be greater or equal to 2.
     min_points_in_model: int
-        number of observations to start building a KDE. Default 'None' means
-        dim+1, the bare minimum.
+        Number of observations to start building a KDE. Default 'None' means dim+1;
+        when the number of completed trials in this budget is equal to or larger than ``max{dim+1, min_points_in_model}``,
+        BOHB will start to build a KDE model of this budget then use said KDE model to guide configuration selection.
+        Needs to be positive. (dim means the number of hyperparameters in search space)
     top_n_percent: int
-        percentage ( between 1 and 99, default 15) of the observations that are considered good.
+        Percentage (between 1 and 99, default 15) of the observations which are considered good.
+        Good points and bad points are used for building KDE models.
+        For example, if you have 100 observed trials and top_n_percent is 15,
+        then the top 15% of points will be used for building the good points models "l(x)".
+        The remaining 85% of points will be used for building the bad point models "g(x)".
     num_samples: int
-        number of samples to optimize EI (default 64)
+        Number of samples to optimize EI (default 64).
+        In this case, it will sample "num_samples" points and compare the result of l(x)/g(x).
+        Then it will return the one with the maximum l(x)/g(x) value as the next configuration
+        if the optimize_mode is ``maximize``. Otherwise, it returns the smallest one.
     random_fraction: float
-    fraction of purely random configurations that are sampled from the
-        prior without the model.
+        Fraction of purely random configurations that are sampled from the prior without the model.
     bandwidth_factor: float
-        to encourage diversity, the points proposed to optimize EI, are sampled
-        from a 'widened' KDE where the bandwidth is multiplied by this factor (default: 3)
+        To encourage diversity, the points proposed to optimize EI are sampled
+        from a 'widened' KDE where the bandwidth is multiplied by this factor (default: 3).
+        It is suggested to use the default value if you are not familiar with KDE.
     min_bandwidth: float
-        to keep diversity, even when all (good) samples have the same value for one of the parameters,
-        a minimum bandwidth (Default: 1e-3) is used instead of zero.
+        To keep diversity, even when all (good) samples have the same value for one of the parameters,
+        a minimum bandwidth (default: 1e-3) is used instead of zero.
+        It is suggested to use the default value if you are not familiar with KDE.
+    config_space: str
+        Directly use a .pcs file serialized by `ConfigSpace <https://automl.github.io/ConfigSpace/>` in "pcs new" format.
+        In this case, search space file (if provided in config) will be ignored.
+        Note that this path needs to be an absolute path. Relative path is currently not supported.
+
+    Notes
+    -----
+
+    Below is the introduction of the BOHB process separated in two parts:
+
+    **The first part HB (Hyperband).**
+    BOHB follows Hyperband’s way of choosing the budgets and continue to use SuccessiveHalving.
+    For more details, you can refer to the :class:`nni.algorithms.hpo.hyperband_advisor.Hyperband`
+    and the `reference paper for Hyperband <https://arxiv.org/abs/1603.06560>`__.
+    This procedure is summarized by the pseudocode below.
+
+    .. image:: ../../img/bohb_1.png
+        :scale: 80 %
+        :align: center
+
+    **The second part BO (Bayesian Optimization)**
+    The BO part of BOHB closely resembles TPE with one major difference:
+    It opted for a single multidimensional KDE compared to the hierarchy of one-dimensional KDEs used in TPE
+    in order to better handle interaction effects in the input space.
+    Tree Parzen Estimator(TPE): uses a KDE (kernel density estimator) to model the densities.
+
+    .. image:: ../../img/bohb_2.png
+        :scale: 80 %
+        :align: center
+
+    To fit useful KDEs, we require a minimum number of data points Nmin;
+    this is set to d + 1 for our experiments, where d is the number of hyperparameters.
+    To build a model as early as possible, we do not wait until Nb = \|Db\|,
+    where the number of observations for budget b is large enough to satisfy q · Nb ≥ Nmin.
+    Instead, after initializing with Nmin + 2 random configurations, we choose the
+    best and worst configurations, respectively, to model the two densities.
+    Note that it also samples a constant fraction named **random fraction** of the configurations uniformly at random.
+
+    .. image:: ../../img/bohb_3.png
+        :scale: 80 %
+        :align: center
+
+
+    .. image:: ../../img/bohb_6.jpg
+        :scale: 65 %
+        :align: center
+
+    **The above image shows the workflow of BOHB.**
+    Here set max_budget = 9, min_budget = 1, eta = 3, others as default.
+    In this case, s_max = 2, so we will continuously run the {s=2, s=1, s=0, s=2, s=1, s=0, ...} cycle.
+    In each stage of SuccessiveHalving (the orange box), it will pick the top 1/eta configurations and run them again with more budget,
+    repeating the SuccessiveHalving stage until the end of this iteration.
+    At the same time, it collects the configurations, budgets and final metrics of each trial
+    and use these to build a multidimensional KDEmodel with the key "budget".
+    Multidimensional KDE is used to guide the selection of configurations for the next iteration.
+    The sampling procedure (using Multidimensional KDE to guide selection) is summarized by the pseudocode below.
+
+    .. image:: ../../img/bohb_4.png
+        :scale: 80 %
+        :align: center
+
+    **Here is a simple experiment which tunes MNIST with BOHB.**
+    Code implementation: :githublink:`examples/trials/mnist-advisor <examples/trials/mnist-advisor>`
+    The following is the experimental final results:
+
+    .. image:: ../../img/bohb_5.png
+        :scale: 80 %
+        :align: center
+
+    More experimental results can be found in the `reference paper <https://arxiv.org/abs/1807.01774>`__.
+    It shows that BOHB makes good use of previous results and has a balanced trade-off in exploration and exploitation.
     """
 
     def __init__(self,
diff --git a/nni/algorithms/hpo/curvefitting_assessor/curvefitting_assessor.py b/nni/algorithms/hpo/curvefitting_assessor/curvefitting_assessor.py
index b91583bb3..e81b9afde 100644
--- a/nni/algorithms/hpo/curvefitting_assessor/curvefitting_assessor.py
+++ b/nni/algorithms/hpo/curvefitting_assessor/curvefitting_assessor.py
@@ -22,18 +22,52 @@ class CurvefittingClassArgsValidator(ClassArgsValidator):
         }).validate(kwargs)
 
 class CurvefittingAssessor(Assessor):
-    """CurvefittingAssessor uses learning curve fitting algorithm to predict the learning curve performance in the future.
+    """
+    CurvefittingAssessor uses learning curve fitting algorithm to predict the learning curve performance in the future.
+
+    The intermediate result **must** be accuracy. Curve fitting does not support minimizing loss.
+
+    Curve fitting assessor is an LPA (learning, predicting, assessing) algorithm.
     It stops a pending trial X at step S if the trial's forecast result at target step is convergence and lower than the
     best performance in the history.
 
+    Paper: `Speeding up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves
+    <https://ml.informatik.uni-freiburg.de/wp-content/uploads/papers/15-IJCAI-Extrapolation_of_Learning_Curves.pdf>`__
+
+    Examples
+    --------
+
+    .. code-block::
+
+        config.assessor.name = 'Curvefitting'
+        config.tuner.class_args = {
+            'epoch_num': 20,
+            'start_step': 6,
+            'threshold': 9,
+            'gap': 1,
+        }
+
     Parameters
     ----------
     epoch_num : int
-        The total number of epoch
+        The total number of epochs.
+
+        We need to know the number of epochs to determine which points we need to predict.
+
     start_step : int
-        only after receiving start_step number of reported intermediate results
+        A trial is determined to be stopped or not only after receiving start_step number of intermediate results.
+
     threshold : float
-        The threshold that we decide to early stop the worse performance curve.
+        The threshold that we use to decide to early stop the worst performance curve.
+
+        For example: if threshold = 0.95, and the best performance in the history is 0.9,
+        then we will stop the trial who's predicted value is lower than 0.95 * 0.9 = 0.855.
+
+    gap : int
+        The gap interval between assessor judgements.
+
+        For example: if gap = 2, start_step = 6,
+        then we will assess the result when we get 6, 8, 10, 12, ... intermediate results.
     """
 
     def __init__(self, epoch_num=20, start_step=6, threshold=0.95, gap=1):
@@ -56,15 +90,6 @@ class CurvefittingAssessor(Assessor):
         logger.info('Successfully initials the curvefitting assessor')
 
     def trial_end(self, trial_job_id, success):
-        """update the best performance of completed trial job
-
-        Parameters
-        ----------
-        trial_job_id : int
-            trial job id
-        success : bool
-            True if succssfully finish the experiment, False otherwise
-        """
         if success:
             if self.set_best_performance:
                 self.completed_best_performance = max(self.completed_best_performance, self.trial_history[-1])
@@ -76,25 +101,6 @@ class CurvefittingAssessor(Assessor):
             logger.info('No need to update, trial job id: %s', trial_job_id)
 
     def assess_trial(self, trial_job_id, trial_history):
-        """assess whether a trial should be early stop by curve fitting algorithm
-
-        Parameters
-        ----------
-        trial_job_id : int
-            trial job id
-        trial_history : list
-            The history performance matrix of each trial
-
-        Returns
-        -------
-        bool
-            AssessResult.Good or AssessResult.Bad
-
-        Raises
-        ------
-        Exception
-            unrecognize exception in curvefitting_assessor
-        """
         scalar_trial_history = extract_scalar_history(trial_history)
         self.trial_history = scalar_trial_history
         if not self.set_best_performance:
diff --git a/nni/algorithms/hpo/dngo_tuner.py b/nni/algorithms/hpo/dngo_tuner.py
index e71a15843..3664169b5 100644
--- a/nni/algorithms/hpo/dngo_tuner.py
+++ b/nni/algorithms/hpo/dngo_tuner.py
@@ -1,3 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import logging
 import warnings
 
@@ -44,7 +47,20 @@ def _random_config(search_space, random_state):
 
 
 class DNGOTuner(Tuner):
-
+    """
+    Use neural networks as an alternative to GPs to model distributions over functions in bayesian optimization.
+
+    Parameters
+    ----------
+    optimize : maximize | minimize, default = maximize
+        If 'maximize', the tuner will target to maximize metrics. If 'minimize', the tuner will target to minimize metrics.
+    sample_size : int, default = 1000
+        Number of samples to select in each iteration. The best one will be picked from the samples as the next trial.
+    trials_per_update : int, default = 20
+        Number of trials to collect before updating the model.
+    num_epochs_per_training : int, default = 500
+        Number of epochs to train DNGO model.
+    """
     def __init__(self, optimize_mode='maximize', sample_size=1000, trials_per_update=20, num_epochs_per_training=500):
         self.searchspace_json = None
         self.random_state = None
diff --git a/nni/algorithms/hpo/evolution_tuner.py b/nni/algorithms/hpo/evolution_tuner.py
index 9277dcca3..3a427442c 100644
--- a/nni/algorithms/hpo/evolution_tuner.py
+++ b/nni/algorithms/hpo/evolution_tuner.py
@@ -4,6 +4,7 @@
 """
 evolution_tuner.py
 """
+from __future__ import annotations
 
 import copy
 import random
@@ -22,28 +23,19 @@ logger = logging.getLogger(__name__)
 
 class Individual:
     """
-    Indicidual class to store the indv info.
+    Individual class to store the indv info.
 
-    Attributes
+    Parameters
     ----------
-    config : str
+    config : str, default = None
         Search space.
-    info : str
+    info : str, default = None
         The str to save information of individual.
-    result : float
+    result : float, None = None
         The final metric of a individual.
     """
 
     def __init__(self, config=None, info=None, result=None):
-        """
-        Parameters
-        ----------
-        config : str
-            A config to represent a group of parameters.
-        info : str
-        result : float
-        save_dir : str
-        """
         self.config = config
         self.result = result
         self.info = info
@@ -61,18 +53,36 @@ class EvolutionClassArgsValidator(ClassArgsValidator):
 
 class EvolutionTuner(Tuner):
     """
-    EvolutionTuner is tuner using navie evolution algorithm.
+    Naive Evolution comes from `Large-Scale Evolution of Image Classifiers <https://arxiv.org/pdf/1703.01041.pdf>`__
+    It randomly initializes a population based on the search space.
+    For each generation, it chooses better ones and does some mutation.
+    (e.g., changes a hyperparameter, adds/removes one layer, etc.) on them to get the next generation.
+    Naive Evolution requires many trials to works but it’s very simple and it’s easily expanded with new features.
+
+    Examples
+    --------
+
+    .. code-block::
+
+        config.tuner.name = 'Evolution'
+        config.tuner.class_args = {
+                'optimize_mode': 'maximize',
+                'population_size': 100
+        }
+
+    Parameters
+    ----------
+    optimize_mode: str
+        Optimize mode, 'maximize' or 'minimize'.
+        If 'maximize', the tuner will try to maximize metrics. If 'minimize', the tuner will try to minimize metrics.
+    population_size: int
+        The initial size of the population (trial num) in the evolution tuner(default=32).
+        The larger population size, the better evolution performance.
+        It's suggested that ``population_size`` be much larger than ``concurrency`` so users can get the most out of the algorithm.
+        And at least ``concurrency``, or the tuner will fail on its first generation of parameters.
     """
 
-    def __init__(self, optimize_mode="maximize", population_size=32):
-        """
-        Parameters
-        ----------
-        optimize_mode : str, default 'maximize'
-        population_size : int
-            initial population size. The larger population size,
-        the better evolution performance.
-        """
+    def __init__(self, optimize_mode='maximize', population_size=32):
         self.optimize_mode = OptimizeMode(optimize_mode)
         self.population_size = population_size
 
@@ -89,11 +99,11 @@ class EvolutionTuner(Tuner):
     def update_search_space(self, search_space):
         """
         Update search space.
-
         Search_space contains the information that user pre-defined.
 
         Parameters
         ----------
+
         search_space : dict
         """
         self.searchspace_json = search_space
@@ -109,8 +119,10 @@ class EvolutionTuner(Tuner):
         """
         To deal with trial failure. If a trial fails,
         random generate the parameters and add into the population.
+
         Parameters
         ----------
+
         parameter_id : int
             Unique identifier for hyper-parameters used by this trial.
         success : bool
@@ -136,12 +148,15 @@ class EvolutionTuner(Tuner):
     def generate_multiple_parameters(self, parameter_id_list, **kwargs):
         """
         Returns multiple sets of trial (hyper-)parameters, as iterable of serializable objects.
+
         Parameters
         ----------
+
         parameter_id_list : list of int
             Unique identifiers for each set of requested hyper-parameters.
         **kwargs
             Not used
+
         Returns
         -------
         list
@@ -182,12 +197,13 @@ class EvolutionTuner(Tuner):
 
         Parameters
         ----------
+
         parameter_id : int
 
         Returns
         -------
         dict
-            A group of candaidte parameters that evolution tuner generated.
+            A group of candidate parameters that evolution tuner generated.
         """
         pos = -1
 
@@ -234,10 +250,12 @@ class EvolutionTuner(Tuner):
 
         Parameters
         ----------
+
         parameter_id : int
 
         Returns
         -------
+
         dict
             One newly generated configuration.
         """
@@ -258,6 +276,7 @@ class EvolutionTuner(Tuner):
 
         Parameters
         ----------
+
         parameter_id : int
         parameters : dict
         value : dict/float
diff --git a/nni/algorithms/hpo/gp_tuner/__init__.py b/nni/algorithms/hpo/gp_tuner/__init__.py
index 17bedd38f..a2b489569 100644
--- a/nni/algorithms/hpo/gp_tuner/__init__.py
+++ b/nni/algorithms/hpo/gp_tuner/__init__.py
@@ -1 +1,4 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from .gp_tuner import GPTuner, GPClassArgsValidator
diff --git a/nni/algorithms/hpo/gp_tuner/gp_tuner.py b/nni/algorithms/hpo/gp_tuner/gp_tuner.py
index 6beaf2c3a..1043a2d40 100644
--- a/nni/algorithms/hpo/gp_tuner/gp_tuner.py
+++ b/nni/algorithms/hpo/gp_tuner/gp_tuner.py
@@ -41,29 +41,77 @@ class GPClassArgsValidator(ClassArgsValidator):
 
 class GPTuner(Tuner):
     """
-    GPTuner is a Bayesian Optimization method where Gaussian Process is used for modeling loss functions.
+    GPTuner is a Bayesian Optimization method where Gaussian Process
+    is used for modeling loss functions.
+
+    Bayesian optimization works by constructing a posterior distribution of functions
+    (a Gaussian Process) that best describes the function you want to optimize.
+    As the number of observations grows, the posterior distribution improves,
+    and the algorithm becomes more certain of which regions in parameter space
+    are worth exploring and which are not.
+
+    GPTuner is designed to minimize/maximize the number of steps required to find
+    a combination of parameters that are close to the optimal combination.
+    To do so, this method uses a proxy optimization problem (finding the maximum of
+    the acquisition function) that, albeit still a hard problem, is cheaper
+    (in the computational sense) to solve, and it's amenable to common tools.
+    Therefore, Bayesian Optimization is suggested for situations where sampling the function
+    to be optimized is very expensive.
+
+    Note that the only acceptable types in the :doc:`search space </hpo/search_space>` are
+    ``randint``, ``uniform``, ``quniform``, ``loguniform``, ``qloguniform``, and numerical ``choice``.
+
+    This optimization approach is described in Section 3 of the paper
+    `Algorithms for Hyper-Parameter Optimization <https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf>`__
+    ( :footcite:t:`bergstra2011algorithms` ).
+
+    Examples
+    --------
+
+    .. code-block::
+
+        config.tuner.name = 'GPTuner'
+        config.tuner.class_args = {
+            'optimize_mode': 'maximize',
+            'utility': 'ei',
+            'kappa': 5.0,
+            'xi': 0.0,
+            'nu': 2.5,
+            'alpha': 1e-6,
+            'cold_start_num': 10,
+            'selection_num_warm_up': 100000,
+            'selection_num_starting_points': 250
+        }
 
     Parameters
     ----------
     optimize_mode : str
-        optimize mode, 'maximize' or 'minimize', by default 'maximize'
+        Optimize mode, 'maximize' or 'minimize', by default 'maximize'
     utility : str
-        utility function (also called 'acquisition funcition') to use, which can be 'ei', 'ucb' or 'poi'. By default 'ei'.
+        Utility function (also called 'acquisition funcition') to use,
+        which can be 'ei', 'ucb' or 'poi'. By default 'ei'.
     kappa : float
-        value used by utility function 'ucb'. The bigger kappa is, the more the tuner will be exploratory. By default 5.
+        Value used by utility function 'ucb'. The bigger kappa is,
+        the more the tuner will be exploratory. By default 5.
     xi : float
-        used by utility function 'ei' and 'poi'. The bigger xi is, the more the tuner will be exploratory. By default 0.
+        Used by utility function 'ei' and 'poi'. The bigger xi is,
+        the more the tuner will be exploratory. By default 0.
     nu : float
-        used to specify Matern kernel. The smaller nu, the less smooth the approximated function is. By default 2.5.
+        Used to specify Matern kernel. The smaller nu,
+        the less smooth the approximated function is. By default 2.5.
     alpha : float
-        Used to specify Gaussian Process Regressor. Larger values correspond to increased noise level in the observations.
+        Used to specify Gaussian Process Regressor.
+        Larger values correspond to increased noise level in the observations.
         By default 1e-6.
     cold_start_num : int
-        Number of random exploration to perform before Gaussian Process. By default 10.
+        Number of random exploration to perform before Gaussian Process.
+        By default 10.
     selection_num_warm_up : int
-        Number of random points to evaluate for getting the point which maximizes the acquisition function. By default 100000
+        Number of random points to evaluate for getting the point which
+        maximizes the acquisition function. By default 100000
     selection_num_starting_points : int
-        Number of times to run L-BFGS-B from a random starting point after the warmup. By default 250.
+        Number of times to run L-BFGS-B from a random starting point after the warmup.
+        By default 250.
     """
 
     def __init__(self, optimize_mode="maximize", utility='ei', kappa=5, xi=0, nu=2.5, alpha=1e-6, cold_start_num=10,
diff --git a/nni/algorithms/hpo/gridsearch_tuner.py b/nni/algorithms/hpo/gridsearch_tuner.py
index 37c73d163..ffdd049f7 100644
--- a/nni/algorithms/hpo/gridsearch_tuner.py
+++ b/nni/algorithms/hpo/gridsearch_tuner.py
@@ -2,14 +2,10 @@
 # Licensed under the MIT license.
 
 """
-Grid search tuner for hyper-parameter optimization.
+Grid search tuner.
 
 For categorical parameters this tuner fully explore all combinations.
 For numerical parameters it samples them at progressively decreased intervals.
-
-Use this tuner if you have abundant resource and want to find strictly optimal parameters.
-
-Grid search tuner has no argument.
 """
 
 __all__ = ['GridSearchTuner']
@@ -63,6 +59,35 @@ _logger = logging.getLogger('nni.tuner.gridsearch')
 ##
 
 class GridSearchTuner(Tuner):
+    """
+    Grid search tuner divides search space into evenly spaced grid, and performs brute-force traverse.
+
+    Recommended when the search space is small, or if you want to find strictly optimal hyperparameters.
+
+    **Implementation**
+
+    The original grid search approach performs an exhaustive search through a space consists of ``choice`` and ``randint``.
+
+    NNI's implementation extends grid search to support all search spaces types.
+
+    When the search space contains continuous parameters like ``normal`` and ``loguniform``,
+    grid search tuner works in following steps:
+
+    1. Divide the search space into a grid.
+    2. Perform an exhaustive searth through the grid.
+    3. Subdivide the grid into a finer-grained new grid.
+    4. Goto step 2, until experiment end.
+
+    As a deterministic algorithm, grid search has no argument.
+
+    Examples
+    --------
+
+    .. code-block::
+
+        config.tuner.name = 'GridSearch'
+    """
+
     def __init__(self):
         self.space = None
 
@@ -175,13 +200,18 @@ class GridSearchTuner(Tuner):
                     mid = (l + r) / 2
                     diff_l = _less(l, mid, spec)
                     diff_r = _less(mid, r, spec)
-                    if diff_l and diff_r:  # we can skip these for non-q, but it will complicate the code
+                    # if l != 0 and r != 1, then they are already in the grid, else they are not
+                    # the special case is needed because for normal distribution 0 and 1 will generate infinity
+                    if (diff_l or l == 0.0) and (diff_r or r == 1.0):
+                        # we can skip these for non-q, but it will complicate the code
                         new_vals.append(mid)
                         updated = True
                     if diff_l:
                         new_divs.append((l, mid))
+                        updated = (updated or l == 0.0)
                     if diff_r:
                         new_divs.append((mid, r))
+                        updated = (updated or r == 1.0)
                 self.grid[i] += new_vals
                 self.divisions[i] = new_divs
 
diff --git a/nni/algorithms/hpo/hyperband_advisor.py b/nni/algorithms/hpo/hyperband_advisor.py
index f42d59369..2dd0bbf25 100644
--- a/nni/algorithms/hpo/hyperband_advisor.py
+++ b/nni/algorithms/hpo/hyperband_advisor.py
@@ -105,7 +105,8 @@ def json2parameter(ss_spec, random_state):
 
 
 class Bracket():
-    """A bracket in Hyperband, all the information of a bracket is managed by an instance of this class
+    """
+    A bracket in Hyperband, all the information of a bracket is managed by an instance of this class
 
     Parameters
     ----------
@@ -267,24 +268,136 @@ class HyperbandClassArgsValidator(ClassArgsValidator):
 
 class Hyperband(MsgDispatcherBase):
     """
-    Hyperband inherit from MsgDispatcherBase rather than Tuner, because it integrates both tuner's functions and assessor's functions.
-    This is an implementation that could fully leverage available resources or follow the algorithm process,
-    i.e., high parallelism or serial.
-    A single execution of Hyperband takes a finite budget of (s_max + 1)B.
+    `Hyperband <https://arxiv.org/pdf/1603.06560.pdf>`__ is a multi-fidelity hyperparameter tuning algorithm
+    based on successive halving.
+
+    The basic idea of Hyperband is to create several buckets,
+    each having ``n`` randomly generated hyperparameter configurations,
+    each configuration using ``r`` resources (e.g., epoch number, batch number).
+    After the ``n`` configurations are finished, it chooses the top ``n/eta`` configurations
+    and runs them using increased ``r*eta`` resources.
+    At last, it chooses the best configuration it has found so far.
+    Please refer to the paper :footcite:t:`li2017hyperband` for detailed algorithm.
+
+    Examples
+    --------
+
+    .. code-block::
+
+        config.advisor.name = 'Hyperband'
+        config.advisor.class_args = {
+            'optimize_mode': 'maximize',
+            'R': 60,
+            'eta': 3
+        }
+
+
+    Note that once you use Advisor, you are not allowed to add a Tuner and Assessor spec in the config file.
+    When Hyperband is used, the dict returned by :func:`nni.get_next_parameter` one more key
+    called ``TRIAL_BUDGET`` besides the hyperparameters and their values.
+    **With this TRIAL_BUDGET, users can control in trial code how long a trial runs by following
+    the suggested trial budget from Hyperband.** ``TRIAL_BUDGET`` is a relative number,
+    users can interpret them as number of epochs, number of mini-batches, running time, etc.
+
+    Here is a concrete example of ``R=81`` and ``eta=3``:
+
+    .. list-table::
+        :header-rows: 1
+        :widths: auto
+
+        * -
+          - s=4
+          - s=3
+          - s=2
+          - s=1
+          - s=0
+        * - i
+          - n r
+          - n r
+          - n r
+          - n r
+          - n r
+        * - 0
+          - 81 1
+          - 27 3
+          - 9 9
+          - 6 27
+          - 5 81
+        * - 1
+          - 27 3
+          - 9 9
+          - 3 27
+          - 2 81
+          -
+        * - 2
+          - 9 9
+          - 3 27
+          - 1 81
+          -
+          -
+        * - 3
+          - 3 27
+          - 1 81
+          -
+          -
+          -
+        * - 4
+          - 1 81
+          -
+          -
+          -
+          -
+
+
+    ``s`` means bucket, ``n`` means the number of configurations that are generated,
+    the corresponding ``r`` means how many budgets these configurations run.
+    ``i`` means round, for example, bucket 4 has 5 rounds, bucket 3 has 4 rounds.
+
+    A complete example can be found :githublink:`examples/trials/mnist-advisor`.
 
     Parameters
     ----------
+    optimize_mode: str
+        Optimize mode, 'maximize' or 'minimize'.
+
     R: int
-        the maximum amount of resource that can be allocated to a single configuration
+        The maximum amount of budget that can be allocated to a single configuration.
+        Here, trial budget could mean the number of epochs, number of mini-batches, etc.,
+        depending on how users interpret it.
+        Each trial should use ``TRIAL_BUDGET`` to control how long it runs.
+
     eta: int
-        the variable that controls the proportion of configurations discarded in each round of SuccessiveHalving
-    optimize_mode: str
-        optimize mode, 'maximize' or 'minimize'
+        The variable that controls the proportion of configurations discarded in each round of SuccessiveHalving.
+        ``1/eta`` configurations will survive and rerun using more budgets in each round.
+
     exec_mode: str
-        execution mode, 'serial' or 'parallelism'
+        Execution mode, 'serial' or 'parallelism'.
+        If 'parallelism', the tuner will try to use available resources to start new bucket immediately.
+        If 'serial', the tuner will only start new bucket after the current bucket is done.
+
+
+    Notes
+    -----
+
+    First, Hyperband an example of how to write an autoML algorithm based on MsgDispatcherBase,
+    rather than based on Tuner and Assessor. Hyperband is implemented in this way
+    because it integrates the functions of both Tuner and Assessor,thus, we call it Advisor.
+
+    Second, this implementation fully leverages Hyperband's internal parallelism.
+    Specifically, the next bucket is not started strictly after the current bucket.
+    Instead, it starts when there are available resources. If you want to use full parallelism mode,
+    set ``exec_mode`` to ``parallelism``.
+
+    Or if you want to set ``exec_mode`` with ``serial`` according to the original algorithm.
+    In this mode, the next bucket will start strictly after the current bucket.
+
+    ``parallelism`` mode may lead to multiple unfinished buckets,
+    in contrast, there is at most one unfinished bucket under ``serial`` mode.
+    The advantage of ``parallelism`` mode is to make full use of resources,
+    which may reduce the experiment duration multiple times.
     """
 
-    def __init__(self, R=60, eta=3, optimize_mode='maximize', exec_mode='parallelism'):
+    def __init__(self, optimize_mode='maximize', R=60, eta=3, exec_mode='parallelism'):
         """B = (s_max + 1)R"""
         super(Hyperband, self).__init__()
         self.R = R
diff --git a/nni/algorithms/hpo/hyperopt_tuner.py b/nni/algorithms/hpo/hyperopt_tuner.py
index 07aaa1246..91dac9743 100644
--- a/nni/algorithms/hpo/hyperopt_tuner.py
+++ b/nni/algorithms/hpo/hyperopt_tuner.py
@@ -191,23 +191,31 @@ class HyperoptClassArgsValidator(ClassArgsValidator):
 
 class HyperoptTuner(Tuner):
     """
-    HyperoptTuner is a tuner which using hyperopt algorithm.
+    NNI wraps `hyperopt <https://github.com/hyperopt/hyperopt>`__ to provide anneal tuner.
+
+    This simple annealing algorithm begins by sampling from the prior
+    but tends over time to sample from points closer and closer to the best ones observed.
+    This algorithm is a simple variation of random search that leverages smoothness in the response surface.
+    The annealing rate is not adaptive.
+
+    Examples
+    --------
+
+    .. code-block::
+
+        config.tuner.name = 'Anneal'
+        config.tuner.class_args = {
+            'optimize_mode': 'minimize'
+        }
+
+    Parameters
+    ----------
+    optimze_mode: 'minimize' or 'maximize'
+        Whether optimize to minimize or maximize trial result.
     """
 
     def __init__(self, algorithm_name, optimize_mode='minimize',
                  parallel_optimize=False, constant_liar_type='min'):
-        """
-        Parameters
-        ----------
-        algorithm_name : str
-            algorithm_name includes "tpe", "random_search" and anneal".
-        optimize_mode : str
-        parallel_optimize : bool
-            More detail could reference: docs/en_US/Tuner/HyperoptTuner.md
-        constant_liar_type : str
-            constant_liar_type including "min", "max" and "mean"
-            More detail could reference: docs/en_US/Tuner/HyperoptTuner.md
-        """
         self.algorithm_name = algorithm_name
         self.optimize_mode = OptimizeMode(optimize_mode)
         self.json = None
@@ -238,15 +246,6 @@ class HyperoptTuner(Tuner):
         raise RuntimeError('Not support tuner algorithm in hyperopt.')
 
     def update_search_space(self, search_space):
-        """
-        Update search space definition in tuner by search_space in parameters.
-
-        Will called when first setup experiemnt or update search space in WebUI.
-
-        Parameters
-        ----------
-        search_space : dict
-        """
         validate_search_space(search_space)
         self.json = search_space
 
@@ -266,22 +265,11 @@ class HyperoptTuner(Tuner):
         self.rval.catch_eval_exceptions = False
 
     def generate_parameters(self, parameter_id, **kwargs):
-        """
-        Returns a set of trial (hyper-)parameters, as a serializable object.
-
-        Parameters
-        ----------
-        parameter_id : int
-
-        Returns
-        -------
-        params : dict
-        """
-        total_params = self.get_suggestion(random_search=False)
+        total_params = self._get_suggestion(random_search=False)
         # avoid generating same parameter with concurrent trials because hyperopt doesn't support parallel mode
         if total_params in self.total_data.values():
             # but it can cause duplicate parameter rarely
-            total_params = self.get_suggestion(random_search=True)
+            total_params = self._get_suggestion(random_search=True)
         self.total_data[parameter_id] = total_params
 
         if self.parallel:
@@ -291,17 +279,6 @@ class HyperoptTuner(Tuner):
         return params
 
     def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
-        """
-        Record an observation of the objective function
-
-        Parameters
-        ----------
-        parameter_id : int
-        parameters : dict
-        value : dict/float
-            if value is dict, it should have "default" key.
-            value is final metrics of the trial.
-        """
         reward = extract_scalar_reward(value)
         # restore the paramsters contains '_index'
         if parameter_id not in self.total_data:
@@ -369,7 +346,7 @@ class HyperoptTuner(Tuner):
                 idxs[key] = [new_id]
                 vals[key] = [vals[key]]
 
-        self.miscs_update_idxs_vals(rval_miscs,
+        self._miscs_update_idxs_vals(rval_miscs,
                                     idxs,
                                     vals,
                                     idxs_map={new_id: new_id},
@@ -382,7 +359,7 @@ class HyperoptTuner(Tuner):
         trials.insert_trial_docs([trial])
         trials.refresh()
 
-    def miscs_update_idxs_vals(self,
+    def _miscs_update_idxs_vals(self,
                                miscs,
                                idxs,
                                vals,
@@ -416,7 +393,7 @@ class HyperoptTuner(Tuner):
                     misc_by_id[tid]['idxs'][key] = [tid]
                     misc_by_id[tid]['vals'][key] = [val]
 
-    def get_suggestion(self, random_search=False):
+    def _get_suggestion(self, random_search=False):
         """
         get suggestion from hyperopt
 
@@ -469,14 +446,6 @@ class HyperoptTuner(Tuner):
         return total_params
 
     def import_data(self, data):
-        """
-        Import additional data for tuning
-
-        Parameters
-        ----------
-        data:
-            a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
-        """
         _completed_num = 0
         for trial_info in data:
             logger.info("Importing data, current processing progress %s / %s", _completed_num, len(data))
diff --git a/nni/algorithms/hpo/medianstop_assessor.py b/nni/algorithms/hpo/medianstop_assessor.py
index 56eb82a3c..aeafafefc 100644
--- a/nni/algorithms/hpo/medianstop_assessor.py
+++ b/nni/algorithms/hpo/medianstop_assessor.py
@@ -1,11 +1,14 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
 
+from __future__ import annotations
+
 import logging
 from schema import Schema, Optional
 
 from nni import ClassArgsValidator
 from nni.assessor import Assessor, AssessResult
+from nni.typehint import Literal
 from nni.utils import extract_scalar_history
 
 logger = logging.getLogger('medianstop_Assessor')
@@ -18,18 +21,35 @@ class MedianstopClassArgsValidator(ClassArgsValidator):
         }).validate(kwargs)
 
 class MedianstopAssessor(Assessor):
-    """MedianstopAssessor is The median stopping rule stops a pending trial X at step S
+    """
+    The median stopping rule stops a pending trial X at step S
     if the trial’s best objective value by step S is strictly worse than the median value
     of the running averages of all completed trials’ objectives reported up to step S
 
+    Paper: `Google Vizer: A Service for Black-Box Optimization
+    <https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/46180.pdf>`__
+
+    Examples
+    --------
+
+    .. code-block::
+
+        config.assessor.name = 'Medianstop'
+        config.tuner.class_args = {
+            'optimize_mode': 'maximize',
+            'start_step': 5
+        }
+
     Parameters
     ----------
-    optimize_mode : str
-        optimize mode, 'maximize' or 'minimize'
-    start_step : int
-        only after receiving start_step number of reported intermediate results
+    optimize_mode
+        Whether optimize to minimize or maximize trial result.
+    start_step
+        A trial is determined to be stopped or not
+        only after receiving start_step number of reported intermediate results.
     """
-    def __init__(self, optimize_mode='maximize', start_step=0):
+
+    def __init__(self, optimize_mode: Literal['minimize', 'maximize'] = 'maximize', start_step: int = 0):
         self._start_step = start_step
         self._running_history = dict()
         self._completed_avg_history = dict()
@@ -56,15 +76,6 @@ class MedianstopAssessor(Assessor):
         self._running_history[trial_job_id].extend(trial_history[len(self._running_history[trial_job_id]):])
 
     def trial_end(self, trial_job_id, success):
-        """trial_end
-
-        Parameters
-        ----------
-        trial_job_id : int
-            trial job id
-        success : bool
-            True if succssfully finish the experiment, False otherwise
-        """
         if trial_job_id in self._running_history:
             if success:
                 cnt = 0
@@ -79,25 +90,6 @@ class MedianstopAssessor(Assessor):
             logger.warning('trial_end: trial_job_id does not exist in running_history')
 
     def assess_trial(self, trial_job_id, trial_history):
-        """assess_trial
-
-        Parameters
-        ----------
-        trial_job_id : int
-            trial job id
-        trial_history : list
-            The history performance matrix of each trial
-
-        Returns
-        -------
-        bool
-            AssessResult.Good or AssessResult.Bad
-
-        Raises
-        ------
-        Exception
-            unrecognize exception in medianstop_assessor
-        """
         curr_step = len(trial_history)
         if curr_step < self._start_step:
             return AssessResult.Good
diff --git a/nni/algorithms/hpo/metis_tuner/__init__.py b/nni/algorithms/hpo/metis_tuner/__init__.py
index f4f9ceba6..9d799fb5a 100644
--- a/nni/algorithms/hpo/metis_tuner/__init__.py
+++ b/nni/algorithms/hpo/metis_tuner/__init__.py
@@ -1 +1,4 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from .metis_tuner import MetisTuner, MetisClassArgsValidator
diff --git a/nni/algorithms/hpo/metis_tuner/metis_tuner.py b/nni/algorithms/hpo/metis_tuner/metis_tuner.py
index 0575f463e..77b25a4d6 100644
--- a/nni/algorithms/hpo/metis_tuner/metis_tuner.py
+++ b/nni/algorithms/hpo/metis_tuner/metis_tuner.py
@@ -46,39 +46,74 @@ class MetisClassArgsValidator(ClassArgsValidator):
 
 class MetisTuner(Tuner):
     """
-    Metis Tuner
+    `Metis tuner <https://www.microsoft.com/en-us/research/publication/metis-robustly-tuning-tail-latencies-cloud-systems/>`__ offers
+    several benefits over other tuning algorithms.
+    While most tools only predict the optimal configuration, Metis gives you two outputs,
+    a prediction for the optimal configuration and a suggestion for the next trial.
+    No more guess work!
 
-    More algorithm information you could reference here:
-    https://www.microsoft.com/en-us/research/publication/metis-robustly-tuning-tail-latencies-cloud-systems/
+    While most tools assume training datasets do not have noisy data,
+    Metis actually tells you if you need to resample a particular hyper-parameter.
 
-    Attributes
+    While most tools have problems of being exploitation-heavy,
+    Metis' search strategy balances exploration, exploitation, and (optional) resampling.
+
+    Metis belongs to the class of sequential model-based optimization (SMBO) algorithms
+    and it is based on the Bayesian Optimization framework. To model the parameter-vs-performance space,
+    Metis uses both a Gaussian Process and GMM. Since each trial can impose a high time cost,
+    Metis heavily trades inference computations with naive trials.
+    At each iteration, Metis does two tasks (refer to :footcite:t:`li2018metis` for details):
+
+
+    1. It finds the global optimal point in the Gaussian Process space.
+       This point represents the optimal configuration.
+
+    2. It identifies the next hyper-parameter candidate.
+       This is achieved by inferring the potential information gain of
+       exploration, exploitation, and resampling.
+
+    Note that the only acceptable types in the :doc:`search space </hpo/search_space>` are
+    ``quniform``, ``uniform``, ``randint``, and numerical ``choice``.
+
+
+    Examples
+    --------
+
+    .. code-block::
+
+        config.tuner.name = 'MetisTuner'
+        config.tuner.class_args = {
+            'optimize_mode': 'maximize'
+        }
+
+    Parameters
     ----------
-        optimize_mode : str
-            optimize_mode is a string that including two mode "maximize" and "minimize"
-
-        no_resampling : bool
-            True or False.
-            Should Metis consider re-sampling as part of the search strategy?
-            If you are confident that the training dataset is noise-free,
-            then you do not need re-sampling.
-
-        no_candidates : bool
-            True or False.
-            Should Metis suggest parameters for the next benchmark?
-            If you do not plan to do more benchmarks,
-            Metis can skip this step.
-
-        selection_num_starting_points : int
-            How many times Metis should try to find the global optimal in the search space?
-            The higher the number, the longer it takes to output the solution.
-
-        cold_start_num : int
-            Metis need some trial result to get cold start.
-            when the number of trial result is less than
-            cold_start_num, Metis will randomly sample hyper-parameter for trial.
-
-        exploration_probability: float
-            The probability of Metis to select parameter from exploration instead of exploitation.
+    optimize_mode : str
+        optimize_mode is a string that including two mode "maximize" and "minimize"
+
+    no_resampling : bool
+        True or False.
+        Should Metis consider re-sampling as part of the search strategy?
+        If you are confident that the training dataset is noise-free,
+        then you do not need re-sampling.
+
+    no_candidates : bool
+        True or False.
+        Should Metis suggest parameters for the next benchmark?
+        If you do not plan to do more benchmarks,
+        Metis can skip this step.
+
+    selection_num_starting_points : int
+        How many times Metis should try to find the global optimal in the search space?
+        The higher the number, the longer it takes to output the solution.
+
+    cold_start_num : int
+        Metis need some trial result to get cold start.
+        when the number of trial result is less than
+        cold_start_num, Metis will randomly sample hyper-parameter for trial.
+
+    exploration_probability: float
+        The probability of Metis to select parameter from exploration instead of exploitation.
     """
 
     def __init__(
@@ -89,43 +124,6 @@ class MetisTuner(Tuner):
             selection_num_starting_points=600,
             cold_start_num=10,
             exploration_probability=0.9):
-        """
-        Parameters
-        ----------
-        optimize_mode : str
-            optimize_mode is a string that including two mode "maximize" and "minimize"
-
-        no_resampling : bool
-            True or False.
-            Should Metis consider re-sampling as part of the search strategy?
-            If you are confident that the training dataset is noise-free,
-            then you do not need re-sampling.
-
-        no_candidates : bool
-            True or False.
-            Should Metis suggest parameters for the next benchmark?
-            If you do not plan to do more benchmarks,
-            Metis can skip this step.
-
-        selection_num_starting_points : int
-            How many times Metis should try to find the global optimal in the search space?
-            The higher the number, the longer it takes to output the solution.
-
-        cold_start_num : int
-            Metis need some trial result to get cold start.
-            when the number of trial result is less than
-            cold_start_num, Metis will randomly sample hyper-parameter for trial.
-
-        exploration_probability : float
-            The probability of Metis to select parameter from exploration instead of exploitation.
-
-        x_bounds : list
-            The constration of parameters.
-
-        x_types : list
-            The type of parameters.
-        """
-
         self.samples_x = []
         self.samples_y = []
         self.samples_y_aggregation = []
@@ -141,7 +139,9 @@ class MetisTuner(Tuner):
         self.minimize_constraints_fun = None
         self.minimize_starting_points = None
         self.supplement_data_num = 0
+        # The constration of parameters
         self.x_bounds = []
+        # The type of parameters
         self.x_types = []
 
 
diff --git a/nni/algorithms/hpo/networkmorphism_tuner/__init__.py b/nni/algorithms/hpo/networkmorphism_tuner/__init__.py
index b60da9c38..95a7a41d2 100644
--- a/nni/algorithms/hpo/networkmorphism_tuner/__init__.py
+++ b/nni/algorithms/hpo/networkmorphism_tuner/__init__.py
@@ -1 +1,4 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from .networkmorphism_tuner import NetworkMorphismTuner, NetworkMorphismClassArgsValidator
diff --git a/nni/algorithms/hpo/pbt_tuner.py b/nni/algorithms/hpo/pbt_tuner.py
index 507c519a2..13778f22e 100644
--- a/nni/algorithms/hpo/pbt_tuner.py
+++ b/nni/algorithms/hpo/pbt_tuner.py
@@ -170,26 +170,92 @@ class PBTClassArgsValidator(ClassArgsValidator):
         }).validate(kwargs)
 
 class PBTTuner(Tuner):
+    """
+    Population Based Training (PBT) comes from `Population Based Training of Neural Networks <https://arxiv.org/abs/1711.09846v1>`__.
+    It's a simple asynchronous optimization algorithm which effectively utilizes a fixed computational budget to jointly optimize
+    a population of models and their hyperparameters to maximize performance.
+    Importantly, PBT discovers a schedule of hyperparameter settings rather than following the generally sub-optimal strategy of
+    trying to find a single fixed set to use for the whole course of training.
+
+    .. image:: ../../img/pbt.jpg
+
+    PBTTuner initializes a population with several trials (i.e., ``population_size``).
+    There are four steps in the above figure, each trial only runs by one step. How long is one step is controlled by trial code,
+    e.g., one epoch. When a trial starts, it loads a checkpoint specified by PBTTuner and continues to run one step,
+    then saves checkpoint to a directory specified by PBTTuner and exits.
+    The trials in a population run steps synchronously, that is, after all the trials finish the ``i``-th step,
+    the ``(i+1)``-th step can be started. Exploitation and exploration of PBT are executed between two consecutive steps.
+
+    Two important steps to follow if you are trying to use PBTTuner:
+
+    1. **Provide checkpoint directory**. Since some trials need to load other trial's checkpoint,
+       users should provide a directory (i.e., ``all_checkpoint_dir``) which is accessible by every trial.
+       It is easy for local mode, users could directly use the default directory or specify any directory on the local machine.
+       For other training services, users should follow
+       :doc:`the document of those training services </experiment/training_service/shared_storage>`
+       to provide a directory in a shared storage, such as NFS, Azure storage.
+
+    2. **Modify your trial code**. Before running a step, a trial needs to load a checkpoint,
+       the checkpoint directory is specified in hyper-parameter configuration generated by PBTTuner,
+       i.e., ``params['load_checkpoint_dir']``. Similarly, the directory for saving checkpoint is also included in the configuration,
+       i.e., ``params['save_checkpoint_dir']``. Here, ``all_checkpoint_dir`` is base folder of ``load_checkpoint_dir``
+       and ``save_checkpoint_dir`` whose format is ``all_checkpoint_dir/<population-id>/<step>``.
+
+       .. code-block:: python
+
+        params = nni.get_next_parameter()
+        # the path of the checkpoint to load
+        load_path = os.path.join(params['load_checkpoint_dir'], 'model.pth')
+        # load checkpoint from `load_path`
+        ...
+        # run one step
+        ...
+        # the path for saving a checkpoint
+        save_path = os.path.join(params['save_checkpoint_dir'], 'model.pth')
+        # save checkpoint to `save_path`
+        ...
+
+    The complete example code can be found :githublink:`here <examples/trials/mnist-pbt-tuner-pytorch>`.
+
+    Parameters
+    ----------
+    optimize_mode : ``maximize`` or ``minimize``, default: ``maximize``
+        If ``maximize``, the tuner will target to maximize metrics. If ``minimize``, the tuner will target to minimize metrics.
+    all_checkpoint_dir : str
+        Directory for trials to load and save checkpoint.
+        If not specified, the directory would be ``~/nni/checkpoint/``.
+        Note that if the experiment is not local mode,
+        users should provide a path in a shared storage which can be accessed by all the trials.
+    population_size : int, default = 10
+        Number of trials in a population. Each step has this number of trials.
+        In our implementation, one step is running each trial by specific training epochs set by users.
+    factor : float, default = (1.2, 0.8)
+        Factors for perturbation of hyperparameters.
+    resample_probability : float, default = 0.25
+        Probability for resampling.
+    fraction : float, default = 0.2
+        Fraction for selecting bottom and top trials.
+
+    Examples
+    --------
+    Below is an example of PBTTuner configuration in experiment config file.
+
+    .. code-block:: yaml
+
+        tuner:
+          name: PBTTuner
+          classArgs:
+            optimize_mode: maximize
+            all_checkpoint_dir: /the/path/to/store/checkpoints
+            population_size: 10
+
+    Notes
+    -----
+    Assessor is not allowed if PBTTuner is used.
+    """
+
     def __init__(self, optimize_mode="maximize", all_checkpoint_dir=None, population_size=10, factor=0.2,
                  resample_probability=0.25, fraction=0.2):
-        """
-        Initialization
-
-        Parameters
-        ----------
-        optimize_mode : str
-            maximize or minimize
-        all_checkpoint_dir : str
-            directory to store training model checkpoint
-        population_size : int
-            number of trials for each epoch
-        factor : float
-            factor for perturbation
-        resample_probability : float
-            probability for resampling
-        fraction : float
-            fraction for selecting bottom and top trials
-        """
         self.optimize_mode = OptimizeMode(optimize_mode)
         if all_checkpoint_dir is None:
             all_checkpoint_dir = os.getenv('NNI_CHECKPOINT_DIRECTORY')
diff --git a/nni/algorithms/hpo/ppo_tuner/__init__.py b/nni/algorithms/hpo/ppo_tuner/__init__.py
index 854090c93..c2ea27403 100644
--- a/nni/algorithms/hpo/ppo_tuner/__init__.py
+++ b/nni/algorithms/hpo/ppo_tuner/__init__.py
@@ -1 +1,4 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from .ppo_tuner import PPOTuner, PPOClassArgsValidator
diff --git a/nni/algorithms/hpo/ppo_tuner/ppo_tuner.py b/nni/algorithms/hpo/ppo_tuner/ppo_tuner.py
index 33b62d600..a96a5984c 100644
--- a/nni/algorithms/hpo/ppo_tuner/ppo_tuner.py
+++ b/nni/algorithms/hpo/ppo_tuner/ppo_tuner.py
@@ -306,40 +306,37 @@ class PPOClassArgsValidator(ClassArgsValidator):
 class PPOTuner(Tuner):
     """
     PPOTuner, the implementation inherits the main logic of the implementation
-    [ppo2 from openai](https://github.com/openai/baselines/tree/master/baselines/ppo2), and is adapted for NAS scenario.
+    `ppo2 from openai <https://github.com/openai/baselines/tree/master/baselines/ppo2>`__ and is adapted for NAS scenario.
     It uses ``lstm`` for its policy network and value network, policy and value share the same network.
+
+    Parameters
+    ----------
+    optimize_mode : str
+        maximize or minimize
+    trials_per_update : int
+        Number of trials to have for each model update
+    epochs_per_update : int
+        Number of epochs to run for each model update
+    minibatch_size : int
+        Minibatch size (number of trials) for the update
+    ent_coef : float
+        Policy entropy coefficient in the optimization objective
+    lr : float
+        Learning rate of the model (lstm network), constant
+    vf_coef : float
+        Value function loss coefficient in the optimization objective
+    max_grad_norm : float
+        Gradient norm clipping coefficient
+    gamma : float
+        Discounting factor
+    lam : float
+        Advantage estimation discounting factor (lambda in the paper)
+    cliprange : float
+        Cliprange in the PPO algorithm, constant
     """
 
     def __init__(self, optimize_mode, trials_per_update=20, epochs_per_update=4, minibatch_size=4,
                  ent_coef=0.0, lr=3e-4, vf_coef=0.5, max_grad_norm=0.5, gamma=0.99, lam=0.95, cliprange=0.2):
-        """
-        Initialization, PPO model is not initialized here as search space is not received yet.
-
-        Parameters
-        ----------
-        optimize_mode : str
-            maximize or minimize
-        trials_per_update : int
-            Number of trials to have for each model update
-        epochs_per_update : int
-            Number of epochs to run for each model update
-        minibatch_size : int
-            Minibatch size (number of trials) for the update
-        ent_coef : float
-            Policy entropy coefficient in the optimization objective
-        lr : float
-            Learning rate of the model (lstm network), constant
-        vf_coef : float
-            Value function loss coefficient in the optimization objective
-        max_grad_norm : float
-            Gradient norm clipping coefficient
-        gamma : float
-            Discounting factor
-        lam : float
-            Advantage estimation discounting factor (lambda in the paper)
-        cliprange : float
-            Cliprange in the PPO algorithm, constant
-        """
         self.optimize_mode = OptimizeMode(optimize_mode)
         self.model_config = ModelConfig()
         self.model = None
diff --git a/nni/algorithms/hpo/random_tuner.py b/nni/algorithms/hpo/random_tuner.py
index 741311f28..caf9e3abb 100644
--- a/nni/algorithms/hpo/random_tuner.py
+++ b/nni/algorithms/hpo/random_tuner.py
@@ -2,12 +2,14 @@
 # Licensed under the MIT license.
 
 """
-Naive random tuner for hyper-parameter optimization.
+Naive random tuner.
 
 You can specify an integer seed to determine random result.
 """
 
-__all__ = ['RandomTuner', 'suggest', 'suggest_parameter']
+from __future__ import annotations
+
+__all__ = ['RandomTuner']
 
 import logging
 
@@ -15,24 +17,46 @@ import numpy as np
 import schema
 
 from nni import ClassArgsValidator
-from nni.common.hpo_utils import format_search_space, deformat_parameters
+from nni.common.hpo_utils import Deduplicator, format_search_space, deformat_parameters
 from nni.tuner import Tuner
 
 _logger = logging.getLogger('nni.tuner.random')
 
 class RandomTuner(Tuner):
-    def __init__(self, seed=None):
+    """
+    A naive tuner that generates fully random hyperparameters.
+
+    Examples
+    --------
+
+    .. code-block::
+
+        config.tuner.name = 'Random'
+        config.tuner.class_args = {
+            'seed': 100
+        }
+
+    Parameters
+    ----------
+    seed
+        The random seed.
+    """
+
+    def __init__(self, seed: int | None = None):
         self.space = None
         if seed is None:  # explicitly generate a seed to make the experiment reproducible
             seed = np.random.default_rng().integers(2 ** 31)
         self.rng = np.random.default_rng(seed)
+        self.dedup = None
         _logger.info(f'Using random seed {seed}')
 
     def update_search_space(self, space):
         self.space = format_search_space(space)
+        self.dedup = Deduplicator(self.space)
 
     def generate_parameters(self, *args, **kwargs):
         params = suggest(self.rng, self.space)
+        params = self.dedup(params)
         return deformat_parameters(params, self.space)
 
     def receive_trial_result(self, *args, **kwargs):
diff --git a/nni/algorithms/hpo/regularized_evolution_tuner.py b/nni/algorithms/hpo/regularized_evolution_tuner.py
index ac756c338..e3139b8e4 100644
--- a/nni/algorithms/hpo/regularized_evolution_tuner.py
+++ b/nni/algorithms/hpo/regularized_evolution_tuner.py
@@ -1,3 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import copy
 import logging
 import random
diff --git a/nni/algorithms/hpo/smac_tuner/smac_tuner.py b/nni/algorithms/hpo/smac_tuner/smac_tuner.py
index 68f1d07ff..7b17a2b77 100644
--- a/nni/algorithms/hpo/smac_tuner/smac_tuner.py
+++ b/nni/algorithms/hpo/smac_tuner/smac_tuner.py
@@ -38,20 +38,46 @@ class SMACClassArgsValidator(ClassArgsValidator):
 
 class SMACTuner(Tuner):
     """
-    This is a wrapper of [SMAC](https://github.com/automl/SMAC3) following NNI tuner interface.
-    It only supports ``SMAC`` mode, and does not support the multiple instances of SMAC3 (i.e.,
-    the same configuration is run multiple times).
+    `SMAC <https://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf>`__ is based on Sequential Model-Based Optimization (SMBO).
+    It adapts the most prominent previously used model class (Gaussian stochastic process models)
+    and introduces the model class of random forests to SMBO in order to handle categorical parameters.
+
+    The SMAC supported by nni is a wrapper on `the SMAC3 github repo <https://github.com/automl/SMAC3>`__,
+    following NNI tuner interface :class:`nni.tuner.Tuner`. For algorithm details of SMAC, please refer to the paper
+    :footcite:t:`hutter2011sequential`.
+
+    Note that SMAC on nni only supports a subset of the types in
+    :doc:`search space </hpo/search_space>`:
+    ``choice``, ``randint``, ``uniform``, ``loguniform``, and ``quniform``.
+
+    Note that SMAC needs additional installation using the following command:
+
+    .. code-block:: bash
+
+        pip install nni[SMAC]
+
+    ``swig`` is required for SMAC. for Ubuntu ``swig`` can be installed with ``apt``.
+
+    Examples
+    --------
+
+    .. code-block::
+
+        config.tuner.name = 'SMAC'
+        config.tuner.class_args = {
+            'optimize_mode': 'maximize'
+        }
+
+    Parameters
+    ----------
+    optimize_mode : str
+        Optimize mode, 'maximize' or 'minimize', by default 'maximize'
+    config_dedup : bool
+        If True, the tuner will not generate a configuration that has been already generated.
+        If False, a configuration may be generated twice, but it is rare for relatively large search space.
     """
+
     def __init__(self, optimize_mode="maximize", config_dedup=False):
-        """
-        Parameters
-        ----------
-        optimize_mode : str
-            Optimize mode, 'maximize' or 'minimize', by default 'maximize'
-        config_dedup : bool
-            If True, the tuner will not generate a configuration that has been already generated.
-            If False, a configuration may be generated twice, but it is rare for relatively large search space.
-        """
         self.logger = logger
         self.optimize_mode = OptimizeMode(optimize_mode)
         self.total_data = {}
diff --git a/nni/algorithms/hpo/tpe_tuner.py b/nni/algorithms/hpo/tpe_tuner.py
index c233e97b2..fdfed6a5d 100644
--- a/nni/algorithms/hpo/tpe_tuner.py
+++ b/nni/algorithms/hpo/tpe_tuner.py
@@ -2,26 +2,30 @@
 # Licensed under the MIT license.
 
 """
-Tree-structured Parzen Estimator (TPE) tuner for hyper-parameter optimization.
+Tree-structured Parzen Estimator (TPE) tuner.
 
 Paper: https://proceedings.neurips.cc/paper/2011/file/86e8f7ab32cfd12577bc2619bc635690-Paper.pdf
+
 Official code: https://github.com/hyperopt/hyperopt/blob/master/hyperopt/tpe.py
 
 This is a slightly modified re-implementation of the algorithm.
 """
 
-__all__ = ['TpeTuner', 'TpeArguments', 'suggest', 'suggest_parameter']
+from __future__ import annotations
+
+__all__ = ['TpeTuner', 'TpeArguments']
 
 from collections import defaultdict
 import logging
 import math
-from typing import NamedTuple, Optional, Union
+from typing import Any, NamedTuple
 
 import numpy as np
 from scipy.special import erf  # pylint: disable=no-name-in-module
 
+from nni.common.hpo_utils import Deduplicator, OptimizeMode, format_search_space, deformat_parameters, format_parameters
 from nni.tuner import Tuner
-from nni.common.hpo_utils import OptimizeMode, format_search_space, deformat_parameters, format_parameters
+from nni.typehint import Literal
 from nni.utils import extract_scalar_reward
 from . import random_tuner
 
@@ -31,12 +35,13 @@ _logger = logging.getLogger('nni.tuner.tpe')
 
 class TpeArguments(NamedTuple):
     """
-    These are the hyper-parameters of TPE algorithm itself.
-    To avoid confusing with trials' hyper-parameters, they are called "arguments" in this code.
+    Hyperparameters of TPE algorithm itself.
+
+    To avoid confusing with trials' hyperparameters to be tuned, these are called "arguments" here.
 
     Parameters
-    ==========
-    constant_liar_type: 'best' | 'worst' | 'mean' | None (default: 'best')
+    ----------
+    constant_liar_type
         TPE algorithm itself does not support parallel tuning.
         This parameter specifies how to optimize for trial_concurrency > 1.
 
@@ -44,20 +49,21 @@ class TpeArguments(NamedTuple):
 
         How each liar works is explained in paper's section 6.1.
         In general "best" suit for small trial number and "worst" suit for large trial number.
+        (:doc:`experiment result </sharings/parallelizing_tpe_search>`)
 
-    n_startup_jobs: int (default: 20)
-        The first N hyper-parameters are generated fully randomly for warming up.
+    n_startup_jobs
+        The first N hyperparameters are generated fully randomly for warming up.
         If the search space is large, you can increase this value.
         Or if max_trial_number is small, you may want to decrease it.
 
-    n_ei_candidates: int (default: 24)
+    n_ei_candidates
         For each iteration TPE samples EI for N sets of parameters and choose the best one. (loosely speaking)
 
-    linear_forgetting: int (default: 25)
+    linear_forgetting
         TPE will lower the weights of old trials.
         This controls how many iterations it takes for a trial to start decay.
 
-    prior_weight: float (default: 1.0)
+    prior_weight
         TPE treats user provided search space as prior.
         When generating new trials, it also incorporates the prior in trial history by transforming the search space to
         one trial configuration (i.e., each parameter of this configuration chooses the mean of its candidate range).
@@ -66,11 +72,11 @@ class TpeArguments(NamedTuple):
         With prior weight 1.0, the search space is treated as one good trial.
         For example, "normal(0, 1)" effectly equals to a trial with x = 0 which has yielded good result.
 
-    gamma: float (default: 0.25)
+    gamma
         Controls how many trials are considered "good".
         The number is calculated as "min(gamma * sqrt(N), linear_forgetting)".
     """
-    constant_liar_type: Optional[str] = 'best'
+    constant_liar_type: Literal['best', 'worst', 'mean'] | None = 'best'
     n_startup_jobs: int = 20
     n_ei_candidates: int = 24
     linear_forgetting: int = 25
@@ -79,24 +85,75 @@ class TpeArguments(NamedTuple):
 
 class TpeTuner(Tuner):
     """
+    Tree-structured Parzen Estimator (TPE) tuner.
+
+    TPE is a lightweight tuner that has no extra dependency and supports all search space types,
+    designed to be the default tuner.
+
+    It has the drawback that TPE cannot discover relationship between different hyperparameters.
+
+    **Implementation**
+
+    TPE is an SMBO algorithm.
+    It models P(x|y) and P(y) where x represents hyperparameters and y the evaluation result.
+    P(x|y) is modeled by transforming the generative process of hyperparameters,
+    replacing the distributions of the configuration prior with non-parametric densities.
+
+    Paper: `Algorithms for Hyper-Parameter Optimization
+    <https://proceedings.neurips.cc/paper/2011/file/86e8f7ab32cfd12577bc2619bc635690-Paper.pdf>`__
+
+    Examples
+    --------
+
+    .. code-block::
+
+        ## minimal config ##
+
+        config.tuner.name = 'TPE'
+        config.tuner.class_args = {
+            'optimize_mode': 'maximize'
+        }
+
+    .. code-block::
+
+        ## advanced config ##
+
+        config.tuner.name = 'TPE'
+        config.tuner.class_args = {
+            'optimize_mode': maximize,
+            'seed': 12345,
+            'tpe_args': {
+                'constant_liar_type': 'mean',
+                'n_startup_jobs': 10,
+                'n_ei_candidates': 20,
+                'linear_forgetting': 100,
+                'prior_weight': 0,
+                'gamma': 0.5
+            }
+        }
+
     Parameters
-    ==========
-    optimze_mode: 'minimize' | 'maximize' (default: 'minimize')
+    ----------
+    optimze_mode: Literal['minimize', 'maximize']
         Whether optimize to minimize or maximize trial result.
-    seed: int | None
+    seed
         The random seed.
-    tpe_args: dict[string, Any] | None
+    tpe_args
         Advanced users can use this to customize TPE tuner.
-        See `TpeArguments` for details.
+        See :class:`TpeArguments` for details.
     """
 
-    def __init__(self, optimize_mode='minimize', seed=None, tpe_args=None):
+    def __init__(self,
+            optimize_mode: Literal['minimize', 'maximize'] = 'minimize',
+            seed: int | None = None,
+            tpe_args: dict[str, Any] | None = None):
         self.optimize_mode = OptimizeMode(optimize_mode)
         self.args = TpeArguments(**(tpe_args or {}))
         self.space = None
         # concurrent generate_parameters() calls are likely to yield similar result, because they use same history
         # the liar solves this problem by adding fake results to history
         self.liar = create_liar(self.args.constant_liar_type)
+        self.dedup = None
 
         if seed is None:  # explicitly generate a seed to make the experiment reproducible
             seed = np.random.default_rng().integers(2 ** 31)
@@ -109,6 +166,7 @@ class TpeTuner(Tuner):
 
     def update_search_space(self, space):
         self.space = format_search_space(space)
+        self.dedup = Deduplicator(self.space)
 
     def generate_parameters(self, parameter_id, **kwargs):
         if self.liar and self._running_params:
@@ -122,6 +180,7 @@ class TpeTuner(Tuner):
             history = self._history
 
         params = suggest(self.args, self.rng, self.space, history)
+        params = self.dedup(params)
 
         self._params[parameter_id] = params
         self._running_params[parameter_id] = params
@@ -183,7 +242,7 @@ def suggest_parameter(args, rng, spec, parameter_history):
 ## Utilities part ##
 
 class Record(NamedTuple):
-    param: Union[int, float]
+    param: int | float
     loss: float
 
 class BestLiar:  # assume running parameters have best result, it accelerates "converging"
@@ -305,7 +364,7 @@ def adaptive_parzen_normal(args, history_mus, prior_mu, prior_sigma):
     this function is used for everything other than "choice" and "randint".
 
     Parameters
-    ==========
+    ----------
     args: TpeArguments
         Algorithm arguments.
     history_mus: 1-d array of float
@@ -317,7 +376,7 @@ def adaptive_parzen_normal(args, history_mus, prior_mu, prior_sigma):
         σ value of normal search space.
 
     Returns
-    =======
+    -------
     Tuple of three 1-d float arrays: (weight, µ, σ).
 
     The tuple represents N+1 "vicinity of observations" and each one's weight,
diff --git a/nni/algorithms/nas/pytorch/fbnet/utils.py b/nni/algorithms/nas/pytorch/fbnet/utils.py
index 925b612a5..77e71746b 100644
--- a/nni/algorithms/nas/pytorch/fbnet/utils.py
+++ b/nni/algorithms/nas/pytorch/fbnet/utils.py
@@ -3,6 +3,7 @@
 
 from __future__ import absolute_import, division, print_function
 
+import ast
 import os
 import timeit
 import torch
@@ -10,17 +11,20 @@ import torch
 import numpy as np
 import torch.nn as nn
 
-from nni.compression.pytorch.utils.counter import count_flops_params
+from nni.compression.pytorch.utils import count_flops_params
 
 LUT_FILE = "lut.npy"
+LUT_JSON_FILE = "lut.txt"
 LUT_PATH = "lut"
 
+DATA_TYPE = "float"
 
 class NASConfig:
     def __init__(
         self,
         perf_metric="flops",
         lut_load=False,
+        lut_load_format="json",
         model_dir=None,
         nas_lr=0.01,
         nas_weight_decay=5e-4,
@@ -41,6 +45,13 @@ class NASConfig:
         ], "perf_metric should be ['flops', 'latency']"
         # wether load or create lut file
         self.lut_load = lut_load
+
+        assert lut_load_format in [
+            "json",
+            "numpy",
+        ], "lut_load_format should be ['json', 'numpy']"
+        self.lut_load_format = lut_load_format
+
         # necessary dirs
         self.lut_en = model_dir is not None
         if self.lut_en:
@@ -252,8 +263,14 @@ class LookUpTable:
         if config.lut_en:
             self.lut_perf = None
             self.lut_file = os.path.join(config.lut_path, LUT_FILE)
+            self.lut_json_file = LUT_JSON_FILE
             if config.lut_load:
-                self._load_from_file()
+                if config.lut_load_format == "numpy":
+                    # Load data from numpy file
+                    self._load_from_file()
+                else:
+                    # Load data from json file
+                    self._load_from_json_file()
             else:
                 self._create_perfs()
 
@@ -349,3 +366,68 @@ class LookUpTable:
     def _load_from_file(self):
         """Load numpy file."""
         self.lut_perf = np.load(self.lut_file, allow_pickle=True)
+
+    def _load_from_json_file(self):
+        """Load json file."""
+
+        """
+        lut_json_file ('lut.txt') format:
+            {'op_name': operator_name,
+             'op_data_shape': (input_w, input_h, C_in, C_out, stride),
+             'op_dtype': data_type,
+             'op_latency': latency}
+            {...}
+            {...}
+        """
+        latency_file = open(self.lut_json_file, "r")
+        ops_latency = latency_file.readlines()
+
+        """ops_lut: {'op_name': {'op_data_shape': {'op_dtype': latency}}}"""
+        ops_lut = {}
+
+        for op_latency in ops_latency:
+            assert isinstance(op_latency, str) or isinstance(op_latency, dict)
+
+            if isinstance(op_latency, str):
+                record = ast.literal_eval(op_latency)
+            elif isinstance(op_latency, dict):
+                record = op_latency
+
+            op_name = record["op_name"]
+            """op_data_shape: (input_w, input_h, C_in, C_out, stride)"""
+            op_data_shape = record["op_data_shape"]
+            op_dtype = record["op_dtype"]
+            op_latency = record["op_latency"]
+
+            if op_name not in ops_lut:
+                ops_lut[op_name] = {}
+
+            if op_data_shape not in ops_lut[op_name]:
+                ops_lut[op_name][op_data_shape] = {}
+
+            ops_lut[op_name][op_data_shape][op_dtype] = op_latency
+
+        self.lut_perf = [{} for i in range(self.cnt_layers)]
+        layer_id = 0
+
+        for stage_name in self.lut_ops:
+            stage_ops = self.lut_ops[stage_name]
+            ops_num = self.layer_num[stage_name]
+
+            for _ in range(ops_num):
+                for op_name in stage_ops:
+                    layer_config = self.layer_configs[layer_id]
+                    layer_in_shape = self.layer_in_shapes[layer_id]
+
+                    input_w = layer_in_shape[1]
+                    input_h = layer_in_shape[2]
+                    c_in = layer_config[0]
+                    c_out = layer_config[1]
+                    stride = layer_config[2]
+                    op_data_shape = (input_w, input_h, c_in, c_out, stride)
+
+                    if op_name in ops_lut and op_data_shape in ops_lut[op_name]:
+                        self.lut_perf[layer_id][op_name] = \
+                            ops_lut[op_name][op_data_shape][DATA_TYPE]
+
+                layer_id += 1
diff --git a/nni/assessor.py b/nni/assessor.py
index 7cd83e923..36c78bbbf 100644
--- a/nni/assessor.py
+++ b/nni/assessor.py
@@ -8,10 +8,13 @@ to tell whether this trial can be early stopped or not.
 See :class:`Assessor`' specification and ``docs/en_US/assessors.rst`` for details.
 """
 
+from __future__ import annotations
+
 from enum import Enum
 import logging
 
 from .recoverable import Recoverable
+from .typehint import TrialMetric
 
 __all__ = ['AssessResult', 'Assessor']
 
@@ -54,7 +57,7 @@ class Assessor(Recoverable):
     :class:`~nni.algorithms.hpo.curvefitting_assessor.CurvefittingAssessor`
     """
 
-    def assess_trial(self, trial_job_id, trial_history):
+    def assess_trial(self, trial_job_id: str, trial_history: list[TrialMetric]) -> AssessResult:
         """
         Abstract method for determining whether a trial should be killed. Must override.
 
@@ -91,7 +94,7 @@ class Assessor(Recoverable):
         """
         raise NotImplementedError('Assessor: assess_trial not implemented')
 
-    def trial_end(self, trial_job_id, success):
+    def trial_end(self, trial_job_id: str, success: bool) -> None:
         """
         Abstract method invoked when a trial is completed or terminated. Do nothing by default.
 
@@ -103,22 +106,22 @@ class Assessor(Recoverable):
             True if the trial successfully completed; False if failed or terminated.
         """
 
-    def load_checkpoint(self):
+    def load_checkpoint(self) -> None:
         """
         Internal API under revising, not recommended for end users.
         """
         checkpoin_path = self.get_checkpoint_path()
         _logger.info('Load checkpoint ignored by assessor, checkpoint path: %s', checkpoin_path)
 
-    def save_checkpoint(self):
+    def save_checkpoint(self) -> None:
         """
         Internal API under revising, not recommended for end users.
         """
         checkpoin_path = self.get_checkpoint_path()
         _logger.info('Save checkpoint ignored by assessor, checkpoint path: %s', checkpoin_path)
 
-    def _on_exit(self):
+    def _on_exit(self) -> None:
         pass
 
-    def _on_error(self):
+    def _on_error(self) -> None:
         pass
diff --git a/nni/common/__init__.py b/nni/common/__init__.py
index f18054727..c29323fd6 100644
--- a/nni/common/__init__.py
+++ b/nni/common/__init__.py
@@ -1 +1,4 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from .serializer import trace, dump, load, is_traceable
diff --git a/nni/common/graph_utils.py b/nni/common/graph_utils.py
index 1c1f4aaf9..4844d870d 100644
--- a/nni/common/graph_utils.py
+++ b/nni/common/graph_utils.py
@@ -47,7 +47,7 @@ class TorchGraph:
         Parameters
         ----------
         model : pytorch model
-            The model user wants to speed up
+            The model user wants to speedup
         dummy_input : pytorch tensor
             The dummy input for ```jit.trace```, users should put it on right device before pass in
         traced_model : torch._C.torch.jit.TopLevelTracedModule
diff --git a/nni/common/hpo_utils/__init__.py b/nni/common/hpo_utils/__init__.py
index ae6bd87bc..eed7f4616 100644
--- a/nni/common/hpo_utils/__init__.py
+++ b/nni/common/hpo_utils/__init__.py
@@ -1,6 +1,7 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
 
+from .dedup import Deduplicator
 from .validation import validate_search_space
 from .formatting import *
 from .optimize_mode import OptimizeMode
diff --git a/nni/common/hpo_utils/dedup.py b/nni/common/hpo_utils/dedup.py
new file mode 100644
index 000000000..cc0772514
--- /dev/null
+++ b/nni/common/hpo_utils/dedup.py
@@ -0,0 +1,90 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+"""
+Deduplicate repeated parameters.
+
+No guarantee for forward-compatibility.
+"""
+
+from __future__ import annotations
+
+import logging
+import typing
+
+import nni
+from .formatting import FormattedParameters, FormattedSearchSpace, ParameterSpec, deformat_parameters
+
+# TODO:
+# Move main logic of basic tuners (random and grid search) into SDK,
+# so we can get rid of private methods and circular dependency.
+if typing.TYPE_CHECKING:
+    from nni.algorithms.hpo.gridsearch_tuner import GridSearchTuner
+
+_logger = logging.getLogger(__name__)
+
+class Deduplicator:
+    """
+    A helper for tuners to deduplicate generated parameters.
+
+    When the tuner generates an already existing parameter,
+    calling this will return a new parameter generated with grid search.
+    Otherwise it returns the orignial parameter object.
+
+    If all parameters have been generated, raise ``NoMoreTrialError``.
+
+    All search space types, including nested choice, are supported.
+
+    Resuming and updating search space are not supported for now.
+    It will not raise error, but may return duplicate parameters.
+
+    See random tuner's source code for example usage.
+    """
+
+    def __init__(self, formatted_search_space: FormattedSearchSpace):
+        self._space: FormattedSearchSpace = formatted_search_space
+        self._never_dup: bool = any(_spec_never_dup(spec) for spec in self._space.values())
+        self._history: set[str] = set()
+        self._grid_search: GridSearchTuner | None = None
+
+    def __call__(self, formatted_parameters: FormattedParameters) -> FormattedParameters:
+        if self._never_dup or self._not_dup(formatted_parameters):
+            return formatted_parameters
+
+        if self._grid_search is None:
+            _logger.info(f'Tuning algorithm generated duplicate parameter: {formatted_parameters}')
+            _logger.info(f'Use grid search for deduplication.')
+            self._init_grid_search()
+
+        while True:
+            new = self._grid_search._suggest()  # type: ignore
+            if new is None:
+                raise nni.NoMoreTrialError()
+            if self._not_dup(new):
+                return new
+
+    def _init_grid_search(self) -> None:
+        from nni.algorithms.hpo.gridsearch_tuner import GridSearchTuner
+        self._grid_search = GridSearchTuner()
+        self._grid_search.history = self._history
+        self._grid_search.space = self._space
+        self._grid_search._init_grid()
+
+    def _not_dup(self, formatted_parameters: FormattedParameters) -> bool:
+        params = deformat_parameters(formatted_parameters, self._space)
+        params_str = typing.cast(str, nni.dump(params, sort_keys=True))
+        if params_str in self._history:
+            return False
+        else:
+            self._history.add(params_str)
+            return True
+
+def _spec_never_dup(spec: ParameterSpec) -> bool:
+    if spec.is_nested():
+        return False  # "not chosen" duplicates with "not chosen"
+    if spec.categorical or spec.q is not None:
+        return False
+    if spec.normal_distributed:
+        return spec.sigma > 0
+    else:
+        return spec.low < spec.high
diff --git a/nni/common/hpo_utils/formatting.py b/nni/common/hpo_utils/formatting.py
index 6bb54b004..a6b1a3710 100644
--- a/nni/common/hpo_utils/formatting.py
+++ b/nni/common/hpo_utils/formatting.py
@@ -2,6 +2,8 @@
 # Licensed under the MIT license.
 
 """
+Helper class and functions for tuners to deal with search space.
+
 This script provides a more program-friendly representation of HPO search space.
 The format is considered internal helper and is not visible to end users.
 
@@ -9,8 +11,16 @@ You will find this useful when you want to support nested search space.
 
 The random tuner is an intuitive example for this utility.
 You should check its code before reading docstrings in this file.
+
+.. attention::
+
+    This module does not guarantee forward-compatibility.
+
+    If you want to use it outside official NNI repo, it is recommended to copy the script.
 """
 
+from __future__ import annotations
+
 __all__ = [
     'ParameterSpec',
     'deformat_parameters',
@@ -20,10 +30,16 @@ __all__ = [
 
 import math
 from types import SimpleNamespace
-from typing import Any, List, NamedTuple, Optional, Tuple
+from typing import Any, Dict, NamedTuple, Tuple, cast
 
 import numpy as np
 
+from nni.typehint import Parameters, SearchSpace
+
+ParameterKey = Tuple['str | int', ...]
+FormattedParameters = Dict[ParameterKey, 'float | int']
+FormattedSearchSpace = Dict[ParameterKey, 'ParameterSpec']
+
 class ParameterSpec(NamedTuple):
     """
     Specification (aka space / range / domain) of one single parameter.
@@ -33,52 +49,59 @@ class ParameterSpec(NamedTuple):
 
     name: str                       # The object key in JSON
     type: str                       # "_type" in JSON
-    values: List[Any]               # "_value" in JSON
+    values: list[Any]               # "_value" in JSON
 
-    key: Tuple[str]                 # The "path" of this parameter
+    key: ParameterKey               # The "path" of this parameter
 
     categorical: bool               # Whether this paramter is categorical (unordered) or numerical (ordered)
-    size: int = None                # If it's categorical, how many candidates it has
-    chosen_size: Optional[int] = 1  # If it's categorical, it should choose how many candidates.
+    size: int = cast(int, None)     # If it's categorical, how many candidates it has
+    chosen_size: int | None = 1     # If it's categorical, it should choose how many candidates.
                                     # By default, 1. If none, arbitrary number of candidates can be chosen.
 
     # uniform distributed
-    low: float = None               # Lower bound of uniform parameter
-    high: float = None              # Upper bound of uniform parameter
+    low: float = cast(float, None)  # Lower bound of uniform parameter
+    high: float = cast(float, None) # Upper bound of uniform parameter
 
-    normal_distributed: bool = None # Whether this parameter is uniform or normal distrubuted
-    mu: float = None                # µ of normal parameter
-    sigma: float = None             # σ of normal parameter
+    normal_distributed: bool = cast(bool, None)
+                                    # Whether this parameter is uniform or normal distrubuted
+    mu: float = cast(float, None)   # µ of normal parameter
+    sigma: float = cast(float, None)# σ of normal parameter
 
-    q: Optional[float] = None       # If not `None`, the parameter value should be an integer multiple of this
-    clip: Optional[Tuple[float, float]] = None
+    q: float | None = None          # If not `None`, the parameter value should be an integer multiple of this
+    clip: tuple[float, float] | None = None
                                     # For q(log)uniform, this equals to "values[:2]"; for others this is None
 
-    log_distributed: bool = None    # Whether this parameter is log distributed
+    log_distributed: bool = cast(bool, None)
+                                    # Whether this parameter is log distributed
                                     # When true, low/high/mu/sigma describes log of parameter value (like np.lognormal)
 
-    def is_activated_in(self, partial_parameters):
+    def is_activated_in(self, partial_parameters: FormattedParameters) -> bool:
         """
         For nested search space, check whether this parameter should be skipped for current set of paremters.
         This function must be used in a pattern similar to random tuner. Otherwise it will misbehave.
         """
-        if len(self.key) < 2 or isinstance(self.key[-2], str):
+        if self.is_nested():
+            return partial_parameters[self.key[:-2]] == self.key[-2]
+        else:
             return True
-        return partial_parameters[self.key[:-2]] == self.key[-2]
 
-def format_search_space(search_space):
+    def is_nested(self):
+        """
+        Check whether this parameter is inside a nested choice.
+        """
+        return len(self.key) >= 2 and isinstance(self.key[-2], int)
+
+def format_search_space(search_space: SearchSpace) -> FormattedSearchSpace:
     """
     Convert user provided search space into a dict of ParameterSpec.
     The dict key is dict value's `ParameterSpec.key`.
     """
     formatted = _format_search_space(tuple(), search_space)
-    # In CPython 3.6, dicts preserve order by internal implementation.
-    # In Python 3.7+, dicts preserve order by language spec.
-    # Python 3.6 is crappy enough. Don't bother to do extra work for it.
-    # Remove these comments when we drop 3.6 support.
     return {spec.key: spec for spec in formatted}
 
-def deformat_parameters(formatted_parameters, formatted_search_space):
+def deformat_parameters(
+        formatted_parameters: FormattedParameters,
+        formatted_search_space: FormattedSearchSpace) -> Parameters:
     """
     Convert internal format parameters to users' expected format.
 
@@ -90,10 +113,11 @@ def deformat_parameters(formatted_parameters, formatted_search_space):
      3. For "q*", convert x to `round(x / q) * q`, then clip into range.
      4. For nested choices, convert flatten key-value pairs into nested structure.
     """
-    ret = {}
+    ret: Parameters = {}
     for key, x in formatted_parameters.items():
         spec = formatted_search_space[key]
         if spec.categorical:
+            x = cast(int, x)
             if spec.type == 'randint':
                 lower = min(math.ceil(float(x)) for x in spec.values)
                 _assign(ret, key, int(lower + x))
@@ -114,7 +138,7 @@ def deformat_parameters(formatted_parameters, formatted_search_space):
             _assign(ret, key, x)
     return ret
 
-def format_parameters(parameters, formatted_search_space):
+def format_parameters(parameters: Parameters, formatted_search_space: FormattedSearchSpace) -> FormattedParameters:
     """
     Convert end users' parameter format back to internal format, mainly for resuming experiments.
 
@@ -125,7 +149,7 @@ def format_parameters(parameters, formatted_search_space):
     for key, spec in formatted_search_space.items():
         if not spec.is_activated_in(ret):
             continue
-        value = parameters
+        value: Any = parameters
         for name in key:
             if isinstance(name, str):
                 value = value[name]
@@ -144,8 +168,8 @@ def format_parameters(parameters, formatted_search_space):
             ret[key] = value
     return ret
 
-def _format_search_space(parent_key, space):
-    formatted = []
+def _format_search_space(parent_key: ParameterKey, space: SearchSpace) -> list[ParameterSpec]:
+    formatted: list[ParameterSpec] = []
     for name, spec in space.items():
         if name == '_name':
             continue
@@ -157,7 +181,7 @@ def _format_search_space(parent_key, space):
                 formatted += _format_search_space(key, sub_space)
     return formatted
 
-def _format_parameter(key, type_, values):
+def _format_parameter(key: ParameterKey, type_: str, values: list[Any]):
     spec = SimpleNamespace(
         name = key[-1],
         type = type_,
@@ -199,7 +223,7 @@ def _format_parameter(key, type_, values):
 
     return ParameterSpec(**spec.__dict__)
 
-def _is_nested_choices(values):
+def _is_nested_choices(values: list[Any]) -> bool:
     assert values  # choices should not be empty
     for value in values:
         if not isinstance(value, dict):
@@ -208,9 +232,9 @@ def _is_nested_choices(values):
             return False
     return True
 
-def _assign(params, key, x):
+def _assign(params: Parameters, key: ParameterKey, x: Any) -> None:
     if len(key) == 1:
-        params[key[0]] = x
+        params[cast(str, key[0])] = x
     elif isinstance(key[0], int):
         _assign(params, key[1:], x)
     else:
diff --git a/nni/common/hpo_utils/optimize_mode.py b/nni/common/hpo_utils/optimize_mode.py
index 6d7a034c8..e91834d73 100644
--- a/nni/common/hpo_utils/optimize_mode.py
+++ b/nni/common/hpo_utils/optimize_mode.py
@@ -1,3 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from enum import Enum
 
 class OptimizeMode(Enum):
diff --git a/nni/common/hpo_utils/validation.py b/nni/common/hpo_utils/validation.py
index 3c1ea71d5..729c0b517 100644
--- a/nni/common/hpo_utils/validation.py
+++ b/nni/common/hpo_utils/validation.py
@@ -1,8 +1,10 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
 
+from __future__ import annotations
+
 import logging
-from typing import Any, List, Optional
+from typing import Any
 
 common_search_space_types = [
     'choice',
@@ -19,7 +21,7 @@ common_search_space_types = [
 
 def validate_search_space(
         search_space: Any,
-        support_types: Optional[List[str]] = None,
+        support_types: list[str] | None = None,
         raise_exception: bool = False  # for now, in case false positive
     ) -> bool:
 
diff --git a/nni/common/serializer.py b/nni/common/serializer.py
index 41d73f819..93aa9665d 100644
--- a/nni/common/serializer.py
+++ b/nni/common/serializer.py
@@ -1,3 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import abc
 import base64
 import collections.abc
@@ -5,6 +8,8 @@ import copy
 import functools
 import inspect
 import numbers
+import os
+import sys
 import types
 import warnings
 from io import IOBase
@@ -13,7 +18,7 @@ from typing import Any, Dict, List, Optional, TypeVar, Union
 import cloudpickle  # use cloudpickle as backend for unserializable types and instances
 import json_tricks  # use json_tricks as serializer backend
 
-__all__ = ['trace', 'dump', 'load', 'PayloadTooLarge', 'Translatable', 'Traceable', 'is_traceable']
+__all__ = ['trace', 'dump', 'load', 'PayloadTooLarge', 'Translatable', 'Traceable', 'is_traceable', 'is_wrapped_with_trace']
 
 
 T = TypeVar('T')
@@ -23,46 +28,49 @@ class PayloadTooLarge(Exception):
     pass
 
 
-class Traceable(abc.ABC):
+class Traceable:
     """
     A traceable object have copy and dict. Copy and mutate are used to copy the object for further mutations.
     Dict returns a TraceDictType to enable serialization.
     """
-    @abc.abstractmethod
+
     def trace_copy(self) -> 'Traceable':
         """
         Perform a shallow copy.
         NOTE: NONE of the attributes will be preserved.
         This is the one that should be used when you want to "mutate" a serializable object.
         """
-        ...
+        raise NotImplementedError()
 
     @property
-    @abc.abstractmethod
     def trace_symbol(self) -> Any:
         """
         Symbol object. Could be a class or a function.
         ``get_hybrid_cls_or_func_name`` and ``import_cls_or_func_from_hybrid_name`` is a pair to
         convert the symbol into a string and convert the string back to symbol.
         """
-        ...
+        raise NotImplementedError()
 
     @property
-    @abc.abstractmethod
     def trace_args(self) -> List[Any]:
         """
         List of positional arguments passed to symbol. Usually empty if ``kw_only`` is true,
         in which case all the positional arguments are converted into keyword arguments.
         """
-        ...
+        raise NotImplementedError()
 
     @property
-    @abc.abstractmethod
     def trace_kwargs(self) -> Dict[str, Any]:
         """
         Dict of keyword arguments.
         """
-        ...
+        raise NotImplementedError()
+
+    def get(self) -> Any:
+        """
+        Get the original object. Usually used together with ``trace_copy``.
+        """
+        raise NotImplementedError()
 
 
 class Translatable(abc.ABC):
@@ -84,13 +92,27 @@ class Translatable(abc.ABC):
 
 def is_traceable(obj: Any) -> bool:
     """
-    Check whether an object is a traceable instance (not type).
+    Check whether an object is a traceable instance or type.
+
+    Note that an object is traceable only means that it implements the "Traceable" interface,
+    and the properties have been implemented. It doesn't necessary mean that its type is wrapped with trace,
+    because the properties could be added **after** the instance has been created.
     """
     return hasattr(obj, 'trace_copy') and \
         hasattr(obj, 'trace_symbol') and \
         hasattr(obj, 'trace_args') and \
-        hasattr(obj, 'trace_kwargs') and \
-        not inspect.isclass(obj)
+        hasattr(obj, 'trace_kwargs')
+
+
+def is_wrapped_with_trace(cls_or_func: Any) -> bool:
+    """
+    Check whether a function or class is already wrapped with ``@nni.trace``.
+    If a class or function is already wrapped with trace, then the created object must be "traceable".
+    """
+    return getattr(cls_or_func, '_traced', False) and (
+        not hasattr(cls_or_func, '__dict__') or  # in case it's a function
+        '_traced' in cls_or_func.__dict__  # must be in this class, super-class traced doesn't count
+    )
 
 
 class SerializableObject(Traceable):
@@ -120,6 +142,13 @@ class SerializableObject(Traceable):
             {k: copy.copy(v) for k, v in self.trace_kwargs.items()},
         )
 
+    def get(self) -> T:
+        if not self._get_nni_attr('call_super'):
+            # Reinitialize
+            return trace(self.trace_symbol)(*self.trace_args, **self.trace_kwargs)
+
+        return self
+
     @property
     def trace_symbol(self) -> Any:
         return self._get_nni_attr('symbol')
@@ -160,6 +189,15 @@ class SerializableObject(Traceable):
 
 def inject_trace_info(obj: Any, symbol: T, args: List[Any], kwargs: Dict[str, Any]) -> Any:
     # If an object is already created, this can be a fix so that the necessary info are re-injected into the object.
+    # Make obj complying with the interface of traceable, though we cannot change its base class.
+    obj.__dict__.update(_nni_symbol=symbol, _nni_args=args, _nni_kwargs=kwargs)
+
+    return obj
+
+
+def _make_class_traceable(cls: T, create_wrapper: bool = False) -> T:
+    # Make an already exist class traceable, without creating a new class.
+    # Should be used together with `inject_trace_info`.
 
     def getter_factory(x):
         return lambda self: self.__dict__['_nni_' + x]
@@ -177,27 +215,29 @@ def inject_trace_info(obj: Any, symbol: T, args: List[Any], kwargs: Dict[str, An
             {k: copy.copy(v) for k, v in self.trace_kwargs.items()},
         )
 
+    def get(self):
+        return self
+
     attributes = {
         'trace_symbol': property(getter_factory('symbol'), setter_factory('symbol')),
         'trace_args': property(getter_factory('args'), setter_factory('args')),
         'trace_kwargs': property(getter_factory('kwargs'), setter_factory('kwargs')),
-        'trace_copy': trace_copy
+        'trace_copy': trace_copy,
+        'get': get,
     }
 
-    if hasattr(obj, '__class__') and hasattr(obj, '__dict__'):
+    if not create_wrapper:
         for name, method in attributes.items():
-            setattr(obj.__class__, name, method)
+            setattr(cls, name, method)
+        return cls
     else:
-        wrapper = type('wrapper', (Traceable, type(obj)), attributes)
-        obj = wrapper(obj)  # pylint: disable=abstract-class-instantiated
-
-    # make obj complying with the interface of traceable, though we cannot change its base class
-    obj.__dict__.update(_nni_symbol=symbol, _nni_args=args, _nni_kwargs=kwargs)
-
-    return obj
+        # sometimes create_wrapper is mandatory, e.g., for built-in types like list/int.
+        # but I don't want to check here because it's unreliable.
+        wrapper = type('wrapper', (Traceable, cls), attributes)
+        return wrapper
 
 
-def trace(cls_or_func: T = None, *, kw_only: bool = True) -> Union[T, Traceable]:
+def trace(cls_or_func: T = None, *, kw_only: bool = True, inheritable: bool = False) -> Union[T, Traceable]:
     """
     Annotate a function or a class if you want to preserve where it comes from.
     This is usually used in the following scenarios:
@@ -221,6 +261,9 @@ def trace(cls_or_func: T = None, *, kw_only: bool = True) -> Union[T, Traceable]
     list and types. This can be useful to extract semantics, but can be tricky in some corner cases.
     Therefore, in some cases, some positional arguments will still be kept.
 
+    If ``inheritable`` is true, the trace information from superclass will also be available in subclass.
+    This however, will make the subclass un-trace-able. Note that this argument has no effect when tracing functions.
+
     .. warning::
 
         Generators will be first expanded into a list, and the resulting list will be further passed into the wrapped function/class.
@@ -235,12 +278,19 @@ def trace(cls_or_func: T = None, *, kw_only: bool = True) -> Union[T, Traceable]
             pass
     """
 
+    # This is an internal flag to control the behavior of trace.
+    # Useful in doc build and tests.
+    # Might be changed in future.
+    nni_trace_flag = os.environ.get('NNI_TRACE_FLAG', '')
+    if nni_trace_flag.lower() == 'disable':
+        return cls_or_func
+
     def wrap(cls_or_func):
         # already annotated, do nothing
-        if getattr(cls_or_func, '_traced', False):
+        if is_wrapped_with_trace(cls_or_func):
             return cls_or_func
         if isinstance(cls_or_func, type):
-            cls_or_func = _trace_cls(cls_or_func, kw_only)
+            cls_or_func = _trace_cls(cls_or_func, kw_only, inheritable=inheritable)
         elif _is_function(cls_or_func):
             cls_or_func = _trace_func(cls_or_func, kw_only)
         else:
@@ -353,11 +403,60 @@ def load(string: Optional[str] = None, *, fp: Optional[Any] = None, ignore_comme
         return json_tricks.load(fp, obj_pairs_hooks=hooks, **json_tricks_kwargs)
 
 
-def _trace_cls(base, kw_only, call_super=True):
+def _trace_cls(base, kw_only, call_super=True, inheritable=False):
     # the implementation to trace a class is to store a copy of init arguments
     # this won't support class that defines a customized new but should work for most cases
 
-    class wrapper(SerializableObject, base):
+    if sys.platform != 'linux':
+        if not call_super:
+            raise ValueError("'call_super' is mandatory to be set true on non-linux platform")
+
+        try:
+            # In non-linux envs, dynamically creating new classes doesn't work with pickle.
+            # We have to replace the ``__init__`` with a new ``__init__``.
+            # This, however, causes side-effects where the replacement is not intended.
+            # This also doesn't work built-in types (e.g., OrderedDict), and the replacement
+            # won't be effective any more if ``nni.trace`` is called in-place (e.g., ``nni.trace(nn.Conv2d)(...)``).
+            original_init = base.__init__
+
+            # Makes the new init have the exact same signature as the old one,
+            # so as to make pytorch-lightning happy.
+            # https://github.com/PyTorchLightning/pytorch-lightning/blob/4cc05b2cf98e49168a5f5dc265647d75d1d3aae9/pytorch_lightning/utilities/parsing.py#L143
+            @functools.wraps(original_init)
+            def new_init(self, *args, **kwargs):
+                args, kwargs = _formulate_arguments(original_init, args, kwargs, kw_only, is_class_init=True)
+                original_init(
+                    self,
+                    *[_argument_processor(arg) for arg in args],
+                    **{kw: _argument_processor(arg) for kw, arg in kwargs.items()}
+                )
+                inject_trace_info(self, base, args, kwargs)
+
+            base.__init__ = new_init
+
+            base = _make_class_traceable(base)
+            return base
+
+        except TypeError:
+            warnings.warn("In-place __init__ replacement failed in `@nni.trace`, probably because the type is a built-in/extension type, "
+                          "and it's __init__ can't be replaced. `@nni.trace` is now falling back to the 'inheritance' approach. "
+                          "However, this could cause issues when using pickle. See https://github.com/microsoft/nni/issues/4434",
+                          RuntimeWarning)
+
+    # This is trying to solve the case where superclass and subclass are both decorated with @nni.trace.
+    # We use a metaclass to "unwrap" the superclass.
+    # However, this doesn't work if:
+    # 1. Base class already has a customized metaclass. We will raise error in that class.
+    # 2. SerializableObject in ancester (instead of parent). I think this case is rare and I didn't handle this case yet. FIXME
+    if type(base) is type and not inheritable:
+        metaclass = _unwrap_metaclass
+    else:
+        metaclass = type
+        if SerializableObject in inspect.getmro(base):
+            raise TypeError(f"{base} has a superclass already decorated with trace, and it's using a customized metaclass {type(base)}. "
+                            "Please either use the default metaclass, or remove trace from the super-class.")
+
+    class wrapper(SerializableObject, base, metaclass=metaclass):
         def __init__(self, *args, **kwargs):
             # store a copy of initial parameters
             args, kwargs = _formulate_arguments(base.__init__, args, kwargs, kw_only, is_class_init=True)
@@ -365,6 +464,32 @@ def _trace_cls(base, kw_only, call_super=True):
             # calling serializable object init to initialize the full object
             super().__init__(symbol=base, args=args, kwargs=kwargs, call_super=call_super)
 
+        def __reduce__(self):
+            # The issue that decorator and pickler doesn't play well together is well known.
+            # The workaround solution is to use a fool class (_pickling_object) which pretends to be the pickled object.
+            # We then put the original type, as well as args and kwargs in its `__new__` argument.
+            # I suspect that their could still be problems when things get complex,
+            # e.g., the wrapped class has a custom pickling (`__reduce__``) or `__new__`.
+            # But it can't be worse because the previous pickle doesn't work at all.
+            #
+            # Linked issue: https://github.com/microsoft/nni/issues/4434
+            # SO: https://stackoverflow.com/questions/52185507/pickle-and-decorated-classes-picklingerror-not-the-same-object
+
+            # Store the inner class. The wrapped class couldn't be properly pickled.
+            type_ = cloudpickle.dumps(type(self).__wrapped__)
+
+            # in case they have customized ``__getstate__``.
+            if hasattr(self, '__getstate__'):
+                obj_ = self.__getstate__()
+            else:
+                obj_ = self.__dict__
+
+            # Pickle can't handle type objects.
+            if '_nni_symbol' in obj_:
+                obj_['_nni_symbol'] = cloudpickle.dumps(obj_['_nni_symbol'])
+
+            return _pickling_object, (type_, kw_only, obj_)
+
     _copy_class_wrapper_attributes(base, wrapper)
 
     return wrapper
@@ -391,6 +516,8 @@ def _trace_func(func, kw_only):
         elif hasattr(res, '__class__') and hasattr(res, '__dict__'):
             # is a class, inject interface directly
             # need to be done before primitive types because there could be inheritance here.
+            if not getattr(type(res), '_traced', False):
+                _make_class_traceable(type(res), False)  # in-place
             res = inject_trace_info(res, func, args, kwargs)
         elif isinstance(res, (collections.abc.Callable, types.ModuleType, IOBase)):
             raise TypeError(f'Try to add trace info to {res}, but functions and modules are not supported.')
@@ -400,6 +527,8 @@ def _trace_func(func, kw_only):
             # will be directly captured by python json encoder
             # and thus not possible to restore the trace parameters after dump and reload.
             # this is a known limitation.
+            new_type = _make_class_traceable(type(res), True)
+            res = new_type(res)  # re-creating the object
             res = inject_trace_info(res, func, args, kwargs)
         else:
             raise TypeError(f'Try to add trace info to {res}, but the type "{type(res)}" is unknown. '
@@ -425,6 +554,48 @@ def _copy_class_wrapper_attributes(base, wrapper):
     wrapper.__wrapped__ = base
 
 
+class _unwrap_metaclass(type):
+    # When a subclass is created, it detects whether the super-class is already annotated with @nni.trace.
+    # If yes, it gets the ``__wrapped__`` inner class, so that it doesn't inherit SerializableObject twice.
+    # Note that this doesn't work when metaclass is already defined (such as ABCMeta). We give up in that case.
+
+    def __new__(cls, name, bases, dct):
+        bases = tuple([getattr(base, '__wrapped__', base) for base in bases])
+        return super().__new__(cls, name, bases, dct)
+
+    # Using a customized "bases" breaks default isinstance and issubclass.
+    # We recover this by overriding the subclass and isinstance behavior, which conerns wrapped class only.
+    def __subclasscheck__(cls, subclass):
+        inner_cls = getattr(cls, '__wrapped__', cls)
+        return inner_cls in inspect.getmro(subclass)
+
+    def __instancecheck__(cls, instance):
+        inner_cls = getattr(cls, '__wrapped__', cls)
+        return inner_cls in inspect.getmro(type(instance))
+
+
+class _pickling_object:
+    # Need `cloudpickle.load` on the callable because the callable is pickled with cloudpickle.
+    # Used in `_trace_cls`.
+
+    def __new__(cls, type_, kw_only, data):
+        type_ = _wrapped_cloudpickle_loads(type_)
+        # Restore the trace type
+        type_ = _trace_cls(type_, kw_only)
+
+        # restore type
+        if '_nni_symbol' in data:
+            data['_nni_symbol'] = _wrapped_cloudpickle_loads(data['_nni_symbol'])
+
+        # https://docs.python.org/3/library/pickle.html#pickling-class-instances
+        obj = type_.__new__(type_)
+        if hasattr(obj, '__setstate__'):
+            obj.__setstate__(data)
+        else:
+            obj.__dict__.update(data)
+        return obj
+
+
 def _argument_processor(arg):
     # 1) translate
     # handle cases like ValueChoice
@@ -520,7 +691,7 @@ def _formulate_arguments(func, args, kwargs, kw_only, is_class_init=False):
 def _is_function(obj: Any) -> bool:
     # https://stackoverflow.com/questions/624926/how-do-i-detect-whether-a-python-variable-is-a-function
     return isinstance(obj, (types.FunctionType, types.BuiltinFunctionType, types.MethodType,
-                            types.BuiltinMethodType))
+                            types.BuiltinMethodType)) and obj is not None
 
 
 def _import_cls_or_func_from_name(target: str) -> Any:
@@ -533,7 +704,9 @@ def _import_cls_or_func_from_name(target: str) -> Any:
 
 def _strip_trace_type(traceable: Any) -> Any:
     if getattr(traceable, '_traced', False):
-        return traceable.__wrapped__
+        # sometimes, ``__wrapped__`` could be unavailable (e.g., with `inject_trace_info`)
+        # need to have a default value
+        return getattr(traceable, '__wrapped__', traceable)
     return traceable
 
 
@@ -571,7 +744,7 @@ def get_hybrid_cls_or_func_name(cls_or_func: Any, pickle_size_limit: int = 4096)
 def import_cls_or_func_from_hybrid_name(s: str) -> Any:
     if s.startswith('bytes:'):
         b = base64.b64decode(s.split(':', 1)[-1])
-        return cloudpickle.loads(b)
+        return _wrapped_cloudpickle_loads(b)
     if s.startswith('path:'):
         s = s.split(':', 1)[-1]
     return _import_cls_or_func_from_name(s)
@@ -598,7 +771,7 @@ def _json_tricks_serializable_object_encode(obj: Any, primitives: bool = False,
     # Encodes a serializable object instance to json.
 
     # do nothing to instance that is not a serializable object and do not use trace
-    if not use_trace or not is_traceable(obj):
+    if not (use_trace and hasattr(obj, '__class__') and is_traceable(type(obj))):
         return obj
 
     if isinstance(obj.trace_symbol, property):
@@ -644,5 +817,14 @@ def _json_tricks_any_object_decode(obj: Dict[str, Any]) -> Any:
     if isinstance(obj, dict) and '__nni_obj__' in obj:
         obj = obj['__nni_obj__']
         b = base64.b64decode(obj)
-        return cloudpickle.loads(b)
+        return _wrapped_cloudpickle_loads(b)
     return obj
+
+
+def _wrapped_cloudpickle_loads(b: bytes) -> Any:
+    try:
+        return cloudpickle.loads(b)
+    except TypeError:
+        warnings.warn('TypeError encountered during deserializing object. This could be caused by '
+                      'inconsistency between Python versions where dump and load happens.')
+        raise
diff --git a/nni/common/version.py b/nni/common/version.py
index b8881f48a..e9dc4d817 100644
--- a/nni/common/version.py
+++ b/nni/common/version.py
@@ -1,7 +1,83 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+from __future__ import annotations
+
 import logging
+import sys
+import warnings
+
+import cloudpickle
+import json_tricks
+import numpy
+import yaml
+
+import nni
+
+def _minor_version_tuple(version_str: str) -> tuple[int, int]:
+    # If not a number, returns -1 (e.g., 999.dev0 -> (999, -1))
+    return tuple(int(x) if x.isdigit() else -1 for x in version_str.split(".")[:2])
+
+PYTHON_VERSION = sys.version_info[:2]
+NUMPY_VERSION = _minor_version_tuple(numpy.__version__)
+
 try:
     import torch
-    TORCH_VERSION = tuple(int(x) for x in torch.__version__.split(".")[:2])
-except Exception:
+    TORCH_VERSION = _minor_version_tuple(torch.__version__)
+except ImportError:
     logging.info("PyTorch is not installed.")
     TORCH_VERSION = None
+
+try:
+    import pytorch_lightning
+    PYTORCH_LIGHTNING_VERSION = _minor_version_tuple(pytorch_lightning.__version__)
+except ImportError:
+    logging.info("PyTorch Lightning is not installed.")
+    PYTORCH_LIGHTNING_VERSION = None
+
+try:
+    import tensorflow
+    TENSORFLOW_VERSION = _minor_version_tuple(tensorflow.__version__)
+except ImportError:
+    logging.info("Tensorflow is not installed.")
+    TENSORFLOW_VERSION = None
+
+# Serialization version check are needed because they are prone to be inconsistent between versions
+
+CLOUDPICKLE_VERSION = _minor_version_tuple(cloudpickle.__version__)
+JSON_TRICKS_VERSION = _minor_version_tuple(json_tricks.__version__)
+PYYAML_VERSION = _minor_version_tuple(yaml.__version__)
+
+NNI_VERSION = _minor_version_tuple(nni.__version__)
+
+def version_dump() -> dict[str, tuple[int, int] | None]:
+    return {
+        'python': PYTHON_VERSION,
+        'numpy': NUMPY_VERSION,
+        'torch': TORCH_VERSION,
+        'pytorch_lightning': PYTORCH_LIGHTNING_VERSION,
+        'tensorflow': TENSORFLOW_VERSION,
+        'cloudpickle': CLOUDPICKLE_VERSION,
+        'json_tricks': JSON_TRICKS_VERSION,
+        'pyyaml': PYYAML_VERSION,
+        'nni': NNI_VERSION
+    }
+
+def version_check(expect: dict, raise_error: bool = False) -> None:
+    current_ver = version_dump()
+    for package in expect:
+        # version could be list due to serialization
+        exp_version: tuple | None = tuple(expect[package]) if expect[package] else None
+        if exp_version is None:
+            continue
+
+        err_message: str | None = None
+        if package not in current_ver:
+            err_message = f'{package} is missing in current environment'
+        elif current_ver[package] != exp_version:
+            err_message = f'Expect {package} to have version {exp_version}, but {current_ver[package]} found'
+        if err_message:
+            if raise_error:
+                raise RuntimeError('Version check failed: ' + err_message)
+            else:
+                warnings.warn('Version check with warning: ' + err_message)
diff --git a/nni/compression/pytorch/__init__.py b/nni/compression/pytorch/__init__.py
index 10e2fd050..a9e5b79f4 100644
--- a/nni/compression/pytorch/__init__.py
+++ b/nni/compression/pytorch/__init__.py
@@ -3,4 +3,4 @@
 
 from .speedup import ModelSpeedup
 from .compressor import Compressor, Pruner, Quantizer
-from .pruning import apply_compression_results
+from .utils.apply_compression import apply_compression_results
diff --git a/nni/compression/pytorch/compressor.py b/nni/compression/pytorch/compressor.py
index f8f054ac1..456c01964 100644
--- a/nni/compression/pytorch/compressor.py
+++ b/nni/compression/pytorch/compressor.py
@@ -631,6 +631,7 @@ class Quantizer(Compressor):
         """
         quantize should overload this method to quantize weight.
         This method is effectively hooked to :meth:`forward` of the model.
+
         Parameters
         ----------
         wrapper : QuantizerModuleWrapper
@@ -642,6 +643,7 @@ class Quantizer(Compressor):
         """
         quantize should overload this method to quantize output.
         This method is effectively hooked to :meth:`forward` of the model.
+
         Parameters
         ----------
         output : Tensor
@@ -655,6 +657,7 @@ class Quantizer(Compressor):
         """
         quantize should overload this method to quantize input.
         This method is effectively hooked to :meth:`forward` of the model.
+
         Parameters
         ----------
         inputs : Tensor
@@ -908,6 +911,7 @@ class QuantGrad(torch.autograd.Function):
     def _quantize(cls, x, scale, zero_point):
         """
         Reference function for quantizing x -- non-clamped.
+
         Parameters
         ----------
         x : Tensor
@@ -916,6 +920,7 @@ class QuantGrad(torch.autograd.Function):
             scale for quantizing x
         zero_point : Tensor
             zero_point for quantizing x
+
         Returns
         -------
         tensor
@@ -927,12 +932,14 @@ class QuantGrad(torch.autograd.Function):
     def get_bits_length(cls, config, quant_type):
         """
         Get bits for quantize config
+
         Parameters
         ----------
         config : Dict
             the configuration for quantization
         quant_type : str
             quant type
+
         Returns
         -------
         int
@@ -948,6 +955,7 @@ class QuantGrad(torch.autograd.Function):
         """
         This method should be overrided by subclass to provide customized backward function,
         default implementation is Straight-Through Estimator
+
         Parameters
         ----------
         tensor : Tensor
@@ -963,6 +971,7 @@ class QuantGrad(torch.autograd.Function):
             quant_min for quantizing tensor
         qmax : Tensor
             quant_max for quantizng tensor
+
         Returns
         -------
         tensor
diff --git a/nni/compression/pytorch/pruning/__init__.py b/nni/compression/pytorch/pruning/__init__.py
index 9d3a7d2ca..62714943d 100644
--- a/nni/compression/pytorch/pruning/__init__.py
+++ b/nni/compression/pytorch/pruning/__init__.py
@@ -1,4 +1,4 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
 
-from .apply_compression import apply_compression_results
+from nni.algorithms.compression.v2.pytorch.pruning import *
diff --git a/nni/compression/pytorch/quantization/literal.py b/nni/compression/pytorch/quantization/literal.py
index eaad1dcf2..f89882c2e 100644
--- a/nni/compression/pytorch/quantization/literal.py
+++ b/nni/compression/pytorch/quantization/literal.py
@@ -1,3 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from enum import Enum, EnumMeta
 
 
diff --git a/nni/compression/pytorch/quantization/observers.py b/nni/compression/pytorch/quantization/observers.py
index bd7b2bc28..15f7bb472 100644
--- a/nni/compression/pytorch/quantization/observers.py
+++ b/nni/compression/pytorch/quantization/observers.py
@@ -1,3 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from torch.quantization import default_weight_observer, default_histogram_observer
 from torch.quantization import RecordingObserver as _RecordingObserver
 
diff --git a/nni/compression/pytorch/quantization/settings.py b/nni/compression/pytorch/quantization/settings.py
index b4206e239..e6a523de6 100644
--- a/nni/compression/pytorch/quantization/settings.py
+++ b/nni/compression/pytorch/quantization/settings.py
@@ -1,3 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from typing import Any, Optional
 
 from .literal import QuantDtype, QuantType, QuantScheme
diff --git a/nni/compression/pytorch/quantization/utils.py b/nni/compression/pytorch/quantization/utils.py
index c0735c52c..3c7002eee 100644
--- a/nni/compression/pytorch/quantization/utils.py
+++ b/nni/compression/pytorch/quantization/utils.py
@@ -1,3 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import torch
 
 from nni.common.version import TORCH_VERSION
diff --git a/nni/compression/pytorch/quantization_speedup/__init__.py b/nni/compression/pytorch/quantization_speedup/__init__.py
index 636c82a5b..060545ecb 100644
--- a/nni/compression/pytorch/quantization_speedup/__init__.py
+++ b/nni/compression/pytorch/quantization_speedup/__init__.py
@@ -1 +1,4 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from .integrated_tensorrt import CalibrateType, ModelSpeedupTensorRT
\ No newline at end of file
diff --git a/nni/compression/pytorch/quantization_speedup/backend.py b/nni/compression/pytorch/quantization_speedup/backend.py
index 7d139d48f..f99687763 100644
--- a/nni/compression/pytorch/quantization_speedup/backend.py
+++ b/nni/compression/pytorch/quantization_speedup/backend.py
@@ -10,7 +10,7 @@ class BaseModelSpeedup:
         Parameters
         ----------
         model : pytorch model
-            The model to speed up by quantization.
+            The model to speedup by quantization.
         config : dict
             Config recording bit number and name of layers.
         """
diff --git a/nni/compression/pytorch/quantization_speedup/frontend_to_onnx.py b/nni/compression/pytorch/quantization_speedup/frontend_to_onnx.py
index b6c323f2e..84216a2ab 100644
--- a/nni/compression/pytorch/quantization_speedup/frontend_to_onnx.py
+++ b/nni/compression/pytorch/quantization_speedup/frontend_to_onnx.py
@@ -37,7 +37,7 @@ def _setattr(model, name, module):
     Parameters
     ----------
     model : pytorch model
-        The model to speed up by quantization
+        The model to speedup by quantization
     name : str
         name of pytorch module
     module : torch.nn.Module
@@ -98,7 +98,7 @@ def torch_to_onnx(model, config, input_shape, model_path, input_names, output_na
     Parameters
     ----------
     model : pytorch model
-        The model to speed up by quantization
+        The model to speedup by quantization
     config : dict
         Config recording bits number and name of layers
     input_shape : tuple
diff --git a/nni/compression/pytorch/quantization_speedup/integrated_tensorrt.py b/nni/compression/pytorch/quantization_speedup/integrated_tensorrt.py
index b4a0ab412..5cfb5e0b1 100644
--- a/nni/compression/pytorch/quantization_speedup/integrated_tensorrt.py
+++ b/nni/compression/pytorch/quantization_speedup/integrated_tensorrt.py
@@ -228,40 +228,41 @@ def build_engine(model_file, config=None, extra_layer_bits=32, strict_datatype=F
         return engine
 
 class ModelSpeedupTensorRT(BaseModelSpeedup):
+    r"""
+    Parameters
+    ----------
+    model : pytorch model
+        The model to speedup by quantization.
+    input_shape : tuple
+        The input shape of model, shall pass it to torch.onnx.export.
+    config : dict
+        Config recording bits number and name of layers.
+    onnx_path : str
+        The path user want to store onnx model which is converted from pytorch model.
+    extra_layer_bits : int
+        Other layers which are not in config will be quantized to corresponding bits number.
+    strict_datatype : bool
+        Whether constrain layer bits to the number given in config or not. If true, all the layer
+        will be set to given bits strictly. Otherwise, these layers will be set automatically by
+        tensorrt.
+    calibrate_type : tensorrt.tensorrt.CalibrationAlgoType
+        The algorithm of calibrating. Please refer to https://docs.nvidia.com/deeplearning/
+        tensorrt/api/python_api/infer/Int8/Calibrator.html for detail
+    calibrate_data : numpy array
+        The data using to calibrate quantization model
+    calibration_cache : str
+        The path user want to store calibrate cache file
+    batchsize : int
+        The batch size of calibration and inference
+    input_names : list
+        Input name of onnx model providing for torch.onnx.export to generate onnx model
+    output_name : list
+        Output name of onnx model providing for torch.onnx.export to generate onnx model
+    """
+
     def __init__(self, model, input_shape, config=None, onnx_path="default_model.onnx", extra_layer_bits=32, strict_datatype=True,
         calibrate_type=CalibrateType.ENTROPY2, calib_data_loader=None, calibration_cache = "calibration.cache", batchsize=1,
         input_names=["actual_input_1"], output_names=["output1"]):
-        """
-        Parameters
-        ----------
-        model : pytorch model
-            The model to speed up by quantization.
-        input_shape : tuple
-            The input shape of model, shall pass it to torch.onnx.export.
-        config : dict
-            Config recording bits number and name of layers.
-        onnx_path : str
-            The path user want to store onnx model which is converted from pytorch model.
-        extra_layer_bits : int
-            Other layers which are not in config will be quantized to corresponding bits number.
-        strict_datatype : bool
-            Whether constrain layer bits to the number given in config or not. If true, all the layer
-            will be set to given bits strictly. Otherwise, these layers will be set automatically by
-            tensorrt.
-        calibrate_type : tensorrt.tensorrt.CalibrationAlgoType
-            The algorithm of calibrating. Please refer to https://docs.nvidia.com/deeplearning/
-            tensorrt/api/python_api/infer/Int8/Calibrator.html for detail
-        calibrate_data : numpy array
-            The data using to calibrate quantization model
-        calibration_cache : str
-            The path user want to store calibrate cache file
-        batchsize : int
-            The batch size of calibration and inference
-        input_names : list
-            Input name of onnx model providing for torch.onnx.export to generate onnx model
-        output_name : list
-            Output name of onnx model providing for torch.onnx.export to generate onnx model
-        """
         super().__init__(model, config)
         self.model = model
         self.onnx_path = onnx_path
diff --git a/nni/compression/pytorch/speedup/__init__.py b/nni/compression/pytorch/speedup/__init__.py
index cef8ebd76..5c97b5b37 100644
--- a/nni/compression/pytorch/speedup/__init__.py
+++ b/nni/compression/pytorch/speedup/__init__.py
@@ -1 +1,4 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from .compressor import ModelSpeedup
\ No newline at end of file
diff --git a/nni/compression/pytorch/speedup/compress_modules.py b/nni/compression/pytorch/speedup/compress_modules.py
index 64cb0abfb..716d131da 100644
--- a/nni/compression/pytorch/speedup/compress_modules.py
+++ b/nni/compression/pytorch/speedup/compress_modules.py
@@ -16,6 +16,7 @@ replace_module = {
     'MaxPool2d': lambda module, masks: no_replace(module, masks),
     'AvgPool2d': lambda module, masks: no_replace(module, masks),
     'AdaptiveAvgPool2d': lambda module, masks: no_replace(module, masks),
+    'ZeroPad2d': lambda module, masks: no_replace(module, masks),
     'ReLU': lambda module, masks: no_replace(module, masks),
     'ReLU6': lambda module, masks: no_replace(module, masks),
     'LeakyReLU': lambda module, masks: no_replace(module, masks),
@@ -41,7 +42,8 @@ replace_module = {
     'Dropout3d': lambda module, masks: no_replace(module, masks),
     'Upsample': lambda module, masks: no_replace(module, masks),
     'LayerNorm': lambda module, masks: replace_layernorm(module, masks),
-    'ConvTranspose2d': lambda module, masks: replace_convtranspose2d(module, masks)
+    'ConvTranspose2d': lambda module, masks: replace_convtranspose2d(module, masks),
+    'Flatten': lambda module, masks: no_replace(module, masks)
 }
 
 
diff --git a/nni/compression/pytorch/speedup/compressor.py b/nni/compression/pytorch/speedup/compressor.py
index 41b5c38cd..ed489b526 100644
--- a/nni/compression/pytorch/speedup/compressor.py
+++ b/nni/compression/pytorch/speedup/compressor.py
@@ -29,7 +29,7 @@ class ModelSpeedup:
     Parameters
     ----------
     model : pytorch model
-        The model user wants to speed up
+        The model user wants to speedup
     dummy_input : pytorch tensor, tuple of tensor, list of tensor
         Note: The first dimension of the dummy_input should be the batchsize.
         The dummy input for ```jit.trace```, users should put it on the right
@@ -388,6 +388,9 @@ class ModelSpeedup:
     def replace_submodule(self, unique_name, reindex_dim=None, reindex=None):
         """
         Replace the submodule according to the inferred sparsity.
+
+        Parameters
+        ----------
         unique_name: str
             The unique_name of the submodule to replace.
         reindex_dim: int
@@ -496,7 +499,7 @@ class ModelSpeedup:
         second, replace modules.
         """
 
-        _logger.info("start to speed up the model")
+        _logger.info("start to speedup the model")
         self.initialize_speedup()
         training = self.bound_model.training
         # set to the evaluation mode
diff --git a/nni/compression/pytorch/speedup/infer_mask.py b/nni/compression/pytorch/speedup/infer_mask.py
index 8ace63920..312f60ecd 100644
--- a/nni/compression/pytorch/speedup/infer_mask.py
+++ b/nni/compression/pytorch/speedup/infer_mask.py
@@ -171,10 +171,14 @@ class AutoMaskInference:
             # apply the input mask
             for tid, in_tensor in enumerate(self.dummy_input):
                 if isinstance(in_tensor, torch.Tensor) and self.in_masks[tid] is not None:
+                    # in_tensor.data = in_tensor.data * \
+                    #     self.in_masks[tid] + \
+                    #     (1-self.in_masks[tid]) * self.in_constants[tid]
+                    # issue-4540 when two tensors are multiplied, the constants part make
+                    # the propagation weaker, and lead to shape misaligment. Currently, we
+                    # donnot support the constant folding, so, we just remove the constant here
                     in_tensor.data = in_tensor.data * \
-                        self.in_masks[tid] + \
-                        (1-self.in_masks[tid]) * self.in_constants[tid]
-
+                        self.in_masks[tid]
 
     def __apply_weight_mask(self):
         """
diff --git a/nni/compression/pytorch/speedup/jit_translate.py b/nni/compression/pytorch/speedup/jit_translate.py
index f0e109809..05acc2728 100644
--- a/nni/compression/pytorch/speedup/jit_translate.py
+++ b/nni/compression/pytorch/speedup/jit_translate.py
@@ -10,16 +10,31 @@ import torch
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.INFO)
 
+# to exclude partial
+
+__all__ = [
+    'adaptive_avgpool_python', 'add_python', 'avgpool2d_python', 'cat_python', 'contiguous_python',
+    'div_python', 'dropout_python', 'exp_python', 'flatten_python', 'floor_div_python', 'gelu_python',
+    'getattr_python', 'jit_to_python_function', 'matmul_python', 'mean_python',
+    'mul_python', 'num2tensor_python', 'parse_constant', 'permute_python', 'relu_inplace_python',
+    'relu_python', 'reshape_python', 'select_python', 'sigmoid_python', 'size_python', 'slice_python',
+    'softmax_python', 'squeeze_python', 'to_python', 'toint_python', 'torch', 'trans_from_jit_to_python',
+    'translate_list', 'transpose2_python', 'transpose_python', 'tupleunpack_python', 'typeas_python',
+    'unsqueeze_python', 'upsample_bilinear2d_python', 'view_python'
+]
+
 
 def translate_list(list_node, speedup=None):
     """
     Get the list of values from the list construct node.
+
     Parameters
-    ---------
+    ----------
     list_node: Torch.C.Value
         The cpp node of the target list.
     speedup: ModuleSpeed
         The Module speedup module.
+
     Returns
     -------
     values: list
@@ -45,12 +60,14 @@ def translate_list(list_node, speedup=None):
 def parse_constant(cvalue, speedup):
     """
     Parse the constant values from this Node
+
     Parameters
     ----------
     cvalue: Torch.C.Value
         The cpp node of the target constant value.
     speedup: ModelSpeedup
         The Model speedup module.
+
     Returns
     -------
     value: int/float/tensor
@@ -125,6 +142,29 @@ def add_python(node, speedup):
         return new_add
 
 
+def sub_python(node, speedup):
+    c_node = node.key_node
+    inputs = list(c_node.inputs())
+    constant = [None, None]
+    for i in range(2):
+        input_i = inputs[i]
+        debug_name = input_i.debugName()
+        if debug_name not in speedup.internal_result:
+            # this input is a constant value
+            # TODO: what if this input is a constant tensor
+
+            if input_i.toIValue() is not None:
+                constant[i] = parse_constant(input_i, speedup)
+                break
+    if constant[0] is None and constant[1] is None:
+        new_sub = torch.sub
+    elif constant[0] is not None:
+        new_sub = partial(torch.sub, input=constant)
+    else:
+        new_sub = partial(torch.sub, other=constant)
+    return new_sub
+
+
 def floor_div_python(node, speedup):
     c_node = node.key_node
     inputs = list(c_node.inputs())
@@ -211,6 +251,10 @@ def gelu_python(node, speedup):
     return torch.nn.GELU()
 
 
+def silu_python(node, speedup):
+    return torch.nn.SiLU()
+
+
 def avgpool2d_python(node, speedup):
     c_node = node.key_node
     inputs = list(c_node.inputs())
@@ -260,6 +304,14 @@ def unsqueeze_python(node, speedup):
     new_unsqueeze = partial(torch.unsqueeze, dim=dim)
     return new_unsqueeze
 
+def constant_pad_nd_python(node, speedup):
+    c_node = node.key_node
+    inputs = list(c_node.inputs())
+    pad = translate_list(inputs[1], speedup)
+    value = parse_constant(inputs[2], speedup)
+    new_constant_pad_nd = partial(torch.nn.functional.pad, pad=pad, value=value)
+    return new_constant_pad_nd
+
 ##########################################################
 # Split Line
 # Following module/functions cannot be translated into a
@@ -362,7 +414,7 @@ def reshape_python(node, speedup):
             logger.info('Reshape Module output size: %s', str(self.shape))
 
         def forward(self, *args):
-            return args[0].view(self.shape)
+            return args[0].reshape(self.shape)
     c_node = node.key_node
     inputs = list(c_node.inputs())
     shape = translate_list(inputs[1], speedup)
@@ -488,6 +540,8 @@ def cat_python(node, speedup):
 trans_from_jit_to_python = {
     'aten::add': add_python,
     'aten::add_': add_python,
+    'aten::sub': sub_python,
+    'aten::sub_': sub_python,
     'aten::mul': mul_python,
     'aten::mul_': mul_python,
     'aten::relu': relu_python,
@@ -525,6 +579,8 @@ trans_from_jit_to_python = {
     'aten::exp': exp_python,
     'aten::squeeze': squeeze_python,
     'aten::unsqueeze': unsqueeze_python,
+    'aten::constant_pad_nd': constant_pad_nd_python,
+    'aten::silu': silu_python,
     'prim::TupleUnpack': tupleunpack_python,
     'prim::ListUnpack': tupleunpack_python,
     'prim::NumToTensor': num2tensor_python,
diff --git a/nni/compression/pytorch/utils/__init__.py b/nni/compression/pytorch/utils/__init__.py
index 19f3dc51a..017cd1a03 100644
--- a/nni/compression/pytorch/utils/__init__.py
+++ b/nni/compression/pytorch/utils/__init__.py
@@ -1,6 +1,11 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+from .counter import count_flops_params
+from .mask_conflict import ChannelMaskConflict, GroupMaskConflict
 from .utils import *
+from .sensitivity_analysis import SensitivityAnalysis
 from .shape_dependency import *
-from .shape_dependency import ReshapeDependency
 
 def not_safe_to_prune(model, dummy_input):
     """
diff --git a/nni/compression/pytorch/pruning/apply_compression.py b/nni/compression/pytorch/utils/apply_compression.py
similarity index 100%
rename from nni/compression/pytorch/pruning/apply_compression.py
rename to nni/compression/pytorch/utils/apply_compression.py
diff --git a/nni/compression/pytorch/utils/mask_conflict.py b/nni/compression/pytorch/utils/mask_conflict.py
index f72d67de2..1d95bbf4f 100644
--- a/nni/compression/pytorch/utils/mask_conflict.py
+++ b/nni/compression/pytorch/utils/mask_conflict.py
@@ -81,23 +81,23 @@ class MaskFix:
 
 
 class GroupMaskConflict(MaskFix):
+    """
+    GroupMaskConflict fix the mask conflict between the layers that
+    has group dependecy with each other.
+
+    Parameters
+    ----------
+    masks : dict
+        a dict object that stores the masks
+    model : torch.nn.Module
+        model to fix the mask conflict
+    dummy_input : torch.Tensor
+        input example to trace the model
+    traced : torch._C.torch.jit.TopLevelTracedModule
+        the traced model of the target model, is this parameter is not None,
+        we donnot use the model and dummpy_input to get the trace graph.
+    """
     def __init__(self, masks, model, dummy_input, traced=None):
-        """
-        GroupMaskConflict fix the mask conflict between the layers that
-        has group dependecy with each other.
-
-        Parameters
-        ----------
-        masks : dict
-            a dict object that stores the masks
-        model : torch.nn.Module
-            model to fix the mask conflict
-        dummy_input : torch.Tensor
-            input example to trace the model
-        traced : torch._C.torch.jit.TopLevelTracedModule
-            the traced model of the target model, is this parameter is not None,
-            we donnot use the model and dummpy_input to get the trace graph.
-        """
         super(GroupMaskConflict, self).__init__(
             masks, model, dummy_input, traced)
 
@@ -168,23 +168,24 @@ class GroupMaskConflict(MaskFix):
 
 
 class ChannelMaskConflict(MaskFix):
+    """
+    ChannelMaskConflict fix the mask conflict between the layers that
+    has channel dependecy with each other.
+
+    Parameters
+    ----------
+    masks : dict
+        a dict object that stores the masks
+    model : torch.nn.Module
+        model to fix the mask conflict
+    dummy_input : torch.Tensor
+        input example to trace the model
+    graph : torch._C.torch.jit.TopLevelTracedModule
+        the traced graph of the target model, is this parameter is not None,
+        we donnot use the model and dummpy_input to get the trace graph.
+    """
+
     def __init__(self, masks, model, dummy_input, traced=None):
-        """
-        ChannelMaskConflict fix the mask conflict between the layers that
-        has channel dependecy with each other.
-
-        Parameters
-        ----------
-        masks : dict
-            a dict object that stores the masks
-        model : torch.nn.Module
-            model to fix the mask conflict
-        dummy_input : torch.Tensor
-            input example to trace the model
-        graph : torch._C.torch.jit.TopLevelTracedModule
-            the traced graph of the target model, is this parameter is not None,
-            we donnot use the model and dummpy_input to get the trace graph.
-        """
         super(ChannelMaskConflict, self).__init__(
             masks, model, dummy_input, traced)
         self.conv_prune_dim = detect_mask_prune_dim(masks, model)
diff --git a/nni/compression/pytorch/utils/num_param_counter.py b/nni/compression/pytorch/utils/num_param_counter.py
index 89ad09799..68bdf6fcd 100644
--- a/nni/compression/pytorch/utils/num_param_counter.py
+++ b/nni/compression/pytorch/utils/num_param_counter.py
@@ -1,3 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 def get_total_num_weights(model, op_types=['default']):
         '''
         calculate the total number of weights
diff --git a/nni/compression/pytorch/utils/sensitivity_analysis.py b/nni/compression/pytorch/utils/sensitivity_analysis.py
index a36a523fe..275acc617 100644
--- a/nni/compression/pytorch/utils/sensitivity_analysis.py
+++ b/nni/compression/pytorch/utils/sensitivity_analysis.py
@@ -18,51 +18,52 @@ logger.setLevel(logging.INFO)
 
 
 class SensitivityAnalysis:
-    def __init__(self, model, val_func, sparsities=None, prune_type='l1', early_stop_mode=None, early_stop_value=None):
-        """
-        Perform sensitivity analysis for this model.
-        Parameters
-        ----------
-        model : torch.nn.Module
-            the model to perform sensitivity analysis
-        val_func : function
-            validation function for the model. Due to
-            different models may need different dataset/criterion
-            , therefore the user need to cover this part by themselves.
-            In the val_func, the model should be tested on the validation dateset,
-            and the validation accuracy/loss should be returned as the output of val_func.
-            There are no restrictions on the input parameters of the val_function.
-            User can use the val_args, val_kwargs parameters in analysis
-            to pass all the parameters that val_func needed.
-        sparsities : list
-            The sparsity list provided by users. This parameter is set when the user
-            only wants to test some specific sparsities. In the sparsity list, each element
-            is a sparsity value which means how much weight the pruner should prune. Take
-            [0.25, 0.5, 0.75] for an example, the SensitivityAnalysis will prune 25% 50% 75%
-            weights gradually for each layer.
-        prune_type : str
-            The pruner type used to prune the conv layers, default is 'l1',
-            and 'l2', 'fine-grained' is also supported.
-        early_stop_mode : str
-            If this flag is set, the sensitivity analysis
-            for a conv layer will early stop when the validation metric(
-            for example, accurracy/loss) has alreay meet the threshold. We
-            support four different early stop modes: minimize, maximize, dropped,
-            raised. The default value is None, which means the analysis won't stop
-            until all given sparsities are tested. This option should be used with
-            early_stop_value together.
-
-            minimize: The analysis stops when the validation metric return by the val_func
-            lower than early_stop_value.
-            maximize: The analysis stops when the validation metric return by the val_func
-            larger than early_stop_value.
-            dropped: The analysis stops when the validation metric has dropped by early_stop_value.
-            raised: The analysis stops when the validation metric has raised by early_stop_value.
-        early_stop_value : float
-            This value is used as the threshold for different earlystop modes.
-            This value is effective only when the early_stop_mode is set.
+    """
+    Perform sensitivity analysis for this model.
+
+    Parameters
+    ----------
+    model : torch.nn.Module
+        the model to perform sensitivity analysis
+    val_func : function
+        validation function for the model. Due to
+        different models may need different dataset/criterion
+        , therefore the user need to cover this part by themselves.
+        In the val_func, the model should be tested on the validation dateset,
+        and the validation accuracy/loss should be returned as the output of val_func.
+        There are no restrictions on the input parameters of the val_function.
+        User can use the val_args, val_kwargs parameters in analysis
+        to pass all the parameters that val_func needed.
+    sparsities : list
+        The sparsity list provided by users. This parameter is set when the user
+        only wants to test some specific sparsities. In the sparsity list, each element
+        is a sparsity value which means how much weight the pruner should prune. Take
+        [0.25, 0.5, 0.75] for an example, the SensitivityAnalysis will prune 25% 50% 75%
+        weights gradually for each layer.
+    prune_type : str
+        The pruner type used to prune the conv layers, default is 'l1',
+        and 'l2', 'fine-grained' is also supported.
+    early_stop_mode : str
+        If this flag is set, the sensitivity analysis
+        for a conv layer will early stop when the validation metric(
+        for example, accurracy/loss) has alreay meet the threshold. We
+        support four different early stop modes: minimize, maximize, dropped,
+        raised. The default value is None, which means the analysis won't stop
+        until all given sparsities are tested. This option should be used with
+        early_stop_value together.
+
+        minimize: The analysis stops when the validation metric return by the val_func
+        lower than early_stop_value.
+        maximize: The analysis stops when the validation metric return by the val_func
+        larger than early_stop_value.
+        dropped: The analysis stops when the validation metric has dropped by early_stop_value.
+        raised: The analysis stops when the validation metric has raised by early_stop_value.
+    early_stop_value : float
+        This value is used as the threshold for different earlystop modes.
+        This value is effective only when the early_stop_mode is set.
+    """
 
-        """
+    def __init__(self, model, val_func, sparsities=None, prune_type='l1', early_stop_mode=None, early_stop_value=None):
         from nni.algorithms.compression.pytorch.pruning.constants_pruner import PRUNER_DICT
 
         self.model = model
diff --git a/nni/compression/pytorch/utils/shape_dependency.py b/nni/compression/pytorch/utils/shape_dependency.py
index f972212a5..28a28d623 100644
--- a/nni/compression/pytorch/utils/shape_dependency.py
+++ b/nni/compression/pytorch/utils/shape_dependency.py
@@ -10,7 +10,7 @@ from nni.algorithms.compression.v2.pytorch.base import PrunerModuleWrapper as Pr
 from .utils import get_module_by_name
 
 
-__all__ = ['ChannelDependency', 'GroupDependency',
+__all__ = ['ChannelDependency', 'GroupDependency', 'ReshapeDependency',
            'InputChannelDependency', 'AttentionWeightDependency']
 
 
@@ -91,24 +91,26 @@ def reshape_break_channel_dependency(op_node):
 
 
 class ChannelDependency(Dependency):
+    """
+    This model analyze the channel dependencies between the conv
+    layers in a model.
+
+    Parameters
+    ----------
+    model : torch.nn.Module
+        The model to be analyzed.
+    data : torch.Tensor
+        The example input data to trace the network architecture.
+    traced_model : torch._C.Graph
+        if we alreay has the traced graph of the target model, we donnot
+        need to trace the model again.
+    prune_type: str
+        This parameter indicates the channel pruning type: 1) `Filter`
+        prune the filter of the convolution layer to prune the corresponding
+        channels 2) `Batchnorm`: prune the channel in the batchnorm layer
+    """
+
     def __init__(self, model, dummy_input, traced_model=None, prune_type='Filter'):
-        """
-        This model analyze the channel dependencies between the conv
-        layers in a model.
-        Parameters
-        ----------
-        model : torch.nn.Module
-            The model to be analyzed.
-        data : torch.Tensor
-            The example input data to trace the network architecture.
-        traced_model : torch._C.Graph
-            if we alreay has the traced graph of the target model, we donnot
-            need to trace the model again.
-        prune_type: str
-            This parameter indicates the channel pruning type: 1) `Filter`
-            prune the filter of the convolution layer to prune the corresponding
-            channels 2) `Batchnorm`: prune the channel in the batchnorm layer
-        """
         self.prune_type = prune_type
         self.target_types = []
         if self.prune_type == 'Filter':
@@ -163,7 +165,13 @@ class ChannelDependency(Dependency):
             parent_layers = []
             # find the node that contains aten::add
             # or aten::cat operations
-            if node.op_type in ADD_TYPES:
+            if node.op_type in ADD_TYPES or node.op_type in MUL_TYPES:
+                # refer issue 4540 for more details. Multiplication actually
+                # will not introduce the channel dependency, cause the misaligned
+                # channels can propagate to each other. However, when one of the input
+                # tensor is from skip connection(residual), the channel propagation
+                # may be failed(the input is also used by another layer and cannot be
+                # pruned), in this case, we need to fix the conflict maunally.
                 parent_layers = self._get_parent_layers(node)
             elif node.op_type == CAT_TYPE:
                 # To determine if this cat operation will introduce channel
@@ -271,6 +279,7 @@ class InputChannelDependency(ChannelDependency):
         """
         This model analyze the input channel dependencies between the conv
         layers in a model.
+
         Parameters
         ----------
         model : torch.nn.Module
@@ -329,20 +338,22 @@ class InputChannelDependency(ChannelDependency):
 
 
 class GroupDependency(Dependency):
+    """
+    This model analyze the group dependencis between the conv
+    layers in a model.
+
+    Parameters
+    ----------
+    model : torch.nn.Module
+        The model to be analyzed.
+    data : torch.Tensor
+        The example input data to trace the network architecture.
+    traced_model : torch._C.Graph
+        if we alreay has the traced graph of the target model, we donnot
+        need to trace the model again.
+    """
+
     def __init__(self, model, dummy_input, traced_model=None):
-        """
-        This model analyze the group dependencis between the conv
-        layers in a model.
-        Parameters
-        ----------
-        model : torch.nn.Module
-            The model to be analyzed.
-        data : torch.Tensor
-            The example input data to trace the network architecture.
-        traced_model : torch._C.Graph
-            if we alreay has the traced graph of the target model, we donnot
-            need to trace the model again.
-        """
         self.min_groups = {}
         super(GroupDependency, self).__init__(model, dummy_input, traced_model)
 
diff --git a/nni/experiment/config/algorithm.py b/nni/experiment/config/algorithm.py
index bd79f90e5..96b8d3560 100644
--- a/nni/experiment/config/algorithm.py
+++ b/nni/experiment/config/algorithm.py
@@ -47,6 +47,7 @@ class _AlgorithmConfig(ConfigBase):
         else:  # custom algorithm
             assert self.name is None
             assert self.class_name
+            assert self.code_directory is not None
             if not Path(self.code_directory).is_dir():
                 raise ValueError(f'CustomAlgorithmConfig: code_directory "{self.code_directory}" is not a directory')
 
diff --git a/nni/experiment/config/convert.py b/nni/experiment/config/convert.py
index 860b3c1ff..f1a98e492 100644
--- a/nni/experiment/config/convert.py
+++ b/nni/experiment/config/convert.py
@@ -37,6 +37,8 @@ def to_v2(v1):
         _move_field(v1_trial, v2, 'command', 'trialCommand')
         _move_field(v1_trial, v2, 'codeDir', 'trialCodeDirectory')
         _move_field(v1_trial, v2, 'gpuNum', 'trialGpuNumber')
+    else:
+        v1_trial = {}
 
     for algo_type in ['tuner', 'assessor', 'advisor']:
         v1_algo = v1.pop(algo_type, None)
diff --git a/nni/experiment/config/training_services/frameworkcontroller.py b/nni/experiment/config/training_services/frameworkcontroller.py
index 567612717..e196ac708 100644
--- a/nni/experiment/config/training_services/frameworkcontroller.py
+++ b/nni/experiment/config/training_services/frameworkcontroller.py
@@ -46,6 +46,7 @@ class FrameworkControllerConfig(TrainingServiceConfig):
     service_account_name: Optional[str]
     task_roles: List[FrameworkControllerRoleConfig]
     reuse_mode: Optional[bool] = True
+    namespace: str = 'default'
 
     def _canonicalize(self, parents):
         super()._canonicalize(parents)
diff --git a/nni/experiment/config/training_services/kubeflow.py b/nni/experiment/config/training_services/kubeflow.py
index 15d198169..9c76e649c 100644
--- a/nni/experiment/config/training_services/kubeflow.py
+++ b/nni/experiment/config/training_services/kubeflow.py
@@ -43,6 +43,7 @@ class KubeflowConfig(TrainingServiceConfig):
     ps: Optional[KubeflowRoleConfig] = None
     master: Optional[KubeflowRoleConfig] = None
     reuse_mode: Optional[bool] = True #set reuse mode as true for v2 config
+    namespace: str = 'default'
 
     def _canonicalize(self, parents):
         super()._canonicalize(parents)
diff --git a/nni/experiment/config/training_services/remote.py b/nni/experiment/config/training_services/remote.py
index 4a1a49d81..5b5c476d5 100644
--- a/nni/experiment/config/training_services/remote.py
+++ b/nni/experiment/config/training_services/remote.py
@@ -53,7 +53,7 @@ class RemoteMachineConfig(ConfigBase):
 
         if self.password is not None:
             warnings.warn('SSH password will be exposed in web UI as plain text. We recommend to use SSH key file.')
-        elif not Path(self.ssh_key_file).is_file():
+        elif not Path(self.ssh_key_file).is_file():  # type: ignore
             raise ValueError(
                 f'RemoteMachineConfig: You must either provide password or a valid SSH key file "{self.ssh_key_file}"'
             )
diff --git a/nni/experiment/config/utils/internal.py b/nni/experiment/config/utils/internal.py
index b34b46023..49dca2342 100644
--- a/nni/experiment/config/utils/internal.py
+++ b/nni/experiment/config/utils/internal.py
@@ -20,6 +20,15 @@ import nni.runtime.config
 
 from .public import is_missing
 
+__all__ = [
+    'get_base_path', 'set_base_path', 'unset_base_path', 'resolve_path',
+    'case_insensitive', 'camel_case',
+    'is_instance', 'validate_type', 'is_path_like',
+    'guess_config_type', 'guess_list_config_type',
+    'training_service_config_factory', 'load_training_service_config',
+    'get_ipv4_address'
+]
+
 ## handle relative path ##
 
 _current_base_path = None
diff --git a/nni/experiment/config/utils/public.py b/nni/experiment/config/utils/public.py
index 623a95ff6..fe29604d9 100644
--- a/nni/experiment/config/utils/public.py
+++ b/nni/experiment/config/utils/public.py
@@ -10,6 +10,12 @@ import math
 from pathlib import Path
 from typing import Union
 
+__all__ = [
+    'PathLike', 'is_missing',
+    'canonical_gpu_indices', 'validate_gpu_indices',
+    'parse_time', 'parse_memory_size'
+]
+
 PathLike = Union[Path, str]
 
 def is_missing(value):
diff --git a/nni/experiment/data.py b/nni/experiment/data.py
index d58f4671f..026c9c04c 100644
--- a/nni/experiment/data.py
+++ b/nni/experiment/data.py
@@ -1,3 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from dataclasses import dataclass
 import json
 from typing import List
@@ -111,6 +114,8 @@ class TrialJob:
         Stderr log path.
     sequenceId: int
         Sequence Id.
+    message: str
+        Message including platform/environment.
     """
     trialJobId: str
     status: str
@@ -121,9 +126,11 @@ class TrialJob:
     finalMetricData: List[TrialMetricData]
     stderrPath: str
     sequenceId: int
+    message: str
 
-    def __init__(self, trialJobId: str, status: str, logPath: str, startTime: int, sequenceId: int,
-                 endTime: int = -1, stderrPath: str = '', hyperParameters: List = [], finalMetricData: List = []):
+    def __init__(self, trialJobId: str, status: str, startTime: int, sequenceId: int, logPath: str = '',
+                 endTime: int = -1, stderrPath: str = '', hyperParameters: List = [], finalMetricData: List = [],
+                 message: str = '--'):
         self.trialJobId = trialJobId
         self.status = status
         self.hyperParameters = [TrialHyperParameters(**json.loads(e)) for e in hyperParameters]
@@ -133,3 +140,4 @@ class TrialJob:
         self.finalMetricData = [TrialMetricData(**e) for e in finalMetricData]
         self.stderrPath = stderrPath
         self.sequenceId = sequenceId
+        self.message = message
diff --git a/nni/experiment/experiment.py b/nni/experiment/experiment.py
index 57ff2356b..b1ddd105e 100644
--- a/nni/experiment/experiment.py
+++ b/nni/experiment/experiment.py
@@ -1,3 +1,8 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+from __future__ import annotations
+
 import atexit
 from enum import Enum
 import logging
@@ -5,7 +10,7 @@ from pathlib import Path
 import socket
 from subprocess import Popen
 import time
-from typing import Optional, Union, List, overload, Any
+from typing import Any
 
 import colorama
 import psutil
@@ -25,80 +30,61 @@ class RunMode(Enum):
     """
     Config lifecycle and ouput redirection of NNI manager process.
 
-      - Background: stop NNI manager when Python script exits; do not print NNI manager log. (default)
-      - Foreground: stop NNI manager when Python script exits; print NNI manager log to stdout.
-      - Detach: do not stop NNI manager when Python script exits.
+    - Background: stop NNI manager when Python script exits; do not print NNI manager log. (default)
+    - Foreground: stop NNI manager when Python script exits; print NNI manager log to stdout.
+    - Detach: do not stop NNI manager when Python script exits.
 
-    NOTE:
-    This API is non-stable and is likely to get refactored in next release.
-    NNI manager should treat log level more seriously so we can default to "foreground" without being too verbose.
+    NOTE: This API is non-stable and is likely to get refactored in upcoming release.
     """
+    # TODO:
+    # NNI manager should treat log level more seriously so we can default to "foreground" without being too verbose.
     Background = 'background'
     Foreground = 'foreground'
     Detach = 'detach'
 
 class Experiment:
     """
-    Create and stop an NNI experiment.
+    Manage NNI experiment.
+
+    You can either specify an :class:`ExperimentConfig` object, or a training service name.
+    If a platform name is used, a blank config template for that training service will be generated.
+
+    When configuration is completed, use :meth:`Experiment.run` to launch the experiment.
+
+    Example
+    -------
+    .. code-block::
+
+        experiment = Experiment('remote')
+        experiment.config.trial_command = 'python3 trial.py'
+        experiment.config.machines.append(RemoteMachineConfig(ip=..., user_name=...))
+        ...
+        experiment.run(8080)
 
     Attributes
     ----------
     config
         Experiment configuration.
+    id
+        Experiment ID.
     port
-        Web UI port of the experiment, or `None` if it is not running.
+        Web portal port. Or ``None`` if the experiment is not running.
     """
 
-    @overload
-    def __init__(self, config: ExperimentConfig) -> None:
-        """
-        Prepare an experiment.
-
-        Use `Experiment.run()` to launch it.
-
-        Parameters
-        ----------
-        config
-            Experiment configuration.
-        """
-        ...
-
-    @overload
-    def __init__(self, training_service: Union[str, List[str]]) -> None:
-        """
-        Prepare an experiment, leaving configuration fields to be set later.
-
-        Example usage::
-
-            experiment = Experiment('remote')
-            experiment.config.trial_command = 'python3 trial.py'
-            experiment.config.machines.append(RemoteMachineConfig(ip=..., user_name=...))
-            ...
-            experiment.run(8080)
-
-        Parameters
-        ----------
-        training_service
-            Name of training service.
-            Supported value: "local", "remote", "openpai", "aml", "kubeflow", "frameworkcontroller", "adl" and hybrid training service.
-        """
-        ...
-
-    def __init__(self, config=None, training_service=None):
+    def __init__(self, config_or_platform: ExperimentConfig | str | list[str] | None):
         nni.runtime.log.init_logger_for_command_line()
 
-        self.config: Optional[ExperimentConfig] = None
+        self.config: ExperimentConfig | None = None
         self.id: str = management.generate_experiment_id()
-        self.port: Optional[int] = None
-        self._proc: Optional[Popen] = None
-        self.mode = 'new'
-        self.url_prefix: Optional[str] = None
-
-        args = [config, training_service]  # deal with overloading
-        if isinstance(args[0], (str, list)):
-            self.config = ExperimentConfig(args[0])
+        self.port: int | None = None
+        self._proc: Popen | psutil.Process | None = None
+        self._action = 'create'
+        self.url_prefix: str | None = None
+
+        if isinstance(config_or_platform, (str, list)):
+            self.config = ExperimentConfig(config_or_platform)
         else:
-            self.config = args[0]
+            self.config = config_or_platform
 
     def start(self, port: int = 8080, debug: bool = False, run_mode: RunMode = RunMode.Background) -> None:
         """
@@ -114,6 +100,7 @@ class Experiment:
         debug
             Whether to start in debug mode.
         """
+        assert self.config is not None
         if run_mode is not RunMode.Detach:
             atexit.register(self.stop)
 
@@ -127,7 +114,7 @@ class Experiment:
             log_dir = Path.home() / f'nni-experiments/{self.id}/log'
         nni.runtime.log.start_experiment_log(self.id, log_dir, debug)
 
-        self._proc = launcher.start_experiment(self.mode, self.id, config, port, debug, run_mode, self.url_prefix)
+        self._proc = launcher.start_experiment(self._action, self.id, config, port, debug, run_mode, self.url_prefix)
         assert self._proc is not None
 
         self.port = port  # port will be None if start up failed
@@ -138,12 +125,12 @@ class Experiment:
                 if interface.family == socket.AF_INET:
                     ips.append(interface.address)
         ips = [f'http://{ip}:{port}' for ip in ips if ip]
-        msg = 'Web UI URLs: ' + colorama.Fore.CYAN + ' '.join(ips) + colorama.Style.RESET_ALL
+        msg = 'Web portal URLs: ' + colorama.Fore.CYAN + ' '.join(ips) + colorama.Style.RESET_ALL
         _logger.info(msg)
 
     def stop(self) -> None:
         """
-        Stop background experiment.
+        Stop the experiment.
         """
         _logger.info('Stopping experiment, please wait...')
         atexit.unregister(self.stop)
@@ -157,20 +144,20 @@ class Experiment:
                 _logger.warning('Cannot gracefully stop experiment, killing NNI process...')
                 kill_command(self._proc.pid)
 
-        self.id = None
+        self.id = None  # type: ignore
         self.port = None
         self._proc = None
         _logger.info('Experiment stopped')
 
-    def run(self, port: int = 8080, wait_completion: bool = True, debug: bool = False) -> bool:
+    def run(self, port: int = 8080, wait_completion: bool = True, debug: bool = False) -> bool | None:
         """
         Run the experiment.
 
-        If wait_completion is True, this function will block until experiment finish or error.
+        If ``wait_completion`` is ``True``, this function will block until experiment finish or error.
 
-        Return `True` when experiment done; or return `False` when experiment failed.
+        Return ``True`` when experiment done; or return ``False`` when experiment failed.
 
-        Else if wait_completion is False, this function will non-block and return None immediately.
+        Else if ``wait_completion`` is ``False``, this function will non-block and return None immediately.
         """
         self.start(port, debug)
         if wait_completion:
@@ -184,7 +171,6 @@ class Experiment:
                         return False
             except KeyboardInterrupt:
                 _logger.warning('KeyboardInterrupt detected')
-            finally:
                 self.stop()
 
     @classmethod
@@ -197,7 +183,7 @@ class Experiment:
         port
             The port of web UI.
         """
-        experiment = Experiment()
+        experiment = Experiment(None)
         experiment.port = port
         experiment.id = experiment.get_experiment_profile().get('id')
         status = experiment.get_status()
@@ -259,17 +245,17 @@ class Experiment:
 
     @staticmethod
     def _resume(exp_id, exp_dir=None):
-        exp = Experiment()
+        exp = Experiment(None)
         exp.id = exp_id
-        exp.mode = 'resume'
+        exp._action = 'resume'
         exp.config = launcher.get_stopped_experiment_config(exp_id, exp_dir)
         return exp
 
     @staticmethod
     def _view(exp_id, exp_dir=None):
-        exp = Experiment()
+        exp = Experiment(None)
         exp.id = exp_id
-        exp.mode = 'view'
+        exp._action = 'view'
         exp.config = launcher.get_stopped_experiment_config(exp_id, exp_dir)
         return exp
 
diff --git a/nni/experiment/launcher.py b/nni/experiment/launcher.py
index 8eff304cc..d356500e4 100644
--- a/nni/experiment/launcher.py
+++ b/nni/experiment/launcher.py
@@ -1,6 +1,8 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
 
+from __future__ import annotations
+
 import contextlib
 from dataclasses import dataclass, fields
 from datetime import datetime
@@ -27,23 +29,27 @@ _logger = logging.getLogger('nni.experiment')
 
 @dataclass(init=False)
 class NniManagerArgs:
+    # argv sent to "ts/nni_manager/main.js"
+
     port: int
     experiment_id: int
-    start_mode: str  # new or resume
-    mode: str  # training service platform
-    log_dir: str
+    action: str  # 'new', 'resume', 'view'
+    mode: str  # training service platform, to be removed
+    experiments_directory: str  # renamed "config.nni_experiments_directory", must be absolute
     log_level: str
-    readonly: bool = False
     foreground: bool = False
-    url_prefix: Optional[str] = None
+    url_prefix: Optional[str] = None  # leading and trailing "/" must be stripped
     dispatcher_pipe: Optional[str] = None
 
     def __init__(self, action, exp_id, config, port, debug, foreground, url_prefix):
         self.port = port
         self.experiment_id = exp_id
+        self.action = action
         self.foreground = foreground
         self.url_prefix = url_prefix
-        self.log_dir = config.experiment_working_directory
+        # config field name "experiment_working_directory" is a mistake
+        # see "ts/nni_manager/common/globals/arguments.ts" for details
+        self.experiments_directory = config.experiment_working_directory
 
         if isinstance(config.training_service, list):
             self.mode = 'hybrid'
@@ -54,20 +60,14 @@ class NniManagerArgs:
         if debug and self.log_level not in ['debug', 'trace']:
             self.log_level = 'debug'
 
-        if action == 'resume':
-            self.start_mode = 'resume'
-        elif action == 'view':
-            self.start_mode = 'resume'
-            self.readonly = True
-        else:
-            self.start_mode = 'new'
-
     def to_command_line_args(self):
+        # reformat fields to meet yargs library's format
+        # see "ts/nni_manager/common/globals/arguments.ts" for details
         ret = []
         for field in fields(self):
             value = getattr(self, field.name)
             if value is not None:
-                ret.append('--' + field.name)
+                ret.append('--' + field.name.replace('_', '-'))
                 if isinstance(value, bool):
                     ret.append(str(value).lower())
                 else:
@@ -76,6 +76,8 @@ class NniManagerArgs:
 
 def start_experiment(action, exp_id, config, port, debug, run_mode, url_prefix):
     foreground = run_mode.value == 'foreground'
+    if url_prefix is not None:
+        url_prefix = url_prefix.strip('/')
     nni_manager_args = NniManagerArgs(action, exp_id, config, port, debug, foreground, url_prefix)
 
     _ensure_port_idle(port)
@@ -118,7 +120,11 @@ def start_experiment(action, exp_id, config, port, debug, run_mode, url_prefix):
 
     link = Path(config.experiment_working_directory, '_latest')
     try:
-        link.unlink(missing_ok=True)
+        if sys.version_info >= (3, 8):
+            link.unlink(missing_ok=True)
+        else:
+            if link.exists():
+                link.unlink()
         link.symlink_to(exp_id, target_is_directory=True)
     except Exception:
         if sys.platform != 'win32':
@@ -126,16 +132,16 @@ def start_experiment(action, exp_id, config, port, debug, run_mode, url_prefix):
 
     return proc
 
-def _start_rest_server(nni_manager_args, run_mode) -> Tuple[int, Popen]:
+def _start_rest_server(nni_manager_args, run_mode) -> Popen:
     import nni_node
-    node_dir = Path(nni_node.__path__[0])
+    node_dir = Path(nni_node.__path__[0])  # type: ignore
     node = str(node_dir / ('node.exe' if sys.platform == 'win32' else 'node'))
     main_js = str(node_dir / 'main.js')
     cmd = [node, '--max-old-space-size=4096', main_js]
     cmd += nni_manager_args.to_command_line_args()
 
     if run_mode.value == 'detach':
-        log = Path(nni_manager_args.log_dir, nni_manager_args.experiment_id, 'log')
+        log = Path(nni_manager_args.experiments_directory, nni_manager_args.experiment_id, 'log')
         out = (log / 'nnictl_stdout.log').open('a')
         err = (log / 'nnictl_stderr.log').open('a')
         header = f'Experiment {nni_manager_args.experiment_id} start: {datetime.now()}'
@@ -151,10 +157,10 @@ def _start_rest_server(nni_manager_args, run_mode) -> Tuple[int, Popen]:
         from subprocess import CREATE_NEW_PROCESS_GROUP
         return Popen(cmd, stdout=out, stderr=err, cwd=node_dir, creationflags=CREATE_NEW_PROCESS_GROUP)
     else:
-        return Popen(cmd, stdout=out, stderr=err, cwd=node_dir, preexec_fn=os.setpgrp)
+        return Popen(cmd, stdout=out, stderr=err, cwd=node_dir, preexec_fn=os.setpgrp)  # type: ignore
 
 
-def start_experiment_retiarii(exp_id: str, config: ExperimentConfig, port: int, debug: bool) -> Popen:
+def start_experiment_retiarii(exp_id, config, port, debug):
     pipe = None
     proc = None
 
@@ -201,7 +207,7 @@ def _ensure_port_idle(port: int, message: Optional[str] = None) -> None:
 
 
 def _start_rest_server_retiarii(config: ExperimentConfig, port: int, debug: bool, experiment_id: str,
-                                pipe_path: str = None, mode: str = 'new') -> Tuple[int, Popen]:
+                                pipe_path: str, mode: str = 'create') -> Tuple[int, Popen]:
     if isinstance(config.training_service, list):
         ts = 'hybrid'
     else:
@@ -213,24 +219,20 @@ def _start_rest_server_retiarii(config: ExperimentConfig, port: int, debug: bool
         'port': port,
         'mode': ts,
         'experiment_id': experiment_id,
-        'start_mode': mode,
-        'log_dir': config.experiment_working_directory,
+        'action': mode,
+        'experiments_directory': config.experiment_working_directory,
         'log_level': 'debug' if debug else 'info'
     }
     if pipe_path is not None:
         args['dispatcher_pipe'] = pipe_path
 
-    if mode == 'view':
-        args['start_mode'] = 'resume'
-        args['readonly'] = 'true'
-
     import nni_node
-    node_dir = Path(nni_node.__path__[0])
+    node_dir = Path(nni_node.__path__[0])  # type: ignore
     node = str(node_dir / ('node.exe' if sys.platform == 'win32' else 'node'))
     main_js = str(node_dir / 'main.js')
     cmd = [node, '--max-old-space-size=4096', main_js]
     for arg_key, arg_value in args.items():
-        cmd.append('--' + arg_key)
+        cmd.append('--' + arg_key.replace('_', '-'))
         cmd.append(str(arg_value))
 
     if sys.platform == 'win32':
@@ -263,8 +265,8 @@ def _save_experiment_information(experiment_id: str, port: int, start_time: int,
 
 
 def get_stopped_experiment_config(exp_id, exp_dir=None):
-    config_json = get_stopped_experiment_config_json(exp_id, exp_dir)
-    config = ExperimentConfig(**config_json)
+    config_json = get_stopped_experiment_config_json(exp_id, exp_dir)  # type: ignore
+    config = ExperimentConfig(**config_json)  # type: ignore
     if exp_dir and not os.path.samefile(exp_dir, config.experiment_working_directory):
         msg = 'Experiment working directory provided in command line (%s) is different from experiment config (%s)'
         _logger.warning(msg, exp_dir, config.experiment_working_directory)
diff --git a/nni/experiment/management.py b/nni/experiment/management.py
index b15c4d6d2..b74cd033b 100644
--- a/nni/experiment/management.py
+++ b/nni/experiment/management.py
@@ -1,3 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from pathlib import Path
 import random
 import string
diff --git a/nni/experiment/pipe.py b/nni/experiment/pipe.py
index e59fd8270..7a29ca41e 100644
--- a/nni/experiment/pipe.py
+++ b/nni/experiment/pipe.py
@@ -1,4 +1,6 @@
-from io import BufferedIOBase
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import logging
 import os
 import sys
@@ -25,7 +27,7 @@ if sys.platform == 'win32':
                 _winapi.NULL
             )
 
-        def connect(self) -> BufferedIOBase:
+        def connect(self):
             _winapi.ConnectNamedPipe(self._handle, _winapi.NULL)
             fd = msvcrt.open_osfhandle(self._handle, 0)
             self.file = os.fdopen(fd, 'w+b')
@@ -55,7 +57,7 @@ else:
             self._socket.bind(self.path)
             self._socket.listen(1)  # only accepts one connection
 
-        def connect(self) -> BufferedIOBase:
+        def connect(self):
             conn, _ = self._socket.accept()
             self.file = conn.makefile('rwb')
             return self.file
diff --git a/nni/experiment/rest.py b/nni/experiment/rest.py
index c2fbaa824..3ab266311 100644
--- a/nni/experiment/rest.py
+++ b/nni/experiment/rest.py
@@ -1,3 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import logging
 from typing import Any, Optional
 
diff --git a/nni/recoverable.py b/nni/recoverable.py
index 70f11e634..4ff419b8f 100644
--- a/nni/recoverable.py
+++ b/nni/recoverable.py
@@ -1,17 +1,19 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
 
+from __future__ import annotations
+
 import os
 
 class Recoverable:
 
-    def load_checkpoint(self):
+    def load_checkpoint(self) -> None:
         pass
 
-    def save_checkpoint(self):
+    def save_checkpoint(self) -> None:
         pass
 
-    def get_checkpoint_path(self):
+    def get_checkpoint_path(self) -> str | None:
         ckp_path = os.getenv('NNI_CHECKPOINT_DIRECTORY')
         if ckp_path is not None and os.path.isdir(ckp_path):
             return ckp_path
diff --git a/nni/retiarii/codegen/pytorch.py b/nni/retiarii/codegen/pytorch.py
index f8a1f46c4..555c5ea1c 100644
--- a/nni/retiarii/codegen/pytorch.py
+++ b/nni/retiarii/codegen/pytorch.py
@@ -6,6 +6,7 @@ import re
 from typing import Dict, List, Tuple, Any
 
 from nni.retiarii.operation_def.torch_op_def import ToDevice
+from nni.retiarii.utils import STATE_DICT_PY_MAPPING
 from nni.common.device import Device, GPUDevice
 
 from ..graph import IllegalGraphError, Edge, Graph, Node, Model
@@ -97,7 +98,18 @@ def _format_variable_name(name: str, graph_name: str) -> str:
     name = name.replace('/', '__')
 
     # https://stackoverflow.com/questions/3303312/how-do-i-convert-a-string-to-a-valid-variable-name-in-python
-    return re.sub('\W|^(?=\d)','_', name)
+    name = re.sub('\W|^(?=\d)','_', name)
+
+    if name.startswith('__') and (len(name) > 2 and name[2] != '_'):
+        # name can't start with double underscore
+        # it's reserved in Python: https://stackoverflow.com/a/1301409/6837658
+        # but it's actually very common in our generated code
+        name = name[1:]
+    elif name.startswith('_'):
+        # to avoid conflicts between '_' and '__'
+        name = 'i' + name
+
+    return name
 
 
 def generate_cuda_mapping(placement: Dict[Node, Device]) -> Dict[Device, int]:
@@ -125,6 +137,7 @@ def graph_to_pytorch_model(graph_name: str, graph: Graph, placement=None) -> str
     # only need to generate code for module here
     import_pkgs = set()
     node_codes = []
+    node_python_mappings = {}
     cuda_remapped_id = None
     if placement:
         cuda_remapped_id = generate_cuda_mapping(placement)
@@ -138,7 +151,9 @@ def graph_to_pytorch_model(graph_name: str, graph: Graph, placement=None) -> str
             pkg_name = node.operation.get_import_pkg()
             if pkg_name is not None:
                 import_pkgs.add(pkg_name)
-            node_code = node.operation.to_init_code(_format_variable_name(node.name, graph_name))
+
+            py_variable_name = _format_variable_name(node.name, graph_name)
+            node_code = node.operation.to_init_code(py_variable_name)
             if node_code is not None:
                 if placement and node in placement and len(node_code) > 0:
                     if isinstance(placement[node], GPUDevice):
@@ -149,6 +164,11 @@ def graph_to_pytorch_model(graph_name: str, graph: Graph, placement=None) -> str
                 else:
                     node_codes.append(node_code)
 
+                # Map to module hierarchies in original search space python code
+                node_python_mappings[py_variable_name] = node.python_name
+
+    node_codes.append(f'self.{STATE_DICT_PY_MAPPING} = {node_python_mappings}')
+
     if graph.input_node.operation.io_names is None:
         input_code = '*_inputs'
     else:
diff --git a/nni/retiarii/converter/graph_gen.py b/nni/retiarii/converter/graph_gen.py
index 70222abec..e18f7ab2d 100644
--- a/nni/retiarii/converter/graph_gen.py
+++ b/nni/retiarii/converter/graph_gen.py
@@ -660,8 +660,9 @@ class GraphConverter:
             attrs = {
                 'mutation': 'repeat',
                 'label': module.label,
+                'depth': module.depth_choice,
+                'max_depth': module.max_depth,
                 'min_depth': module.min_depth,
-                'max_depth': module.max_depth
             }
             return ir_graph, attrs
 
@@ -695,15 +696,17 @@ class GraphConverter:
 class GraphConverterWithShape(GraphConverter):
     """
     Convert a pytorch model to nni ir along with input/output shape info.
-    Based ir acquired through `torch.jit.script`
-    and shape info acquired through `torch.jit.trace`.
-
-    Known issues
-    ------------
-    1. `InputChoice` and `ValueChoice` not supported yet.
-    2. Currently random inputs are fed while tracing layerchoice.
-       If forward path of candidates depends on input data, then wrong path will be traced.
-       This will result in incomplete shape info.
+    Based ir acquired through ``torch.jit.script``
+    and shape info acquired through ``torch.jit.trace``.
+
+    .. warning::
+
+        Known issues:
+
+        1. ``InputChoice`` and ``ValueChoice`` not supported yet.
+        2. Currently random inputs are fed while tracing layerchoice.
+           If forward path of candidates depends on input data, then wrong path will be traced.
+           This will result in incomplete shape info.
     """
     def convert_module(self, script_module, module, module_name, ir_model, dummy_input):
         module.eval()
@@ -815,7 +818,7 @@ class GraphConverterWithShape(GraphConverter):
 
 def convert_to_graph(script_module, module, converter=None, **kwargs):
     """
-    Convert module to our graph ir, i.e., build a ```Model``` type
+    Convert module to our graph ir, i.e., build a :class:`Model` type
 
     Parameters
     ----------
diff --git a/nni/retiarii/evaluator/pytorch/cgo/evaluator.py b/nni/retiarii/evaluator/pytorch/cgo/evaluator.py
index 967ad6dba..21c00e009 100644
--- a/nni/retiarii/evaluator/pytorch/cgo/evaluator.py
+++ b/nni/retiarii/evaluator/pytorch/cgo/evaluator.py
@@ -29,8 +29,20 @@ class _MultiModelSupervisedLearningModule(LightningModule):
         self.criterion_cls = criterion
         self.optimizer = optimizer
         self.metrics = nn.ModuleDict({name: cls() for name, cls in metrics.items()})
+        self.metrics_args = metrics
         self.n_models = n_models
 
+    def dump_kwargs(self):
+        kwargs = {}
+        kwargs['criterion'] = self.criterion_cls
+        kwargs['metrics'] = self.metrics_args
+        kwargs['n_models'] = self.n_models
+        kwargs['learning_rate'] = self.hparams['learning_rate']
+        kwargs['weight_decay'] = self.hparams['weight_decay']
+        kwargs['optimizer'] = self.optimizer
+        return kwargs
+
+
     def forward(self, x):
         y_hat = self.model(x)
         return y_hat
@@ -125,8 +137,7 @@ class MultiModelSupervisedLearningModule(_MultiModelSupervisedLearningModule):
         super().__init__(criterion, metrics, learning_rate=learning_rate, weight_decay=weight_decay, optimizer=optimizer)
 
 
-@nni.trace
-class _ClassificationModule(MultiModelSupervisedLearningModule):
+class _ClassificationModule(_MultiModelSupervisedLearningModule):
     def __init__(self, criterion: nn.Module = nn.CrossEntropyLoss,
                  learning_rate: float = 0.001,
                  weight_decay: float = 0.,
@@ -157,7 +168,7 @@ class Classification(Lightning):
         If the ``lightning_module`` has a predefined val_dataloaders method this will be skipped.
     trainer_kwargs : dict
         Optional keyword arguments passed to trainer. See
-        `Lightning documentation <https://pytorch-lightning.readthedocs.io/en/stable/trainer.html>`__ for details.
+        `Lightning documentation <https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html>`__ for details.
     """
 
     def __init__(self, criterion: nn.Module = nn.CrossEntropyLoss,
@@ -172,9 +183,7 @@ class Classification(Lightning):
         super().__init__(module, Trainer(use_cgo=True, **trainer_kwargs),
                          train_dataloader=train_dataloader, val_dataloaders=val_dataloaders)
 
-
-@nni.trace
-class _RegressionModule(MultiModelSupervisedLearningModule):
+class _RegressionModule(_MultiModelSupervisedLearningModule):
     def __init__(self, criterion: nn.Module = nn.MSELoss,
                  learning_rate: float = 0.001,
                  weight_decay: float = 0.,
@@ -205,7 +214,7 @@ class Regression(Lightning):
         If the ``lightning_module`` has a predefined val_dataloaders method this will be skipped.
     trainer_kwargs : dict
         Optional keyword arguments passed to trainer. See
-        `Lightning documentation <https://pytorch-lightning.readthedocs.io/en/stable/trainer.html>`__ for details.
+        `Lightning documentation <https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html>`__ for details.
     """
 
     def __init__(self, criterion: nn.Module = nn.MSELoss,
diff --git a/nni/retiarii/evaluator/pytorch/cgo/trainer.py b/nni/retiarii/evaluator/pytorch/cgo/trainer.py
index b6cb0cf3b..fd5128448 100644
--- a/nni/retiarii/evaluator/pytorch/cgo/trainer.py
+++ b/nni/retiarii/evaluator/pytorch/cgo/trainer.py
@@ -2,11 +2,8 @@
 # Licensed under the MIT license.
 
 import pytorch_lightning as pl
-import nni
 from .accelerator import BypassAccelerator
 
-
-@nni.trace
 class Trainer(pl.Trainer):
     """
     Trainer for cross-graph optimization.
@@ -20,7 +17,7 @@ class Trainer(pl.Trainer):
         default: False
     trainer_kwargs : dict
         Optional keyword arguments passed to trainer. See
-        `Lightning documentation <https://pytorch-lightning.readthedocs.io/en/stable/trainer.html>`__ for details.
+        `Lightning documentation <https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html>`__ for details.
     """
 
     def __init__(self, use_cgo=False, **trainer_kwargs):
diff --git a/nni/retiarii/evaluator/pytorch/lightning.py b/nni/retiarii/evaluator/pytorch/lightning.py
index 550f97417..9cdfcef71 100644
--- a/nni/retiarii/evaluator/pytorch/lightning.py
+++ b/nni/retiarii/evaluator/pytorch/lightning.py
@@ -4,7 +4,7 @@
 import os
 import warnings
 from pathlib import Path
-from typing import Dict, Union, Optional, List, Type
+from typing import Dict, Union, Optional, List, Callable
 
 import pytorch_lightning as pl
 import torch.nn as nn
@@ -29,11 +29,20 @@ __all__ = ['LightningModule', 'Trainer', 'DataLoader', 'Lightning', 'Classificat
 class LightningModule(pl.LightningModule):
     """
     Basic wrapper of generated model.
-
     Lightning modules used in NNI should inherit this class.
+
+    It's a subclass of ``pytorch_lightning.LightningModule``.
+    See https://pytorch-lightning.readthedocs.io/en/stable/common/lightning_module.html
     """
 
-    def set_model(self, model: Union[Type[nn.Module], nn.Module]) -> None:
+    def set_model(self, model: Union[Callable[[], nn.Module], nn.Module]) -> None:
+        """Set the inner model (architecture) to train / evaluate.
+
+        Parameters
+        ----------
+        model : callable or nn.Module
+            Can be a callable returning nn.Module or nn.Module.
+        """
         if isinstance(model, nn.Module):
             self.model = model
         else:
@@ -41,7 +50,13 @@ class LightningModule(pl.LightningModule):
 
 
 Trainer = nni.trace(pl.Trainer)
+Trainer.__doc__ = """
+Traced version of ``pytorch_lightning.Trainer``. See https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html
+"""
 DataLoader = nni.trace(torch_data.DataLoader)
+DataLoader.__doc__ = """
+Traced version of ``torch.utils.data.DataLoader``. See https://pytorch.org/docs/stable/data.html
+"""
 
 @nni.trace
 class Lightning(Evaluator):
@@ -162,7 +177,6 @@ class _SupervisedLearningModule(LightningModule):
             self.export_onnx = Path(export_onnx)
         else:
             self.export_onnx = None
-        self._already_exported = False
 
     def forward(self, x):
         y_hat = self.model(x)
@@ -181,12 +195,12 @@ class _SupervisedLearningModule(LightningModule):
         x, y = batch
         y_hat = self(x)
 
-        if not self._already_exported:
+        if self.export_onnx is not None:
             try:
                 self.to_onnx(self.export_onnx, x, export_params=True)
             except RuntimeError as e:
                 warnings.warn(f'ONNX conversion failed. As a result, you might not be able to use visualization. Error message: {e}')
-            self._already_exported = True
+            self.export_onnx = None
 
         self.log('val_loss', self.criterion(y_hat, y), prog_bar=True)
         for name, metric in self.metrics.items():
@@ -236,7 +250,7 @@ class _ClassificationModule(_SupervisedLearningModule):
 
 class Classification(Lightning):
     """
-    Trainer that is used for classification.
+    Evaluator that is used for classification.
 
     Parameters
     ----------
@@ -289,7 +303,7 @@ class _RegressionModule(_SupervisedLearningModule):
 
 class Regression(Lightning):
     """
-    Trainer that is used for regression.
+    Evaluator that is used for regression.
 
     Parameters
     ----------
diff --git a/nni/retiarii/execution/api.py b/nni/retiarii/execution/api.py
index 8027e7e36..d0028e5e7 100644
--- a/nni/retiarii/execution/api.py
+++ b/nni/retiarii/execution/api.py
@@ -21,7 +21,9 @@ def set_execution_engine(engine: AbstractExecutionEngine) -> None:
     if _execution_engine is None:
         _execution_engine = engine
     else:
-        raise RuntimeError('Execution engine is already set.')
+        raise RuntimeError('Execution engine is already set. '
+                           'You should avoid instantiating RetiariiExperiment twice in one process. '
+                           'If you are running in a Jupyter notebook, please restart the kernel.')
 
 
 def get_execution_engine() -> AbstractExecutionEngine:
diff --git a/nni/retiarii/execution/base.py b/nni/retiarii/execution/base.py
index 54eb041c3..8ee1f0ff5 100644
--- a/nni/retiarii/execution/base.py
+++ b/nni/retiarii/execution/base.py
@@ -17,6 +17,8 @@ _logger = logging.getLogger(__name__)
 
 class BaseGraphData:
     """
+    Data sent between strategy and trial, in graph-based execution engine.
+
     Attributes
     ----------
     model_script
diff --git a/nni/retiarii/execution/cgo_engine.py b/nni/retiarii/execution/cgo_engine.py
index e6e204dbe..f35eb1056 100644
--- a/nni/retiarii/execution/cgo_engine.py
+++ b/nni/retiarii/execution/cgo_engine.py
@@ -200,7 +200,7 @@ class CGOExecutionEngine(AbstractExecutionEngine):
 
             # replace the module with a new instance whose n_models is set
             # n_models must be set in __init__, otherwise it cannot be captured by serialize_cls
-            new_module_init_params = model.evaluator.module.trace_kwargs.copy()
+            new_module_init_params = model.evaluator.module.dump_kwargs().copy()
 
             # MultiModelSupervisedLearningModule hides n_models of _MultiModelSupervisedLearningModule from users
             new_module_init_params['n_models'] = len(multi_model)
diff --git a/nni/retiarii/experiment/pytorch.py b/nni/retiarii/experiment/pytorch.py
index d94e6cd6c..60f65b2e2 100644
--- a/nni/retiarii/experiment/pytorch.py
+++ b/nni/retiarii/experiment/pytorch.py
@@ -45,6 +45,9 @@ from ..strategy.utils import dry_run_for_formatted_search_space
 _logger = logging.getLogger(__name__)
 
 
+__all__ = ['RetiariiExeConfig', 'RetiariiExperiment']
+
+
 @dataclass(init=False)
 class RetiariiExeConfig(ConfigBase):
     experiment_name: Optional[str] = None
@@ -145,6 +148,10 @@ def preprocess_model(base_model, evaluator, applied_mutators, full_ir=True, dumm
                            'do not use mutators when you use LayerChoice/InputChoice')
     if mutators is not None:
         applied_mutators = mutators
+
+    # Add mutations on evaluators
+    applied_mutators += process_evaluator_mutations(evaluator, applied_mutators)
+
     return base_model_ir, applied_mutators
 
 
@@ -284,7 +291,6 @@ class RetiariiExperiment(Experiment):
             full_ir=self.config.execution_engine not in ['py', 'benchmark'],
             dummy_input=self.config.dummy_input
         )
-        self.applied_mutators += process_evaluator_mutations(self.evaluator, self.applied_mutators)
 
         _logger.info('Start strategy...')
         search_space = dry_run_for_formatted_search_space(base_model_ir, self.applied_mutators)
@@ -475,6 +481,8 @@ class RetiariiExperiment(Experiment):
         For one-shot algorithms, only top-1 is supported. For others, ``optimize_mode`` and ``formatter`` are
         available for customization.
 
+        Parameters
+        ----------
         top_k : int
             How many models are intended to be exported.
         optimize_mode : str
diff --git a/nni/retiarii/graph.py b/nni/retiarii/graph.py
index 559d5175d..6f1b63744 100644
--- a/nni/retiarii/graph.py
+++ b/nni/retiarii/graph.py
@@ -75,12 +75,12 @@ class Model:
     """
     Represents a neural network model.
 
-    During mutation, one `Model` object is created for each trainable snapshot.
+    During mutation, one :class:`Model` object is created for each trainable snapshot.
     For example, consider a mutator that insert a node at an edge for each iteration.
     In one iteration, the mutator invokes 4 primitives: add node, remove edge, add edge to head, add edge to tail.
-    These 4 primitives operates in one `Model` object.
+    These 4 primitives operates in one :class:`Model` object.
     When they are all done the model will be set to "frozen" (trainable) status and be submitted to execution engine.
-    And then a new iteration starts, and a new `Model` object is created by forking last model.
+    And then a new iteration starts, and a new :class:`Model` object is created by forking last model.
 
     Attributes
     ----------
@@ -91,7 +91,7 @@ class Model:
     python_init_params
         Initialization parameters of python class.
     status
-        See `ModelStatus`.
+        See :class:`ModelStatus`.
     root_graph
         The outermost graph which usually takes dataset as input and feeds output to loss function.
     graphs
@@ -100,11 +100,11 @@ class Model:
         Model evaluator
     history
         Mutation history.
-        `self` is directly mutated from `self.history[-1]`;
-        `self.history[-1] is mutated from `self.history[-2]`, and so on.
-        `self.history[0]` is the base graph.
+        ``self`` is directly mutated from ``self.history[-1]``;
+        ``self.history[-1]`` is mutated from ``self.history[-2]``, and so on.
+        ``self.history[0]`` is the base graph.
     metric
-        Training result of the model, or `None` if it's not yet trained or has failed to train.
+        Training result of the model, or ``None`` if it's not yet trained or has failed to train.
     intermediate_metrics
         Intermediate training metrics. If the model is not trained, it's an empty list.
     """
@@ -265,9 +265,9 @@ class Graph:
     Graph topology.
 
     This class simply represents the topology, with no semantic meaning.
-    All other information like metric, non-graph functions, mutation history, etc should go to `Model`.
+    All other information like metric, non-graph functions, mutation history, etc should go to :class:`Model`.
 
-    Each graph belongs to and only belongs to one `Model`.
+    Each graph belongs to and only belongs to one :class:`Model`.
 
     Attributes
     ----------
@@ -284,15 +284,15 @@ class Graph:
     output_names
         Optional mnemonic names of output values.
     input_node
-        ...
+        Incoming node.
     output_node
-        ...
+        Output node.
     hidden_nodes
-        ...
+        Hidden nodes
     nodes
         All input/output/hidden nodes.
     edges
-        ...
+        Edges.
     python_name
         The name of torch.nn.Module, should have one-to-one mapping with items in python model.
     """
@@ -532,16 +532,16 @@ class Node:
     """
     An operation or an opaque subgraph inside a graph.
 
-    Each node belongs to and only belongs to one `Graph`.
-    Nodes should never be created with constructor. Use `Graph.add_node()` instead.
+    Each node belongs to and only belongs to one :class:`Graph`.
+    Nodes should never be created with constructor. Use :meth:`Graph.add_node` instead.
 
     The node itself is for topology only.
-    Information of tensor calculation should all go inside `operation` attribute.
+    Information of tensor calculation should all go inside ``operation`` attribute.
 
     TODO: parameter of subgraph (cell)
     It's easy to assign parameters on cell node, but it's hard to "use" them.
     We need to design a way to reference stored cell parameters in inner node operations.
-    e.g. `self.fc = Linear(self.units)`  <-  how to express `self.units` in IR?
+    e.g. ``self.fc = Linear(self.units)``  <-  how to express ``self.units`` in IR?
 
     Attributes
     ----------
@@ -557,10 +557,10 @@ class Node:
     label
         Optional. If two nodes have the same label, they are considered same by the mutator.
     operation
-        ...
+        Operation.
     cell
         Read only shortcut to get the referenced subgraph.
-        If this node is not a subgraph (is a primitive operation), accessing `cell` will raise an error.
+        If this node is not a subgraph (is a primitive operation), accessing ``cell`` will raise an error.
     predecessors
         Predecessor nodes of this node in the graph. This is an optional mutation helper.
     successors
@@ -677,36 +677,36 @@ class Edge:
     """
     A tensor, or "data flow", between two nodes.
 
-    Example forward code snippet:
-    ```
-    a, b, c = split(x)
-    p = concat(a, c)
-    q = sum(b, p)
-    z = relu(q)
-    ```
-
-    Edges in above snippet:
-      + head: (split, 0), tail: (concat, 0)  # a in concat
-      + head: (split, 2), tail: (concat, 1)  # c in concat
-      + head: (split, 1), tail: (sum, -1 or 0)  # b in sum
-      + head: (concat, null), tail: (sum, -1 or 1)  # p in sum
-      + head: (sum, null), tail: (relu, null)  # q in relu
+    Example forward code snippet: ::
+
+        a, b, c = split(x)
+        p = concat(a, c)
+        q = sum(b, p)
+        z = relu(q)
+
+    Edges in above snippet: ::
+
+        + head: (split, 0), tail: (concat, 0)  # a in concat
+        + head: (split, 2), tail: (concat, 1)  # c in concat
+        + head: (split, 1), tail: (sum, -1 or 0)  # b in sum
+        + head: (concat, null), tail: (sum, -1 or 1)  # p in sum
+        + head: (sum, null), tail: (relu, null)  # q in relu
 
     Attributes
     ----------
     graph
-        ...
+        Graph.
     head
         Head node.
     tail
         Tail node.
     head_slot
         Index of outputs in head node.
-        If the node has only one output, this should be `null`.
+        If the node has only one output, this should be ``null``.
     tail_slot
         Index of inputs in tail node.
-        If the node has only one input, this should be `null`.
-        If the node does not care about order, this can be `-1`.
+        If the node has only one input, this should be ``null``.
+        If the node does not care about order, this can be ``-1``.
     """
 
     def __init__(self, head: EdgeEndpoint, tail: EdgeEndpoint, _internal: bool = False):
diff --git a/nni/retiarii/hub/README.md b/nni/retiarii/hub/README.md
new file mode 100644
index 000000000..e36b034ed
--- /dev/null
+++ b/nni/retiarii/hub/README.md
@@ -0,0 +1,29 @@
+This README will be deleted once this hub got stabilized, after which we will promote it in the documentation.
+
+## Why
+
+We hereby provides a series of state-of-the-art search space, which is PyTorch model + mutations + training recipe.
+
+For further motivations and plans, please see https://github.com/microsoft/nni/issues/4249.
+
+## Reproduction Roadmap
+
+1. Runnable
+2. Load checkpoint of searched architecture and evaluate
+3. Reproduce searched architecture
+4. Runnable with built-in algos
+5. Reproduce result with at least one algo
+
+|                        | 1      | 2      | 3      | 4      | 5      |
+|------------------------|--------|--------|--------|--------|--------|
+| NasBench101            | Y      |        |        |        |        |
+| NasBench201            | Y      |        |        |        |        |
+| NASNet                 | Y      |        |        |        |        |
+| ENAS                   | Y      |        |        |        |        |
+| AmoebaNet              | Y      |        |        |        |        |
+| PNAS                   | Y      |        |        |        |        |
+| DARTS                  | Y      |        |        |        |        |
+| ProxylessNAS           | Y      |        |        |        |        |
+| MobileNetV3Space       | Y      |        |        |        |        |
+| ShuffleNetSpace        | Y      |        |        |        |        |
+| ShuffleNetSpace (ch)   | Y      |        |        |        |        |
diff --git a/nni/retiarii/hub/__init__.py b/nni/retiarii/hub/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/nni/retiarii/hub/pytorch/__init__.py b/nni/retiarii/hub/pytorch/__init__.py
new file mode 100644
index 000000000..adc0c11b9
--- /dev/null
+++ b/nni/retiarii/hub/pytorch/__init__.py
@@ -0,0 +1,9 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+from .mobilenetv3 import MobileNetV3Space
+from .nasbench101 import NasBench101
+from .nasbench201 import NasBench201
+from .nasnet import NDS, NASNet, ENAS, AmoebaNet, PNAS, DARTS
+from .proxylessnas import ProxylessNAS
+from .shufflenet import ShuffleNetSpace
diff --git a/nni/retiarii/hub/pytorch/mobilenetv3.py b/nni/retiarii/hub/pytorch/mobilenetv3.py
new file mode 100644
index 000000000..be7e2a0bd
--- /dev/null
+++ b/nni/retiarii/hub/pytorch/mobilenetv3.py
@@ -0,0 +1,155 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+from typing import Tuple, Optional, Callable
+
+import nni.retiarii.nn.pytorch as nn
+from nni.retiarii import model_wrapper
+
+from .proxylessnas import ConvBNReLU, InvertedResidual, SeparableConv, make_divisible, reset_parameters
+
+
+class h_sigmoid(nn.Module):
+    def __init__(self, inplace=True):
+        super(h_sigmoid, self).__init__()
+        self.relu = nn.ReLU6(inplace=inplace)
+
+    def forward(self, x):
+        return self.relu(x + 3) / 6
+
+
+class h_swish(nn.Module):
+    def __init__(self, inplace=True):
+        super(h_swish, self).__init__()
+        self.sigmoid = h_sigmoid(inplace=inplace)
+
+    def forward(self, x):
+        return x * self.sigmoid(x)
+
+
+class SELayer(nn.Module):
+    """Squeeze-and-excite layer."""
+
+    def __init__(self,
+                 channels: int,
+                 reduction: int = 4,
+                 activation_layer: Optional[Callable[..., nn.Module]] = None):
+        super().__init__()
+        if activation_layer is None:
+            activation_layer = nn.Sigmoid
+        self.avg_pool = nn.AdaptiveAvgPool2d(1)
+        self.fc = nn.Sequential(
+            nn.Linear(channels, make_divisible(channels // reduction, 8)),
+            nn.ReLU(inplace=True),
+            nn.Linear(make_divisible(channels // reduction, 8), channels),
+            activation_layer()
+        )
+
+    def forward(self, x):
+        b, c, _, _ = x.size()
+        y = self.avg_pool(x).view(b, c)
+        y = self.fc(y).view(b, c, 1, 1)
+        return x * y
+
+
+@model_wrapper
+class MobileNetV3Space(nn.Module):
+    """
+    MobileNetV3Space implements the largest search space in `TuNAS <https://arxiv.org/abs/2008.06120>`__.
+
+    The search dimensions include widths, expand ratios, kernel sizes, SE ratio.
+    Some of them can be turned off via arguments to narrow down the search space.
+
+    Different from ProxylessNAS search space, this space is implemented with :class:`nn.ValueChoice`.
+
+    We use the following snipppet as reference.
+    https://github.com/google-research/google-research/blob/20736344591f774f4b1570af64624ed1e18d2867/tunas/mobile_search_space_v3.py#L728
+    """
+
+    def __init__(self, num_labels: int = 1000,
+                 base_widths: Tuple[int, ...] = (16, 16, 32, 64, 128, 256, 512, 1024),
+                 width_multipliers: Tuple[float, ...] = (0.5, 0.625, 0.75, 1.0, 1.25, 1.5, 2.0),
+                 expand_ratios: Tuple[int, ...] = (1, 2, 3, 4, 5, 6),
+                 dropout_rate: float = 0.2,
+                 bn_eps: float = 1e-3,
+                 bn_momentum: float = 0.1):
+        super().__init__()
+
+        self.widths = [
+            nn.ValueChoice([make_divisible(base_width * mult, 8) for mult in width_multipliers], label=f'width_{i}')
+            for i, base_width in enumerate(base_widths)
+        ]
+        self.expand_ratios = expand_ratios
+
+        blocks = [
+            # Stem
+            ConvBNReLU(
+                3, self.widths[0],
+                nn.ValueChoice([3, 5], label='ks_0'),
+                stride=2, activation_layer=h_swish
+            ),
+            SeparableConv(self.widths[0], self.widths[0], activation_layer=nn.ReLU),
+        ]
+
+        # counting for kernel sizes and expand ratios
+        self.layer_count = 2
+
+        blocks += [
+            # Body
+            self._make_stage(1, self.widths[0], self.widths[1], False, 2, nn.ReLU),
+            self._make_stage(2, self.widths[1], self.widths[2], True, 2, nn.ReLU),
+            self._make_stage(1, self.widths[2], self.widths[3], False, 2, h_swish),
+            self._make_stage(1, self.widths[3], self.widths[4], True, 1, h_swish),
+            self._make_stage(1, self.widths[4], self.widths[5], True, 2, h_swish),
+        ]
+
+        # Head
+        blocks += [
+            ConvBNReLU(self.widths[5], self.widths[6], 1, 1, activation_layer=h_swish),
+            nn.AdaptiveAvgPool2d(1),
+
+            ConvBNReLU(self.widths[6], self.widths[7], 1, 1, norm_layer=nn.Identity, activation_layer=h_swish),
+        ]
+
+        self.blocks = nn.Sequential(*blocks)
+
+        self.classifier = nn.Sequential(
+            nn.Dropout(dropout_rate),
+            nn.Linear(self.widths[7], num_labels),
+        )
+
+        reset_parameters(self, bn_momentum=bn_momentum, bn_eps=bn_eps)
+
+    def forward(self, x):
+        x = self.blocks(x)
+        x = x.view(x.size(0), -1)
+        x = self.classifier(x)
+        return x
+
+    def _make_stage(self, stage_idx, inp, oup, se, stride, act):
+        # initialize them first because they are related to layer_count.
+        exp, ks, se_blocks = [], [], []
+        for _ in range(4):
+            exp.append(nn.ValueChoice(list(self.expand_ratios), label=f'exp_{self.layer_count}'))
+            ks.append(nn.ValueChoice([3, 5, 7], label=f'ks_{self.layer_count}'))
+            if se:
+                # if SE is true, assign a layer choice to SE
+                se_blocks.append(
+                    lambda hidden_ch: nn.LayerChoice([nn.Identity(), SELayer(hidden_ch)], label=f'se_{self.layer_count}')
+                )
+            else:
+                se_blocks.append(None)
+            self.layer_count += 1
+
+        blocks = [
+            # stride = 2
+            InvertedResidual(inp, oup, exp[0], ks[0],
+                             stride, squeeze_and_excite=se_blocks[0], activation_layer=act),
+            # stride = 1, residual connection should be automatically enabled
+            InvertedResidual(oup, oup, exp[1], ks[1], squeeze_and_excite=se_blocks[1], activation_layer=act),
+            InvertedResidual(oup, oup, exp[2], ks[2], squeeze_and_excite=se_blocks[2], activation_layer=act),
+            InvertedResidual(oup, oup, exp[3], ks[3], squeeze_and_excite=se_blocks[3], activation_layer=act)
+        ]
+
+        # mutable depth
+        return nn.Repeat(blocks, depth=(1, 4), label=f'depth_{stage_idx}')
diff --git a/nni/retiarii/hub/pytorch/nasbench101.py b/nni/retiarii/hub/pytorch/nasbench101.py
new file mode 100644
index 000000000..6ab5ab84f
--- /dev/null
+++ b/nni/retiarii/hub/pytorch/nasbench101.py
@@ -0,0 +1,125 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+import math
+
+import torch.nn as nn
+from nni.retiarii import model_wrapper
+from nni.retiarii.nn.pytorch import NasBench101Cell
+
+
+__all__ = ['NasBench101']
+
+
+def truncated_normal_(tensor, mean=0, std=1):
+    # https://discuss.pytorch.org/t/implementing-truncated-normal-initializer/4778/15
+    size = tensor.shape
+    tmp = tensor.new_empty(size + (4,)).normal_()
+    valid = (tmp < 2) & (tmp > -2)
+    ind = valid.max(-1, keepdim=True)[1]
+    tensor.data.copy_(tmp.gather(-1, ind).squeeze(-1))
+    tensor.data.mul_(std).add_(mean)
+
+
+class ConvBNReLU(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0):
+        super(ConvBNReLU, self).__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.conv_bn_relu = nn.Sequential(
+            nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, bias=False),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(inplace=True)
+        )
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                fan_in = m.kernel_size[0] * m.kernel_size[1] * m.in_channels
+                truncated_normal_(m.weight.data, mean=0., std=math.sqrt(1. / fan_in))
+            if isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+
+    def forward(self, x):
+        return self.conv_bn_relu(x)
+
+
+class Conv3x3BNReLU(ConvBNReLU):
+    def __init__(self, in_channels, out_channels):
+        super(Conv3x3BNReLU, self).__init__(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+
+
+class Conv1x1BNReLU(ConvBNReLU):
+    def __init__(self, in_channels, out_channels):
+        super(Conv1x1BNReLU, self).__init__(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+
+
+Projection = Conv1x1BNReLU
+
+
+@model_wrapper
+class NasBench101(nn.Module):
+    """The full search space, proposed by `NAS-Bench-101 <http://proceedings.mlr.press/v97/ying19a/ying19a.pdf>`__.
+
+    It's simply a stack of :class:`NasBench101Cell`. Operations are conv3x3, conv1x1 and maxpool respectively.
+    """
+
+    def __init__(self,
+                 stem_out_channels: int = 128,
+                 num_stacks: int = 3,
+                 num_modules_per_stack: int = 3,
+                 max_num_vertices: int = 7,
+                 max_num_edges: int = 9,
+                 num_labels: int = 10,
+                 bn_eps: float = 1e-5,
+                 bn_momentum: float = 0.003):
+        super().__init__()
+
+        op_candidates = {
+            'conv3x3-bn-relu': lambda num_features: Conv3x3BNReLU(num_features, num_features),
+            'conv1x1-bn-relu': lambda num_features: Conv1x1BNReLU(num_features, num_features),
+            'maxpool3x3': lambda num_features: nn.MaxPool2d(3, 1, 1)
+        }
+
+        # initial stem convolution
+        self.stem_conv = Conv3x3BNReLU(3, stem_out_channels)
+
+        layers = []
+        in_channels = out_channels = stem_out_channels
+        for stack_num in range(num_stacks):
+            if stack_num > 0:
+                downsample = nn.MaxPool2d(kernel_size=2, stride=2)
+                layers.append(downsample)
+                out_channels *= 2
+            for _ in range(num_modules_per_stack):
+                cell = NasBench101Cell(op_candidates, in_channels, out_channels,
+                                       lambda cin, cout: Projection(cin, cout),
+                                       max_num_vertices, max_num_edges, label='cell')
+                layers.append(cell)
+                in_channels = out_channels
+
+        self.features = nn.ModuleList(layers)
+        self.gap = nn.AdaptiveAvgPool2d(1)
+        self.classifier = nn.Linear(out_channels, num_labels)
+
+        for module in self.modules():
+            if isinstance(module, nn.BatchNorm2d):
+                module.eps = bn_eps
+                module.momentum = bn_momentum
+
+    def forward(self, x):
+        bs = x.size(0)
+        out = self.stem_conv(x)
+        for layer in self.features:
+            out = layer(out)
+        out = self.gap(out).view(bs, -1)
+        out = self.classifier(out)
+        return out
+
+    def reset_parameters(self):
+        for module in self.modules():
+            if isinstance(module, nn.BatchNorm2d):
+                module.eps = self.config.bn_eps
+                module.momentum = self.config.bn_momentum
diff --git a/nni/retiarii/hub/pytorch/nasbench201.py b/nni/retiarii/hub/pytorch/nasbench201.py
new file mode 100644
index 000000000..3494f9229
--- /dev/null
+++ b/nni/retiarii/hub/pytorch/nasbench201.py
@@ -0,0 +1,201 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+import torch
+import torch.nn as nn
+
+from nni.retiarii import model_wrapper
+from nni.retiarii.nn.pytorch import NasBench201Cell
+
+
+__all__ = ['NasBench201']
+
+
+OPS_WITH_STRIDE = {
+    'none': lambda C_in, C_out, stride: Zero(C_in, C_out, stride),
+    'avg_pool_3x3': lambda C_in, C_out, stride: Pooling(C_in, C_out, stride, 'avg'),
+    'max_pool_3x3': lambda C_in, C_out, stride: Pooling(C_in, C_out, stride, 'max'),
+    'conv_3x3': lambda C_in, C_out, stride: ReLUConvBN(C_in, C_out, (3, 3), (stride, stride), (1, 1), (1, 1)),
+    'conv_1x1': lambda C_in, C_out, stride: ReLUConvBN(C_in, C_out, (1, 1), (stride, stride), (0, 0), (1, 1)),
+    'skip_connect': lambda C_in, C_out, stride: nn.Identity() if stride == 1 and C_in == C_out
+    else FactorizedReduce(C_in, C_out, stride),
+}
+
+PRIMITIVES = ['none', 'skip_connect', 'conv_1x1', 'conv_3x3', 'avg_pool_3x3']
+
+
+class ReLUConvBN(nn.Module):
+    def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation):
+        super(ReLUConvBN, self).__init__()
+        self.op = nn.Sequential(
+            nn.ReLU(inplace=False),
+            nn.Conv2d(C_in, C_out, kernel_size, stride=stride,
+                      padding=padding, dilation=dilation, bias=False),
+            nn.BatchNorm2d(C_out)
+        )
+
+    def forward(self, x):
+        return self.op(x)
+
+
+class SepConv(nn.Module):
+    def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation):
+        super(SepConv, self).__init__()
+        self.op = nn.Sequential(
+            nn.ReLU(inplace=False),
+            nn.Conv2d(C_in, C_in, kernel_size=kernel_size, stride=stride,
+                      padding=padding, dilation=dilation, groups=C_in, bias=False),
+            nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=False),
+            nn.BatchNorm2d(C_out),
+        )
+
+    def forward(self, x):
+        return self.op(x)
+
+
+class Pooling(nn.Module):
+    def __init__(self, C_in, C_out, stride, mode):
+        super(Pooling, self).__init__()
+        if C_in == C_out:
+            self.preprocess = None
+        else:
+            self.preprocess = ReLUConvBN(C_in, C_out, 1, 1, 0, 1)
+        if mode == 'avg':
+            self.op = nn.AvgPool2d(3, stride=stride, padding=1, count_include_pad=False)
+        elif mode == 'max':
+            self.op = nn.MaxPool2d(3, stride=stride, padding=1)
+        else:
+            raise ValueError('Invalid mode={:} in Pooling'.format(mode))
+
+    def forward(self, x):
+        if self.preprocess:
+            x = self.preprocess(x)
+        return self.op(x)
+
+
+class Zero(nn.Module):
+    def __init__(self, C_in, C_out, stride):
+        super(Zero, self).__init__()
+        self.C_in = C_in
+        self.C_out = C_out
+        self.stride = stride
+        self.is_zero = True
+
+    def forward(self, x):
+        if self.C_in == self.C_out:
+            if self.stride == 1:
+                return x.mul(0.)
+            else:
+                return x[:, :, ::self.stride, ::self.stride].mul(0.)
+        else:
+            shape = list(x.shape)
+            shape[1] = self.C_out
+            zeros = x.new_zeros(shape, dtype=x.dtype, device=x.device)
+            return zeros
+
+
+class FactorizedReduce(nn.Module):
+    def __init__(self, C_in, C_out, stride):
+        super(FactorizedReduce, self).__init__()
+        self.stride = stride
+        self.C_in = C_in
+        self.C_out = C_out
+        self.relu = nn.ReLU(inplace=False)
+        if stride == 2:
+            C_outs = [C_out // 2, C_out - C_out // 2]
+            self.convs = nn.ModuleList()
+            for i in range(2):
+                self.convs.append(nn.Conv2d(C_in, C_outs[i], 1, stride=stride, padding=0, bias=False))
+            self.pad = nn.ConstantPad2d((0, 1, 0, 1), 0)
+        else:
+            raise ValueError('Invalid stride : {:}'.format(stride))
+        self.bn = nn.BatchNorm2d(C_out)
+
+    def forward(self, x):
+        x = self.relu(x)
+        y = self.pad(x)
+        out = torch.cat([self.convs[0](x), self.convs[1](y[:, :, 1:, 1:])], dim=1)
+        out = self.bn(out)
+        return out
+
+
+class ResNetBasicblock(nn.Module):
+    def __init__(self, inplanes, planes, stride):
+        super(ResNetBasicblock, self).__init__()
+        assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
+        self.conv_a = ReLUConvBN(inplanes, planes, 3, stride, 1, 1)
+        self.conv_b = ReLUConvBN(planes, planes, 3, 1, 1, 1)
+        if stride == 2:
+            self.downsample = nn.Sequential(
+                nn.AvgPool2d(kernel_size=2, stride=2, padding=0),
+                nn.Conv2d(inplanes, planes, kernel_size=1, stride=1, padding=0, bias=False))
+        elif inplanes != planes:
+            self.downsample = ReLUConvBN(inplanes, planes, 1, 1, 0, 1)
+        else:
+            self.downsample = None
+        self.in_dim = inplanes
+        self.out_dim = planes
+        self.stride = stride
+        self.num_conv = 2
+
+    def forward(self, inputs):
+        basicblock = self.conv_a(inputs)
+        basicblock = self.conv_b(basicblock)
+
+        if self.downsample is not None:
+            inputs = self.downsample(inputs)  # residual
+        return inputs + basicblock
+
+
+@model_wrapper
+class NasBench201(nn.Module):
+    """The full search space proposed by `NAS-Bench-201 <https://arxiv.org/abs/2001.00326>`__.
+
+    It's a stack of :class:`NasBench201Cell`.
+    """
+    def __init__(self,
+                 stem_out_channels: int = 16,
+                 num_modules_per_stack: int = 5,
+                 num_labels: int = 10):
+        super().__init__()
+        self.channels = C = stem_out_channels
+        self.num_modules = N = num_modules_per_stack
+        self.num_labels = num_labels
+
+        self.stem = nn.Sequential(
+            nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False),
+            nn.BatchNorm2d(C)
+        )
+
+        layer_channels = [C] * N + [C * 2] + [C * 2] * N + [C * 4] + [C * 4] * N
+        layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N
+
+        C_prev = C
+        self.cells = nn.ModuleList()
+        for C_curr, reduction in zip(layer_channels, layer_reductions):
+            if reduction:
+                cell = ResNetBasicblock(C_prev, C_curr, 2)
+            else:
+                cell = NasBench201Cell({prim: lambda C_in, C_out: OPS_WITH_STRIDE[prim](C_in, C_out, 1) for prim in PRIMITIVES},
+                                       C_prev, C_curr, label='cell')
+            self.cells.append(cell)
+            C_prev = C_curr
+
+        self.lastact = nn.Sequential(
+            nn.BatchNorm2d(C_prev),
+            nn.ReLU(inplace=True)
+        )
+        self.global_pooling = nn.AdaptiveAvgPool2d(1)
+        self.classifier = nn.Linear(C_prev, self.num_labels)
+
+    def forward(self, inputs):
+        feature = self.stem(inputs)
+        for cell in self.cells:
+            feature = cell(feature)
+
+        out = self.lastact(feature)
+        out = self.global_pooling(out)
+        out = out.view(out.size(0), -1)
+        logits = self.classifier(out)
+
+        return logits
diff --git a/nni/retiarii/hub/pytorch/nasnet.py b/nni/retiarii/hub/pytorch/nasnet.py
new file mode 100644
index 000000000..143e5185b
--- /dev/null
+++ b/nni/retiarii/hub/pytorch/nasnet.py
@@ -0,0 +1,673 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+"""File containing NASNet-series search space.
+
+The implementation is based on NDS.
+It's called ``nasnet.py`` simply because NASNet is the first to propose such structure.
+"""
+
+from collections import OrderedDict
+from typing import Tuple, List, Union, Iterable, Dict, Callable
+
+try:
+    from typing import Literal
+except ImportError:
+    from typing_extensions import Literal
+
+import torch
+
+import nni.retiarii.nn.pytorch as nn
+from nni.retiarii import model_wrapper
+
+
+# the following are NAS operations from
+# https://github.com/facebookresearch/unnas/blob/main/pycls/models/nas/operations.py
+
+OPS = {
+    'none': lambda C, stride, affine:
+        Zero(stride),
+    'avg_pool_2x2': lambda C, stride, affine:
+        nn.AvgPool2d(2, stride=stride, padding=0, count_include_pad=False),
+    'avg_pool_3x3': lambda C, stride, affine:
+        nn.AvgPool2d(3, stride=stride, padding=1, count_include_pad=False),
+    'avg_pool_5x5': lambda C, stride, affine:
+        nn.AvgPool2d(5, stride=stride, padding=2, count_include_pad=False),
+    'max_pool_2x2': lambda C, stride, affine:
+        nn.MaxPool2d(2, stride=stride, padding=0),
+    'max_pool_3x3': lambda C, stride, affine:
+        nn.MaxPool2d(3, stride=stride, padding=1),
+    'max_pool_5x5': lambda C, stride, affine:
+        nn.MaxPool2d(5, stride=stride, padding=2),
+    'max_pool_7x7': lambda C, stride, affine:
+        nn.MaxPool2d(7, stride=stride, padding=3),
+    'skip_connect': lambda C, stride, affine:
+        nn.Identity() if stride == 1 else FactorizedReduce(C, C, affine=affine),
+    'conv_1x1': lambda C, stride, affine:
+        nn.Sequential(
+            nn.ReLU(inplace=False),
+            nn.Conv2d(C, C, 1, stride=stride, padding=0, bias=False),
+            nn.BatchNorm2d(C, affine=affine)
+        ),
+    'conv_3x3': lambda C, stride, affine:
+        nn.Sequential(
+            nn.ReLU(inplace=False),
+            nn.Conv2d(C, C, 3, stride=stride, padding=1, bias=False),
+            nn.BatchNorm2d(C, affine=affine)
+        ),
+    'sep_conv_3x3': lambda C, stride, affine:
+        SepConv(C, C, 3, stride, 1, affine=affine),
+    'sep_conv_5x5': lambda C, stride, affine:
+        SepConv(C, C, 5, stride, 2, affine=affine),
+    'sep_conv_7x7': lambda C, stride, affine:
+        SepConv(C, C, 7, stride, 3, affine=affine),
+    'dil_conv_3x3': lambda C, stride, affine:
+        DilConv(C, C, 3, stride, 2, 2, affine=affine),
+    'dil_conv_5x5': lambda C, stride, affine:
+        DilConv(C, C, 5, stride, 4, 2, affine=affine),
+    'dil_sep_conv_3x3': lambda C, stride, affine:
+        DilSepConv(C, C, 3, stride, 2, 2, affine=affine),
+    'conv_3x1_1x3': lambda C, stride, affine:
+        nn.Sequential(
+            nn.ReLU(inplace=False),
+            nn.Conv2d(C, C, (1, 3), stride=(1, stride), padding=(0, 1), bias=False),
+            nn.Conv2d(C, C, (3, 1), stride=(stride, 1), padding=(1, 0), bias=False),
+            nn.BatchNorm2d(C, affine=affine)
+        ),
+    'conv_7x1_1x7': lambda C, stride, affine:
+        nn.Sequential(
+            nn.ReLU(inplace=False),
+            nn.Conv2d(C, C, (1, 7), stride=(1, stride), padding=(0, 3), bias=False),
+            nn.Conv2d(C, C, (7, 1), stride=(stride, 1), padding=(3, 0), bias=False),
+            nn.BatchNorm2d(C, affine=affine)
+        ),
+}
+
+
+class ReLUConvBN(nn.Sequential):
+
+    def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
+        super().__init__(
+            nn.ReLU(inplace=False),
+            nn.Conv2d(
+                C_in, C_out, kernel_size, stride=stride,
+                padding=padding, bias=False
+            ),
+            nn.BatchNorm2d(C_out, affine=affine)
+        )
+
+
+class DilConv(nn.Sequential):
+
+    def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation, affine=True):
+        super().__init__(
+            nn.ReLU(inplace=False),
+            nn.Conv2d(
+                C_in, C_in, kernel_size=kernel_size, stride=stride,
+                padding=padding, dilation=dilation, groups=C_in, bias=False
+            ),
+            nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=False),
+            nn.BatchNorm2d(C_out, affine=affine),
+        )
+
+
+class SepConv(nn.Sequential):
+
+    def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
+        super().__init__(
+            nn.ReLU(inplace=False),
+            nn.Conv2d(
+                C_in, C_in, kernel_size=kernel_size, stride=stride,
+                padding=padding, groups=C_in, bias=False
+            ),
+            nn.Conv2d(C_in, C_in, kernel_size=1, padding=0, bias=False),
+            nn.BatchNorm2d(C_in, affine=affine),
+            nn.ReLU(inplace=False),
+            nn.Conv2d(
+                C_in, C_in, kernel_size=kernel_size, stride=1,
+                padding=padding, groups=C_in, bias=False
+            ),
+            nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=False),
+            nn.BatchNorm2d(C_out, affine=affine),
+        )
+
+
+class DilSepConv(nn.Sequential):
+
+    def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation, affine=True):
+        super().__init__(
+            nn.ReLU(inplace=False),
+            nn.Conv2d(
+                C_in, C_in, kernel_size=kernel_size, stride=stride,
+                padding=padding, dilation=dilation, groups=C_in, bias=False
+            ),
+            nn.Conv2d(C_in, C_in, kernel_size=1, padding=0, bias=False),
+            nn.BatchNorm2d(C_in, affine=affine),
+            nn.ReLU(inplace=False),
+            nn.Conv2d(
+                C_in, C_in, kernel_size=kernel_size, stride=1,
+                padding=padding, dilation=dilation, groups=C_in, bias=False
+            ),
+            nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=False),
+            nn.BatchNorm2d(C_out, affine=affine),
+        )
+
+
+class Zero(nn.Module):
+
+    def __init__(self, stride):
+        super().__init__()
+        self.stride = stride
+
+    def forward(self, x):
+        if self.stride == 1:
+            return x.mul(0.)
+        return x[:, :, ::self.stride, ::self.stride].mul(0.)
+
+
+class FactorizedReduce(nn.Module):
+
+    def __init__(self, C_in, C_out, affine=True):
+        super().__init__()
+        if isinstance(C_out, int):
+            assert C_out % 2 == 0
+        else:   # is a value choice
+            assert all(c % 2 == 0 for c in C_out.all_options())
+        self.relu = nn.ReLU(inplace=False)
+        self.conv_1 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False)
+        self.conv_2 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False)
+        self.bn = nn.BatchNorm2d(C_out, affine=affine)
+        self.pad = nn.ConstantPad2d((0, 1, 0, 1), 0)
+
+    def forward(self, x):
+        x = self.relu(x)
+        y = self.pad(x)
+        out = torch.cat([self.conv_1(x), self.conv_2(y[:, :, 1:, 1:])], dim=1)
+        out = self.bn(out)
+        return out
+
+
+class DropPath_(nn.Module):
+    # https://github.com/khanrc/pt.darts/blob/0.1/models/ops.py
+    def __init__(self, drop_prob=0.):
+        super().__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, x):
+        if self.training and self.drop_prob > 0.:
+            keep_prob = 1. - self.drop_prob
+            mask = torch.zeros((x.size(0), 1, 1, 1), dtype=torch.float, device=x.device).bernoulli_(keep_prob)
+            return x.div(keep_prob).mul(mask)
+        return x
+
+
+class AuxiliaryHead(nn.Module):
+    def __init__(self, C: int, num_labels: int, dataset: Literal['imagenet', 'cifar']):
+        super().__init__()
+        if dataset == 'imagenet':
+            # assuming input size 14x14
+            stride = 2
+        elif dataset == 'cifar':
+            stride = 3
+
+        self.features = nn.Sequential(
+            nn.ReLU(inplace=True),
+            nn.AvgPool2d(5, stride=stride, padding=0, count_include_pad=False),
+            nn.Conv2d(C, 128, 1, bias=False),
+            nn.BatchNorm2d(128),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(128, 768, 2, bias=False),
+            nn.BatchNorm2d(768),
+            nn.ReLU(inplace=True)
+        )
+        self.classifier = nn.Linear(768, num_labels)
+
+    def forward(self, x):
+        x = self.features(x)
+        x = self.classifier(x.view(x.size(0), -1))
+        return x
+
+
+class SequentialBreakdown(nn.Sequential):
+    """Return all layers of a sequential."""
+
+    def __init__(self, sequential: nn.Sequential):
+        super().__init__(OrderedDict(sequential.named_children()))
+
+    def forward(self, inputs):
+        result = []
+        for module in self:
+            inputs = module(inputs)
+            result.append(inputs)
+        return result
+
+
+class CellPreprocessor(nn.Module):
+    """
+    Aligning the shape of predecessors.
+
+    If the last cell is a reduction cell, ``pre0`` should be ``FactorizedReduce`` instead of ``ReLUConvBN``.
+    See :class:`CellBuilder` on how to calculate those channel numbers.
+    """
+
+    def __init__(self, C_pprev: int, C_prev: int, C: int, last_cell_reduce: bool) -> None:
+        super().__init__()
+
+        if last_cell_reduce:
+            self.pre0 = FactorizedReduce(C_pprev, C)
+        else:
+            self.pre0 = ReLUConvBN(C_pprev, C, 1, 1, 0)
+        self.pre1 = ReLUConvBN(C_prev, C, 1, 1, 0)
+
+    def forward(self, cells):
+        assert len(cells) == 2
+        pprev, prev = cells
+        pprev = self.pre0(pprev)
+        prev = self.pre1(prev)
+
+        return [pprev, prev]
+
+
+class CellPostprocessor(nn.Module):
+    """
+    The cell outputs previous cell + this cell, so that cells can be directly chained.
+    """
+
+    def forward(self, this_cell, previous_cells):
+        return [previous_cells[-1], this_cell]
+
+
+class CellBuilder:
+    """The cell builder is used in Repeat.
+    Builds an cell each time it's "called".
+    Note that the builder is ephemeral, it can only be called once for every index.
+    """
+
+    def __init__(self, op_candidates: List[str], C_prev_in: int, C_in: int, C: int,
+                 num_nodes: int, merge_op: Literal['all', 'loose_end'],
+                 first_cell_reduce: bool, last_cell_reduce: bool):
+        self.C_prev_in = C_prev_in      # This is the out channels of the cell before last cell.
+        self.C_in = C_in                # This is the out channesl of last cell.
+        self.C = C                      # This is NOT C_out of this stage, instead, C_out = C * len(cell.output_node_indices)
+        self.op_candidates = op_candidates
+        self.num_nodes = num_nodes
+        self.merge_op = merge_op
+        self.first_cell_reduce = first_cell_reduce
+        self.last_cell_reduce = last_cell_reduce
+        self._expect_idx = 0
+
+    def __call__(self, repeat_idx: int):
+        if self._expect_idx != repeat_idx:
+            raise ValueError(f'Expect index {self._expect_idx}, found {repeat_idx}')
+
+        # It takes an index that is the index in the repeat.
+        # Number of predecessors for each cell is fixed to 2.
+        num_predecessors = 2
+        # Number of ops per node is fixed to 2.
+        num_ops_per_node = 2
+
+        # Reduction cell means stride = 2 and channel multiplied by 2.
+        is_reduction_cell = repeat_idx == 0 and self.first_cell_reduce
+
+        # self.C_prev_in, self.C_in, self.last_cell_reduce are updated after each cell is built.
+        preprocessor = CellPreprocessor(self.C_prev_in, self.C_in, self.C, self.last_cell_reduce)
+
+        ops_factory: Dict[str, Callable[[int, int, int], nn.Module]] = {
+            op:  # make final chosen ops named with their aliases
+            lambda node_index, op_index, input_index:
+            OPS[op](self.C, 2 if is_reduction_cell and (
+                    input_index is None or input_index < num_predecessors  # could be none when constructing search sapce
+                    ) else 1, True)
+            for op in self.op_candidates
+        }
+
+        cell = nn.Cell(ops_factory, self.num_nodes, num_ops_per_node, num_predecessors, self.merge_op,
+                       preprocessor=preprocessor, postprocessor=CellPostprocessor(),
+                       label='reduce' if is_reduction_cell else 'normal')
+
+        # update state
+        self.C_prev_in = self.C_in
+        self.C_in = self.C * len(cell.output_node_indices)
+        self.last_cell_reduce = is_reduction_cell
+        self._expect_idx += 1
+
+        return cell
+
+
+_INIT_PARAMETER_DOCS = """
+
+    Parameters
+    ----------
+    width : int or tuple of int
+        A fixed initial width or a tuple of widths to choose from.
+    num_cells : int or tuple of int
+        A fixed number of cells (depths) to stack, or a tuple of depths to choose from.
+    dataset : "cifar" | "imagenet"
+        The essential differences are in "stem" cells, i.e., how they process the raw image input.
+        Choosing "imagenet" means more downsampling at the beginning of the network.
+    auxiliary_loss : bool
+        If true, another auxiliary classification head will produce the another prediction.
+        This makes the output of network two logits in the training phase.
+
+"""
+
+
+class NDS(nn.Module):
+    """
+    The unified version of NASNet search space.
+
+    We follow the implementation in
+    `unnas <https://github.com/facebookresearch/unnas/blob/main/pycls/models/nas/nas.py>`__.
+    See `On Network Design Spaces for Visual Recognition <https://arxiv.org/abs/1905.13214>`__ for details.
+
+    Different NAS papers usually differ in the way that they specify ``op_candidates`` and ``merge_op``.
+    ``dataset`` here is to give a hint about input resolution, so as to create reasonable stem and auxiliary heads.
+
+    NDS has a speciality that it has mutable depths/widths.
+    This is implemented by accepting a list of int as ``num_cells`` / ``width``.
+    """ + _INIT_PARAMETER_DOCS + """
+    op_candidates : list of str
+        List of operator candidates. Must be from ``OPS``.
+    merge_op : ``all`` or ``loose_end``
+        See :class:`~nni.retiarii.nn.pytorch.Cell`.
+    num_nodes_per_cell : int
+        See :class:`~nni.retiarii.nn.pytorch.Cell`.
+    """
+
+    def __init__(self,
+                 op_candidates: List[str],
+                 merge_op: Literal['all', 'loose_end'] = 'all',
+                 num_nodes_per_cell: int = 4,
+                 width: Union[Tuple[int], int] = 16,
+                 num_cells: Union[Tuple[int], int] = 20,
+                 dataset: Literal['cifar', 'imagenet'] = 'imagenet',
+                 auxiliary_loss: bool = False):
+        super().__init__()
+
+        self.dataset = dataset
+        self.num_labels = 10 if dataset == 'cifar' else 1000
+        self.auxiliary_loss = auxiliary_loss
+
+        # preprocess the specified width and depth
+        if isinstance(width, Iterable):
+            C = nn.ValueChoice(list(width), label='width')
+        else:
+            C = width
+
+        if isinstance(num_cells, Iterable):
+            num_cells = nn.ValueChoice(list(num_cells), label='depth')
+        num_cells_per_stage = [i * num_cells // 3 - (i - 1) * num_cells // 3 for i in range(3)]
+
+        # auxiliary head is different for network targetted at different datasets
+        if dataset == 'imagenet':
+            self.stem0 = nn.Sequential(
+                nn.Conv2d(3, C // 2, kernel_size=3, stride=2, padding=1, bias=False),
+                nn.BatchNorm2d(C // 2),
+                nn.ReLU(inplace=True),
+                nn.Conv2d(C // 2, C, 3, stride=2, padding=1, bias=False),
+                nn.BatchNorm2d(C),
+            )
+            self.stem1 = nn.Sequential(
+                nn.ReLU(inplace=True),
+                nn.Conv2d(C, C, 3, stride=2, padding=1, bias=False),
+                nn.BatchNorm2d(C),
+            )
+            C_pprev = C_prev = C_curr = C
+            last_cell_reduce = True
+        elif dataset == 'cifar':
+            self.stem = nn.Sequential(
+                nn.Conv2d(3, 3 * C, 3, padding=1, bias=False),
+                nn.BatchNorm2d(3 * C)
+            )
+            C_pprev = C_prev = 3 * C
+            C_curr = C
+            last_cell_reduce = False
+
+        self.stages = nn.ModuleList()
+        for stage_idx in range(3):
+            if stage_idx > 0:
+                C_curr *= 2
+            # For a stage, we get C_in, C_curr, and C_out.
+            # C_in is only used in the first cell.
+            # C_curr is number of channels for each operator in current stage.
+            # C_out is usually `C * num_nodes_per_cell` because of concat operator.
+            cell_builder = CellBuilder(op_candidates, C_pprev, C_prev, C_curr, num_nodes_per_cell,
+                                       merge_op, stage_idx > 0, last_cell_reduce)
+            stage = nn.Repeat(cell_builder, num_cells_per_stage[stage_idx])
+            self.stages.append(stage)
+
+            # C_pprev is output channel number of last second cell among all the cells already built.
+            if len(stage) > 1:
+                # Contains more than one cell
+                C_pprev = len(stage[-2].output_node_indices) * C_curr
+            else:
+                # Look up in the out channels of last stage.
+                C_pprev = C_prev
+
+            # This was originally,
+            # C_prev = num_nodes_per_cell * C_curr.
+            # but due to loose end, it becomes,
+            C_prev = len(stage[-1].output_node_indices) * C_curr
+
+            # Useful in aligning the pprev and prev cell.
+            last_cell_reduce = cell_builder.last_cell_reduce
+
+            if stage_idx == 2:
+                C_to_auxiliary = C_prev
+
+        if auxiliary_loss:
+            assert isinstance(self.stages[2], nn.Sequential), 'Auxiliary loss can only be enabled in retrain mode.'
+            self.stages[2] = SequentialBreakdown(self.stages[2])
+            self.auxiliary_head = AuxiliaryHead(C_to_auxiliary, self.num_labels, dataset=self.dataset)
+
+        self.global_pooling = nn.AdaptiveAvgPool2d((1, 1))
+        self.classifier = nn.Linear(C_prev, self.num_labels)
+
+    def forward(self, inputs):
+        if self.dataset == 'imagenet':
+            s0 = self.stem0(inputs)
+            s1 = self.stem1(s0)
+        else:
+            s0 = s1 = self.stem(inputs)
+
+        for stage_idx, stage in enumerate(self.stages):
+            if stage_idx == 2 and self.auxiliary_loss:
+                s = list(stage([s0, s1]).values())
+                s0, s1 = s[-1]
+                if self.training:
+                    # auxiliary loss is attached to the first cell of the last stage.
+                    logits_aux = self.auxiliary_head(s[0][1])
+            else:
+                s0, s1 = stage([s0, s1])
+
+        out = self.global_pooling(s1)
+        logits = self.classifier(out.view(out.size(0), -1))
+        if self.training and self.auxiliary_loss:
+            return logits, logits_aux
+        else:
+            return logits
+
+    def set_drop_path_prob(self, drop_prob):
+        """
+        Set the drop probability of Drop-path in the network.
+        Reference: `FractalNet: Ultra-Deep Neural Networks without Residuals <https://arxiv.org/pdf/1605.07648v4.pdf>`__.
+        """
+        for module in self.modules():
+            if isinstance(module, DropPath_):
+                module.drop_prob = drop_prob
+
+
+@model_wrapper
+class NASNet(NDS):
+    __doc__ = """
+    Search space proposed in `Learning Transferable Architectures for Scalable Image Recognition <https://arxiv.org/abs/1707.07012>`__.
+
+    It is built upon :class:`~nni.retiarii.nn.pytorch.Cell`, and implemented based on :class:`~NDS`.
+    Its operator candidates are :attribute:`~NASNet.NASNET_OPS`.
+    It has 5 nodes per cell, and the output is concatenation of nodes not used as input to other nodes.
+    """ + _INIT_PARAMETER_DOCS
+
+    NASNET_OPS = [
+        'skip_connect',
+        'conv_3x1_1x3',
+        'conv_7x1_1x7',
+        'dil_conv_3x3',
+        'avg_pool_3x3',
+        'max_pool_3x3',
+        'max_pool_5x5',
+        'max_pool_7x7',
+        'conv_1x1',
+        'conv_3x3',
+        'sep_conv_3x3',
+        'sep_conv_5x5',
+        'sep_conv_7x7',
+    ]
+
+    def __init__(self,
+                 width: Union[Tuple[int], int] = (16, 24, 32),
+                 num_cells: Union[Tuple[int], int] = (4, 8, 12, 16, 20),
+                 dataset: Literal['cifar', 'imagenet'] = 'cifar',
+                 auxiliary_loss: bool = False):
+        super().__init__(self.NASNET_OPS,
+                         merge_op='loose_end',
+                         num_nodes_per_cell=5,
+                         width=width,
+                         num_cells=num_cells,
+                         dataset=dataset,
+                         auxiliary_loss=auxiliary_loss)
+
+
+@model_wrapper
+class ENAS(NDS):
+    __doc__ = """Search space proposed in `Efficient neural architecture search via parameter sharing <https://arxiv.org/abs/1802.03268>`__.
+
+    It is built upon :class:`~nni.retiarii.nn.pytorch.Cell`, and implemented based on :class:`~NDS`.
+    Its operator candidates are :attribute:`~ENAS.ENAS_OPS`.
+    It has 5 nodes per cell, and the output is concatenation of nodes not used as input to other nodes.
+    """ + _INIT_PARAMETER_DOCS
+
+    ENAS_OPS = [
+        'skip_connect',
+        'sep_conv_3x3',
+        'sep_conv_5x5',
+        'avg_pool_3x3',
+        'max_pool_3x3',
+    ]
+
+    def __init__(self,
+                 width: Union[Tuple[int], int] = (16, 24, 32),
+                 num_cells: Union[Tuple[int], int] = (4, 8, 12, 16, 20),
+                 dataset: Literal['cifar', 'imagenet'] = 'cifar',
+                 auxiliary_loss: bool = False):
+        super().__init__(self.ENAS_OPS,
+                         merge_op='loose_end',
+                         num_nodes_per_cell=5,
+                         width=width,
+                         num_cells=num_cells,
+                         dataset=dataset,
+                         auxiliary_loss=auxiliary_loss)
+
+
+@model_wrapper
+class AmoebaNet(NDS):
+    __doc__ = """Search space proposed in
+    `Regularized evolution for image classifier architecture search <https://arxiv.org/abs/1802.01548>`__.
+
+    It is built upon :class:`~nni.retiarii.nn.pytorch.Cell`, and implemented based on :class:`~NDS`.
+    Its operator candidates are :attribute:`~AmoebaNet.AMOEBA_OPS`.
+    It has 5 nodes per cell, and the output is concatenation of nodes not used as input to other nodes.
+    """ + _INIT_PARAMETER_DOCS
+
+    AMOEBA_OPS = [
+        'skip_connect',
+        'sep_conv_3x3',
+        'sep_conv_5x5',
+        'sep_conv_7x7',
+        'avg_pool_3x3',
+        'max_pool_3x3',
+        'dil_sep_conv_3x3',
+        'conv_7x1_1x7',
+    ]
+
+    def __init__(self,
+                 width: Union[Tuple[int], int] = (16, 24, 32),
+                 num_cells: Union[Tuple[int], int] = (4, 8, 12, 16, 20),
+                 dataset: Literal['cifar', 'imagenet'] = 'cifar',
+                 auxiliary_loss: bool = False):
+
+        super().__init__(self.AMOEBA_OPS,
+                         merge_op='loose_end',
+                         num_nodes_per_cell=5,
+                         width=width,
+                         num_cells=num_cells,
+                         dataset=dataset,
+                         auxiliary_loss=auxiliary_loss)
+
+
+@model_wrapper
+class PNAS(NDS):
+    __doc__ = """Search space proposed in
+    `Progressive neural architecture search <https://arxiv.org/abs/1712.00559>`__.
+
+    It is built upon :class:`~nni.retiarii.nn.pytorch.Cell`, and implemented based on :class:`~NDS`.
+    Its operator candidates are :attribute:`~PNAS.PNAS_OPS`.
+    It has 5 nodes per cell, and the output is concatenation of all nodes in the cell.
+    """ + _INIT_PARAMETER_DOCS
+
+    PNAS_OPS = [
+        'sep_conv_3x3',
+        'sep_conv_5x5',
+        'sep_conv_7x7',
+        'conv_7x1_1x7',
+        'skip_connect',
+        'avg_pool_3x3',
+        'max_pool_3x3',
+        'dil_conv_3x3',
+    ]
+
+    def __init__(self,
+                 width: Union[Tuple[int], int] = (16, 24, 32),
+                 num_cells: Union[Tuple[int], int] = (4, 8, 12, 16, 20),
+                 dataset: Literal['cifar', 'imagenet'] = 'cifar',
+                 auxiliary_loss: bool = False):
+        super().__init__(self.PNAS_OPS,
+                         merge_op='all',
+                         num_nodes_per_cell=5,
+                         width=width,
+                         num_cells=num_cells,
+                         dataset=dataset,
+                         auxiliary_loss=auxiliary_loss)
+
+
+@model_wrapper
+class DARTS(NDS):
+    __doc__ = """Search space proposed in `Darts: Differentiable architecture search <https://arxiv.org/abs/1806.09055>`__.
+
+    It is built upon :class:`~nni.retiarii.nn.pytorch.Cell`, and implemented based on :class:`~NDS`.
+    Its operator candidates are :attribute:`~DARTS.DARTS_OPS`.
+    It has 4 nodes per cell, and the output is concatenation of all nodes in the cell.
+    """ + _INIT_PARAMETER_DOCS
+
+    DARTS_OPS = [
+        'none',
+        'max_pool_3x3',
+        'avg_pool_3x3',
+        'skip_connect',
+        'sep_conv_3x3',
+        'sep_conv_5x5',
+        'dil_conv_3x3',
+        'dil_conv_5x5',
+    ]
+
+    def __init__(self,
+                 width: Union[Tuple[int], int] = (16, 24, 32),
+                 num_cells: Union[Tuple[int], int] = (4, 8, 12, 16, 20),
+                 dataset: Literal['cifar', 'imagenet'] = 'cifar',
+                 auxiliary_loss: bool = False):
+        super().__init__(self.DARTS_OPS,
+                         merge_op='all',
+                         num_nodes_per_cell=4,
+                         width=width,
+                         num_cells=num_cells,
+                         dataset=dataset,
+                         auxiliary_loss=auxiliary_loss)
diff --git a/nni/retiarii/hub/pytorch/proxylessnas.py b/nni/retiarii/hub/pytorch/proxylessnas.py
new file mode 100644
index 000000000..47955b8c1
--- /dev/null
+++ b/nni/retiarii/hub/pytorch/proxylessnas.py
@@ -0,0 +1,291 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+import math
+from typing import Optional, Callable, List, Tuple
+
+import torch
+import nni.retiarii.nn.pytorch as nn
+from nni.retiarii import model_wrapper
+
+
+def make_divisible(v, divisor, min_val=None):
+    """
+    This function is taken from the original tf repo.
+    It ensures that all layers have a channel number that is divisible by 8
+    It can be seen here:
+    https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
+    """
+    if min_val is None:
+        min_val = divisor
+    # This should work for both value choices and constants.
+    new_v = nn.ValueChoice.max(min_val, round(v + divisor // 2) // divisor * divisor)
+    # Make sure that round down does not go down by more than 10%.
+    return nn.ValueChoice.condition(new_v < 0.9 * v, new_v + divisor, new_v)
+
+
+class ConvBNReLU(nn.Sequential):
+    """
+    The template for a conv-bn-relu block.
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int = 3,
+        stride: int = 1,
+        groups: int = 1,
+        norm_layer: Optional[Callable[..., nn.Module]] = None,
+        activation_layer: Optional[Callable[..., nn.Module]] = None,
+        dilation: int = 1,
+    ) -> None:
+        padding = (kernel_size - 1) // 2 * dilation
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+        if activation_layer is None:
+            activation_layer = nn.ReLU6
+        super().__init__(
+            nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation=dilation, groups=groups,
+                      bias=False),
+            norm_layer(out_channels),
+            activation_layer(inplace=True)
+        )
+        self.out_channels = out_channels
+
+
+class SeparableConv(nn.Sequential):
+    """
+    In the original MobileNetV2 implementation, this is InvertedResidual when expand ratio = 1.
+    Residual connection is added if input and output shape are the same.
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int = 3,
+        stride: int = 1,
+        norm_layer: Optional[Callable[..., nn.Module]] = None,
+        activation_layer: Optional[Callable[..., nn.Module]] = None,
+    ) -> None:
+        super().__init__(
+            # dw
+            ConvBNReLU(in_channels, in_channels, stride=stride, kernel_size=kernel_size, groups=in_channels,
+                       norm_layer=norm_layer, activation_layer=activation_layer),
+            # pw-linear
+            ConvBNReLU(in_channels, out_channels, kernel_size=1, norm_layer=norm_layer, activation_layer=nn.Identity)
+        )
+        self.residual_connection = stride == 1 and in_channels == out_channels
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if self.residual_connection:
+            return x + super().forward(x)
+        else:
+            return super().forward(x)
+
+
+class InvertedResidual(nn.Sequential):
+    """
+    An Inverted Residual Block, sometimes called an MBConv Block, is a type of residual block used for image models
+    that uses an inverted structure for efficiency reasons.
+    It was originally proposed for the `MobileNetV2 <https://arxiv.org/abs/1801.04381>`__ CNN architecture.
+    It has since been reused for several mobile-optimized CNNs.
+    It follows a narrow -> wide -> narrow approach, hence the inversion.
+    It first widens with a 1x1 convolution, then uses a 3x3 depthwise convolution (which greatly reduces the number of parameters),
+    then a 1x1 convolution is used to reduce the number of channels so input and output can be added.
+
+    Follow implementation of:
+    https://github.com/google-research/google-research/blob/20736344591f774f4b1570af64624ed1e18d2867/tunas/rematlib/mobile_model_v3.py#L453
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        expand_ratio: int,
+        kernel_size: int = 3,
+        stride: int = 1,
+        squeeze_and_excite: Optional[Callable[[int], nn.Module]] = None,
+        norm_layer: Optional[Callable[..., nn.Module]] = None,
+        activation_layer: Optional[Callable[..., nn.Module]] = None,
+    ) -> None:
+        super().__init__()
+        self.stride = stride
+        self.out_channels = out_channels
+        assert stride in [1, 2]
+
+        hidden_ch = nn.ValueChoice.to_int(round(in_channels * expand_ratio))
+
+        # FIXME: check whether this equal works
+        # Residual connection is added here stride = 1 and input channels and output channels are the same.
+        self.residual_connection = stride == 1 and in_channels == out_channels
+
+        layers: List[nn.Module] = [
+            # point-wise convolution
+            # NOTE: some paper omit this point-wise convolution when stride = 1.
+            # In our implementation, if this pw convolution is intended to be omitted,
+            # please use SepConv instead.
+            ConvBNReLU(in_channels, hidden_ch, kernel_size=1,
+                       norm_layer=norm_layer, activation_layer=activation_layer),
+            # depth-wise
+            ConvBNReLU(hidden_ch, hidden_ch, stride=stride, kernel_size=kernel_size, groups=hidden_ch,
+                       norm_layer=norm_layer, activation_layer=activation_layer)
+        ]
+
+        if squeeze_and_excite:
+            layers.append(squeeze_and_excite(hidden_ch))
+
+        layers += [
+            # pw-linear
+            ConvBNReLU(hidden_ch, out_channels, kernel_size=1, norm_layer=norm_layer, activation_layer=nn.Identity)
+        ]
+
+        super().__init__(*layers)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if self.residual_connection:
+            return x + super().forward(x)
+        else:
+            return super().forward(x)
+
+
+def inverted_residual_choice_builder(
+    expand_ratios: List[int],
+    kernel_sizes: List[int],
+    downsample: bool,
+    stage_input_width: int,
+    stage_output_width: int,
+    label: str
+):
+    def builder(index):
+        stride = 1
+        inp = stage_output_width
+
+        if index == 0:
+            # first layer in stage
+            # do downsample and width reshape
+            inp = stage_input_width
+            if downsample:
+                stride = 2
+
+        oup = stage_output_width
+
+        op_choices = {}
+        for exp_ratio in expand_ratios:
+            for kernel_size in kernel_sizes:
+                op_choices[f'k{kernel_size}e{exp_ratio}'] = InvertedResidual(inp, oup, exp_ratio, kernel_size, stride)
+
+        # It can be implemented with ValueChoice, but we use LayerChoice here
+        # to be aligned with the intention of the original ProxylessNAS.
+        return nn.LayerChoice(op_choices, label=f'{label}_i{index}')
+
+    return builder
+
+
+@model_wrapper
+class ProxylessNAS(nn.Module):
+    """
+    The search space proposed by `ProxylessNAS <https://arxiv.org/abs/1812.00332>`__.
+
+    Following the official implementation, the inverted residual with kernel size / expand ratio variations in each layer
+    is implemented with a :class:`nn.LayerChoice` with all-combination candidates. That means,
+    when used in weight sharing, these candidates will be treated as separate layers, and won't be fine-grained shared.
+    We note that ``MobileNetV3Space`` is different in this perspective.
+    """
+
+    def __init__(self, num_labels: int = 1000,
+                 base_widths: Tuple[int, ...] = (32, 16, 32, 40, 80, 96, 192, 320, 1280),
+                 dropout_rate: float = 0.,
+                 width_mult: float = 1.0,
+                 bn_eps: float = 1e-3,
+                 bn_momentum: float = 0.1):
+
+        super().__init__()
+
+        assert len(base_widths) == 9
+        # include the last stage info widths here
+        widths = [make_divisible(width * width_mult, 8) for width in base_widths]
+        downsamples = [True, False, True, True, True, False, True, False]
+
+        self.num_labels = num_labels
+        self.dropout_rate = dropout_rate
+        self.bn_eps = bn_eps
+        self.bn_momentum = bn_momentum
+
+        self.first_conv = ConvBNReLU(3, widths[0], stride=2, norm_layer=nn.BatchNorm2d)
+
+        blocks = [
+            # first stage is fixed
+            SeparableConv(widths[0], widths[1], kernel_size=3, stride=1)
+        ]
+
+        # https://github.com/ultmaster/AceNAS/blob/46c8895fd8a05ffbc61a6b44f1e813f64b4f66b7/searchspace/proxylessnas/__init__.py#L21
+        for stage in range(2, 8):
+            # Rather than returning a fixed module here,
+            # we return a builder that dynamically creates module for different `repeat_idx`.
+            builder = inverted_residual_choice_builder(
+                [3, 6], [3, 5, 7], downsamples[stage], widths[stage - 1], widths[stage], f's{stage}')
+            if stage < 6:
+                blocks.append(nn.Repeat(builder, (1, 4), label=f's{stage}_depth'))
+            else:
+                # No mutation for depth in the last stage.
+                # Directly call builder to initiate one block
+                blocks.append(builder(0))
+
+        self.blocks = nn.Sequential(*blocks)
+
+        # final layers
+        self.feature_mix_layer = ConvBNReLU(widths[7], widths[8], kernel_size=1, norm_layer=nn.BatchNorm2d)
+        self.global_avg_pooling = nn.AdaptiveAvgPool2d(1)
+        self.dropout_layer = nn.Dropout(dropout_rate)
+        self.classifier = nn.Linear(widths[-1], num_labels)
+
+        reset_parameters(self, bn_momentum=bn_momentum, bn_eps=bn_eps)
+
+    def forward(self, x):
+        x = self.first_conv(x)
+        x = self.blocks(x)
+        x = self.feature_mix_layer(x)
+        x = self.global_avg_pooling(x)
+        x = x.view(x.size(0), -1)  # flatten
+        x = self.dropout_layer(x)
+        x = self.classifier(x)
+        return x
+
+    def no_weight_decay(self):
+        # this is useful for timm optimizer
+        # no regularizer to linear layer
+        if hasattr(self, 'classifier'):
+            return {'classifier.weight', 'classifier.bias'}
+        return set()
+
+
+def reset_parameters(model, model_init='he_fout', init_div_groups=False,
+                     bn_momentum=0.1, bn_eps=1e-5):
+    for m in model.modules():
+        if isinstance(m, nn.Conv2d):
+            if model_init == 'he_fout':
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                if init_div_groups:
+                    n /= m.groups
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+            elif model_init == 'he_fin':
+                n = m.kernel_size[0] * m.kernel_size[1] * m.in_channels
+                if init_div_groups:
+                    n /= m.groups
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+            else:
+                raise NotImplementedError
+        elif isinstance(m, nn.BatchNorm2d):
+            m.weight.data.fill_(1)
+            m.bias.data.zero_()
+            m.momentum = bn_momentum
+            m.eps = bn_eps
+        elif isinstance(m, nn.Linear):
+            m.weight.data.normal_(0, 0.01)
+            if m.bias is not None:
+                m.bias.data.zero_()
+        elif isinstance(m, nn.BatchNorm1d):
+            m.weight.data.fill_(1)
+            m.bias.data.zero_()
diff --git a/nni/retiarii/hub/pytorch/shufflenet.py b/nni/retiarii/hub/pytorch/shufflenet.py
new file mode 100644
index 000000000..dcc846676
--- /dev/null
+++ b/nni/retiarii/hub/pytorch/shufflenet.py
@@ -0,0 +1,239 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+import torch
+import nni.retiarii.nn.pytorch as nn
+from nni.retiarii import model_wrapper
+
+
+class ShuffleNetBlock(nn.Module):
+    """
+    Describe the basic building block of shuffle net, as described in
+    `ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices <https://arxiv.org/pdf/1707.01083.pdf>`__.
+
+    When stride = 1, the block expects an input with ``2 * input channels``. Otherwise input channels.
+    """
+
+    def __init__(self, in_channels: int, out_channels: int, mid_channels: int, *,
+                 kernel_size: int, stride: int, sequence: str = "pdp", affine: bool = True):
+        super().__init__()
+        assert stride in [1, 2]
+        assert kernel_size in [3, 5, 7]
+        self.channels = in_channels // 2 if stride == 1 else in_channels
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.mid_channels = mid_channels
+        self.kernel_size = kernel_size
+        self.stride = stride
+        self.pad = kernel_size // 2
+        self.oup_main = out_channels - self.channels
+        self.affine = affine
+        assert self.oup_main > 0
+
+        self.branch_main = nn.Sequential(*self._decode_point_depth_conv(sequence))
+
+        if stride == 2:
+            self.branch_proj = nn.Sequential(
+                # dw
+                nn.Conv2d(self.channels, self.channels, kernel_size, stride, self.pad,
+                          groups=self.channels, bias=False),
+                nn.BatchNorm2d(self.channels, affine=affine),
+                # pw-linear
+                nn.Conv2d(self.channels, self.channels, 1, 1, 0, bias=False),
+                nn.BatchNorm2d(self.channels, affine=affine),
+                nn.ReLU(inplace=True)
+            )
+        else:
+            # empty block to be compatible with torchscript
+            self.branch_proj = nn.Sequential()
+
+    def forward(self, x):
+        if self.stride == 2:
+            x_proj, x = self.branch_proj(x), x
+        else:
+            x_proj, x = self._channel_shuffle(x)
+        return torch.cat((x_proj, self.branch_main(x)), 1)
+
+    def _decode_point_depth_conv(self, sequence):
+        result = []
+        first_depth = first_point = True
+        pc = c = self.channels
+        for i, token in enumerate(sequence):
+            # compute output channels of this conv
+            if i + 1 == len(sequence):
+                assert token == "p", "Last conv must be point-wise conv."
+                c = self.oup_main
+            elif token == "p" and first_point:
+                c = self.mid_channels
+            if token == "d":
+                # depth-wise conv
+                if isinstance(pc, int) and isinstance(c, int):
+                    # check can only be done for static channels
+                    assert pc == c, "Depth-wise conv must not change channels."
+                result.append(nn.Conv2d(pc, c, self.kernel_size, self.stride if first_depth else 1, self.pad,
+                                        groups=c, bias=False))
+                result.append(nn.BatchNorm2d(c, affine=self.affine))
+                first_depth = False
+            elif token == "p":
+                # point-wise conv
+                result.append(nn.Conv2d(pc, c, 1, 1, 0, bias=False))
+                result.append(nn.BatchNorm2d(c, affine=self.affine))
+                result.append(nn.ReLU(inplace=True))
+                first_point = False
+            else:
+                raise ValueError("Conv sequence must be d and p.")
+            pc = c
+        return result
+
+    def _channel_shuffle(self, x):
+        bs, num_channels, height, width = x.size()
+        # NOTE: this line is commented for torchscript
+        # assert (num_channels % 4 == 0)
+        x = x.reshape(bs * num_channels // 2, 2, height * width)
+        x = x.permute(1, 0, 2)
+        x = x.reshape(2, -1, num_channels // 2, height, width)
+        return x[0], x[1]
+
+
+class ShuffleXceptionBlock(ShuffleNetBlock):
+    """
+    The ``choice_x`` version of shuffle net block, described in
+    `Single Path One-shot <https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123610528.pdf>`__.
+    """
+
+    def __init__(self, in_channels: int, out_channels: int, mid_channels: int, *, stride: int, affine: bool = True):
+        super().__init__(in_channels, out_channels, mid_channels,
+                         kernel_size=3, stride=stride, sequence="dpdpdp", affine=affine)
+
+
+@model_wrapper
+class ShuffleNetSpace(nn.Module):
+    """
+    The search space proposed in `Single Path One-shot <https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123610528.pdf>`__.
+
+    The basic building block design is inspired by a state-of-the-art manually-designed network --
+    `ShuffleNetV2 <https://openaccess.thecvf.com/content_ECCV_2018/html/Ningning_Light-weight_CNN_Architecture_ECCV_2018_paper.html>`__.
+    There are 20 choice blocks in total. Each choice block has 4 candidates, namely ``choice 3``, ``choice 5``,
+    ``choice_7`` and ``choice_x`` respectively. They differ in kernel sizes and the number of depthwise convolutions.
+    The size of the search space is :math:`4^{20}`.
+
+    Parameters
+    ----------
+    num_labels : int
+        Number of classes for the classification head. Default: 1000.
+    channel_search : bool
+        If true, for each building block, the number of ``mid_channels``
+        (output channels of the first 1x1 conv in each building block) varies from 0.2x to 1.6x (quantized to multiple of 0.2).
+        Here, "k-x" means k times the number of default channels.
+        Otherwise, 1.0x is used by default. Default: false.
+    affine : bool
+        Apply affine to all batch norm. Default: false.
+    """
+
+    def __init__(self,
+                 num_labels: int = 1000,
+                 channel_search: bool = False,
+                 affine: bool = False):
+        super().__init__()
+
+        self.num_labels = num_labels
+        self.channel_search = channel_search
+        self.affine = affine
+
+        # the block number in each stage. 4 stages in total. 20 blocks in total.
+        self.stage_repeats = [4, 4, 8, 4]
+
+        # output channels for all stages, including the very first layer and the very last layer
+        self.stage_out_channels = [-1, 16, 64, 160, 320, 640, 1024]
+
+        # building first layer
+        out_channels = self.stage_out_channels[1]
+        self.first_conv = nn.Sequential(
+            nn.Conv2d(3, out_channels, 3, 2, 1, bias=False),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU(inplace=True),
+        )
+
+        self.features = []
+
+        global_block_idx = 0
+        for stage_idx, num_repeat in enumerate(self.stage_repeats):
+            for block_idx in range(num_repeat):
+                # count global index to give names to choices
+                global_block_idx += 1
+
+                # get ready for input and output
+                in_channels = out_channels
+                out_channels = self.stage_out_channels[stage_idx + 2]
+                stride = 2 if block_idx == 0 else 1
+
+                # mid channels can be searched
+                base_mid_channels = out_channels // 2
+                if self.channel_search:
+                    k_choice_list = [int(base_mid_channels * (.2 * k)) for k in range(1, 9)]
+                    mid_channels = nn.ValueChoice(k_choice_list, label=f'channel_{global_block_idx}')
+                else:
+                    mid_channels = int(base_mid_channels)
+
+                choice_block = nn.LayerChoice([
+                    ShuffleNetBlock(in_channels, out_channels, mid_channels=mid_channels, kernel_size=3, stride=stride, affine=affine),
+                    ShuffleNetBlock(in_channels, out_channels, mid_channels=mid_channels, kernel_size=5, stride=stride, affine=affine),
+                    ShuffleNetBlock(in_channels, out_channels, mid_channels=mid_channels, kernel_size=7, stride=stride, affine=affine),
+                    ShuffleXceptionBlock(in_channels, out_channels, mid_channels=mid_channels, stride=stride, affine=affine)
+                ], label=f'layer_{global_block_idx}')
+                self.features.append(choice_block)
+
+        self.features = nn.Sequential(*self.features)
+
+        # final layers
+        last_conv_channels = self.stage_out_channels[-1]
+        self.conv_last = nn.Sequential(
+            nn.Conv2d(out_channels, last_conv_channels, 1, 1, 0, bias=False),
+            nn.BatchNorm2d(last_conv_channels, affine=affine),
+            nn.ReLU(inplace=True),
+        )
+        self.globalpool = nn.AdaptiveAvgPool2d((1, 1))
+        self.dropout = nn.Dropout(0.1)
+        self.classifier = nn.Sequential(
+            nn.Linear(last_conv_channels, num_labels, bias=False),
+        )
+
+        self._initialize_weights()
+
+    def forward(self, x):
+        x = self.first_conv(x)
+        x = self.features(x)
+        x = self.conv_last(x)
+
+        x = self.globalpool(x)
+
+        x = self.dropout(x)
+        x = x.contiguous().view(-1, self.stage_out_channels[-1])
+        x = self.classifier(x)
+        return x
+
+    def _initialize_weights(self):
+        for name, m in self.named_modules():
+            if isinstance(m, nn.Conv2d):
+                if 'first' in name:
+                    torch.nn.init.normal_(m.weight, 0, 0.01)
+                else:
+                    torch.nn.init.normal_(m.weight, 0, 1.0 / m.weight.shape[1])
+                if m.bias is not None:
+                    torch.nn.init.constant_(m.bias, 0)
+            elif isinstance(m, nn.BatchNorm2d):
+                if m.weight is not None:
+                    torch.nn.init.constant_(m.weight, 1)
+                if m.bias is not None:
+                    torch.nn.init.constant_(m.bias, 0.0001)
+                torch.nn.init.constant_(m.running_mean, 0)
+            elif isinstance(m, nn.BatchNorm1d):
+                if m.weight is not None:
+                    torch.nn.init.constant_(m.weight, 1)
+                if m.bias is not None:
+                    torch.nn.init.constant_(m.bias, 0.0001)
+                torch.nn.init.constant_(m.running_mean, 0)
+            elif isinstance(m, nn.Linear):
+                torch.nn.init.normal_(m.weight, 0, 0.01)
+                if m.bias is not None:
+                    torch.nn.init.constant_(m.bias, 0)
diff --git a/nni/retiarii/integration.py b/nni/retiarii/integration.py
index 2f2e3d0a1..f37bd2d01 100644
--- a/nni/retiarii/integration.py
+++ b/nni/retiarii/integration.py
@@ -7,6 +7,7 @@ from typing import Any, Callable
 
 import nni
 from nni.common.serializer import PayloadTooLarge
+from nni.common.version import version_dump
 from nni.runtime.msg_dispatcher_base import MsgDispatcherBase
 from nni.runtime.protocol import CommandType, send
 from nni.utils import MetricType
@@ -120,7 +121,8 @@ class RetiariiAdvisor(MsgDispatcherBase):
             'parameter_id': self.parameters_count,
             'parameters': parameters,
             'parameter_source': 'algorithm',
-            'placement_constraint': placement_constraint
+            'placement_constraint': placement_constraint,
+            'version_info': version_dump()
         }
         _logger.debug('New trial sent: %s', new_trial)
 
diff --git a/nni/retiarii/integration_api.py b/nni/retiarii/integration_api.py
index cceff57ca..dfc77bdc2 100644
--- a/nni/retiarii/integration_api.py
+++ b/nni/retiarii/integration_api.py
@@ -1,9 +1,11 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
 
+import warnings
 from typing import NewType, Any
 
 import nni
+from nni.common.version import version_check
 
 # NOTE: this is only for passing flake8, we cannot import RetiariiAdvisor
 # because it would induce cycled import
@@ -37,6 +39,14 @@ def receive_trial_parameters() -> dict:
     Reload with our json loads because NNI didn't use Retiarii serializer to load the data.
     """
     params = nni.get_next_parameter()
+
+    # version check, optional
+    raw_params = nni.trial._params
+    if raw_params is not None and 'version_info' in raw_params:
+        version_check(raw_params['version_info'])
+    else:
+        warnings.warn('Version check failed because `version_info` is not found.')
+
     return params
 
 
diff --git a/nni/retiarii/mutator.py b/nni/retiarii/mutator.py
index abf69703f..e40bdb94a 100644
--- a/nni/retiarii/mutator.py
+++ b/nni/retiarii/mutator.py
@@ -32,8 +32,8 @@ class Mutator:
     Mutates graphs in model to generate new model.
     `Mutator` class will be used in two places:
 
-        1. Inherit `Mutator` to implement graph mutation logic.
-        2. Use `Mutator` subclass to implement NAS strategy.
+    1. Inherit `Mutator` to implement graph mutation logic.
+    2. Use `Mutator` subclass to implement NAS strategy.
 
     In scenario 1, the subclass should implement `Mutator.mutate()` interface with `Mutator.choice()`.
     In scenario 2, strategy should use constructor or `Mutator.bind_sampler()` to initialize subclass,
diff --git a/nni/retiarii/nn/pytorch/.gitignore b/nni/retiarii/nn/pytorch/.gitignore
new file mode 100644
index 000000000..5c6df96e1
--- /dev/null
+++ b/nni/retiarii/nn/pytorch/.gitignore
@@ -0,0 +1 @@
+_nn.py
diff --git a/nni/retiarii/nn/pytorch/__init__.py b/nni/retiarii/nn/pytorch/__init__.py
index bcc8c45f3..8a493274c 100644
--- a/nni/retiarii/nn/pytorch/__init__.py
+++ b/nni/retiarii/nn/pytorch/__init__.py
@@ -1,4 +1,7 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from .api import *
 from .component import *
 from .nn import *
-from .hypermodule import *
\ No newline at end of file
+from .hypermodule import *
diff --git a/nni/retiarii/nn/pytorch/api.py b/nni/retiarii/nn/pytorch/api.py
index 8c78e253e..81d694185 100644
--- a/nni/retiarii/nn/pytorch/api.py
+++ b/nni/retiarii/nn/pytorch/api.py
@@ -2,26 +2,31 @@
 # Licensed under the MIT license.
 
 import math
+import itertools
 import operator
 import warnings
-from typing import Any, List, Union, Dict, Optional, Callable, Iterable, NoReturn, TypeVar
+from typing import Any, List, Union, Dict, Optional, Callable, Iterable, NoReturn, TypeVar, Sequence
 
 import torch
 import torch.nn as nn
 
+from nni.common.hpo_utils import ParameterSpec
 from nni.common.serializer import Translatable
 from nni.retiarii.serializer import basic_unit
-from .utils import Mutable, generate_new_label, get_fixed_value
+from nni.retiarii.utils import STATE_DICT_PY_MAPPING_PARTIAL, ModelNamespace, NoContextError
+from .mutation_utils import Mutable, generate_new_label, get_fixed_value
 
 
-__all__ = ['LayerChoice', 'InputChoice', 'ValueChoice', 'Placeholder', 'ChosenInputs']
+__all__ = ['LayerChoice', 'InputChoice', 'ValueChoice', 'ModelParameterChoice', 'Placeholder', 'ChosenInputs']
 
 
 class LayerChoice(Mutable):
     """
     Layer choice selects one of the ``candidates``, then apply it on inputs and return results.
 
-    Layer choice does not allow itself to be nested.
+    It allows users to put several candidate operations (e.g., PyTorch modules), one of them is chosen in each explored model.
+
+    *New in v2.2:* Layer choice can be nested.
 
     Parameters
     ----------
@@ -42,6 +47,21 @@ class LayerChoice(Mutable):
         Deprecated. A list of all candidate modules in the layer choice module.
         ``list(layer_choice)`` is recommended, which will serve the same purpose.
 
+    Examples
+    --------
+
+    ::
+
+        # import nni.retiarii.nn.pytorch as nn
+        # declared in `__init__` method
+        self.layer = nn.LayerChoice([
+            ops.PoolBN('max', channels, 3, stride, 1),
+            ops.SepConv(channels, channels, 3, stride, 1),
+            nn.Identity()
+        ])
+        # invoked in `forward` method
+        out = self.layer(x)
+
     Notes
     -----
     ``candidates`` can be a list of modules or a ordered dict of named modules, for example,
@@ -65,9 +85,22 @@ class LayerChoice(Mutable):
                             label: Optional[str] = None, **kwargs):
         chosen = get_fixed_value(label)
         if isinstance(candidates, list):
-            return candidates[int(chosen)]
+            result = candidates[int(chosen)]
         else:
-            return candidates[chosen]
+            result = candidates[chosen]
+
+        # map the named hierarchies to support weight inheritance for python engine
+        if hasattr(result, STATE_DICT_PY_MAPPING_PARTIAL):
+            # handle cases where layer choices are nested
+            # already has a mapping, will merge with it
+            prev_mapping = getattr(result, STATE_DICT_PY_MAPPING_PARTIAL)
+            setattr(result, STATE_DICT_PY_MAPPING_PARTIAL, {k: f'{chosen}.{v}' for k, v in prev_mapping.items()})
+        else:
+            # "result" needs to know where to map itself.
+            # Ideally, we should put a _mapping_ in the module where "result" is located,
+            # but it's impossible to put mapping into parent module here.
+            setattr(result, STATE_DICT_PY_MAPPING_PARTIAL, {'__self__': str(chosen)})
+        return result
 
     def __init__(self, candidates: Union[Dict[str, nn.Module], List[nn.Module]], *,
                  prior: Optional[List[float]] = None, label: Optional[str] = None, **kwargs):
@@ -150,6 +183,10 @@ class LayerChoice(Mutable):
         return list(self)
 
     def forward(self, x):
+        """
+        The forward of layer choice is simply running the first candidate module.
+        It shouldn't be called directly by users in most cases.
+        """
         warnings.warn('You should not run forward of this module directly.')
         return self._first_module(x)
 
@@ -168,6 +205,10 @@ ReductionType = Literal['mean', 'concat', 'sum', 'none']
 class InputChoice(Mutable):
     """
     Input choice selects ``n_chosen`` inputs from ``choose_from`` (contains ``n_candidates`` keys).
+
+    It is mainly for choosing (or trying) different connections. It takes several tensors and chooses ``n_chosen`` tensors from them.
+    When specific inputs are chosen, ``InputChoice`` will become :class:`ChosenInputs`.
+
     Use ``reduction`` to specify how chosen inputs are reduced into one output. A few options are:
 
     * ``none``: do nothing and return the list directly.
@@ -189,6 +230,16 @@ class InputChoice(Mutable):
         Prior distribution used in random sampling.
     label : str
         Identifier of the input choice.
+
+    Examples
+    --------
+    ::
+
+        # import nni.retiarii.nn.pytorch as nn
+        # declared in `__init__` method
+        self.input_switch = nn.InputChoice(n_chosen=1)
+        # invoked in `forward` method, choose one from the three
+        out = self.input_switch([tensor1, tensor2, tensor3])
     """
 
     @classmethod
@@ -230,6 +281,10 @@ class InputChoice(Mutable):
         return self._label
 
     def forward(self, candidate_inputs: List[torch.Tensor]) -> torch.Tensor:
+        """
+        The forward of input choice is simply the first item of ``candidate_inputs``.
+        It shouldn't be called directly by users in most cases.
+        """
         warnings.warn('You should not run forward of this module directly.')
         return candidate_inputs[0]
 
@@ -260,6 +315,9 @@ class ChosenInputs(nn.Module):
         self.reduction = reduction
 
     def forward(self, candidate_inputs):
+        """
+        Compute the reduced input based on ``chosen`` and ``reduction``.
+        """
         return self._tensor_reduction(self.reduction, [candidate_inputs[i] for i in self.chosen])
 
     def _tensor_reduction(self, reduction_type, tensor_list):
@@ -330,7 +388,7 @@ def _valuechoice_staticmethod_helper(orig_func):
     return orig_func
 
 
-class ValueChoiceX(Translatable):
+class ValueChoiceX(Translatable, nn.Module):
     """Internal API. Implementation note:
 
     The transformed (X) version of value choice.
@@ -346,6 +404,8 @@ class ValueChoiceX(Translatable):
     2. For graph-engine, it uses evaluate to calculate the result.
 
     Potentially, we have to implement the evaluation logic in oneshot algorithms. I believe we can postpone the discussion till then.
+
+    This class is implemented as a ``nn.Module`` so that it can be scanned by python engine / torchscript.
     """
 
     def __init__(self, function: Callable[..., Any], repr_template: str, arguments: List[Any], dry_run: bool = True):
@@ -366,6 +426,9 @@ class ValueChoiceX(Translatable):
             # for sanity check
             self.dry_run()
 
+    def forward(self) -> None:
+        raise RuntimeError('You should never call forward of the composition of a value-choice.')
+
     def inner_choices(self) -> Iterable['ValueChoice']:
         """
         Return an iterable of all leaf value choices.
@@ -383,6 +446,30 @@ class ValueChoiceX(Translatable):
         # values are not used
         return self._evaluate(iter([]), True)
 
+    def all_options(self) -> Iterable[Any]:
+        """Explore all possibilities of a value choice.
+        """
+        # Record all inner choices: label -> candidates, no duplicates.
+        dedup_inner_choices: Dict[str, List[Any]] = {}
+        # All labels of leaf nodes on tree, possibly duplicates.
+        all_labels: List[str] = []
+
+        for choice in self.inner_choices():
+            all_labels.append(choice.label)
+            if choice.label in dedup_inner_choices:
+                if choice.candidates != dedup_inner_choices[choice.label]:
+                    # check for choice with the same label
+                    raise ValueError(f'"{choice.candidates}" is not equal to "{dedup_inner_choices[choice.label]}", '
+                                     f'but they share the same label: {choice.label}')
+            else:
+                dedup_inner_choices[choice.label] = choice.candidates
+
+        dedup_labels, dedup_candidates = list(dedup_inner_choices.keys()), list(dedup_inner_choices.values())
+
+        for chosen in itertools.product(*dedup_candidates):
+            chosen = dict(zip(dedup_labels, chosen))
+            yield self.evaluate([chosen[label] for label in all_labels])
+
     def evaluate(self, values: Iterable[Any]) -> Any:
         """
         Evaluate the result of this group.
@@ -525,7 +612,8 @@ class ValueChoiceX(Translatable):
     def __index__(self) -> NoReturn:
         # https://docs.python.org/3/reference/datamodel.html#object.__index__
         raise RuntimeError("`__index__` is not allowed on ValueChoice, which means you can't "
-                           "use int(), float(), complex(), range() on a ValueChoice.")
+                           "use int(), float(), complex(), range() on a ValueChoice. "
+                           "To cast the type of ValueChoice, please try `ValueChoice.to_int()` or `ValueChoice.to_float()`.")
 
     def __bool__(self) -> NoReturn:
         raise RuntimeError('Cannot use bool() on ValueChoice. That means, using ValueChoice in a if-clause is illegal. '
@@ -661,11 +749,13 @@ ValueChoiceOrAny = TypeVar('ValueChoiceOrAny', ValueChoiceX, Any)
 
 class ValueChoice(ValueChoiceX, Mutable):
     """
-    ValueChoice is to choose one from ``candidates``.
+    ValueChoice is to choose one from ``candidates``. The most common use cases are:
 
-    In most use scenarios, ValueChoice should be passed to the init parameters of a serializable module. For example,
+    * Used as input arguments of :class:`~nni.retiarii.basic_unit`
+      (i.e., modules in ``nni.retiarii.nn.pytorch`` and user-defined modules decorated with ``@basic_unit``).
+    * Used as input arguments of evaluator (*new in v2.7*).
 
-    .. code-block:: python
+    It can be used in parameters of operators (i.e., a sub-module of the model): ::
 
         class Net(nn.Module):
             def __init__(self):
@@ -675,37 +765,84 @@ class ValueChoice(ValueChoiceX, Mutable):
             def forward(self, x):
                 return self.conv(x)
 
-    In case, you want to search a parameter that is used repeatedly, this is also possible by sharing the same value choice instance.
-    (Sharing the label should have the same effect.) For example,
+    Or evaluator (only if the evaluator is :doc:`traceable </nas/serialization>`, e.g.,
+    :class:`FunctionalEvaluator <nni.retiarii.evaluator.FunctionalEvaluator>`): ::
 
-    .. code-block:: python
+        def train_and_evaluate(model_cls, learning_rate):
+            ...
 
-        class Net(nn.Module):
-            def __init__(self):
-                super().__init__()
-                hidden_dim = nn.ValueChoice([128, 512])
-                self.fc = nn.Sequential(
-                    nn.Linear(64, hidden_dim),
-                    nn.Linear(hidden_dim, 10)
-                )
-
-                # the following code has the same effect.
-                # self.fc = nn.Sequential(
-                #     nn.Linear(64, nn.ValueChoice([128, 512], label='dim')),
-                #     nn.Linear(nn.ValueChoice([128, 512], label='dim'), 10)
-                # )
+        self.evaluator = FunctionalEvaluator(train_and_evaluate, learning_rate=nn.ValueChoice([1e-3, 1e-2, 1e-1]))
 
-            def forward(self, x):
-                return self.fc(x)
+    Value choices supports arithmetic operators, which is particularly useful when searching for a network width multiplier: ::
 
-    Note that ValueChoice should be used directly. Transformations like ``nn.Linear(32, nn.ValueChoice([64, 128]) * 2)``
-    are not supported.
+        # init
+        scale = nn.ValueChoice([1.0, 1.5, 2.0])
+        self.conv1 = nn.Conv2d(3, round(scale * 16))
+        self.conv2 = nn.Conv2d(round(scale * 16), round(scale * 64))
+        self.conv3 = nn.Conv2d(round(scale * 64), round(scale * 256))
 
-    Another common use case is to initialize the values to choose from in init and call the module in forward to get the chosen value.
-    Usually, this is used to pass a mutable value to a functional API like ``torch.xxx`` or ``nn.functional.xxx```.
-    For example,
+        # forward
+        return self.conv3(self.conv2(self.conv1(x)))
 
-    .. code-block:: python
+    Or when kernel size and padding are coupled so as to keep the output size constant: ::
+
+        # init
+        ks = nn.ValueChoice([3, 5, 7])
+        self.conv = nn.Conv2d(3, 16, kernel_size=ks, padding=(ks - 1) // 2)
+
+        # forward
+        return self.conv(x)
+
+    Or when several layers are concatenated for a final layer. ::
+
+        # init
+        self.linear1 = nn.Linear(3, nn.ValueChoice([1, 2, 3], label='a'))
+        self.linear2 = nn.Linear(3, nn.ValueChoice([4, 5, 6], label='b'))
+        self.final = nn.Linear(nn.ValueChoice([1, 2, 3], label='a') + nn.ValueChoice([4, 5, 6], label='b'), 2)
+
+        # forward
+        return self.final(torch.cat([self.linear1(x), self.linear2(x)], 1))
+
+    Some advanced operators are also provided, such as :meth:`ValueChoice.max` and :meth:`ValueChoice.cond`.
+
+    .. tip::
+
+        All the APIs have an optional argument called ``label``,
+        mutations with the same label will share the same choice. A typical example is, ::
+
+            self.net = nn.Sequential(
+                nn.Linear(10, nn.ValueChoice([32, 64, 128], label='hidden_dim')),
+                nn.Linear(nn.ValueChoice([32, 64, 128], label='hidden_dim'), 3)
+            )
+
+        Sharing the same value choice instance has the similar effect. ::
+
+            class Net(nn.Module):
+                def __init__(self):
+                    super().__init__()
+                    hidden_dim = nn.ValueChoice([128, 512])
+                    self.fc = nn.Sequential(
+                        nn.Linear(64, hidden_dim),
+                        nn.Linear(hidden_dim, 10)
+                    )
+
+    .. warning::
+
+        It looks as if a specific candidate has been chosen (e.g., how it looks like when you can put ``ValueChoice``
+        as a parameter of ``nn.Conv2d``), but in fact it's a syntax sugar as because the basic units and evaluators
+        do all the underlying works. That means, you cannot assume that ``ValueChoice`` can be used in the same way
+        as its candidates. For example, the following usage will NOT work: ::
+
+            self.blocks = []
+            for i in range(nn.ValueChoice([1, 2, 3])):
+                self.blocks.append(Block())
+
+            # NOTE: instead you should probably write
+            # self.blocks = nn.Repeat(Block(), (1, 3))
+
+    Another use case is to initialize the values to choose from in init and call the module in forward to get the chosen value.
+    Usually, this is used to pass a mutable value to a functional API like ``torch.xxx`` or ``nn.functional.xxx```.
+    For example, ::
 
         class Net(nn.Module):
             def __init__(self):
@@ -740,13 +877,16 @@ class ValueChoice(ValueChoiceX, Mutable):
         self.prior = prior or [1 / len(candidates) for _ in range(len(candidates))]
         assert abs(sum(self.prior) - 1) < 1e-5, 'Sum of prior distribution is not 1.'
         self._label = generate_new_label(label)
-        self._accessor = []
 
     @property
     def label(self):
         return self._label
 
     def forward(self):
+        """
+        The forward of input choice is simply the first value of ``candidates``.
+        It shouldn't be called directly by users in most cases.
+        """
         warnings.warn('You should not run forward of this module directly.')
         return self.candidates[0]
 
@@ -772,6 +912,149 @@ class ValueChoice(ValueChoiceX, Mutable):
         return f'ValueChoice({self.candidates}, label={repr(self.label)})'
 
 
+ValueType = TypeVar('ValueType')
+
+
+class ModelParameterChoice:
+    """ModelParameterChoice chooses one hyper-parameter from ``candidates``.
+
+    .. attention::
+
+       This API is internal, and does not guarantee forward-compatibility.
+
+    It's quite similar to :class:`ValueChoice`, but unlike :class:`ValueChoice`,
+    it always returns a fixed value, even at the construction of base model.
+
+    This makes it highly flexible (e.g., can be used in for-loop, if-condition, as argument of any function). For example: ::
+
+        self.has_auxiliary_head = ModelParameterChoice([False, True])
+        # this will raise error if you use `ValueChoice`
+        if self.has_auxiliary_head is True:  # or self.has_auxiliary_head
+            self.auxiliary_head = Head()
+        else:
+            self.auxiliary_head = None
+        print(type(self.has_auxiliary_head))  # <class 'bool'>
+
+    The working mechanism of :class:`ModelParameterChoice` is that, it registers itself
+    in the ``model_wrapper``, as a hyper-parameter of the model, and then returns the value specified with ``default``.
+    At base model construction, the default value will be used (as a mocked hyper-parameter).
+    In trial, the hyper-parameter selected by strategy will be used.
+
+    Although flexible, we still recommend using :class:`ValueChoice` in favor of :class:`ModelParameterChoice`,
+    because information are lost when using :class:`ModelParameterChoice` in exchange of its flexibility,
+    making it incompatible with one-shot strategies and non-python execution engines.
+
+    .. warning::
+
+        :class:`ModelParameterChoice` can NOT be nested.
+
+    .. tip::
+
+        Although called :class:`ModelParameterChoice`, it's meant to tune hyper-parameter of architecture.
+        It's NOT used to tune model-training hyper-parameters like ``learning_rate``.
+        If you need to tune ``learning_rate``, please use :class:`ValueChoice` on arguments of :class:`nni.retiarii.Evaluator`.
+
+    Parameters
+    ----------
+    candidates : list of any
+        List of values to choose from.
+    prior : list of float
+        Prior distribution to sample from. Currently has no effect.
+    default : Callable[[List[Any]], Any] or Any
+        Function that selects one from ``candidates``, or a candidate.
+        Use :meth:`ModelParameterChoice.FIRST` or :meth:`ModelParameterChoice.LAST` to take the first or last item.
+        Default: :meth:`ModelParameterChoice.FIRST`
+    label : str
+        Identifier of the value choice.
+
+    Warnings
+    --------
+    :class:`ModelParameterChoice` is incompatible with one-shot strategies and non-python execution engines.
+
+    Sometimes, the same search space implemented **without** :class:`ModelParameterChoice` can be simpler, and explored
+    with more types of search strategies. For example, the following usages are equivalent: ::
+
+        # with ModelParameterChoice
+        depth = nn.ModelParameterChoice(list(range(3, 10)))
+        blocks = []
+        for i in range(depth):
+            blocks.append(Block())
+
+        # w/o HyperParmaeterChoice
+        blocks = Repeat(Block(), (3, 9))
+
+    Examples
+    --------
+    Get a dynamic-shaped parameter. Because ``torch.zeros`` is not a basic unit, we can't use :class:`ValueChoice` on it.
+    >>> parameter_dim = nn.ModelParameterChoice([64, 128, 256])
+    >>> self.token = nn.Parameter(torch.zeros(1, parameter_dim, 32, 32))
+    """
+
+    # FIXME: fix signature in docs
+
+    # FIXME: prior is designed but not supported yet
+
+    def __new__(cls, candidates: List[ValueType], *,
+                prior: Optional[List[float]] = None,
+                default: Union[Callable[[List[ValueType]], ValueType], ValueType] = None,
+                label: Optional[str] = None) -> ValueType:
+        # Actually, creating a `ModelParameterChoice` never creates one.
+        # It always return a fixed value, and register a ParameterSpec
+
+        if default is None:
+            default = cls.FIRST
+
+        try:
+            return cls.create_fixed_module(candidates, label=label)
+        except NoContextError:
+            return cls.create_default(candidates, default, label)
+
+    @staticmethod
+    def create_default(candidates: List[ValueType],
+                       default: Union[Callable[[List[ValueType]], ValueType], ValueType],
+                       label: Optional[str]) -> ValueType:
+        if default not in candidates:
+            # could be callable
+            try:
+                default = default(candidates)
+            except TypeError as e:
+                if 'not callable' in str(e):
+                    raise TypeError("`default` is not in `candidates`, and it's also not callable.")
+                raise
+
+        label = generate_new_label(label)
+        parameter_spec = ParameterSpec(
+            label,          # name
+            'choice',       # TODO: support more types
+            candidates,     # value
+            (label,),       # we don't have nested now
+            True,           # yes, categorical
+        )
+
+        # there could be duplicates. Dedup is done in mutator
+        ModelNamespace.current_context().parameter_specs.append(parameter_spec)
+
+        return default
+
+    @classmethod
+    def create_fixed_module(cls, candidates: List[ValueType], *, label: Optional[str] = None, **kwargs) -> ValueType:
+        # same as ValueChoice
+        value = get_fixed_value(label)
+        if value not in candidates:
+            raise ValueError(f'Value {value} does not belong to the candidates: {candidates}.')
+        return value
+
+    @staticmethod
+    def FIRST(sequence: Sequence[ValueType]) -> ValueType:
+        """Get the first item of sequence. Useful in ``default`` argument."""
+        return sequence[0]
+
+    @staticmethod
+    def LAST(sequence: Sequence[ValueType]) -> ValueType:
+        """Get the last item of sequence. Useful in ``default`` argument."""
+        return sequence[-1]
+
+
 @basic_unit
 class Placeholder(nn.Module):
     """
@@ -785,4 +1068,8 @@ class Placeholder(nn.Module):
         super().__init__()
 
     def forward(self, x):
+        """
+        Forward of placeholder is not meaningful.
+        It returns input directly.
+        """
         return x
diff --git a/nni/retiarii/nn/pytorch/cell.py b/nni/retiarii/nn/pytorch/cell.py
index 34cb5532d..c4725cdbb 100644
--- a/nni/retiarii/nn/pytorch/cell.py
+++ b/nni/retiarii/nn/pytorch/cell.py
@@ -10,8 +10,8 @@ import torch
 import torch.nn as nn
 
 from .api import ChosenInputs, LayerChoice, InputChoice
-from .nn import ModuleList
-from .utils import generate_new_label
+from .nn import ModuleList  # pylint: disable=no-name-in-module
+from .mutation_utils import generate_new_label
 
 
 class _ListIdentity(nn.Identity):
@@ -32,12 +32,70 @@ _cell_op_factory_type = Callable[[int, int, Optional[int]], nn.Module]
 
 class Cell(nn.Module):
     """
-    Cell structure [zophnas]_ [zophnasnet]_ that is popularly used in NAS literature.
-    [nds]_ is a good summary of how this structure works in practice.
+    Cell structure that is popularly used in NAS literature.
+
+    Find the details in:
+
+    * `Neural Architecture Search with Reinforcement Learning <https://arxiv.org/abs/1611.01578>`__.
+    * `Learning Transferable Architectures for Scalable Image Recognition <https://arxiv.org/abs/1707.07012>`__.
+    * `DARTS: Differentiable Architecture Search <https://arxiv.org/abs/1806.09055>`__
+
+    `On Network Design Spaces for Visual Recognition <https://arxiv.org/abs/1905.13214>`__
+    is a good summary of how this structure works in practice.
 
     A cell consists of multiple "nodes". Each node is a sum of multiple operators. Each operator is chosen from
     ``op_candidates``, and takes one input from previous nodes and predecessors. Predecessor means the input of cell.
-    The output of cell is the concatenation of some of the nodes in the cell (currently all the nodes).
+    The output of cell is the concatenation of some of the nodes in the cell (by default all the nodes).
+
+    Two examples of searched cells are illustrated in the figure below.
+    In these two cells, ``op_candidates`` are series of convolutions and pooling operations.
+    ``num_nodes_per_node`` is set to 2. ``num_nodes`` is set to 5. ``merge_op`` is ``loose_end``.
+    Assuming nodes are enumerated from bottom to top, left to right,
+    ``output_node_indices`` for the normal cell is ``[2, 3, 4, 5, 6]``.
+    For the reduction cell, it's ``[4, 5, 6]``.
+    Please take a look at this
+    `review article <https://sh-tsang.medium.com/review-nasnet-neural-architecture-search-network-image-classification-23139ea0425d>`__
+    if you are interested in details.
+
+    .. image:: ../../../img/nasnet_cell.png
+       :width: 900
+       :align: center
+
+    Here is a glossary table, which could help better understand the terms used above:
+
+    .. list-table::
+        :widths: 25 75
+        :header-rows: 1
+
+        * - Name
+          - Brief Description
+        * - Cell
+          - A cell consists of ``num_nodes`` nodes.
+        * - Node
+          - A node is the **sum** of ``num_ops_per_node`` operators.
+        * - Operator
+          - Each operator is independently chosen from a list of user-specified candidate operators.
+        * - Operator's input
+          - Each operator has one input, chosen from previous nodes as well as predecessors.
+        * - Predecessors
+          - Input of cell. A cell can have multiple predecessors. Predecessors are sent to *preprocessor* for preprocessing.
+        * - Cell's output
+          - Output of cell. Usually concatenation of some nodes (possibly all nodes) in the cell. Cell's output,
+            along with predecessors, are sent to *postprocessor* for postprocessing.
+        * - Preprocessor
+          - Extra preprocessing to predecessors. Usually used in shape alignment (e.g., predecessors have different shapes).
+            By default, do nothing.
+        * - Postprocessor
+          - Extra postprocessing for cell's output. Usually used to chain cells with multiple Predecessors
+            (e.g., the next cell wants to have the outputs of both this cell and previous cell as its input).
+            By default, directly use this cell's output.
+
+    .. tip::
+
+        It's highly recommended to make the candidate operators have an output of the same shape as input.
+        This is because, there can be dynamic connections within cell. If there's shape change within operations,
+        the input shape of the subsequent operation becomes unknown.
+        In addition, the final concatenation could have shape mismatch issues.
 
     Parameters
     ----------
@@ -58,7 +116,7 @@ class Cell(nn.Module):
         will be ``list(range(num_predecessors, num_predecessors + num_nodes))``.
         If "loose_end", only the nodes that have never been used as other nodes' inputs will be concatenated to the output.
         Predecessors are not considered when calculating unused nodes.
-        Details can be found in reference [nds]. Default: all.
+        Details can be found in `NDS paper <https://arxiv.org/abs/1905.13214>`__. Default: all.
     preprocessor : callable
         Override this if some extra transformation on cell's input is intended.
         It should be a callable (``nn.Module`` is also acceptable) that takes a list of tensors which are predecessors,
@@ -70,27 +128,66 @@ class Cell(nn.Module):
         Its return type should be either one tensor, or a tuple of tensors.
         The return value of postprocessor is the return value of the cell's forward.
         By default, it returns only the output of the current cell.
+    concat_dim : int
+        The result will be a concatenation of several nodes on this dim. Default: 1.
     label : str
         Identifier of the cell. Cell sharing the same label will semantically share the same choice.
 
+    Examples
+    --------
+    Choose between conv2d and maxpool2d.
+    The cell have 4 nodes, 1 op per node, and 2 predecessors.
+
+    >>> cell = nn.Cell([nn.Conv2d(32, 32, 3, padding=1), nn.MaxPool2d(3, padding=1)], 4, 1, 2)
+
+    In forward:
+
+    >>> cell([input1, input2])
+
+    The "list bracket" can be omitted:
+
+    >>> cell(only_input)                    # only one input
+    >>> cell(tensor1, tensor2, tensor3)     # multiple inputs
+
+    Use ``merge_op`` to specify how to construct the output.
+    The output will then have dynamic shape, depending on which input has been used in the cell.
+
+    >>> cell = nn.Cell([nn.Conv2d(32, 32, 3), nn.MaxPool2d(3)], 4, 1, 2, merge_op='loose_end')
+    >>> cell_out_channels = len(cell.output_node_indices) * 32
+
+    The op candidates can be callable that accepts node index in cell, op index in node, and input index.
+
+    >>> cell = nn.Cell([
+    ...     lambda node_index, op_index, input_index: nn.Conv2d(32, 32, 3, stride=2 if input_index < 1 else 1),
+    ... ], 4, 1, 2)
+
+    Predecessor example: ::
+
+        class Preprocessor:
+            def __init__(self):
+                self.conv1 = nn.Conv2d(16, 32, 1)
+                self.conv2 = nn.Conv2d(64, 32, 1)
+
+            def forward(self, x):
+                return [self.conv1(x[0]), self.conv2(x[1])]
+
+        cell = nn.Cell([nn.Conv2d(32, 32, 3), nn.MaxPool2d(3)], 4, 1, 2, preprocessor=Preprocessor())
+        cell([torch.randn(1, 16, 48, 48), torch.randn(1, 64, 48, 48)])  # the two inputs will be sent to conv1 and conv2 respectively
+
+    Warnings
+    --------
+    :class:`Cell` is not supported in :ref:`graph-based execution engine <graph-based-execution-engine>`.
+
     Attributes
     ----------
     output_node_indices : list of int
-        Indices of the nodes concatenated to the output. For example, if the following operation is a 2d-convolution,
-        its input channels is ``len(output_node_indices) * channels``.
+        An attribute that contains indices of the nodes concatenated to the output (a list of integers).
 
-    Examples
-    --------
-    >>> cell = nn.Cell([nn.Conv2d(32, 32, 3), nn.MaxPool2d(3)], 4, 1, 2)
-    >>> output = cell([input1, input2])
+        When the cell is first instantiated in the base model, or when ``merge_op`` is ``all``,
+        ``output_node_indices`` must be ``range(num_predecessors, num_predecessors + num_nodes)``.
 
-    References
-    ----------
-    .. [zophnas] Barret Zoph, Quoc V. Le, "Neural Architecture Search with Reinforcement Learning". https://arxiv.org/abs/1611.01578
-    .. [zophnasnet] Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le,
-        "Learning Transferable Architectures for Scalable Image Recognition". https://arxiv.org/abs/1707.07012
-    .. [nds] Radosavovic, Ilija and Johnson, Justin and Xie, Saining and Lo, Wan-Yen and Dollar, Piotr,
-        "On Network Design Spaces for Visual Recognition". https://arxiv.org/abs/1905.13214
+        When ``merge_op`` is ``loose_end``, ``output_node_indices`` is useful to compute the shape of this cell's output,
+        because the output shape depends on the connection in the cell, and which nodes are "loose ends" depends on mutation.
     """
 
     def __init__(self,
@@ -106,6 +203,7 @@ class Cell(nn.Module):
                  preprocessor: Optional[Callable[[List[torch.Tensor]], List[torch.Tensor]]] = None,
                  postprocessor: Optional[Callable[[torch.Tensor, List[torch.Tensor]],
                                          Union[Tuple[torch.Tensor, ...], torch.Tensor]]] = None,
+                 concat_dim: int = 1,
                  *,
                  label: Optional[str] = None):
         super().__init__()
@@ -127,6 +225,8 @@ class Cell(nn.Module):
         self.merge_op = merge_op
         self.output_node_indices = list(range(num_predecessors, num_predecessors + num_nodes))
 
+        self.concat_dim = concat_dim
+
         # fill-in the missing modules
         self._create_modules(op_candidates)
 
@@ -158,11 +258,28 @@ class Cell(nn.Module):
     def label(self):
         return self._label
 
-    def forward(self, x: List[torch.Tensor]):
-        # The return type should be 'Union[Tuple[torch.Tensor, ...], torch.Tensor]'.
-        # Cannot decorate it as annotation. Otherwise torchscript will complain.
-        assert isinstance(x, list), 'We currently only support input of cell as a list, even if you have only one predecessor.'
-        states = self.preprocessor(x)
+    def forward(self, *inputs: Union[List[torch.Tensor], torch.Tensor]) -> Union[Tuple[torch.Tensor, ...], torch.Tensor]:
+        """Forward propagation of cell.
+
+        Parameters
+        ----------
+        inputs
+            Can be a list of tensors, or several tensors.
+            The length should be equal to ``num_predecessors``.
+
+        Returns
+        -------
+        Tuple[torch.Tensor] | torch.Tensor
+            The return type depends on the output of ``postprocessor``.
+            By default, it's the output of ``merge_op``, which is a contenation (on ``concat_dim``)
+            of some of (possibly all) the nodes' outputs in the cell.
+        """
+        if len(inputs) == 1 and isinstance(inputs[0], list):
+            inputs = inputs[0]
+        else:
+            inputs = list(inputs)
+        assert len(inputs) == self.num_predecessors, 'The number of inputs must be equal to `num_predecessors`.'
+        states = self.preprocessor(inputs)
         for ops, inps in zip(self.ops, self.inputs):
             current_state = []
             for op, inp in zip(ops, inps):
@@ -171,10 +288,10 @@ class Cell(nn.Module):
             states.append(current_state)
         if self.merge_op == 'all':
             # a special case for graph engine
-            this_cell = torch.cat(states[self.num_predecessors:], 1)
+            this_cell = torch.cat(states[self.num_predecessors:], self.concat_dim)
         else:
-            this_cell = torch.cat([states[k] for k in self.output_node_indices], 1)
-        return self.postprocessor(this_cell, x)
+            this_cell = torch.cat([states[k] for k in self.output_node_indices], self.concat_dim)
+        return self.postprocessor(this_cell, inputs)
 
     @staticmethod
     def _convert_op_candidates(op_candidates, node_index, op_index, chosen) -> Union[Dict[str, nn.Module], List[nn.Module]]:
diff --git a/nni/retiarii/nn/pytorch/component.py b/nni/retiarii/nn/pytorch/component.py
index 7c681b024..946c43eae 100644
--- a/nni/retiarii/nn/pytorch/component.py
+++ b/nni/retiarii/nn/pytorch/component.py
@@ -1,14 +1,17 @@
 import copy
+import warnings
 from collections import OrderedDict
 from typing import Callable, List, Union, Tuple, Optional
 
 import torch
 import torch.nn as nn
 
-from .api import LayerChoice
+from nni.retiarii.utils import NoContextError, STATE_DICT_PY_MAPPING_PARTIAL
+
+from .api import LayerChoice, ValueChoice, ValueChoiceX
 from .cell import Cell
 from .nasbench101 import NasBench101Cell, NasBench101Mutator
-from .utils import Mutable, generate_new_label, get_fixed_value
+from .mutation_utils import Mutable, generate_new_label, get_fixed_value
 
 
 __all__ = ['Repeat', 'Cell', 'NasBench101Cell', 'NasBench101Mutator', 'NasBench201Cell']
@@ -21,13 +24,38 @@ class Repeat(Mutable):
     Parameters
     ----------
     blocks : function, list of function, module or list of module
-        The block to be repeated. If not a list, it will be replicated into a list.
+        The block to be repeated. If not a list, it will be replicated (**deep-copied**) into a list.
         If a list, it should be of length ``max_depth``, the modules will be instantiated in order and a prefix will be taken.
         If a function, it will be called (the argument is the index) to instantiate a module.
         Otherwise the module will be deep-copied.
     depth : int or tuple of int
         If one number, the block will be repeated by a fixed number of times. If a tuple, it should be (min, max),
-        meaning that the block will be repeated at least `min` times and at most `max` times.
+        meaning that the block will be repeated at least ``min`` times and at most ``max`` times.
+        If a ValueChoice, it should choose from a series of positive integers.
+
+    Examples
+    --------
+    Block() will be deep copied and repeated 3 times. ::
+
+        self.blocks = nn.Repeat(Block(), 3)
+
+    Block() will be repeated 1, 2, or 3 times. ::
+
+        self.blocks = nn.Repeat(Block(), (1, 3))
+
+    Can be used together with layer choice.
+    With deep copy, the 3 layers will have the same label, thus share the choice. ::
+
+        self.blocks = nn.Repeat(nn.LayerChoice([...]), (1, 3))
+
+    To make the three layer choices independent,
+    we need a factory function that accepts index (0, 1, 2, ...) and returns the module of the ``index``-th layer. ::
+
+        self.blocks = nn.Repeat(lambda index: nn.LayerChoice([...], label=f'layer{index}'), (1, 3))
+
+    Depth can be a ValueChoice to support arbitrary depth candidate list. ::
+
+        self.blocks = nn.Repeat(Block(), nn.ValueChoice([1, 3, 5]))
     """
 
     @classmethod
@@ -36,9 +64,26 @@ class Repeat(Mutable):
                                           List[Callable[[int], nn.Module]],
                                           nn.Module,
                                           List[nn.Module]],
-                            depth: Union[int, Tuple[int, int]], *, label: Optional[str] = None):
-        repeat = get_fixed_value(label)
-        return nn.Sequential(*cls._replicate_and_instantiate(blocks, repeat))
+                            depth: Union[int, Tuple[int, int], ValueChoice], *, label: Optional[str] = None):
+        if isinstance(depth, tuple):
+            # we can't create a value choice here,
+            # otherwise we will have two value choices, one created here, another in init.
+            depth = get_fixed_value(label)
+
+        if isinstance(depth, int):
+            # if depth is a valuechoice, it should be already an int
+            result = nn.Sequential(*cls._replicate_and_instantiate(blocks, depth))
+
+            if hasattr(result, STATE_DICT_PY_MAPPING_PARTIAL):
+                # already has a mapping, will merge with it
+                prev_mapping = getattr(result, STATE_DICT_PY_MAPPING_PARTIAL)
+                setattr(result, STATE_DICT_PY_MAPPING_PARTIAL, {k: f'blocks.{v}' for k, v in prev_mapping.items()})
+            else:
+                setattr(result, STATE_DICT_PY_MAPPING_PARTIAL, {'__self__': 'blocks'})
+
+            return result
+
+        raise NoContextError(f'Not in fixed mode, or {depth} not an integer.')
 
     def __init__(self,
                  blocks: Union[Callable[[int], nn.Module],
@@ -47,14 +92,39 @@ class Repeat(Mutable):
                                List[nn.Module]],
                  depth: Union[int, Tuple[int, int]], *, label: Optional[str] = None):
         super().__init__()
-        self._label = generate_new_label(label)
-        self.min_depth = depth if isinstance(depth, int) else depth[0]
-        self.max_depth = depth if isinstance(depth, int) else depth[1]
+
+        self._label = None  # by default, no label
+
+        if isinstance(depth, ValueChoiceX):
+            if label is not None:
+                warnings.warn(
+                    'In repeat, `depth` is already a ValueChoice, but `label` is still set. It will be ignored.',
+                    RuntimeWarning
+                )
+            self.depth_choice = depth
+            all_values = list(self.depth_choice.all_options())
+            self.min_depth = min(all_values)
+            self.max_depth = max(all_values)
+
+            if isinstance(depth, ValueChoice):
+                self._label = depth.label  # if a leaf node
+
+        elif isinstance(depth, tuple):
+            self.min_depth = depth if isinstance(depth, int) else depth[0]
+            self.max_depth = depth if isinstance(depth, int) else depth[1]
+            self.depth_choice = ValueChoice(list(range(self.min_depth, self.max_depth + 1)), label=label)
+            self._label = self.depth_choice.label
+
+        elif isinstance(depth, int):
+            self.min_depth = self.max_depth = depth
+            self.depth_choice = depth
+        else:
+            raise TypeError(f'Unsupported "depth" type: {type(depth)}')
         assert self.max_depth >= self.min_depth > 0
         self.blocks = nn.ModuleList(self._replicate_and_instantiate(blocks, self.max_depth))
 
     @property
-    def label(self):
+    def label(self) -> Optional[str]:
         return self._label
 
     def forward(self, x):
@@ -76,10 +146,19 @@ class Repeat(Mutable):
             blocks = [b(i) for i, b in enumerate(blocks)]
         return blocks
 
+    def __getitem__(self, index):
+        # shortcut for blocks[index]
+        return self.blocks[index]
+
+    def __len__(self):
+        return self.max_depth
+
 
 class NasBench201Cell(nn.Module):
     """
-    Cell structure that is proposed in NAS-Bench-201 [nasbench201]_ .
+    Cell structure that is proposed in NAS-Bench-201.
+
+    Proposed by `NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search <https://arxiv.org/abs/2001.00326>`__.
 
     This cell is a densely connected DAG with ``num_tensors`` nodes, where each node is tensor.
     For every i < j, there is an edge from i-th node to j-th node.
@@ -105,11 +184,6 @@ class NasBench201Cell(nn.Module):
         Number of tensors in the cell (input included). Default: 4
     label : str
         Identifier of the cell. Cell sharing the same label will semantically share the same choice.
-
-    References
-    ----------
-    .. [nasbench201] Dong, X. and Yang, Y., 2020. Nas-bench-201: Extending the scope of reproducible neural architecture search.
-        arXiv preprint arXiv:2001.00326.
     """
 
     @staticmethod
@@ -141,6 +215,10 @@ class NasBench201Cell(nn.Module):
             self.layers.append(node_ops)
 
     def forward(self, inputs):
+        """
+        The forward of input choice is simply selecting first on all choices.
+        It shouldn't be called directly by users in most cases.
+        """
         tensors = [inputs]
         for layer in self.layers:
             current_tensor = []
diff --git a/nni/retiarii/nn/pytorch/hypermodule.py b/nni/retiarii/nn/pytorch/hypermodule.py
index 771d84162..649489b7f 100644
--- a/nni/retiarii/nn/pytorch/hypermodule.py
+++ b/nni/retiarii/nn/pytorch/hypermodule.py
@@ -1,14 +1,14 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
 
+from packaging.version import Version
 import torch
 import torch.nn as nn
 
 from nni.retiarii.serializer import basic_unit
 
 from .api import LayerChoice
-from .utils import generate_new_label
-from ...utils import version_larger_equal
+from .mutation_utils import generate_new_label
 
 __all__ = ['AutoActivation']
 
@@ -99,7 +99,7 @@ class UnaryTanh(nn.Module):
     def forward(self, x):
         return torch.tanh(x)
 
-if not version_larger_equal(torch.__version__, TorchVersion):
+if not Version(torch.__version__) >= Version(TorchVersion):
     @basic_unit
     class UnaryAsinh(nn.Module):
         def forward(self, x):
@@ -110,7 +110,7 @@ class UnaryAtan(nn.Module):
     def forward(self, x):
         return torch.atan(x)
 
-if not version_larger_equal(torch.__version__, TorchVersion):
+if not Version(torch.__version__) >= Version(TorchVersion):
     @basic_unit
     class UnarySinc(nn.Module):
         def forward(self, x):
@@ -151,7 +151,7 @@ unary_modules = ['UnaryIdentity', 'UnaryNegative', 'UnaryAbs', 'UnarySquare', 'U
     'UnarySinh', 'UnaryCosh', 'UnaryTanh', 'UnaryAtan', 'UnaryMax',
     'UnaryMin', 'UnarySigmoid', 'UnaryLogExp', 'UnaryExpSquare', 'UnaryErf']
 
-if not version_larger_equal(torch.__version__, TorchVersion):
+if not Version(torch.__version__) >= Version(TorchVersion):
     unary_modules.append('UnaryAsinh')
     unary_modules.append('UnarySinc')
 
@@ -222,14 +222,16 @@ binary_modules = ['BinaryAdd', 'BinaryMul', 'BinaryMinus', 'BinaryDivide', 'Bina
 
 class AutoActivation(nn.Module):
     """
-    This module is an implementation of the paper "Searching for Activation Functions"
-    (https://arxiv.org/abs/1710.05941).
-    NOTE: current `beta` is not per-channel parameter
+    This module is an implementation of the paper `Searching for Activation Functions <https://arxiv.org/abs/1710.05941>`__.
 
     Parameters
     ----------
     unit_num : int
         the number of core units
+
+    Notes
+    -----
+    Current `beta` is not per-channel parameter.
     """
     def __init__(self, unit_num: int = 1, label: str = None):
         super().__init__()
diff --git a/nni/retiarii/nn/pytorch/utils.py b/nni/retiarii/nn/pytorch/mutation_utils.py
similarity index 100%
rename from nni/retiarii/nn/pytorch/utils.py
rename to nni/retiarii/nn/pytorch/mutation_utils.py
diff --git a/nni/retiarii/nn/pytorch/mutator.py b/nni/retiarii/nn/pytorch/mutator.py
index 6d1be83d4..3103faa2b 100644
--- a/nni/retiarii/nn/pytorch/mutator.py
+++ b/nni/retiarii/nn/pytorch/mutator.py
@@ -2,19 +2,18 @@
 # Licensed under the MIT license.
 
 import inspect
-from collections import defaultdict
-from typing import Any, List, Optional, Tuple, Dict
+from typing import Any, List, Optional, Tuple, Dict, Iterator
 
 import torch.nn as nn
 
-from nni.common.serializer import is_traceable
+from nni.common.serializer import is_traceable, is_wrapped_with_trace
 from nni.retiarii.graph import Cell, Graph, Model, ModelStatus, Node, Evaluator
 from nni.retiarii.mutator import Mutator
 from nni.retiarii.serializer import is_basic_unit, is_model_wrapped
-from nni.retiarii.utils import uid
+from nni.retiarii.utils import ModelNamespace, uid
 
 from .api import LayerChoice, InputChoice, ValueChoice, ValueChoiceX, Placeholder
-from .component import Repeat, NasBench101Cell, NasBench101Mutator
+from .component import NasBench101Cell, NasBench101Mutator
 
 
 class LayerChoiceMutator(Mutator):
@@ -144,14 +143,15 @@ class RepeatMutator(Mutator):
         return chain
 
     def mutate(self, model):
-        min_depth = self.nodes[0].operation.parameters['min_depth']
-        max_depth = self.nodes[0].operation.parameters['max_depth']
-        if min_depth < max_depth:
-            chosen_depth = self.choice(list(range(min_depth, max_depth + 1)))
         for node in self.nodes:
             # the logic here is similar to layer choice. We find cell attached to each node.
             target: Graph = model.graphs[node.operation.cell_name]
             chain = self._retrieve_chain_from_graph(target)
+            # and we get the chosen depth (by value choice)
+            node_in_model = model.get_node_by_name(node.name)
+            # depth is a value choice in base model
+            # but it's already mutated by a ParameterChoiceMutator here
+            chosen_depth = node_in_model.operation.parameters['depth']
             for edge in chain[chosen_depth - 1].outgoing_edges:
                 edge.remove()
             target.add_edge((chain[chosen_depth - 1], None), (target.output_node, None))
@@ -184,6 +184,8 @@ def process_inline_mutation(model: Model) -> Optional[List[Mutator]]:
     # `pc_nodes` are arguments of basic units. They can be compositions.
     pc_nodes: List[Tuple[Node, str, ValueChoiceX]] = []
     for node in model.get_nodes():
+        # arguments used in operators like Conv2d
+        # argument `valuechoice` used in generated repeat cell
         for name, choice in node.operation.parameters.items():
             if isinstance(choice, ValueChoiceX):
                 # e.g., (conv_node, "out_channels", ValueChoice([1, 3]))
@@ -219,9 +221,10 @@ def process_inline_mutation(model: Model) -> Optional[List[Mutator]]:
     repeat_nodes = _group_by_label(filter(lambda d: d.operation.parameters.get('mutation') == 'repeat',
                                           model.get_nodes_by_type('_cell')))
     for node_list in repeat_nodes:
+        # this check is not completely reliable, because it only checks max and min
         assert _is_all_equal(map(lambda node: node.operation.parameters['max_depth'], node_list)) and \
             _is_all_equal(map(lambda node: node.operation.parameters['min_depth'], node_list)), \
-            'Repeat with the same label must have the same number of candidates.'
+            'Repeat with the same label must have the same candidates.'
         mutator = RepeatMutator(node_list)
         applied_mutators.append(mutator)
 
@@ -281,6 +284,13 @@ def extract_mutation_from_pt_module(pytorch_model: nn.Module) -> Tuple[Model, Op
     else:
         model.python_init_params = {}
 
+    # hyper-parameter choice
+    namespace: ModelNamespace = pytorch_model._model_namespace
+    for param_spec in namespace.parameter_specs:
+        assert param_spec.categorical and param_spec.type == 'choice'
+        node = graph.add_node(f'param_spec_{param_spec.name}', 'ModelParameterChoice', {'candidates': param_spec.values})
+        node.label = param_spec.name
+
     for name, module in pytorch_model.named_modules():
         # tricky case: value choice that serves as parameters are stored in traced arguments
         if is_basic_unit(module):
@@ -300,14 +310,10 @@ def extract_mutation_from_pt_module(pytorch_model: nn.Module) -> Tuple[Model, Op
             node = graph.add_node(name, 'InputChoice',
                                   {'n_candidates': module.n_candidates, 'n_chosen': module.n_chosen})
             node.label = module.label
-        if isinstance(module, ValueChoice):
-            node = graph.add_node(name, 'ValueChoice', {'candidates': module.candidates})
-            node.label = module.label
-        if isinstance(module, Repeat) and module.min_depth <= module.max_depth:
-            node = graph.add_node(name, 'Repeat', {
-                'candidates': list(range(module.min_depth, module.max_depth + 1))
-            })
-            node.label = module.label
+        if isinstance(module, ValueChoiceX):
+            for i, choice in enumerate(module.inner_choices()):
+                node = graph.add_node(f'{name}.{i}', 'ValueChoice', {'candidates': choice.candidates})
+                node.label = choice.label
         if isinstance(module, NasBench101Cell):
             node = graph.add_node(name, 'NasBench101Cell', {
                 'max_num_edges': module.max_num_edges
@@ -354,55 +360,68 @@ class EvaluatorValueChoiceMutator(Mutator):
     # works in the same way as `ParameterChoiceMutator`
     # we only need one such mutator for one model/evaluator
 
-    def mutate(self, model: Model):
-        # make a copy to mutate the evaluator
-        model.evaluator = model.evaluator.trace_copy()
-
-        value_choice_decisions = {}
-        for mutation in model.history:
-            if isinstance(mutation.mutator, EvaluatorValueChoiceLeafMutator):
-                value_choice_decisions[mutation.mutator.label] = mutation.samples[0]
+    def _mutate_traceable_object(self, obj: Any, value_choice_decisions: Dict[str, Any]) -> Any:
+        if not _is_traceable_object(obj):
+            return obj
 
-        result = {}
+        updates = {}
 
-        # for each argument that is a composition of value choice
+        # For each argument that is a composition of value choice
         # we find all the leaf-value-choice in the mutation
-        # and compute the final result
-        for key, param in model.evaluator.trace_kwargs.items():
+        # and compute the final updates
+        for key, param in obj.trace_kwargs.items():
             if isinstance(param, ValueChoiceX):
                 leaf_node_values = [value_choice_decisions[choice.label] for choice in param.inner_choices()]
-                result[key] = param.evaluate(leaf_node_values)
+                updates[key] = param.evaluate(leaf_node_values)
+            elif is_traceable(param):
+                # Recursively
+                sub_update = self._mutate_traceable_object(param, value_choice_decisions)
+                if sub_update is not param:  # if mutated
+                    updates[key] = sub_update
+
+        if updates:
+            mutated_obj = obj.trace_copy()                  # Make a copy
+            mutated_obj.trace_kwargs.update(updates)        # Mutate
+            mutated_obj = mutated_obj.get()                 # Instantiate the full mutated object
+
+            return mutated_obj
 
-        model.evaluator.trace_kwargs.update(result)
+        return obj
+
+    def mutate(self, model: Model):
+        value_choice_decisions = {}
+        for mutation in model.history:
+            if isinstance(mutation.mutator, EvaluatorValueChoiceLeafMutator):
+                value_choice_decisions[mutation.mutator.label] = mutation.samples[0]
+
+        model.evaluator = self._mutate_traceable_object(model.evaluator, value_choice_decisions)
 
 
 def process_evaluator_mutations(evaluator: Evaluator, existing_mutators: List[Mutator]) -> List[Mutator]:
     # take all the value choice in the kwargs of evaluaator into a list
     # `existing_mutators` can mutators generated from `model`
-    if not is_traceable(evaluator):
+    if not _is_traceable_object(evaluator):
         return []
     mutator_candidates = {}
-    mutator_keys = defaultdict(list)
-    for key, param in evaluator.trace_kwargs.items():
+    for param in _expand_nested_trace_kwargs(evaluator):
         if isinstance(param, ValueChoiceX):
             for choice in param.inner_choices():
                 # merge duplicate labels
                 for mutator in existing_mutators:
-                    if mutator.name == choice.label:
+                    if mutator.label == choice.label:
                         raise ValueError(
                             f'Found duplicated labels “{choice.label}”. When two value choices have the same name, '
-                            'they would share choices. However, sharing choices between model and evaluator is not yet supported.'
+                            'they would share choices. However, sharing choices between model and evaluator is not supported.'
                         )
                 if choice.label in mutator_candidates and mutator_candidates[choice.label] != choice.candidates:
                     raise ValueError(
                         f'Duplicate labels for evaluator ValueChoice {choice.label}. They should share choices.'
                         f'But their candidate list is not equal: {mutator_candidates[choice.label][1]} vs. {choice.candidates}'
                     )
-                mutator_keys[choice.label].append(key)
                 mutator_candidates[choice.label] = choice.candidates
     mutators = []
-    for label in mutator_keys:
-        mutators.append(EvaluatorValueChoiceLeafMutator(mutator_candidates[label], label))
+    for label, candidates in mutator_candidates.items():
+        mutators.append(EvaluatorValueChoiceLeafMutator(candidates, label))
     if mutators:
         # one last mutator to actually apply the mutations
         mutators.append(EvaluatorValueChoiceMutator())
@@ -453,3 +472,18 @@ def _group_by_label(nodes: List[Node]) -> List[List[Node]]:
             result[label] = []
         result[label].append(node)
     return list(result.values())
+
+
+def _expand_nested_trace_kwargs(obj: Any) -> Iterator[Any]:
+    # Get items from `trace_kwargs`.
+    # If some item is traceable itself, get items recursively.
+
+    if _is_traceable_object(obj):
+        for param in obj.trace_kwargs.values():
+            yield param
+            yield from _expand_nested_trace_kwargs(param)
+
+
+def _is_traceable_object(obj: Any) -> bool:
+    # Is it a traceable "object" (not class)?
+    return is_traceable(obj) and not is_wrapped_with_trace(obj)
diff --git a/nni/retiarii/nn/pytorch/nasbench101.py b/nni/retiarii/nn/pytorch/nasbench101.py
index 5671af1f6..02b6582c0 100644
--- a/nni/retiarii/nn/pytorch/nasbench101.py
+++ b/nni/retiarii/nn/pytorch/nasbench101.py
@@ -9,7 +9,7 @@ import torch.nn as nn
 from nni.retiarii.mutator import InvalidMutation, Mutator
 from nni.retiarii.graph import Model
 from .api import InputChoice, ValueChoice, LayerChoice
-from .utils import Mutable, generate_new_label, get_fixed_dict
+from .mutation_utils import Mutable, generate_new_label, get_fixed_dict
 
 _logger = logging.getLogger(__name__)
 
@@ -219,11 +219,13 @@ class _NasBench101CellFixed(nn.Module):
 
 class NasBench101Cell(Mutable):
     """
-    Cell structure that is proposed in NAS-Bench-101 [nasbench101]_ .
+    Cell structure that is proposed in NAS-Bench-101.
 
-    This cell is usually used in evaluation of NAS algorithms because there is a ``comprehensive analysis'' of this search space
-    available, which includes a full architecture-dataset that ``maps 423k unique architectures to metrics
-    including run time and accuracy''. You can also use the space in your own space design, in which scenario it should be possible
+    Proposed by `NAS-Bench-101: Towards Reproducible Neural Architecture Search <http://proceedings.mlr.press/v97/ying19a/ying19a.pdf>`__.
+
+    This cell is usually used in evaluation of NAS algorithms because there is a "comprehensive analysis" of this search space
+    available, which includes a full architecture-dataset that "maps 423k unique architectures to metrics
+    including run time and accuracy". You can also use the space in your own space design, in which scenario it should be possible
     to leverage results in the benchmark to narrow the huge space down to a few efficient architectures.
 
     The space of this cell architecture consists of all possible directed acyclic graphs on no more than ``max_num_nodes`` nodes,
@@ -232,7 +234,7 @@ class NasBench101Cell(Mutable):
     To align with the paper settings, two vertices specially labeled as operation IN and OUT, are also counted into
     ``max_num_nodes`` in our implementaion, the default value of ``max_num_nodes`` is 7 and ``max_num_edges`` is 9.
 
-    Input of this cell should be of shape :math:`[N, C_{in}, *]`, while output should be `[N, C_{out}, *]`. The shape
+    Input of this cell should be of shape :math:`[N, C_{in}, *]`, while output should be :math:`[N, C_{out}, *]`. The shape
     of each hidden nodes will be first automatically computed, depending on the cell structure. Each of the ``op_candidates``
     should be a callable that accepts computed ``num_features`` and returns a ``Module``. For example,
 
@@ -276,10 +278,9 @@ class NasBench101Cell(Mutable):
     label : str
         Identifier of the cell. Cell sharing the same label will semantically share the same choice.
 
-    References
-    ----------
-    .. [nasbench101] Ying, Chris, et al. "Nas-bench-101: Towards reproducible neural architecture search."
-        International Conference on Machine Learning. PMLR, 2019.
+    Warnings
+    --------
+    :class:`NasBench101Cell` is not supported in :ref:`graph-based execution engine <graph-based-execution-engine>`.
     """
 
     @staticmethod
@@ -304,6 +305,8 @@ class NasBench101Cell(Mutable):
             [op_candidates[selected[f'{label}/op{i}']] for i in range(1, num_nodes - 1)],
             adjacency_list, in_features, out_features, num_nodes, projection)
 
+        # FIXME: weight inheritance on nasbench101 is not supported yet
+
     def __init__(self, op_candidates: Union[Dict[str, Callable[[int], nn.Module]], List[Callable[[int], nn.Module]]],
                  in_features: int, out_features: int, projection: Callable[[int, int], nn.Module],
                  max_num_nodes: int = 7, max_num_edges: int = 9, label: Optional[str] = None):
@@ -339,7 +342,10 @@ class NasBench101Cell(Mutable):
         return self._label
 
     def forward(self, x):
-        # This is a dummy forward and actually not used
+        """
+        The forward of input choice is simply selecting first on all choices.
+        It shouldn't be called directly by users in most cases.
+        """
         tensors = [x]
         for i in range(1, self.max_num_nodes):
             node_input = self.inputs[i]([self.projections[i](tensors[0])] + [t for t in tensors[1:]])
diff --git a/nni/retiarii/nn/pytorch/nn.py b/nni/retiarii/nn/pytorch/nn.py
index 6b79af636..7de8ea000 100644
--- a/nni/retiarii/nn/pytorch/nn.py
+++ b/nni/retiarii/nn/pytorch/nn.py
@@ -1,158 +1,89 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
 
+import inspect
+import warnings
+from pathlib import Path
+
 import torch
 import torch.nn as nn
 
-from ...serializer import basic_unit
-from ...utils import version_larger_equal
-
-# NOTE: support pytorch version >= 1.5.0
-
-__all__ = [
-    'Module', 'Sequential', 'ModuleList',  # TODO: 'ModuleDict', 'ParameterList', 'ParameterDict',
-    'Identity', 'Linear', 'Conv1d', 'Conv2d', 'Conv3d', 'ConvTranspose1d',
-    'ConvTranspose2d', 'ConvTranspose3d', 'Threshold', 'ReLU', 'Hardtanh', 'ReLU6',
-    'Sigmoid', 'Tanh', 'Softmax', 'Softmax2d', 'LogSoftmax', 'ELU', 'SELU', 'CELU', 'GLU', 'GELU', 'Hardshrink',
-    'LeakyReLU', 'LogSigmoid', 'Softplus', 'Softshrink', 'MultiheadAttention', 'PReLU', 'Softsign', 'Softmin',
-    'Tanhshrink', 'RReLU', 'AvgPool1d', 'AvgPool2d', 'AvgPool3d', 'MaxPool1d', 'MaxPool2d',
-    'MaxPool3d', 'MaxUnpool1d', 'MaxUnpool2d', 'MaxUnpool3d', 'FractionalMaxPool2d', "FractionalMaxPool3d",
-    'LPPool1d', 'LPPool2d', 'LocalResponseNorm', 'BatchNorm1d', 'BatchNorm2d', 'BatchNorm3d', 'InstanceNorm1d',
-    'InstanceNorm2d', 'InstanceNorm3d', 'LayerNorm', 'GroupNorm', 'SyncBatchNorm',
-    'Dropout', 'Dropout2d', 'Dropout3d', 'AlphaDropout', 'FeatureAlphaDropout',
-    'ReflectionPad1d', 'ReflectionPad2d', 'ReplicationPad2d', 'ReplicationPad1d', 'ReplicationPad3d',
-    'CrossMapLRN2d', 'Embedding', 'EmbeddingBag', 'RNNBase', 'RNN', 'LSTM', 'GRU', 'RNNCellBase', 'RNNCell',
-    'LSTMCell', 'GRUCell', 'PixelShuffle', 'Upsample', 'UpsamplingNearest2d', 'UpsamplingBilinear2d',
-    'PairwiseDistance', 'AdaptiveMaxPool1d', 'AdaptiveMaxPool2d', 'AdaptiveMaxPool3d', 'AdaptiveAvgPool1d',
-    'AdaptiveAvgPool2d', 'AdaptiveAvgPool3d', 'TripletMarginLoss', 'ZeroPad2d', 'ConstantPad1d', 'ConstantPad2d',
-    'ConstantPad3d', 'Bilinear', 'CosineSimilarity', 'Unfold', 'Fold',
-    'AdaptiveLogSoftmaxWithLoss', 'TransformerEncoder', 'TransformerDecoder',
-    'TransformerEncoderLayer', 'TransformerDecoderLayer', 'Transformer',
-    'Flatten', 'Hardsigmoid'
-]
-
-if version_larger_equal(torch.__version__, '1.6.0'):
-    __all__.append('Hardswish')
-
-if version_larger_equal(torch.__version__, '1.7.0'):
-    __all__.extend(['Unflatten', 'SiLU', 'TripletMarginWithDistanceLoss'])
-
-
-Module = nn.Module
-
-Sequential = nn.Sequential
-ModuleList = basic_unit(nn.ModuleList, basic_unit_tag=False)
-
-Identity = basic_unit(nn.Identity)
-Linear = basic_unit(nn.Linear)
-Conv1d = basic_unit(nn.Conv1d)
-Conv2d = basic_unit(nn.Conv2d)
-Conv3d = basic_unit(nn.Conv3d)
-ConvTranspose1d = basic_unit(nn.ConvTranspose1d)
-ConvTranspose2d = basic_unit(nn.ConvTranspose2d)
-ConvTranspose3d = basic_unit(nn.ConvTranspose3d)
-Threshold = basic_unit(nn.Threshold)
-ReLU = basic_unit(nn.ReLU)
-Hardtanh = basic_unit(nn.Hardtanh)
-ReLU6 = basic_unit(nn.ReLU6)
-Sigmoid = basic_unit(nn.Sigmoid)
-Tanh = basic_unit(nn.Tanh)
-Softmax = basic_unit(nn.Softmax)
-Softmax2d = basic_unit(nn.Softmax2d)
-LogSoftmax = basic_unit(nn.LogSoftmax)
-ELU = basic_unit(nn.ELU)
-SELU = basic_unit(nn.SELU)
-CELU = basic_unit(nn.CELU)
-GLU = basic_unit(nn.GLU)
-GELU = basic_unit(nn.GELU)
-Hardshrink = basic_unit(nn.Hardshrink)
-LeakyReLU = basic_unit(nn.LeakyReLU)
-LogSigmoid = basic_unit(nn.LogSigmoid)
-Softplus = basic_unit(nn.Softplus)
-Softshrink = basic_unit(nn.Softshrink)
-MultiheadAttention = basic_unit(nn.MultiheadAttention)
-PReLU = basic_unit(nn.PReLU)
-Softsign = basic_unit(nn.Softsign)
-Softmin = basic_unit(nn.Softmin)
-Tanhshrink = basic_unit(nn.Tanhshrink)
-RReLU = basic_unit(nn.RReLU)
-AvgPool1d = basic_unit(nn.AvgPool1d)
-AvgPool2d = basic_unit(nn.AvgPool2d)
-AvgPool3d = basic_unit(nn.AvgPool3d)
-MaxPool1d = basic_unit(nn.MaxPool1d)
-MaxPool2d = basic_unit(nn.MaxPool2d)
-MaxPool3d = basic_unit(nn.MaxPool3d)
-MaxUnpool1d = basic_unit(nn.MaxUnpool1d)
-MaxUnpool2d = basic_unit(nn.MaxUnpool2d)
-MaxUnpool3d = basic_unit(nn.MaxUnpool3d)
-FractionalMaxPool2d = basic_unit(nn.FractionalMaxPool2d)
-FractionalMaxPool3d = basic_unit(nn.FractionalMaxPool3d)
-LPPool1d = basic_unit(nn.LPPool1d)
-LPPool2d = basic_unit(nn.LPPool2d)
-LocalResponseNorm = basic_unit(nn.LocalResponseNorm)
-BatchNorm1d = basic_unit(nn.BatchNorm1d)
-BatchNorm2d = basic_unit(nn.BatchNorm2d)
-BatchNorm3d = basic_unit(nn.BatchNorm3d)
-InstanceNorm1d = basic_unit(nn.InstanceNorm1d)
-InstanceNorm2d = basic_unit(nn.InstanceNorm2d)
-InstanceNorm3d = basic_unit(nn.InstanceNorm3d)
-LayerNorm = basic_unit(nn.LayerNorm)
-GroupNorm = basic_unit(nn.GroupNorm)
-SyncBatchNorm = basic_unit(nn.SyncBatchNorm)
-Dropout = basic_unit(nn.Dropout)
-Dropout2d = basic_unit(nn.Dropout2d)
-Dropout3d = basic_unit(nn.Dropout3d)
-AlphaDropout = basic_unit(nn.AlphaDropout)
-FeatureAlphaDropout = basic_unit(nn.FeatureAlphaDropout)
-ReflectionPad1d = basic_unit(nn.ReflectionPad1d)
-ReflectionPad2d = basic_unit(nn.ReflectionPad2d)
-ReplicationPad2d = basic_unit(nn.ReplicationPad2d)
-ReplicationPad1d = basic_unit(nn.ReplicationPad1d)
-ReplicationPad3d = basic_unit(nn.ReplicationPad3d)
-CrossMapLRN2d = basic_unit(nn.CrossMapLRN2d)
-Embedding = basic_unit(nn.Embedding)
-EmbeddingBag = basic_unit(nn.EmbeddingBag)
-RNNBase = basic_unit(nn.RNNBase)
-RNN = basic_unit(nn.RNN)
-LSTM = basic_unit(nn.LSTM)
-GRU = basic_unit(nn.GRU)
-RNNCellBase = basic_unit(nn.RNNCellBase)
-RNNCell = basic_unit(nn.RNNCell)
-LSTMCell = basic_unit(nn.LSTMCell)
-GRUCell = basic_unit(nn.GRUCell)
-PixelShuffle = basic_unit(nn.PixelShuffle)
-Upsample = basic_unit(nn.Upsample)
-UpsamplingNearest2d = basic_unit(nn.UpsamplingNearest2d)
-UpsamplingBilinear2d = basic_unit(nn.UpsamplingBilinear2d)
-PairwiseDistance = basic_unit(nn.PairwiseDistance)
-AdaptiveMaxPool1d = basic_unit(nn.AdaptiveMaxPool1d)
-AdaptiveMaxPool2d = basic_unit(nn.AdaptiveMaxPool2d)
-AdaptiveMaxPool3d = basic_unit(nn.AdaptiveMaxPool3d)
-AdaptiveAvgPool1d = basic_unit(nn.AdaptiveAvgPool1d)
-AdaptiveAvgPool2d = basic_unit(nn.AdaptiveAvgPool2d)
-AdaptiveAvgPool3d = basic_unit(nn.AdaptiveAvgPool3d)
-TripletMarginLoss = basic_unit(nn.TripletMarginLoss)
-ZeroPad2d = basic_unit(nn.ZeroPad2d)
-ConstantPad1d = basic_unit(nn.ConstantPad1d)
-ConstantPad2d = basic_unit(nn.ConstantPad2d)
-ConstantPad3d = basic_unit(nn.ConstantPad3d)
-Bilinear = basic_unit(nn.Bilinear)
-CosineSimilarity = basic_unit(nn.CosineSimilarity)
-Unfold = basic_unit(nn.Unfold)
-Fold = basic_unit(nn.Fold)
-AdaptiveLogSoftmaxWithLoss = basic_unit(nn.AdaptiveLogSoftmaxWithLoss)
-TransformerEncoder = basic_unit(nn.TransformerEncoder)
-TransformerDecoder = basic_unit(nn.TransformerDecoder)
-TransformerEncoderLayer = basic_unit(nn.TransformerEncoderLayer)
-TransformerDecoderLayer = basic_unit(nn.TransformerDecoderLayer)
-Transformer = basic_unit(nn.Transformer)
-Flatten = basic_unit(nn.Flatten)
-Hardsigmoid = basic_unit(nn.Hardsigmoid)
-
-if version_larger_equal(torch.__version__, '1.6.0'):
-    Hardswish = basic_unit(nn.Hardswish)
-
-if version_larger_equal(torch.__version__, '1.7.0'):
-    SiLU = basic_unit(nn.SiLU)
-    Unflatten = basic_unit(nn.Unflatten)
-    TripletMarginWithDistanceLoss = basic_unit(nn.TripletMarginWithDistanceLoss)
+# To make auto-completion happy, we generate a _nn.py that lists out all the classes.
+
+nn_cache_file_path = Path(__file__).parent / '_nn.py'
+
+cache_valid = False
+
+if nn_cache_file_path.exists():
+    from . import _nn  # pylint: disable=no-name-in-module
+    # valid only when torch version match
+    if _nn._torch_version == torch.__version__:
+        cache_valid = True
+
+if not cache_valid:
+    _NO_WRAP_CLASSES = [
+        # not an nn.Module
+        'Parameter',
+        'ParameterList',
+        'UninitializedBuffer',
+        'UninitializedParameter',
+
+        # arguments are special
+        'Module',
+        'Sequential',
+
+        # utilities
+        'Container',
+        'DataParallel',
+    ]
+
+    _WRAP_WITHOUT_TAG_CLASSES = [
+        # special support on graph engine
+        'ModuleList',
+        'ModuleDict',
+    ]
+
+    code = [
+        '# This file is auto-generated to make auto-completion work.',
+        '# When pytorch version does not match, it will get automatically updated.',
+        '# pylint: skip-file',
+        f'_torch_version = "{torch.__version__}"',
+        'import torch.nn as nn',
+        'from nni.retiarii.serializer import basic_unit',
+    ]
+
+    all_names = []
+
+    # Add modules, classes, functions in torch.nn into this module.
+    for name, obj in inspect.getmembers(torch.nn):
+        if inspect.isclass(obj):
+            if name in _NO_WRAP_CLASSES:
+                code.append(f'{name} = nn.{name}')
+            elif not issubclass(obj, nn.Module):
+                # It should never go here
+                # We did it to play safe
+                warnings.warn(f'{obj} is found to be not a nn.Module, which is unexpected. '
+                              'It means your PyTorch version might not be supported.', RuntimeWarning)
+                code.append(f'{name} = nn.{name}')
+            elif name in _WRAP_WITHOUT_TAG_CLASSES:
+                code.append(f'{name} = basic_unit(nn.{name}, basic_unit_tag=False)')
+            else:
+                code.append(f'{name} = basic_unit(nn.{name})')
+
+            all_names.append(name)
+
+        elif inspect.isfunction(obj) or inspect.ismodule(obj):
+            code.append(f'{name} = nn.{name}')  # no modification
+            all_names.append(name)
+
+    code.append(f'__all__ = {all_names}')
+
+    with nn_cache_file_path.open('w') as fp:
+        fp.write('\n'.join(code))
+
+
+# Import all modules from generated _nn.py
+
+from . import _nn  # pylint: disable=no-name-in-module
+__all__ = _nn.__all__
+from ._nn import *  # pylint: disable=import-error, wildcard-import
diff --git a/nni/retiarii/oneshot/pytorch/random.py b/nni/retiarii/oneshot/pytorch/random.py
index 794639c68..a47a667bd 100644
--- a/nni/retiarii/oneshot/pytorch/random.py
+++ b/nni/retiarii/oneshot/pytorch/random.py
@@ -143,7 +143,8 @@ class SinglePathTrainer(BaseOneShotTrainer):
 
         self.train_loader = torch.utils.data.DataLoader(self.dataset_train,
                                                         batch_size=batch_size,
-                                                        num_workers=workers)
+                                                        num_workers=workers,
+                                                        shuffle=True)
         self.valid_loader = torch.utils.data.DataLoader(self.dataset_valid,
                                                         batch_size=batch_size,
                                                         num_workers=workers)
diff --git a/nni/retiarii/serializer.py b/nni/retiarii/serializer.py
index 933475628..66827ec32 100644
--- a/nni/retiarii/serializer.py
+++ b/nni/retiarii/serializer.py
@@ -2,10 +2,11 @@
 # Licensed under the MIT license.
 
 import inspect
+import os
 import warnings
 from typing import Any, TypeVar, Union
 
-from nni.common.serializer import Traceable, is_traceable, trace, _copy_class_wrapper_attributes
+from nni.common.serializer import Traceable, is_traceable, is_wrapped_with_trace, trace, _copy_class_wrapper_attributes
 from .utils import ModelNamespace
 
 __all__ = ['get_init_parameters_or_fail', 'serialize', 'serialize_cls', 'basic_unit', 'model_wrapper',
@@ -64,7 +65,14 @@ def basic_unit(cls: T, basic_unit_tag: bool = True) -> Union[T, Traceable]:
         class PrimitiveOp(nn.Module):
             ...
     """
-    _check_wrapped(cls)
+
+    # Internal flag. See nni.trace
+    nni_trace_flag = os.environ.get('NNI_TRACE_FLAG', '')
+    if nni_trace_flag.lower() == 'disable':
+        return cls
+
+    if _check_wrapped(cls, 'basic_unit'):
+        return cls
 
     import torch.nn as nn
     assert issubclass(cls, nn.Module), 'When using @basic_unit, the class must be a subclass of nn.Module.'
@@ -72,15 +80,7 @@ def basic_unit(cls: T, basic_unit_tag: bool = True) -> Union[T, Traceable]:
     cls = trace(cls)
     cls._nni_basic_unit = basic_unit_tag
 
-    # HACK: for torch script
-    # https://github.com/pytorch/pytorch/pull/45261
-    # https://github.com/pytorch/pytorch/issues/54688
-    # I'm not sure whether there will be potential issues
-    import torch
-    cls._get_nni_attr = torch.jit.ignore(cls._get_nni_attr)
-    cls.trace_symbol = torch.jit.unused(cls.trace_symbol)
-    cls.trace_args = torch.jit.unused(cls.trace_args)
-    cls.trace_kwargs = torch.jit.unused(cls.trace_kwargs)
+    _torchscript_patch(cls)
 
     return cls
 
@@ -97,27 +97,40 @@ def model_wrapper(cls: T) -> Union[T, Traceable]:
 
     The wrapper serves two purposes:
 
-        1. Capture the init parameters of python class so that it can be re-instantiated in another process.
-        2. Reset uid in namespace so that the auto label counting in each model stably starts from zero.
+    1. Capture the init parameters of python class so that it can be re-instantiated in another process.
+    2. Reset uid in namespace so that the auto label counting in each model stably starts from zero.
 
     Currently, NNI might not complain in simple cases where ``@model_wrapper`` is actually not needed.
     But in future, we might enforce ``@model_wrapper`` to be required for base model.
     """
-    _check_wrapped(cls)
+
+    # Internal flag. See nni.trace
+    nni_trace_flag = os.environ.get('NNI_TRACE_FLAG', '')
+    if nni_trace_flag.lower() == 'disable':
+        return cls
+
+    if _check_wrapped(cls, 'model_wrapper'):
+        return cls
 
     import torch.nn as nn
     assert issubclass(cls, nn.Module)
 
-    wrapper = trace(cls)
+    # subclass can still use trace info
+    wrapper = trace(cls, inheritable=True)
 
     class reset_wrapper(wrapper):
         def __init__(self, *args, **kwargs):
-            with ModelNamespace():
+            self._model_namespace = ModelNamespace()
+            with self._model_namespace:
                 super().__init__(*args, **kwargs)
 
     _copy_class_wrapper_attributes(wrapper, reset_wrapper)
-    reset_wrapper.__wrapped__ = wrapper.__wrapped__
+    reset_wrapper.__wrapped__ = getattr(wrapper, '__wrapped__', wrapper)
     reset_wrapper._nni_model_wrapper = True
+    reset_wrapper._traced = True
+
+    _torchscript_patch(cls)
+
     return reset_wrapper
 
 
@@ -133,6 +146,38 @@ def is_model_wrapped(cls_or_instance) -> bool:
     return getattr(cls_or_instance, '_nni_model_wrapper', False)
 
 
-def _check_wrapped(cls: T) -> bool:
-    if getattr(cls, '_traced', False) or getattr(cls, '_nni_model_wrapper', False):
-        raise TypeError(f'{cls} is already wrapped with trace wrapper (basic_unit / model_wrapper / trace). Cannot wrap again.')
+def _check_wrapped(cls: T, rewrap: str) -> bool:
+    wrapped = None
+    if is_model_wrapped(cls):
+        wrapped = 'model_wrapper'
+    elif is_basic_unit(cls):
+        wrapped = 'basic_unit'
+    elif is_wrapped_with_trace(cls):
+        wrapped = 'nni.trace'
+    if wrapped:
+        if wrapped != rewrap:
+            raise TypeError(f'{cls} is already wrapped with {wrapped}. Cannot rewrap with {rewrap}.')
+        return True
+    return False
+
+
+def _torchscript_patch(cls) -> None:
+    # HACK: for torch script
+    # https://github.com/pytorch/pytorch/pull/45261
+    # https://github.com/pytorch/pytorch/issues/54688
+    # I'm not sure whether there will be potential issues
+    import torch
+    if hasattr(cls, '_get_nni_attr'):  # could not exist on non-linux
+        cls._get_nni_attr = torch.jit.ignore(cls._get_nni_attr)
+    if hasattr(cls, 'trace_symbol'):
+        # these must all exist or all non-exist
+        try:
+            cls.trace_symbol = torch.jit.unused(cls.trace_symbol)
+            cls.trace_args = torch.jit.unused(cls.trace_args)
+            cls.trace_kwargs = torch.jit.unused(cls.trace_kwargs)
+            cls.trace_copy = torch.jit.ignore(cls.trace_copy)
+        except AttributeError as e:
+            if 'property' in str(e):
+                raise RuntimeError('Trace on PyTorch module failed. Your PyTorch version might be outdated. '
+                                   'Please try to upgrade PyTorch.')
+            raise
diff --git a/nni/retiarii/strategy/__init__.py b/nni/retiarii/strategy/__init__.py
index 1614a7938..39cfaf6de 100644
--- a/nni/retiarii/strategy/__init__.py
+++ b/nni/retiarii/strategy/__init__.py
@@ -4,7 +4,7 @@
 from .base import BaseStrategy
 from .bruteforce import Random, GridSearch
 from .evolution import RegularizedEvolution
-from .tpe_strategy import TPEStrategy
+from .tpe_strategy import TPEStrategy, TPE
 from .local_debug_strategy import _LocalDebugStrategy
 from .rl import PolicyBasedRL
 from .oneshot import DARTS, Proxyless, GumbelDARTS, ENAS, RandomOneShot
diff --git a/nni/retiarii/strategy/_rl_impl.py b/nni/retiarii/strategy/_rl_impl.py
index ce51487a7..fb9bab0be 100644
--- a/nni/retiarii/strategy/_rl_impl.py
+++ b/nni/retiarii/strategy/_rl_impl.py
@@ -43,6 +43,17 @@ class MultiThreadEnvWorker(EnvWorker):
     def reset(self):
         return self.env.reset()
 
+    def send(self, action):
+        # for tianshou >= 0.4.6
+        if action is None:
+            self.result = self.pool.apply_async(self.env.reset)
+        else:
+            self.send_action(action)
+
+    def recv(self):
+        # for tianshou >= 0.4.6
+        return self.result.get()
+
     @staticmethod
     def wait(*args, **kwargs):
         raise NotImplementedError('Async collect is not supported yet.')
diff --git a/nni/retiarii/strategy/rl.py b/nni/retiarii/strategy/rl.py
index 23a05ed1e..5bc964e4d 100644
--- a/nni/retiarii/strategy/rl.py
+++ b/nni/retiarii/strategy/rl.py
@@ -23,7 +23,9 @@ class PolicyBasedRL(BaseStrategy):
     """
     Algorithm for policy-based reinforcement learning.
     This is a wrapper of algorithms provided in tianshou (PPO by default),
-    and can be easily customized with other algorithms that inherit ``BasePolicy`` (e.g., REINFORCE [1]_).
+    and can be easily customized with other algorithms that inherit ``BasePolicy``
+    (e.g., `REINFORCE <https://link.springer.com/content/pdf/10.1007/BF00992696.pdf>`__
+    as in `this paper <https://arxiv.org/abs/1611.01578>`__).
 
     Parameters
     ----------
@@ -33,13 +35,8 @@ class PolicyBasedRL(BaseStrategy):
         How many trials (trajectories) each time collector collects.
         After each collect, trainer will sample batch from replay buffer and do the update. Default: 20.
     policy_fn : function
-        Takes ``ModelEvaluationEnv`` as input and return a policy. See ``_default_policy_fn`` for an example.
-
-    References
-    ----------
-
-    .. [1] Barret Zoph and Quoc V. Le, "Neural Architecture Search with Reinforcement Learning".
-        https://arxiv.org/abs/1611.01578
+        Takes :class:`ModelEvaluationEnv` as input and return a policy.
+        See :meth:`PolicyBasedRL._default_policy_fn` for an example.
     """
 
     def __init__(self, max_collect: int = 100, trial_per_collect = 20,
diff --git a/nni/retiarii/strategy/tpe_strategy.py b/nni/retiarii/strategy/tpe_strategy.py
index 7f55ad302..68f1e11dc 100644
--- a/nni/retiarii/strategy/tpe_strategy.py
+++ b/nni/retiarii/strategy/tpe_strategy.py
@@ -39,17 +39,15 @@ class TPESampler(Sampler):
         return chosen
 
 
-class TPEStrategy(BaseStrategy):
+class TPE(BaseStrategy):
     """
-    The Tree-structured Parzen Estimator (TPE) [bergstrahpo]_ is a sequential model-based optimization (SMBO) approach.
-    SMBO methods sequentially construct models to approximate the performance of hyperparameters based on historical measurements,
-    and then subsequently choose new hyperparameters to test based on this model.
+    The Tree-structured Parzen Estimator (TPE) is a sequential model-based optimization (SMBO) approach.
 
-    References
-    ----------
+    Find the details in
+    `Algorithms for Hyper-Parameter Optimization <https://papers.nips.cc/paper/2011/file/86e8f7ab32cfd12577bc2619bc635690-Paper.pdf>`__.
 
-    .. [bergstrahpo] Bergstra et al., "Algorithms for Hyper-Parameter Optimization".
-        https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf
+    SMBO methods sequentially construct models to approximate the performance of hyperparameters based on historical measurements,
+    and then subsequently choose new hyperparameters to test based on this model.
     """
 
     def __init__(self):
@@ -92,3 +90,7 @@ class TPEStrategy(BaseStrategy):
                     to_be_deleted.append(_id)
             for _id in to_be_deleted:
                 del self.running_models[_id]
+
+
+# alias for backward compatibility
+TPEStrategy = TPE
diff --git a/nni/retiarii/utils.py b/nni/retiarii/utils.py
index 425d707ec..16e627fa3 100644
--- a/nni/retiarii/utils.py
+++ b/nni/retiarii/utils.py
@@ -2,11 +2,17 @@
 # Licensed under the MIT license.
 
 import inspect
+import itertools
 import warnings
 from collections import defaultdict
+from contextlib import contextmanager
 from typing import Any, List, Dict
 from pathlib import Path
 
+from nni.common.hpo_utils import ParameterSpec
+
+__all__ = ['NoContextError', 'ContextStack', 'ModelNamespace', 'original_state_dict_hooks']
+
 
 def import_(target: str, allow_none: bool = False) -> Any:
     if target is None:
@@ -16,13 +22,6 @@ def import_(target: str, allow_none: bool = False) -> Any:
     return getattr(module, identifier)
 
 
-def version_larger_equal(a: str, b: str) -> bool:
-    # TODO: refactor later
-    a = a.split('+')[0]
-    b = b.split('+')[0]
-    return tuple(map(int, a.split('.'))) >= tuple(map(int, b.split('.')))
-
-
 _last_uid = defaultdict(int)
 
 _DEFAULT_MODEL_NAMESPACE = 'model'
@@ -70,12 +69,13 @@ def get_importable_name(cls, relocate_module=False):
 
 
 class NoContextError(Exception):
+    """Exception raised when context is missing."""
     pass
 
 
 class ContextStack:
     """
-    This is to maintain a globally-accessible context envinronment that is visible to everywhere.
+    This is to maintain a globally-accessible context environment that is visible to everywhere.
 
     Use ``with ContextStack(namespace, value):`` to initiate, and use ``get_current_context(namespace)`` to
     get the corresponding value in the namespace.
@@ -113,44 +113,195 @@ class ContextStack:
 
 class ModelNamespace:
     """
-    To create an individual namespace for models to enable automatic numbering.
+    To create an individual namespace for models:
+
+    1. to enable automatic numbering;
+    2. to trace general information (like creation of hyper-parameters) of model.
+
+    A namespace is bounded to a key. Namespace bounded to different keys are completed isolated.
+    Namespace can have sub-namespaces (with the same key). The numbering will be chained (e.g., ``model_1_4_2``).
     """
 
     def __init__(self, key: str = _DEFAULT_MODEL_NAMESPACE):
         # for example, key: "model_wrapper"
         self.key = key
 
+        # the "path" of current name
+        # By default, it's ``[]``
+        # If a ``@model_wrapper`` is nested inside a model_wrapper, it will become something like ``[1, 3, 2]``.
+        # See ``__enter__``.
+        self.name_path: List[int] = []
+
+        # parameter specs.
+        # Currently only used trace calls of ModelParameterChoice.
+        self.parameter_specs: List[ParameterSpec] = []
+
     def __enter__(self):
         # For example, currently the top of stack is [1, 2, 2], and [1, 2, 2, 3] is used,
         # the next thing up is [1, 2, 2, 4].
         # `reset_uid` to count from zero for "model_wrapper_1_2_2_4"
         try:
-            current_context = ContextStack.top(self.key)
-            next_uid = uid(self._simple_name(self.key, current_context))
-            ContextStack.push(self.key, current_context + [next_uid])
-            reset_uid(self._simple_name(self.key, current_context + [next_uid]))
+            parent_context: 'ModelNamespace' = ModelNamespace.current_context(self.key)
+            next_uid = uid(parent_context._simple_name())
+            self.name_path = parent_context.name_path + [next_uid]
+            ContextStack.push(self.key, self)
+            reset_uid(self._simple_name())
         except NoContextError:
-            ContextStack.push(self.key, [])
-            reset_uid(self._simple_name(self.key, []))
+            # not found, no existing namespace
+            self.name_path = []
+            ContextStack.push(self.key, self)
+            reset_uid(self._simple_name())
 
     def __exit__(self, *args, **kwargs):
         ContextStack.pop(self.key)
 
+    def _simple_name(self) -> str:
+        return self.key + ''.join(['_' + str(k) for k in self.name_path])
+
+    def __repr__(self):
+        return f'ModelNamespace(name={self._simple_name()}, num_specs={len(self.parameter_specs)})'
+
+    # Access the current context in the model #
+
+    @staticmethod
+    def current_context(key: str = _DEFAULT_MODEL_NAMESPACE) -> 'ModelNamespace':
+        """Get the current context in key."""
+        try:
+            return ContextStack.top(key)
+        except NoContextError:
+            raise NoContextError('ModelNamespace context is missing. You might have forgotten to use `@model_wrapper`.')
+
     @staticmethod
     def next_label(key: str = _DEFAULT_MODEL_NAMESPACE) -> str:
+        """Get the next label for API calls, with automatic numbering."""
         try:
             current_context = ContextStack.top(key)
         except NoContextError:
             # fallback to use "default" namespace
-            return ModelNamespace._simple_name('default', [uid()])
-
-        next_uid = uid(ModelNamespace._simple_name(key, current_context))
-        return ModelNamespace._simple_name(key, current_context + [next_uid])
+            # it won't be registered
+            warnings.warn('ModelNamespace is missing. You might have forgotten to use `@model_wrapper`. '
+                          'Some features might not work. This will be an error in future releases.', RuntimeWarning)
+            current_context = ModelNamespace('default')
 
-    @staticmethod
-    def _simple_name(key: str, lst: List[Any]) -> str:
-        return key + ''.join(['_' + str(k) for k in lst])
+        next_uid = uid(current_context._simple_name())
+        return current_context._simple_name() + '_' + str(next_uid)
 
 
 def get_current_context(key: str) -> Any:
     return ContextStack.top(key)
+
+
+# map variables to prefix in the state dict
+# e.g., {'upsample': 'mynet.module.deconv2.upsample_layer'}
+STATE_DICT_PY_MAPPING = '_mapping_'
+
+# map variables to `prefix`.`value` in the state dict
+# e.g., {'upsample': 'choice3.upsample_layer'},
+# which actually means {'upsample': 'mynet.module.choice3.upsample_layer'},
+# and 'upsample' is also in `mynet.module`.
+STATE_DICT_PY_MAPPING_PARTIAL = '_mapping_partial_'
+
+
+@contextmanager
+def original_state_dict_hooks(model: Any):
+    """
+    Use this patch if you want to save/load state dict in the original state dict hierarchy.
+
+    For example, when you already have a state dict for the base model / search space (which often
+    happens when you have trained a supernet with one-shot strategies), the state dict isn't organized
+    in the same way as when a sub-model is sampled from the search space. This patch will help
+    the modules in the sub-model find the corresponding module in the base model.
+
+    The code looks like,
+
+    .. code-block:: python
+
+        with original_state_dict_hooks(model):
+            model.load_state_dict(state_dict_from_supernet, strict=False)  # supernet has extra keys
+
+    Or vice-versa,
+
+    .. code-block:: python
+
+        with original_state_dict_hooks(model):
+            supernet_style_state_dict = model.state_dict()
+    """
+
+    import torch.nn as nn
+    assert isinstance(model, nn.Module), 'PyTorch is the only supported framework for now.'
+
+    # the following are written for pytorch only
+
+    # first get the full mapping
+    full_mapping = {}
+
+    def full_mapping_in_module(src_prefix, tar_prefix, module):
+        if hasattr(module, STATE_DICT_PY_MAPPING):
+            # only values are complete
+            local_map = getattr(module, STATE_DICT_PY_MAPPING)
+        elif hasattr(module, STATE_DICT_PY_MAPPING_PARTIAL):
+            # keys and values are both incomplete
+            local_map = getattr(module, STATE_DICT_PY_MAPPING_PARTIAL)
+            local_map = {k: tar_prefix + v for k, v in local_map.items()}
+        else:
+            # no mapping
+            local_map = {}
+
+        if '__self__' in local_map:
+            # special case, overwrite prefix
+            tar_prefix = local_map['__self__'] + '.'
+
+        for key, value in local_map.items():
+            if key != '' and key not in module._modules:  # not a sub-module, probably a parameter
+                full_mapping[src_prefix + key] = value
+
+        if src_prefix != tar_prefix:  # To deal with leaf nodes.
+            for name, value in itertools.chain(module._parameters.items(), module._buffers.items()):  # direct children
+                if value is None or name in module._non_persistent_buffers_set:
+                    # it won't appear in state dict
+                    continue
+                if (src_prefix + name) not in full_mapping:
+                    full_mapping[src_prefix + name] = tar_prefix + name
+
+        for name, child in module.named_children():
+            # sub-modules
+            full_mapping_in_module(
+                src_prefix + name + '.',
+                local_map.get(name, tar_prefix + name) + '.',  # if mapping doesn't exist, respect the prefix
+                child
+            )
+
+    full_mapping_in_module('', '', model)
+
+    def load_state_dict_hook(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
+        reverse_mapping = defaultdict(list)
+        for src, tar in full_mapping.items():
+            reverse_mapping[tar].append(src)
+
+        transf_state_dict = {}
+        for src, tar_keys in reverse_mapping.items():
+            if src in state_dict:
+                value = state_dict.pop(src)
+                for tar in tar_keys:
+                    transf_state_dict[tar] = value
+            else:
+                missing_keys.append(src)
+        state_dict.update(transf_state_dict)
+
+    def state_dict_hook(module, destination, prefix, local_metadata):
+        result = {}
+        for src, tar in full_mapping.items():
+            if src in destination:
+                result[tar] = destination.pop(src)
+            else:
+                raise KeyError(f'"{src}" not in state dict, but found in mapping.')
+        destination.update(result)
+
+    try:
+        hooks = []
+        hooks.append(model._register_load_state_dict_pre_hook(load_state_dict_hook))
+        hooks.append(model._register_state_dict_hook(state_dict_hook))
+        yield
+    finally:
+        for hook in hooks:
+            hook.remove()
diff --git a/nni/runtime/config.py b/nni/runtime/config.py
index 8c353c8f2..10f077cb9 100644
--- a/nni/runtime/config.py
+++ b/nni/runtime/config.py
@@ -14,7 +14,7 @@ def get_config_directory() -> Path:
     Create it if not exist.
     """
     if os.getenv('NNI_CONFIG_DIR') is not None:
-        config_dir = Path(os.getenv('NNI_CONFIG_DIR'))
+        config_dir = Path(os.getenv('NNI_CONFIG_DIR'))  # type: ignore
     elif sys.prefix != sys.base_prefix or Path(sys.prefix, 'conda-meta').is_dir():
         config_dir = Path(sys.prefix, 'nni')
     elif sys.platform == 'win32':
@@ -39,4 +39,4 @@ def get_builtin_config_file(name: str) -> Path:
     """
     Get a readonly builtin config file.
     """
-    return Path(nni.__path__[0], 'runtime/default_config', name)
+    return Path(nni.__path__[0], 'runtime/default_config', name)  # type: ignore
diff --git a/nni/runtime/env_vars.py b/nni/runtime/env_vars.py
index 810ab2f4f..12870ac57 100644
--- a/nni/runtime/env_vars.py
+++ b/nni/runtime/env_vars.py
@@ -27,7 +27,7 @@ _dispatcher_env_var_names = [
 
 def _load_env_vars(env_var_names):
     env_var_dict = {k: os.environ.get(k) for k in env_var_names}
-    return namedtuple('EnvVars', env_var_names)(**env_var_dict)
+    return namedtuple('EnvVars', env_var_names)(**env_var_dict)  # pylint: disable=unused-variable
 
 trial_env_vars = _load_env_vars(_trial_env_var_names)
 
diff --git a/nni/runtime/log.py b/nni/runtime/log.py
index afe583eae..3d6ecde9c 100644
--- a/nni/runtime/log.py
+++ b/nni/runtime/log.py
@@ -1,3 +1,8 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+from __future__ import annotations
+
 import logging
 import sys
 from datetime import datetime
@@ -61,6 +66,8 @@ def init_logger_for_command_line() -> None:
         _cli_log_initialized = True
         colorful_formatter = Formatter(log_format, time_format)
         colorful_formatter.format = _colorful_format
+        if '_default_' not in handlers:  # this happens when building sphinx gallery
+            _register_handler(StreamHandler(sys.stdout), logging.INFO)
         handlers['_default_'].setFormatter(colorful_formatter)
 
 def start_experiment_log(experiment_id: str, log_directory: Path, debug: bool) -> None:
@@ -103,7 +110,7 @@ def _init_logger_standalone() -> None:
     _register_handler(StreamHandler(sys.stdout), logging.INFO)
 
 
-def _prepare_log_dir(path: Optional[str]) -> Path:
+def _prepare_log_dir(path: Path | str) -> Path:
     if path is None:
         return Path()
     ret = Path(path)
@@ -146,7 +153,7 @@ class _LogFileWrapper(TextIOBase):
     def __init__(self, log_file: TextIOBase):
         self.file: TextIOBase = log_file
         self.line_buffer: Optional[str] = None
-        self.line_start_time: Optional[datetime] = None
+        self.line_start_time: datetime = datetime.fromtimestamp(0)
 
     def write(self, s: str) -> int:
         cur_time = datetime.now()
diff --git a/nni/runtime/msg_dispatcher.py b/nni/runtime/msg_dispatcher.py
index 9f8481daf..442d50f9c 100644
--- a/nni/runtime/msg_dispatcher.py
+++ b/nni/runtime/msg_dispatcher.py
@@ -212,6 +212,7 @@ class MsgDispatcher(MsgDispatcherBase):
         except Exception as e:
             _logger.error('Assessor error')
             _logger.exception(e)
+            raise
 
         if isinstance(result, bool):
             result = AssessResult.Good if result else AssessResult.Bad
diff --git a/nni/runtime/platform/standalone.py b/nni/runtime/platform/standalone.py
index f8926822b..2a1847ea9 100644
--- a/nni/runtime/platform/standalone.py
+++ b/nni/runtime/platform/standalone.py
@@ -23,10 +23,10 @@ def get_next_parameter():
     warning_message = ''.join([
         colorama.Style.BRIGHT,
         colorama.Fore.RED,
-        'Running NNI code without runtime. ',
-        'Check the following tutorial if you are new to NNI: ',
+        'Running trial code without runtime. ',
+        'Please check the tutorial if you are new to NNI: ',
         colorama.Fore.YELLOW,
-        'https://nni.readthedocs.io/en/stable/Tutorial/QuickStart.html#id1',
+        'https://nni.readthedocs.io/en/stable/tutorials/hpo_quickstart_pytorch/main.html',
         colorama.Style.RESET_ALL
     ])
     warnings.warn(warning_message, RuntimeWarning)
diff --git a/nni/tools/gpu_tool/gpu_metrics_collector.py b/nni/tools/gpu_tool/gpu_metrics_collector.py
index 8e50cf9ec..c36998c27 100644
--- a/nni/tools/gpu_tool/gpu_metrics_collector.py
+++ b/nni/tools/gpu_tool/gpu_metrics_collector.py
@@ -28,8 +28,8 @@ def main(argv):
 
 
 def parse_nvidia_smi_result(smi, outputDir):
+    old_umask = os.umask(0)
     try:
-        old_umask = os.umask(0)
         xmldoc = minidom.parseString(smi)
         gpuList = xmldoc.getElementsByTagName('gpu')
         with open(os.path.join(outputDir, "gpu_metrics"), 'a') as outputFile:
@@ -62,8 +62,8 @@ def parse_nvidia_smi_result(smi, outputDir):
 
 
 def gen_empty_gpu_metric(outputDir):
+    old_umask = os.umask(0)
     try:
-        old_umask = os.umask(0)
         with open(os.path.join(outputDir, "gpu_metrics"), 'a') as outputFile:
             outPut = {}
             outPut["Timestamp"] = time.asctime(time.localtime())
diff --git a/nni/tools/jupyter_extension/__init__.py b/nni/tools/jupyter_extension/__init__.py
index df86a70a8..ac2a0e6e8 100644
--- a/nni/tools/jupyter_extension/__init__.py
+++ b/nni/tools/jupyter_extension/__init__.py
@@ -1,3 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 from . import proxy
 
 load_jupyter_server_extension = proxy.setup
diff --git a/nni/tools/jupyter_extension/management.py b/nni/tools/jupyter_extension/management.py
index 276160b66..d3e70a3f6 100644
--- a/nni/tools/jupyter_extension/management.py
+++ b/nni/tools/jupyter_extension/management.py
@@ -1,3 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import json
 from pathlib import Path
 import shutil
diff --git a/nni/tools/jupyter_extension/proxy.py b/nni/tools/jupyter_extension/proxy.py
index f5457379d..c0bd571d7 100644
--- a/nni/tools/jupyter_extension/proxy.py
+++ b/nni/tools/jupyter_extension/proxy.py
@@ -1,3 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import json
 from pathlib import Path
 
diff --git a/nni/tools/nnictl/config_schema.py b/nni/tools/nnictl/config_schema.py
index 997ad1fa3..a659ce5e1 100644
--- a/nni/tools/nnictl/config_schema.py
+++ b/nni/tools/nnictl/config_schema.py
@@ -359,6 +359,7 @@ kubeflow_config_schema = {
             'path': setType('path', str)
         },
         Optional('reuse'): setType('reuse', bool),
+        Optional('namespace'): setType('namespace', str),
     }, {
         'operator': setChoice('operator', 'tf-operator', 'pytorch-operator'),
         'apiVersion': setType('apiVersion', str),
@@ -377,6 +378,7 @@ kubeflow_config_schema = {
         },
         Optional('uploadRetryCount'): setNumberRange('uploadRetryCount', int, 1, 99999),
         Optional('reuse'): setType('reuse', bool),
+        Optional('namespace'): setType('namespace', str),
     })
 }
 
diff --git a/nni/tools/nnictl/hello.py b/nni/tools/nnictl/hello.py
new file mode 100644
index 000000000..2cc84786c
--- /dev/null
+++ b/nni/tools/nnictl/hello.py
@@ -0,0 +1,26 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+"""
+Provide ``nnictl hello`` command to generate quickstart example.
+"""
+
+from pathlib import Path
+import shutil
+
+from colorama import Fore
+
+import nni_assets
+
+def create_example(_args):
+    example_path = Path(nni_assets.__path__[0], 'hello_hpo')
+    try:
+        shutil.copytree(example_path, 'nni_hello_hpo')
+    except PermissionError:
+        print(Fore.RED + 'Permission denied. Please run the command in a writable directory.' + Fore.RESET)
+        exit(1)
+    except FileExistsError:
+        print('File exists. Please run "python nni_hello_hpo/main.py" to start the example.')
+        exit(1)
+    print('A hyperparameter optimization example has been created at "nni_hello_hpo" directory.')
+    print('Please run "python nni_hello_hpo/main.py" to try it out.')
diff --git a/nni/tools/nnictl/legacy_launcher.py b/nni/tools/nnictl/legacy_launcher.py
index 8641d11d5..16f3fc09a 100644
--- a/nni/tools/nnictl/legacy_launcher.py
+++ b/nni/tools/nnictl/legacy_launcher.py
@@ -69,22 +69,18 @@ def start_rest_server(port, platform, mode, experiment_id, foreground=False, log
     else:
         node_command = os.path.join(entry_dir, 'node')
     cmds = [node_command, '--max-old-space-size=4096', entry_file, '--port', str(port), '--mode', platform, \
-            '--experiment_id', experiment_id]
-    if mode == 'view':
-        cmds += ['--start_mode', 'resume']
-        cmds += ['--readonly', 'true']
-    else:
-        cmds += ['--start_mode', mode]
+            '--experiment-id', experiment_id]
+    cmds += ['--action', mode]
     if log_dir is not None:
-        cmds += ['--log_dir', log_dir]
+        cmds += ['--experiments-directory', log_dir]
     if log_level is not None:
-        cmds += ['--log_level', log_level]
+        cmds += ['--log-level', log_level]
     if foreground:
         cmds += ['--foreground', 'true']
     if url_prefix:
         _validate_prefix_path(url_prefix)
         set_prefix_url(url_prefix)
-        cmds += ['--url_prefix', url_prefix]
+        cmds += ['--url-prefix', url_prefix.strip('/')]
 
     stdout_full_path, stderr_full_path = get_log_path(experiment_id)
     with open(stdout_full_path, 'a+') as stdout_file, open(stderr_full_path, 'a+') as stderr_file:
@@ -520,9 +516,9 @@ def create_experiment(args):
 
     try:
         if schema == 1:
-            launch_experiment(args, config_v1, 'new', experiment_id, 1)
+            launch_experiment(args, config_v1, 'create', experiment_id, 1)
         else:
-            launch_experiment(args, config_v2, 'new', experiment_id, 2)
+            launch_experiment(args, config_v2, 'create', experiment_id, 2)
     except Exception as exception:
         restServerPid = Experiments().get_all_experiments().get(experiment_id, {}).get('pid')
         if restServerPid:
diff --git a/nni/tools/nnictl/nnictl.py b/nni/tools/nnictl/nnictl.py
index f1ebc3db5..d9afe4547 100644
--- a/nni/tools/nnictl/nnictl.py
+++ b/nni/tools/nnictl/nnictl.py
@@ -16,7 +16,8 @@ from .nnictl_utils import stop_experiment, trial_ls, trial_kill, list_experiment
     save_experiment, load_experiment
 from .algo_management import algo_reg, algo_unreg, algo_show, algo_list
 from .constants import DEFAULT_REST_PORT
-from .import ts_management
+from . import hello
+from . import ts_management
 
 init(autoreset=True)
 
@@ -483,6 +484,10 @@ def get_parser():
     jupyter_uninstall_parser = jupyter_subparsers.add_parser('uninstall', description='Uninstall JupyterLab extension.')
     jupyter_uninstall_parser.set_defaults(func=_jupyter_uninstall)
 
+    # hello command
+    parser_hello = subparsers.add_parser('hello', description='Create "hello nni" example in current directory.')
+    parser_hello.set_defaults(func=hello.create_example)
+
     return parser
 
 
diff --git a/nni/tools/nnictl/nnictl_utils.py b/nni/tools/nnictl/nnictl_utils.py
index 8f7d0f689..554f90803 100644
--- a/nni/tools/nnictl/nnictl_utils.py
+++ b/nni/tools/nnictl/nnictl_utils.py
@@ -105,7 +105,7 @@ def parse_ids(args):
     3.If there is an id specified, return the corresponding id
     4.If there is no id specified, and there is an experiment running, return the id, or return Error
     5.If the id matches an experiment, nnictl will return the id.
-    6.If the id ends with *, nnictl will match all ids matchs the regular
+    6.If the id ends with ``*``, nnictl will match all ids matchs the regular
     7.If the id does not exist but match the prefix of an experiment id, nnictl will return the matched id
     8.If the id does not exist but match multiple prefix of the experiment ids, nnictl will give id information
     '''
@@ -222,9 +222,22 @@ def stop_experiment(args):
     if experiment_id_list:
         for experiment_id in experiment_id_list:
             print_normal('Stopping experiment %s' % experiment_id)
-            experiments_config = Experiments()
-            experiments_dict = experiments_config.get_all_experiments()
-            rest_pid = experiments_dict.get(experiment_id).get('pid')
+            # FIXME: Retry should be placed to `Experiments`, need review both python and ts code.
+            # retry up to 10 times to get the experiment metadata
+            for i in range(1, 11):
+                experiments_dict = Experiments().get_all_experiments()
+                experiment_info = experiments_dict.get(experiment_id)
+                if experiment_info is None:
+                    print_warning('Get experiment {} metadata failed, {} time retry...'.format(experiment_id, i))
+                    time.sleep(0.5)
+                else:
+                    break
+            if experiment_info is None:
+                print_error('Experiment {} metadata getting failed.'.format(experiment_id))
+                print_error('The experiments metadata in `.experiment` is:')
+                print_error(json.dumps(Experiments().get_all_experiments(), indent=4))
+                exit(1)
+            rest_pid = experiment_info.get('pid')
             if rest_pid:
                 kill_command(rest_pid)
             print_normal('Stop experiment success.')
diff --git a/nni/tools/nnictl/rest_utils.py b/nni/tools/nnictl/rest_utils.py
index 74a836bda..d3f0c8eda 100644
--- a/nni/tools/nnictl/rest_utils.py
+++ b/nni/tools/nnictl/rest_utils.py
@@ -73,7 +73,7 @@ def check_rest_server(rest_port):
                 return False, response
         else:
             time.sleep(1)
-    return  False, response
+    return False, response
 
 def check_rest_server_quick(rest_port):
     '''Check if restful server is ready, only check once'''
diff --git a/nni/tools/nnictl/ts_management.py b/nni/tools/nnictl/ts_management.py
index 4dffe21eb..f3145e015 100644
--- a/nni/tools/nnictl/ts_management.py
+++ b/nni/tools/nnictl/ts_management.py
@@ -1,3 +1,6 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import importlib
 import json
 
diff --git a/nni/trial.py b/nni/trial.py
index e8eec281b..e8afc036a 100644
--- a/nni/trial.py
+++ b/nni/trial.py
@@ -1,13 +1,18 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
 
+from __future__ import annotations
+
+from typing import Any
+
 from .common.serializer import dump
 from .runtime.env_vars import trial_env_vars
 from .runtime import platform
-
+from .typehint import Parameters, TrialMetric
 
 __all__ = [
     'get_next_parameter',
+    'get_next_parameters',
     'get_current_parameter',
     'report_intermediate_result',
     'report_final_result',
@@ -23,35 +28,51 @@ _trial_id = platform.get_trial_id()
 _sequence_id = platform.get_sequence_id()
 
 
-def get_next_parameter():
+def get_next_parameter() -> Parameters:
     """
-    Get the hyper paremeters generated by tuner. For a multiphase experiment, it returns a new group of hyper
-    parameters at each call of get_next_parameter. For a non-multiphase (multiPhase is not configured or set to False)
-    experiment, it returns hyper parameters only on the first call for each trial job, it returns None since second call.
-    This API should be called only once in each trial job of an experiment which is not specified as multiphase.
+    Get the hyperparameters generated by tuner.
+
+    Each trial should and should only invoke this function once.
+    Otherwise the behavior is undefined.
+
+    Examples
+    --------
+    Assuming the :doc:`search space </hpo/search_space>` is:
+
+    .. code-block::
+
+        {
+            'activation': {'_type': 'choice', '_value': ['relu', 'tanh', 'sigmoid']},
+            'learning_rate': {'_type': 'loguniform', '_value': [0.0001, 0.1]}
+        }
+
+    Then this function might return:
+
+    .. code-block::
+
+        {
+            'activation': 'relu',
+            'learning_rate': 0.02
+        }
 
     Returns
     -------
-    dict
-        A dict object contains the hyper parameters generated by tuner, the keys of the dict are defined in
-        search space. Returns None if no more hyper parameters can be generated by tuner.
+    :class:`~nni.typehint.Parameters`
+        A hyperparameter set sampled from search space.
     """
     global _params
     _params = platform.get_next_parameter()
     if _params is None:
-        return None
+        return None  # type: ignore
     return _params['parameters']
 
-def get_current_parameter(tag=None):
+def get_next_parameters() -> Parameters:
     """
-    Get current hyper parameters generated by tuner. It returns the same group of hyper parameters as the last
-    call of get_next_parameter returns.
-
-    Parameters
-    ----------
-    tag: str
-        hyper parameter key
+    Alias of :func:`get_next_parameter`
     """
+    return get_next_parameter()
+
+def get_current_parameter(tag: str | None = None) -> Any:
     global _params
     if _params is None:
         return None
@@ -59,72 +80,57 @@ def get_current_parameter(tag=None):
         return _params['parameters']
     return _params['parameters'][tag]
 
-def get_experiment_id():
+def get_experiment_id() -> str:
     """
-    Get experiment ID.
-
-    Returns
-    -------
-    str
-        Identifier of current experiment
+    Return experiment ID.
     """
     return _experiment_id
 
-def get_trial_id():
+def get_trial_id() -> str:
     """
-    Get trial job ID which is string identifier of a trial job, for example 'MoXrp'. In one experiment, each trial
-    job has an unique string ID.
+    Return unique ID of the trial that is current running.
 
-    Returns
-    -------
-    str
-        Identifier of current trial job which is calling this API.
+    This is shown as "ID" in the web portal's trial table.
     """
     return _trial_id
 
-def get_sequence_id():
+def get_sequence_id() -> int:
     """
-    Get trial job sequence nubmer. A sequence number is an integer value assigned to each trial job base on the
-    order they are submitted, incremental starting from 0. In one experiment, both trial job ID and sequence number
-    are unique for each trial job, they are of different data types.
+    Return sequence nubmer of the trial that is currently running.
 
-    Returns
-    -------
-    int
-        Sequence number of current trial job which is calling this API.
+    This is shown as "Trial No." in the web portal's trial table.
     """
     return _sequence_id
 
 _intermediate_seq = 0
 
 
-def overwrite_intermediate_seq(value):
-    """
-    Overwrite intermediate sequence value.
-
-    Parameters
-    ----------
-    value:
-        int
-    """
+def overwrite_intermediate_seq(value: int) -> None:
     assert isinstance(value, int)
     global _intermediate_seq
     _intermediate_seq = value
 
 
-def report_intermediate_result(metric):
+def report_intermediate_result(metric: TrialMetric | dict[str, Any]) -> None:
     """
     Reports intermediate result to NNI.
 
+    ``metric`` should either be a float, or a dict that ``metric['default']`` is a float.
+
+    If ``metric`` is a dict, ``metric['default']`` will be used by tuner,
+    and other items can be visualized with web portal.
+
+    Typically ``metric`` is per-epoch accuracy or loss.
+
     Parameters
     ----------
-    metric:
-        serializable object.
+    metric : :class:`~nni.typehint.TrialMetric`
+        The intermeidate result.
     """
     global _intermediate_seq
     assert _params or trial_env_vars.NNI_PLATFORM is None, \
         'nni.get_next_parameter() needs to be called before report_intermediate_result'
-    metric = dump({
+    dumped_metric = dump({
         'parameter_id': _params['parameter_id'] if _params else None,
         'trial_job_id': trial_env_vars.NNI_TRIAL_JOB_ID,
         'type': 'PERIODICAL',
@@ -132,25 +138,31 @@ def report_intermediate_result(metric):
         'value': dump(metric)
     })
     _intermediate_seq += 1
-    platform.send_metric(metric)
+    platform.send_metric(dumped_metric)
 
-def report_final_result(metric):
+def report_final_result(metric: TrialMetric | dict[str, Any]) -> None:
     """
     Reports final result to NNI.
 
+    ``metric`` should either be a float, or a dict that ``metric['default']`` is a float.
+
+    If ``metric`` is a dict, ``metric['default']`` will be used by tuner,
+    and other items can be visualized with web portal.
+
+    Typically ``metric`` is the final accuracy or loss.
+
     Parameters
     ----------
-    metric: serializable object
-        Usually (for built-in tuners to work), it should be a number, or
-        a dict with key "default" (a number), and any other extra keys.
+    metric : :class:`~nni.typehint.TrialMetric`
+        The final result.
     """
     assert _params or trial_env_vars.NNI_PLATFORM is None, \
         'nni.get_next_parameter() needs to be called before report_final_result'
-    metric = dump({
+    dumped_metric = dump({
         'parameter_id': _params['parameter_id'] if _params else None,
         'trial_job_id': trial_env_vars.NNI_TRIAL_JOB_ID,
         'type': 'FINAL',
         'sequence': 0,
         'value': dump(metric)
     })
-    platform.send_metric(metric)
+    platform.send_metric(dumped_metric)
diff --git a/nni/tuner.py b/nni/tuner.py
index 4fbcc011d..87b168db6 100644
--- a/nni/tuner.py
+++ b/nni/tuner.py
@@ -8,11 +8,14 @@ A new trial will run with this configuration.
 See :class:`Tuner`' specification and ``docs/en_US/tuners.rst`` for details.
 """
 
+from __future__ import annotations
+
 import logging
 
 import nni
 
 from .recoverable import Recoverable
+from .typehint import Parameters, SearchSpace, TrialMetric, TrialRecord
 
 __all__ = ['Tuner']
 
@@ -67,7 +70,7 @@ class Tuner(Recoverable):
     :class:`~nni.algorithms.hpo.gp_tuner.gp_tuner.GPTuner`
     """
 
-    def generate_parameters(self, parameter_id, **kwargs):
+    def generate_parameters(self, parameter_id: int, **kwargs) -> Parameters:
         """
         Abstract method which provides a set of hyper-parameters.
 
@@ -100,7 +103,7 @@ class Tuner(Recoverable):
         # we need to design a new exception for this purpose
         raise NotImplementedError('Tuner: generate_parameters not implemented')
 
-    def generate_multiple_parameters(self, parameter_id_list, **kwargs):
+    def generate_multiple_parameters(self, parameter_id_list: list[int], **kwargs) -> list[Parameters]:
         """
         Callback method which provides multiple sets of hyper-parameters.
 
@@ -135,7 +138,7 @@ class Tuner(Recoverable):
             result.append(res)
         return result
 
-    def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
+    def receive_trial_result(self, parameter_id: int, parameters: Parameters, value: TrialMetric, **kwargs) -> None:
         """
         Abstract method invoked when a trial reports its final result. Must override.
 
@@ -165,7 +168,7 @@ class Tuner(Recoverable):
         # pylint: disable=attribute-defined-outside-init
         self._accept_customized = accept
 
-    def trial_end(self, parameter_id, success, **kwargs):
+    def trial_end(self, parameter_id: int, success: bool, **kwargs) -> None:
         """
         Abstract method invoked when a trial is completed or terminated. Do nothing by default.
 
@@ -179,7 +182,7 @@ class Tuner(Recoverable):
             Unstable parameters which should be ignored by normal users.
         """
 
-    def update_search_space(self, search_space):
+    def update_search_space(self, search_space: SearchSpace) -> None:
         """
         Abstract method for updating the search space. Must override.
 
@@ -194,21 +197,21 @@ class Tuner(Recoverable):
         """
         raise NotImplementedError('Tuner: update_search_space not implemented')
 
-    def load_checkpoint(self):
+    def load_checkpoint(self) -> None:
         """
         Internal API under revising, not recommended for end users.
         """
         checkpoin_path = self.get_checkpoint_path()
         _logger.info('Load checkpoint ignored by tuner, checkpoint path: %s', checkpoin_path)
 
-    def save_checkpoint(self):
+    def save_checkpoint(self) -> None:
         """
         Internal API under revising, not recommended for end users.
         """
         checkpoin_path = self.get_checkpoint_path()
         _logger.info('Save checkpoint ignored by tuner, checkpoint path: %s', checkpoin_path)
 
-    def import_data(self, data):
+    def import_data(self, data: list[TrialRecord]) -> None:
         """
         Internal API under revising, not recommended for end users.
         """
@@ -216,8 +219,8 @@ class Tuner(Recoverable):
         # data: a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
         pass
 
-    def _on_exit(self):
+    def _on_exit(self) -> None:
         pass
 
-    def _on_error(self):
+    def _on_error(self) -> None:
         pass
diff --git a/nni/typehint.py b/nni/typehint.py
new file mode 100644
index 000000000..33b82a22c
--- /dev/null
+++ b/nni/typehint.py
@@ -0,0 +1,58 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+"""
+Types for static checking.
+"""
+
+__all__ = [
+    'Literal',
+    'Parameters', 'SearchSpace', 'TrialMetric', 'TrialRecord',
+]
+
+import sys
+from typing import Any, Dict, List, TYPE_CHECKING
+
+if TYPE_CHECKING or sys.version_info >= (3, 8):
+    from typing import Literal, TypedDict
+else:
+    from typing_extensions import Literal, TypedDict
+
+Parameters = Dict[str, Any]
+"""
+Return type of :func:`nni.get_next_parameter`.
+
+For built-in tuners, this is a ``dict`` whose content is defined by :doc:`search space </hpo/search_space>`.
+
+Customized tuners do not need to follow the constraint and can use anything serializable.
+"""
+
+class _ParameterSearchSpace(TypedDict):
+    _type: Literal[
+        'choice', 'randint',
+        'uniform', 'loguniform', 'quniform', 'qloguniform',
+        'normal', 'lognormal', 'qnormal', 'qlognormal',
+    ]
+    _value: List[Any]
+
+SearchSpace = Dict[str, _ParameterSearchSpace]
+"""
+Type of ``experiment.config.search_space``.
+
+For built-in tuners, the format is detailed in :doc:`/hpo/search_space`.
+
+Customized tuners do not need to follow the constraint and can use anything serializable, except ``None``.
+"""
+
+TrialMetric = float
+"""
+Type of the metrics sent to :func:`nni.report_final_result` and :func:`nni.report_intermediate_result`.
+
+For built-in tuners it must be a number (``float``, ``int``, ``numpy.float32``, etc).
+
+Customized tuners do not need to follow this constraint and can use anything serializable.
+"""
+
+class TrialRecord(TypedDict):
+    parameter: Parameters
+    value: TrialMetric
diff --git a/nni_assets/__init__.py b/nni_assets/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/nni_assets/hello_hpo/main.py b/nni_assets/hello_hpo/main.py
new file mode 100644
index 000000000..79e6e4158
--- /dev/null
+++ b/nni_assets/hello_hpo/main.py
@@ -0,0 +1,33 @@
+"""
+NNI hyperparameter optimization example.
+
+Check the online tutorial for details:
+https://nni.readthedocs.io/en/stable/tutorials/hpo_quickstart_pytorch/main.html
+"""
+
+from pathlib import Path
+import signal
+
+from nni.experiment import Experiment
+
+# Define search space
+search_space = {
+    'features': {'_type': 'choice', '_value': [128, 256, 512, 1024]},
+    'lr': {'_type': 'loguniform', '_value': [0.0001, 0.1]},
+    'momentum': {'_type': 'uniform', '_value': [0, 1]},
+}
+
+# Configure experiment
+experiment = Experiment('local')
+experiment.config.trial_command = 'python model.py'
+experiment.config.trial_code_directory = Path(__file__).parent
+experiment.config.search_space = search_space
+experiment.config.tuner.name = 'Random'
+experiment.config.max_trial_number = 10
+experiment.config.trial_concurrency = 2
+
+# Run it!
+experiment.run(port=8080, wait_completion=False)
+
+print('Experiment is running. Press Ctrl-C to quit.')
+signal.pause()
diff --git a/nni_assets/hello_hpo/model.py b/nni_assets/hello_hpo/model.py
new file mode 100644
index 000000000..eb663b385
--- /dev/null
+++ b/nni_assets/hello_hpo/model.py
@@ -0,0 +1,67 @@
+"""
+Run main.py to start.
+
+This script is modified from PyTorch quickstart:
+https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
+"""
+
+import nni
+import torch
+from torch import nn
+from torch.utils.data import DataLoader
+from torchvision import datasets
+from torchvision.transforms import ToTensor
+
+# Get optimized hyperparameters
+params = {'features': 512, 'lr': 0.001, 'momentum': 0}
+optimized_params = nni.get_next_parameter()
+params.update(optimized_params)
+
+# Load dataset
+training_data = datasets.FashionMNIST(root='data', train=True, download=True, transform=ToTensor())
+test_data = datasets.FashionMNIST(root='data', train=False, download=True, transform=ToTensor())
+train_dataloader = DataLoader(training_data, batch_size=64)
+test_dataloader = DataLoader(test_data, batch_size=64)
+
+# Build model
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+model = nn.Sequential(
+    nn.Flatten(),
+    nn.Linear(28*28, params['features']),
+    nn.ReLU(),
+    nn.Linear(params['features'], params['features']),
+    nn.ReLU(),
+    nn.Linear(params['features'], 10)
+).to(device)
+
+# Training functions
+loss_fn = nn.CrossEntropyLoss()
+optimizer = torch.optim.SGD(model.parameters(), lr=params['lr'], momentum=params['momentum'])
+
+def train(dataloader, model, loss_fn, optimizer):
+    model.train()
+    for batch, (X, y) in enumerate(dataloader):
+        X, y = X.to(device), y.to(device)
+        pred = model(X)
+        loss = loss_fn(pred, y)
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+def test(dataloader, model, loss_fn):
+    model.eval()
+    correct = 0
+    with torch.no_grad():
+        for X, y in dataloader:
+            X, y = X.to(device), y.to(device)
+            pred = model(X)
+            correct += (pred.argmax(1) == y).type(torch.float).sum().item()
+    return correct / len(dataloader.dataset)
+
+# Train the model
+epochs = 5
+for t in range(epochs):
+    train(train_dataloader, model, loss_fn, optimizer)
+    accuracy = test(test_dataloader, model, loss_fn)
+    nni.report_intermediate_result(accuracy)
+nni.report_final_result(accuracy)
diff --git a/pipelines/fast-test.yml b/pipelines/fast-test.yml
index 1b382a07b..5e3ad4d45 100644
--- a/pipelines/fast-test.yml
+++ b/pipelines/fast-test.yml
@@ -1,6 +1,4 @@
-trigger:
-  branches:
-    exclude: [ l10n_master ]
+trigger: none
 
 stages:
 - stage: lint
@@ -12,7 +10,6 @@ stages:
     - template: templates/install-dependencies.yml
       parameters:
         platform: ubuntu-latest
-        useCache: ${{ eq('$(USE_CACHE)', 'true') }}
 
     - script: |
         sudo apt-get install -y pandoc
@@ -20,24 +17,40 @@ stages:
 
     - script: |
         cd docs
-        # rstcheck -r source
-      displayName: rstcheck (disabled for now)  # TODO: rstcheck
+        rstcheck -r source \
+          --ignore-directives automodule,autoclass,autofunction,cardlinkitem,codesnippetcard,argparse,tabs \
+          --ignore-roles githublink --ignore-substitutions release \
+          --report warning
+      displayName: rstcheck
 
     - script: |
         cd docs
-        make -e SPHINXOPTS="-W --keep-going -T" html
+        make -e SPHINXOPTS="-W --keep-going -T -q" html
       displayName: Sphinx sanity check
 
     - script: |
+        set -e
         cd docs
-        make -e SPHINXOPTS="-W --keep-going -T -D language='zh'" html
+        rm -rf build
+        sudo apt-get install -y gettext
+        python tools/translation_patch.py
+        make -e SPHINXOPTS="-W --keep-going -T -q -D language='zh'" html
       displayName: Sphinx sanity check (Chinese)
 
     - script: |
+        set -e
         cd docs
-        python tools/chineselink.py check
+        rm -rf build
+        make i18n
+        git diff --exit-code source/locales
       displayName: Translation up-to-date
 
+    - script: |
+        cd docs
+        rm -rf build
+        make -e SPHINXOPTS="-W -T -b linkcheck -q --keep-going" html
+      displayName: External links integrity check
+
   - job: python
     pool:
       vmImage: ubuntu-latest
@@ -45,7 +58,6 @@ stages:
     - template: templates/install-dependencies.yml
       parameters:
         platform: ubuntu-latest
-        useCache: ${{ eq('$(USE_CACHE)', 'true') }}
 
     - script: |
         # pylint requires newer typing extension. Override requirements in tensorflow
@@ -63,6 +75,10 @@ stages:
         python -m flake8 examples --count --exclude=$EXCLUDES --select=E9,F63,F72,F82 --show-source --statistics
       displayName: flake8
 
+    - script: |
+        python -m pyright nni
+      displayName: Type Check
+
   - job: typescript
     pool:
       vmImage: ubuntu-latest
@@ -70,7 +86,6 @@ stages:
     - template: templates/install-dependencies.yml
       parameters:
         platform: ubuntu-latest
-        useCache: ${{ eq('$(USE_CACHE)', 'true') }}
 
     - script: |
         set -e
@@ -97,7 +112,6 @@ stages:
     - template: templates/install-dependencies.yml
       parameters:
         platform: ubuntu-latest
-        useCache: ${{ eq('$(USE_CACHE)', 'true') }}
 
     - script: |
         python setup.py develop
@@ -151,7 +165,6 @@ stages:
     - template: templates/install-dependencies.yml
       parameters:
         platform: ubuntu-legacy
-        useCache: ${{ eq('$(USE_CACHE)', 'true') }}
 
     - script: |
         python setup.py develop
@@ -177,13 +190,12 @@ stages:
   - job: windows
     pool:
       vmImage: windows-latest
-    timeoutInMinutes: 70
+    timeoutInMinutes: 75
 
     steps:
     - template: templates/install-dependencies.yml
       parameters:
         platform: windows
-        useCache: ${{ eq('$(USE_CACHE)', 'true') }}
 
     - script: |
         python setup.py develop --no-user
@@ -211,7 +223,6 @@ stages:
     - template: templates/install-dependencies.yml
       parameters:
         platform: macos
-        useCache: ${{ eq('$(USE_CACHE)', 'true') }}
 
     - script: |
         python setup.py develop
diff --git a/pipelines/release.yml b/pipelines/release.yml
index 9874760cf..63555287c 100644
--- a/pipelines/release.yml
+++ b/pipelines/release.yml
@@ -56,7 +56,7 @@ jobs:
 
   - script: |
       python -m pip install --upgrade pip setuptools wheel twine
-      python -m pip install jupyter jupyterlab
+      python -m pip install -r dependencies/develop.txt
       python test/vso_tools/build_wheel.py $(NNI_RELEASE)
     displayName: Build wheel
 
@@ -102,7 +102,7 @@ jobs:
 
   - script: |
       python -m pip install --upgrade pip setuptools wheel twine
-      python -m pip install jupyter jupyterlab
+      python -m pip install -r dependencies/develop.txt
       python test/vso_tools/build_wheel.py $(NNI_RELEASE)
     displayName: Build wheel
 
@@ -130,7 +130,7 @@ jobs:
 
   - powershell: |
       python -m pip install --upgrade pip setuptools wheel twine
-      python -m pip install jupyter jupyterlab
+      python -m pip install -r dependencies/develop.txt
       python test/vso_tools/build_wheel.py $(NNI_RELEASE)
     displayName: Build wheel
 
diff --git a/pipelines/templates/cache-dependencies-template.yml b/pipelines/templates/cache-dependencies-template.yml
index 6135c66bc..737584de4 100644
--- a/pipelines/templates/cache-dependencies-template.yml
+++ b/pipelines/templates/cache-dependencies-template.yml
@@ -2,6 +2,16 @@ parameters:
 - name: platform
   type: string
 
+# variables set on VSO: (for security concern)
+#   sas_token
+#
+# its value should be:
+#   sp=racw&st=2022-04-01T00:00:00Z&se=......
+#
+# how to re-generate:
+#   1. find the storage on azure portal: nni (resource group) -> nni (storage account) -> cache (container)
+#   2. settings - shared access tokens - generate sas token and url
+
 steps:
 - template: config-version.yml
   parameters:
@@ -63,12 +73,15 @@ steps:
   displayName: Install Web UI dependencies
 
 - script: |
-    python test/vso_tools/pack_dependencies.py $(Build.ArtifactStagingDirectory)
+    python test/vso_tools/pack_dependencies.py dependencies.zip
   displayName: Create cache archive
 
-- task: UniversalPackages@0
-  inputs:
-    command: publish
-    vstsFeedPublish: NNIOpenSource/dependencies
-    vstsFeedPackagePublish: dependencies-${{ parameters.platform }}
-  displayName: Upload cache archive
+- script: |
+    azcopy copy dependencies.zip 'https://nni.blob.core.windows.net/cache/dependencies-${{ parameters.platform }}.zip?$(sas_token)'
+  displayName: (POSIX) Upload cache archive
+  condition: and(succeeded(), not(contains('${{ parameters.platform }}', 'windows')))
+
+- powershell: |
+    azcopy copy dependencies.zip 'https://nni.blob.core.windows.net/cache/dependencies-${{ parameters.platform }}.zip?$(sas_token)'
+  displayName: (Windows) Upload cache archive
+  condition: and(succeeded(), contains('${{ parameters.platform }}', 'windows'))
diff --git a/pipelines/templates/install-dependencies.yml b/pipelines/templates/install-dependencies.yml
index 81f1b5b23..24e1501f4 100644
--- a/pipelines/templates/install-dependencies.yml
+++ b/pipelines/templates/install-dependencies.yml
@@ -1,8 +1,6 @@
 parameters:
 - name: platform
   type: string
-- name: useCache
-  type: boolean
 
 steps:
 - template: config-version.yml
@@ -25,18 +23,20 @@ steps:
   displayName: (macOS) Downgrade swig
   condition: and(succeeded(), contains('${{ parameters.platform }}', 'macos'))
 
-- task: UniversalPackages@0
-  inputs:
-    vstsFeed: NNIOpenSource/dependencies
-    vstsFeedPackage: dependencies-${{ parameters.platform }}
-    vstsPackageVersion: "*"
-  displayName: Download cache
-  condition: and(succeeded(), ${{ parameters.useCache }})
-
 - script: |
-    python test/vso_tools/unpack_dependencies.py
-  displayName: Unpack cache
-  condition: and(succeeded(), ${{ parameters.useCache }})
+    set -e
+    azcopy copy 'https://nni.blob.core.windows.net/cache/dependencies-${{ parameters.platform }}.zip' dependencies.zip
+    python test/vso_tools/unpack_dependencies.py dependencies.zip
+  displayName: (POSIX) Download cache
+  condition: and(succeeded(), not(contains('${{ parameters.platform }}', 'windows')))
+  continueOnError: true
+
+- powershell: |
+    azcopy copy 'https://nni.blob.core.windows.net/cache/dependencies-${{ parameters.platform }}.zip' dependencies.zip
+    python test/vso_tools/unpack_dependencies.py dependencies.zip
+  displayName: (Windows) Download cache
+  condition: and(succeeded(), contains('${{ parameters.platform }}', 'windows'))
+  continueOnError: true
 
 - script: |
     mv dependencies/recommended_legacy.txt dependencies/recommended.txt
@@ -54,14 +54,11 @@ steps:
     python -m pip install -r dependencies/recommended.txt
   displayName: Install Python dependencies
 
-# FIXME: See cache-dependencies-template.yml on why it needs rebuild.
-- script: |
-    python -m pip uninstall -y ConfigSpaceNNI
-    python -m pip install --no-cache-dir ConfigSpaceNNI
-  displayName: Rebuild ConfigSpaceNNI
-  condition: and(succeeded(), not(${{ parameters.useCache }}), contains('${{ parameters.platform }}', 'ubuntu'))
-
 # TODO: Delete this after upgrading to PyTorch 1.11.
 - script: |
     python test/vso_tools/interim_patch.py
   displayName: Torch utils tensorboard interim patch
+
+- script: |
+    pip list
+  displayName: List pip dependencies
diff --git a/pyrightconfig.json b/pyrightconfig.json
new file mode 100644
index 000000000..ebe2e6cee
--- /dev/null
+++ b/pyrightconfig.json
@@ -0,0 +1,14 @@
+{
+    "ignore": [
+        "nni/algorithms",
+        "nni/common/device.py",
+        "nni/common/graph_utils.py",
+        "nni/common/serializer.py",
+        "nni/compression",
+        "nni/nas",
+        "nni/retiarii",
+        "nni/smartparam.py",
+        "nni/tools"
+    ],
+    "reportMissingImports": false
+}
diff --git a/setup.py b/setup.py
index 2ae7062e8..d31b9b0aa 100644
--- a/setup.py
+++ b/setup.py
@@ -27,7 +27,7 @@ Uninstall:
 
   $ pip uninstall nni
 
-Remove generated files: (use "--all" to remove toolchain and built wheel)
+Remove generated files: (use "--all" to remove built wheel)
 
   $ python setup.py clean [--all]
 
@@ -112,6 +112,7 @@ def _setup():
         packages = _find_python_packages(),
         package_data = {
             'nni': _find_requirements_txt() + _find_default_config(),  # setuptools issue #1806
+            'nni_assets': _find_asset_files(),
             'nni_node': _find_node_files()  # note: this does not work before building
         },
 
@@ -124,6 +125,7 @@ def _setup():
             'BOHB': _read_requirements_txt('dependencies/required_extra.txt', 'BOHB'),
             'PPOTuner': _read_requirements_txt('dependencies/required_extra.txt', 'PPOTuner'),
             'DNGO': _read_requirements_txt('dependencies/required_extra.txt', 'DNGO'),
+            'all': _read_requirements_txt('dependencies/required_extra.txt'),
         },
         setup_requires = ['requests'],
 
@@ -153,7 +155,7 @@ def _find_python_packages():
     for dirpath, dirnames, filenames in os.walk('nni'):
         if '/__pycache__' not in dirpath and '/.mypy_cache' not in dirpath and '/default_config' not in dirpath:
             packages.append(dirpath.replace('/', '.'))
-    return sorted(packages) + ['nni_node']
+    return sorted(packages) + ['nni_assets', 'nni_node']
 
 def _find_requirements_txt():
     requirement_files = []
@@ -165,6 +167,14 @@ def _find_requirements_txt():
 def _find_default_config():
     return ['runtime/default_config/' + name for name in os.listdir('nni/runtime/default_config')]
 
+def _find_asset_files():
+    files = []
+    for dirpath, dirnames, filenames in os.walk('nni_assets'):
+        for filename in filenames:
+            if os.path.splitext(filename)[1] == '.py':
+                files.append(os.path.join(dirpath[len('nni_assets/'):], filename))
+    return sorted(files)
+
 def _find_node_files():
     if not os.path.exists('nni_node'):
         if release and 'build_ts' not in sys.argv and 'clean' not in sys.argv:
@@ -221,7 +231,7 @@ class Build(build):
         check_jupyter_lab_version()
 
         if os.path.islink('nni_node/main.js'):
-            sys.exit('A development build already exists. Please uninstall NNI and run "python3 setup.py clean --all".')
+            sys.exit('A development build already exists. Please uninstall NNI and run "python3 setup.py clean".')
         open('nni/version.py', 'w').write(f"__version__ = '{release}'")
         super().run()
 
@@ -259,7 +269,7 @@ class Clean(clean):
 
     def run(self):
         super().run()
-        setup_ts.clean(self._all)
+        setup_ts.clean()
         _clean_temp_files()
         shutil.rmtree('nni.egg-info', ignore_errors=True)
         if self._all:
@@ -279,7 +289,10 @@ _temp_files = [
     'test/model_path/',
     'test/temp.json',
     'test/ut/sdk/*.pth',
-    'test/ut/tools/annotation/_generated/'
+    'test/ut/tools/annotation/_generated/',
+
+    # example
+    'nni_assets/**/data/',
 ]
 
 
diff --git a/setup_ts.py b/setup_ts.py
index 23bc97321..ba9e76271 100644
--- a/setup_ts.py
+++ b/setup_ts.py
@@ -15,6 +15,7 @@ from io import BytesIO
 import json
 import os
 from pathlib import Path
+import platform
 import shutil
 import subprocess
 import sys
@@ -56,12 +57,13 @@ def build(release):
         symlink_nni_node()
     restore_package()
 
-def clean(clean_all=False):
+def clean():
     """
     Remove TypeScript-related intermediate files.
     Python intermediate files are not touched here.
     """
     shutil.rmtree('nni_node', ignore_errors=True)
+    shutil.rmtree('toolchain', ignore_errors=True)
 
     for file_or_dir in generated_files:
         path = Path(file_or_dir)
@@ -70,13 +72,11 @@ def clean(clean_all=False):
         elif path.is_dir():
             shutil.rmtree(path)
 
-    if clean_all:
-        shutil.rmtree('toolchain', ignore_errors=True)
-
 
 if sys.platform == 'linux' or sys.platform == 'darwin':
     node_executable = 'node'
-    node_spec = f'node-{node_version}-{sys.platform}-x64'
+    _arch = 'x64' if platform.machine() == 'x86_64' else platform.machine()
+    node_spec = f'node-{node_version}-{sys.platform}-' + _arch
     node_download_url = f'https://nodejs.org/dist/{node_version}/{node_spec}.tar.xz'
     node_extractor = lambda data: tarfile.open(fileobj=BytesIO(data), mode='r:xz')
     node_executable_in_tarball = 'bin/node'
@@ -183,16 +183,14 @@ def compile_ts(release):
         _yarn('ts/webui', 'build')
 
     _print('Building JupyterLab extension')
-    if release:
+    try:
         _yarn('ts/jupyter_extension')
         _yarn('ts/jupyter_extension', 'build')
-    else:
-        try:
-            _yarn('ts/jupyter_extension')
-            _yarn('ts/jupyter_extension', 'build')
-        except Exception:
-            _print('Failed to build JupyterLab extension, skip for develop mode', color='yellow')
-            _print(traceback.format_exc(), color='yellow')
+    except Exception:
+        if release:
+            raise
+        _print('Failed to build JupyterLab extension, skip for develop mode', color='yellow')
+        _print(traceback.format_exc(), color='yellow')
 
 
 def symlink_nni_node():
@@ -225,12 +223,18 @@ def copy_nni_node(version):
     """
     _print('Copying files')
 
-    # copytree(..., dirs_exist_ok=True) is not supported by Python 3.6
-    for path in Path('ts/nni_manager/dist').iterdir():
-        if path.is_dir():
-            shutil.copytree(path, Path('nni_node', path.name))
-        elif path.name != 'nni_manager.tsbuildinfo':
-            shutil.copyfile(path, Path('nni_node', path.name))
+    if sys.version_info >= (3, 8):
+        shutil.copytree('ts/nni_manager/dist', 'nni_node', dirs_exist_ok=True)
+    else:
+        for item in os.listdir('ts/nni_manager/dist'):
+            subsrc = os.path.join('ts/nni_manager/dist', item)
+            subdst = os.path.join('nni_node', item)
+            if os.path.isdir(subsrc):
+                shutil.copytree(subsrc, subdst)
+            else:
+                shutil.copy2(subsrc, subdst)
+    shutil.copyfile('ts/nni_manager/yarn.lock', 'nni_node/yarn.lock')
+    Path('nni_node/nni_manager.tsbuildinfo').unlink()
 
     package_json = json.load(open('ts/nni_manager/package.json'))
     if version:
diff --git a/test/config/examples/cifar10_search_space.json b/test/config/examples/cifar10_search_space.json
index ca1c0d203..5cd40c18f 100644
--- a/test/config/examples/cifar10_search_space.json
+++ b/test/config/examples/cifar10_search_space.json
@@ -1,5 +1,5 @@
 {
     "lr":{"_type":"choice", "_value":[0.1, 0.01, 0.001, 0.0001]},
     "optimizer":{"_type":"choice", "_value":["SGD", "Adadelta", "Adagrad", "Adam", "Adamax"]},
-    "model":{"_type":"choice", "_value":["vgg", "resnet18"]}
+    "model":{"_type":"choice", "_value":["vgg"]}
 }
diff --git a/test/config/training_service.yml b/test/config/training_service.yml
index 07e809414..4e52786de 100644
--- a/test/config/training_service.yml
+++ b/test/config/training_service.yml
@@ -18,6 +18,7 @@ kubeflow:
     azureStorage:
       accountName:
       azureShare:
+    namespace: kubeflow
   trial:
     worker:
       replicas: 1
@@ -35,7 +36,7 @@ frameworkcontroller:
   maxTrialNum: 2
   trialConcurrency: 2
   frameworkcontrollerConfig:
-    serviceAccountName: frameworkbarrier
+    serviceAccountName: frameworkcontroller
     storage: azureStorage
     keyVault:
       vaultName:
@@ -43,6 +44,7 @@ frameworkcontroller:
     azureStorage:
       accountName:
       azureShare:
+    namespace: kubeflow
   trial:
     taskRoles:
       - name: worker
diff --git a/test/config/training_service_v2.yml b/test/config/training_service_v2.yml
index 85ee63781..0623ca587 100644
--- a/test/config/training_service_v2.yml
+++ b/test/config/training_service_v2.yml
@@ -20,6 +20,7 @@ kubeflow:
   trainingService:
     reuseMode: true
     platform: kubeflow
+    namespace: kubeflow
     worker:
       command:
       code_directory:
@@ -44,6 +45,7 @@ frameworkcontroller:
   trainingService:
     reuseMode: true
     platform: frameworkcontroller
+    namespace: kubeflow
     serviceAccountName: frameworkcontroller
     taskRoles:
       - name: worker
diff --git a/test/nni_test/nnitest/utils.py b/test/nni_test/nnitest/utils.py
index 300799003..e3f527603 100644
--- a/test/nni_test/nnitest/utils.py
+++ b/test/nni_test/nnitest/utils.py
@@ -122,12 +122,18 @@ def print_file_content(filepath):
         print(content, flush=True)
 
 def print_trial_job_log(training_service, trial_jobs_url):
-    trial_jobs = get_trial_jobs(trial_jobs_url)
-    for trial_job in trial_jobs:
-        trial_log_dir = os.path.join(get_experiment_dir(EXPERIMENT_URL), 'trials', trial_job['trialJobId'])
+    trial_log_root = os.path.join(get_experiment_dir(EXPERIMENT_URL), 'trials')
+    if not os.path.exists(trial_log_root):
+        print('trial log folder does not exist: {}'.format(trial_log_root), flush=True)
+        return
+    folders = os.listdir(trial_log_root)
+    for name in folders:
+        trial_log_dir = os.path.join(trial_log_root, name)
         log_files = ['stderr', 'trial.log'] if training_service == 'local' else ['stdout_log_collection.log']
         for log_file in log_files:
-            print_file_content(os.path.join(trial_log_dir, log_file))
+            log_file_path = os.path.join(trial_log_dir, log_file)
+            if os.path.exists(log_file_path):
+                print_file_content(log_file_path)
 
 def print_experiment_log(experiment_id):
     log_dir = get_nni_log_dir(experiment_id=experiment_id)
diff --git a/test/pytest.ini b/test/pytest.ini
index 5daf6aa42..d970f35aa 100644
--- a/test/pytest.ini
+++ b/test/pytest.ini
@@ -1,2 +1,7 @@
 [pytest]
 addopts = --cov=nni --cov-config=.coveragerc --junitxml=junit/test-results.xml --cov-report=xml --cov-report=html --cov-config=.coveragerc
+filterwarnings =
+    ignore:Using key to access the identifier of:DeprecationWarning
+    ignore:layer_choice.choices is deprecated.:DeprecationWarning
+    ignore:The truth value of an empty array is ambiguous.:DeprecationWarning
+    ignore:nni.retiarii.serialize is deprecated and will be removed in future release.:DeprecationWarning
diff --git a/test/retiarii_test/cgo/darts_model.py b/test/retiarii_test/cgo/darts_model.py
deleted file mode 100644
index 1354bdc14..000000000
--- a/test/retiarii_test/cgo/darts_model.py
+++ /dev/null
@@ -1,165 +0,0 @@
-from collections import OrderedDict
-from typing import (List, Optional)
-
-import torch
-import torch.nn as torch_nn
-#sys.path.append(str(Path(__file__).resolve().parents[2]))
-
-import ops
-import nni.retiarii.nn.pytorch as nn
-from nni.retiarii import basic_unit
-
-@basic_unit
-class AuxiliaryHead(nn.Module):
-    """ Auxiliary head in 2/3 place of network to let the gradient flow well """
-
-    def __init__(self, input_size, C, n_classes):
-        """ assuming input size 7x7 or 8x8 """
-        assert input_size in [7, 8]
-        super().__init__()
-        self.net = nn.Sequential(
-            nn.ReLU(inplace=True),
-            nn.AvgPool2d(5, stride=input_size - 5, padding=0, count_include_pad=False),  # 2x2 out
-            nn.Conv2d(C, 128, kernel_size=1, bias=False),
-            nn.BatchNorm2d(128),
-            nn.ReLU(inplace=True),
-            nn.Conv2d(128, 768, kernel_size=2, bias=False),  # 1x1 out
-            nn.BatchNorm2d(768),
-            nn.ReLU(inplace=True)
-        )
-        self.linear = nn.Linear(768, n_classes)
-
-    def forward(self, x):
-        out = self.net(x)
-        out = out.view(out.size(0), -1)  # flatten
-        logits = self.linear(out)
-        return logits
-
-class Node(nn.Module):
-    def __init__(self, node_id, num_prev_nodes, channels, num_downsample_connect):
-        super().__init__()
-        self.ops = nn.ModuleList()
-        choice_keys = []
-        for i in range(num_prev_nodes):
-            stride = 2 if i < num_downsample_connect else 1
-            choice_keys.append("{}_p{}".format(node_id, i))
-            self.ops.append(
-                nn.LayerChoice([
-                    ops.PoolBN('max', channels, 3, stride, 1, affine=False),
-                    ops.PoolBN('avg', channels, 3, stride, 1, affine=False),
-                    nn.Identity() if stride == 1 else ops.FactorizedReduce(channels, channels, affine=False),
-                    ops.SepConv(channels, channels, 3, stride, 1, affine=False),
-                    ops.SepConv(channels, channels, 5, stride, 2, affine=False),
-                    ops.DilConv(channels, channels, 3, stride, 2, 2, affine=False),
-                    ops.DilConv(channels, channels, 5, stride, 4, 2, affine=False)
-                ]))
-        self.drop_path = ops.DropPath()
-        self.input_switch = nn.InputChoice(n_candidates=num_prev_nodes, n_chosen=2)
-
-    def forward(self, prev_nodes: List['Tensor']) -> 'Tensor':
-        #assert self.ops.__len__() == len(prev_nodes)
-        #out = [op(node) for op, node in zip(self.ops, prev_nodes)]
-        out = []
-        for i, op in enumerate(self.ops):
-            out.append(op(prev_nodes[i]))
-        #out = [self.drop_path(o) if o is not None else None for o in out]
-        return self.input_switch(out)
-
-class Cell(nn.Module):
-
-    def __init__(self, n_nodes, channels_pp, channels_p, channels, reduction_p, reduction):
-        super().__init__()
-        self.reduction = reduction
-        self.n_nodes = n_nodes
-
-        # If previous cell is reduction cell, current input size does not match with
-        # output size of cell[k-2]. So the output[k-2] should be reduced by preprocessing.
-        if reduction_p:
-            self.preproc0 = ops.FactorizedReduce(channels_pp, channels, affine=False)
-        else:
-            self.preproc0 = ops.StdConv(channels_pp, channels, 1, 1, 0, affine=False)
-        self.preproc1 = ops.StdConv(channels_p, channels, 1, 1, 0, affine=False)
-
-        # generate dag
-        self.mutable_ops = nn.ModuleList()
-        for depth in range(2, self.n_nodes + 2):
-            self.mutable_ops.append(Node("{}_n{}".format("reduce" if reduction else "normal", depth),
-                                         depth, channels, 2 if reduction else 0))
-
-    def forward(self, s0, s1):
-        # s0, s1 are the outputs of previous previous cell and previous cell, respectively.
-        tensors = [self.preproc0(s0), self.preproc1(s1)]
-        new_tensors = []
-        for node in self.mutable_ops:
-            tmp = tensors + new_tensors
-            cur_tensor = node(tmp)
-            new_tensors.append(cur_tensor)
-
-        output = torch.cat(new_tensors, dim=1)
-        return output
-
-class CNN(nn.Module):
-
-    def __init__(self, input_size, in_channels, channels, n_classes, n_layers, n_nodes=4,
-                 stem_multiplier=3, auxiliary=False):
-        super().__init__()
-        self.in_channels = in_channels
-        self.channels = channels
-        self.n_classes = n_classes
-        self.n_layers = n_layers
-        self.aux_pos = 2 * n_layers // 3 if auxiliary else -1
-
-        c_cur = stem_multiplier * self.channels
-        self.stem = nn.Sequential(
-            nn.Conv2d(in_channels, c_cur, 3, 1, 1, bias=False),
-            nn.BatchNorm2d(c_cur)
-        )
-
-        # for the first cell, stem is used for both s0 and s1
-        # [!] channels_pp and channels_p is output channel size, but c_cur is input channel size.
-        channels_pp, channels_p, c_cur = c_cur, c_cur, channels
-
-        self.cells = nn.ModuleList()
-        reduction_p, reduction = False, False
-        for i in range(n_layers):
-            reduction_p, reduction = reduction, False
-            # Reduce featuremap size and double channels in 1/3 and 2/3 layer.
-            if i in [n_layers // 3, 2 * n_layers // 3]:
-                c_cur *= 2
-                reduction = True
-
-            cell = Cell(n_nodes, channels_pp, channels_p, c_cur, reduction_p, reduction)
-            self.cells.append(cell)
-            c_cur_out = c_cur * n_nodes
-            channels_pp, channels_p = channels_p, c_cur_out
-
-            #if i == self.aux_pos:
-            #    self.aux_head = AuxiliaryHead(input_size // 4, channels_p, n_classes)
-
-        self.gap = nn.AdaptiveAvgPool2d(1)
-        self.linear = nn.Linear(channels_p, n_classes)
-
-    def forward(self, x):
-        s0 = s1 = self.stem(x)
-
-        #aux_logits = None
-        for i, cell in enumerate(self.cells):
-            s0, s1 = s1, cell(s0, s1)
-            #if i == self.aux_pos and self.training:
-            #    aux_logits = self.aux_head(s1)
-
-        out = self.gap(s1)
-        out = out.view(out.size(0), -1)  # flatten
-        logits = self.linear(out)
-
-        #if aux_logits is not None:
-        #    return logits, aux_logits
-        return logits
-
-    def drop_path_prob(self, p):
-        for module in self.modules():
-            if isinstance(module, ops.DropPath):
-                module.p = p
-
-if __name__ == '__main__':
-    base_model = CNN(32, 3, 16, 10, 8)
diff --git a/test/retiarii_test/cgo/ops.py b/test/retiarii_test/cgo/ops.py
deleted file mode 100644
index 45b1e79ea..000000000
--- a/test/retiarii_test/cgo/ops.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import torch
-import nni.retiarii.nn.pytorch as nn
-from nni.retiarii import basic_unit
-
-@basic_unit
-class DropPath(nn.Module):
-    def __init__(self, p=0.):
-        """
-        Drop path with probability.
-        Parameters
-        ----------
-        p : float
-            Probability of an path to be zeroed.
-        """
-        super().__init__()
-        self.p = p
-
-    def forward(self, x):
-        if self.training and self.p > 0.:
-            keep_prob = 1. - self.p
-            # per data point mask
-            mask = torch.zeros((x.size(0), 1, 1, 1), device=x.device).bernoulli_(keep_prob)
-            return x / keep_prob * mask
-
-        return x
-
-@basic_unit
-class PoolBN(nn.Module):
-    """
-    AvgPool or MaxPool with BN. `pool_type` must be `max` or `avg`.
-    """
-    def __init__(self, pool_type, C, kernel_size, stride, padding, affine=True):
-        super().__init__()
-        if pool_type.lower() == 'max':
-            self.pool = nn.MaxPool2d(kernel_size, stride, padding)
-        elif pool_type.lower() == 'avg':
-            self.pool = nn.AvgPool2d(kernel_size, stride, padding, count_include_pad=False)
-        else:
-            raise ValueError()
-
-        self.bn = nn.BatchNorm2d(C, affine=affine)
-
-    def forward(self, x):
-        out = self.pool(x)
-        out = self.bn(out)
-        return out
-
-@basic_unit
-class StdConv(nn.Module):
-    """
-    Standard conv: ReLU - Conv - BN
-    """
-    def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
-        super().__init__()
-        self.net = nn.Sequential(
-            nn.ReLU(),
-            nn.Conv2d(C_in, C_out, kernel_size, stride, padding, bias=False),
-            nn.BatchNorm2d(C_out, affine=affine)
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
-@basic_unit
-class FacConv(nn.Module):
-    """
-    Factorized conv: ReLU - Conv(Kx1) - Conv(1xK) - BN
-    """
-    def __init__(self, C_in, C_out, kernel_length, stride, padding, affine=True):
-        super().__init__()
-        self.net = nn.Sequential(
-            nn.ReLU(),
-            nn.Conv2d(C_in, C_in, (kernel_length, 1), stride, padding, bias=False),
-            nn.Conv2d(C_in, C_out, (1, kernel_length), stride, padding, bias=False),
-            nn.BatchNorm2d(C_out, affine=affine)
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
-@basic_unit
-class DilConv(nn.Module):
-    """
-    (Dilated) depthwise separable conv.
-    ReLU - (Dilated) depthwise separable - Pointwise - BN.
-    If dilation == 2, 3x3 conv => 5x5 receptive field, 5x5 conv => 9x9 receptive field.
-    """
-    def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation, affine=True):
-        super().__init__()
-        self.net = nn.Sequential(
-            nn.ReLU(),
-            nn.Conv2d(C_in, C_in, kernel_size, stride, padding, dilation=dilation, groups=C_in,
-                      bias=False),
-            nn.Conv2d(C_in, C_out, 1, stride=1, padding=0, bias=False),
-            nn.BatchNorm2d(C_out, affine=affine)
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
-@basic_unit
-class SepConv(nn.Module):
-    """
-    Depthwise separable conv.
-    DilConv(dilation=1) * 2.
-    """
-    def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True):
-        super().__init__()
-        self.net = nn.Sequential(
-            DilConv(C_in, C_in, kernel_size, stride, padding, dilation=1, affine=affine),
-            DilConv(C_in, C_out, kernel_size, 1, padding, dilation=1, affine=affine)
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
-@basic_unit
-class FactorizedReduce(nn.Module):
-    """
-    Reduce feature map size by factorized pointwise (stride=2).
-    """
-    def __init__(self, C_in, C_out, affine=True):
-        super().__init__()
-        self.relu = nn.ReLU()
-        self.conv1 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False)
-        self.conv2 = nn.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False)
-        self.bn = nn.BatchNorm2d(C_out, affine=affine)
-
-    def forward(self, x):
-        x = self.relu(x)
-        out = torch.cat([self.conv1(x), self.conv2(x[:, :, 1:, 1:])], dim=1)
-        out = self.bn(out)
-        return out
diff --git a/test/retiarii_test/cgo/test.py b/test/retiarii_test/cgo/test.py
deleted file mode 100644
index 3fdbfdcdf..000000000
--- a/test/retiarii_test/cgo/test.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import json
-import os
-import sys
-import torch
-from pathlib import Path
-
-import nni.retiarii.evaluator.pytorch.cgo.evaluator as cgo
-import nni.retiarii.evaluator.pytorch.lightning as pl
-import nni.retiarii.strategy as strategy
-from nni.retiarii import serialize
-from nni.retiarii.experiment.pytorch import RetiariiExperiment, RetiariiExeConfig
-from torchvision import transforms
-from torchvision.datasets import CIFAR10
-
-from darts_model import CNN
-
-if __name__ == '__main__':
-    base_model = CNN(32, 3, 16, 10, 8)
-
-    train_transform = transforms.Compose([
-        transforms.RandomCrop(32, padding=4),
-        transforms.RandomHorizontalFlip(),
-        transforms.ToTensor(),
-        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
-    ])
-    valid_transform = transforms.Compose([
-        transforms.ToTensor(),
-        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
-    ])
-
-    train_dataset = serialize(CIFAR10, root='data/cifar10', train=True, download=True, transform=train_transform)
-    test_dataset = serialize(CIFAR10, root='data/cifar10', train=False, download=True, transform=valid_transform)
-    trainer = cgo.Classification(train_dataloader=pl.DataLoader(train_dataset, batch_size=100),
-                                 val_dataloaders=pl.DataLoader(test_dataset, batch_size=100),
-                                 max_epochs=1, limit_train_batches=0.2)
-
-    simple_strategy = strategy.Random()
-
-    exp = RetiariiExperiment(base_model, trainer, [], simple_strategy)
-
-    exp_config = RetiariiExeConfig('local')
-    exp_config.experiment_name = 'darts_search'
-    exp_config.execution_engine = 'cgo'
-    exp_config.trial_concurrency = 3
-    # since CGO may merge multiple trials into one, RetiariiExperiment may run more trials than max_trial_number
-    # when max_trial_number = 3, it actually runs 9 models since each merged trial contains 3 trials from strategy
-    exp_config.max_trial_number = 100
-    exp_config.devices = ['cuda:0', 'cuda:1', 'cuda:2']
-    exp_config.trial_gpu_number = 1
-    exp_config.batch_waiting_time = 100
-    exp_config.training_service.use_active_gpu = True
-    exp_config.training_service.gpu_indices = [0, 1, 2]
-
-    exp.run(exp_config, 8081)
diff --git a/test/retiarii_test/cgo_mnasnet/.nniignore b/test/retiarii_test/cgo_mnasnet/.nniignore
new file mode 100644
index 000000000..1269488f7
--- /dev/null
+++ b/test/retiarii_test/cgo_mnasnet/.nniignore
@@ -0,0 +1 @@
+data
diff --git a/test/retiarii_test/cgo_mnasnet/test.py b/test/retiarii_test/cgo_mnasnet/test.py
index 4569fa45e..eac4956f3 100644
--- a/test/retiarii_test/cgo_mnasnet/test.py
+++ b/test/retiarii_test/cgo_mnasnet/test.py
@@ -58,10 +58,8 @@ if __name__ == '__main__':
     exp_config.trial_concurrency = 3
     exp_config.max_trial_number = 10
     exp_config.trial_gpu_number = 1
-    exp_config.training_service.use_active_gpu = True
     exp_config.training_service.reuse_mode = True
-    exp_config.training_service.gpu_indices = [0, 1, 2]
-    exp_config.max_concurrency_cgo = 1
+    exp_config.max_concurrency_cgo = 3
     exp_config.batch_waiting_time = 0
 
     rm_conf = RemoteMachineConfig()
diff --git a/test/scripts/model_compression.sh b/test/scripts/model_compression.sh
index 1be9c15d0..431800593 100644
--- a/test/scripts/model_compression.sh
+++ b/test/scripts/model_compression.sh
@@ -4,7 +4,7 @@ CWD=${PWD}
 
 echo ""
 echo "===========================Testing: pruning and speedup==========================="
-cd ${CWD}/../examples/model_compress/pruning
+cd ${CWD}/../examples/model_compress/pruning/legacy
 
 echo "testing fpgm pruning and speedup..."
 python3 basic_pruners_torch.py --pruner fpgm --pretrain-epochs 1 --fine-tune-epochs 1 --model vgg16 --dataset cifar10
diff --git a/test/ut/compression/v1/test_compression_utils.py b/test/ut/compression/v1/test_compression_utils.py
index 2a7af8c4b..a41d61132 100644
--- a/test/ut/compression/v1/test_compression_utils.py
+++ b/test/ut/compression/v1/test_compression_utils.py
@@ -12,7 +12,7 @@ import numpy as np
 from nni.algorithms.compression.pytorch.pruning import L1FilterPruner
 from nni.compression.pytorch.utils.shape_dependency import ChannelDependency
 from nni.compression.pytorch.utils.mask_conflict import fix_mask_conflict
-from nni.compression.pytorch.utils.counter import count_flops_params
+from nni.compression.pytorch.utils import count_flops_params
 
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 prefix = 'analysis_test'
diff --git a/test/ut/compression/v1/test_model_speedup.py b/test/ut/compression/v1/test_model_speedup.py
index 9d0ff7cf8..a70f010f6 100644
--- a/test/ut/compression/v1/test_model_speedup.py
+++ b/test/ut/compression/v1/test_model_speedup.py
@@ -512,6 +512,46 @@ class SpeedupTestCase(TestCase):
         print("Fine-grained speeduped model")
         print(model)
 
+    def test_multiplication_speedup(self):
+        """
+        Model from issue 4540.
+        """
+        class Net(torch.nn.Module):
+            def __init__(self,):
+                super(Net, self).__init__()
+                self.avgpool = torch.nn.AdaptiveAvgPool2d(1)
+                self.input = torch.nn.Conv2d(3, 8, 3)
+                self.bn = torch.nn.BatchNorm2d(8)
+                self.fc1 = torch.nn.Conv2d(8, 16, 1)
+                self.fc2 = torch.nn.Conv2d(16, 8, 1)
+                self.activation = torch.nn.ReLU()
+                self.scale_activation = torch.nn.Hardsigmoid()
+                self.out = torch.nn.Conv2d(8, 12, 1)
+
+            def forward(self, input):
+                input = self.activation(self.bn(self.input(input)))
+                scale = self.avgpool(input)
+                out1 = self.activation(self.fc1(scale))
+                out1 = self.scale_activation(self.fc2(out1))
+                return self.out(out1 * input)
+
+        model = Net().to(device)
+        model.eval()
+        im = torch.ones(1, 3, 512, 512).to(device)
+        model(im)
+        cfg_list = []
+
+        for name, module in model.named_modules():
+            if isinstance(module, torch.nn.Conv2d):
+                cfg_list.append({'op_types':['Conv2d'], 'sparsity':0.3, 'op_names':[name]})
+
+        pruner = L1FilterPruner(model, cfg_list)
+        pruner.compress()
+        pruner.export_model(MODEL_FILE, MASK_FILE)
+        pruner._unwrap_model()
+        ms=ModelSpeedup(model, im, MASK_FILE)
+        ms.speedup_model()
+
     def tearDown(self):
         if os.path.exists(MODEL_FILE):
             os.remove(MODEL_FILE)
diff --git a/test/ut/compression/v2/test_iterative_pruner_torch.py b/test/ut/compression/v2/test_iterative_pruner_torch.py
index 7e3209073..85c561fe1 100644
--- a/test/ut/compression/v2/test_iterative_pruner_torch.py
+++ b/test/ut/compression/v2/test_iterative_pruner_torch.py
@@ -4,11 +4,12 @@
 import random
 import unittest
 
+import numpy
 import torch
 import torch.nn.functional as F
 
 import nni
-from nni.algorithms.compression.v2.pytorch.pruning import (
+from nni.compression.pytorch.pruning import (
     LinearPruner,
     AGPPruner,
     LotteryTicketPruner,
@@ -105,6 +106,17 @@ class IterativePrunerTestCase(unittest.TestCase):
         sparsity_list = compute_sparsity_mask2compact(pruned_model, masks, config_list)
         assert 0.78 < sparsity_list[0]['total_sparsity'] < 0.82
 
+    def test_amc_pruner(self):
+        model = TorchModel()
+        config_list = [{'op_types': ['Conv2d'], 'total_sparsity': 0.5, 'max_sparsity_per_layer': 0.8}]
+        dummy_input = torch.rand(10, 1, 28, 28)
+        ddpg_params = {'hidden1': 300, 'hidden2': 300, 'lr_c': 1e-3, 'lr_a': 1e-4, 'warmup': 5, 'discount': 1.,
+                       'bsize': 64, 'rmsize': 100, 'window_length': 1, 'tau': 0.01, 'init_delta': 0.5, 'delta_decay': 0.99,
+                       'max_episode_length': 1e9, 'epsilon': 50000}
+        pruner = AMCPruner(10, model, config_list, dummy_input, evaluator, finetuner=finetuner, ddpg_params=ddpg_params, target='flops', log_dir='../../../logs')
+        pruner.compress()
+
+class FixSeedPrunerTestCase(unittest.TestCase):
     def test_auto_compress_pruner(self):
         model = TorchModel()
         config_list = [{'op_types': ['Conv2d'], 'total_sparsity': 0.8}]
@@ -126,15 +138,21 @@ class IterativePrunerTestCase(unittest.TestCase):
         print(sparsity_list)
         assert 0.78 < sparsity_list[0]['total_sparsity'] < 0.82
 
-    def test_amc_pruner(self):
-        model = TorchModel()
-        config_list = [{'op_types': ['Conv2d'], 'total_sparsity': 0.5, 'max_sparsity_per_layer': 0.8}]
-        dummy_input = torch.rand(10, 1, 28, 28)
-        ddpg_params = {'hidden1': 300, 'hidden2': 300, 'lr_c': 1e-3, 'lr_a': 1e-4, 'warmup': 5, 'discount': 1.,
-                       'bsize': 64, 'rmsize': 100, 'window_length': 1, 'tau': 0.01, 'init_delta': 0.5, 'delta_decay': 0.99,
-                       'max_episode_length': 1e9, 'epsilon': 50000}
-        pruner = AMCPruner(10, model, config_list, dummy_input, evaluator, finetuner=finetuner, ddpg_params=ddpg_params, target='flops', log_dir='../../../logs')
-        pruner.compress()
+    def setUp(self) -> None:
+        # fix seed in order to solve the random failure of ut
+        random.seed(1024)
+        numpy.random.seed(1024)
+        torch.manual_seed(1024)
+
+    def tearDown(self) -> None:
+        # reset seed
+        import time
+        now = int(time.time() * 100)
+        random.seed(now)
+        seed = random.randint(0, 2 ** 32 - 1)
+        random.seed(seed)
+        numpy.random.seed(seed)
+        torch.manual_seed(seed)
 
 if __name__ == '__main__':
     unittest.main()
diff --git a/test/ut/compression/v2/test_pruner_torch.py b/test/ut/compression/v2/test_pruner_torch.py
index 7e462447a..8aabe536e 100644
--- a/test/ut/compression/v2/test_pruner_torch.py
+++ b/test/ut/compression/v2/test_pruner_torch.py
@@ -1,13 +1,15 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
 
+import random
 import unittest
 
+import numpy
 import torch
 import torch.nn.functional as F
 
 import nni
-from nni.algorithms.compression.v2.pytorch.pruning import (
+from nni.compression.pytorch.pruning import (
     LevelPruner,
     L1NormPruner,
     L2NormPruner,
@@ -122,18 +124,6 @@ class PrunerTestCase(unittest.TestCase):
         sparsity_list = compute_sparsity_mask2compact(pruned_model, masks, config_list)
         assert 0.78 < sparsity_list[0]['total_sparsity'] < 0.82
 
-    def test_activation_apoz_rank_pruner(self):
-        model = TorchModel()
-        config_list = [{'op_types': ['Conv2d'], 'sparsity': 0.8}]
-        pruner = ActivationAPoZRankPruner(model=model, config_list=config_list, trainer=trainer,
-                                          traced_optimizer=get_optimizer(model), criterion=criterion, training_batches=5,
-                                          activation='relu', mode='dependency_aware',
-                                          dummy_input=torch.rand(10, 1, 28, 28))
-        pruned_model, masks = pruner.compress()
-        pruner._unwrap_model()
-        sparsity_list = compute_sparsity_mask2compact(pruned_model, masks, config_list)
-        assert 0.78 < sparsity_list[0]['total_sparsity'] < 0.82
-
     def test_activation_mean_rank_pruner(self):
         model = TorchModel()
         config_list = [{'op_types': ['Conv2d'], 'sparsity': 0.8}]
@@ -177,6 +167,34 @@ class PrunerTestCase(unittest.TestCase):
         sparsity_list = compute_sparsity_mask2compact(pruned_model, masks, config_list)
         assert 0.78 < sparsity_list[0]['total_sparsity'] < 0.82
 
+class FixSeedPrunerTestCase(unittest.TestCase):
+    def test_activation_apoz_rank_pruner(self):
+        model = TorchModel()
+        config_list = [{'op_types': ['Conv2d'], 'sparsity': 0.8}]
+        pruner = ActivationAPoZRankPruner(model=model, config_list=config_list, trainer=trainer,
+                                            traced_optimizer=get_optimizer(model), criterion=criterion, training_batches=5,
+                                            activation='relu', mode='dependency_aware',
+                                            dummy_input=torch.rand(10, 1, 28, 28))
+        pruned_model, masks = pruner.compress()
+        pruner._unwrap_model()
+        sparsity_list = compute_sparsity_mask2compact(pruned_model, masks, config_list)
+        assert 0.78 < sparsity_list[0]['total_sparsity'] < 0.82
+
+    def setUp(self) -> None:
+        # fix seed in order to solve the random failure of ut
+        random.seed(1024)
+        numpy.random.seed(1024)
+        torch.manual_seed(1024)
+
+    def tearDown(self) -> None:
+        # reset seed
+        import time
+        now = int(time.time() * 100)
+        random.seed(now)
+        seed = random.randint(0, 2 ** 32 - 1)
+        random.seed(seed)
+        numpy.random.seed(seed)
+        torch.manual_seed(seed)
 
 if __name__ == '__main__':
     unittest.main()
diff --git a/test/ut/compression/v2/test_pruning_tools_torch.py b/test/ut/compression/v2/test_pruning_tools_torch.py
index be7010dc2..8e04bd7f5 100644
--- a/test/ut/compression/v2/test_pruning_tools_torch.py
+++ b/test/ut/compression/v2/test_pruning_tools_torch.py
@@ -83,17 +83,17 @@ class PruningToolsTestCase(unittest.TestCase):
         # Test WeightDataCollector
         data_collector = WeightDataCollector(pruner)
         data = data_collector.collect()
-        assert all(torch.equal(get_module_by_name(model, module_name)[1].module.weight.data, data[module_name]) for module_name in ['conv1', 'conv2'])
+        assert all(torch.equal(get_module_by_name(model, module_name)[1].weight.data, data[module_name]) for module_name in ['conv1', 'conv2'])
 
         # Test WeightTrainerBasedDataCollector
         def opt_after():
-            model.conv1.module.weight.data = torch.ones(5, 1, 5, 5)
-            model.conv2.module.weight.data = torch.ones(10, 5, 5, 5)
+            model.conv1.weight.data = torch.ones(5, 1, 5, 5)
+            model.conv2.weight.data = torch.ones(10, 5, 5, 5)
 
         optimizer_helper = OptimizerConstructHelper.from_trace(model, get_optimizer(model))
         data_collector = WeightTrainerBasedDataCollector(pruner, trainer, optimizer_helper, criterion, 1, opt_after_tasks=[opt_after])
         data = data_collector.collect()
-        assert all(torch.equal(get_module_by_name(model, module_name)[1].module.weight.data, data[module_name]) for module_name in ['conv1', 'conv2'])
+        assert all(torch.equal(get_module_by_name(model, module_name)[1].weight.data, data[module_name]) for module_name in ['conv1', 'conv2'])
         assert all(t.numel() == (t == 1).type_as(t).sum().item() for t in data.values())
 
         # Test SingleHookTrainerBasedDataCollector
@@ -102,7 +102,7 @@ class PruningToolsTestCase(unittest.TestCase):
                 if len(buffer) < 2:
                     buffer.append(grad.clone().detach())
             return collect_taylor
-        hook_targets = {'conv1': model.conv1.module.weight, 'conv2': model.conv2.module.weight}
+        hook_targets = {'conv1': model.conv1.weight, 'conv2': model.conv2.weight}
         collector_info = HookCollectorInfo(hook_targets, 'tensor', _collector)
 
         optimizer_helper = OptimizerConstructHelper.from_trace(model, get_optimizer(model))
diff --git a/test/ut/compression/v2/test_pruning_wrapper.py b/test/ut/compression/v2/test_pruning_wrapper.py
new file mode 100644
index 000000000..d3a305a6c
--- /dev/null
+++ b/test/ut/compression/v2/test_pruning_wrapper.py
@@ -0,0 +1,47 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+import unittest
+
+import torch
+import torch.nn.functional as F
+
+from nni.compression.pytorch.pruning import L1NormPruner
+
+class TorchModel(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv1 = torch.nn.Conv2d(1, 5, 5, 1)
+        self.bn1 = torch.nn.BatchNorm2d(5)
+        self.conv2 = torch.nn.Conv2d(5, 10, 5, 1)
+        self.bn2 = torch.nn.BatchNorm2d(10)
+        self.fc1 = torch.nn.Linear(4 * 4 * 10, 100)
+        self.fc2 = torch.nn.Linear(100, 10)
+
+    def forward(self, x):
+        x = F.relu(self.bn1(self.conv1(x)))
+        x = F.max_pool2d(x, 2, 2)
+        x = F.relu(self.bn2(self.conv2(x)))
+        x = F.max_pool2d(x, 2, 2)
+        x = x.view(-1, 4 * 4 * 10)
+        x = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return F.log_softmax(x, dim=1)
+
+
+class PrunerTestCase(unittest.TestCase):
+    def test_pruner_module_wrapper(self):
+        model = TorchModel()
+        conv1_weight = model.conv1.weight.data.clone()
+        conv2_weight = model.conv2.weight.data.clone()
+        config_list = [{'op_types': ['Conv2d'], 'sparsity': 0.8}]
+        pruner = L1NormPruner(model, config_list)
+        _, masks = pruner.compress()
+        model(torch.rand(10, 1, 28, 28))
+        assert torch.equal(model.conv1.weight.data, conv1_weight)
+        assert torch.equal(model.conv2.weight.data, conv2_weight)
+        assert torch.equal(model.conv1.module.weight.data, conv1_weight * masks['conv1']['weight'])
+        assert torch.equal(model.conv2.module.weight.data, conv2_weight * masks['conv2']['weight'])
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/test/ut/compression/v2/test_scheduler.py b/test/ut/compression/v2/test_scheduler.py
index 916b7f763..629355eef 100644
--- a/test/ut/compression/v2/test_scheduler.py
+++ b/test/ut/compression/v2/test_scheduler.py
@@ -6,7 +6,7 @@ import unittest
 import torch
 import torch.nn.functional as F
 
-from nni.algorithms.compression.v2.pytorch.pruning import PruningScheduler, L1NormPruner
+from nni.compression.pytorch.pruning import PruningScheduler, L1NormPruner
 from nni.algorithms.compression.v2.pytorch.pruning.tools import AGPTaskGenerator
 
 
diff --git a/test/ut/retiarii/debug_mnist_pytorch.py b/test/ut/retiarii/debug_mnist_pytorch.py
index c7511a5db..80d7355a0 100644
--- a/test/ut/retiarii/debug_mnist_pytorch.py
+++ b/test/ut/retiarii/debug_mnist_pytorch.py
@@ -16,6 +16,7 @@ class _model(nn.Module):
         self.fc1 = torch.nn.Linear(out_features=256, in_features=1024)
         self.fc2 = torch.nn.Linear(out_features=10, in_features=256)
         self.softmax = torch.nn.Softmax()
+        self._mapping_ = {'stem': None, 'flatten': None, 'fc1': None, 'fc2': None, 'softmax': None}
 
     def forward(self, image):
         stem = self.stem(image)
@@ -34,6 +35,7 @@ class stem(nn.Module):
         self.pool1 = torch.nn.MaxPool2d(kernel_size=2)
         self.conv2 = torch.nn.Conv2d(out_channels=64, in_channels=32, kernel_size=5)
         self.pool2 = torch.nn.MaxPool2d(kernel_size=2)
+        self._mapping_ = {'conv1': None, 'pool1': None, 'conv2': None, 'pool2': None}
 
     def forward(self, *_inputs):
         conv1 = self.conv1(_inputs[0])
diff --git a/test/ut/retiarii/test_cgo_engine.py b/test/ut/retiarii/test_cgo_engine.py
index 0b8fa626b..4d6666626 100644
--- a/test/ut/retiarii/test_cgo_engine.py
+++ b/test/ut/retiarii/test_cgo_engine.py
@@ -9,6 +9,8 @@ from pytorch_lightning.utilities.seed import seed_everything
 from pathlib import Path
 
 import nni
+import nni.runtime.platform.test
+import json
 
 try:
     from nni.common.device import GPUDevice
@@ -146,7 +148,7 @@ def _new_trainer():
     train_dataset = serialize(MNIST, root='data/mnist', train=True, download=True, transform=transform)
     test_dataset = serialize(MNIST, root='data/mnist', train=False, download=True, transform=transform)
 
-    multi_module = MultiModelSupervisedLearningModule(nn.CrossEntropyLoss, {'acc': pl._AccuracyWithLogits})
+    multi_module = _MultiModelSupervisedLearningModule(nn.CrossEntropyLoss, {'acc': pl._AccuracyWithLogits})
 
     lightning = pl.Lightning(multi_module, cgo_trainer.Trainer(use_cgo=True,
                                                                max_epochs=1,
diff --git a/test/ut/retiarii/test_convert.py b/test/ut/retiarii/test_convert.py
index c79c7696a..15b3bba00 100644
--- a/test/ut/retiarii/test_convert.py
+++ b/test/ut/retiarii/test_convert.py
@@ -14,6 +14,7 @@ import torchvision
 import nni.retiarii.nn.pytorch as nn
 from nni.retiarii import basic_unit
 from nni.retiarii.codegen import model_to_pytorch_script
+from nni.retiarii.utils import original_state_dict_hooks
 
 from .convert_mixin import ConvertMixin, ConvertWithShapeMixin
 
@@ -50,16 +51,6 @@ class Linear(nn.Module):
         return out.view(size[0], size[1], -1)
 
 class TestConvert(unittest.TestCase, ConvertMixin):
-    @staticmethod
-    def _match_state_dict(current_values, expected_format):
-        result = {}
-        for k, v in expected_format.items():
-            for idx, cv in enumerate(current_values):
-                if cv.shape == v.shape:
-                    result[k] = cv
-                    current_values.pop(idx)
-                    break
-        return result
 
     def checkExportImport(self, model, input):
         model_ir = self._convert_model(model, input)
@@ -68,9 +59,8 @@ class TestConvert(unittest.TestCase, ConvertMixin):
         exec_vars = {}
         exec(model_code + '\n\nconverted_model = _model()', exec_vars)
         converted_model = exec_vars['converted_model']
-        converted_state_dict = self._match_state_dict(list(model.state_dict().values()),
-                                                      dict(converted_model.state_dict()))
-        converted_model.load_state_dict(converted_state_dict)
+        with original_state_dict_hooks(converted_model):
+            converted_model.load_state_dict(dict(model.state_dict()))
         with torch.no_grad():
             expected_output = model.eval()(*input)
             converted_output = converted_model.eval()(*input)
diff --git a/test/ut/retiarii/test_convert_basic.py b/test/ut/retiarii/test_convert_basic.py
index 145f62f63..48f0ba183 100644
--- a/test/ut/retiarii/test_convert_basic.py
+++ b/test/ut/retiarii/test_convert_basic.py
@@ -12,20 +12,11 @@ from nni.retiarii import basic_unit
 
 from .convert_mixin import ConvertMixin, ConvertWithShapeMixin
 from nni.retiarii.codegen import model_to_pytorch_script
+from nni.retiarii.utils import original_state_dict_hooks
 
 # following pytorch v1.7.1
 
 class TestConvert(unittest.TestCase, ConvertMixin):
-    @staticmethod
-    def _match_state_dict(current_values, expected_format):
-        result = {}
-        for k, v in expected_format.items():
-            for idx, cv in enumerate(current_values):
-                if cv.shape == v.shape:
-                    result[k] = cv
-                    current_values.pop(idx)
-                    break
-        return result
 
     def checkExportImport(self, model, input, check_value=True):
         model_ir = self._convert_model(model, input)
@@ -35,9 +26,10 @@ class TestConvert(unittest.TestCase, ConvertMixin):
         exec_vars = {}
         exec(model_code + '\n\nconverted_model = _model()', exec_vars)
         converted_model = exec_vars['converted_model']
-        converted_state_dict = self._match_state_dict(list(model.state_dict().values()),
-                                                      dict(converted_model.state_dict()))
-        converted_model.load_state_dict(converted_state_dict)
+
+        with original_state_dict_hooks(converted_model):
+            converted_model.load_state_dict(model.state_dict())
+
         with torch.no_grad():
             expected_output = model.eval()(*input)
             converted_output = converted_model.eval()(*input)
diff --git a/test/ut/retiarii/test_convert_models.py b/test/ut/retiarii/test_convert_models.py
index 26851d1e0..8dba5bbae 100644
--- a/test/ut/retiarii/test_convert_models.py
+++ b/test/ut/retiarii/test_convert_models.py
@@ -9,23 +9,13 @@ import torch.nn.functional as F
 import torchvision
 
 import nni.retiarii.nn.pytorch as nn
-from nni.retiarii import serialize
 from nni.retiarii.codegen import model_to_pytorch_script
+from nni.retiarii.utils import original_state_dict_hooks
 
 from .convert_mixin import ConvertMixin, ConvertWithShapeMixin
 
 
 class TestModels(unittest.TestCase, ConvertMixin):
-    @staticmethod
-    def _match_state_dict(current_values, expected_format):
-        result = {}
-        for k, v in expected_format.items():
-            for idx, cv in enumerate(current_values):
-                if cv.shape == v.shape:
-                    result[k] = cv
-                    current_values.pop(idx)
-                    break
-        return result
 
     def run_test(self, model, input, check_value=True):
         model_ir = self._convert_model(model, input)
@@ -35,9 +25,10 @@ class TestModels(unittest.TestCase, ConvertMixin):
         exec_vars = {}
         exec(model_code + '\n\nconverted_model = _model()', exec_vars)
         converted_model = exec_vars['converted_model']
-        converted_state_dict = self._match_state_dict(list(model.state_dict().values()),
-                                                      dict(converted_model.state_dict()))
-        converted_model.load_state_dict(converted_state_dict)
+
+        with original_state_dict_hooks(converted_model):
+            converted_model.load_state_dict(model.state_dict())
+
         with torch.no_grad():
             expected_output = model.eval()(*input)
             converted_output = converted_model.eval()(*input)
diff --git a/test/ut/retiarii/test_convert_operators.py b/test/ut/retiarii/test_convert_operators.py
index 2bc24fff6..73f607973 100644
--- a/test/ut/retiarii/test_convert_operators.py
+++ b/test/ut/retiarii/test_convert_operators.py
@@ -16,6 +16,7 @@ import torchvision
 
 import nni.retiarii.nn.pytorch as nn
 from nni.retiarii.codegen import model_to_pytorch_script
+from nni.retiarii.utils import original_state_dict_hooks
 
 from .convert_mixin import ConvertMixin, ConvertWithShapeMixin
 
@@ -23,16 +24,6 @@ from .convert_mixin import ConvertMixin, ConvertWithShapeMixin
 
 
 class TestOperators(unittest.TestCase, ConvertMixin):
-    @staticmethod
-    def _match_state_dict(current_values, expected_format):
-        result = {}
-        for k, v in expected_format.items():
-            for idx, cv in enumerate(current_values):
-                if cv.shape == v.shape:
-                    result[k] = cv
-                    current_values.pop(idx)
-                    break
-        return result
 
     def checkExportImport(self, model, input, check_value=True):
         model_ir = self._convert_model(model, input)
@@ -42,9 +33,10 @@ class TestOperators(unittest.TestCase, ConvertMixin):
         exec_vars = {}
         exec(model_code + '\n\nconverted_model = _model()', exec_vars)
         converted_model = exec_vars['converted_model']
-        converted_state_dict = self._match_state_dict(list(model.state_dict().values()),
-                                                      dict(converted_model.state_dict()))
-        converted_model.load_state_dict(converted_state_dict)
+
+        with original_state_dict_hooks(converted_model):
+            converted_model.load_state_dict(model.state_dict())
+
         with torch.no_grad():
             expected_output = model.eval()(*input)
             converted_output = converted_model.eval()(*input)
diff --git a/test/ut/retiarii/test_convert_pytorch.py b/test/ut/retiarii/test_convert_pytorch.py
index 692910642..6036cde48 100644
--- a/test/ut/retiarii/test_convert_pytorch.py
+++ b/test/ut/retiarii/test_convert_pytorch.py
@@ -14,28 +14,17 @@ import torch.nn.functional as F
 import torchvision
 
 import nni.retiarii.nn.pytorch as nn
-from nni.retiarii import serialize
 from nni.retiarii.codegen import model_to_pytorch_script
+from nni.retiarii.utils import original_state_dict_hooks
 
 from .convert_mixin import ConvertMixin, ConvertWithShapeMixin
 
 
 class TestPytorch(unittest.TestCase, ConvertMixin):
-    @staticmethod
-    def _match_state_dict(current_values, expected_format):
-        result = {}
-        for k, v in expected_format.items():
-            for idx, cv in enumerate(current_values):
-                if cv.shape == v.shape:
-                    result[k] = cv
-                    current_values.pop(idx)
-                    break
-        return result
-
-    def run_test(self, model, input, check_value=True):
+
+    def run_test(self, model, input, check_value=True, strict_load=True):
         model_ir = self._convert_model(model, input)
         model_code = model_to_pytorch_script(model_ir)
-        print(model_code)
 
         from .inject_nn import remove_inject_pytorch_nn
         remove_inject_pytorch_nn()
@@ -43,9 +32,10 @@ class TestPytorch(unittest.TestCase, ConvertMixin):
         exec_vars = {}
         exec(model_code + '\n\nconverted_model = _model()', exec_vars)
         converted_model = exec_vars['converted_model']
-        converted_state_dict = self._match_state_dict(list(model.state_dict().values()),
-                                                      dict(converted_model.state_dict()))
-        converted_model.load_state_dict(converted_state_dict)
+
+        with original_state_dict_hooks(converted_model):
+            converted_model.load_state_dict(model.state_dict(), strict=strict_load)
+
         with torch.no_grad():
             expected_output = model.eval()(*input)
             converted_output = converted_model.eval()(*input)
@@ -76,7 +66,8 @@ class TestPytorch(unittest.TestCase, ConvertMixin):
 
         model = LargeModel()
         x = torch.tensor([2], dtype=torch.long)
-        self.run_test(model, (x, ))
+        # emb and lin1 is actually not used so they won't appear in generated model
+        self.run_test(model, (x, ), strict_load=False)
 
     @unittest.skip('skip for now, as it needs inject_nn')
     def test_mobilenet_v2_with_external_data(self):
diff --git a/test/ut/retiarii/test_highlevel_apis.py b/test/ut/retiarii/test_highlevel_apis.py
index 9e8b79fc4..dc354096d 100644
--- a/test/ut/retiarii/test_highlevel_apis.py
+++ b/test/ut/retiarii/test_highlevel_apis.py
@@ -5,7 +5,10 @@ from collections import Counter
 
 import pytest
 
+import nni
+import nni.retiarii.evaluator.pytorch.lightning as pl
 import nni.retiarii.nn.pytorch as nn
+import pytorch_lightning
 import torch
 import torch.nn.functional as F
 from nni.retiarii import InvalidMutation, Sampler, basic_unit
@@ -13,11 +16,12 @@ from nni.retiarii.converter import convert_to_graph
 from nni.retiarii.codegen import model_to_pytorch_script
 from nni.retiarii.evaluator import FunctionalEvaluator
 from nni.retiarii.execution.utils import _unpack_if_only_one
+from nni.retiarii.experiment.pytorch import preprocess_model
 from nni.retiarii.graph import Model
 from nni.retiarii.nn.pytorch.api import ValueChoice
 from nni.retiarii.nn.pytorch.mutator import process_evaluator_mutations, process_inline_mutation, extract_mutation_from_pt_module
 from nni.retiarii.serializer import model_wrapper
-from nni.retiarii.utils import ContextStack
+from nni.retiarii.utils import ContextStack, NoContextError, original_state_dict_hooks
 
 
 class EnumerateSampler(Sampler):
@@ -66,6 +70,10 @@ def _apply_all_mutators(model, mutators, samplers):
 class GraphIR(unittest.TestCase):
     # graph engine will have an extra mutator for parameter choices
     value_choice_incr = 1
+    # graph engine has an extra mutator to apply the depth choice to nodes
+    repeat_incr = 1
+    # graph engine parse the model into graph
+    graph_engine = True
 
     def _convert_to_ir(self, model):
         script_module = torch.jit.script(model)
@@ -123,6 +131,29 @@ class GraphIR(unittest.TestCase):
             self.assertEqual(self._get_converted_pytorch_model(model_new)(torch.randn(1, 3, 3, 3)).size(),
                              torch.Size([1, i, 3, 3]))
 
+    def test_layer_choice_weight_inheritance(self):
+        @model_wrapper
+        class Net(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.module = nn.LayerChoice([nn.Conv2d(3, i, kernel_size=1) for i in range(1, 11)])
+
+            def forward(self, x):
+                return self.module(x)
+
+        orig_model = Net()
+        model, mutators = self._get_model_with_mutators(orig_model)
+        mutator = mutators[0].bind_sampler(EnumerateSampler())
+        for i in range(1, 11):
+            model_new = mutator.apply(model)
+            model_new = self._get_converted_pytorch_model(model_new)
+            with original_state_dict_hooks(model_new):
+                model_new.load_state_dict(orig_model.state_dict(), strict=False)
+            inp = torch.randn(1, 3, 3, 3)
+            a = getattr(orig_model.module, str(i - 1))(inp)
+            b = model_new(inp)
+            self.assertLess((a - b).abs().max().item(), 1E-4)
+
     def test_nested_layer_choice(self):
         @model_wrapper
         class Net(nn.Module):
@@ -150,6 +181,40 @@ class GraphIR(unittest.TestCase):
         self.assertEqual(self._get_converted_pytorch_model(mutators[1].apply(mutators[0].apply(model)))(input).size(),
                          torch.Size([1, 5, 5, 5]))
 
+    def test_nested_layer_choice_weight_inheritance(self):
+        @model_wrapper
+        class Net(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.module = nn.LayerChoice([
+                    nn.LayerChoice([nn.Conv2d(3, 3, kernel_size=1),
+                                    nn.Conv2d(3, 4, kernel_size=1),
+                                    nn.Conv2d(3, 5, kernel_size=1)]),
+                    nn.Conv2d(3, 1, kernel_size=1)
+                ])
+
+            def forward(self, x):
+                return self.module(x)
+
+        orig_model = Net()
+        model, mutators = self._get_model_with_mutators(orig_model)
+        mutators[0].bind_sampler(EnumerateSampler())
+        mutators[1].bind_sampler(EnumerateSampler())
+        input = torch.randn(1, 3, 5, 5)
+
+        for i in range(3):
+            model_new = self._get_converted_pytorch_model(mutators[1].apply(mutators[0].apply(model)))
+            with original_state_dict_hooks(model_new):
+                model_new.load_state_dict(orig_model.state_dict(), strict=False)
+            if i == 0:
+                a = getattr(getattr(orig_model.module, '0'), '0')(input)
+            elif i == 1:
+                a = getattr(orig_model.module, '1')(input)
+            elif i == 2:
+                a = getattr(getattr(orig_model.module, '0'), '2')(input)
+            b = model_new(input)
+            self.assertLess((a - b).abs().max().item(), 1E-4)
+
     def test_input_choice(self):
         @model_wrapper
         class Net(nn.Module):
@@ -506,6 +571,48 @@ class GraphIR(unittest.TestCase):
         with pytest.raises(AssertionError):
             self._get_model_with_mutators(Net())
 
+    def test_valuechoice_hybrid_arch_hparams(self):
+        @model_wrapper
+        class Net(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.conv = nn.Conv2d(3, 5, kernel_size=nn.ValueChoice([3, 5]))
+
+            def forward(self, x):
+                return self.conv(x)
+
+        def foo():
+            pass
+
+        evaluator = FunctionalEvaluator(foo, t=1, x=ValueChoice([1, 2]), y=ValueChoice([3, 4]))
+        model, mutators = preprocess_model(Net(), evaluator, [], full_ir=self.graph_engine)
+        samplers = [EnumerateSampler() for _ in range(len(mutators))]
+        model1 = _apply_all_mutators(model, mutators, samplers)
+        model2 = _apply_all_mutators(model, mutators, samplers)
+        self.assertEqual(self._get_converted_pytorch_model(model1)(torch.randn(1, 3, 5, 5)).size(),
+                         torch.Size([1, 5, 3, 3]))
+        self.assertEqual(model1.evaluator.trace_kwargs['x'], 1)
+        self.assertEqual(self._get_converted_pytorch_model(model2)(torch.randn(1, 3, 5, 5)).size(),
+                         torch.Size([1, 5, 1, 1]))
+        self.assertEqual(model2.evaluator.trace_kwargs['y'], 4)
+
+    def test_valuechoice_hybrid_arch_hparams_conflict_label(self):
+        @model_wrapper
+        class Net(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.conv = nn.Conv2d(3, 5, kernel_size=nn.ValueChoice([3, 5], label='123'))
+
+            def forward(self, x):
+                return self.conv(x)
+
+        def foo():
+            pass
+
+        evaluator = FunctionalEvaluator(foo, t=1, x=ValueChoice([3, 5], label='123'))
+        with pytest.raises(ValueError, match='share'):
+            preprocess_model(Net(), evaluator, [], full_ir=self.graph_engine)
+
     def test_repeat(self):
         class AddOne(nn.Module):
             def forward(self, x):
@@ -521,14 +628,39 @@ class GraphIR(unittest.TestCase):
                 return self.block(x)
 
         model, mutators = self._get_model_with_mutators(Net())
-        self.assertEqual(len(mutators), 1)
-        mutator = mutators[0].bind_sampler(EnumerateSampler())
-        model1 = mutator.apply(model)
-        model2 = mutator.apply(model)
-        model3 = mutator.apply(model)
-        self.assertTrue((self._get_converted_pytorch_model(model1)(torch.zeros(1, 16)) == 3).all())
-        self.assertTrue((self._get_converted_pytorch_model(model2)(torch.zeros(1, 16)) == 4).all())
-        self.assertTrue((self._get_converted_pytorch_model(model3)(torch.zeros(1, 16)) == 5).all())
+        self.assertEqual(len(mutators), 1 + self.repeat_incr + self.value_choice_incr)
+        samplers = [EnumerateSampler() for _ in range(len(mutators))]
+        for target in [3, 4, 5]:
+            new_model = _apply_all_mutators(model, mutators, samplers)
+            self.assertTrue((self._get_converted_pytorch_model(new_model)(torch.zeros(1, 16)) == target).all())
+
+    def test_repeat_static(self):
+        class AddOne(nn.Module):
+            def forward(self, x):
+                return x + 1
+
+        @model_wrapper
+        class Net(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.block = nn.Repeat(lambda index: nn.LayerChoice([AddOne(), nn.Identity()]), 4)
+
+            def forward(self, x):
+                return self.block(x)
+
+        model, mutators = self._get_model_with_mutators(Net())
+        self.assertEqual(len(mutators), 4)
+        sampler = RandomSampler()
+
+        result = []
+        for _ in range(50):
+            new_model = model
+            for mutator in mutators:
+                new_model = mutator.bind_sampler(sampler).apply(new_model)
+            result.append(self._get_converted_pytorch_model(new_model)(torch.zeros(1, 1)).item())
+
+        for x in [1, 2, 3]:
+            self.assertIn(float(x), result)
 
     def test_repeat_complex(self):
         class AddOne(nn.Module):
@@ -545,8 +677,8 @@ class GraphIR(unittest.TestCase):
                 return self.block(x)
 
         model, mutators = self._get_model_with_mutators(Net())
-        self.assertEqual(len(mutators), 2)
-        self.assertEqual(set([mutator.label for mutator in mutators]), {'lc', 'rep'})
+        self.assertEqual(len(mutators), 2 + self.repeat_incr + self.value_choice_incr)
+        self.assertEqual(set([mutator.label for mutator in mutators if mutator.label is not None]), {'lc', 'rep'})
 
         sampler = RandomSampler()
         for _ in range(10):
@@ -567,7 +699,7 @@ class GraphIR(unittest.TestCase):
                 return self.block(x)
 
         model, mutators = self._get_model_with_mutators(Net())
-        self.assertEqual(len(mutators), 4)
+        self.assertEqual(len(mutators), 4 + self.repeat_incr + self.value_choice_incr)
 
         result = []
         for _ in range(20):
@@ -578,81 +710,71 @@ class GraphIR(unittest.TestCase):
 
         self.assertIn(1., result)
 
-    def test_cell(self):
-        @model_wrapper
-        class Net(nn.Module):
-            def __init__(self):
-                super().__init__()
-                self.cell = nn.Cell([nn.Linear(16, 16), nn.Linear(16, 16, bias=False)],
-                                    num_nodes=4, num_ops_per_node=2, num_predecessors=2, merge_op='all')
-
-            def forward(self, x, y):
-                return self.cell([x, y])
-
-        raw_model, mutators = self._get_model_with_mutators(Net())
-        for _ in range(10):
-            sampler = EnumerateSampler()
-            model = raw_model
-            for mutator in mutators:
-                model = mutator.bind_sampler(sampler).apply(model)
-            self.assertTrue(self._get_converted_pytorch_model(model)(
-                torch.randn(1, 16), torch.randn(1, 16)).size() == torch.Size([1, 64]))
+    def test_repeat_valuechoice(self):
+        class AddOne(nn.Module):
+            def forward(self, x):
+                return x + 1
 
         @model_wrapper
-        class Net2(nn.Module):
+        class Net(nn.Module):
             def __init__(self):
                 super().__init__()
-                self.cell = nn.Cell([nn.Linear(16, 16), nn.Linear(16, 16, bias=False)], num_nodes=4)
+                self.block = nn.Repeat(AddOne(), nn.ValueChoice([1, 3, 5]))
 
             def forward(self, x):
-                return self.cell([x])
+                return self.block(x)
 
-        raw_model, mutators = self._get_model_with_mutators(Net2())
-        for _ in range(10):
-            sampler = EnumerateSampler()
-            model = raw_model
-            for mutator in mutators:
-                model = mutator.bind_sampler(sampler).apply(model)
-            self.assertTrue(self._get_converted_pytorch_model(model)(torch.randn(1, 16)).size() == torch.Size([1, 64]))
+        model, mutators = self._get_model_with_mutators(Net())
+        self.assertEqual(len(mutators), 1 + self.repeat_incr + self.value_choice_incr)
+        samplers = [EnumerateSampler() for _ in range(len(mutators))]
+        for target in [1, 3, 5]:
+            new_model = _apply_all_mutators(model, mutators, samplers)
+            self.assertTrue((self._get_converted_pytorch_model(new_model)(torch.zeros(1, 16)) == target).all())
 
-    def test_cell_predecessors(self):
-        from typing import List, Tuple
+    def test_repeat_valuechoicex(self):
+        class AddOne(nn.Module):
+            def forward(self, x):
+                return x + 1
 
-        class Preprocessor(nn.Module):
+        @model_wrapper
+        class Net(nn.Module):
             def __init__(self):
                 super().__init__()
-                self.linear = nn.Linear(3, 16)
+                self.block = nn.Repeat(AddOne(), nn.ValueChoice([0, 2, 4]) + 1)
 
-            def forward(self, x: List[torch.Tensor]) -> List[torch.Tensor]:
-                return [self.linear(x[0]), x[1]]
+            def forward(self, x):
+                return self.block(x)
 
-        class Postprocessor(nn.Module):
-            def forward(self, this: torch.Tensor, prev: List[torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]:
-                return prev[-1], this
+        model, mutators = self._get_model_with_mutators(Net())
+        self.assertEqual(len(mutators), 1 + self.repeat_incr + self.value_choice_incr)
+        samplers = [EnumerateSampler() for _ in range(len(mutators))]
+        for target in [1, 3, 5]:
+            new_model = _apply_all_mutators(model, mutators, samplers)
+            self.assertTrue((self._get_converted_pytorch_model(new_model)(torch.zeros(1, 16)) == target).all())
 
+    def test_repeat_weight_inheritance(self):
         @model_wrapper
         class Net(nn.Module):
             def __init__(self):
                 super().__init__()
-                self.cell = nn.Cell({
-                    'first': nn.Linear(16, 16),
-                    'second': nn.Linear(16, 16, bias=False)
-                }, num_nodes=4, num_ops_per_node=2, num_predecessors=2,
-                preprocessor=Preprocessor(), postprocessor=Postprocessor(), merge_op='all')
+                self.module = nn.Repeat(lambda index: nn.Conv2d(3, 3, 1), (2, 5))
 
-            def forward(self, x, y):
-                return self.cell([x, y])
+            def forward(self, x):
+                return self.module(x)
 
-        raw_model, mutators = self._get_model_with_mutators(Net())
-        for _ in range(10):
-            sampler = EnumerateSampler()
-            model = raw_model
-            for mutator in mutators:
-                model = mutator.bind_sampler(sampler).apply(model)
-            result = self._get_converted_pytorch_model(model)(
-                torch.randn(1, 3), torch.randn(1, 16))
-            self.assertTrue(result[0].size() == torch.Size([1, 16]))
-            self.assertTrue(result[1].size() == torch.Size([1, 64]))
+        orig_model = Net()
+        model, mutators = self._get_model_with_mutators(orig_model)
+        samplers = [EnumerateSampler() for _ in range(len(mutators))]
+        inp = torch.randn(1, 3, 5, 5)
+
+        for i in range(4):
+            model_new = self._get_converted_pytorch_model(_apply_all_mutators(model, mutators, samplers))
+            with original_state_dict_hooks(model_new):
+                model_new.load_state_dict(orig_model.state_dict(), strict=False)
+
+            a = nn.Sequential(*orig_model.module.blocks[:i + 2])(inp)
+            b = model_new(inp)
+            self.assertLess((a - b).abs().max().item(), 1E-4)
 
     def test_nasbench201_cell(self):
         @model_wrapper
@@ -697,6 +819,8 @@ class GraphIR(unittest.TestCase):
 class Python(GraphIR):
     # Python engine doesn't have the extra mutator
     value_choice_incr = 0
+    repeat_incr = 0
+    graph_engine = False
 
     def _get_converted_pytorch_model(self, model_ir):
         mutation = {mut.mutator.label: _unpack_if_only_one(mut.samples) for mut in model_ir.history}
@@ -719,6 +843,141 @@ class Python(GraphIR):
     @unittest.skip
     def test_valuechoice_getitem_functional_expression(self): ...
 
+    def test_hyperparameter_choice(self):
+        @model_wrapper
+        class Net(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.aux = nn.ModelParameterChoice([False, True])
+
+            def forward(self, x):
+                return x
+
+        model, mutators = self._get_model_with_mutators(Net())
+        self.assertEqual(len(mutators), 1)
+        sampler = EnumerateSampler()
+        model1 = _apply_all_mutators(model, mutators, sampler)
+        model2 = _apply_all_mutators(model, mutators, sampler)
+        self.assertEqual(self._get_converted_pytorch_model(model1).aux, False)
+        self.assertEqual(self._get_converted_pytorch_model(model2).aux, True)
+
+    def test_hyperparameter_choice_parameter(self):
+        class Inner(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.aux = torch.nn.Parameter(
+                    torch.zeros(1, nn.ModelParameterChoice([64, 128, 256], label='a'), 3, 3)
+                )
+
+            def forward(self):
+                return self.aux
+        @model_wrapper
+        class Net(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.choice = nn.ModelParameterChoice([64, 128, 256], label='a')
+                self.inner = Inner()
+
+            def forward(self):
+                param = self.inner()
+                assert param.size(1) == self.choice
+                return param
+
+        model, mutators = self._get_model_with_mutators(Net())
+        self.assertEqual(len(mutators), 1)
+        sampler = RandomSampler()
+        result_pool = set()
+        for _ in range(20):
+            model = _apply_all_mutators(model, mutators, sampler)
+            result = self._get_converted_pytorch_model(model)()
+            result_pool.add(result.size(1))
+        self.assertSetEqual(result_pool, {64, 128, 256})
+
+    def test_hyperparameter_choice_no_model_wrapper(self):
+        class Net(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.choice = nn.ModelParameterChoice([64, 128, 256], label='a')
+
+        with self.assertRaises(NoContextError):
+            model = Net()
+
+    def test_cell(self):
+        @model_wrapper
+        class Net(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.cell = nn.Cell([nn.Linear(16, 16), nn.Linear(16, 16, bias=False)],
+                                    num_nodes=4, num_ops_per_node=2, num_predecessors=2, merge_op='all')
+
+            def forward(self, x, y):
+                return self.cell(x, y)
+
+        raw_model, mutators = self._get_model_with_mutators(Net())
+        for _ in range(10):
+            sampler = EnumerateSampler()
+            model = raw_model
+            for mutator in mutators:
+                model = mutator.bind_sampler(sampler).apply(model)
+            self.assertTrue(self._get_converted_pytorch_model(model)(
+                torch.randn(1, 16), torch.randn(1, 16)).size() == torch.Size([1, 64]))
+
+        @model_wrapper
+        class Net2(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.cell = nn.Cell([nn.Linear(16, 16), nn.Linear(16, 16, bias=False)], num_nodes=4)
+
+            def forward(self, x):
+                return self.cell(x)
+
+        raw_model, mutators = self._get_model_with_mutators(Net2())
+        for _ in range(10):
+            sampler = EnumerateSampler()
+            model = raw_model
+            for mutator in mutators:
+                model = mutator.bind_sampler(sampler).apply(model)
+            self.assertTrue(self._get_converted_pytorch_model(model)(torch.randn(1, 16)).size() == torch.Size([1, 64]))
+
+    def test_cell_predecessors(self):
+        from typing import List, Tuple
+
+        class Preprocessor(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.linear = nn.Linear(3, 16)
+
+            def forward(self, x: List[torch.Tensor]) -> List[torch.Tensor]:
+                return [self.linear(x[0]), x[1]]
+
+        class Postprocessor(nn.Module):
+            def forward(self, this: torch.Tensor, prev: List[torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]:
+                return prev[-1], this
+
+        @model_wrapper
+        class Net(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.cell = nn.Cell({
+                    'first': nn.Linear(16, 16),
+                    'second': nn.Linear(16, 16, bias=False)
+                }, num_nodes=4, num_ops_per_node=2, num_predecessors=2,
+                preprocessor=Preprocessor(), postprocessor=Postprocessor(), merge_op='all')
+
+            def forward(self, x, y):
+                return self.cell([x, y])
+
+        raw_model, mutators = self._get_model_with_mutators(Net())
+        for _ in range(10):
+            sampler = EnumerateSampler()
+            model = raw_model
+            for mutator in mutators:
+                model = mutator.bind_sampler(sampler).apply(model)
+            result = self._get_converted_pytorch_model(model)(
+                torch.randn(1, 3), torch.randn(1, 16))
+            self.assertTrue(result[0].size() == torch.Size([1, 16]))
+            self.assertTrue(result[1].size() == torch.Size([1, 64]))
+
     def test_cell_loose_end(self):
         @model_wrapper
         class Net(nn.Module):
@@ -810,6 +1069,8 @@ class Shared(unittest.TestCase):
             elif i == 2:
                 assert choice.candidates == [5, 6]
         assert d.evaluate([2, 3, 5]) == 20
+        expect = [x + y + 3 * z for x in [1, 2] for y in [3, 4] for z in [5, 6]]
+        assert list(d.all_options()) == expect
 
         a = nn.ValueChoice(['cat', 'dog'])
         b = nn.ValueChoice(['milk', 'coffee'])
@@ -886,6 +1147,9 @@ class Shared(unittest.TestCase):
                 lst = [value if choice.label == 'value' else divisor for choice in result.inner_choices()]
                 assert result.evaluate(lst) == original_make_divisible(value, divisor)
 
+        assert len(list(result.all_options())) == 30
+        assert max(result.all_options()) == 135
+
     def test_valuechoice_in_evaluator(self):
         def foo():
             pass
@@ -920,3 +1184,59 @@ class Shared(unittest.TestCase):
         for _ in range(10):
             model = _apply_all_mutators(init_model, mutators, sampler)
             assert (model.evaluator.trace_kwargs['x'], model.evaluator.trace_kwargs['y']) in [(1, 2), (3, 4)]
+
+    def test_valuechoice_in_evaluator_nested(self):
+        @nni.trace
+        class FooClass:
+            def __init__(self, a):
+                self.a = a
+
+        obj = FooClass(ValueChoice([1, 2, 3], label='t'))
+
+        def foo():
+            pass
+
+        evaluator = FunctionalEvaluator(foo, t=obj, v=ValueChoice([1, 2, 3], label='t') + ValueChoice([10, 20, 30]))
+        mutators = process_evaluator_mutations(evaluator, [])
+        assert len(mutators) == 3
+        init_model = Model(_internal=True)
+        init_model.evaluator = evaluator
+        samplers = [RandomSampler() for _ in range(3)]
+        for _ in range(10):
+            model = _apply_all_mutators(init_model, mutators, samplers)
+            a, v = model.evaluator.trace_kwargs['t'].a, model.evaluator.trace_kwargs['v']
+            assert v % 10 == a
+            assert a in [1, 2, 3]
+            assert v // 10 in [1, 2, 3]
+
+    @unittest.skipIf(pytorch_lightning.__version__ < '1.0', 'Legacy PyTorch-lightning not supported')
+    def test_valuechoice_lightning(self):
+        @nni.trace
+        class AnyModule(pl.LightningModule):
+            pass
+
+        evaluator = pl.Lightning(AnyModule(), pl.Trainer(max_epochs=nn.ValueChoice([1, 2, 3])))
+        mutators = process_evaluator_mutations(evaluator, [])
+        assert len(mutators) == 2
+        init_model = Model(_internal=True)
+        init_model.evaluator = evaluator
+        samplers = [RandomSampler() for _ in range(2)]
+        values = []
+        for _ in range(20):
+            model = _apply_all_mutators(init_model, mutators, samplers)
+            values.append(model.evaluator.trainer.max_epochs)
+            model._dump()
+
+        assert len(set(values)) == 3
+
+    @unittest.skipIf(pytorch_lightning.__version__ < '1.0', 'Legacy PyTorch-lightning not supported')
+    def test_valuechoice_classification(self):
+        evaluator = pl.Classification(criterion=nn.CrossEntropyLoss)
+        process_evaluator_mutations(evaluator, [])
+
+    def test_retiarii_nn_import(self):
+        dummy = torch.zeros(1, 16, 32, 24)
+        nn.init.uniform_(dummy)
+
+        conv = nn.Conv2d(1, 3, 1)
+        param = nn.Parameter(torch.zeros(1, 3, 24, 24))
diff --git a/test/ut/retiarii/test_space_hub.py b/test/ut/retiarii/test_space_hub.py
new file mode 100644
index 000000000..c44423825
--- /dev/null
+++ b/test/ut/retiarii/test_space_hub.py
@@ -0,0 +1,194 @@
+"""Currently, this is only a sanity-check (runnable) of spaces provided in hub."""
+
+import random
+
+from torchvision import transforms
+from torchvision.datasets import FakeData
+
+import pytest
+
+import pytorch_lightning
+
+import nni
+import nni.runtime.platform.test
+import nni.retiarii.evaluator.pytorch.lightning as pl
+import nni.retiarii.hub.pytorch as searchspace
+from nni.retiarii.utils import ContextStack
+from nni.retiarii.execution.utils import _unpack_if_only_one
+from nni.retiarii.mutator import InvalidMutation, Sampler
+from nni.retiarii.nn.pytorch.mutator import extract_mutation_from_pt_module
+
+
+pytestmark = pytest.mark.skipif(pytorch_lightning.__version__ < '1.0', reason='Incompatible APIs.')
+
+
+def _reset():
+    # this is to not affect other tests in sdk
+    nni.trial._intermediate_seq = 0
+    nni.trial._params = {'foo': 'bar', 'parameter_id': 0}
+    nni.runtime.platform.test._last_metric = None
+
+
+class RandomSampler(Sampler):
+    def __init__(self):
+        self.counter = 0
+
+    def choice(self, candidates, *args, **kwargs):
+        self.counter += 1
+        return random.choice(candidates)
+
+
+def try_mutation_until_success(base_model, mutators, retry):
+    if not retry:
+        raise ValueError('Retry exhausted.')
+    try:
+        model = base_model
+        for mutator in mutators:
+            model = mutator.bind_sampler(RandomSampler()).apply(model)
+        return model
+    except InvalidMutation:
+        return try_mutation_until_success(base_model, mutators, retry - 1)
+
+
+def _test_searchspace_on_dataset(searchspace, dataset='cifar10', arch=None):
+    _reset()
+
+    # dataset supports cifar10 and imagenet
+    model, mutators = extract_mutation_from_pt_module(searchspace)
+
+    if arch is None:
+        model = try_mutation_until_success(model, mutators, 10)
+        arch = {mut.mutator.label: _unpack_if_only_one(mut.samples) for mut in model.history}
+
+    print('Selected model:', arch)
+    with ContextStack('fixed', arch):
+        model = model.python_class(**model.python_init_params)
+
+    if dataset == 'cifar10':
+        train_data = FakeData(size=200, image_size=(3, 32, 32), num_classes=10, transform=transforms.ToTensor())
+        valid_data = FakeData(size=200, image_size=(3, 32, 32), num_classes=10, transform=transforms.ToTensor())
+
+    elif dataset == 'imagenet':
+        train_data = FakeData(size=200, image_size=(3, 224, 224), num_classes=1000, transform=transforms.ToTensor())
+        valid_data = FakeData(size=200, image_size=(3, 224, 224), num_classes=1000, transform=transforms.ToTensor())
+
+    train_dataloader = pl.DataLoader(train_data, batch_size=4, shuffle=True)
+    valid_dataloader = pl.DataLoader(valid_data, batch_size=6)
+
+    evaluator = pl.Classification(
+        train_dataloader=train_dataloader,
+        val_dataloaders=valid_dataloader,
+        export_onnx=False,
+        max_epochs=1,
+        limit_train_batches=2,
+        limit_val_batches=3,
+    )
+    evaluator.fit(model)
+
+    # cleanup to avoid affecting later test cases
+    _reset()
+
+
+def test_nasbench101():
+    ss = searchspace.NasBench101()
+    _test_searchspace_on_dataset(ss)
+
+
+def test_nasbench201():
+    ss = searchspace.NasBench101()
+    _test_searchspace_on_dataset(ss)
+
+
+def test_nasnet():
+    _test_searchspace_on_dataset(searchspace.NASNet())
+    _test_searchspace_on_dataset(searchspace.ENAS())
+    _test_searchspace_on_dataset(searchspace.AmoebaNet())
+    _test_searchspace_on_dataset(searchspace.PNAS())
+    _test_searchspace_on_dataset(searchspace.DARTS())
+
+
+def test_nasnet_corner_case():
+    # The case is that output channel of reduce cell and normal cell are different
+    # CellPreprocessor needs to know whether its predecessors are normal cell / reduction cell
+    arch = {
+        "width": 32,
+        "depth": 8,
+        "normal/op_2_0": "max_pool_7x7",
+        "normal/op_2_1": "conv_1x1",
+        "normal/op_3_0": "sep_conv_5x5",
+        "normal/op_3_1": "max_pool_7x7",
+        "normal/op_4_0": "sep_conv_5x5",
+        "normal/op_4_1": "conv_1x1",
+        "normal/op_5_0": "max_pool_3x3",
+        "normal/op_5_1": "sep_conv_5x5",
+        "normal/op_6_0": "max_pool_7x7",
+        "normal/op_6_1": "sep_conv_5x5",
+        "normal/input_2_0": 0,
+        "normal/input_2_1": 0,
+        "normal/input_3_0": 0,
+        "normal/input_3_1": 1,
+        "normal/input_4_0": 1,
+        "normal/input_4_1": 2,
+        "normal/input_5_0": 0,
+        "normal/input_5_1": 1,
+        "normal/input_6_0": 0,
+        "normal/input_6_1": 2,
+        "reduce/op_2_0": "dil_conv_3x3",
+        "reduce/op_2_1": "max_pool_7x7",
+        "reduce/op_3_0": "dil_conv_3x3",
+        "reduce/op_3_1": "dil_conv_3x3",
+        "reduce/op_4_0": "conv_7x1_1x7",
+        "reduce/op_4_1": "conv_7x1_1x7",
+        "reduce/op_5_0": "max_pool_3x3",
+        "reduce/op_5_1": "conv_1x1",
+        "reduce/op_6_0": "sep_conv_7x7",
+        "reduce/op_6_1": "sep_conv_3x3",
+        "reduce/input_2_0": 1,
+        "reduce/input_2_1": 1,
+        "reduce/input_3_0": 0,
+        "reduce/input_3_1": 1,
+        "reduce/input_4_0": 2,
+        "reduce/input_4_1": 1,
+        "reduce/input_5_0": 0,
+        "reduce/input_5_1": 4,
+        "reduce/input_6_0": 3,
+        "reduce/input_6_1": 3,
+    }
+
+    _test_searchspace_on_dataset(searchspace.NASNet(), arch=arch)
+
+
+def test_nasnet_fixwd():
+    # minimum
+    ss = searchspace.DARTS(width=16, num_cells=4)
+    _test_searchspace_on_dataset(ss)
+
+    # medium
+    ss = searchspace.NASNet(width=16, num_cells=12)
+    _test_searchspace_on_dataset(ss)
+
+
+def test_nasnet_imagenet():
+    ss = searchspace.ENAS(dataset='imagenet')
+    _test_searchspace_on_dataset(ss, dataset='imagenet')
+
+    ss = searchspace.PNAS(dataset='imagenet')
+    _test_searchspace_on_dataset(ss, dataset='imagenet')
+
+
+def test_proxylessnas():
+    ss = searchspace.ProxylessNAS()
+    _test_searchspace_on_dataset(ss, dataset='imagenet')
+
+
+def test_mobilenetv3():
+    ss = searchspace.MobileNetV3Space()
+    _test_searchspace_on_dataset(ss, dataset='imagenet')
+
+
+def test_shufflenet():
+    ss = searchspace.ShuffleNetSpace()
+    _test_searchspace_on_dataset(ss, dataset='imagenet')
+
+    ss = searchspace.ShuffleNetSpace(channel_search=True)
+    _test_searchspace_on_dataset(ss, dataset='imagenet')
diff --git a/test/ut/sdk/test_builtin_tuners.py b/test/ut/sdk/test_builtin_tuners.py
index c201ae8a9..65f85a0c2 100644
--- a/test/ut/sdk/test_builtin_tuners.py
+++ b/test/ut/sdk/test_builtin_tuners.py
@@ -324,11 +324,13 @@ class BuiltinTunersTestCase(TestCase):
         self.import_data_test(tuner_fn, support_middle=False)
 
     def test_tpe(self):
+        self.exhaustive = True
         tuner_fn = TpeTuner
         self.search_space_test_all(TpeTuner)
         self.import_data_test(tuner_fn)
 
     def test_random_search(self):
+        self.exhaustive = True
         tuner_fn = RandomTuner
         self.search_space_test_all(tuner_fn)
         self.import_data_test(tuner_fn)
diff --git a/test/ut/sdk/test_hpo_dedup.py b/test/ut/sdk/test_hpo_dedup.py
new file mode 100644
index 000000000..2a38f3b77
--- /dev/null
+++ b/test/ut/sdk/test_hpo_dedup.py
@@ -0,0 +1,74 @@
+import numpy as np
+
+import nni
+from nni.algorithms.hpo.random_tuner import suggest
+from nni.common.hpo_utils import Deduplicator, deformat_parameters, format_search_space
+
+seed = np.random.default_rng().integers(2 ** 31)
+print(seed)
+rng = np.random.default_rng(seed)
+
+finite_space = {
+    'x': {'_type': 'choice', '_value': ['a', 'b']},
+    'y': {'_type': 'quniform', '_value': [0, 1, 0.6]},
+    'z': {'_type': 'normal', '_value': [1, 0]},
+}
+
+infinite_space = {
+    'x': {'_type': 'choice', '_value': ['a', 'b']},
+    'y': {'_type': 'uniform', '_value': [0, 1]},
+}
+
+nested_space = {
+    'outer': {
+        '_type': 'choice',
+        '_value': [
+            {'_name': 'A', 'x': {'_type': 'choice', '_value': ['a', 'b']}},
+            {'_name': 'B', 'y': {'_type': 'uniform', '_value': [0, 1]}},
+        ]
+    }
+}
+
+def test_dedup_finite():
+    space = format_search_space(finite_space)
+    dedup = Deduplicator(space)
+    params = []
+    exhausted = False
+    try:
+        for i in range(7):
+            p = dedup(suggest(rng, space))
+            params.append(deformat_parameters(p, space))
+    except nni.NoMoreTrialError:
+        exhausted = True
+    params = sorted(params, key=(lambda p: (p['x'], p['y'], p['z'])))
+    assert exhausted
+    assert params == [
+        {'x': 'a', 'y': 0.0, 'z': 1.0},
+        {'x': 'a', 'y': 0.6, 'z': 1.0},
+        {'x': 'a', 'y': 1.0, 'z': 1.0},
+        {'x': 'b', 'y': 0.0, 'z': 1.0},
+        {'x': 'b', 'y': 0.6, 'z': 1.0},
+        {'x': 'b', 'y': 1.0, 'z': 1.0},
+    ]
+
+def test_dedup_infinite():
+    space = format_search_space(infinite_space)
+    dedup = Deduplicator(space)
+    for i in range(10):
+        p = suggest(rng, space)
+        assert dedup(p) is p
+
+def test_dedup_nested():
+    space = format_search_space(nested_space)
+    dedup = Deduplicator(space)
+    params = set()
+    for i in range(10):
+        p = dedup(suggest(rng, space))
+        s = nni.dump(deformat_parameters(p, space), sort_keys=True)
+        assert s not in params
+        params.add(s)
+
+if __name__ == '__main__':
+    test_dedup_finite()
+    test_dedup_infinite()
+    test_dedup_nested()
diff --git a/test/ut/sdk/test_serializer.py b/test/ut/sdk/test_serializer.py
index 6a7515b67..e897d7d87 100644
--- a/test/ut/sdk/test_serializer.py
+++ b/test/ut/sdk/test_serializer.py
@@ -1,4 +1,5 @@
 import math
+import pickle
 import sys
 from pathlib import Path
 
@@ -27,6 +28,11 @@ class SimpleClass:
         self._b = b
 
 
+@nni.trace
+class EmptyClass:
+    pass
+
+
 class UnserializableSimpleClass:
     def __init__(self):
         self._a = 1
@@ -124,7 +130,8 @@ def test_custom_class():
 
     module = nni.trace(Foo)(Foo(1), 5)
     dumped_module = nni.dump(module)
-    assert len(dumped_module) > 200  # should not be too longer if the serialization is correct
+    module = nni.load(dumped_module)
+    assert module.bb[0] == module.bb[999] == 6
 
     module = nni.trace(Foo)(nni.trace(Foo)(1), 5)
     dumped_module = nni.dump(module)
@@ -193,6 +200,20 @@ def test_dataset():
     assert y.size() == torch.Size([10])
 
 
+def test_pickle():
+    pickle.dumps(EmptyClass())
+    obj = SimpleClass(1)
+    obj = pickle.loads(pickle.dumps(obj))
+
+    assert obj._a == 1
+    assert obj._b == 1
+
+    obj = SimpleClass(1)
+    obj.xxx = 3
+    obj = pickle.loads(pickle.dumps(obj))
+    assert obj.xxx == 3
+
+
 @pytest.mark.skipif(sys.platform != 'linux', reason='https://github.com/microsoft/nni/issues/4434')
 def test_multiprocessing_dataloader():
     # check whether multi-processing works
@@ -208,6 +229,28 @@ def test_multiprocessing_dataloader():
     assert y.size() == torch.Size([10])
 
 
+def _test_multiprocessing_dataset_worker(dataset):
+    if sys.platform == 'linux':
+        # on non-linux, the loaded object will become non-traceable
+        # due to an implementation limitation
+        assert is_traceable(dataset)
+    else:
+        from torch.utils.data import Dataset
+        assert isinstance(dataset, Dataset)
+
+
+def test_multiprocessing_dataset():
+    from torch.utils.data import Dataset
+
+    dataset = nni.trace(Dataset)()
+
+    import multiprocessing
+    process = multiprocessing.Process(target=_test_multiprocessing_dataset_worker, args=(dataset, ))
+    process.start()
+    process.join()
+    assert process.exitcode == 0
+
+
 def test_type():
     assert nni.dump(torch.optim.Adam) == '{"__nni_type__": "path:torch.optim.adam.Adam"}'
     assert nni.load('{"__nni_type__": "path:torch.optim.adam.Adam"}') == torch.optim.Adam
@@ -220,10 +263,20 @@ def test_lightning_earlystop():
     import nni.retiarii.evaluator.pytorch.lightning as pl
     from pytorch_lightning.callbacks.early_stopping import EarlyStopping
     trainer = pl.Trainer(callbacks=[nni.trace(EarlyStopping)(monitor="val_loss")])
-    trainer = nni.load(nni.dump(trainer))
+    pickle_size_limit = 4096 if sys.platform == 'linux' else 32768
+    trainer = nni.load(nni.dump(trainer, pickle_size_limit=pickle_size_limit))
     assert any(isinstance(callback, EarlyStopping) for callback in trainer.callbacks)
 
 
+def test_pickle_trainer():
+    import nni.retiarii.evaluator.pytorch.lightning as pl
+    from pytorch_lightning import Trainer
+    trainer = pl.Trainer(max_epochs=1)
+    data = pickle.dumps(trainer)
+    trainer = pickle.loads(data)
+    assert isinstance(trainer, Trainer)
+
+
 def test_generator():
     import torch.nn as nn
     import torch.optim as optim
@@ -272,11 +325,53 @@ def test_arguments_kind():
     assert lstm.trace_kwargs == {'input_size': 2, 'hidden_size': 2}
 
 
-if __name__ == '__main__':
-    # test_simple_class()
-    # test_external_class()
-    # test_nested_class()
-    # test_unserializable()
-    # test_basic_unit()
-    # test_generator()
-    test_arguments_kind()
+def test_subclass():
+    @nni.trace
+    class Super:
+        def __init__(self, a, b):
+            self._a = a
+            self._b = b
+
+    class Sub1(Super):
+        def __init__(self, c, d):
+            super().__init__(3, 4)
+            self._c = c
+            self._d = d
+
+    @nni.trace
+    class Sub2(Super):
+        def __init__(self, c, d):
+            super().__init__(3, 4)
+            self._c = c
+            self._d = d
+
+    obj = Sub1(1, 2)
+    # There could be trace_kwargs for obj. Behavior is undefined.
+    assert obj._a == 3 and obj._c == 1
+    assert isinstance(obj, Super)
+    obj = Sub2(1, 2)
+    assert obj.trace_kwargs == {'c': 1, 'd': 2}
+    assert issubclass(type(obj), Super)
+    assert isinstance(obj, Super)
+
+
+def test_get():
+    @nni.trace
+    class Foo:
+        def __init__(self, a = 1):
+            self._a = a
+
+        def bar(self):
+            return self._a + 1
+
+    obj = Foo(3)
+    assert nni.load(nni.dump(obj)).bar() == 4
+    obj1 = obj.trace_copy()
+    with pytest.raises(AttributeError):
+        obj1.bar()
+    obj1.trace_kwargs['a'] = 5
+    obj1 = obj1.get()
+    assert obj1.bar() == 6
+    obj2 = obj1.trace_copy()
+    obj2.trace_kwargs['a'] = -1
+    assert obj2.get().bar() == 0
diff --git a/test/ut/sdk/test_version.py b/test/ut/sdk/test_version.py
new file mode 100644
index 000000000..3ef65086c
--- /dev/null
+++ b/test/ut/sdk/test_version.py
@@ -0,0 +1,21 @@
+import pytest
+import sys
+
+from nni.common.version import version_dump, version_check
+
+
+def test_version_dump():
+    dump_ver = version_dump()
+    assert len(dump_ver) >= 9
+    print(dump_ver)
+
+
+def test_version_check():
+    version_check(version_dump(), raise_error=True)
+    version_check({'python': sys.version_info[:2]}, raise_error=True)
+
+    with pytest.warns(UserWarning):
+        version_check({'nni': (99999, 99999)})
+
+    with pytest.raises(RuntimeError):
+        version_check({'python': (2, 7)}, raise_error=True)
diff --git a/test/vso_tools/pack_dependencies.py b/test/vso_tools/pack_dependencies.py
index cedd2c419..5cc40bb01 100644
--- a/test/vso_tools/pack_dependencies.py
+++ b/test/vso_tools/pack_dependencies.py
@@ -1,6 +1,10 @@
 """
-Create an archive in sys.argv[1], containing python-packages and node_modules.
+Create a zip archive named sys.argv[1], containing python-packages and node_modules.
 Use unpack_dependencies.py to extract the archive.
+
+Example usage:
+
+    python test/vso_tools/pack_dependencies.py dependencies.zip
 """
 
 import json
@@ -18,7 +22,7 @@ def main() -> None:
     shutil.move('ts/nni_manager/node_modules', 'cache/nni-manager-dependencies')
     shutil.move('ts/webui/node_modules', 'cache/webui-dependencies')
 
-    archive = ZipFile('cache.zip', 'w', ZIP_DEFLATED, compresslevel=9)
+    archive = ZipFile(sys.argv[1], 'w', ZIP_DEFLATED, compresslevel=9)
     symlinks = {}
     empty_dirs = set()
     for file in sorted(cache.rglob('*')):
@@ -36,8 +40,5 @@ def main() -> None:
     archive.writestr('directories.json', json.dumps(list(empty_dirs), indent=4))
     archive.close()
 
-    assert Path(sys.argv[1]).is_dir()
-    shutil.move('cache.zip', sys.argv[1])
-
 if __name__ == '__main__':
     main()
diff --git a/test/vso_tools/unpack_dependencies.py b/test/vso_tools/unpack_dependencies.py
index 566945d19..092e399ab 100644
--- a/test/vso_tools/unpack_dependencies.py
+++ b/test/vso_tools/unpack_dependencies.py
@@ -1,5 +1,5 @@
 """
-Extract an archive created by pack_dependencies.py.
+Extract archive sys.argv[1] created by pack_dependencies.py.
 """
 
 from __future__ import annotations
@@ -23,7 +23,7 @@ def main() -> None:
     print('All Python paths:')
     print('\n'.join(sys.path), flush=True)
 
-    extract_all(ZipFile('cache.zip'))
+    extract_all(ZipFile(sys.argv[1]))
     empty_dirs = json.loads(Path('directories.json').read_text())
     symlinks = json.loads(Path('symlinks.json').read_text())
     for dir_ in empty_dirs:
diff --git a/ts/jupyter_extension/package.json b/ts/jupyter_extension/package.json
index 895daaa70..f14a54540 100644
--- a/ts/jupyter_extension/package.json
+++ b/ts/jupyter_extension/package.json
@@ -6,11 +6,27 @@
         "build": "tsc && jupyter labextension build ."
     },
     "dependencies": {
-        "@jupyterlab/application": "^3.0.11",
-        "@jupyterlab/launcher": "^3.0.9"
+        "@jupyterlab/application": "<3.1.0",
+        "@jupyterlab/launcher": "<3.1.0"
     },
     "devDependencies": {
-        "@jupyterlab/builder": "^3.0.9"
+        "@jupyterlab/builder": "3.0.9"
+    },
+    "resolutions": {
+        "@jupyterlab/apputils": "<3.1.0",
+        "@jupyterlab/buildutils": "<3.1.0",
+        "@jupyterlab/codeeditor": "<3.1.0",
+        "@jupyterlab/codemirror": "<3.1.0",
+        "@jupyterlab/docregistry": "<3.1.0",
+        "@jupyterlab/nbformat": "<3.1.0",
+        "@jupyterlab/rendermime-interfaces": "<3.1.0",
+        "@jupyterlab/rendermime": "<3.1.0",
+        "@jupyterlab/services": "<6.1.0",
+        "@jupyterlab/settingregistry": "<3.1.0",
+        "@jupyterlab/statedb": "<3.1.0",
+        "@jupyterlab/statusbar": "<3.1.0",
+        "@jupyterlab/translation": "<3.1.0",
+        "@jupyterlab/ui-components": "<3.1.0"
     },
     "jupyterlab": {
         "extension": true,
diff --git a/ts/jupyter_extension/yarn.lock b/ts/jupyter_extension/yarn.lock
index b8ae1f606..f68ff7bca 100644
--- a/ts/jupyter_extension/yarn.lock
+++ b/ts/jupyter_extension/yarn.lock
@@ -3,18 +3,25 @@
 
 
 "@babel/runtime@^7.1.2":
-  version "7.14.8"
-  resolved "https://registry.yarnpkg.com/@babel/runtime/-/runtime-7.14.8.tgz#7119a56f421018852694290b9f9148097391b446"
-  integrity sha512-twj3L8Og5SaCRCErB4x4ajbvBIVV77CGeFglHpeg5WC5FF8TZzBWXtTJ4MqaD9QszLYTtr+IsaAL2rEUevb+eg==
+  version "7.17.8"
+  resolved "https://registry.yarnpkg.com/@babel/runtime/-/runtime-7.17.8.tgz#3e56e4aff81befa55ac3ac6a0967349fd1c5bca2"
+  integrity sha512-dQpEpK0O9o6lj6oPu0gRDbbnk+4LeHlNcBpspf6Olzt3GIX4P1lWF1gS+pHLDFlaJvbR6q7jCfQ08zA4QJBnmA==
   dependencies:
     regenerator-runtime "^0.13.4"
 
-"@blueprintjs/core@^3.36.0", "@blueprintjs/core@^3.47.0":
-  version "3.47.0"
-  resolved "https://registry.yarnpkg.com/@blueprintjs/core/-/core-3.47.0.tgz#bf33155d224b742ba51c6e1cf5be4523290337a7"
-  integrity sha512-u+bfmCyPXwKZMnwY4+e/iWjO2vDUvr8hA8ydmV0afyvcEe7Sh85UPEorIgQ/CBuRIbVMNm8FpLsFzDxgkfrCNA==
+"@blueprintjs/colors@^4.0.0-alpha.3":
+  version "4.0.3"
+  resolved "https://registry.yarnpkg.com/@blueprintjs/colors/-/colors-4.0.3.tgz#7002e2490dea8454c19ea78bc52b4eb99e8846f5"
+  integrity sha512-lRL4YE9qSiqxV+1vNMQYCm5pdUOfGajsvIekFJgmkS+Ks9fVjXZ8n0VBoIzm2POPJtEvMxUab2CVyME3p54r4w==
+
+"@blueprintjs/core@^3.36.0", "@blueprintjs/core@^3.54.0":
+  version "3.54.0"
+  resolved "https://registry.yarnpkg.com/@blueprintjs/core/-/core-3.54.0.tgz#7269f34eccdf0d2874377c5ad973ca2a31562221"
+  integrity sha512-u2c1s6MNn0ocxhnC6CuiG5g3KV6b4cKUvSobznepA9SC3/AL1s3XOvT7DLWoHRv2B/vBOHFYEDzLw2/vlcGGZg==
   dependencies:
-    "@blueprintjs/icons" "^3.27.0"
+    "@blueprintjs/colors" "^4.0.0-alpha.3"
+    "@blueprintjs/icons" "^3.33.0"
+    "@juggle/resize-observer" "^3.3.1"
     "@types/dom4" "^2.0.1"
     classnames "^2.2"
     dom4 "^2.1.5"
@@ -23,35 +30,39 @@
     react-lifecycles-compat "^3.0.4"
     react-popper "^1.3.7"
     react-transition-group "^2.9.0"
-    resize-observer-polyfill "^1.5.1"
-    tslib "~1.13.0"
+    tslib "~2.3.1"
 
-"@blueprintjs/icons@^3.27.0":
-  version "3.27.0"
-  resolved "https://registry.yarnpkg.com/@blueprintjs/icons/-/icons-3.27.0.tgz#f4c03e8bc2f9310f7eaefaab26dd91f65935da43"
-  integrity sha512-ItRioyrr2s70chclj5q38HS9omKOa15b3JZXv9JcMIFz+6w6rAcoAH7DA+5xIs27bFjax/SdAZp/eYXSw0+QpA==
+"@blueprintjs/icons@^3.33.0":
+  version "3.33.0"
+  resolved "https://registry.yarnpkg.com/@blueprintjs/icons/-/icons-3.33.0.tgz#4dacdb7731abdf08d1ab240f3a23a185df60918b"
+  integrity sha512-Q6qoSDIm0kRYQZISm59UUcDCpV3oeHulkLuh3bSlw0HhcSjvEQh2PSYbtaifM60Q4aK4PCd6bwJHg7lvF1x5fQ==
   dependencies:
     classnames "^2.2"
-    tslib "~1.13.0"
+    tslib "~2.3.1"
 
 "@blueprintjs/select@^3.15.0":
-  version "3.16.6"
-  resolved "https://registry.yarnpkg.com/@blueprintjs/select/-/select-3.16.6.tgz#ae41a73bc7c23b07a20b0c50c71273c9d5d0d83d"
-  integrity sha512-lg2duuzlRw18+pbET6vlRY/TVSuuSI6wI4DObUiBAfU7G3fMa6d10Sp+0Yn00XaMPQ5y3MGn1gz0EbIJ3/A5OA==
+  version "3.19.1"
+  resolved "https://registry.yarnpkg.com/@blueprintjs/select/-/select-3.19.1.tgz#b5e8baa6f182a0647651a57fde8d1d97eaa1e997"
+  integrity sha512-8UJIZMaWXRMQHr14wbmzJc/CklcSKxOU5JUux0xXKQz/hDW/g1a650tlwJmnxufvRdShbGinlVfHupCs0EL6sw==
   dependencies:
-    "@blueprintjs/core" "^3.47.0"
+    "@blueprintjs/core" "^3.54.0"
     classnames "^2.2"
-    tslib "~1.13.0"
+    tslib "~2.3.1"
 
 "@discoveryjs/json-ext@^0.5.0":
-  version "0.5.3"
-  resolved "https://registry.yarnpkg.com/@discoveryjs/json-ext/-/json-ext-0.5.3.tgz#90420f9f9c6d3987f176a19a7d8e764271a2f55d"
-  integrity sha512-Fxt+AfXgjMoin2maPIYzFZnQjAXjAL0PHscM5pRTtatFqB+vZxAM9tLp2Optnuw3QOQC40jTNeGYFOMvyf7v9g==
+  version "0.5.7"
+  resolved "https://registry.yarnpkg.com/@discoveryjs/json-ext/-/json-ext-0.5.7.tgz#1d572bfbbe14b7704e0ba0f39b74815b84870d70"
+  integrity sha512-dBVuXR082gk3jsFp7Rd/JI4kytwGHecnCoTtXFb7DB6CNHp4rg5k1bhg0nWdLGLnOV71lmDzGQaLMy8iPLY0pw==
 
 "@fortawesome/fontawesome-free@^5.12.0":
-  version "5.15.3"
-  resolved "https://registry.yarnpkg.com/@fortawesome/fontawesome-free/-/fontawesome-free-5.15.3.tgz#c36ffa64a2a239bf948541a97b6ae8d729e09a9a"
-  integrity sha512-rFnSUN/QOtnOAgqFRooTA3H57JLDm0QEG/jPdk+tLQNL/eWd+Aok8g3qCI+Q1xuDPWpGW/i9JySpJVsq8Q0s9w==
+  version "5.15.4"
+  resolved "https://registry.yarnpkg.com/@fortawesome/fontawesome-free/-/fontawesome-free-5.15.4.tgz#ecda5712b61ac852c760d8b3c79c96adca5554e5"
+  integrity sha512-eYm8vijH/hpzr/6/1CJ/V/Eb1xQFW2nnUKArb3z+yUWv7HTwj6M7SP957oMjfZjAHU6qpoNc2wQvIxBLWYa/Jg==
+
+"@gar/promisify@^1.0.1":
+  version "1.1.3"
+  resolved "https://registry.yarnpkg.com/@gar/promisify/-/promisify-1.1.3.tgz#555193ab2e3bb3b6adc3d551c9c030d9e860daf6"
+  integrity sha512-k2Ty1JcVojjJFwrg/ThKi2ujJ7XNLYaFGNB/bWT9wGR+oSMJHMa5w+CUq6p/pVrKeNNgA7pCqEcjSnHVoqJQFw==
 
 "@hypnosphi/create-react-context@^0.3.1":
   version "0.3.1"
@@ -61,21 +72,26 @@
     gud "^1.0.0"
     warning "^4.0.3"
 
-"@jupyterlab/application@^3.0.11":
-  version "3.0.11"
-  resolved "https://registry.yarnpkg.com/@jupyterlab/application/-/application-3.0.11.tgz#01d502656db1aa07afc439a58171897af2a2fdd1"
-  integrity sha512-UBqnRcXSy/Iz5vq1dCYkQvSkCFGPqjQdDFvOhvXacGxHklVjiku5Epltdbe2kQl+uhhn7VC4HEh1kzxiYamwcg==
+"@juggle/resize-observer@^3.3.1":
+  version "3.3.1"
+  resolved "https://registry.yarnpkg.com/@juggle/resize-observer/-/resize-observer-3.3.1.tgz#b50a781709c81e10701004214340f25475a171a0"
+  integrity sha512-zMM9Ds+SawiUkakS7y94Ymqx+S0ORzpG3frZirN3l+UlXUmSUR7hF4wxCVqW+ei94JzV5kt0uXBcoOEAuiydrw==
+
+"@jupyterlab/application@<3.1.0":
+  version "3.0.13"
+  resolved "https://registry.yarnpkg.com/@jupyterlab/application/-/application-3.0.13.tgz#546f1cba3c37c177db627e523aa2841b0a8e4a38"
+  integrity sha512-XAc5u4tETGWZIZbX2gulK5LwuQ2P725BGfd4OS5JoQIQJgPUGqLIYEr9QH2H57PLkoaaKCqwhiGLvkSqOw5Olg==
   dependencies:
     "@fortawesome/fontawesome-free" "^5.12.0"
-    "@jupyterlab/apputils" "^3.0.9"
-    "@jupyterlab/coreutils" "^5.0.6"
-    "@jupyterlab/docregistry" "^3.0.11"
-    "@jupyterlab/rendermime" "^3.0.10"
-    "@jupyterlab/rendermime-interfaces" "^3.0.9"
-    "@jupyterlab/services" "^6.0.9"
-    "@jupyterlab/statedb" "^3.0.6"
-    "@jupyterlab/translation" "^3.0.9"
-    "@jupyterlab/ui-components" "^3.0.7"
+    "@jupyterlab/apputils" "^3.0.10"
+    "@jupyterlab/coreutils" "^5.0.7"
+    "@jupyterlab/docregistry" "^3.0.13"
+    "@jupyterlab/rendermime" "^3.0.12"
+    "@jupyterlab/rendermime-interfaces" "^3.0.10"
+    "@jupyterlab/services" "^6.0.10"
+    "@jupyterlab/statedb" "^3.0.7"
+    "@jupyterlab/translation" "^3.0.10"
+    "@jupyterlab/ui-components" "^3.0.8"
     "@lumino/algorithm" "^1.3.3"
     "@lumino/application" "^1.13.1"
     "@lumino/commands" "^1.12.0"
@@ -87,17 +103,17 @@
     "@lumino/signaling" "^1.4.3"
     "@lumino/widgets" "^1.16.1"
 
-"@jupyterlab/apputils@^3.0.9":
-  version "3.0.9"
-  resolved "https://registry.yarnpkg.com/@jupyterlab/apputils/-/apputils-3.0.9.tgz#504273fc0b69f74d8a8b87b7a89ee3d4decd679d"
-  integrity sha512-fsJjl+NX2+e+1FM7SMfpI1VsaPQsaIPnPGsdpQoboJJqdQJHuj1oPXNwc/aI1daEElirB15fYGCUGc2oUrv6RQ==
-  dependencies:
-    "@jupyterlab/coreutils" "^5.0.6"
-    "@jupyterlab/services" "^6.0.9"
-    "@jupyterlab/settingregistry" "^3.0.6"
-    "@jupyterlab/statedb" "^3.0.6"
-    "@jupyterlab/translation" "^3.0.9"
-    "@jupyterlab/ui-components" "^3.0.7"
+"@jupyterlab/apputils@<3.1.0", "@jupyterlab/apputils@^3.0.10":
+  version "3.0.10"
+  resolved "https://registry.yarnpkg.com/@jupyterlab/apputils/-/apputils-3.0.10.tgz#77c262aa26f4286dce04916b45b23e5177009f24"
+  integrity sha512-M2MPHbGIFxkGOhj6TTlDugyaAo/MEYLLw++LV/DKQGvUDkiCWihBff9GnDWINJmJowkeM2niWxKOHAV83XSk0Q==
+  dependencies:
+    "@jupyterlab/coreutils" "^5.0.7"
+    "@jupyterlab/services" "^6.0.10"
+    "@jupyterlab/settingregistry" "^3.0.7"
+    "@jupyterlab/statedb" "^3.0.7"
+    "@jupyterlab/translation" "^3.0.10"
+    "@jupyterlab/ui-components" "^3.0.8"
     "@lumino/algorithm" "^1.3.3"
     "@lumino/commands" "^1.12.0"
     "@lumino/coreutils" "^1.5.3"
@@ -115,7 +131,7 @@
     sanitize-html "~2.3.3"
     url "^0.11.0"
 
-"@jupyterlab/builder@^3.0.9":
+"@jupyterlab/builder@3.0.9":
   version "3.0.9"
   resolved "https://registry.yarnpkg.com/@jupyterlab/builder/-/builder-3.0.9.tgz#60b184accd63afced196d2369c886f6fe238acc4"
   integrity sha512-IjWHk/xTgufTyggbT/0tGFeRdsHe3rNMQkOMqDN5+8YqFV4uCTUNokgvyysedgwB7JP+tcPclN/a3QoIPjAq/w==
@@ -155,7 +171,7 @@
     webpack-merge "^5.1.2"
     worker-loader "^3.0.2"
 
-"@jupyterlab/buildutils@^3.0.7":
+"@jupyterlab/buildutils@<3.1.0", "@jupyterlab/buildutils@^3.0.7":
   version "3.0.7"
   resolved "https://registry.yarnpkg.com/@jupyterlab/buildutils/-/buildutils-3.0.7.tgz#e83d3303b5f2bddd0b6b39fdab81864b67574dec"
   integrity sha512-V3A9foBIP9CJcblyZTEKHtkY6o/3pRodxNAZqveiDDpBdQhM8xv4uOLBKkkMxroa5Eh5Goub4fw+JHxI2nKGXw==
@@ -175,16 +191,16 @@
     sort-package-json "~1.44.0"
     typescript "~4.1.3"
 
-"@jupyterlab/codeeditor@^3.0.9":
-  version "3.0.9"
-  resolved "https://registry.yarnpkg.com/@jupyterlab/codeeditor/-/codeeditor-3.0.9.tgz#e06f82ad3c5199be8e9eb97b598d212af0c4ca08"
-  integrity sha512-OUymghTH6CsAXc4z8EA7BqwdT99mdjJ/X488EOgXCBgeKz3QKB1gQ3GpH26soUv4S0prAs8RKU7rHjfb+DLYBQ==
-  dependencies:
-    "@jupyterlab/coreutils" "^5.0.6"
-    "@jupyterlab/nbformat" "^3.0.6"
-    "@jupyterlab/observables" "^4.0.6"
-    "@jupyterlab/translation" "^3.0.9"
-    "@jupyterlab/ui-components" "^3.0.7"
+"@jupyterlab/codeeditor@<3.1.0", "@jupyterlab/codeeditor@^3.0.10":
+  version "3.0.10"
+  resolved "https://registry.yarnpkg.com/@jupyterlab/codeeditor/-/codeeditor-3.0.10.tgz#7130782b813a01ed8312ade89b3de34cc454d5d2"
+  integrity sha512-6E9J5aYpnjTwshIT3wmZ95RrXSvcAzZw+tsd/NpQswr9oow4uS4DcaKcaDQ4dEU2KzpYVEiRUYM/sQQIj7TdHw==
+  dependencies:
+    "@jupyterlab/coreutils" "^5.0.7"
+    "@jupyterlab/nbformat" "^3.0.7"
+    "@jupyterlab/observables" "^4.0.7"
+    "@jupyterlab/translation" "^3.0.10"
+    "@jupyterlab/ui-components" "^3.0.8"
     "@lumino/coreutils" "^1.5.3"
     "@lumino/disposable" "^1.4.3"
     "@lumino/dragdrop" "^1.7.1"
@@ -192,18 +208,18 @@
     "@lumino/signaling" "^1.4.3"
     "@lumino/widgets" "^1.16.1"
 
-"@jupyterlab/codemirror@^3.0.9":
-  version "3.0.9"
-  resolved "https://registry.yarnpkg.com/@jupyterlab/codemirror/-/codemirror-3.0.9.tgz#2b66c998547ce30a6162141bfb168fb7d2db2ea0"
-  integrity sha512-RgB4ZS1Rhzvk20VDvnP7oQ8Bh9fC0dWDO/hZZwLJamlJLgtQNsCnU3Qw/K2dxhCMWBexI3n+E+0mcv1IXbEtLQ==
-  dependencies:
-    "@jupyterlab/apputils" "^3.0.9"
-    "@jupyterlab/codeeditor" "^3.0.9"
-    "@jupyterlab/coreutils" "^5.0.6"
-    "@jupyterlab/nbformat" "^3.0.6"
-    "@jupyterlab/observables" "^4.0.6"
-    "@jupyterlab/statusbar" "^3.0.9"
-    "@jupyterlab/translation" "^3.0.9"
+"@jupyterlab/codemirror@<3.1.0", "@jupyterlab/codemirror@^3.0.10":
+  version "3.0.10"
+  resolved "https://registry.yarnpkg.com/@jupyterlab/codemirror/-/codemirror-3.0.10.tgz#1ce0b319d7fda6ac12a473f649d6c819827d8414"
+  integrity sha512-zWx8up4YIHuf5eIzikCovtzWnMJXy0bNmrmUpPNFbBU1Nd+35REhm4LWai7k3wLCX+5jgUwAcXH/tNqf5iGjWg==
+  dependencies:
+    "@jupyterlab/apputils" "^3.0.10"
+    "@jupyterlab/codeeditor" "^3.0.10"
+    "@jupyterlab/coreutils" "^5.0.7"
+    "@jupyterlab/nbformat" "^3.0.7"
+    "@jupyterlab/observables" "^4.0.7"
+    "@jupyterlab/statusbar" "^3.0.10"
+    "@jupyterlab/translation" "^3.0.10"
     "@lumino/algorithm" "^1.3.3"
     "@lumino/commands" "^1.12.0"
     "@lumino/coreutils" "^1.5.3"
@@ -214,10 +230,10 @@
     codemirror "~5.58.0"
     react "^17.0.1"
 
-"@jupyterlab/coreutils@^5.0.6":
-  version "5.0.6"
-  resolved "https://registry.yarnpkg.com/@jupyterlab/coreutils/-/coreutils-5.0.6.tgz#dd36591d01191762ff35e3b096f324e990e0e617"
-  integrity sha512-nXGpI1IJw+4pNq6Afy+oI3LrTsaQ14xG7Kxbhg9UPfoDgsNt2rdG4pwYe4NZyj2GJHAkUj00lcUD9eBTrxMWvw==
+"@jupyterlab/coreutils@^5.0.7":
+  version "5.3.2"
+  resolved "https://registry.yarnpkg.com/@jupyterlab/coreutils/-/coreutils-5.3.2.tgz#ef5c4ef374fd56745b6373aaddc737e6fef9f732"
+  integrity sha512-5nDi44CxdRBn1wwPC6pUWt8fpTbWz1BGwqcVx1M/kXQNOVRIODatsorvvoHnVyaSQOz6CP8jch+4PrBXdHFwyQ==
   dependencies:
     "@lumino/coreutils" "^1.5.3"
     "@lumino/disposable" "^1.4.3"
@@ -227,21 +243,21 @@
     path-browserify "^1.0.0"
     url-parse "~1.5.1"
 
-"@jupyterlab/docregistry@^3.0.11":
-  version "3.0.11"
-  resolved "https://registry.yarnpkg.com/@jupyterlab/docregistry/-/docregistry-3.0.11.tgz#21ffbabbbac56b6c8a7db5547068790a4b077bd6"
-  integrity sha512-kx+ZXgM2UcBXvy+LDwGOVa/zP3+CjKMj0jM5qaUW+sHFZzkFIV/ke/MuiX2p6J+78s2VY5Hyy2Tq07jZhMEACg==
-  dependencies:
-    "@jupyterlab/apputils" "^3.0.9"
-    "@jupyterlab/codeeditor" "^3.0.9"
-    "@jupyterlab/codemirror" "^3.0.9"
-    "@jupyterlab/coreutils" "^5.0.6"
-    "@jupyterlab/observables" "^4.0.6"
-    "@jupyterlab/rendermime" "^3.0.10"
-    "@jupyterlab/rendermime-interfaces" "^3.0.9"
-    "@jupyterlab/services" "^6.0.9"
-    "@jupyterlab/translation" "^3.0.9"
-    "@jupyterlab/ui-components" "^3.0.7"
+"@jupyterlab/docregistry@<3.1.0", "@jupyterlab/docregistry@^3.0.13":
+  version "3.0.13"
+  resolved "https://registry.yarnpkg.com/@jupyterlab/docregistry/-/docregistry-3.0.13.tgz#3e733358474f7c9baaf310fb0952f8174cefd512"
+  integrity sha512-DaEqAV/q/Tzu0gyhr/tDm4KiGg6IMIhUHKHNmfeplB6O7NyvP+4jkLPBNNBM6cNmGK/KP98n4KFZXlm0v7OgVA==
+  dependencies:
+    "@jupyterlab/apputils" "^3.0.10"
+    "@jupyterlab/codeeditor" "^3.0.10"
+    "@jupyterlab/codemirror" "^3.0.10"
+    "@jupyterlab/coreutils" "^5.0.7"
+    "@jupyterlab/observables" "^4.0.7"
+    "@jupyterlab/rendermime" "^3.0.12"
+    "@jupyterlab/rendermime-interfaces" "^3.0.10"
+    "@jupyterlab/services" "^6.0.10"
+    "@jupyterlab/translation" "^3.0.10"
+    "@jupyterlab/ui-components" "^3.0.8"
     "@lumino/algorithm" "^1.3.3"
     "@lumino/coreutils" "^1.5.3"
     "@lumino/disposable" "^1.4.3"
@@ -249,14 +265,14 @@
     "@lumino/signaling" "^1.4.3"
     "@lumino/widgets" "^1.16.1"
 
-"@jupyterlab/launcher@^3.0.9":
-  version "3.0.9"
-  resolved "https://registry.yarnpkg.com/@jupyterlab/launcher/-/launcher-3.0.9.tgz#ed1cf1a93e793d336ea0a3c0a27aa113f4bbb114"
-  integrity sha512-UoPTtRukvViRlmASr7XeecmUu8VGbhV4Ui4lQ3YQh2k6LG6t1Whgu/uS6XzjIzeIB5RtAFQ1Psk4TnjASJXs/A==
+"@jupyterlab/launcher@<3.1.0":
+  version "3.0.10"
+  resolved "https://registry.yarnpkg.com/@jupyterlab/launcher/-/launcher-3.0.10.tgz#f6589398332150c8945c4dc00c82273c2caedce2"
+  integrity sha512-WWJ9ferJih6kZigrb5Y6UiyK6RrxH26zPlQ8dFVYzARemyUEEhJPEwtng6+CdKb/NNySbURGT3lXKgUwiEDcuw==
   dependencies:
-    "@jupyterlab/apputils" "^3.0.9"
-    "@jupyterlab/translation" "^3.0.9"
-    "@jupyterlab/ui-components" "^3.0.7"
+    "@jupyterlab/apputils" "^3.0.10"
+    "@jupyterlab/translation" "^3.0.10"
+    "@jupyterlab/ui-components" "^3.0.8"
     "@lumino/algorithm" "^1.3.3"
     "@lumino/commands" "^1.12.0"
     "@lumino/coreutils" "^1.5.3"
@@ -265,17 +281,17 @@
     "@lumino/widgets" "^1.16.1"
     react "^17.0.1"
 
-"@jupyterlab/nbformat@^3.0.6":
-  version "3.0.6"
-  resolved "https://registry.yarnpkg.com/@jupyterlab/nbformat/-/nbformat-3.0.6.tgz#858a6567cdd60879bc7f9dad6c9dcb5587417b5d"
-  integrity sha512-4+u770JYPmRpLyEPpnG0crj8ePUkg/vCF1W4hnDDxnLTVjzKw5kv6KVb5yJGEHAihUOf51bjceNUOp/+nLVBTg==
+"@jupyterlab/nbformat@<3.1.0", "@jupyterlab/nbformat@^3.0.7":
+  version "3.0.7"
+  resolved "https://registry.yarnpkg.com/@jupyterlab/nbformat/-/nbformat-3.0.7.tgz#bd01cea3d03d45b1c8e65c04b32d4c86c8458ebd"
+  integrity sha512-Pzz2ZDtYZB9Nv8lP1wUYlLZ7+XwehZqmz/VYqfpVRQATXWIfrBuIvCmzM796OS1DJ3qe4ShAZYvNOEPFlsFGmQ==
   dependencies:
     "@lumino/coreutils" "^1.5.3"
 
-"@jupyterlab/observables@^4.0.6":
-  version "4.0.6"
-  resolved "https://registry.yarnpkg.com/@jupyterlab/observables/-/observables-4.0.6.tgz#be3bb0f08d2e79f86f4553857ed0aa90d7b293f2"
-  integrity sha512-PYJosNXGSkLExaEXqpUuDjEXTEcxTpvM6kG8I6NFJyDQVD6E50LggC6NofY5EIcEsJsO771BLvI4kwNk7LRQSA==
+"@jupyterlab/observables@^4.0.7":
+  version "4.3.2"
+  resolved "https://registry.yarnpkg.com/@jupyterlab/observables/-/observables-4.3.2.tgz#fd22b9cdb496ca9fe1520230fe550d3b7ac62346"
+  integrity sha512-Xa4lOA71en4kGq881Numqk3S10J7e3s8PmA/zFi21qrnTEKYMvpidoA0rGdzVUc3qT+iJ7hqr9kZFHV9JNTiQA==
   dependencies:
     "@lumino/algorithm" "^1.3.3"
     "@lumino/coreutils" "^1.5.3"
@@ -283,28 +299,28 @@
     "@lumino/messaging" "^1.4.3"
     "@lumino/signaling" "^1.4.3"
 
-"@jupyterlab/rendermime-interfaces@^3.0.9":
-  version "3.0.9"
-  resolved "https://registry.yarnpkg.com/@jupyterlab/rendermime-interfaces/-/rendermime-interfaces-3.0.9.tgz#13badf733d79b34bed0392e8a34d30291090e536"
-  integrity sha512-KvoDcIzgvDhvCGDYqFhRM753iOryWFujAEzXjpzvYz/1yNUh5weYsdwdmdCjUTkToM9rFiIDMwjferJPU54thw==
+"@jupyterlab/rendermime-interfaces@<3.1.0", "@jupyterlab/rendermime-interfaces@^3.0.10":
+  version "3.0.10"
+  resolved "https://registry.yarnpkg.com/@jupyterlab/rendermime-interfaces/-/rendermime-interfaces-3.0.10.tgz#420c205f8daafdaa10f3e31d51c329f2a1e73496"
+  integrity sha512-pPlWfpjRVN02L8AloZoG16UqC8a2VJdlS+W31IFyXNqMq3blu61fyrdxhEKn/CH01rsV1PToU9o8VA2aHwKedg==
   dependencies:
-    "@jupyterlab/translation" "^3.0.9"
+    "@jupyterlab/translation" "^3.0.10"
     "@lumino/coreutils" "^1.5.3"
     "@lumino/widgets" "^1.16.1"
 
-"@jupyterlab/rendermime@^3.0.10":
-  version "3.0.10"
-  resolved "https://registry.yarnpkg.com/@jupyterlab/rendermime/-/rendermime-3.0.10.tgz#7592155ea00c3a81f0d9a5662d3ccdeb37f722b1"
-  integrity sha512-9Q32zYpBkbrlAkuHJ7760ZETWQYZkKT9UcJWOMVF7iNgoBfRohAYvPHsoc6JFZyFEFhKzkLwa+CTcL48aGjg7A==
-  dependencies:
-    "@jupyterlab/apputils" "^3.0.9"
-    "@jupyterlab/codemirror" "^3.0.9"
-    "@jupyterlab/coreutils" "^5.0.6"
-    "@jupyterlab/nbformat" "^3.0.6"
-    "@jupyterlab/observables" "^4.0.6"
-    "@jupyterlab/rendermime-interfaces" "^3.0.9"
-    "@jupyterlab/services" "^6.0.9"
-    "@jupyterlab/translation" "^3.0.9"
+"@jupyterlab/rendermime@<3.1.0", "@jupyterlab/rendermime@^3.0.12":
+  version "3.0.12"
+  resolved "https://registry.yarnpkg.com/@jupyterlab/rendermime/-/rendermime-3.0.12.tgz#6f539d51c69e5a546461dcdee7cfc172347cdffa"
+  integrity sha512-7eq2aYNyoNXBMBe8VozVvD+JdXy0KPA13/vvsQmmKOrRTp7qrYie7n/wViJMB8sKfJ43HR4/x1YDkPBjVO1JjA==
+  dependencies:
+    "@jupyterlab/apputils" "^3.0.10"
+    "@jupyterlab/codemirror" "^3.0.10"
+    "@jupyterlab/coreutils" "^5.0.7"
+    "@jupyterlab/nbformat" "^3.0.7"
+    "@jupyterlab/observables" "^4.0.7"
+    "@jupyterlab/rendermime-interfaces" "^3.0.10"
+    "@jupyterlab/services" "^6.0.10"
+    "@jupyterlab/translation" "^3.0.10"
     "@lumino/algorithm" "^1.3.3"
     "@lumino/coreutils" "^1.5.3"
     "@lumino/messaging" "^1.4.3"
@@ -313,16 +329,16 @@
     lodash.escape "^4.0.1"
     marked "^2.0.0"
 
-"@jupyterlab/services@^6.0.9":
-  version "6.0.9"
-  resolved "https://registry.yarnpkg.com/@jupyterlab/services/-/services-6.0.9.tgz#70a10d7f6883b8fafff81216663d96858b0cf46b"
-  integrity sha512-zeN9roqwbYo6b2I5BXWx+Mr4KzTpe2UcVwrcAGw9NXqIieb0ZnvtHqtNj/vcHCM2xQKuPup9W1X1bE5b3wF5Yw==
+"@jupyterlab/services@<6.1.0", "@jupyterlab/services@^6.0.10":
+  version "6.0.10"
+  resolved "https://registry.yarnpkg.com/@jupyterlab/services/-/services-6.0.10.tgz#73ed3776930e12decbb3e692d46b20c094886774"
+  integrity sha512-iaK6NofKKhgXXbxEUfqPIHJQBxbJbmVYLFdb96ZiQT6e9RI2yQvCUp5RkWGNbRPPWtbZtzCA1LeaGpSS7hfadw==
   dependencies:
-    "@jupyterlab/coreutils" "^5.0.6"
-    "@jupyterlab/nbformat" "^3.0.6"
-    "@jupyterlab/observables" "^4.0.6"
-    "@jupyterlab/settingregistry" "^3.0.6"
-    "@jupyterlab/statedb" "^3.0.6"
+    "@jupyterlab/coreutils" "^5.0.7"
+    "@jupyterlab/nbformat" "^3.0.7"
+    "@jupyterlab/observables" "^4.0.7"
+    "@jupyterlab/settingregistry" "^3.0.7"
+    "@jupyterlab/statedb" "^3.0.7"
     "@lumino/algorithm" "^1.3.3"
     "@lumino/coreutils" "^1.5.3"
     "@lumino/disposable" "^1.4.3"
@@ -331,12 +347,12 @@
     node-fetch "^2.6.0"
     ws "^7.2.0"
 
-"@jupyterlab/settingregistry@^3.0.6":
-  version "3.0.6"
-  resolved "https://registry.yarnpkg.com/@jupyterlab/settingregistry/-/settingregistry-3.0.6.tgz#000cd9dc4984a1ccac01d73c7967893befe14b8d"
-  integrity sha512-fIeVJjkaf8FYSJ4jwJobwNeco8J2CEuWzmEJKiDjhmzmRZApS9Jjx+CJXDkTxoSMDQ41ELxQKJq5bcbih/90zQ==
+"@jupyterlab/settingregistry@<3.1.0", "@jupyterlab/settingregistry@^3.0.7":
+  version "3.0.7"
+  resolved "https://registry.yarnpkg.com/@jupyterlab/settingregistry/-/settingregistry-3.0.7.tgz#aab27c460077244dc1cb09fb641fb28ab6ce79f4"
+  integrity sha512-VtKuPpftnMhF8uuMBLBx2CSzyJG/PTn7w0U0FUal07uVmminYhBupw8oZmhTISG1H+ivk5E518ifE4oJfqf09g==
   dependencies:
-    "@jupyterlab/statedb" "^3.0.6"
+    "@jupyterlab/statedb" "^3.0.7"
     "@lumino/commands" "^1.12.0"
     "@lumino/coreutils" "^1.5.3"
     "@lumino/disposable" "^1.4.3"
@@ -344,10 +360,10 @@
     ajv "^6.12.3"
     json5 "^2.1.1"
 
-"@jupyterlab/statedb@^3.0.6":
-  version "3.0.6"
-  resolved "https://registry.yarnpkg.com/@jupyterlab/statedb/-/statedb-3.0.6.tgz#d331c815496f80083d53277e1972095da954f31f"
-  integrity sha512-hXewp5TAKneWJcYXenTZuzSUagGjyWv5vRHDFarw1O4pkEg7zz8IyN2yAvbYH6+GDqIhF/91rgGu9alkx/yjjA==
+"@jupyterlab/statedb@<3.1.0", "@jupyterlab/statedb@^3.0.7":
+  version "3.0.7"
+  resolved "https://registry.yarnpkg.com/@jupyterlab/statedb/-/statedb-3.0.7.tgz#df70f7953b877227b5d0b76343134966f638d956"
+  integrity sha512-BWjna7IiMRi3bL3AMIYJKefTmTY48clVvEt23ldko+WOdEe/TPovSPpFMny6JaKGvErkuLGoaAqhu65/1J4TfQ==
   dependencies:
     "@lumino/commands" "^1.12.0"
     "@lumino/coreutils" "^1.5.3"
@@ -355,17 +371,17 @@
     "@lumino/properties" "^1.2.3"
     "@lumino/signaling" "^1.4.3"
 
-"@jupyterlab/statusbar@^3.0.9":
-  version "3.0.9"
-  resolved "https://registry.yarnpkg.com/@jupyterlab/statusbar/-/statusbar-3.0.9.tgz#b00d8b74e813bb9534e7a57d0419579e9367da7a"
-  integrity sha512-MaA6GVi59mH3YRkV5iJPcpdS9opMTgFvcfMQLzKeMJvEQvM2fFGMVixp+q2U6Pa8iJsCp59CUoTyuQQdkw1UFw==
-  dependencies:
-    "@jupyterlab/apputils" "^3.0.9"
-    "@jupyterlab/codeeditor" "^3.0.9"
-    "@jupyterlab/coreutils" "^5.0.6"
-    "@jupyterlab/services" "^6.0.9"
-    "@jupyterlab/translation" "^3.0.9"
-    "@jupyterlab/ui-components" "^3.0.7"
+"@jupyterlab/statusbar@<3.1.0", "@jupyterlab/statusbar@^3.0.10":
+  version "3.0.10"
+  resolved "https://registry.yarnpkg.com/@jupyterlab/statusbar/-/statusbar-3.0.10.tgz#ff8a8a629a8b8d1f725e635e59ee7936322f14b6"
+  integrity sha512-e+OJVHVQz8T5NvKsz3alOgyos3RKSdmRaHEFYnTqA0+ZlEzJwVNM1pZHd/ctnymo1XYDAUWvLnLHpitQZkJ3aw==
+  dependencies:
+    "@jupyterlab/apputils" "^3.0.10"
+    "@jupyterlab/codeeditor" "^3.0.10"
+    "@jupyterlab/coreutils" "^5.0.7"
+    "@jupyterlab/services" "^6.0.10"
+    "@jupyterlab/translation" "^3.0.10"
+    "@jupyterlab/ui-components" "^3.0.8"
     "@lumino/algorithm" "^1.3.3"
     "@lumino/coreutils" "^1.5.3"
     "@lumino/disposable" "^1.4.3"
@@ -376,24 +392,24 @@
     react "^17.0.1"
     typestyle "^2.0.4"
 
-"@jupyterlab/translation@^3.0.9":
-  version "3.0.9"
-  resolved "https://registry.yarnpkg.com/@jupyterlab/translation/-/translation-3.0.9.tgz#54472d3d2fef0d56dfa61c2711a9155f3308ad5b"
-  integrity sha512-XsIUt08HDpA2zqhJFmNV9iuxMriV4sAdx4rM1rA0tEUuvWSXerLvpzNUw4LAz+iaJgyUgqqV1gKrOgoMTjtvWA==
+"@jupyterlab/translation@<3.1.0", "@jupyterlab/translation@^3.0.10":
+  version "3.0.10"
+  resolved "https://registry.yarnpkg.com/@jupyterlab/translation/-/translation-3.0.10.tgz#cd6203b69f86a570879b91d7643e3c8d7c70f7d4"
+  integrity sha512-O+jGspAqhH8jO6OLr1hRD8MBEkn7PgyNY1AQUKyg22uM2DGOv47LNIRSHIjF31cFbHrziuh4pMMs1nZ2WitU1g==
   dependencies:
-    "@jupyterlab/coreutils" "^5.0.6"
-    "@jupyterlab/services" "^6.0.9"
-    "@jupyterlab/statedb" "^3.0.6"
+    "@jupyterlab/coreutils" "^5.0.7"
+    "@jupyterlab/services" "^6.0.10"
+    "@jupyterlab/statedb" "^3.0.7"
     "@lumino/coreutils" "^1.5.3"
 
-"@jupyterlab/ui-components@^3.0.7":
-  version "3.0.7"
-  resolved "https://registry.yarnpkg.com/@jupyterlab/ui-components/-/ui-components-3.0.7.tgz#83525d98051e9c74bd415da9e4a0fb20ec6bd609"
-  integrity sha512-kuq2aZ3DcCQNqf5ucsXWREHxbYq23+S12zMertOs+74KQr8jm8chX9HmqpmefNKnSIqqi/RKVSS2PWuSTpkEEw==
+"@jupyterlab/ui-components@<3.1.0", "@jupyterlab/ui-components@^3.0.8":
+  version "3.0.8"
+  resolved "https://registry.yarnpkg.com/@jupyterlab/ui-components/-/ui-components-3.0.8.tgz#ee772780506815d00ff11c08a695abe204d4ce9e"
+  integrity sha512-WXOwA42jrgWIs2iHPZ1HvcI6YLyRNDHYD8XfMKneenddiWMm2ozgIhTrVssmLim6oRE2ipRzBqNkUIKBgYWwFA==
   dependencies:
     "@blueprintjs/core" "^3.36.0"
     "@blueprintjs/select" "^3.15.0"
-    "@jupyterlab/coreutils" "^5.0.6"
+    "@jupyterlab/coreutils" "^5.0.7"
     "@lumino/coreutils" "^1.5.3"
     "@lumino/signaling" "^1.4.3"
     "@lumino/virtualdom" "^1.8.0"
@@ -402,123 +418,123 @@
     react-dom "^17.0.1"
     typestyle "^2.0.4"
 
-"@lumino/algorithm@^1.3.3", "@lumino/algorithm@^1.6.0":
-  version "1.6.0"
-  resolved "https://registry.yarnpkg.com/@lumino/algorithm/-/algorithm-1.6.0.tgz#771e7896cd94e660f9b58a52f80e1bb255de1d41"
-  integrity sha512-NMOcm5Yr9nXz5gokS/K4jHBbUMQYBkvDXl1n51XWdcz0LY+oGuIKPhjazhUgmbNRehzdZBj5hMMd1+htYWeVKQ==
+"@lumino/algorithm@^1.3.3", "@lumino/algorithm@^1.9.1":
+  version "1.9.1"
+  resolved "https://registry.yarnpkg.com/@lumino/algorithm/-/algorithm-1.9.1.tgz#a870598e031f5ee85e20e77ce7bfffbb0dffd7f5"
+  integrity sha512-d0rj7IYRzYj6WbWSrbJbKvrfO4H0NUnXT2yjSWS/sCklpTpSp0IGmndK/X4r6gG+ev5lb5+wBg9ofUDBvoAlAw==
 
 "@lumino/application@^1.13.1":
-  version "1.20.0"
-  resolved "https://registry.yarnpkg.com/@lumino/application/-/application-1.20.0.tgz#b50ca4180bc400589fdfcfcaab08c4af937fccd0"
-  integrity sha512-FAoQcq4L3ZswTK0lWfLKnG1ecG26cwqjzg2fyoBeuWGBi1TG9BYjFBdV7ErTFMxW8jE1CLOLuxsZaKFLNErcKA==
-  dependencies:
-    "@lumino/commands" "^1.15.0"
-    "@lumino/coreutils" "^1.8.0"
-    "@lumino/widgets" "^1.23.0"
-
-"@lumino/collections@^1.6.0":
-  version "1.6.0"
-  resolved "https://registry.yarnpkg.com/@lumino/collections/-/collections-1.6.0.tgz#7d3e94cee26409b0cd719c1934bdda471e6a5662"
-  integrity sha512-ZETm0/xF0oUHV03sOXNOfFI1EEpS317HvN5n+fZBJvCNZIrJkWmKD8QuxcfwHb7AChKUhXlVHhDbWlb1LKnd7g==
-  dependencies:
-    "@lumino/algorithm" "^1.6.0"
-
-"@lumino/commands@^1.12.0", "@lumino/commands@^1.15.0":
-  version "1.15.0"
-  resolved "https://registry.yarnpkg.com/@lumino/commands/-/commands-1.15.0.tgz#06eb94fb4b34cad59f35b1fcaf473e8d2047f779"
-  integrity sha512-JOE68KfbR9xw5YTfcwo+9E0PSWidifEMAcOC/aXd7iSzfsCRknMTcMQIUGL277IU7J7CJvoe10DUE5QKwTmX+g==
-  dependencies:
-    "@lumino/algorithm" "^1.6.0"
-    "@lumino/coreutils" "^1.8.0"
-    "@lumino/disposable" "^1.7.0"
-    "@lumino/domutils" "^1.5.0"
-    "@lumino/keyboard" "^1.5.0"
-    "@lumino/signaling" "^1.7.0"
-    "@lumino/virtualdom" "^1.11.0"
-
-"@lumino/coreutils@^1.5.3", "@lumino/coreutils@^1.8.0":
-  version "1.8.0"
-  resolved "https://registry.yarnpkg.com/@lumino/coreutils/-/coreutils-1.8.0.tgz#4feb3ccbfbc3efc8e395a90f22b5a938fbad959a"
-  integrity sha512-OvCsaASUqOE7R6Dxngyk4/b5QMOjyRUNxuZuuL+fx+JvGKZFZ/B2c9LYtAJ9mDmQ1BQiGNV/qSpL4o7x8PCfjw==
+  version "1.28.1"
+  resolved "https://registry.yarnpkg.com/@lumino/application/-/application-1.28.1.tgz#dfefe82ad414f51659e5931e3d07989364d7625e"
+  integrity sha512-BRRtWJ3mG2abZ9XwB/olGJWXeJjtflDGB/uW6ZsG53Pfu7ekyXKv0wUcijvW+HM9o3bMR+PwM7ELyXtHKkodig==
+  dependencies:
+    "@lumino/commands" "^1.20.0"
+    "@lumino/coreutils" "^1.12.0"
+    "@lumino/widgets" "^1.31.1"
 
-"@lumino/disposable@^1.4.3", "@lumino/disposable@^1.7.0":
-  version "1.7.0"
-  resolved "https://registry.yarnpkg.com/@lumino/disposable/-/disposable-1.7.0.tgz#539463490cb42e8d2dc46b5ff7cc291f4f1a8d07"
-  integrity sha512-3mWi11ko3XVY63BPwvys7MXrbFddA2i+gp72d0wAKM2NDDUopVPikMHhJpjGJcw+otjahzXYiTewxPDEau9dYg==
+"@lumino/collections@^1.9.1":
+  version "1.9.1"
+  resolved "https://registry.yarnpkg.com/@lumino/collections/-/collections-1.9.1.tgz#268f1ec6850d5e131cfc8db232c7e1e106144aa0"
+  integrity sha512-5RaRGUY7BJ/1j173sc9DCfiVf70Z0hopRnBV8/AeAaK9bJJRAYjDhlZ9O8xTyouegh6krkOfiDyjl3pwogLrQw==
   dependencies:
-    "@lumino/algorithm" "^1.6.0"
-    "@lumino/signaling" "^1.7.0"
+    "@lumino/algorithm" "^1.9.1"
 
-"@lumino/domutils@^1.2.3", "@lumino/domutils@^1.5.0":
-  version "1.5.0"
-  resolved "https://registry.yarnpkg.com/@lumino/domutils/-/domutils-1.5.0.tgz#fdba0cfe404b4817e63aa064f63b3c965655e76e"
-  integrity sha512-dZ0Aa+/qhvfPc1aa5kX4LLGE3B6BW1XmJa0R1XVCEpAFY3cZiujuQWmhYHJtZPrOiqn0UtioT2OpqnWdtCWc0A==
+"@lumino/commands@^1.12.0", "@lumino/commands@^1.20.0":
+  version "1.20.0"
+  resolved "https://registry.yarnpkg.com/@lumino/commands/-/commands-1.20.0.tgz#44c797134bb33946141a490c506420bd5f12ce0f"
+  integrity sha512-xyrzDIJ9QEbcbRAwmXrjb7A7/E5MDNbnLANKwqmFVNF+4LSnF62obdvY4On3Rify3HmfX0u16Xr9gfoWPX9wLQ==
+  dependencies:
+    "@lumino/algorithm" "^1.9.1"
+    "@lumino/coreutils" "^1.12.0"
+    "@lumino/disposable" "^1.10.1"
+    "@lumino/domutils" "^1.8.1"
+    "@lumino/keyboard" "^1.8.1"
+    "@lumino/signaling" "^1.10.1"
+    "@lumino/virtualdom" "^1.14.1"
+
+"@lumino/coreutils@^1.12.0", "@lumino/coreutils@^1.5.3":
+  version "1.12.0"
+  resolved "https://registry.yarnpkg.com/@lumino/coreutils/-/coreutils-1.12.0.tgz#fbdef760f736eaf2bd396a5c6fc3a68a4b449b15"
+  integrity sha512-DSglh4ylmLi820CNx9soJmDJCpUgymckdWeGWuN0Ash5g60oQvrQDfosVxEhzmNvtvXv45WZEqSBzDP6E5SEmQ==
+
+"@lumino/disposable@^1.10.1", "@lumino/disposable@^1.4.3":
+  version "1.10.1"
+  resolved "https://registry.yarnpkg.com/@lumino/disposable/-/disposable-1.10.1.tgz#58fddc619cf89335802d168564b76ff5315d5a84"
+  integrity sha512-mZQILc8sVGZC7mJNOGVmehDRO9/u3sIRdjZ+pCYjDgXKcINLd6HoPhZDquKCWiRBfHTL1B3tOHjnBhahBc2N/Q==
+  dependencies:
+    "@lumino/algorithm" "^1.9.1"
+    "@lumino/signaling" "^1.10.1"
+
+"@lumino/domutils@^1.2.3", "@lumino/domutils@^1.8.1":
+  version "1.8.1"
+  resolved "https://registry.yarnpkg.com/@lumino/domutils/-/domutils-1.8.1.tgz#cf118e4eba90c3bf1e3edf7f19cce8846ec7875c"
+  integrity sha512-QUVXwmDMIfcHC3yslhmyGK4HYBKaJ3xX5MTwDrjsSX7J7AZ4jwL4zfsxyF9ntdqEKraoJhLQ6BaUBY+Ur1cnYw==
+
+"@lumino/dragdrop@^1.14.0", "@lumino/dragdrop@^1.7.1":
+  version "1.14.0"
+  resolved "https://registry.yarnpkg.com/@lumino/dragdrop/-/dragdrop-1.14.0.tgz#48baacc190518d0cb563698daa0d5b976d6fe5c3"
+  integrity sha512-hO8sgF0BkpihKIP6UZgVJgiOEhz89i7Oxtp9FR9Jqw5alGocxSXt7q3cteMvqpcL6o2/s3CafZNRkVLRXmepNw==
+  dependencies:
+    "@lumino/coreutils" "^1.12.0"
+    "@lumino/disposable" "^1.10.1"
+
+"@lumino/keyboard@^1.8.1":
+  version "1.8.1"
+  resolved "https://registry.yarnpkg.com/@lumino/keyboard/-/keyboard-1.8.1.tgz#e7850e2fb973fbb4c6e737ca8d9307f2dc3eb74b"
+  integrity sha512-8x0y2ZQtEvOsblpI2gfTgf+gboftusP+5aukKEsgNQtzFl28RezQXEOSVd8iD3K6+Q1MaPQF0OALYP0ASqBjBg==
+
+"@lumino/messaging@^1.10.1", "@lumino/messaging@^1.4.3":
+  version "1.10.1"
+  resolved "https://registry.yarnpkg.com/@lumino/messaging/-/messaging-1.10.1.tgz#b29575cca46e2f23b84626b793ec8e2be46a53ba"
+  integrity sha512-XZSdt9ih94rdeeLL0cryUw6HHD51D7TP8c+MFf+YRF6VKwOFB9RoajfQWadeqpmH+schTs3EsrFfA9KHduzC7w==
+  dependencies:
+    "@lumino/algorithm" "^1.9.1"
+    "@lumino/collections" "^1.9.1"
 
-"@lumino/dragdrop@^1.10.0", "@lumino/dragdrop@^1.7.1":
+"@lumino/polling@^1.3.3":
   version "1.10.0"
-  resolved "https://registry.yarnpkg.com/@lumino/dragdrop/-/dragdrop-1.10.0.tgz#2fddacfee055e660dd33dd9a3cfbd8fbba811673"
-  integrity sha512-A3cNLcp09zygOprWmLTkLZCQYNq3dJfN+mhni4IZizqCTkKbTCEzo2/IwoCWvy+ABKft8d/A9Y40wFW6yJ9OyA==
+  resolved "https://registry.yarnpkg.com/@lumino/polling/-/polling-1.10.0.tgz#94a92811edf4c2534c741510b30f500d8c16a395"
+  integrity sha512-ZNXObJQfugnS41Yrlr7yWcFiRK+xAGGOXO08JJ0Mctsg5mT30UEGFVWJY2AjZ6N5aQuLyGed/pMkBzLzrzt8OA==
   dependencies:
-    "@lumino/coreutils" "^1.8.0"
-    "@lumino/disposable" "^1.7.0"
+    "@lumino/coreutils" "^1.12.0"
+    "@lumino/disposable" "^1.10.1"
+    "@lumino/signaling" "^1.10.1"
 
-"@lumino/keyboard@^1.5.0":
-  version "1.5.0"
-  resolved "https://registry.yarnpkg.com/@lumino/keyboard/-/keyboard-1.5.0.tgz#c12213822dd2645c412e8689aecd4a2726113ac6"
-  integrity sha512-/uF9xqHYVbIkser2Q6UIv7VWrzThr1fxAmSOShjSoKGocL0XHeaBaCOMezSaVxnJ1wm1ciNdhMsjscVM8Inp7g==
+"@lumino/properties@^1.2.3", "@lumino/properties@^1.8.1":
+  version "1.8.1"
+  resolved "https://registry.yarnpkg.com/@lumino/properties/-/properties-1.8.1.tgz#47eb8516e92c987dcb2c404db83a258159efec3d"
+  integrity sha512-O+CCcAqP64Di32DUZ4Jqq0DtUyE5RJREN5vbkgGZGu+WauJ/RYoiLDe1ubbAeSaHk71OrS60ZBV7QyC8ZaBVsA==
 
-"@lumino/messaging@^1.4.3", "@lumino/messaging@^1.7.0":
-  version "1.7.0"
-  resolved "https://registry.yarnpkg.com/@lumino/messaging/-/messaging-1.7.0.tgz#32542f9e9a266fd5b3f71842f70cfe141e016d93"
-  integrity sha512-QYWf9QGIGD0Oes104zw7mVln4S8yRije2mZhNNRBjkYcDuQlPW+eRSuC5LwAMsFnGymBlUPwPbKOUEO2RbhAtg==
+"@lumino/signaling@^1.10.1", "@lumino/signaling@^1.4.3":
+  version "1.10.1"
+  resolved "https://registry.yarnpkg.com/@lumino/signaling/-/signaling-1.10.1.tgz#c8a1cb5b661b6744ea817c99c758fdc897847c26"
+  integrity sha512-GZVbX4cfk/ZqLwkemPD/NwqToaTL/6q7qdLpEhgkiPlaH1S5/V7fDpP7N1uFy4n3BDITId8cpYgH/Ds32Mdp3A==
   dependencies:
-    "@lumino/algorithm" "^1.6.0"
-    "@lumino/collections" "^1.6.0"
+    "@lumino/algorithm" "^1.9.1"
 
-"@lumino/polling@^1.3.3":
-  version "1.6.0"
-  resolved "https://registry.yarnpkg.com/@lumino/polling/-/polling-1.6.0.tgz#64f40bba4602fe9eceb9f3fae8f3647831e5b7e9"
-  integrity sha512-jG1nqw6UO5XEN7QamOr6iDW8WvYeZQcBVRjM38fszz62dwJ/VGPvO2hlNl6QWWIfCynbJudms0LQm+z0BT1EdA==
-  dependencies:
-    "@lumino/coreutils" "^1.8.0"
-    "@lumino/disposable" "^1.7.0"
-    "@lumino/signaling" "^1.7.0"
-
-"@lumino/properties@^1.2.3", "@lumino/properties@^1.5.0":
-  version "1.5.0"
-  resolved "https://registry.yarnpkg.com/@lumino/properties/-/properties-1.5.0.tgz#7e8638e84c51bb110c5a69f91ca8b0e40b2c3fca"
-  integrity sha512-YqpJE6/1Wkjrie0E+ypu+yzd55B5RlvKYMnQs3Ox+SrJsnNBhA6Oj44EhVf8SUTuHgn1t/mm+LvbswKN5RM4+g==
-
-"@lumino/signaling@^1.4.3", "@lumino/signaling@^1.7.0":
-  version "1.7.0"
-  resolved "https://registry.yarnpkg.com/@lumino/signaling/-/signaling-1.7.0.tgz#76da4738bf8f19e7da6de1d457a54220e2140670"
-  integrity sha512-a5kd11Sf04jTfpzxCr7TOBD2o5YvItA4IGwiOoG+QR6sPR0Rwmcf47fPItqXo5st58iNIblC3F+c264N+Me+gg==
-  dependencies:
-    "@lumino/algorithm" "^1.6.0"
-
-"@lumino/virtualdom@^1.11.0", "@lumino/virtualdom@^1.8.0":
-  version "1.11.0"
-  resolved "https://registry.yarnpkg.com/@lumino/virtualdom/-/virtualdom-1.11.0.tgz#468b4d28a07e2b8988dc583b4aab40e37dc6955e"
-  integrity sha512-G0sIx4pLYbgJ4w+SIgsCYQgKP/GBrWgjh8wcumD6XpaYZNivJv4c01xITYYlh7FU61jZmMWMrxtZztArNRDSqg==
-  dependencies:
-    "@lumino/algorithm" "^1.6.0"
-
-"@lumino/widgets@^1.16.1", "@lumino/widgets@^1.23.0":
-  version "1.23.0"
-  resolved "https://registry.yarnpkg.com/@lumino/widgets/-/widgets-1.23.0.tgz#096c7574de75fa67b32bcb914c5dae290fbee6f3"
-  integrity sha512-0Akt9ESgc06SJ3EJG3VK1Liw+AAjRWkKMfm8VUTwT/1QJYYGZ8kfHNO97mkBLv+0EkLEkZIeaQb8fIoU6vh7bw==
-  dependencies:
-    "@lumino/algorithm" "^1.6.0"
-    "@lumino/commands" "^1.15.0"
-    "@lumino/coreutils" "^1.8.0"
-    "@lumino/disposable" "^1.7.0"
-    "@lumino/domutils" "^1.5.0"
-    "@lumino/dragdrop" "^1.10.0"
-    "@lumino/keyboard" "^1.5.0"
-    "@lumino/messaging" "^1.7.0"
-    "@lumino/properties" "^1.5.0"
-    "@lumino/signaling" "^1.7.0"
-    "@lumino/virtualdom" "^1.11.0"
+"@lumino/virtualdom@^1.14.1", "@lumino/virtualdom@^1.8.0":
+  version "1.14.1"
+  resolved "https://registry.yarnpkg.com/@lumino/virtualdom/-/virtualdom-1.14.1.tgz#2551b146cbe87c48d23754f370c1331a60c9fe62"
+  integrity sha512-imIJd/wtRkoR1onEiG5nxPEaIrf70nn4PgD/56ri3/Lo6AJEX2CusF6iIA27GVB8yl/7CxgTHUnzzCwTFPypcA==
+  dependencies:
+    "@lumino/algorithm" "^1.9.1"
+
+"@lumino/widgets@^1.16.1", "@lumino/widgets@^1.31.1":
+  version "1.31.1"
+  resolved "https://registry.yarnpkg.com/@lumino/widgets/-/widgets-1.31.1.tgz#c9c0b8c7940b412e55369fa277392bf86c6e4136"
+  integrity sha512-4RzAMqWwWHa5IiaQaeIbiZdIBm/FOg6ub0w8dG3km0k+zIQyA4LFq2dbB1w6SHT1d06N+L/ebYfgvMFswPENag==
+  dependencies:
+    "@lumino/algorithm" "^1.9.1"
+    "@lumino/commands" "^1.20.0"
+    "@lumino/coreutils" "^1.12.0"
+    "@lumino/disposable" "^1.10.1"
+    "@lumino/domutils" "^1.8.1"
+    "@lumino/dragdrop" "^1.14.0"
+    "@lumino/keyboard" "^1.8.1"
+    "@lumino/messaging" "^1.10.1"
+    "@lumino/properties" "^1.8.1"
+    "@lumino/signaling" "^1.10.1"
+    "@lumino/virtualdom" "^1.14.1"
 
 "@nodelib/fs.scandir@2.1.5":
   version "2.1.5"
@@ -541,6 +557,14 @@
     "@nodelib/fs.scandir" "2.1.5"
     fastq "^1.6.0"
 
+"@npmcli/fs@^1.0.0":
+  version "1.1.1"
+  resolved "https://registry.yarnpkg.com/@npmcli/fs/-/fs-1.1.1.tgz#72f719fe935e687c56a4faecf3c03d06ba593257"
+  integrity sha512-8KG5RD0GVP4ydEzRn/I4BNDuxDtqVbOdm8675T49OIG/NGhaK0pjPX7ZcDlvKYbA+ulvVK3ztfcF4uBdOxuJbQ==
+  dependencies:
+    "@gar/promisify" "^1.0.1"
+    semver "^7.3.5"
+
 "@npmcli/move-file@^1.0.1":
   version "1.1.2"
   resolved "https://registry.yarnpkg.com/@npmcli/move-file/-/move-file-1.1.2.tgz#1a82c3e372f7cae9253eb66d72543d6b8685c674"
@@ -566,39 +590,39 @@
   resolved "https://registry.yarnpkg.com/@types/dom4/-/dom4-2.0.2.tgz#6495303f049689ce936ed328a3e5ede9c51408ee"
   integrity sha512-Rt4IC1T7xkCWa0OG1oSsPa0iqnxlDeQqKXZAHrQGLb7wFGncWm85MaxKUjAGejOrUynOgWlFi4c6S6IyJwoK4g==
 
-"@types/eslint-scope@^3.7.0":
-  version "3.7.1"
-  resolved "https://registry.yarnpkg.com/@types/eslint-scope/-/eslint-scope-3.7.1.tgz#8dc390a7b4f9dd9f1284629efce982e41612116e"
-  integrity sha512-SCFeogqiptms4Fg29WpOTk5nHIzfpKCemSN63ksBQYKTcXoJEmJagV+DhVmbapZzY4/5YaOV1nZwrsU79fFm1g==
+"@types/eslint-scope@^3.7.3":
+  version "3.7.3"
+  resolved "https://registry.yarnpkg.com/@types/eslint-scope/-/eslint-scope-3.7.3.tgz#125b88504b61e3c8bc6f870882003253005c3224"
+  integrity sha512-PB3ldyrcnAicT35TWPs5IcwKD8S333HMaa2VVv4+wdvebJkjWuW/xESoB8IwRcog8HYVYamb1g/R31Qv5Bx03g==
   dependencies:
     "@types/eslint" "*"
     "@types/estree" "*"
 
 "@types/eslint@*":
-  version "7.28.0"
-  resolved "https://registry.yarnpkg.com/@types/eslint/-/eslint-7.28.0.tgz#7e41f2481d301c68e14f483fe10b017753ce8d5a"
-  integrity sha512-07XlgzX0YJUn4iG1ocY4IX9DzKSmMGUs6ESKlxWhZRaa0fatIWaHWUVapcuGa8r5HFnTqzj+4OCjd5f7EZ/i/A==
+  version "8.4.1"
+  resolved "https://registry.yarnpkg.com/@types/eslint/-/eslint-8.4.1.tgz#c48251553e8759db9e656de3efc846954ac32304"
+  integrity sha512-GE44+DNEyxxh2Kc6ro/VkIj+9ma0pO0bwv9+uHSyBrikYOHr8zYcdPvnBOp1aw8s+CjRvuSx7CyWqRrNFQ59mA==
   dependencies:
     "@types/estree" "*"
     "@types/json-schema" "*"
 
-"@types/estree@*", "@types/estree@^0.0.50":
-  version "0.0.50"
-  resolved "https://registry.yarnpkg.com/@types/estree/-/estree-0.0.50.tgz#1e0caa9364d3fccd2931c3ed96fdbeaa5d4cca83"
-  integrity sha512-C6N5s2ZFtuZRj54k2/zyRhNDjJwwcViAM3Nbm8zjBpbqAdZ00mr0CFxvSKeO8Y/e03WVFLpQMdHYVfUd6SB+Hw==
+"@types/estree@*", "@types/estree@^0.0.51":
+  version "0.0.51"
+  resolved "https://registry.yarnpkg.com/@types/estree/-/estree-0.0.51.tgz#cfd70924a25a3fd32b218e5e420e6897e1ac4f40"
+  integrity sha512-CuPgU6f3eT/XgKKPqKd/gLZV1Xmvf1a2R5POBOGQa6uv82xpls89HU5zKeVoyR8XzHd1RGNOlQlvUe3CFkjWNQ==
 
 "@types/glob@^7.1.1":
-  version "7.1.4"
-  resolved "https://registry.yarnpkg.com/@types/glob/-/glob-7.1.4.tgz#ea59e21d2ee5c517914cb4bc8e4153b99e566672"
-  integrity sha512-w+LsMxKyYQm347Otw+IfBXOv9UWVjpHpCDdbBMt8Kz/xbvCYNjP+0qPh91Km3iKfSRLBB0P7fAMf0KHrPu+MyA==
+  version "7.2.0"
+  resolved "https://registry.yarnpkg.com/@types/glob/-/glob-7.2.0.tgz#bc1b5bf3aa92f25bd5dd39f35c57361bdce5b2eb"
+  integrity sha512-ZUxbzKl0IfJILTS6t7ip5fQQM/J3TJYubDm3nMbgubNNYS62eXeUpoLUC8/7fJNiFYHTrGPQn7hspDUzIHX3UA==
   dependencies:
     "@types/minimatch" "*"
     "@types/node" "*"
 
 "@types/json-schema@*", "@types/json-schema@^7.0.5", "@types/json-schema@^7.0.8":
-  version "7.0.8"
-  resolved "https://registry.yarnpkg.com/@types/json-schema/-/json-schema-7.0.8.tgz#edf1bf1dbf4e04413ca8e5b17b3b7d7d54b59818"
-  integrity sha512-YSBPTLTVm2e2OoQIDYx8HaeWJ5tTToLH67kXR7zYNGupXMEHa2++G8k+DczX2cFVgalypqtyZIcU19AFcmOpmg==
+  version "7.0.11"
+  resolved "https://registry.yarnpkg.com/@types/json-schema/-/json-schema-7.0.11.tgz#d421b6c527a3037f7c84433fd2c4229e016863d3"
+  integrity sha512-wOuvG1SN4Us4rez+tylwwwCV1psiNVOkJeM3AUWUNWg/jDQY2+HE/444y5gc+jBmRqASOm2Oeh5c1axHobwRKQ==
 
 "@types/minimatch@*":
   version "3.0.5"
@@ -606,9 +630,9 @@
   integrity sha512-Klz949h02Gz2uZCMGwDUSDS1YBlTdDDgbWHi+81l29tQALUtvz4rAYi5uoVhE5Lagoq6DeqAUlbrHvW/mXDgdQ==
 
 "@types/node@*":
-  version "16.4.0"
-  resolved "https://registry.yarnpkg.com/@types/node/-/node-16.4.0.tgz#2c219eaa3b8d1e4d04f4dd6e40bc68c7467d5272"
-  integrity sha512-HrJuE7Mlqcjj+00JqMWpZ3tY8w7EUd+S0U3L1+PQSWiXZbOgyQDvi+ogoUxaHApPJq5diKxYBQwA3iIlNcPqOg==
+  version "17.0.23"
+  resolved "https://registry.yarnpkg.com/@types/node/-/node-17.0.23.tgz#3b41a6e643589ac6442bdbd7a4a3ded62f33f7da"
+  integrity sha512-UxDxWn7dl97rKVeVS61vErvw086aCYhDLyvRQZ5Rk65rZKepaFdm53GeqXaKBuOhED4e9uWq34IC3TdSdJJ2Gw==
 
 "@types/prop-types@*":
   version "15.7.4"
@@ -616,9 +640,9 @@
   integrity sha512-rZ5drC/jWjrArrS8BR6SIr4cWpW09RNTYt9AMZo3Jwwif+iacXAqgVjm0B0Bv/S1jhDXKHqRVNCbACkJ89RAnQ==
 
 "@types/react@^17.0.0":
-  version "17.0.14"
-  resolved "https://registry.yarnpkg.com/@types/react/-/react-17.0.14.tgz#f0629761ca02945c4e8fea99b8177f4c5c61fb0f"
-  integrity sha512-0WwKHUbWuQWOce61UexYuWTGuGY/8JvtUe/dtQ6lR4sZ3UiylHotJeWpf3ArP9+DSGUoLY3wbU59VyMrJps5VQ==
+  version "17.0.43"
+  resolved "https://registry.yarnpkg.com/@types/react/-/react-17.0.43.tgz#4adc142887dd4a2601ce730bc56c3436fdb07a55"
+  integrity sha512-8Q+LNpdxf057brvPu1lMtC5Vn7J119xrP1aq4qiaefNioQUYANF/CYeK4NsKorSZyUGJ66g0IM+4bbjwx45o2A==
   dependencies:
     "@types/prop-types" "*"
     "@types/scheduler" "*"
@@ -750,22 +774,22 @@
     "@webassemblyjs/ast" "1.11.1"
     "@xtuc/long" "4.2.2"
 
-"@webpack-cli/configtest@^1.0.4":
-  version "1.0.4"
-  resolved "https://registry.yarnpkg.com/@webpack-cli/configtest/-/configtest-1.0.4.tgz#f03ce6311c0883a83d04569e2c03c6238316d2aa"
-  integrity sha512-cs3XLy+UcxiP6bj0A6u7MLLuwdXJ1c3Dtc0RkKg+wiI1g/Ti1om8+/2hc2A2B60NbBNAbMgyBMHvyymWm/j4wQ==
+"@webpack-cli/configtest@^1.1.1":
+  version "1.1.1"
+  resolved "https://registry.yarnpkg.com/@webpack-cli/configtest/-/configtest-1.1.1.tgz#9f53b1b7946a6efc2a749095a4f450e2932e8356"
+  integrity sha512-1FBc1f9G4P/AxMqIgfZgeOTuRnwZMten8E7zap5zgpPInnCrP8D4Q81+4CWIch8i/Nf7nXjP0v6CjjbHOrXhKg==
 
-"@webpack-cli/info@^1.3.0":
-  version "1.3.0"
-  resolved "https://registry.yarnpkg.com/@webpack-cli/info/-/info-1.3.0.tgz#9d78a31101a960997a4acd41ffd9b9300627fe2b"
-  integrity sha512-ASiVB3t9LOKHs5DyVUcxpraBXDOKubYu/ihHhU+t1UPpxsivg6Od2E2qU4gJCekfEddzRBzHhzA/Acyw/mlK/w==
+"@webpack-cli/info@^1.4.1":
+  version "1.4.1"
+  resolved "https://registry.yarnpkg.com/@webpack-cli/info/-/info-1.4.1.tgz#2360ea1710cbbb97ff156a3f0f24556e0fc1ebea"
+  integrity sha512-PKVGmazEq3oAo46Q63tpMr4HipI3OPfP7LiNOEJg963RMgT0rqheag28NCML0o3GIzA3DmxP1ZIAv9oTX1CUIA==
   dependencies:
     envinfo "^7.7.3"
 
-"@webpack-cli/serve@^1.5.1":
-  version "1.5.1"
-  resolved "https://registry.yarnpkg.com/@webpack-cli/serve/-/serve-1.5.1.tgz#b5fde2f0f79c1e120307c415a4c1d5eb15a6f278"
-  integrity sha512-4vSVUiOPJLmr45S8rMGy7WDvpWxfFxfP/Qx/cxZFCfvoypTYpPPL1X8VIZMe0WTA+Jr7blUxwUSEZNkjoMTgSw==
+"@webpack-cli/serve@^1.6.1":
+  version "1.6.1"
+  resolved "https://registry.yarnpkg.com/@webpack-cli/serve/-/serve-1.6.1.tgz#0de2875ac31b46b6c5bb1ae0a7d7f0ba5678dffe"
+  integrity sha512-gNGTiTrjEVQ0OcVnzsRSqTxaBSr+dmTfm+qJsCDluky8uhdLWep7Gcr62QsAKHTMxjCS/8nEITsmFAhfIx+QSw==
 
 "@xtuc/ieee754@^1.2.0":
   version "1.2.0"
@@ -782,10 +806,15 @@
   resolved "https://registry.yarnpkg.com/@yarnpkg/lockfile/-/lockfile-1.1.0.tgz#e77a97fbd345b76d83245edcd17d393b1b41fb31"
   integrity sha512-GpSwvyXOcOOlV70vbnzjj4fW5xW/FdUF6nQEt1ENy7m4ZCczi1+/buVUPAqmGfqznsORNFzUMjctTIp8a9tuCQ==
 
-acorn@^8.4.1:
-  version "8.4.1"
-  resolved "https://registry.yarnpkg.com/acorn/-/acorn-8.4.1.tgz#56c36251fc7cabc7096adc18f05afe814321a28c"
-  integrity sha512-asabaBSkEKosYKMITunzX177CXxQ4Q8BSSzMTKD+FefUhipQC70gfW5SiUDhYQ3vk8G+81HqQk7Fv9OXwwn9KA==
+acorn-import-assertions@^1.7.6:
+  version "1.8.0"
+  resolved "https://registry.yarnpkg.com/acorn-import-assertions/-/acorn-import-assertions-1.8.0.tgz#ba2b5939ce62c238db6d93d81c9b111b29b855e9"
+  integrity sha512-m7VZ3jwz4eK6A4Vtt8Ew1/mNbP24u0FhdyfA7fSvnJR6LMdfOYnmuIrrJAgrYfYJ10F/otaHTtrtrtmHdMNzEw==
+
+acorn@^8.4.1, acorn@^8.5.0:
+  version "8.7.0"
+  resolved "https://registry.yarnpkg.com/acorn/-/acorn-8.7.0.tgz#90951fde0f8f09df93549481e5fc141445b791cf"
+  integrity sha512-V/LGr1APy+PXIwKebEWrkZPwoeoF+w1jiOBUmuxuiUIaOHtob8Qc9BTrYo7VuI5fR8tqsy+buA2WFooR5olqvQ==
 
 aggregate-error@^3.0.0:
   version "3.1.0"
@@ -817,7 +846,7 @@ ansi-escapes@^4.2.1:
   dependencies:
     type-fest "^0.21.3"
 
-ansi-regex@^5.0.0:
+ansi-regex@^5.0.1:
   version "5.0.1"
   resolved "https://registry.yarnpkg.com/ansi-regex/-/ansi-regex-5.0.1.tgz#082cb2c89c9fe8659a311a53bd6a4dc5301db304"
   integrity sha512-quJQXlTSUGL2LH9SUXo8VwsY4soanhgo6LNSm84E1LBcE8s3O0wpdiRzyR9z/ZZJMlMWv37qOOb9pdJlMUEKFQ==
@@ -869,7 +898,7 @@ brace-expansion@^1.1.7:
     balanced-match "^1.0.0"
     concat-map "0.0.1"
 
-braces@^3.0.1:
+braces@^3.0.2:
   version "3.0.2"
   resolved "https://registry.yarnpkg.com/braces/-/braces-3.0.2.tgz#3454e1a462ee8d599e236df336cd9ea4f8afe107"
   integrity sha512-b8um+L1RzM3WDSzvhm6gIz1yfTbBt6YTlcEKAvsmqCZZFw46z626lVj9j1yEPW33H5H+lBQpZMP1k8l+78Ha0A==
@@ -877,20 +906,20 @@ braces@^3.0.1:
     fill-range "^7.0.1"
 
 browserslist@^4.14.5:
-  version "4.16.6"
-  resolved "https://registry.yarnpkg.com/browserslist/-/browserslist-4.16.6.tgz#d7901277a5a88e554ed305b183ec9b0c08f66fa2"
-  integrity sha512-Wspk/PqO+4W9qp5iUTJsa1B/QrYn1keNCcEP5OvP7WBwT4KaDly0uONYmC6Xa3Z5IqnUgS0KcgLYu1l74x0ZXQ==
+  version "4.20.2"
+  resolved "https://registry.yarnpkg.com/browserslist/-/browserslist-4.20.2.tgz#567b41508757ecd904dab4d1c646c612cd3d4f88"
+  integrity sha512-CQOBCqp/9pDvDbx3xfMi+86pr4KXIf2FDkTTdeuYw8OxS9t898LA1Khq57gtufFILXpfgsSx5woNgsBgvGjpsA==
   dependencies:
-    caniuse-lite "^1.0.30001219"
-    colorette "^1.2.2"
-    electron-to-chromium "^1.3.723"
+    caniuse-lite "^1.0.30001317"
+    electron-to-chromium "^1.4.84"
     escalade "^3.1.1"
-    node-releases "^1.1.71"
+    node-releases "^2.0.2"
+    picocolors "^1.0.0"
 
 buffer-from@^1.0.0:
-  version "1.1.1"
-  resolved "https://registry.yarnpkg.com/buffer-from/-/buffer-from-1.1.1.tgz#32713bc028f75c02fdb710d7c7bcec1f2c6070ef"
-  integrity sha512-MQcXEUbCKtEo7bhqEs6560Hyd4XaovZlO/k9V3hjVUF/zwW7KBVdSK4gIt/bzwS9MbR5qob+F5jusZsb0YQK2A==
+  version "1.1.2"
+  resolved "https://registry.yarnpkg.com/buffer-from/-/buffer-from-1.1.2.tgz#2b146a6fd72e80b4f55d255f35ed59a3a9a41bd5"
+  integrity sha512-E+XQCRwSbaaiChtv6k6Dwgc+bx+Bs6vuKJHHl5kox/BaKbhiXzqQOwK4cO22yElGp2OCmjwVhT3HmxgyPGnJfQ==
 
 buffer@^5.6.0:
   version "5.7.1"
@@ -901,10 +930,11 @@ buffer@^5.6.0:
     ieee754 "^1.1.13"
 
 cacache@^15.0.5:
-  version "15.2.0"
-  resolved "https://registry.yarnpkg.com/cacache/-/cacache-15.2.0.tgz#73af75f77c58e72d8c630a7a2858cb18ef523389"
-  integrity sha512-uKoJSHmnrqXgthDFx/IU6ED/5xd+NNGe+Bb+kLZy7Ku4P+BaiWEUflAKPZ7eAzsYGcsAGASJZsybXp+quEcHTw==
+  version "15.3.0"
+  resolved "https://registry.yarnpkg.com/cacache/-/cacache-15.3.0.tgz#dc85380fb2f556fe3dda4c719bfa0ec875a7f1eb"
+  integrity sha512-VVdYzXEn+cnbXpFgWs5hTT7OScegHVmLhJIR8Ufqk3iFD6A6j5iSX1KuBTfNEv4tdJWE2PzA6IVFtcLC7fN9wQ==
   dependencies:
+    "@npmcli/fs" "^1.0.0"
     "@npmcli/move-file" "^1.0.1"
     chownr "^2.0.0"
     fs-minipass "^2.0.0"
@@ -936,7 +966,7 @@ cacheable-request@^6.0.0:
     normalize-url "^4.1.0"
     responselike "^1.0.2"
 
-call-bind@^1.0.0, call-bind@^1.0.2:
+call-bind@^1.0.2:
   version "1.0.2"
   resolved "https://registry.yarnpkg.com/call-bind/-/call-bind-1.0.2.tgz#b1d4e89e688119c3c9a903ad30abb2f6a919be3c"
   integrity sha512-7O+FbCihrB5WGbFYesctwmTKae6rOiIzmz1icreWJ+0aA7LJfuqhEso2T9ncpcFtzMQtzXf2QGGueWJGTYsqrA==
@@ -944,10 +974,10 @@ call-bind@^1.0.0, call-bind@^1.0.2:
     function-bind "^1.1.1"
     get-intrinsic "^1.0.2"
 
-caniuse-lite@^1.0.30001219:
-  version "1.0.30001245"
-  resolved "https://registry.yarnpkg.com/caniuse-lite/-/caniuse-lite-1.0.30001245.tgz#45b941bbd833cb0fa53861ff2bae746b3c6ca5d4"
-  integrity sha512-768fM9j1PKXpOCKws6eTo3RHmvTUsG9UrpT4WoREFeZgJBTi4/X9g565azS/rVUGtqb8nt7FjLeF5u4kukERnA==
+caniuse-lite@^1.0.30001317:
+  version "1.0.30001325"
+  resolved "https://registry.yarnpkg.com/caniuse-lite/-/caniuse-lite-1.0.30001325.tgz#2b4ad19b77aa36f61f2eaf72e636d7481d55e606"
+  integrity sha512-sB1bZHjseSjDtijV1Hb7PB2Zd58Kyx+n/9EotvZ4Qcz2K3d0lWB8dB4nb8wN/TsOGFq3UuAm0zQZNQ4SoR7TrQ==
 
 chalk@^2.3.0:
   version "2.4.2"
@@ -959,9 +989,9 @@ chalk@^2.3.0:
     supports-color "^5.3.0"
 
 chalk@^4.1.0:
-  version "4.1.1"
-  resolved "https://registry.yarnpkg.com/chalk/-/chalk-4.1.1.tgz#c80b3fab28bf6371e6863325eee67e618b77e6ad"
-  integrity sha512-diHzdDKxcU+bAsUboHLPEDQiw0qEe0qd7SYUn3HgcFlWgbDcfLGswOHYeGrHKzG9z6UYf01d9VFMfZxPM1xZSg==
+  version "4.1.2"
+  resolved "https://registry.yarnpkg.com/chalk/-/chalk-4.1.2.tgz#aac4e2b7734a740867aeb16bf02aad556a1e7a01"
+  integrity sha512-oKnbhFyRIXpUuez8iBMmyEa4nbj4IOQyuhc/wy9kY7/WVPcwIO9VA668Pu8RkO7+0G76SLROeyw9CpQ061i4mA==
   dependencies:
     ansi-styles "^4.1.0"
     supports-color "^7.1.0"
@@ -1053,10 +1083,10 @@ color-name@~1.1.4:
   resolved "https://registry.yarnpkg.com/color-name/-/color-name-1.1.4.tgz#c2a09a87acbde69543de6f63fa3995c826c536a2"
   integrity sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA==
 
-colorette@^1.2.1, colorette@^1.2.2:
-  version "1.2.2"
-  resolved "https://registry.yarnpkg.com/colorette/-/colorette-1.2.2.tgz#cbcc79d5e99caea2dbf10eb3a26fd8b3e6acfa94"
-  integrity sha512-MKGMzyfeuutC/ZJ1cba9NqcNpfeqMUcYmyF1ZFY6/Cn7CNSAKx6a+s48sqLqyAiZuaP2TcqMhoo+dlwFnVxT9w==
+colorette@^2.0.14:
+  version "2.0.16"
+  resolved "https://registry.yarnpkg.com/colorette/-/colorette-2.0.16.tgz#713b9af84fdb000139f04546bd4a93f62a5085da"
+  integrity sha512-hUewv7oMjCp+wkBv5Rm0v87eJhq4woh5rSR+42YSQJKecCqgIqNkZ6lAlQms/BwHPJA5NKMRlpxPRv0n8HQW6g==
 
 commander@^2.20.0:
   version "2.20.3"
@@ -1118,15 +1148,15 @@ cssesc@^3.0.0:
   resolved "https://registry.yarnpkg.com/cssesc/-/cssesc-3.0.0.tgz#37741919903b868565e1c09ea747445cd18983ee"
   integrity sha512-/Tb/JcjK111nNScGob5MNtsntNM1aCNUDipB/TkwZFhyDrrE47SOx/18wF2bbjgc3ZzCSKW1T5nt5EbFoAz/Vg==
 
-csstype@2.6.9:
-  version "2.6.9"
-  resolved "https://registry.yarnpkg.com/csstype/-/csstype-2.6.9.tgz#05141d0cd557a56b8891394c1911c40c8a98d098"
-  integrity sha512-xz39Sb4+OaTsULgUERcCk+TJj8ylkL4aSVDQiX/ksxbELSqwkgt4d4RD7fovIdgJGSuNYqwZEiVjYY5l0ask+Q==
+csstype@3.0.10:
+  version "3.0.10"
+  resolved "https://registry.yarnpkg.com/csstype/-/csstype-3.0.10.tgz#2ad3a7bed70f35b965707c092e5f30b327c290e5"
+  integrity sha512-2u44ZG2OcNUO9HDp/Jl8C07x6pU/eTR3ncV91SiK3dhG9TWvRVsCoJw14Ckx5DgWkzGA3waZWO3d7pgqpUI/XA==
 
 csstype@^3.0.2, csstype@~3.0.3:
-  version "3.0.8"
-  resolved "https://registry.yarnpkg.com/csstype/-/csstype-3.0.8.tgz#d2266a792729fb227cd216fb572f43728e1ad340"
-  integrity sha512-jXKhWqXPmlUeoQnF/EhTtTl4C9SnrxSH/jZUih3jmO6lBKr99rP3/+FmrMj4EFpOXzMtXHAZkd3x0E6h6Fgflw==
+  version "3.0.11"
+  resolved "https://registry.yarnpkg.com/csstype/-/csstype-3.0.11.tgz#d66700c5eacfac1940deb4e3ee5642792d85cd33"
+  integrity sha512-sa6P2wJ+CAbgyy4KFssIb/JNMLxFvKF1pCYCSXS8ZMuqZnMsrxqI2E5sPyoTpxoPU/gVZMzr2zjOfg8GIZOMsw==
 
 decompress-response@^3.3.0:
   version "3.3.0"
@@ -1218,16 +1248,16 @@ domelementtype@^2.0.1, domelementtype@^2.2.0:
   integrity sha512-DtBMo82pv1dFtUmHyr48beiuq792Sxohr+8Hm9zoxklYPfa6n0Z3Byjj2IV7bmr2IyqClnqEQhfgHJJ5QF0R5A==
 
 domhandler@^4.0.0, domhandler@^4.2.0:
-  version "4.2.0"
-  resolved "https://registry.yarnpkg.com/domhandler/-/domhandler-4.2.0.tgz#f9768a5f034be60a89a27c2e4d0f74eba0d8b059"
-  integrity sha512-zk7sgt970kzPks2Bf+dwT/PLzghLnsivb9CcxkvR8Mzr66Olr0Ofd8neSbglHJHaHa2MadfoSdNlKYAaafmWfA==
+  version "4.3.1"
+  resolved "https://registry.yarnpkg.com/domhandler/-/domhandler-4.3.1.tgz#8d792033416f59d68bc03a5aa7b018c1ca89279c"
+  integrity sha512-GrwoxYN+uWlzO8uhUXRl0P+kHE4GtVPfYzVLcUxPL7KNdHKj66vvlhiweIHqYYXWlw+T8iLMp42Lm67ghw4WMQ==
   dependencies:
     domelementtype "^2.2.0"
 
 domutils@^2.5.2:
-  version "2.7.0"
-  resolved "https://registry.yarnpkg.com/domutils/-/domutils-2.7.0.tgz#8ebaf0c41ebafcf55b0b72ec31c56323712c5442"
-  integrity sha512-8eaHa17IwJUPAiB+SoTYBo5mCdeMgdcAoXJ59m6DT1vw+5iLS3gNoqYaRowaBKtGVrOF1Jz4yDTgYKLK2kvfJg==
+  version "2.8.0"
+  resolved "https://registry.yarnpkg.com/domutils/-/domutils-2.8.0.tgz#4437def5db6e2d1f5d6ee859bd95ca7d02048135"
+  integrity sha512-w96Cjofp72M5IIhpjgobBimYEfoPjx1Vx0BSX9P30WBdZW2WIKU0T1Bd0kz2eNZ9ikjKgHbEyKx8BB6H1L3h3A==
   dependencies:
     dom-serializer "^1.0.1"
     domelementtype "^2.2.0"
@@ -1248,10 +1278,10 @@ duplicate-package-checker-webpack-plugin@^3.0.0:
     lodash "^4.17.4"
     semver "^5.4.1"
 
-electron-to-chromium@^1.3.723:
-  version "1.3.781"
-  resolved "https://registry.yarnpkg.com/electron-to-chromium/-/electron-to-chromium-1.3.781.tgz#aeb54595edc0ae56721ff6428ffeb4c885c09e57"
-  integrity sha512-l2adP72hnTZANALhjUDLm0zt1fY8Cm+kEU0Ikfy3sP/99BNsOGnnPf2nfCO/3fIL4LwmTcbQD+Ap2Deeuwe9yQ==
+electron-to-chromium@^1.4.84:
+  version "1.4.104"
+  resolved "https://registry.yarnpkg.com/electron-to-chromium/-/electron-to-chromium-1.4.104.tgz#60973b0a7d398efa877196e8ccb0c93d48b918d8"
+  integrity sha512-2kjoAyiG7uMyGRM9mx25s3HAzmQG2ayuYXxsFmYugHSDcwxREgLtscZvbL1JcW9S/OemeQ3f/SG6JhDwpnCclQ==
 
 emoji-regex@^8.0.0:
   version "8.0.0"
@@ -1270,10 +1300,10 @@ end-of-stream@^1.1.0:
   dependencies:
     once "^1.4.0"
 
-enhanced-resolve@^5.8.0:
-  version "5.8.2"
-  resolved "https://registry.yarnpkg.com/enhanced-resolve/-/enhanced-resolve-5.8.2.tgz#15ddc779345cbb73e97c611cd00c01c1e7bf4d8b"
-  integrity sha512-F27oB3WuHDzvR2DOGNTaYy0D5o0cnrv8TeI482VM4kYgQd/FT9lUQwuNsJ0oOHtBUq7eiW5ytqzp7nBFknL+GA==
+enhanced-resolve@^5.9.2:
+  version "5.9.2"
+  resolved "https://registry.yarnpkg.com/enhanced-resolve/-/enhanced-resolve-5.9.2.tgz#0224dcd6a43389ebfb2d55efee517e5466772dd9"
+  integrity sha512-GIm3fQfwLJ8YZx2smuHpBKkXC1yOk+OBEmKckVyL0i/ea8mqDEykK3ld5dgH1QYPNyT/lIllxV2LULnxCHaHkA==
   dependencies:
     graceful-fs "^4.2.4"
     tapable "^2.2.0"
@@ -1288,10 +1318,10 @@ envinfo@^7.7.3:
   resolved "https://registry.yarnpkg.com/envinfo/-/envinfo-7.8.1.tgz#06377e3e5f4d379fea7ac592d5ad8927e0c4d475"
   integrity sha512-/o+BXHmB7ocbHEAs6F2EnG0ogybVVUdkRunTT2glZU9XAaGmhqskrvKwqXuDfNjEO0LZKWdejEEpnq8aM0tOaw==
 
-es-module-lexer@^0.7.1:
-  version "0.7.1"
-  resolved "https://registry.yarnpkg.com/es-module-lexer/-/es-module-lexer-0.7.1.tgz#c2c8e0f46f2df06274cdaf0dd3f3b33e0a0b267d"
-  integrity sha512-MgtWFl5No+4S3TmhDmCz2ObFGm6lEpTnzbQi+Dd+pw4mlTIZTmM2iAs5gRlmx5zS9luzobCSBSI90JM/1/JgOw==
+es-module-lexer@^0.9.0:
+  version "0.9.3"
+  resolved "https://registry.yarnpkg.com/es-module-lexer/-/es-module-lexer-0.9.3.tgz#6f13db00cc38417137daf74366f535c8eb438f19"
+  integrity sha512-1HQ2M2sPtxwnvOvT1ZClHyQDiggdNjURWpY2we6aMKCQiUVxTmVs2UYPLIrD84sS+kMdUwfBSylbJPwNnBrnHQ==
 
 escalade@^3.1.1:
   version "3.1.1"
@@ -1329,9 +1359,9 @@ estraverse@^4.1.1:
   integrity sha512-39nnKffWz8xN1BU/2c79n9nB9HDzo0niYUqx6xyqUnyoAnQyyWpOTdZEeiCch8BBu515t4wp9ZmgVfVhn9EBpw==
 
 estraverse@^5.2.0:
-  version "5.2.0"
-  resolved "https://registry.yarnpkg.com/estraverse/-/estraverse-5.2.0.tgz#307df42547e6cc7324d3cf03c155d5cdb8c53880"
-  integrity sha512-BxbNGGNm0RyRYvUdHpIwv9IWzeM9XClbOxwoATuFdOE7ZE6wHL+HQ5T8hoPM+zHvmKzzsEqhgy0GrQ5X13afiQ==
+  version "5.3.0"
+  resolved "https://registry.yarnpkg.com/estraverse/-/estraverse-5.3.0.tgz#2eea5290702f26ab8fe5370370ff86c965d21123"
+  integrity sha512-MMdARuVEQziNTeJD8DgMqmhwR11BRQ/cBP+pLtYdSTnf3MIO8fFeiINEbX36ZdNlfU/7A9f3gUw49B3oQsvwBA==
 
 events@^3.2.0:
   version "3.3.0"
@@ -1368,9 +1398,9 @@ fast-deep-equal@^3.1.1:
   integrity sha512-f3qQ9oQy9j2AhBe/H9VC91wLmKBCCU/gDOnKNAYG5hswO7BLKj09Hc5HYNz9cGI++xlpDCIgDaitVs03ATR84Q==
 
 fast-glob@^3.0.3:
-  version "3.2.7"
-  resolved "https://registry.yarnpkg.com/fast-glob/-/fast-glob-3.2.7.tgz#fd6cb7a2d7e9aa7a7846111e85a196d6b2f766a1"
-  integrity sha512-rYGMRwip6lUMvYD3BTScMwT1HtAs2d71SMv66Vrxs0IekGZEjhM0pcMfjQPnknBt2zeCwQMEupiN02ZP4DiT1Q==
+  version "3.2.11"
+  resolved "https://registry.yarnpkg.com/fast-glob/-/fast-glob-3.2.11.tgz#a1172ad95ceb8a16e20caa5c5e56480e5129c1d9"
+  integrity sha512-xrO3+1bxSo3ZVHAnqzyuewYT6aMFHRAd4Kcs92MAonjwQZLsK9d0SF1IyQ3k5PoirxTW0Oe/RqFgMQ6TcNE5Ew==
   dependencies:
     "@nodelib/fs.stat" "^2.0.2"
     "@nodelib/fs.walk" "^1.2.3"
@@ -1389,9 +1419,9 @@ fastest-levenshtein@^1.0.12:
   integrity sha512-On2N+BpYJ15xIC974QNVuYGMOlEVt4s0EOI3wwMqOmK1fdDY+FN/zltPV8vosq4ad4c/gJ1KHScUn/6AWIgiow==
 
 fastq@^1.6.0:
-  version "1.11.1"
-  resolved "https://registry.yarnpkg.com/fastq/-/fastq-1.11.1.tgz#5d8175aae17db61947f8b162cfc7f63264d22807"
-  integrity sha512-HOnr8Mc60eNYl1gzwp6r5RoUyAn5/glBolUzP/Ez6IFVPMPirxn/9phgL6zhOtaTy7ISwPvQ+wT+hfcRZh/bzw==
+  version "1.13.0"
+  resolved "https://registry.yarnpkg.com/fastq/-/fastq-1.13.0.tgz#616760f88a7526bdfc596b7cab8c18938c36b98c"
+  integrity sha512-YpkpUnK8od0o1hmeSc7UUs/eB/vIPWJYjKck2QKIzAf71Vm1AAQ3EbuZB3g2JIy+pg+ERD0vqI79KyZiB2e2Nw==
   dependencies:
     reusify "^1.0.4"
 
@@ -1418,9 +1448,9 @@ fill-range@^7.0.1:
     to-regex-range "^5.0.1"
 
 find-cache-dir@^3.3.1:
-  version "3.3.1"
-  resolved "https://registry.yarnpkg.com/find-cache-dir/-/find-cache-dir-3.3.1.tgz#89b33fad4a4670daa94f855f7fbe31d6d84fe880"
-  integrity sha512-t2GDMt3oGC/v+BMwzmllWDuJF/xcDtE5j/fCGbqDD7OLuJkj0cfh1YSA5VKPvwMeLFLNDBkwOKZ2X85jGLVftQ==
+  version "3.3.2"
+  resolved "https://registry.yarnpkg.com/find-cache-dir/-/find-cache-dir-3.3.2.tgz#b30c5b6eff0730731aea9bbd9dbecbd80256d64b"
+  integrity sha512-wXZV5emFEjrridIgED11OoUKLxiYjAcqot/NJdAkOhlJ+vGzwhOAfcG5OX1jP+S0PcjEn8bdMJv+g2jwQ3Onig==
   dependencies:
     commondir "^1.0.1"
     make-dir "^3.0.2"
@@ -1516,7 +1546,19 @@ glob-to-regexp@^0.4.1:
   resolved "https://registry.yarnpkg.com/glob-to-regexp/-/glob-to-regexp-0.4.1.tgz#c75297087c851b9a578bd217dd59a92f59fe546e"
   integrity sha512-lkX1HJXwyMcprw/5YUZc2s7DrpAiHB21/V+E1rHUrVNokkvB6bqMzT0VfV6/86ZNabt1k14YOIaT7nDvOX3Iiw==
 
-glob@^7.1.3, glob@^7.1.4, glob@~7.1.6:
+glob@^7.1.3, glob@^7.1.4:
+  version "7.2.0"
+  resolved "https://registry.yarnpkg.com/glob/-/glob-7.2.0.tgz#d15535af7732e02e948f4c41628bd910293f6023"
+  integrity sha512-lmLf6gtyrPq8tTjSmrO94wBeQbFR3HbLHbuyD69wuyQkImp2hWqMGB47OX65FBkPffO641IP9jWa1z4ivqG26Q==
+  dependencies:
+    fs.realpath "^1.0.0"
+    inflight "^1.0.4"
+    inherits "2"
+    minimatch "^3.0.4"
+    once "^1.3.0"
+    path-is-absolute "^1.0.0"
+
+glob@~7.1.6:
   version "7.1.7"
   resolved "https://registry.yarnpkg.com/glob/-/glob-7.1.7.tgz#3b193e9233f01d42d0b3f78294bbeeb418f94a90"
   integrity sha512-OvD9ENzPLbegENnYP5UUfJIirTg4+XwMWGaQfQTY0JenxNvvIKP3U3/tAQSPIu/lHxXYSZmpXlUHeqAIdKzBLQ==
@@ -1559,10 +1601,10 @@ got@^9.6.0:
     to-readable-stream "^1.0.0"
     url-parse-lax "^3.0.0"
 
-graceful-fs@^4.1.2, graceful-fs@^4.1.6, graceful-fs@^4.2.0, graceful-fs@^4.2.4:
-  version "4.2.6"
-  resolved "https://registry.yarnpkg.com/graceful-fs/-/graceful-fs-4.2.6.tgz#ff040b2b0853b23c3d31027523706f1885d76bee"
-  integrity sha512-nTnJ528pbqxYanhpDYsi4Rd8MAeaBA67+RZ10CM1m3bTAVFEDcd5AuA4a6W5YkGZ1iNXHzZz8T6TBKLeBuNriQ==
+graceful-fs@^4.1.2, graceful-fs@^4.1.6, graceful-fs@^4.2.0, graceful-fs@^4.2.4, graceful-fs@^4.2.9:
+  version "4.2.10"
+  resolved "https://registry.yarnpkg.com/graceful-fs/-/graceful-fs-4.2.10.tgz#147d3a006da4ca3ce14728c7aefc287c367d7a6c"
+  integrity sha512-9ByhssR2fPVsNZj478qUUbKfmL0+t5BDVyjShtyZZLiK7ZDAArFFfopyOTj0M05wE2tJPisA4iTnnXl2YoPvOA==
 
 gud@^1.0.0:
   version "1.0.0"
@@ -1580,9 +1622,16 @@ has-flag@^4.0.0:
   integrity sha512-EykJT/Q1KjTWctppgIAgfSO0tKVuZUjhgMr17kqTumMl6Afv3EISleU7qZUzoXDFTAHTDC4NOoG/ZxU3EvlMPQ==
 
 has-symbols@^1.0.1, has-symbols@^1.0.2:
-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/has-symbols/-/has-symbols-1.0.2.tgz#165d3070c00309752a1236a479331e3ac56f1423"
-  integrity sha512-chXa79rL/UC2KlX17jo3vRGz0azaWEx5tGqZg5pO3NUyEJVB17dMruQlzCCOfUvElghKcm5194+BCRvi2Rv/Gw==
+  version "1.0.3"
+  resolved "https://registry.yarnpkg.com/has-symbols/-/has-symbols-1.0.3.tgz#bb7b2c4349251dce87b125f7bdf874aa7c8b39f8"
+  integrity sha512-l3LCuF6MgDNwTDKkdYGEihYjt5pRPbEg46rtlmnSPlUbgmB8LOIrKJbYYFBSbnPaJexMKtiPO8hmeRjRz2Td+A==
+
+has-tostringtag@^1.0.0:
+  version "1.0.0"
+  resolved "https://registry.yarnpkg.com/has-tostringtag/-/has-tostringtag-1.0.0.tgz#7e133818a7d394734f941e73c3d3f9291e658b25"
+  integrity sha512-kFjcSNhnlGV1kyoGk7OXKSawH5JOb/LzUc5w9B02hOTO0dfFRjbHQKvg1d6cf3HbeUmtU9VbbV3qzZ2Teh97WQ==
+  dependencies:
+    has-symbols "^1.0.2"
 
 has@^1.0.3:
   version "1.0.3"
@@ -1629,14 +1678,14 @@ ieee754@^1.1.13:
   integrity sha512-dcyqhDvX1C46lXZcVqCpK+FtMRQVdIMN6/Df5js2zouUsqG7I6sFxitIC+7KYK29KdXOLHdu9zL4sFnoVQnqaA==
 
 ignore@^5.1.1:
-  version "5.1.8"
-  resolved "https://registry.yarnpkg.com/ignore/-/ignore-5.1.8.tgz#f150a8b50a34289b33e22f5889abd4d8016f0e57"
-  integrity sha512-BMpfD7PpiETpBl/A6S498BaIJ6Y/ABT93ETbby2fP00v4EbvPBXWEoaR1UBPKs3iR53pJY7EtZk5KACI57i1Uw==
+  version "5.2.0"
+  resolved "https://registry.yarnpkg.com/ignore/-/ignore-5.2.0.tgz#6d3bac8fa7fe0d45d9f9be7bac2fc279577e345a"
+  integrity sha512-CmxgYGiEPCLhfLnpPp1MoRmifwEIOgjcHXxOBjv7mY96c+eWScsOP9c112ZyLdWHi0FxHjI+4uVhKYp/gcdRmQ==
 
 import-local@^3.0.2:
-  version "3.0.2"
-  resolved "https://registry.yarnpkg.com/import-local/-/import-local-3.0.2.tgz#a8cfd0431d1de4a2199703d003e3e62364fa6db6"
-  integrity sha512-vjL3+w0oulAVZ0hBHnxa/Nm5TAurf9YLQJDhqRZyqb+VKGOB6LU8t9H1Nr5CIo16vh9XfJTOoHwU0B71S557gA==
+  version "3.1.0"
+  resolved "https://registry.yarnpkg.com/import-local/-/import-local-3.1.0.tgz#b4479df8a5fd44f6cdce24070675676063c95cb4"
+  integrity sha512-ASB07uLtnDs1o6EHjKpX34BKYDSqnFerfTOJL2HvMqF70LnxpjkzDB8J44oT9pu4AMPkQwf8jl6szgvNd2tRIg==
   dependencies:
     pkg-dir "^4.2.0"
     resolve-cwd "^3.0.0"
@@ -1699,23 +1748,26 @@ interpret@^2.2.0:
   integrity sha512-Ju0Bz/cEia55xDwUWEa8+olFpCiQoypjnQySseKtmjNrnps3P+xfpUmGr90T7yjlVJmOtybRvPXhKMbHr+fWnw==
 
 is-arguments@^1.0.4:
-  version "1.1.0"
-  resolved "https://registry.yarnpkg.com/is-arguments/-/is-arguments-1.1.0.tgz#62353031dfbee07ceb34656a6bde59efecae8dd9"
-  integrity sha512-1Ij4lOMPl/xB5kBDn7I+b2ttPMKa8szhEIrXDuXQD/oe3HJLTLhqhgGspwgyGd6MOywBUqVvYicF72lkgDnIHg==
+  version "1.1.1"
+  resolved "https://registry.yarnpkg.com/is-arguments/-/is-arguments-1.1.1.tgz#15b3f88fda01f2a97fec84ca761a560f123efa9b"
+  integrity sha512-8Q7EARjzEnKpt/PCD7e1cgUS0a6X8u5tdSiMqXhojOdoV9TsMsiO+9VLC5vAmO8N7/GmXn7yjR8qnA6bVAEzfA==
   dependencies:
-    call-bind "^1.0.0"
+    call-bind "^1.0.2"
+    has-tostringtag "^1.0.0"
 
-is-core-module@^2.2.0:
-  version "2.5.0"
-  resolved "https://registry.yarnpkg.com/is-core-module/-/is-core-module-2.5.0.tgz#f754843617c70bfd29b7bd87327400cda5c18491"
-  integrity sha512-TXCMSDsEHMEEZ6eCA8rwRDbLu55MRGmrctljsBX/2v1d9/GzqHOxW5c5oPSgrUt2vBFXebu9rGqckXGPWOlYpg==
+is-core-module@^2.8.1:
+  version "2.8.1"
+  resolved "https://registry.yarnpkg.com/is-core-module/-/is-core-module-2.8.1.tgz#f59fdfca701d5879d0a6b100a40aa1560ce27211"
+  integrity sha512-SdNCUs284hr40hFTFP6l0IfZ/RSrMXF3qgoRHd3/79unUTvrFO/JoXwkGm+5J/Oe3E/b5GsnG330uUNgRpu1PA==
   dependencies:
     has "^1.0.3"
 
 is-date-object@^1.0.1:
-  version "1.0.4"
-  resolved "https://registry.yarnpkg.com/is-date-object/-/is-date-object-1.0.4.tgz#550cfcc03afada05eea3dd30981c7b09551f73e5"
-  integrity sha512-/b4ZVsG7Z5XVtIxs/h9W8nvfLgSAyKYdtGWQLbqy6jA1icmgjf8WCoTKgeS4wy5tYaPePouzFMANbnj94c2Z+A==
+  version "1.0.5"
+  resolved "https://registry.yarnpkg.com/is-date-object/-/is-date-object-1.0.5.tgz#0841d5536e724c25597bf6ea62e1bd38298df31f"
+  integrity sha512-9YQaSxsAiSwcvS33MBk3wTCVnWK+HhF8VZR2jRxehM16QcVOdHqPn4VPHmRK4lSr38n9JriurInLcP90xsYNfQ==
+  dependencies:
+    has-tostringtag "^1.0.0"
 
 is-extglob@^2.1.1:
   version "2.1.1"
@@ -1728,9 +1780,9 @@ is-fullwidth-code-point@^3.0.0:
   integrity sha512-zymm5+u+sCsSWyD9qNaejV3DFvhCKclKdizYaJUuHA83RLjb7nSuGnddCHGv0hk+KY7BMAlsWeK4Ueg6EV6XQg==
 
 is-glob@^4.0.1:
-  version "4.0.1"
-  resolved "https://registry.yarnpkg.com/is-glob/-/is-glob-4.0.1.tgz#7567dbe9f2f5e2467bc77ab83c4a29482407a5dc"
-  integrity sha512-5G0tKtBTFImOqDnLB2hG6Bp2qcKEFduo4tZu9MT/H6NQv/ghhy30o55ufafxJ/LdH79LLs2Kfrn85TLKyA7BUg==
+  version "4.0.3"
+  resolved "https://registry.yarnpkg.com/is-glob/-/is-glob-4.0.3.tgz#64f61e42cbbb2eec2071a9dac0b28ba1e65d5084"
+  integrity sha512-xelSayHH36ZgE7ZWhli7pW34hNbNl8Ojv5KVmkJD4hBdD3th8Tfk9vYasLM+mXWOZhFkgZfxhLSnrwRr4elSSg==
   dependencies:
     is-extglob "^2.1.1"
 
@@ -1757,17 +1809,17 @@ is-plain-object@^5.0.0:
   integrity sha512-VRSzKkbMm5jMDoKLbltAkFQ5Qr7VDiTFGXxYFXXowVj387GeGNOCsOH6Msy00SGZ3Fp84b1Naa1psqgcCIEP5Q==
 
 is-regex@^1.0.4:
-  version "1.1.3"
-  resolved "https://registry.yarnpkg.com/is-regex/-/is-regex-1.1.3.tgz#d029f9aff6448b93ebbe3f33dac71511fdcbef9f"
-  integrity sha512-qSVXFz28HM7y+IWX6vLCsexdlvzT1PJNFSBuaQLQ5o0IEw8UDYW6/2+eCMVyIsbM8CNLX2a/QWmSpyxYEHY7CQ==
+  version "1.1.4"
+  resolved "https://registry.yarnpkg.com/is-regex/-/is-regex-1.1.4.tgz#eef5663cd59fa4c0ae339505323df6854bb15958"
+  integrity sha512-kvRdxDsxZjhzUX07ZnLydzS1TU/TJlTUHHY4YLL87e37oUA49DfkLqgy+VjFocowy29cKvcSiu+kIv728jTTVg==
   dependencies:
     call-bind "^1.0.2"
-    has-symbols "^1.0.2"
+    has-tostringtag "^1.0.0"
 
 is-stream@^2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/is-stream/-/is-stream-2.0.0.tgz#bde9c32680d6fae04129d6ac9d921ce7815f78e3"
-  integrity sha512-XCoy+WlUr7d1+Z8GgSuXmpuUFC9fOhRXglJMx+dwLKTkL44Cjd4W1Z5P+BQZpr+cR93aGP4S/s7Ftw6Nd/kiEw==
+  version "2.0.1"
+  resolved "https://registry.yarnpkg.com/is-stream/-/is-stream-2.0.1.tgz#fac1e3d53b97ad5a9d0ae9cef2389f5810a5c077"
+  integrity sha512-hFoiJiTl63nn+kstHGBtewWSKnQLpyb155KHheA1l39uvtO9nWIop1p3udqPcUd/xbF1VLMO4n7OI6p7RbngDg==
 
 isexe@^2.0.0:
   version "2.0.0"
@@ -1788,10 +1840,10 @@ jest-worker@^26.5.0:
     merge-stream "^2.0.0"
     supports-color "^7.0.0"
 
-jest-worker@^27.0.2:
-  version "27.0.6"
-  resolved "https://registry.yarnpkg.com/jest-worker/-/jest-worker-27.0.6.tgz#a5fdb1e14ad34eb228cfe162d9f729cdbfa28aed"
-  integrity sha512-qupxcj/dRuA3xHPMUd40gr2EaAurFbkwzOh7wfPaeE9id7hyjURRQoqNfHifHK3XjJU6YJJUQKILGUnwGPEOCA==
+jest-worker@^27.4.5:
+  version "27.5.1"
+  resolved "https://registry.yarnpkg.com/jest-worker/-/jest-worker-27.5.1.tgz#8d146f0900e8973b106b6f73cc1e9a8cb86f8db0"
+  integrity sha512-7vuh85V5cdDofPyxn58nrPjBktZo0u9x1g8WtjQol+jZDaE+fhN+cIvTj11GndBnMnyfrUOG1sZQxCdjKh+DKg==
   dependencies:
     "@types/node" "*"
     merge-stream "^2.0.0"
@@ -1825,11 +1877,9 @@ json5@^1.0.1:
     minimist "^1.2.0"
 
 json5@^2.1.1, json5@^2.1.2:
-  version "2.2.0"
-  resolved "https://registry.yarnpkg.com/json5/-/json5-2.2.0.tgz#2dfefe720c6ba525d9ebd909950f0515316c89a3"
-  integrity sha512-f+8cldu7X/y7RAJurMEJmdoKXGB/X550w2Nr3tTbezL6RwEE/iMcm+tZnXeoZtKuOq6ft8+CqzEkrIgx1fPoQA==
-  dependencies:
-    minimist "^1.2.5"
+  version "2.2.1"
+  resolved "https://registry.yarnpkg.com/json5/-/json5-2.2.1.tgz#655d50ed1e6f95ad1a3caababd2b0efda10b395c"
+  integrity sha512-1hqLFMSrGHRHxav9q9gNjJ5EXznIxGVO09xQRrwplcS8qs28pZ8s8hupZAmqDwZUmVZ2Qb2jnyPOWcDH8m8dlA==
 
 jsonfile@^6.0.1:
   version "6.1.0"
@@ -1853,9 +1903,9 @@ kind-of@^6.0.2:
   integrity sha512-dcS1ul+9tmeD95T+x28/ehLgd9mENa3LsvDTtzm3vyBEO7RPptvAD+t44WVXaUjTBRcrpFeFlC8WCruUR456hw==
 
 klona@^2.0.3:
-  version "2.0.4"
-  resolved "https://registry.yarnpkg.com/klona/-/klona-2.0.4.tgz#7bb1e3affb0cb8624547ef7e8f6708ea2e39dfc0"
-  integrity sha512-ZRbnvdg/NxqzC7L9Uyqzf4psi1OM4Cuc+sJAkQPjO6XkQIJTNbfK2Rsmbw8fx1p2mkZdp2FZYo2+LwXYY/uwIA==
+  version "2.0.5"
+  resolved "https://registry.yarnpkg.com/klona/-/klona-2.0.5.tgz#d166574d90076395d9963aa7a928fabb8d76afbc"
+  integrity sha512-pJiBpiXMbt7dkzXe8Ghj/u4FfXOOa98fPW+bihOJ4SjnoijweJrNThJfd3ifXpXhREjpoF2mZVH1GfS9LV3kHQ==
 
 loader-runner@^4.2.0:
   version "4.2.0"
@@ -1872,9 +1922,9 @@ loader-utils@^1.0.0:
     json5 "^1.0.1"
 
 loader-utils@^2.0.0, loader-utils@~2.0.0:
-  version "2.0.0"
-  resolved "https://registry.yarnpkg.com/loader-utils/-/loader-utils-2.0.0.tgz#e4cace5b816d425a166b5f097e10cd12b36064b0"
-  integrity sha512-rP4F0h2RaWSvPEkD7BLDFQnvSf+nK+wr3ESUjNTyAGobqrijmW92zc+SO6d4p4B1wh7+B/Jg1mkQe5NYUEHtHQ==
+  version "2.0.2"
+  resolved "https://registry.yarnpkg.com/loader-utils/-/loader-utils-2.0.2.tgz#d6e3b4fb81870721ae4e0868ab11dd638368c129"
+  integrity sha512-TM57VeHptv569d/GKh6TAYdzKblwDNiumOdkFnejjD0XwTH87K90w3O7AiJRqdQoXygvi1VQTJTLGhJl7WqA7A==
   dependencies:
     big.js "^5.2.2"
     emojis-list "^3.0.0"
@@ -1944,24 +1994,24 @@ merge2@^1.2.3, merge2@^1.3.0:
   integrity sha512-8q7VEgMJW4J8tcfVPy8g09NcQwZdbwFEqhe/WZkoIzjn/3TGDwtOCYtXGxA3O8tPzpczCCDgv+P2P5y00ZJOOg==
 
 micromatch@^4.0.4:
-  version "4.0.4"
-  resolved "https://registry.yarnpkg.com/micromatch/-/micromatch-4.0.4.tgz#896d519dfe9db25fce94ceb7a500919bf881ebf9"
-  integrity sha512-pRmzw/XUcwXGpD9aI9q/0XOwLNygjETJ8y0ao0wdqprrzDa4YnxLcz7fQRZr8voh8V10kGhABbNcHVk5wHgWwg==
+  version "4.0.5"
+  resolved "https://registry.yarnpkg.com/micromatch/-/micromatch-4.0.5.tgz#bc8999a7cbbf77cdc89f132f6e467051b49090c6"
+  integrity sha512-DMy+ERcEW2q8Z2Po+WNXuw3c5YaUSFjAO5GsJqfEl7UjvtIuFKO6ZrKvcItdy98dwFI2N1tg3zNIdKaQT+aNdA==
   dependencies:
-    braces "^3.0.1"
-    picomatch "^2.2.3"
+    braces "^3.0.2"
+    picomatch "^2.3.1"
 
-mime-db@1.48.0:
-  version "1.48.0"
-  resolved "https://registry.yarnpkg.com/mime-db/-/mime-db-1.48.0.tgz#e35b31045dd7eada3aaad537ed88a33afbef2d1d"
-  integrity sha512-FM3QwxV+TnZYQ2aRqhlKBMHxk10lTbMt3bBkMAp54ddrNeVSfcQYOOKuGuy3Ddrm38I04If834fOUSq1yzslJQ==
+mime-db@1.52.0:
+  version "1.52.0"
+  resolved "https://registry.yarnpkg.com/mime-db/-/mime-db-1.52.0.tgz#bbabcdc02859f4987301c856e3387ce5ec43bf70"
+  integrity sha512-sPU4uV7dYlvtWJxwwxHD0PuihVNiE7TyAbQ5SWxDCB9mUYvOgroQOwYQQOKPJ8CIbE+1ETVlOoK1UC2nU3gYvg==
 
 mime-types@^2.1.27:
-  version "2.1.31"
-  resolved "https://registry.yarnpkg.com/mime-types/-/mime-types-2.1.31.tgz#a00d76b74317c61f9c2db2218b8e9f8e9c5c9e6b"
-  integrity sha512-XGZnNzm3QvgKxa8dpzyhFTHmpP3l5YNusmne07VUOXxou9CqUqYa/HBy124RqtVh/O2pECas/MOcsDgpilPOPg==
+  version "2.1.35"
+  resolved "https://registry.yarnpkg.com/mime-types/-/mime-types-2.1.35.tgz#381a871b62a734450660ae3deee44813f70d959a"
+  integrity sha512-ZDY+bPm5zTTF+YpCrAU9nK0UgICYPT0QtT1NZWFv4s++TNkcgVaT0g6+4R2uI4MjQjzysHB1zxuWL50hzaeXiw==
   dependencies:
-    mime-db "1.48.0"
+    mime-db "1.52.0"
 
 mimic-fn@^2.1.0:
   version "2.1.0"
@@ -1983,16 +2033,16 @@ mini-css-extract-plugin@~1.3.2:
     webpack-sources "^1.1.0"
 
 minimatch@^3.0.4:
-  version "3.0.4"
-  resolved "https://registry.yarnpkg.com/minimatch/-/minimatch-3.0.4.tgz#5166e286457f03306064be5497e8dbb0c3d32083"
-  integrity sha512-yJHVQEhyqPLUTgt9B83PXu6W3rx4MvvHvSUvToogpwoGDOUQ+yDrR0HRot+yOCdCO7u4hX3pWft6kWBBcqh0UA==
+  version "3.1.2"
+  resolved "https://registry.yarnpkg.com/minimatch/-/minimatch-3.1.2.tgz#19cd194bfd3e428f049a70817c038d89ab4be35b"
+  integrity sha512-J7p63hRiAjw1NDEww1W7i37+ByIrOWO5XQQAzZ3VOcL0PNybwpfmV/N05zFAzwQ9USyEcX6t3UO+K5aqBQOIHw==
   dependencies:
     brace-expansion "^1.1.7"
 
-minimist@^1.2.0, minimist@^1.2.5, minimist@~1.2.0:
-  version "1.2.5"
-  resolved "https://registry.yarnpkg.com/minimist/-/minimist-1.2.5.tgz#67d66014b66a6a8aaa0c083c5fd58df4e4e97602"
-  integrity sha512-FM9nNUYrRBAELZQT3xeZQ7fmMOBg6nWNmJKTcgsJeaLstP/UODVpGsr5OhXhhXg6f+qtJ8uiZ+PUxkDWcgIXLw==
+minimist@^1.2.0, minimist@~1.2.0:
+  version "1.2.6"
+  resolved "https://registry.yarnpkg.com/minimist/-/minimist-1.2.6.tgz#8637a5b759ea0d6e98702cfb3a9283323c93af44"
+  integrity sha512-Jsjnk4bw3YJqYzbdyBiNsPWHPfO++UGG749Cxs6peCu5Xg4nrena6OVxOYxrQTqww0Jmwt+Ref8rggumkTLz9Q==
 
 minipass-collect@^1.0.2:
   version "1.0.2"
@@ -2016,9 +2066,9 @@ minipass-pipeline@^1.2.2:
     minipass "^3.0.0"
 
 minipass@^3.0.0, minipass@^3.1.1:
-  version "3.1.3"
-  resolved "https://registry.yarnpkg.com/minipass/-/minipass-3.1.3.tgz#7d42ff1f39635482e15f9cdb53184deebd5815fd"
-  integrity sha512-Mgd2GdMVzY+x3IJ+oHnVM+KG3lA5c8tnabyJKmHSaG2kAGpudxuOf8ToDkhumF7UzME7DecbQE9uOZhNm7PuJg==
+  version "3.1.6"
+  resolved "https://registry.yarnpkg.com/minipass/-/minipass-3.1.6.tgz#3b8150aa688a711a1521af5e8779c1d3bb4f45ee"
+  integrity sha512-rty5kpw9/z8SX9dmxblFA6edItUmwJgMeYDZRrwlIVN27i8gysGbznJwUggw2V/FVqFSDdWy040ZPS811DYAqQ==
   dependencies:
     yallist "^4.0.0"
 
@@ -2036,19 +2086,19 @@ mkdirp@^1.0.3, mkdirp@^1.0.4:
   integrity sha512-vVqVZQyf3WLx2Shd0qJ9xuvqgAyKPLAiqITEtqW0oIUjzo3PePDd6fW9iFz30ef7Ysp/oiWqbhszeGWW2T6Gzw==
 
 moment@^2.24.0:
-  version "2.29.1"
-  resolved "https://registry.yarnpkg.com/moment/-/moment-2.29.1.tgz#b2be769fa31940be9eeea6469c075e35006fa3d3"
-  integrity sha512-kHmoybcPV8Sqy59DwNDY3Jefr64lK/by/da0ViFcuA4DH0vQg5Q6Ze5VimxkfQNSC+Mls/Kx53s7TjP1RhFEDQ==
+  version "2.29.2"
+  resolved "https://registry.yarnpkg.com/moment/-/moment-2.29.2.tgz#00910c60b20843bcba52d37d58c628b47b1f20e4"
+  integrity sha512-UgzG4rvxYpN15jgCmVJwac49h9ly9NurikMWGPdVxm8GZD6XjkKPxDTjQQ43gtGgnV3X0cAyWDdP2Wexoquifg==
 
 mute-stream@0.0.8:
   version "0.0.8"
   resolved "https://registry.yarnpkg.com/mute-stream/-/mute-stream-0.0.8.tgz#1630c42b2251ff81e2a283de96a5497ea92e5e0d"
   integrity sha512-nnbWWOkoWyUsTjKrhgD0dcz22mdkSnpYqbEjIm2nhwhuxlSkpywJmBo8h0ZqJdkp73mb90SssHkN4rsRaBAfAA==
 
-nanoid@^3.1.23:
-  version "3.2.0"
-  resolved "https://registry.yarnpkg.com/nanoid/-/nanoid-3.2.0.tgz#62667522da6673971cca916a6d3eff3f415ff80c"
-  integrity sha512-fmsZYa9lpn69Ad5eDn7FMcnnSR+8R34W9qJEijxYhTbfOWzr22n1QxCMzXLK+ODyW2973V3Fux959iQoUxzUIA==
+nanoid@^3.3.1:
+  version "3.3.2"
+  resolved "https://registry.yarnpkg.com/nanoid/-/nanoid-3.3.2.tgz#c89622fafb4381cd221421c69ec58547a1eec557"
+  integrity sha512-CuHBogktKwpm5g2sRgv83jEy2ijFzBwMoYA60orPDR7ynsLijJDqgsi4RDGj3OJpy3Ieb+LYwiRmIOGyytgITA==
 
 neo-async@^2.6.2:
   version "2.6.2"
@@ -2062,10 +2112,10 @@ node-fetch@^2.6.0:
   dependencies:
     whatwg-url "^5.0.0"
 
-node-releases@^1.1.71:
-  version "1.1.73"
-  resolved "https://registry.yarnpkg.com/node-releases/-/node-releases-1.1.73.tgz#dd4e81ddd5277ff846b80b52bb40c49edf7a7b20"
-  integrity sha512-uW7fodD6pyW2FZNZnp/Z3hvWKeEW1Y8R1+1CnErE8cXFXzl5blBOoVB41CvMer6P6Q0S5FXDwcHgFd1Wj0U9zg==
+node-releases@^2.0.2:
+  version "2.0.2"
+  resolved "https://registry.yarnpkg.com/node-releases/-/node-releases-2.0.2.tgz#7139fe71e2f4f11b47d4d2986aaf8c48699e0c01"
+  integrity sha512-XxYDdcQ6eKqp/YjI+tb2C5WM2LgjnZrfYg4vgQt49EK268b6gYCHsBLrK2qvJo4FmCtqmKezb0WZFK4fkrZNsg==
 
 normalize-url@^4.1.0:
   version "4.5.1"
@@ -2133,7 +2183,7 @@ p-limit@^2.2.0:
   dependencies:
     p-try "^2.0.0"
 
-p-limit@^3.0.2, p-limit@^3.1.0:
+p-limit@^3.0.2:
   version "3.1.0"
   resolved "https://registry.yarnpkg.com/p-limit/-/p-limit-3.1.0.tgz#e1daccbe78d0d1388ca18c64fea38e3e57e3706b"
   integrity sha512-TYOanM3wGwNGsZN2cVTYPArw454xnXj5qmWF1bEoAc4+cU/ol7GVh7odevjp1FNHduHc3KZMcFduxU5Xc6uJRQ==
@@ -2194,7 +2244,7 @@ path-key@^3.0.0, path-key@^3.1.0:
   resolved "https://registry.yarnpkg.com/path-key/-/path-key-3.1.1.tgz#581f6ade658cbba65a0d3380de7753295054f375"
   integrity sha512-ojmeN0qd+y0jszEtoY48r0Peq5dwMEkIlCOu6Q5f41lfkswXuKtYrhgoTpLnyIcHm24Uhqx+5Tqm2InSwLhE6Q==
 
-path-parse@^1.0.6:
+path-parse@^1.0.7:
   version "1.0.7"
   resolved "https://registry.yarnpkg.com/path-parse/-/path-parse-1.0.7.tgz#fbc114b60ca42b30d9daf5858e4bd68bbedb6735"
   integrity sha512-LDJzPVEEEPR+y48z93A0Ed0yXb8pAByGWo/k5YYdYgpY2/2EsOsksJrq7lOHxryrVOn1ejG6oAp8ahvOIQD8sw==
@@ -2204,10 +2254,15 @@ path-type@^4.0.0:
   resolved "https://registry.yarnpkg.com/path-type/-/path-type-4.0.0.tgz#84ed01c0a7ba380afe09d90a8c180dcd9d03043b"
   integrity sha512-gDKb8aZMDeD/tZWs9P6+q0J9Mwkdl6xMV8TjnGP3qJVJ06bdMgkbBlLU8IdfOsIsFz2BW1rNVT3XuNEl8zPAvw==
 
-picomatch@^2.2.3:
-  version "2.3.0"
-  resolved "https://registry.yarnpkg.com/picomatch/-/picomatch-2.3.0.tgz#f1f061de8f6a4bf022892e2d128234fb98302972"
-  integrity sha512-lY1Q/PiJGC2zOv/z391WOTD+Z02bCgsFfvxoXXf6h7kv9o+WmsmzYqrAwY63sNgOxE4xEdq0WyUnXfKeBrSvYw==
+picocolors@^1.0.0:
+  version "1.0.0"
+  resolved "https://registry.yarnpkg.com/picocolors/-/picocolors-1.0.0.tgz#cb5bdc74ff3f51892236eaf79d68bc44564ab81c"
+  integrity sha512-1fygroTLlHu66zi26VoTDv8yRgm0Fccecssto+MhsZ0D/DGW2sm8E8AjW7NU5VVTRt5GxbeZ5qBuJr+HyLYkjQ==
+
+picomatch@^2.3.1:
+  version "2.3.1"
+  resolved "https://registry.yarnpkg.com/picomatch/-/picomatch-2.3.1.tgz#3ba3833733646d9d3e4995946c1365a67fb07a42"
+  integrity sha512-JU3teHTNjmE2VCGFzuY8EXzCDVwEqB2a8fsIvwaStHhAWJEeVd1o1QD80CU6+ZdEXXSLbSsuLwJjkCBWqRQUVA==
 
 pkg-dir@^4.1.0, pkg-dir@^4.2.0:
   version "4.2.0"
@@ -2250,26 +2305,26 @@ postcss-modules-values@^4.0.0:
     icss-utils "^5.0.0"
 
 postcss-selector-parser@^6.0.2, postcss-selector-parser@^6.0.4:
-  version "6.0.6"
-  resolved "https://registry.yarnpkg.com/postcss-selector-parser/-/postcss-selector-parser-6.0.6.tgz#2c5bba8174ac2f6981ab631a42ab0ee54af332ea"
-  integrity sha512-9LXrvaaX3+mcv5xkg5kFwqSzSH1JIObIx51PrndZwlmznwXRfxMddDvo9gve3gVR8ZTKgoFDdWkbRFmEhT4PMg==
+  version "6.0.10"
+  resolved "https://registry.yarnpkg.com/postcss-selector-parser/-/postcss-selector-parser-6.0.10.tgz#79b61e2c0d1bfc2602d549e11d0876256f8df88d"
+  integrity sha512-IQ7TZdoaqbT+LCpShg46jnZVlhWD2w6iQYAcYXfHARZ7X1t/UGhhceQDs5X0cGqKvYlHNOuv7Oa1xmb0oQuA3w==
   dependencies:
     cssesc "^3.0.0"
     util-deprecate "^1.0.2"
 
 postcss-value-parser@^4.1.0:
-  version "4.1.0"
-  resolved "https://registry.yarnpkg.com/postcss-value-parser/-/postcss-value-parser-4.1.0.tgz#443f6a20ced6481a2bda4fa8532a6e55d789a2cb"
-  integrity sha512-97DXOFbQJhk71ne5/Mt6cOu6yxsSfM0QGQyl0L25Gca4yGWEGJaig7l7gbCX623VqTBNGLRLaVUCnNkcedlRSQ==
+  version "4.2.0"
+  resolved "https://registry.yarnpkg.com/postcss-value-parser/-/postcss-value-parser-4.2.0.tgz#723c09920836ba6d3e5af019f92bc0971c02e514"
+  integrity sha512-1NNCs6uurfkVbeXG4S8JFT9t19m45ICnif8zWLd5oPSZ50QnwMfK+H3jv408d4jw/7Bttv5axS5IiHoLaVNHeQ==
 
 postcss@^8.0.2, postcss@^8.2.15:
-  version "8.3.5"
-  resolved "https://registry.yarnpkg.com/postcss/-/postcss-8.3.5.tgz#982216b113412bc20a86289e91eb994952a5b709"
-  integrity sha512-NxTuJocUhYGsMiMFHDUkmjSKT3EdH4/WbGF6GCi1NDGk+vbcUTun4fpbOqaPtD8IIsztA2ilZm2DhYCuyN58gA==
+  version "8.4.12"
+  resolved "https://registry.yarnpkg.com/postcss/-/postcss-8.4.12.tgz#1e7de78733b28970fa4743f7da6f3763648b1905"
+  integrity sha512-lg6eITwYe9v6Hr5CncVbK70SoioNQIq81nsaG86ev5hAidQvmOeETBqs7jm43K2F5/Ley3ytDtriImV6TpNiSg==
   dependencies:
-    colorette "^1.2.2"
-    nanoid "^3.1.23"
-    source-map-js "^0.6.2"
+    nanoid "^3.3.1"
+    picocolors "^1.0.0"
+    source-map-js "^1.0.2"
 
 prepend-http@^2.0.0:
   version "2.0.0"
@@ -2277,9 +2332,9 @@ prepend-http@^2.0.0:
   integrity sha1-6SQ0v6XqjBn0HN/UAddBo8gZ2Jc=
 
 prettier@^2.1.1:
-  version "2.3.2"
-  resolved "https://registry.yarnpkg.com/prettier/-/prettier-2.3.2.tgz#ef280a05ec253712e486233db5c6f23441e7342d"
-  integrity sha512-lnJzDfJ66zkMy58OL5/NY5zp70S7Nz6KqcKkXYzn2tMVrNxvbqaBpg7H3qHaLxCJ5lNMsGuM8+ohS7cZrthdLQ==
+  version "2.6.2"
+  resolved "https://registry.yarnpkg.com/prettier/-/prettier-2.6.2.tgz#e26d71a18a74c3d0f0597f55f01fb6c06c206032"
+  integrity sha512-PkUpF+qoXTqhOeWL9fu7As8LXsIUZ1WYaJiY/a7McAQzxjk82OF0tibkFXVCDImZtWxbvojFjerkiLb0/q8mew==
 
 process@^0.11.10:
   version "0.11.10"
@@ -2292,13 +2347,13 @@ promise-inflight@^1.0.1:
   integrity sha1-mEcocL8igTL8vdhoEputEsPAKeM=
 
 prop-types@^15.6.1, prop-types@^15.6.2:
-  version "15.7.2"
-  resolved "https://registry.yarnpkg.com/prop-types/-/prop-types-15.7.2.tgz#52c41e75b8c87e72b9d9360e0206b99dcbffa6c5"
-  integrity sha512-8QQikdH7//R2vurIJSutZ1smHYTcLpRWEOlHnzcWHmBYrOGUysKwSsrC89BCiFj3CbrfJ/nXFdJepOVrY1GCHQ==
+  version "15.8.1"
+  resolved "https://registry.yarnpkg.com/prop-types/-/prop-types-15.8.1.tgz#67d87bf1a694f48435cf332c24af10214a3140b5"
+  integrity sha512-oj87CgZICdulUohogVAR7AjlC0327U4el4L6eAvOqCeudMDVU0NThNaV+b9Df4dXgSP1gXMTnPdhfe/2qDH5cg==
   dependencies:
     loose-envify "^1.4.0"
     object-assign "^4.1.1"
-    react-is "^16.8.1"
+    react-is "^16.13.1"
 
 pump@^3.0.0:
   version "3.0.0"
@@ -2367,7 +2422,7 @@ react-dom@^17.0.1:
     object-assign "^4.1.1"
     scheduler "^0.20.2"
 
-react-is@^16.8.1:
+react-is@^16.13.1:
   version "16.13.1"
   resolved "https://registry.yarnpkg.com/react-is/-/react-is-16.13.1.tgz#789729a4dc36de2999dc156dd6c1d9c18cea56a4"
   integrity sha512-24e6ynE2H+OKt4kqsOvNd8kBpV65zoxbA4BVsEOB3ARVWQki/DHzaUoC5KuON/BiccDaCCTZBuOcfZs70kR8bQ==
@@ -2416,14 +2471,14 @@ rechoir@^0.7.0:
     resolve "^1.9.0"
 
 regenerator-runtime@^0.13.4:
-  version "0.13.7"
-  resolved "https://registry.yarnpkg.com/regenerator-runtime/-/regenerator-runtime-0.13.7.tgz#cac2dacc8a1ea675feaabaeb8ae833898ae46f55"
-  integrity sha512-a54FxoJDIr27pgf7IgeQGxmqUNYrcV338lf/6gH456HZ/PhX+5BcwHXG9ajESmwe6WRO0tAzRUrRmNONWgkrew==
+  version "0.13.9"
+  resolved "https://registry.yarnpkg.com/regenerator-runtime/-/regenerator-runtime-0.13.9.tgz#8925742a98ffd90814988d7566ad30ca3b263b52"
+  integrity sha512-p3VT+cOEgxFsRRA9X4lkI1E+k2/CtnKtU4gcxyaCUreilL/vqI6CdZ3wxVUx3UOUg+gnUOQQcRI7BmSI656MYA==
 
 regexp.prototype.flags@^1.2.0:
-  version "1.3.1"
-  resolved "https://registry.yarnpkg.com/regexp.prototype.flags/-/regexp.prototype.flags-1.3.1.tgz#7ef352ae8d159e758c0eadca6f8fcb4eef07be26"
-  integrity sha512-JiBdRBq91WlY7uRJ0ds7R+dU02i6LKi8r3BuQhNXn+kmeLN+EfHhfjqMRis1zJxnlu88hq/4dx0P2OP3APRTOA==
+  version "1.4.1"
+  resolved "https://registry.yarnpkg.com/regexp.prototype.flags/-/regexp.prototype.flags-1.4.1.tgz#b3f4c0059af9e47eca9f3f660e51d81307e72307"
+  integrity sha512-pMR7hBVUUGI7PMA37m2ofIdQCsomVnas+Jn5UPGAHQ+/LlwKm/aTLJHdasmHRzlfeZwHiAOaRSo2rbBDm3nNUQ==
   dependencies:
     call-bind "^1.0.2"
     define-properties "^1.1.3"
@@ -2447,11 +2502,6 @@ requires-port@^1.0.0:
   resolved "https://registry.yarnpkg.com/requires-port/-/requires-port-1.0.0.tgz#925d2601d39ac485e091cf0da5c6e694dc3dcaff"
   integrity sha1-kl0mAdOaxIXgkc8NpcbmlNw9yv8=
 
-resize-observer-polyfill@^1.5.1:
-  version "1.5.1"
-  resolved "https://registry.yarnpkg.com/resize-observer-polyfill/-/resize-observer-polyfill-1.5.1.tgz#0e9020dd3d21024458d4ebd27e23e40269810464"
-  integrity sha512-LwZrotdHOo12nQuZlHEmtuXdqGoOD0OhaxopaNFxWzInpEgaLWoVuAMbTzixuosCx2nEG58ngzW3vxdWoxIgdg==
-
 resolve-cwd@^3.0.0:
   version "3.0.0"
   resolved "https://registry.yarnpkg.com/resolve-cwd/-/resolve-cwd-3.0.0.tgz#0f0075f1bb2544766cf73ba6a6e2adfebcb13f2d"
@@ -2465,12 +2515,13 @@ resolve-from@^5.0.0:
   integrity sha512-qYg9KP24dD5qka9J47d0aVky0N+b4fTU89LN9iDnjB5waksiC49rvMB0PrUJQGoTmH50XPiqOvAjDfaijGxYZw==
 
 resolve@^1.9.0:
-  version "1.20.0"
-  resolved "https://registry.yarnpkg.com/resolve/-/resolve-1.20.0.tgz#629a013fb3f70755d6f0b7935cc1c2c5378b1975"
-  integrity sha512-wENBPt4ySzg4ybFQW2TT1zMQucPK95HSh/nq2CFTZVOGut2+pQvSsgtda4d26YrYcr067wjbmzOG8byDPBX63A==
+  version "1.22.0"
+  resolved "https://registry.yarnpkg.com/resolve/-/resolve-1.22.0.tgz#5e0b8c67c15df57a89bdbabe603a002f21731198"
+  integrity sha512-Hhtrw0nLeSrFQ7phPp4OOcVjLPIeMnRlr5mcnVuMe7M/7eBn98A3hmFRLoFo3DLZkivSYwhRUJTyPyWAk56WLw==
   dependencies:
-    is-core-module "^2.2.0"
-    path-parse "^1.0.6"
+    is-core-module "^2.8.1"
+    path-parse "^1.0.7"
+    supports-preserve-symlinks-flag "^1.0.0"
 
 responselike@^1.0.2:
   version "1.0.2"
@@ -2558,7 +2609,7 @@ schema-utils@^2.6.5:
     ajv "^6.12.4"
     ajv-keywords "^3.5.2"
 
-schema-utils@^3.0.0, schema-utils@^3.1.0:
+schema-utils@^3.0.0, schema-utils@^3.1.0, schema-utils@^3.1.1:
   version "3.1.1"
   resolved "https://registry.yarnpkg.com/schema-utils/-/schema-utils-3.1.1.tgz#bc74c4b6b6995c1d88f76a8b77bea7219e0c8281"
   integrity sha512-Y5PQxS4ITlC+EahLuXaY86TXfR7Dc5lw294alXOq86JAHCihAIZfqv8nNCWvaEJvaC51uN9hbLGeV0cFBdH+Fw==
@@ -2618,9 +2669,9 @@ shebang-regex@^3.0.0:
   integrity sha512-7++dFhtcx3353uBaq8DDR4NuxBetBzC7ZQOhmTQInHEd6bSrXdiEyzCvG07Z44UYdLShWUyXt5M/yhz8ekcb1A==
 
 signal-exit@^3.0.2, signal-exit@^3.0.3:
-  version "3.0.3"
-  resolved "https://registry.yarnpkg.com/signal-exit/-/signal-exit-3.0.3.tgz#a1410c2edd8f077b08b4e253c8eacfcaf057461c"
-  integrity sha512-VUJ49FC8U1OxwZLxIbTTrDvLnf/6TDgxZcK8wxR8zs13xpx7xbG60ndBlhNrFi2EMuFRoeDoJO7wthSLq42EjA==
+  version "3.0.7"
+  resolved "https://registry.yarnpkg.com/signal-exit/-/signal-exit-3.0.7.tgz#a9a1767f8af84155114eaabd73f99273c8f59ad9"
+  integrity sha512-wnD2ZE+l+SPC/uoS0vXeE9L1+0wuaMqKlfz9AMUo38JsyLSBWSFcHR1Rri62LZc12vLr1gb3jl7iwQhgwpAbGQ==
 
 slash@^3.0.0:
   version "3.0.0"
@@ -2644,20 +2695,20 @@ sort-package-json@~1.44.0:
     is-plain-obj "2.1.0"
     sort-object-keys "^1.1.3"
 
-source-list-map@^2.0.0, source-list-map@^2.0.1:
+source-list-map@^2.0.0:
   version "2.0.1"
   resolved "https://registry.yarnpkg.com/source-list-map/-/source-list-map-2.0.1.tgz#3993bd873bfc48479cca9ea3a547835c7c154b34"
   integrity sha512-qnQ7gVMxGNxsiL4lEuJwe/To8UnK7fAnmbGEEH8RpLouuKbeEm0lhbQVFIrNSuB+G7tVrAlVsZgETT5nljf+Iw==
 
-source-map-js@^0.6.2:
-  version "0.6.2"
-  resolved "https://registry.yarnpkg.com/source-map-js/-/source-map-js-0.6.2.tgz#0bb5de631b41cfbda6cfba8bd05a80efdfd2385e"
-  integrity sha512-/3GptzWzu0+0MBQFrDKzw/DvvMTUORvgY6k6jd/VS6iCR4RDTKWH6v6WPwQoUO8667uQEf9Oe38DxAYWY5F/Ug==
+source-map-js@^1.0.2:
+  version "1.0.2"
+  resolved "https://registry.yarnpkg.com/source-map-js/-/source-map-js-1.0.2.tgz#adbc361d9c62df380125e7f161f71c826f1e490c"
+  integrity sha512-R0XvVJ9WusLiqTCEiGCmICCMplcCkIwwR11mOSD9CR5u+IXYdiseeEuXCVAjS54zqwkLcPNnmU4OeJ6tUrWhDw==
 
-source-map-support@~0.5.19:
-  version "0.5.19"
-  resolved "https://registry.yarnpkg.com/source-map-support/-/source-map-support-0.5.19.tgz#a98b62f86dcaf4f67399648c085291ab9e8fed61"
-  integrity sha512-Wonm7zOCIJzBGQdB+thsPar0kYuCIzYvxZwlBa87yi/Mdjv7Tip2cyVbLj5o0cFPN4EVkuTwb3GDDyUx2DGnGw==
+source-map-support@~0.5.20:
+  version "0.5.21"
+  resolved "https://registry.yarnpkg.com/source-map-support/-/source-map-support-0.5.21.tgz#04fe7c7f9e1ed2d662233c28cb2b35b9f63f6e4f"
+  integrity sha512-uBHU3L3czsIyYXKX88fdrGovxdSCoTGDRZ6SYXtSRxLZUzHg5P/66Ht6uoUlHu9EZod+inXhKo3qQgwXUT/y1w==
   dependencies:
     buffer-from "^1.0.0"
     source-map "^0.6.0"
@@ -2680,20 +2731,20 @@ ssri@^8.0.1:
     minipass "^3.1.1"
 
 string-width@^4.1.0:
-  version "4.2.2"
-  resolved "https://registry.yarnpkg.com/string-width/-/string-width-4.2.2.tgz#dafd4f9559a7585cfba529c6a0a4f73488ebd4c5"
-  integrity sha512-XBJbT3N4JhVumXE0eoLU9DCjcaF92KLNqTmFCnG1pf8duUxFGwtP6AD6nkjw9a3IdiRtL3E2w3JDiE/xi3vOeA==
+  version "4.2.3"
+  resolved "https://registry.yarnpkg.com/string-width/-/string-width-4.2.3.tgz#269c7117d27b05ad2e536830a8ec895ef9c6d010"
+  integrity sha512-wKyQRQpjJ0sIp62ErSZdGsjMJWsap5oRNihHhu6G7JVO/9jIB6UyevL+tXuOqrng8j/cxKTWyWUwvSTriiZz/g==
   dependencies:
     emoji-regex "^8.0.0"
     is-fullwidth-code-point "^3.0.0"
-    strip-ansi "^6.0.0"
+    strip-ansi "^6.0.1"
 
-strip-ansi@^6.0.0:
-  version "6.0.0"
-  resolved "https://registry.yarnpkg.com/strip-ansi/-/strip-ansi-6.0.0.tgz#0b1571dd7669ccd4f3e06e14ef1eed26225ae532"
-  integrity sha512-AuvKTrTfQNYNIctbR1K/YGTR1756GycPsg7b9bdV9Duqur4gv6aKqHXah67Z8ImS7WEz5QVcOtlfW2rZEugt6w==
+strip-ansi@^6.0.0, strip-ansi@^6.0.1:
+  version "6.0.1"
+  resolved "https://registry.yarnpkg.com/strip-ansi/-/strip-ansi-6.0.1.tgz#9e26c63d30f53443e9489495b2105d37b67a85d9"
+  integrity sha512-Y38VPSHcqkFrCpFnQ9vuSXmquuv5oXOKpGeT6aGrr3o3Gc9AlVa6JBfUSOCnbxGGZF+/0ooI7KrPuUSztUdU5A==
   dependencies:
-    ansi-regex "^5.0.0"
+    ansi-regex "^5.0.1"
 
 strip-final-newline@^2.0.0:
   version "2.0.0"
@@ -2734,6 +2785,11 @@ supports-color@^8.0.0:
   dependencies:
     has-flag "^4.0.0"
 
+supports-preserve-symlinks-flag@^1.0.0:
+  version "1.0.0"
+  resolved "https://registry.yarnpkg.com/supports-preserve-symlinks-flag/-/supports-preserve-symlinks-flag-1.0.0.tgz#6eda4bd344a3c94aea376d4cc31bc77311039e09"
+  integrity sha512-ot0WnXS9fgdkgIcePe6RHNk1WA8+muPa6cSjeR3V8K27q9BB1rTE3R1p7Hv0z1ZyAc8s6Vvv8DIyWf681MAt0w==
+
 svg-url-loader@~6.0.0:
   version "6.0.0"
   resolved "https://registry.yarnpkg.com/svg-url-loader/-/svg-url-loader-6.0.0.tgz#b94861d9f6badfb8ca3e7d3ec4655c1bf732ac5d"
@@ -2743,9 +2799,9 @@ svg-url-loader@~6.0.0:
     loader-utils "~2.0.0"
 
 tapable@^2.1.1, tapable@^2.2.0:
-  version "2.2.0"
-  resolved "https://registry.yarnpkg.com/tapable/-/tapable-2.2.0.tgz#5c373d281d9c672848213d0e037d1c4165ab426b"
-  integrity sha512-FBk4IesMV1rBxX2tfiK8RAmogtWn53puLOQlvO8XuwlgxcYbP4mVPS9Ph4aeamSyyVjOl24aYWAuc8U5kCVwMw==
+  version "2.2.1"
+  resolved "https://registry.yarnpkg.com/tapable/-/tapable-2.2.1.tgz#1967a73ef4060a82f12ab96af86d52fdb76eeca0"
+  integrity sha512-GNzQvQTOIP6RyTfE2Qxb8ZVlNmw0n88vp1szwWRimP02mnTsx3Wtn5qRdqY9w2XduFNUgvOwhNnQsjwCp+kqaQ==
 
 tar@^6.0.2:
   version "6.1.11"
@@ -2775,25 +2831,25 @@ terser-webpack-plugin@^4.1.0:
     webpack-sources "^1.4.3"
 
 terser-webpack-plugin@^5.1.3:
-  version "5.1.4"
-  resolved "https://registry.yarnpkg.com/terser-webpack-plugin/-/terser-webpack-plugin-5.1.4.tgz#c369cf8a47aa9922bd0d8a94fe3d3da11a7678a1"
-  integrity sha512-C2WkFwstHDhVEmsmlCxrXUtVklS+Ir1A7twrYzrDrQQOIMOaVAYykaoo/Aq1K0QRkMoY2hhvDQY1cm4jnIMFwA==
+  version "5.3.1"
+  resolved "https://registry.yarnpkg.com/terser-webpack-plugin/-/terser-webpack-plugin-5.3.1.tgz#0320dcc270ad5372c1e8993fabbd927929773e54"
+  integrity sha512-GvlZdT6wPQKbDNW/GDQzZFg/j4vKU96yl2q6mcUkzKOgW4gwf1Z8cZToUCrz31XHlPWH8MVb1r2tFtdDtTGJ7g==
   dependencies:
-    jest-worker "^27.0.2"
-    p-limit "^3.1.0"
-    schema-utils "^3.0.0"
+    jest-worker "^27.4.5"
+    schema-utils "^3.1.1"
     serialize-javascript "^6.0.0"
     source-map "^0.6.1"
-    terser "^5.7.0"
+    terser "^5.7.2"
 
-terser@^5.3.4, terser@^5.7.0:
-  version "5.7.1"
-  resolved "https://registry.yarnpkg.com/terser/-/terser-5.7.1.tgz#2dc7a61009b66bb638305cb2a824763b116bf784"
-  integrity sha512-b3e+d5JbHAe/JSjwsC3Zn55wsBIM7AsHLjKxT31kGCldgbpFePaFo+PiddtO6uwRZWRw7sPXmAN8dTW61xmnSg==
+terser@^5.3.4, terser@^5.7.2:
+  version "5.12.1"
+  resolved "https://registry.yarnpkg.com/terser/-/terser-5.12.1.tgz#4cf2ebed1f5bceef5c83b9f60104ac4a78b49e9c"
+  integrity sha512-NXbs+7nisos5E+yXwAD+y7zrcTkMqb0dEJxIGtSKPdCBzopf7ni4odPul2aechpV7EXNvOudYOX2bb5tln1jbQ==
   dependencies:
+    acorn "^8.5.0"
     commander "^2.20.0"
     source-map "~0.7.2"
-    source-map-support "~0.5.19"
+    source-map-support "~0.5.20"
 
 through@^2.3.6:
   version "2.3.8"
@@ -2820,9 +2876,9 @@ to-regex-range@^5.0.1:
     is-number "^7.0.0"
 
 to-string-loader@^1.1.6:
-  version "1.1.6"
-  resolved "https://registry.yarnpkg.com/to-string-loader/-/to-string-loader-1.1.6.tgz#230529ccc63dd0ecca052a85e1fb82afe946b0ab"
-  integrity sha512-VNg62//PS1WfNwrK3n7t6wtK5Vdtx/qeYLLEioW46VMlYUwAYT6wnfB+OwS2FMTCalIHu0tk79D3RXX8ttmZTQ==
+  version "1.2.0"
+  resolved "https://registry.yarnpkg.com/to-string-loader/-/to-string-loader-1.2.0.tgz#4364aa044b9aa876473f4d7a36ef7d216a276e9c"
+  integrity sha512-KsWUL8FccgBW9FPFm4vYoQbOOcO5m6hKOGYoXjbseD9/4Ft+ravXN5jolQ9kTKYcK4zPt1j+khx97GPGnVoi6A==
   dependencies:
     loader-utils "^1.0.0"
 
@@ -2836,10 +2892,10 @@ tslib@^1.9.0:
   resolved "https://registry.yarnpkg.com/tslib/-/tslib-1.14.1.tgz#cf2d38bdc34a134bcaf1091c41f6619e2f672d00"
   integrity sha512-Xni35NKzjgMrwevysHTCArtLDpPvye8zV/0E4EyYn43P7/7qvQwPh9BGkHewbMulVntbigmcT7rdX3BNo9wRJg==
 
-tslib@~1.13.0:
-  version "1.13.0"
-  resolved "https://registry.yarnpkg.com/tslib/-/tslib-1.13.0.tgz#c881e13cc7015894ed914862d276436fa9a47043"
-  integrity sha512-i/6DQjL8Xf3be4K/E6Wgpekn5Qasl1usyw++dAA35Ue5orEn65VIxOA+YvNNl9HV3qv70T7CNwjODHZrLwvd1Q==
+tslib@~2.3.1:
+  version "2.3.1"
+  resolved "https://registry.yarnpkg.com/tslib/-/tslib-2.3.1.tgz#e8a335add5ceae51aa261d32a490158ef042ef01"
+  integrity sha512-77EbyPPpMz+FRFRuAFlWMtmgUWGe9UOG2Z25NqCwiIjRhOf5iKGuzSe5P2w1laq+FkRy4p+PCuVkJSGkzTEKVw==
 
 type-fest@^0.21.3:
   version "0.21.3"
@@ -2857,11 +2913,11 @@ typescript@~4.1.3:
   integrity sha512-pxnwLxeb/Z5SP80JDRzVjh58KsM6jZHRAOtTpS7sXLS4ogXNKC9ANxHHZqLLeVHZN35jCtI4JdmLLbLiC1kBow==
 
 typestyle@^2.0.4:
-  version "2.1.0"
-  resolved "https://registry.yarnpkg.com/typestyle/-/typestyle-2.1.0.tgz#7c5cc567de72cd8bfb686813150b92791aaa7636"
-  integrity sha512-6uCYPdG4xWLeEcl9O0GtNFnNGhami+irKiLsXSuvWHC/aTS7wdj49WeikWAKN+xHN3b1hm+9v0svwwgSBhCsNA==
+  version "2.3.0"
+  resolved "https://registry.yarnpkg.com/typestyle/-/typestyle-2.3.0.tgz#eff768918d5799d12009ec1543369b76b7e5c3ce"
+  integrity sha512-JZd1R5cWxRvwXzPAKVFsbxO/QjDkFeQlqGF6ZxR7IREEXyFQrf1f1mmlx5EynOTItLLx4aBbcVpCH+CDsl5ZTw==
   dependencies:
-    csstype "2.6.9"
+    csstype "3.0.10"
     free-style "3.1.0"
 
 unique-filename@^1.1.1:
@@ -2907,9 +2963,9 @@ url-parse-lax@^3.0.0:
     prepend-http "^2.0.0"
 
 url-parse@~1.5.1:
-  version "1.5.7"
-  resolved "https://registry.yarnpkg.com/url-parse/-/url-parse-1.5.7.tgz#00780f60dbdae90181f51ed85fb24109422c932a"
-  integrity sha512-HxWkieX+STA38EDk7CE9MEryFeHCKzgagxlGvsdS7WBImq9Mk+PGwiT56w82WI3aicwJA8REp42Cxo98c8FZMA==
+  version "1.5.10"
+  resolved "https://registry.yarnpkg.com/url-parse/-/url-parse-1.5.10.tgz#9d3c2f736c1d75dd3bd2be507dcc111f1e2ea9c1"
+  integrity sha512-WypcfiRhfeUP9vvF0j6rw0J3hrWrw6iZv3+22h6iRMJ/8z1Tj6XfLP4DsUix5MhMPnXpiHDoKyoZ/bdCkwBCiQ==
   dependencies:
     querystringify "^2.1.1"
     requires-port "^1.0.0"
@@ -2927,11 +2983,6 @@ util-deprecate@^1.0.2:
   resolved "https://registry.yarnpkg.com/util-deprecate/-/util-deprecate-1.0.2.tgz#450d4dc9fa70de732762fbd2d4a28981419a0ccf"
   integrity sha1-RQ1Nyfpw3nMnYvvS1KKJgUGaDM8=
 
-v8-compile-cache@^2.2.0:
-  version "2.3.0"
-  resolved "https://registry.yarnpkg.com/v8-compile-cache/-/v8-compile-cache-2.3.0.tgz#2de19618c66dc247dcfb6f99338035d8245a2cee"
-  integrity sha512-l8lCEmLcLYZh4nbunNZvQCJc5pv7+RCwa8q/LdUx8u7lsWvPDKmpodJAJNwkAhJC//dFY48KuIEmjtd4RViDrA==
-
 warning@^4.0.2, warning@^4.0.3:
   version "4.0.3"
   resolved "https://registry.yarnpkg.com/warning/-/warning-4.0.3.tgz#16e9e077eb8a86d6af7d64aa1e05fd85b4678ca3"
@@ -2939,10 +2990,10 @@ warning@^4.0.2, warning@^4.0.3:
   dependencies:
     loose-envify "^1.0.0"
 
-watchpack@^2.2.0:
-  version "2.2.0"
-  resolved "https://registry.yarnpkg.com/watchpack/-/watchpack-2.2.0.tgz#47d78f5415fe550ecd740f99fe2882323a58b1ce"
-  integrity sha512-up4YAn/XHgZHIxFBVCdlMiWDj6WaLKpwVeGQk2I5thdYxF/KmF0aaz6TfJZ/hfl1h/XlcDr7k1KH7ThDagpFaA==
+watchpack@^2.3.1:
+  version "2.3.1"
+  resolved "https://registry.yarnpkg.com/watchpack/-/watchpack-2.3.1.tgz#4200d9447b401156eeca7767ee610f8809bc9d25"
+  integrity sha512-x0t0JuydIo8qCNctdDrn1OzH/qDzk2+rdCOC3YzumZ42fiMqmQ7T3xQurykYMhYfHaPHTp4ZxAx2NfUo1K6QaA==
   dependencies:
     glob-to-regexp "^0.4.1"
     graceful-fs "^4.1.2"
@@ -2953,22 +3004,21 @@ webidl-conversions@^3.0.0:
   integrity sha1-JFNCdeKnvGvnvIZhHMFq4KVlSHE=
 
 webpack-cli@^4.1.0:
-  version "4.7.2"
-  resolved "https://registry.yarnpkg.com/webpack-cli/-/webpack-cli-4.7.2.tgz#a718db600de6d3906a4357e059ae584a89f4c1a5"
-  integrity sha512-mEoLmnmOIZQNiRl0ebnjzQ74Hk0iKS5SiEEnpq3dRezoyR3yPaeQZCMCe+db4524pj1Pd5ghZXjT41KLzIhSLw==
+  version "4.9.2"
+  resolved "https://registry.yarnpkg.com/webpack-cli/-/webpack-cli-4.9.2.tgz#77c1adaea020c3f9e2db8aad8ea78d235c83659d"
+  integrity sha512-m3/AACnBBzK/kMTcxWHcZFPrw/eQuY4Df1TxvIWfWM2x7mRqBQCqKEd96oCUa9jkapLBaFfRce33eGDb4Pr7YQ==
   dependencies:
     "@discoveryjs/json-ext" "^0.5.0"
-    "@webpack-cli/configtest" "^1.0.4"
-    "@webpack-cli/info" "^1.3.0"
-    "@webpack-cli/serve" "^1.5.1"
-    colorette "^1.2.1"
+    "@webpack-cli/configtest" "^1.1.1"
+    "@webpack-cli/info" "^1.4.1"
+    "@webpack-cli/serve" "^1.6.1"
+    colorette "^2.0.14"
     commander "^7.0.0"
     execa "^5.0.0"
     fastest-levenshtein "^1.0.12"
     import-local "^3.0.2"
     interpret "^2.2.0"
     rechoir "^0.7.0"
-    v8-compile-cache "^2.2.0"
     webpack-merge "^5.7.3"
 
 webpack-merge@^5.1.2, webpack-merge@^5.7.3:
@@ -2987,33 +3037,31 @@ webpack-sources@^1.1.0, webpack-sources@^1.4.3:
     source-list-map "^2.0.0"
     source-map "~0.6.1"
 
-webpack-sources@^2.3.0:
-  version "2.3.1"
-  resolved "https://registry.yarnpkg.com/webpack-sources/-/webpack-sources-2.3.1.tgz#570de0af163949fe272233c2cefe1b56f74511fd"
-  integrity sha512-y9EI9AO42JjEcrTJFOYmVywVZdKVUfOvDUPsJea5GIr1JOEGFVqwlY2K098fFoIjOkDzHn2AjRvM8dsBZu+gCA==
-  dependencies:
-    source-list-map "^2.0.1"
-    source-map "^0.6.1"
+webpack-sources@^3.2.3:
+  version "3.2.3"
+  resolved "https://registry.yarnpkg.com/webpack-sources/-/webpack-sources-3.2.3.tgz#2d4daab8451fd4b240cc27055ff6a0c2ccea0cde"
+  integrity sha512-/DyMEOrDgLKKIG0fmvtz+4dUX/3Ghozwgm6iPp8KRhvn+eQf9+Q7GWxVNMk3+uCPWfdXYC4ExGBckIXdFEfH1w==
 
 webpack@^5.3.1:
-  version "5.45.1"
-  resolved "https://registry.yarnpkg.com/webpack/-/webpack-5.45.1.tgz#d78dcbeda18a872dc62b0455d3ed3dcfd1c886bb"
-  integrity sha512-68VT2ZgG9EHs6h6UxfV2SEYewA9BA3SOLSnC2NEbJJiEwbAiueDL033R1xX0jzjmXvMh0oSeKnKgbO2bDXIEyQ==
+  version "5.71.0"
+  resolved "https://registry.yarnpkg.com/webpack/-/webpack-5.71.0.tgz#b01fcf379570b8c5ee06ca06c829ca168c951884"
+  integrity sha512-g4dFT7CFG8LY0iU5G8nBL6VlkT21Z7dcYDpJAEJV5Q1WLb9UwnFbrem1k7K52ILqEmomN7pnzWFxxE6SlDY56A==
   dependencies:
-    "@types/eslint-scope" "^3.7.0"
-    "@types/estree" "^0.0.50"
+    "@types/eslint-scope" "^3.7.3"
+    "@types/estree" "^0.0.51"
     "@webassemblyjs/ast" "1.11.1"
     "@webassemblyjs/wasm-edit" "1.11.1"
     "@webassemblyjs/wasm-parser" "1.11.1"
     acorn "^8.4.1"
+    acorn-import-assertions "^1.7.6"
     browserslist "^4.14.5"
     chrome-trace-event "^1.0.2"
-    enhanced-resolve "^5.8.0"
-    es-module-lexer "^0.7.1"
+    enhanced-resolve "^5.9.2"
+    es-module-lexer "^0.9.0"
     eslint-scope "5.1.1"
     events "^3.2.0"
     glob-to-regexp "^0.4.1"
-    graceful-fs "^4.2.4"
+    graceful-fs "^4.2.9"
     json-parse-better-errors "^1.0.2"
     loader-runner "^4.2.0"
     mime-types "^2.1.27"
@@ -3021,8 +3069,8 @@ webpack@^5.3.1:
     schema-utils "^3.1.0"
     tapable "^2.1.1"
     terser-webpack-plugin "^5.1.3"
-    watchpack "^2.2.0"
-    webpack-sources "^2.3.0"
+    watchpack "^2.3.1"
+    webpack-sources "^3.2.3"
 
 whatwg-url@^5.0.0:
   version "5.0.0"
@@ -3058,9 +3106,9 @@ wrappy@1:
   integrity sha1-tSQ9jz7BqjXxNkYFvA0QNuMKtp8=
 
 ws@^7.2.0:
-  version "7.5.3"
-  resolved "https://registry.yarnpkg.com/ws/-/ws-7.5.3.tgz#160835b63c7d97bfab418fc1b8a9fced2ac01a74"
-  integrity sha512-kQ/dHIzuLrS6Je9+uv81ueZomEwH0qVYstcAQ4/Z93K8zeko9gtAbttJWzoC5ukqXY1PpoouV3+VSOqEAFt5wg==
+  version "7.5.7"
+  resolved "https://registry.yarnpkg.com/ws/-/ws-7.5.7.tgz#9e0ac77ee50af70d58326ecff7e85eb3fa375e67"
+  integrity sha512-KMvVuFzpKBuiIXW3E4u3mySRO2/mCHSyZDJQM5NQ9Q9KHWHWh0NHgfbRMLLrceUK5qAL4ytALJbpRMjixFZh8A==
 
 yallist@^4.0.0:
   version "4.0.0"
diff --git a/ts/nni_manager/common/experimentConfig.ts b/ts/nni_manager/common/experimentConfig.ts
index a4a38dcbf..a5454d6a7 100644
--- a/ts/nni_manager/common/experimentConfig.ts
+++ b/ts/nni_manager/common/experimentConfig.ts
@@ -132,6 +132,7 @@ export interface KubeflowConfig extends TrainingServiceConfig {
     master?: KubeflowRoleConfig;
     reuseMode: boolean;
     maxTrialNumberPerGpu?: number;
+    namespace?: string;
 }
 
 export interface FrameworkControllerTaskRoleConfig {
@@ -156,7 +157,7 @@ export interface FrameworkControllerConfig extends TrainingServiceConfig {
     taskRoles: FrameworkControllerTaskRoleConfig[];
     reuseMode: boolean;
     maxTrialNumberPerGpu?: number;
-    namespace?: 'default';
+    namespace?: string;
     apiVersion?: string;
 }
 
diff --git a/ts/nni_manager/common/experimentStartupInfo.ts b/ts/nni_manager/common/experimentStartupInfo.ts
index eb17b531a..ba57297d3 100644
--- a/ts/nni_manager/common/experimentStartupInfo.ts
+++ b/ts/nni_manager/common/experimentStartupInfo.ts
@@ -1,131 +1,48 @@
 // Copyright (c) Microsoft Corporation.
 // Licensed under the MIT license.
 
-import assert from 'assert';
-import os from 'os';
-import path from 'path';
-
-const API_ROOT_URL: string = '/api/v1/nni';
-
-let singleton: ExperimentStartupInfo | null = null;
+import globals from 'common/globals';
 
 export class ExperimentStartupInfo {
-
-    public experimentId: string = '';
-    public newExperiment: boolean = true;
-    public basePort: number = -1;
-    public initialized: boolean = false;
-    public logDir: string = '';
-    public logLevel: string = '';
-    public readonly: boolean = false;
-    public dispatcherPipe: string | null = null;
-    public platform: string = '';
-    public urlprefix: string = '';
-
-    constructor(
-            newExperiment: boolean,
-            experimentId: string,
-            basePort: number,
-            platform: string,
-            logDir?: string,
-            logLevel?: string,
-            readonly?: boolean,
-            dispatcherPipe?: string,
-            urlprefix?: string) {
-        this.newExperiment = newExperiment;
-        this.experimentId = experimentId;
-        this.basePort = basePort;
-        this.platform = platform;
-
-        if (logDir !== undefined && logDir.length > 0) {
-            this.logDir = path.join(path.normalize(logDir), experimentId);
-        } else {
-            this.logDir = path.join(os.homedir(), 'nni-experiments', experimentId);
-        }
-
-        if (logLevel !== undefined && logLevel.length > 1) {
-            this.logLevel = logLevel;
-        }
-
-        if (readonly !== undefined) {
-            this.readonly = readonly;
-        }
-
-        if (dispatcherPipe != undefined && dispatcherPipe.length > 0) {
-            this.dispatcherPipe = dispatcherPipe;
-        }
-
-        if(urlprefix != undefined && urlprefix.length > 0){
-            this.urlprefix = urlprefix;
-        }
-    }
-
-    public get apiRootUrl(): string {
-        return this.urlprefix === '' ? API_ROOT_URL : `/${this.urlprefix}${API_ROOT_URL}`;
-    }
+    public experimentId: string = globals.args.experimentId;
+    public newExperiment: boolean = (globals.args.action === 'create');
+    public basePort: number = globals.args.port;
+    public logDir: string = globals.paths.experimentRoot;
+    public logLevel: string = globals.args.logLevel;
+    public readonly: boolean = (globals.args.action === 'view');
+    public dispatcherPipe: string | null = globals.args.dispatcherPipe ?? null;
+    public platform: string = globals.args.mode as string;
+    public urlprefix: string = globals.args.urlPrefix;
 
     public static getInstance(): ExperimentStartupInfo {
-        assert(singleton !== null);
-        return singleton!;
+        return new ExperimentStartupInfo();
     }
 }
 
 export function getExperimentStartupInfo(): ExperimentStartupInfo {
-    return ExperimentStartupInfo.getInstance();
-}
-
-export function setExperimentStartupInfo(
-        newExperiment: boolean,
-        experimentId: string,
-        basePort: number,
-        platform: string,
-        logDir?: string,
-        logLevel?: string,
-        readonly?: boolean,
-        dispatcherPipe?: string,
-        urlprefix?: string): void {
-    singleton = new ExperimentStartupInfo(
-        newExperiment,
-        experimentId,
-        basePort,
-        platform,
-        logDir,
-        logLevel,
-        readonly,
-        dispatcherPipe,
-        urlprefix
-    );
+    return new ExperimentStartupInfo();
 }
 
 export function getExperimentId(): string {
-    return getExperimentStartupInfo().experimentId;
+    return globals.args.experimentId;
 }
 
 export function getBasePort(): number {
-    return getExperimentStartupInfo().basePort;
+    return globals.args.port;
 }
 
 export function isNewExperiment(): boolean {
-    return getExperimentStartupInfo().newExperiment;
+    return globals.args.action === 'create';
 }
 
 export function getPlatform(): string {
-    return getExperimentStartupInfo().platform;
+    return globals.args.mode as string;
 }
 
 export function isReadonly(): boolean {
-    return getExperimentStartupInfo().readonly;
+    return globals.args.action === 'view';
 }
 
 export function getDispatcherPipe(): string | null {
-    return getExperimentStartupInfo().dispatcherPipe;
-}
-
-export function getAPIRootUrl(): string {
-    return getExperimentStartupInfo().apiRootUrl;
-}
-
-export function getPrefixUrl(): string {
-    const prefix = getExperimentStartupInfo().urlprefix === '' ? '' : `/${getExperimentStartupInfo().urlprefix}`;
-    return prefix;
+    return globals.args.dispatcherPipe ?? null;
 }
diff --git a/ts/nni_manager/common/globals/arguments.ts b/ts/nni_manager/common/globals/arguments.ts
new file mode 100644
index 000000000..8b3c76b86
--- /dev/null
+++ b/ts/nni_manager/common/globals/arguments.ts
@@ -0,0 +1,96 @@
+// Copyright (c) Microsoft Corporation.
+// Licensed under the MIT license.
+
+/**
+ *  Parse NNI manager's command line arguments.
+ **/
+
+import assert from 'assert/strict';
+
+import yargs from 'yargs/yargs';
+
+/**
+ *  Command line arguments provided by "nni/experiment/launcher.py".
+ *
+ *  Hyphen-separated words are automatically converted to camelCases by yargs lib, but snake_cases are not.
+ *  So it supports "--log-level" but does not support "--log_level".
+ *
+ *  Unfortunately I misunderstood "experiment_working_directory" config field when deciding the name.
+ *  It defaults to "~/nni-experiments" rather than "~/nni-experiments/<experiment-id>",
+ *  and further more the working directory is "site-packages/nni_node", not either.
+ *  For compatibility concern we cannot change the public API, so there is an inconsistency here.
+ **/
+export interface NniManagerArgs {
+    readonly port: number;
+    readonly experimentId: string;
+    readonly action: 'create' | 'resume' | 'view';
+    readonly experimentsDirectory: string;  // renamed "config.experiment_working_directory", must be absolute
+    readonly logLevel: 'critical' | 'error' | 'warning' | 'info' | 'debug';
+    readonly foreground: boolean;
+    readonly urlPrefix: string;  // leading and trailing "/" must be stripped
+
+    // these are planned to be removed
+    readonly mode: string;
+    readonly dispatcherPipe: string | undefined;
+}
+
+export function parseArgs(rawArgs: string[]): NniManagerArgs {
+    const parser = yargs(rawArgs).options(yargsOptions).strict().fail(false);
+    const parsedArgs: NniManagerArgs = parser.parseSync();
+
+    // strip yargs leftovers
+    const argsAsAny: any = {};
+    for (const key in yargsOptions) {
+        argsAsAny[key] = (parsedArgs as any)[key];
+        assert(!Number.isNaN(argsAsAny[key]), `Command line arg --${key} is not a number`);
+    }
+    if (argsAsAny.dispatcherPipe === '') {
+        argsAsAny.dispatcherPipe = undefined;
+    }
+    const args: NniManagerArgs = argsAsAny;
+
+    const prefixErrMsg = `Command line arg --url-prefix "${args.urlPrefix}" is not stripped`;
+    assert(!args.urlPrefix.startsWith('/') && !args.urlPrefix.endsWith('/'), prefixErrMsg);
+
+    return args;
+}
+
+const yargsOptions = {
+    port: {
+        demandOption: true,
+        type: 'number'
+    },
+    experimentId: {
+        demandOption: true,
+        type: 'string'
+    },
+    action: {
+        choices: [ 'create', 'resume', 'view' ] as const,
+        demandOption: true
+    },
+    experimentsDirectory: {
+        demandOption: true,
+        type: 'string'
+    },
+    logLevel: {
+        choices: [ 'critical', 'error', 'warning', 'info', 'debug' ] as const,
+        demandOption: true
+    },
+    foreground: {
+        default: false,
+        type: 'boolean'
+    },
+    urlPrefix: {
+        default: '',
+        type: 'string'
+    },
+
+    mode: {
+        default: '',
+        type: 'string'
+    },
+    dispatcherPipe: {
+        default: '',
+        type: 'string'
+    }
+} as const;
diff --git a/ts/nni_manager/common/globals/index.ts b/ts/nni_manager/common/globals/index.ts
new file mode 100644
index 000000000..90b02f825
--- /dev/null
+++ b/ts/nni_manager/common/globals/index.ts
@@ -0,0 +1,59 @@
+// Copyright (c) Microsoft Corporation.
+// Licensed under the MIT license.
+
+/**
+ *  Collection of global objects.
+ *
+ *  Although global is anti-pattern in OOP, there are two scenarios NNI uses globals.
+ *
+ *   1. Some constant configs (like command line args) are accessed here and there with util functions.
+ *      It is possible to pass parameters instead, but not worthy the refactor.
+ *
+ *   2. Some singletons (like root logger) are indeed global.
+ *      The singletons need to be registered in `global` to support 3rd-party training services,
+ *      because they are compiled outside NNI manager and therefore module scope singletons will not work.
+ **/
+
+import assert from 'assert/strict';
+
+import { NniManagerArgs, parseArgs } from './arguments';
+import { NniPaths, createPaths } from './paths';
+
+export { NniManagerArgs, NniPaths };
+
+/**
+ *  Collection of global objects.
+ *
+ *  It can be obtained with `import globals from 'common/globals'` or `global.nni`.
+ *  The former is preferred because it exposes less underlying implementations.
+ **/
+export interface NniGlobals {
+    readonly args: NniManagerArgs;
+    readonly paths: NniPaths;
+}
+
+// give type hint to `global.nni` (copied from SO, dunno how it works)
+declare global {
+    var nni: NniGlobals;  // eslint-disable-line
+}
+
+// prepare the namespace object and export it
+if (global.nni === undefined) {
+    global.nni = {} as NniGlobals;
+}
+const globals: NniGlobals = global.nni;
+export default globals;
+
+/**
+ *  Initialize globals.
+ *  Must and must only be invoked once in "main.ts".
+ **/
+export function initGlobals(): void {
+    assert.deepEqual(global.nni, {});
+
+    const args = parseArgs(process.argv.slice(2));
+    const paths = createPaths(args);
+
+    const globals: NniGlobals = { args, paths };
+    Object.assign(global.nni, globals);
+}
diff --git a/ts/nni_manager/common/globals/paths.ts b/ts/nni_manager/common/globals/paths.ts
new file mode 100644
index 000000000..41ab630bc
--- /dev/null
+++ b/ts/nni_manager/common/globals/paths.ts
@@ -0,0 +1,44 @@
+// Copyright (c) Microsoft Corporation.
+// Licensed under the MIT license.
+
+/**
+ *  Manage experiment paths.
+ *
+ *  Ideally all path constants should be put here so other modules (especially training services)
+ *  do not need to know file hierarchy of nni-experiments folder, which is an implicit undocumented protocol.
+ **/
+
+import assert from 'assert/strict';
+import fs from 'fs';
+import path from 'path';
+
+import type { NniManagerArgs } from './arguments';
+
+export interface NniPaths {
+    readonly experimentRoot: string;
+    readonly experimentsDirectory: string;
+    readonly logDirectory: string;  // contains nni manager and dispatcher log; trial logs are not here
+    readonly nniManagerLog: string;
+}
+
+export function createPaths(args: NniManagerArgs): NniPaths {
+    assert(
+        path.isAbsolute(args.experimentsDirectory),
+        `Command line arg --experiments-directory "${args.experimentsDirectory}" is not absoulte`
+    );
+    const experimentRoot = path.join(args.experimentsDirectory, args.experimentId);
+
+    const logDirectory = path.join(experimentRoot, 'log');
+
+    // TODO: move all `mkdir`s here
+    fs.mkdirSync(logDirectory, { recursive: true });
+
+    const nniManagerLog = path.join(logDirectory, 'nnimanager.log');
+
+    return {
+        experimentRoot,
+        experimentsDirectory: args.experimentsDirectory,
+        logDirectory,
+        nniManagerLog,
+    };
+}
diff --git a/ts/nni_manager/common/globals/unittest.ts b/ts/nni_manager/common/globals/unittest.ts
new file mode 100644
index 000000000..b2ddd9521
--- /dev/null
+++ b/ts/nni_manager/common/globals/unittest.ts
@@ -0,0 +1,65 @@
+// Copyright (c) Microsoft Corporation.
+// Licensed under the MIT license.
+
+/**
+ *  Unit test helper.
+ *  It should be inside "test", but must be here for compatibility, until we refactor all test cases.
+ *
+ *  Use this module to replace NNI globals with mocked values:
+ *
+ *      import globals from 'common/globals/unittest';
+ *
+ *  You can then edit these mocked globals and the injection will be visible to all modules.
+ *  Remember to invoke `resetGlobals()` in "after()" hook if you do so.
+ **/
+
+import os from 'os';
+import path from 'path';
+
+import type { NniManagerArgs } from './arguments';
+import { NniPaths, createPaths } from './paths';
+
+// copied from https://www.typescriptlang.org/docs/handbook/2/mapped-types.html
+type Mutable<Type> = {
+    -readonly [Property in keyof Type]: Type[Property];
+};
+
+export interface MutableGlobals {
+    args: Mutable<NniManagerArgs>;
+    paths: Mutable<NniPaths>;
+}
+
+export function resetGlobals(): void {
+    const args: NniManagerArgs = {
+        port: 8080,
+        experimentId: 'unittest',
+        action: 'create',
+        experimentsDirectory: path.join(os.homedir(), 'nni-experiments'),
+        logLevel: 'info',
+        foreground: false,
+        urlPrefix: '',
+        mode: 'unittest',
+        dispatcherPipe: undefined
+    };
+
+    const paths = createPaths(args);
+
+    const globals = { args, paths };
+    if (global.nni === undefined) {
+        global.nni = globals;
+    } else {
+        Object.assign(global.nni, globals);
+    }
+}
+
+function isUnitTest(): boolean {
+    const event = process.env['npm_lifecycle_event'] ?? '';
+    return event.startsWith('test') || event === 'mocha' || event === 'nyc';
+}
+
+if (isUnitTest()) {
+    resetGlobals();
+}
+
+const globals: MutableGlobals = global.nni;
+export default globals;
diff --git a/ts/nni_manager/common/utils.ts b/ts/nni_manager/common/utils.ts
index 74ae81d13..c3943a444 100644
--- a/ts/nni_manager/common/utils.ts
+++ b/ts/nni_manager/common/utils.ts
@@ -18,21 +18,22 @@ import { Container } from 'typescript-ioc';
 import glob from 'glob';
 
 import { Database, DataStore } from './datastore';
-import { getExperimentStartupInfo, setExperimentStartupInfo } from './experimentStartupInfo';
+import globals from './globals';
+import { resetGlobals } from './globals/unittest';  // TODO: this file should not contain unittest helpers
 import { ExperimentConfig, Manager } from './manager';
 import { ExperimentManager } from './experimentManager';
 import { HyperParameters, TrainingService, TrialJobStatus } from './trainingService';
 
 function getExperimentRootDir(): string {
-    return getExperimentStartupInfo().logDir;
+    return globals.paths.experimentRoot;
 }
 
 function getLogDir(): string {
-    return path.join(getExperimentRootDir(), 'log');
+    return globals.paths.logDirectory;
 }
 
 function getLogLevel(): string {
-    return getExperimentStartupInfo().logLevel;
+    return globals.args.logLevel;
 }
 
 function getDefaultDatabaseDir(): string {
@@ -104,18 +105,6 @@ function randomSelect<T>(a: T[]): T {
     return a[Math.floor(Math.random() * a.length)];
 }
 
-function parseArg(names: string[]): string {
-    if (process.argv.length >= 4) {
-        for (let i: number = 2; i < process.argv.length - 1; i++) {
-            if (names.includes(process.argv[i])) {
-                return process.argv[i + 1];
-            }
-        }
-    }
-
-    return '';
-}
-
 function getCmdPy(): string {
     let cmd = 'python3';
     if (process.platform === 'win32') {
@@ -165,10 +154,7 @@ function prepareUnitTest(): void {
     Container.snapshot(Manager);
     Container.snapshot(ExperimentManager);
 
-    const logLevel: string = parseArg(['--log_level', '-ll']);
-
-    setExperimentStartupInfo(true, 'unittest', 8080, 'unittest', undefined, logLevel);
-    mkDirPSync(getLogDir());
+    resetGlobals();
 
     const sqliteFile: string = path.join(getDefaultDatabaseDir(), 'nni.sqlite');
     try {
@@ -188,8 +174,6 @@ function cleanupUnitTest(): void {
     Container.restore(DataStore);
     Container.restore(Database);
     Container.restore(ExperimentManager);
-    const logLevel: string = parseArg(['--log_level', '-ll']);
-    setExperimentStartupInfo(true, 'unittest', 8080, 'unittest', undefined, logLevel);
 }
 
 let cachedIpv4Address: string | null = null;
@@ -434,5 +418,5 @@ export function importModule(modulePath: string): any {
 export {
     countFilesRecursively, generateParamFileName, getMsgDispatcherCommand, getCheckpointDir, getExperimentsInfoPath,
     getLogDir, getExperimentRootDir, getJobCancelStatus, getDefaultDatabaseDir, getIPV4Address, unixPathJoin, withLockSync, getFreePort, isPortOpen,
-    mkDirP, mkDirPSync, delay, prepareUnitTest, parseArg, cleanupUnitTest, uniqueString, randomInt, randomSelect, getLogLevel, getVersion, getCmdPy, getTunerProc, isAlive, killPid, getNewLine
+    mkDirP, mkDirPSync, delay, prepareUnitTest, cleanupUnitTest, uniqueString, randomInt, randomSelect, getLogLevel, getVersion, getCmdPy, getTunerProc, isAlive, killPid, getNewLine
 };
diff --git a/ts/nni_manager/config/aml/amlUtil.py b/ts/nni_manager/config/aml/amlUtil.py
index a5b2a6bc2..2237506b0 100644
--- a/ts/nni_manager/config/aml/amlUtil.py
+++ b/ts/nni_manager/config/aml/amlUtil.py
@@ -52,7 +52,8 @@ if __name__ == "__main__":
                     print('stop_result:failed')
                     exit(0)
                 loop_count += 1
-                time.sleep(500)
+                time.sleep(5)
+                status = run.get_status()
             print('stop_result:success')
             exit(0)
         elif line == 'receive':
diff --git a/ts/nni_manager/main.ts b/ts/nni_manager/main.ts
index 5449e5b13..ba11d4db2 100644
--- a/ts/nni_manager/main.ts
+++ b/ts/nni_manager/main.ts
@@ -1,143 +1,93 @@
 // Copyright (c) Microsoft Corporation.
 // Licensed under the MIT license.
 
-import 'app-module-path/register';
+/**
+ *  Entry point of NNI manager.
+ *
+ *  NNI manager is normally started by "nni/experiment/launcher.py".
+ *  It requires command line arguments defined as NniManagerArgs in "common/globals/arguments.ts".
+ *
+ *  Example usage:
+ *
+ *      node main.js \
+ *          --port 8080 \
+ *          --experiment-id ID \
+ *          --action create \
+ *          --experiments-directory /home/USER/nni-experiments \
+ *          --log-level info \
+ *          --foreground false \  (optional)
+ *          --mode local  (required for now, will be removed later)
+ **/
+
+import 'app-module-path/register';  // so we can use absolute path to import
+
+import fs from 'fs';
+
 import { Container, Scope } from 'typescript-ioc';
 
-import * as fs from 'fs';
-import * as path from 'path';
-import * as component from './common/component';
-import { Database, DataStore } from './common/datastore';
-import { setExperimentStartupInfo } from './common/experimentStartupInfo';
-import { getLogger, setLogLevel, startLogging } from './common/log';
-import { Manager, ExperimentStartUpMode } from './common/manager';
-import { ExperimentManager } from './common/experimentManager';
-import { TensorboardManager } from './common/tensorboardManager';
-import { getLogDir, mkDirP, parseArg } from './common/utils';
-import { NNIDataStore } from './core/nniDataStore';
-import { NNIManager } from './core/nnimanager';
-import { SqlDB } from './core/sqlDatabase';
-import { NNIExperimentsManager } from './core/nniExperimentsManager';
-import { NNITensorboardManager } from './core/nniTensorboardManager';
-import { RestServer } from './rest_server';
-
-function initStartupInfo(
-    startExpMode: string, experimentId: string, basePort: number, platform: string,
-    logDirectory: string, experimentLogLevel: string, readonly: boolean, dispatcherPipe: string, urlprefix: string): void {
-    const createNew: boolean = (startExpMode === ExperimentStartUpMode.NEW);
-    setExperimentStartupInfo(createNew, experimentId, basePort, platform, logDirectory, experimentLogLevel, readonly, dispatcherPipe, urlprefix);
-}
+import * as component from 'common/component';
+import { Database, DataStore } from 'common/datastore';
+import { ExperimentManager } from 'common/experimentManager';
+import globals, { initGlobals } from 'common/globals';
+import { getLogger, setLogLevel, startLogging } from 'common/log';
+import { Manager } from 'common/manager';
+import { TensorboardManager } from 'common/tensorboardManager';
+import { NNIDataStore } from 'core/nniDataStore';
+import { NNIExperimentsManager } from 'core/nniExperimentsManager';
+import { NNITensorboardManager } from 'core/nniTensorboardManager';
+import { NNIManager } from 'core/nnimanager';
+import { SqlDB } from 'core/sqlDatabase';
+import { RestServer } from 'rest_server';
+
+import path from 'path';
+
+async function start(): Promise<void> {
+    getLogger('main').info('Start NNI manager');
+
+    Container.bind(Manager).to(NNIManager).scope(Scope.Singleton);
+    Container.bind(Database).to(SqlDB).scope(Scope.Singleton);
+    Container.bind(DataStore).to(NNIDataStore).scope(Scope.Singleton);
+    Container.bind(ExperimentManager).to(NNIExperimentsManager).scope(Scope.Singleton);
+    Container.bind(TensorboardManager).to(NNITensorboardManager).scope(Scope.Singleton);
 
-async function initContainer(foreground: boolean, _platformMode: string, logFileName?: string): Promise<void> {
-    Container.bind(Manager)
-        .to(NNIManager)
-        .scope(Scope.Singleton);
-    Container.bind(Database)
-        .to(SqlDB)
-        .scope(Scope.Singleton);
-    Container.bind(DataStore)
-        .to(NNIDataStore)
-        .scope(Scope.Singleton);
-    Container.bind(ExperimentManager)
-        .to(NNIExperimentsManager)
-        .scope(Scope.Singleton);
-    Container.bind(TensorboardManager)
-        .to(NNITensorboardManager)
-        .scope(Scope.Singleton);
-    const DEFAULT_LOGFILE: string = path.join(getLogDir(), 'nnimanager.log');
-    if (!foreground) {
-        if (logFileName === undefined) {
-            startLogging(DEFAULT_LOGFILE);
-        } else {
-            startLogging(logFileName);
-        }
-    }
-    // eslint-disable-next-line @typescript-eslint/no-use-before-define
-    setLogLevel(logLevel);
     const ds: DataStore = component.get(DataStore);
-
     await ds.init();
-}
 
-function usage(): void {
-    console.info('usage: node main.js --port <port> --mode \
-    <local/remote/pai/kubeflow/frameworkcontroller/aml/adl/hybrid/dlc> --start_mode <new/resume> --experiment_id <id> --foreground <true/false>');
+    const restServer = new RestServer(globals.args.port, globals.args.urlPrefix);
+    await restServer.start();
 }
 
-const strPort: string = parseArg(['--port', '-p']);
-if (!strPort || strPort.length === 0) {
-    usage();
-    process.exit(1);
-}
-
-const foregroundArg: string = parseArg(['--foreground', '-f']);
-if (foregroundArg && !['true', 'false'].includes(foregroundArg.toLowerCase())) {
-    console.log(`FATAL: foreground property should only be true or false`);
-    usage();
-    process.exit(1);
-}
-const foreground: boolean = (foregroundArg && foregroundArg.toLowerCase() === 'true') ? true : false;
-
-const port: number = parseInt(strPort, 10);
-
-const mode: string = parseArg(['--mode', '-m']);
-
-const startMode: string = parseArg(['--start_mode', '-s']);
-if (![ExperimentStartUpMode.NEW, ExperimentStartUpMode.RESUME].includes(startMode)) {
-    console.log(`FATAL: unknown start_mode: ${startMode}`);
-    usage();
-    process.exit(1);
-}
-
-const experimentId: string = parseArg(['--experiment_id', '-id']);
-if (experimentId.trim().length < 1) {
-    console.log(`FATAL: cannot resume the experiment, invalid experiment_id: ${experimentId}`);
-    usage();
-    process.exit(1);
+function shutdown(): void {
+    (component.get(Manager) as Manager).stopExperiment();
 }
-
-const logDir: string = parseArg(['--log_dir', '-ld']);
-if (logDir.length > 0) {
-    if (!fs.existsSync(logDir)) {
-        console.log(`FATAL: log_dir ${logDir} does not exist`);
+    
+// Register callbacks to free training service resources on unexpected shutdown.
+// A graceful stop should use REST API,
+// because interrupts can cause strange behaviors in children processes.
+process.on('SIGTERM', shutdown);
+process.on('SIGBREAK', shutdown);
+process.on('SIGINT', shutdown);
+
+/* main */
+
+initGlobals();
+
+// TODO: these should be handled inside globals module
+startLogging(globals.paths.nniManagerLog);
+setLogLevel(globals.args.logLevel);
+
+start().then(() => {
+    getLogger('main').debug('start() returned.');
+}).catch((error) => {
+    try {
+        getLogger('main').error('Failed to start:', error);
+    } catch (loggerError) {
+        console.log('Failed to start:', error);
+        console.log('Seems logger is faulty:', loggerError);
     }
-}
-
-const logLevel: string = parseArg(['--log_level', '-ll']);
-
-const readonlyArg: string = parseArg(['--readonly', '-r']);
-if (readonlyArg && !['true', 'false'].includes(readonlyArg.toLowerCase())) {
-    console.log(`FATAL: readonly property should only be true or false`);
-    usage();
     process.exit(1);
-}
-const readonly = (readonlyArg && readonlyArg.toLowerCase() == 'true') ? true : false;
-
-const dispatcherPipe: string = parseArg(['--dispatcher_pipe']);
-
-const urlPrefix: string = parseArg(['--url_prefix']);
-
-initStartupInfo(startMode, experimentId, port, mode, logDir, logLevel, readonly, dispatcherPipe, urlPrefix);
-
-mkDirP(getLogDir())
-    .then(async () => {
-        try {
-            await initContainer(foreground, mode);
-            const restServer: RestServer = component.get(RestServer);
-            await restServer.start();
-        } catch (err) {
-            getLogger('main').error(`${err.stack}`);
-            throw err;
-        }
-    })
-    .catch((err: Error) => {
-        console.error(`Failed to create log dir: ${err.stack}`);
-    });
-
-function cleanUp(): void {
-    (component.get(Manager) as Manager).stopExperiment();
-}
+});
 
-process.on('SIGTERM', cleanUp);
-process.on('SIGBREAK', cleanUp);
-process.on('SIGINT', cleanUp);
+// Node.js exits when there is no active handler,
+// and we have registered a lot of handlers which are never cleaned up.
+// So it runs forever until NNIManager calls `process.exit()`.
diff --git a/ts/nni_manager/package.json b/ts/nni_manager/package.json
index 205ba066a..ea37cc4ce 100644
--- a/ts/nni_manager/package.json
+++ b/ts/nni_manager/package.json
@@ -21,7 +21,6 @@
     "js-base64": "^3.6.1",
     "kubernetes-client": "^6.12.1",
     "lockfile": "^1.0.4",
-    "npm": ">=8.3.0",
     "python-shell": "^3.0.0",
     "rx": "^4.1.0",
     "sqlite3": "5.0.2",
@@ -33,7 +32,8 @@
     "ts-deferred": "^1.0.4",
     "typescript-ioc": "^1.2.6",
     "typescript-string-operations": "^1.4.1",
-    "ws": "^7.4.6"
+    "ws": "^7.4.6",
+    "yargs": "^17.3.1"
   },
   "devDependencies": {
     "@types/chai": "^4.2.18",
@@ -55,6 +55,7 @@
     "@types/tar": "^4.0.4",
     "@types/tmp": "^0.2.0",
     "@types/ws": "^7.4.4",
+    "@types/yargs": "^17.0.8",
     "@typescript-eslint/eslint-plugin": "^2.10.0",
     "@typescript-eslint/parser": "^4.26.0",
     "chai": "^4.3.4",
@@ -63,6 +64,7 @@
     "glob": "^7.1.7",
     "mocha": "^9.0.2",
     "node-fetch": "<3.0.0",
+    "npm": ">=8.3.0",
     "nyc": "^15.1.0",
     "request": "^2.88.2",
     "rmdir": "^1.2.0",
diff --git a/ts/nni_manager/rest_server/index.ts b/ts/nni_manager/rest_server/index.ts
index f06c224ce..e31feba5d 100644
--- a/ts/nni_manager/rest_server/index.ts
+++ b/ts/nni_manager/rest_server/index.ts
@@ -18,11 +18,10 @@
  *    2. Refactor ClusterJobRestServer to an express-ws application so it doesn't require extra port.
  *    3. Provide public API to register express app, so this can be decoupled with other modules' implementation.
  *    4. Refactor NNIRestHandler. It's a mess.
- *    5. Get rid of IOC.
- *    6. Deal with log path mismatch between REST API and file system.
- *    7. Strip slashes of URL prefix inside ExperimentStartupInfo.
+ *    5. Deal with log path mismatch between REST API and file system.
  **/
 
+import assert from 'assert/strict';
 import type { Server } from 'http';
 import type { AddressInfo } from 'net';
 import path from 'path';
@@ -31,10 +30,8 @@ import express, { Request, Response, Router } from 'express';
 import httpProxy from 'http-proxy';
 import { Deferred } from 'ts-deferred';
 
-import { Singleton } from 'common/component';
-import { getBasePort, getPrefixUrl } from 'common/experimentStartupInfo';
+import globals from 'common/globals';
 import { Logger, getLogger } from 'common/log';
-import { getLogDir } from 'common/utils';
 import { createRestHandler } from './restHandler';
 
 /**
@@ -43,32 +40,29 @@ import { createRestHandler } from './restHandler';
  *  RestServer must be initialized with start() after NNI manager constructing, but not necessarily after initializing.
  *  This is because RestServer needs NNI manager instance to register API handlers.
  **/
-@Singleton
 export class RestServer {
     private port: number;
     private urlPrefix: string;
     private server: Server | null = null;
     private logger: Logger = getLogger('RestServer');
 
-    // I would prefer to get port and urlPrefix by constructor parameters,
-    // but this is impossible due to limitation of IOC.
-    constructor() {
-        this.port = getBasePort();
-        // Stripping slashes should be done inside ExperimentInfo, but I don't want to touch it for now.
-        this.urlPrefix = '/' + stripSlashes(getPrefixUrl());
+    constructor(port: number, urlPrefix: string) {
+        assert(!urlPrefix.startsWith('/') && !urlPrefix.endsWith('/'));
+        this.port = port;
+        this.urlPrefix = urlPrefix;
     }
 
     // The promise is resolved when it's ready to serve requests.
     // This worth nothing for now,
     // but for example if we connect to tuner using WebSocket then it must be launched after promise resolved.
     public start(): Promise<void> {
-        this.logger.info(`Starting REST server at port ${this.port}, URL prefix: "${this.urlPrefix}"`);
+        this.logger.info(`Starting REST server at port ${this.port}, URL prefix: "/${this.urlPrefix}"`);
 
         const app = express();
         // FIXME: We should have a global handler for critical errors.
         // `shutdown()` is not a callback and should not be passed to NNIRestHandler.
-        app.use(this.urlPrefix, rootRouter(this.shutdown.bind(this)));
-        app.all('*', (_req: Request, res: Response) => { res.status(404).send(`Outside prefix "${this.urlPrefix}"`); });
+        app.use('/' + this.urlPrefix, rootRouter(this.shutdown.bind(this)));
+        app.all('*', (_req: Request, res: Response) => { res.status(404).send(`Outside prefix "/${this.urlPrefix}"`); });
         this.server = app.listen(this.port);
 
         const deferred = new Deferred<void>();
@@ -126,7 +120,7 @@ function rootRouter(stopCallback: () => Promise<void>): Router {
     // The REST API path "/logs" does not match file system path "/log".
     // Here we use an additional router to workaround this problem.
     const logRouter = Router();
-    logRouter.get('*', express.static(getLogDir()));
+    logRouter.get('*', express.static(globals.paths.logDirectory));
     router.use('/logs', logRouter);
 
     /* NAS model visualization */
@@ -153,16 +147,12 @@ function netronProxy(): Router {
     return router;
 }
 
-function stripSlashes(str: string): string {
-    return str.replace(/^\/+/, '').replace(/\/+$/, '');
-}
-
 let webuiPath: string = path.resolve('static');
 let netronUrl: string = 'https://netron.app';
 
 export namespace UnitTestHelpers {
     export function getPort(server: RestServer): number {
-        return (<any>server).port;
+        return (server as any).port;
     }
 
     export function setWebuiPath(mockPath: string): void {
diff --git a/ts/nni_manager/test/common/globals/arguments.test.ts b/ts/nni_manager/test/common/globals/arguments.test.ts
new file mode 100644
index 000000000..e7ed7d9ed
--- /dev/null
+++ b/ts/nni_manager/test/common/globals/arguments.test.ts
@@ -0,0 +1,69 @@
+// Copyright (c) Microsoft Corporation.
+// Licensed under the MIT license.
+
+import assert from 'assert/strict';
+
+import { parseArgs } from 'common/globals/arguments';
+
+const command = '--port 80 --experiment-id ID --action resume --experiments-directory DIR --log-level error';
+const expected = {
+    port: 80,
+    experimentId: 'ID',
+    action: 'resume',
+    experimentsDirectory: 'DIR',
+    logLevel: 'error',
+    foreground: false,
+    urlPrefix: '',
+
+    mode: '',
+    dispatcherPipe: undefined,
+};
+
+function testGoodShort(): void {
+    const args = parseArgs(command.split(' '));
+    assert.deepEqual(args, expected);
+}
+
+function testGoodLong(): void {
+    const cmd = command + ' --url-prefix URL/prefix --foreground true';
+    const args = parseArgs(cmd.split(' '));
+    const expectedLong = Object.assign({}, expected);
+    expectedLong.urlPrefix = 'URL/prefix';
+    expectedLong.foreground = true;
+    assert.deepEqual(args, expectedLong);
+}
+
+function testBadKey(): void {
+    const cmd = command + ' --bad 1';
+    assert.throws(() => parseArgs(cmd.split(' ')));
+}
+
+function testBadPos(): void {
+    const cmd = command.replace('--port', 'port');
+    assert.throws(() => parseArgs(cmd.split(' ')));
+}
+
+function testBadNum(): void {
+    const cmd = command.replace('80', '8o');
+    assert.throws(() => parseArgs(cmd.split(' ')));
+}
+
+function testBadBool(): void {
+    const cmd = command + ' --foreground 1';
+    assert.throws(() => parseArgs(cmd.split(' ')));
+}
+
+function testBadChoice(): void {
+    const cmd = command.replace('resume', 'new');
+    assert.throws(() => parseArgs(cmd.split(' ')));
+}
+
+describe('## globals.arguments ##', () => {
+    it('good short', () => testGoodShort());
+    it('good long', () => testGoodLong());
+    it('bad key arg', () => testBadKey());
+    it('bad positional arg', () => testBadPos());
+    it('bad number', () => testBadNum());
+    it('bad boolean', () => testBadBool());
+    it('bad choice', () => testBadChoice());
+});
diff --git a/ts/nni_manager/test/core/dataStore.test.ts b/ts/nni_manager/test/core/dataStore.test.ts
index cc3fa086f..d0cf6c3db 100644
--- a/ts/nni_manager/test/core/dataStore.test.ts
+++ b/ts/nni_manager/test/core/dataStore.test.ts
@@ -8,7 +8,6 @@ import { Container, Scope } from 'typescript-ioc';
 
 import * as component from '../../common/component';
 import { Database, DataStore, TrialJobInfo } from '../../common/datastore';
-import { setExperimentStartupInfo } from '../../common/experimentStartupInfo';
 import { ExperimentProfile, TrialJobStatistics } from '../../common/manager';
 import { TrialJobStatus } from '../../common/trainingService';
 import { cleanupUnitTest, prepareUnitTest } from '../../common/utils';
diff --git a/ts/nni_manager/test/core/sqlDatabase.test.ts b/ts/nni_manager/test/core/sqlDatabase.test.ts
index d1e8fa729..488398de4 100644
--- a/ts/nni_manager/test/core/sqlDatabase.test.ts
+++ b/ts/nni_manager/test/core/sqlDatabase.test.ts
@@ -9,7 +9,6 @@ import * as path from 'path';
 import { Container } from 'typescript-ioc';
 import * as component from '../../common/component';
 import { Database, MetricDataRecord, TrialJobEvent, TrialJobEventRecord } from '../../common/datastore';
-import { setExperimentStartupInfo } from '../../common/experimentStartupInfo';
 import { ExperimentConfig, ExperimentProfile } from '../../common/manager';
 import { cleanupUnitTest, getDefaultDatabaseDir, mkDirP, prepareUnitTest } from '../../common/utils';
 import { SqlDB } from '../../core/sqlDatabase';
diff --git a/ts/nni_manager/test/rest_server/nni_manager_handlers.test.ts b/ts/nni_manager/test/rest_server/nni_manager_handlers.test.ts
index c83542578..b46a67416 100644
--- a/ts/nni_manager/test/rest_server/nni_manager_handlers.test.ts
+++ b/ts/nni_manager/test/rest_server/nni_manager_handlers.test.ts
@@ -21,6 +21,8 @@ import { testExperimentManagerProvider } from '../mock/experimentManager';
 import { TensorboardManager } from '../../common/tensorboardManager';
 import { NNITensorboardManager } from '../../core/nniTensorboardManager';
 
+let restServer: RestServer;
+
 describe('Unit test for rest server', () => {
 
     let ROOT_URL: string;
@@ -32,7 +34,7 @@ describe('Unit test for rest server', () => {
         Container.bind(TrainingService).to(MockedTrainingService);
         Container.bind(ExperimentManager).provider(testExperimentManagerProvider);
         Container.bind(TensorboardManager).to(NNITensorboardManager);
-        const restServer: RestServer = component.get(RestServer);
+        restServer = new RestServer(8080, '');
         restServer.start().then(() => {
             ROOT_URL = `http://localhost:8080/api/v1/nni`;
             done();
@@ -42,7 +44,7 @@ describe('Unit test for rest server', () => {
     });
 
     after(() => {
-        component.get<RestServer>(RestServer).shutdown();
+        restServer.shutdown();
         cleanupUnitTest();
     });
 
diff --git a/ts/nni_manager/test/rest_server/rest_server.test.ts b/ts/nni_manager/test/rest_server/rest_server.test.ts
index c13cdd6e5..d60b1480f 100644
--- a/ts/nni_manager/test/rest_server/rest_server.test.ts
+++ b/ts/nni_manager/test/rest_server/rest_server.test.ts
@@ -7,7 +7,7 @@ import path from 'path';
 
 import fetch from 'node-fetch';
 
-import { setExperimentStartupInfo } from 'common/experimentStartupInfo';
+import globals, { resetGlobals } from 'common/globals/unittest';
 import { RestServer, UnitTestHelpers } from 'rest_server';
 import * as mock_netron_server from './mock_netron_server';
 
@@ -121,6 +121,7 @@ before(async () => {
 
 after(async () => {
     await restServer.shutdown();
+    resetGlobals();
 });
 
 async function configRestServer(urlPrefix?: string) {
@@ -128,24 +129,10 @@ async function configRestServer(urlPrefix?: string) {
         await restServer.shutdown();
     }
 
-    // Set port, URL prefix, and log path.
-    // There should be a better way to do this.
-    // Maybe rewire? I can't get it work with TypeScript.
-    setExperimentStartupInfo(
-        true,
-        path.basename(__dirname),  // hacking getLogDir()
-        0,  // ask for a random idle port
-        'local',
-        path.dirname(__dirname),
-        undefined,
-        undefined,
-        undefined,
-        urlPrefix
-    );
-
+    globals.paths.logDirectory = path.join(__dirname, 'log');
     UnitTestHelpers.setWebuiPath(path.join(__dirname, 'static'));
 
-    restServer = new RestServer();
+    restServer = new RestServer(0, urlPrefix ?? '');
     await restServer.start();
     const port = UnitTestHelpers.getPort(restServer);
 
diff --git a/ts/nni_manager/training_service/kubernetes/adl/adlApiClient.ts b/ts/nni_manager/training_service/kubernetes/adl/adlApiClient.ts
index a8d8607f5..c93fa4812 100644
--- a/ts/nni_manager/training_service/kubernetes/adl/adlApiClient.ts
+++ b/ts/nni_manager/training_service/kubernetes/adl/adlApiClient.ts
@@ -11,7 +11,7 @@ class AdlClientV1 extends KubernetesCRDClient {
     /**
      * constructor, to initialize adl CRD definition
      */
-    protected readonly namespace: string;
+    public readonly namespace: string;
 
     public constructor(namespace: string) {
         super();
diff --git a/ts/nni_manager/training_service/kubernetes/frameworkcontroller/frameworkcontrollerTrainingService.ts b/ts/nni_manager/training_service/kubernetes/frameworkcontroller/frameworkcontrollerTrainingService.ts
index 8683e6c6c..0acb5a010 100644
--- a/ts/nni_manager/training_service/kubernetes/frameworkcontroller/frameworkcontrollerTrainingService.ts
+++ b/ts/nni_manager/training_service/kubernetes/frameworkcontroller/frameworkcontrollerTrainingService.ts
@@ -118,7 +118,7 @@ class FrameworkControllerTrainingService extends KubernetesTrainingService imple
         } else {
             configTaskRoles = this.parseCustomTaskRoles(this.fcTemplate.spec.taskRoles)
         }
-        const namespace = this.fcClusterConfig.namespace ? this.fcClusterConfig.namespace : "default";
+        const namespace = this.fcClusterConfig.namespace ?? "default";
         this.genericK8sClient.setNamespace = namespace;
 
         if (this.kubernetesRestServerPort === undefined) {
@@ -134,7 +134,7 @@ class FrameworkControllerTrainingService extends KubernetesTrainingService imple
         const trialJobId: string = uniqueString(5);
         // Set trial's NFS working folder
         const trialWorkingFolder: string = path.join(this.CONTAINER_MOUNT_PATH, 'nni', getExperimentId(), trialJobId);
-        const trialLocalTempFolder: string = path.join(getExperimentRootDir(), 'trials-local', trialJobId);
+        const trialLocalTempFolder: string = path.join(getExperimentRootDir(), 'trials', trialJobId);
         let frameworkcontrollerJobName: string = `nniexp${this.experimentId}trial${trialJobId}`.toLowerCase();
 
         let frameworkcontrollerJobConfig: any;
@@ -204,6 +204,7 @@ class FrameworkControllerTrainingService extends KubernetesTrainingService imple
                 let namespace: string | undefined;
                 this.fcClusterConfig = FrameworkControllerClusterConfigFactory
                     .generateFrameworkControllerClusterConfig(frameworkcontrollerClusterJsonObject);
+                this.genericK8sClient.setNamespace = this.fcClusterConfig.namespace ?? "default";
                 if (this.fcClusterConfig.storageType === 'azureStorage') {
                     const azureFrameworkControllerClusterConfig: FrameworkControllerClusterConfigAzure =
                         <FrameworkControllerClusterConfigAzure>this.fcClusterConfig;
@@ -346,8 +347,8 @@ class FrameworkControllerTrainingService extends KubernetesTrainingService imple
         for (const taskRole of configTaskRoles) {
             const runScriptContent: string =
                 await this.generateRunScript('frameworkcontroller', trialJobId, trialWorkingFolder,
-                    this.generateCommandScript(configTaskRoles, taskRole.command), form.sequenceId.toString(),
-                    taskRole.name, taskRole.gpuNum ? taskRole.gpuNum : 0);
+                    this.generateCommandScript(configTaskRoles, taskRole.command),
+                    form.sequenceId.toString(), taskRole.name, taskRole.gpuNum ? taskRole.gpuNum : 0);
             await fs.promises.writeFile(path.join(trialLocalTempFolder, `run_${taskRole.name}.sh`), runScriptContent, {encoding: 'utf8'});
         }
 
@@ -439,7 +440,7 @@ class FrameworkControllerTrainingService extends KubernetesTrainingService imple
             kind: 'Framework',
             metadata: {
                 name: frameworkcontrollerJobName,
-                namespace: this.fcClusterConfig.namespace ? this.fcClusterConfig.namespace : "default",
+                namespace: this.fcClusterConfig.namespace ?? "default",
                 labels: {
                     app: this.NNI_KUBERNETES_TRIAL_LABEL,
                     expId: getExperimentId(),
diff --git a/ts/nni_manager/training_service/kubernetes/kubeflow/kubeflowApiClient.ts b/ts/nni_manager/training_service/kubernetes/kubeflow/kubeflowApiClient.ts
index 978c92478..02a4a30e1 100644
--- a/ts/nni_manager/training_service/kubernetes/kubeflow/kubeflowApiClient.ts
+++ b/ts/nni_manager/training_service/kubernetes/kubeflow/kubeflowApiClient.ts
@@ -17,7 +17,7 @@ class TFOperatorClientV1Alpha2 extends KubernetesCRDClient {
     }
 
     protected get operator(): any {
-        return this.client.apis['kubeflow.org'].v1alpha2.namespaces('default').tfjobs;
+        return this.client.apis['kubeflow.org'].v1alpha2.namespaces(this.namespace).tfjobs;
     }
 
     public get containerName(): string {
@@ -36,7 +36,7 @@ class TFOperatorClientV1Beta1 extends KubernetesCRDClient {
     }
 
     protected get operator(): any {
-        return this.client.apis['kubeflow.org'].v1beta1.namespaces('default').tfjobs;
+        return this.client.apis['kubeflow.org'].v1beta1.namespaces(this.namespace).tfjobs;
     }
 
     public get containerName(): string {
@@ -55,7 +55,7 @@ class TFOperatorClientV1Beta2 extends KubernetesCRDClient {
     }
 
     protected get operator(): any {
-        return this.client.apis['kubeflow.org'].v1beta2.namespaces('default').tfjobs;
+        return this.client.apis['kubeflow.org'].v1beta2.namespaces(this.namespace).tfjobs;
     }
 
     public get containerName(): string {
@@ -74,7 +74,7 @@ class TFOperatorClientV1 extends KubernetesCRDClient {
     }
 
     protected get operator(): any {
-        return this.client.apis['kubeflow.org'].v1.namespaces('default').tfjobs;
+        return this.client.apis['kubeflow.org'].v1.namespaces(this.namespace).tfjobs;
     }
 
     public get containerName(): string {
@@ -92,7 +92,7 @@ class PyTorchOperatorClientV1 extends KubernetesCRDClient {
     }
 
     protected get operator(): any {
-        return this.client.apis['kubeflow.org'].v1.namespaces('default').pytorchjobs;
+        return this.client.apis['kubeflow.org'].v1.namespaces(this.namespace).pytorchjobs;
     }
 
     public get containerName(): string {
@@ -110,7 +110,7 @@ class PyTorchOperatorClientV1Alpha2 extends KubernetesCRDClient {
     }
 
     protected get operator(): any {
-        return this.client.apis['kubeflow.org'].v1alpha2.namespaces('default').pytorchjobs;
+        return this.client.apis['kubeflow.org'].v1alpha2.namespaces(this.namespace).pytorchjobs;
     }
 
     public get containerName(): string {
@@ -129,7 +129,7 @@ class PyTorchOperatorClientV1Beta1 extends KubernetesCRDClient {
     }
 
     protected get operator(): any {
-        return this.client.apis['kubeflow.org'].v1beta1.namespaces('default').pytorchjobs;
+        return this.client.apis['kubeflow.org'].v1beta1.namespaces(this.namespace).pytorchjobs;
     }
 
     public get containerName(): string {
@@ -148,7 +148,7 @@ class PyTorchOperatorClientV1Beta2 extends KubernetesCRDClient {
     }
 
     protected get operator(): any {
-        return this.client.apis['kubeflow.org'].v1beta2.namespaces('default').pytorchjobs;
+        return this.client.apis['kubeflow.org'].v1beta2.namespaces(this.namespace).pytorchjobs;
     }
 
     public get containerName(): string {
diff --git a/ts/nni_manager/training_service/kubernetes/kubeflow/kubeflowConfig.ts b/ts/nni_manager/training_service/kubernetes/kubeflow/kubeflowConfig.ts
index cffc88d37..735937f28 100644
--- a/ts/nni_manager/training_service/kubernetes/kubeflow/kubeflowConfig.ts
+++ b/ts/nni_manager/training_service/kubernetes/kubeflow/kubeflowConfig.ts
@@ -18,8 +18,8 @@ export type OperatorApiVersion = 'v1alpha2' | 'v1beta1' | 'v1beta2' | 'v1';
  */
 export class KubeflowClusterConfig extends KubernetesClusterConfig {
     public readonly operator: KubeflowOperator;
-    constructor(apiVersion: string, operator: KubeflowOperator) {
-        super(apiVersion);
+    constructor(apiVersion: string, operator: KubeflowOperator, namespace?: string) {
+        super(apiVersion, undefined, namespace);
         this.operator = operator;
     }
 }
@@ -30,9 +30,10 @@ export class KubeflowClusterConfigNFS extends KubernetesClusterConfigNFS {
             operator: KubeflowOperator,
             apiVersion: string,
             nfs: NFSConfig,
-            storage?: KubernetesStorageKind
+            storage?: KubernetesStorageKind,
+            namespace?: string
         ) {
-        super(apiVersion, nfs, storage);
+        super(apiVersion, nfs, storage, namespace);
         this.operator = operator;
     }
 
@@ -48,7 +49,8 @@ export class KubeflowClusterConfigNFS extends KubernetesClusterConfigNFS {
             kubeflowClusterConfigObjectNFS.operator,
             kubeflowClusterConfigObjectNFS.apiVersion,
             kubeflowClusterConfigObjectNFS.nfs,
-            kubeflowClusterConfigObjectNFS.storage
+            kubeflowClusterConfigObjectNFS.storage,
+            kubeflowClusterConfigObjectNFS.namespace
         );
     }
 }
@@ -61,9 +63,10 @@ export class KubeflowClusterConfigAzure extends KubernetesClusterConfigAzure {
             apiVersion: string,
             keyVault: KeyVaultConfig,
             azureStorage: AzureStorage,
-            storage?: KubernetesStorageKind
+            storage?: KubernetesStorageKind,
+            namespace?: string
         ) {
-        super(apiVersion, keyVault, azureStorage, storage);
+        super(apiVersion, keyVault, azureStorage, storage, undefined, namespace);
         this.operator = operator;
     }
 
@@ -79,7 +82,8 @@ export class KubeflowClusterConfigAzure extends KubernetesClusterConfigAzure {
             kubeflowClusterConfigObjectAzure.apiVersion,
             kubeflowClusterConfigObjectAzure.keyVault,
             kubeflowClusterConfigObjectAzure.azureStorage,
-            kubeflowClusterConfigObjectAzure.storage
+            kubeflowClusterConfigObjectAzure.storage,
+            kubeflowClusterConfigObjectAzure.namespace
         );
     }
 }
diff --git a/ts/nni_manager/training_service/kubernetes/kubeflow/kubeflowJobRestServer.ts b/ts/nni_manager/training_service/kubernetes/kubeflow/kubeflowJobRestServer.ts
index cee554007..b42e4cb54 100644
--- a/ts/nni_manager/training_service/kubernetes/kubeflow/kubeflowJobRestServer.ts
+++ b/ts/nni_manager/training_service/kubernetes/kubeflow/kubeflowJobRestServer.ts
@@ -14,7 +14,7 @@ export class KubeflowJobRestServer extends KubernetesJobRestServer {
     /**
      * constructor to provide NNIRestServer's own rest property, e.g. port
      */
-    constructor() {
-        super(component.get(KubeflowTrainingService));
+    constructor(kubeflowTrainingService: KubeflowTrainingService) {
+        super(kubeflowTrainingService);
     }
 }
diff --git a/ts/nni_manager/training_service/kubernetes/kubeflow/kubeflowTrainingService.ts b/ts/nni_manager/training_service/kubernetes/kubeflow/kubeflowTrainingService.ts
index 7fee90b2f..bdcac8645 100644
--- a/ts/nni_manager/training_service/kubernetes/kubeflow/kubeflowTrainingService.ts
+++ b/ts/nni_manager/training_service/kubernetes/kubeflow/kubeflowTrainingService.ts
@@ -69,7 +69,7 @@ class KubeflowTrainingService extends KubernetesTrainingService implements Kuber
         }
 
         if (this.kubernetesRestServerPort === undefined) {
-            const restServer: KubeflowJobRestServer = component.get(KubeflowJobRestServer);
+            const restServer: KubeflowJobRestServer = new KubeflowJobRestServer(this);
             this.kubernetesRestServerPort = restServer.clusterRestServerPort;
         }
 
@@ -81,7 +81,7 @@ class KubeflowTrainingService extends KubernetesTrainingService implements Kuber
         const trialJobId: string = uniqueString(5);
         const trialWorkingFolder: string = path.join(this.CONTAINER_MOUNT_PATH, 'nni', getExperimentId(), trialJobId);
         const kubeflowJobName: string = `nni-exp-${this.experimentId}-trial-${trialJobId}`.toLowerCase();
-        const trialLocalTempFolder: string = path.join(getExperimentRootDir(), 'trials-local', trialJobId);
+        const trialLocalTempFolder: string = path.join(getExperimentRootDir(), 'trials', trialJobId);
         //prepare the runscript
         await this.prepareRunScript(trialLocalTempFolder, trialJobId, trialWorkingFolder, form);
         //upload script files to sotrage
@@ -120,6 +120,7 @@ class KubeflowTrainingService extends KubernetesTrainingService implements Kuber
             case TrialConfigMetadataKey.KUBEFLOW_CLUSTER_CONFIG: {
                 const kubeflowClusterJsonObject: object = JSON.parse(value);
                 this.kubeflowClusterConfig = KubeflowClusterConfigFactory.generateKubeflowClusterConfig(kubeflowClusterJsonObject);
+                this.genericK8sClient.setNamespace = this.kubeflowClusterConfig.namespace ?? "default";
                 if (this.kubeflowClusterConfig.storageType === 'azureStorage') {
                     const azureKubeflowClusterConfig: KubeflowClusterConfigAzure = <KubeflowClusterConfigAzure>this.kubeflowClusterConfig;
                     this.azureStorageAccountName = azureKubeflowClusterConfig.azureStorage.accountName;
@@ -137,6 +138,7 @@ class KubeflowTrainingService extends KubernetesTrainingService implements Kuber
                 }
                 this.kubernetesCRDClient = KubeflowOperatorClientFactory.createClient(
                     this.kubeflowClusterConfig.operator, this.kubeflowClusterConfig.apiVersion);
+                this.kubernetesCRDClient.namespace = this.kubeflowClusterConfig.namespace ?? "default";
                 break;
             }
             case TrialConfigMetadataKey.TRIAL_CONFIG: {
@@ -310,7 +312,7 @@ class KubeflowTrainingService extends KubernetesTrainingService implements Kuber
         // Generate kubeflow job resource config object
         const kubeflowJobConfig: any = await this.generateKubeflowJobConfig(trialJobId, trialWorkingFolder, kubeflowJobName, workerPodResources,
                                                                       nonWorkerResources);
-
+        this.log.info('kubeflowJobConfig:', kubeflowJobConfig);
         return Promise.resolve(kubeflowJobConfig);
     }
 
@@ -368,7 +370,7 @@ class KubeflowTrainingService extends KubernetesTrainingService implements Kuber
             kind: this.kubernetesCRDClient.jobKind,
             metadata: {
                 name: kubeflowJobName,
-                namespace: 'default',
+                namespace: this.kubernetesCRDClient.namespace,
                 labels: {
                     app: this.NNI_KUBERNETES_TRIAL_LABEL,
                     expId: getExperimentId(),
diff --git a/ts/nni_manager/training_service/kubernetes/kubernetesApiClient.ts b/ts/nni_manager/training_service/kubernetes/kubernetesApiClient.ts
index f4a735822..72e839010 100644
--- a/ts/nni_manager/training_service/kubernetes/kubernetesApiClient.ts
+++ b/ts/nni_manager/training_service/kubernetes/kubernetesApiClient.ts
@@ -150,6 +150,7 @@ abstract class KubernetesCRDClient {
     protected readonly client: any;
     protected readonly log: Logger = getLogger('KubernetesCRDClient');
     protected crdSchema: any;
+    public namespace: string = 'default';
 
     constructor() {
         this.client = new Client1_10({config: getKubernetesConfig()});
diff --git a/ts/nni_manager/training_service/kubernetes/kubernetesTrainingService.ts b/ts/nni_manager/training_service/kubernetes/kubernetesTrainingService.ts
index 2682570a7..103b8087f 100644
--- a/ts/nni_manager/training_service/kubernetes/kubernetesTrainingService.ts
+++ b/ts/nni_manager/training_service/kubernetes/kubernetesTrainingService.ts
@@ -230,7 +230,7 @@ abstract class KubernetesTrainingService {
             this.azureStorageSecretName = String.Format('nni-secret-{0}', uniqueString(8)
                 .toLowerCase());
 
-            const namespace = this.genericK8sClient.getNamespace ? this.genericK8sClient.getNamespace : "default"
+            const namespace = this.genericK8sClient.getNamespace ?? "default";
             await this.genericK8sClient.createSecret(
                 {
                     apiVersion: 'v1',
@@ -330,7 +330,7 @@ abstract class KubernetesTrainingService {
         const body = fs.readFileSync(filePath).toString('base64');
         const registrySecretName = String.Format('nni-secret-{0}', uniqueString(8)
             .toLowerCase());
-        const namespace = this.genericK8sClient.getNamespace ? this.genericK8sClient.getNamespace : "default"
+        const namespace = this.genericK8sClient.getNamespace ?? "default";
         await this.genericK8sClient.createSecret(
             {
                 apiVersion: 'v1',
diff --git a/ts/nni_manager/training_service/remote_machine/extends/windowsCommands.ts b/ts/nni_manager/training_service/remote_machine/extends/windowsCommands.ts
index c36cbd76b..4ba53c6f5 100644
--- a/ts/nni_manager/training_service/remote_machine/extends/windowsCommands.ts
+++ b/ts/nni_manager/training_service/remote_machine/extends/windowsCommands.ts
@@ -44,7 +44,7 @@ class WindowsCommands extends OsCommands {
     }
 
     public generateGpuStatsScript(scriptFolder: string): string {
-        return `powershell -command $setEnv = Start-Job -script {$env:Path=If($env:prePath){$env:prePath}Else{$env:Path};$env:METRIC_OUTPUT_DIR='${scriptFolder}'};wait-job $setEnv;$app = Start-Process -FilePath python -NoNewWindow -passthru -ArgumentList '-m nni.tools.gpu_tool.gpu_metrics_collector' -RedirectStandardOutput ${scriptFolder}\\scriptstdout -RedirectStandardError ${scriptFolder}\\scriptstderr;Write $PID ^| Out-File ${scriptFolder}\\pid -NoNewline -encoding utf8;wait-process $app.ID`;
+        return `powershell -command $env:Path=If($env:prePath){$env:prePath}Else{$env:Path};$env:METRIC_OUTPUT_DIR='${scriptFolder}';$app = Start-Process -FilePath python -NoNewWindow -passthru -ArgumentList '-m nni.tools.gpu_tool.gpu_metrics_collector' -RedirectStandardOutput ${scriptFolder}\\scriptstdout -RedirectStandardError ${scriptFolder}\\scriptstderr;Write $PID ^| Out-File ${scriptFolder}\\pid -NoNewline -encoding utf8;wait-process $app.ID`;
     }
 
     public createFolder(folderName: string, sharedFolder: boolean = false): string {
diff --git a/ts/nni_manager/training_service/remote_machine/remoteMachineTrainingService.ts b/ts/nni_manager/training_service/remote_machine/remoteMachineTrainingService.ts
index e5b205afd..599f1c54a 100644
--- a/ts/nni_manager/training_service/remote_machine/remoteMachineTrainingService.ts
+++ b/ts/nni_manager/training_service/remote_machine/remoteMachineTrainingService.ts
@@ -467,7 +467,7 @@ class RemoteMachineTrainingService implements TrainingService {
             throw new Error(`Can not get trial job detail for job: ${trialJobId}`);
         }
 
-        const trialLocalTempFolder: string = path.join(this.expRootDir, 'trials-local', trialJobId);
+        const trialLocalTempFolder: string = path.join(this.expRootDir, 'trials', trialJobId);
 
         await executor.createFolder(executor.joinPath(trialJobDetail.workingDirectory, '.nni'));
 
@@ -582,7 +582,7 @@ class RemoteMachineTrainingService implements TrainingService {
         const executor = await this.getExecutor(trialJobId);
 
         const trialWorkingFolder: string = executor.joinPath(executor.getRemoteExperimentRootDir(getExperimentId()), 'trials', trialJobId);
-        const trialLocalTempFolder: string = path.join(this.expRootDir, 'trials-local', trialJobId);
+        const trialLocalTempFolder: string = path.join(this.expRootDir, 'trials', trialJobId);
 
         const fileName: string = generateParamFileName(hyperParameters);
         const localFilepath: string = path.join(trialLocalTempFolder, fileName);
diff --git a/ts/nni_manager/training_service/reusable/environments/kubernetes/frameworkcontrollerEnvironmentService.ts b/ts/nni_manager/training_service/reusable/environments/kubernetes/frameworkcontrollerEnvironmentService.ts
index c823b5ca9..012f86233 100644
--- a/ts/nni_manager/training_service/reusable/environments/kubernetes/frameworkcontrollerEnvironmentService.ts
+++ b/ts/nni_manager/training_service/reusable/environments/kubernetes/frameworkcontrollerEnvironmentService.ts
@@ -29,6 +29,7 @@ export class FrameworkControllerEnvironmentService extends KubernetesEnvironment
         this.config = config;
         // Create kubernetesCRDClient
         this.kubernetesCRDClient = FrameworkControllerClientFactory.createClient(this.config.namespace);
+        this.genericK8sClient.setNamespace = this.config.namespace ?? "default"
         // Create storage
         if (this.config.storage.storageType === 'azureStorage') {
             if (this.config.storage.azureShare === undefined ||
@@ -194,7 +195,7 @@ export class FrameworkControllerEnvironmentService extends KubernetesEnvironment
             kind: 'Framework',
             metadata: {
                 name: frameworkcontrollerJobName,
-                namespace: this.config.namespace ? this.config.namespace : "default",
+                namespace: this.config.namespace ?? "default",
                 labels: {
                     app: this.NNI_KUBERNETES_TRIAL_LABEL,
                     expId: this.experimentId,
diff --git a/ts/nni_manager/training_service/reusable/environments/kubernetes/kubeflowEnvironmentService.ts b/ts/nni_manager/training_service/reusable/environments/kubernetes/kubeflowEnvironmentService.ts
index 2a0b84dde..bd295e9ff 100644
--- a/ts/nni_manager/training_service/reusable/environments/kubernetes/kubeflowEnvironmentService.ts
+++ b/ts/nni_manager/training_service/reusable/environments/kubernetes/kubeflowEnvironmentService.ts
@@ -26,6 +26,7 @@ export class KubeflowEnvironmentService extends KubernetesEnvironmentService {
         // Create kubernetesCRDClient
         this.kubernetesCRDClient = KubeflowOperatorClientFactory.createClient(
             this.config.operator, this.config.apiVersion);
+        this.kubernetesCRDClient.namespace = this.config.namespace ?? "default";
         // Create storage
         if (this.config.storage.storageType === 'azureStorage') {
             if (this.config.storage.azureShare === undefined ||
@@ -41,7 +42,7 @@ export class KubeflowEnvironmentService extends KubernetesEnvironmentService {
                 this.config.operator, this.config.apiVersion, keyValutConfig, azureStorage);
             this.azureStorageAccountName = azureKubeflowClusterConfig.azureStorage.accountName;
             this.azureStorageShare = azureKubeflowClusterConfig.azureStorage.azureShare;
-            
+            this.genericK8sClient.setNamespace = this.config.namespace ?? "default";
             this.createStoragePromise = this.createAzureStorage(
                 azureKubeflowClusterConfig.keyVault.vaultName,
                 azureKubeflowClusterConfig.keyVault.name
@@ -186,7 +187,7 @@ export class KubeflowEnvironmentService extends KubernetesEnvironmentService {
             kind: this.kubernetesCRDClient.jobKind,
             metadata: {
                 name: kubeflowJobName,
-                namespace: 'default',
+                namespace: this.kubernetesCRDClient.namespace,
                 labels: {
                     app: this.NNI_KUBERNETES_TRIAL_LABEL,
                     expId: this.experimentId,
diff --git a/ts/nni_manager/training_service/reusable/environments/kubernetes/kubernetesEnvironmentService.ts b/ts/nni_manager/training_service/reusable/environments/kubernetes/kubernetesEnvironmentService.ts
index 95b6d1c60..268a059bf 100644
--- a/ts/nni_manager/training_service/reusable/environments/kubernetes/kubernetesEnvironmentService.ts
+++ b/ts/nni_manager/training_service/reusable/environments/kubernetes/kubernetesEnvironmentService.ts
@@ -77,7 +77,7 @@ export class KubernetesEnvironmentService extends EnvironmentService {
             if (this.genericK8sClient === undefined) {
                 throw new Error("genericK8sClient undefined!");
             }
-            const namespace = this.genericK8sClient.getNamespace ? this.genericK8sClient.getNamespace : "default"
+            const namespace = this.genericK8sClient.getNamespace ?? "default";
             await this.genericK8sClient.createSecret(
                 {
                     apiVersion: 'v1',
@@ -180,7 +180,7 @@ export class KubernetesEnvironmentService extends EnvironmentService {
         const body = fs.readFileSync(filePath).toString('base64');
         const registrySecretName = String.Format('nni-secret-{0}', uniqueString(8)
             .toLowerCase());
-        const namespace = this.genericK8sClient.getNamespace ? this.genericK8sClient.getNamespace : "default"
+        const namespace = this.genericK8sClient.getNamespace ?? "default";
         await this.genericK8sClient.createSecret(
             {
                 apiVersion: 'v1',
diff --git a/ts/nni_manager/training_service/reusable/trialDispatcher.ts b/ts/nni_manager/training_service/reusable/trialDispatcher.ts
index 4efc4f2b7..13064435f 100644
--- a/ts/nni_manager/training_service/reusable/trialDispatcher.ts
+++ b/ts/nni_manager/training_service/reusable/trialDispatcher.ts
@@ -507,6 +507,7 @@ class TrialDispatcher implements TrainingService {
                                 throw new Error(`${environment.id} does not has environment service!`);
                             }
                             await environment.environmentService.stopEnvironment(environment);
+                            liveEnvironmentsCount--;
                             continue;
                         }
 
diff --git a/ts/nni_manager/yarn.lock b/ts/nni_manager/yarn.lock
index 2ab071269..7d40bfbc9 100644
--- a/ts/nni_manager/yarn.lock
+++ b/ts/nni_manager/yarn.lock
@@ -820,6 +820,18 @@
   dependencies:
     "@types/node" "*"
 
+"@types/yargs-parser@*":
+  version "20.2.1"
+  resolved "https://registry.yarnpkg.com/@types/yargs-parser/-/yargs-parser-20.2.1.tgz#3b9ce2489919d9e4fea439b76916abc34b2df129"
+  integrity sha512-7tFImggNeNBVMsn0vLrpn1H1uPrUBdnARPTpZoitY37ZrdJREzf7I16tMrlK3hen349gr1NYh8CmZQa7CTG6Aw==
+
+"@types/yargs@^17.0.8":
+  version "17.0.8"
+  resolved "https://registry.yarnpkg.com/@types/yargs/-/yargs-17.0.8.tgz#d23a3476fd3da8a0ea44b5494ca7fa677b9dad4c"
+  integrity sha512-wDeUwiUmem9FzsyysEwRukaEdDNcwbROvQ9QGRKaLI6t+IltNzbn4/i4asmB10auvZGQCzSQ6t0GSczEThlUXw==
+  dependencies:
+    "@types/yargs-parser" "*"
+
 "@typescript-eslint/eslint-plugin@^2.10.0":
   version "2.34.0"
   resolved "https://registry.yarnpkg.com/@typescript-eslint/eslint-plugin/-/eslint-plugin-2.34.0.tgz#6f8ce8a46c7dea4a6f1d171d2bb8fbae6dac2be9"
@@ -3550,9 +3562,9 @@ minimatch@3.0.4, minimatch@^3.0.4:
     brace-expansion "^1.1.7"
 
 minimist@^1.2.0, minimist@^1.2.5:
-  version "1.2.5"
-  resolved "https://registry.yarnpkg.com/minimist/-/minimist-1.2.5.tgz#67d66014b66a6a8aaa0c083c5fd58df4e4e97602"
-  integrity sha512-FM9nNUYrRBAELZQT3xeZQ7fmMOBg6nWNmJKTcgsJeaLstP/UODVpGsr5OhXhhXg6f+qtJ8uiZ+PUxkDWcgIXLw==
+  version "1.2.6"
+  resolved "https://registry.yarnpkg.com/minimist/-/minimist-1.2.6.tgz#8637a5b759ea0d6e98702cfb3a9283323c93af44"
+  integrity sha512-Jsjnk4bw3YJqYzbdyBiNsPWHPfO++UGG749Cxs6peCu5Xg4nrena6OVxOYxrQTqww0Jmwt+Ref8rggumkTLz9Q==
 
 minipass-collect@^1.0.2:
   version "1.0.2"
@@ -3765,9 +3777,9 @@ node-fetch@<3.0.0:
     whatwg-url "^5.0.0"
 
 node-forge@>=0.10.0, node-forge@^0.8.5:
-  version "1.2.1"
-  resolved "https://registry.yarnpkg.com/node-forge/-/node-forge-1.2.1.tgz#82794919071ef2eb5c509293325cec8afd0fd53c"
-  integrity sha512-Fcvtbb+zBcZXbTTVwqGA5W+MKBj56UjVRevvchv5XrcyXbmNdesfZL37nlcWOfpgHhgmxApw3tQbTr4CqNmX4w==
+  version "1.3.0"
+  resolved "https://registry.yarnpkg.com/node-forge/-/node-forge-1.3.0.tgz#37a874ea723855f37db091e6c186e5b67a01d4b2"
+  integrity sha512-08ARB91bUi6zNKzVmaj3QO7cr397uiDT2nJ63cHjyNtCTWIgvS47j3eT0WfzUwS9+6Z5YshRaoasFkXCKrIYbA==
 
 node-gyp@3.x, node-gyp@>=8.4.1, node-gyp@^7.1.0, node-gyp@^8.2.0, node-gyp@^8.4.1:
   version "8.4.1"
@@ -5752,7 +5764,7 @@ yallist@^4.0.0:
   resolved "https://registry.yarnpkg.com/yallist/-/yallist-4.0.0.tgz#9bb92790d9c0effec63be73519e11a35019a3a72"
   integrity sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A==
 
-yargs-parser@20.2.4, yargs-parser@>=20.2.7, yargs-parser@^18.1.2, yargs-parser@^20.2.2:
+yargs-parser@20.2.4, yargs-parser@>=20.2.7, yargs-parser@^18.1.2, yargs-parser@^20.2.2, yargs-parser@^21.0.0:
   version "20.2.7"
   resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-20.2.7.tgz#61df85c113edfb5a7a4e36eb8aa60ef423cbc90a"
   integrity sha512-FiNkvbeHzB/syOjIUxFDCnhSfzAL8R5vs40MgLFBorXACCOAEaWu0gRZl14vG8MR9AOJIZbmkjhusqBYZ3HTHw==
@@ -5797,6 +5809,19 @@ yargs@^15.0.2:
     y18n "^4.0.0"
     yargs-parser "^18.1.2"
 
+yargs@^17.3.1:
+  version "17.3.1"
+  resolved "https://registry.yarnpkg.com/yargs/-/yargs-17.3.1.tgz#da56b28f32e2fd45aefb402ed9c26f42be4c07b9"
+  integrity sha512-WUANQeVgjLbNsEmGk20f+nlHgOqzRFpiGWVaBrYGYIGANIIu3lWjoyi0fNlFmJkvfhCZ6BXINe7/W2O2bV4iaA==
+  dependencies:
+    cliui "^7.0.2"
+    escalade "^3.1.1"
+    get-caller-file "^2.0.5"
+    require-directory "^2.1.1"
+    string-width "^4.2.3"
+    y18n "^5.0.5"
+    yargs-parser "^21.0.0"
+
 yn@3.1.1:
   version "3.1.1"
   resolved "https://registry.yarnpkg.com/yn/-/yn-3.1.1.tgz#1e87401a09d767c1d5eab26a6e4c185182d2eb50"
diff --git a/ts/webui/.eslintrc b/ts/webui/.eslintrc
index af15efa22..31c2d3ec2 100644
--- a/ts/webui/.eslintrc
+++ b/ts/webui/.eslintrc
@@ -10,7 +10,8 @@
     },
     "plugins": [
         "@typescript-eslint",
-        "eslint-plugin-prettier"
+        "eslint-plugin-prettier",
+        "import"
     ],
     "extends": [
         "eslint:recommended",
@@ -50,6 +51,15 @@
         "react": {
             "version": "detect"
         },
+        "import/resolver": {
+            "alias": {
+                "@": "src",
+                "@components": "src/components",
+                "@static": "src/static",
+                "@style": "src/static/style",
+                "@model": "src/static/model"
+            }
+        },
         "ignorePatterns": [
             "node_modules/",
             "build/",
diff --git a/ts/webui/README.md b/ts/webui/README.md
index 071e322a4..0e25b7a69 100644
--- a/ts/webui/README.md
+++ b/ts/webui/README.md
@@ -14,6 +14,6 @@ WebUI is built by using [React](https://reactjs.org/docs/getting-started.html) a
 
 ## PR
 
-* WebUI uses [eslint](https://eslint.org/docs/user-guide/getting-started) and [prettier](https://prettier.io/docs/en/index.html) to format code. You could use the command `yarn sanity-check` to check the code error status. And use `yarn sanity-check --fix` could modifiy the most code style error before you send PR.
+* WebUI uses [eslint](https://eslint.org/docs/user-guide/getting-started) and [prettier](https://prettier.io/docs/en/index.html) to format code. You could use the command `yarn sanity-check` to check the code error status. And use `yarn sanity-check --fix` could modifiy the most code style error before you send PR. Also Please use `yarn stylelint --fix` to format `css and scss` files.
 
 * You could send the PR if `yarn release` gets successful build after formatting code.
\ No newline at end of file
diff --git a/ts/webui/package.json b/ts/webui/package.json
index 253e74f24..6e71cf395 100644
--- a/ts/webui/package.json
+++ b/ts/webui/package.json
@@ -28,8 +28,7 @@
     "mini-css-extract-plugin": "^1.6.0",
     "monaco-editor": "^0.25.0",
     "monaco-editor-webpack-plugin": "^4.0.0",
-    "node-sass": "^7.0.0",
-    "npm": ">=8.3.0",
+    "node-sass": "^7.0.1",
     "parcoord-es": "^2.2.10",
     "pnp-webpack-plugin": "^1.6.4",
     "postcss-flexbugs-fixes": "^5.0.2",
@@ -53,7 +52,6 @@
     "style-loader": "^2.0.0",
     "url-loader": "^4.1.1",
     "webpack": "^5.39.0",
-    "webpack-dev-server": ">=4.7.0",
     "webpack-manifest-plugin": "^3.1.1",
     "workbox-webpack-plugin": "^6.1.5"
   },
@@ -83,10 +81,13 @@
     "eslint-plugin-react": "^7.24.0",
     "eslint-plugin-react-hooks": "^4.2.0",
     "express": "^4.17.1",
+    "npm": ">=8.3.0",
     "prettier": "^2.3.1",
     "stylelint": "^13.7.0",
     "stylelint-config-standard": "^22.0.0",
-    "typescript": "^4.3.2"
+    "trim-newlines": "^3.0.0",
+    "typescript": "^4.3.2",
+    "webpack-dev-server": ">=4.7.4"
   },
   "proxy": "http://localhost:12138",
   "scripts": {
diff --git a/ts/webui/scripts/config/webpack.config.dev.js b/ts/webui/scripts/config/webpack.config.dev.js
index 9c8e5adee..3bc18ffb7 100644
--- a/ts/webui/scripts/config/webpack.config.dev.js
+++ b/ts/webui/scripts/config/webpack.config.dev.js
@@ -27,7 +27,14 @@ const config = {
     },
     optimization: { minimize: false },
     resolve: {
-        modules: [ 'node_modules' ],
+        modules: [ 'node_modules', 'src' ],
+        alias: { 
+            '@': paths.appSrc,
+            '@components': `${paths.appSrc}/components`,
+            '@static': `${paths.appSrc}/static`,
+            '@style': `${paths.appSrc}/static/style`,
+            '@model': `${paths.appSrc}/static/model`
+        },
         extensions: paths.moduleFileExtensions.map(ext => `.${ext}`),
     },
     module: {
diff --git a/ts/webui/scripts/config/webpack.config.js b/ts/webui/scripts/config/webpack.config.js
index 8b3487af3..88dff7072 100644
--- a/ts/webui/scripts/config/webpack.config.js
+++ b/ts/webui/scripts/config/webpack.config.js
@@ -19,7 +19,11 @@ const WatchMissingNodeModulesPlugin = require('react-dev-utils/WatchMissingNodeM
 const ModuleScopePlugin = require('react-dev-utils/ModuleScopePlugin');
 const getCSSModuleLocalIdent = require('react-dev-utils/getCSSModuleLocalIdent');
 const paths = require('./paths');
-const modules = require('./modules');
+// note this line because it will cause this error:
+// "Your project's `baseUrl` can only be set to `src` or `node_modules`." +
+// ' Create React App does not support other values at this time.'
+// Only set baseUrl to '.' could fix the `absolute path` issue
+// const modules = require('./modules');
 const getClientEnvironment = require('./env');
 const ModuleNotFoundPlugin = require('react-dev-utils/ModuleNotFoundPlugin');
 const ForkTsCheckerWebpackPlugin = require('react-dev-utils/ForkTsCheckerWebpackPlugin');
@@ -252,7 +256,16 @@ module.exports = function(webpackEnv) {
       // We placed these paths second because we want `node_modules` to "win"
       // if there are any conflicts. This matches Node resolution mechanism.
       // https://github.com/facebook/create-react-app/issues/253
-      modules: ['node_modules', paths.appNodeModules].concat(modules.additionalModulePaths || []),
+      // modules: ['node_modules', paths.appNodeModules].concat(modules.additionalModulePaths || []),
+      modules: ['node_modules', 'src'],
+      alias: {
+        'react-native': 'react-native-web',
+        '@': paths.appSrc,
+        '@components': `${paths.appSrc}/components`,
+        '@static': `${paths.appSrc}/static`,
+        '@style': `${paths.appSrc}/static/style`,
+        '@model': `${paths.appSrc}/static/model`
+      },
       // These are the reasonable defaults supported by the Node ecosystem.
       // We also include JSX as a common component filename extension to support
       // some tools, although we do not recommend using it, see:
@@ -262,11 +275,6 @@ module.exports = function(webpackEnv) {
       extensions: paths.moduleFileExtensions
         .map(ext => `.${ext}`)
         .filter(ext => useTypeScript || !ext.includes('ts')),
-      alias: {
-        // Support React Native Web
-        // https://www.smashingmagazine.com/2016/08/a-glimpse-into-the-future-with-react-native-for-web/
-        'react-native': 'react-native-web',
-      },
       plugins: [
         // Adds support for installing with Plug'n'Play, leading to faster installs and adding
         // guards against forgotten dependencies and such.
diff --git a/ts/webui/src/App.tsx b/ts/webui/src/App.tsx
index ce0ced6d3..3d5b5bda6 100644
--- a/ts/webui/src/App.tsx
+++ b/ts/webui/src/App.tsx
@@ -1,18 +1,20 @@
 import * as React from 'react';
 import { Stack } from '@fluentui/react';
-import { COLUMN } from './static/const';
-import { EXPERIMENT, TRIALS } from './static/datamodel';
-import { isManagerExperimentPage } from './static/function';
-import NavCon from './components/NavCon';
-import MessageInfo from './components/modals/MessageInfo';
-import { SlideNavBtns } from './components/slideNav/SlideNavBtns';
+import { SlideNavBtns } from '@components/nav/slideNav/SlideNavBtns';
+import { EXPERIMENT, TRIALS } from '@static/datamodel';
+import NavCon from '@components/nav/Nav';
+import MessageInfo from '@components/common/MessageInfo';
+import { COLUMN } from '@static/const';
+import { isManagerExperimentPage } from '@static/function';
+
+import '@style/App.scss';
+import '@style/common/common.scss';
+import '@style/experiment/trialdetail/trialsDetail.scss';
+
 const echarts = require('echarts/lib/echarts');
 echarts.registerTheme('nni_theme', {
     color: '#3c8dbc'
 });
-import './App.scss';
-import './static/style/common.scss';
-import './static/style/trialsDetail.scss';
 
 interface AppState {
     interval: number;
diff --git a/ts/webui/src/components/public-child/CopyButton.tsx b/ts/webui/src/components/common/CopyButton.tsx
similarity index 96%
rename from ts/webui/src/components/public-child/CopyButton.tsx
rename to ts/webui/src/components/common/CopyButton.tsx
index 254cbf064..530b7ae8a 100644
--- a/ts/webui/src/components/public-child/CopyButton.tsx
+++ b/ts/webui/src/components/common/CopyButton.tsx
@@ -2,7 +2,7 @@ import React, { useRef } from 'react';
 import PropTypes from 'prop-types';
 import copy from 'copy-to-clipboard';
 import { IconButton, FontSizes, TooltipHost } from '@fluentui/react';
-import { TOOLTIP_BACKGROUND_COLOR } from '../../static/const';
+import { TOOLTIP_BACKGROUND_COLOR } from '@static/const';
 
 const COPIED_TOOLTIP_CLOSE_DELAY = 1000;
 
diff --git a/ts/webui/src/components/public-child/ExpandableDetails.tsx b/ts/webui/src/components/common/ExpandableDetails.tsx
similarity index 92%
rename from ts/webui/src/components/public-child/ExpandableDetails.tsx
rename to ts/webui/src/components/common/ExpandableDetails.tsx
index 34f010f21..61be05755 100644
--- a/ts/webui/src/components/public-child/ExpandableDetails.tsx
+++ b/ts/webui/src/components/common/ExpandableDetails.tsx
@@ -1,6 +1,6 @@
 import * as React from 'react';
 import { DetailsRow, IDetailsRowBaseProps } from '@fluentui/react';
-import OpenRow from '../public-child/OpenRow';
+import OpenRow from './OpenRow';
 
 interface ExpandableDetailsProps {
     detailsProps: IDetailsRowBaseProps;
diff --git a/ts/webui/src/components/public-child/LogPathChild.tsx b/ts/webui/src/components/common/LogPathChild.tsx
similarity index 100%
rename from ts/webui/src/components/public-child/LogPathChild.tsx
rename to ts/webui/src/components/common/LogPathChild.tsx
diff --git a/ts/webui/src/components/modals/MessageInfo.tsx b/ts/webui/src/components/common/MessageInfo.tsx
similarity index 100%
rename from ts/webui/src/components/modals/MessageInfo.tsx
rename to ts/webui/src/components/common/MessageInfo.tsx
diff --git a/ts/webui/src/components/public-child/MonacoEditor.tsx b/ts/webui/src/components/common/MonacoEditor.tsx
similarity index 100%
rename from ts/webui/src/components/public-child/MonacoEditor.tsx
rename to ts/webui/src/components/common/MonacoEditor.tsx
diff --git a/ts/webui/src/components/public-child/OpenRow.tsx b/ts/webui/src/components/common/OpenRow.tsx
similarity index 93%
rename from ts/webui/src/components/public-child/OpenRow.tsx
rename to ts/webui/src/components/common/OpenRow.tsx
index 39225ff15..6ea77cdd8 100644
--- a/ts/webui/src/components/public-child/OpenRow.tsx
+++ b/ts/webui/src/components/common/OpenRow.tsx
@@ -1,18 +1,18 @@
 import * as React from 'react';
 import * as copy from 'copy-to-clipboard';
 import { Stack, PrimaryButton, Pivot, PivotItem, DefaultButton } from '@fluentui/react';
-import { Trial } from '../../static/model/trial';
-import { MANAGER_IP, RETIARIIPARAMETERS } from '../../static/const';
-import { EXPERIMENT, TRIALS } from '../../static/datamodel';
-import { reformatRetiariiParameter } from '../../static/function';
 import JSONTree from 'react-json-tree';
-import PaiTrialLog from '../public-child/PaiTrialLog';
-import TrialLog from '../public-child/TrialLog';
-import MessageInfo from '../modals/MessageInfo';
-import PanelMonacoEditor from '../public-child/PanelMonacoEditor';
-import '../../static/style/overview/overview.scss';
-import '../../static/style/copyParameter.scss';
-import '../../static/style/openRow.scss';
+import { Trial } from '@model/trial';
+import { MANAGER_IP, RETIARIIPARAMETERS } from '@static/const';
+import { EXPERIMENT, TRIALS } from '@static/datamodel';
+import { reformatRetiariiParameter } from '@static/function';
+import PaiTrialLog from './PaiTrialLog';
+import TrialLog from './TrialLog';
+import MessageInfo from './MessageInfo';
+import PanelMonacoEditor from './PanelMonacoEditor';
+import '@style/experiment/overview/overview.scss';
+import '@style/copyParameter.scss';
+import '@style/openRow.scss';
 
 /**
  *  netron URL must be synchronized with ts/nni_manager/rest_server/index.ts`.
diff --git a/ts/webui/src/components/public-child/PaginationTable.tsx b/ts/webui/src/components/common/PaginationTable.tsx
similarity index 100%
rename from ts/webui/src/components/public-child/PaginationTable.tsx
rename to ts/webui/src/components/common/PaginationTable.tsx
diff --git a/ts/webui/src/components/public-child/PaiTrialChild.tsx b/ts/webui/src/components/common/PaiTrialChild.tsx
similarity index 95%
rename from ts/webui/src/components/public-child/PaiTrialChild.tsx
rename to ts/webui/src/components/common/PaiTrialChild.tsx
index 438bc1df2..254e5c778 100644
--- a/ts/webui/src/components/public-child/PaiTrialChild.tsx
+++ b/ts/webui/src/components/common/PaiTrialChild.tsx
@@ -1,5 +1,5 @@
 import * as React from 'react';
-import { DOWNLOAD_IP } from '../../static/const';
+import { DOWNLOAD_IP } from '@static/const';
 import LogPathChild from './LogPathChild';
 
 interface PaiTrialChildProps {
diff --git a/ts/webui/src/components/public-child/PaiTrialLog.tsx b/ts/webui/src/components/common/PaiTrialLog.tsx
similarity index 97%
rename from ts/webui/src/components/public-child/PaiTrialLog.tsx
rename to ts/webui/src/components/common/PaiTrialLog.tsx
index f522cece5..d3ccef1ff 100644
--- a/ts/webui/src/components/public-child/PaiTrialLog.tsx
+++ b/ts/webui/src/components/common/PaiTrialLog.tsx
@@ -1,5 +1,5 @@
 import * as React from 'react';
-import { DOWNLOAD_IP } from '../../static/const';
+import { DOWNLOAD_IP } from '@static/const';
 import PaiTrialChild from './PaiTrialChild';
 import LogPathChild from './LogPathChild';
 
diff --git a/ts/webui/src/components/public-child/PanelMonacoEditor.tsx b/ts/webui/src/components/common/PanelMonacoEditor.tsx
similarity index 83%
rename from ts/webui/src/components/public-child/PanelMonacoEditor.tsx
rename to ts/webui/src/components/common/PanelMonacoEditor.tsx
index fb8a7e513..3d8375c1b 100644
--- a/ts/webui/src/components/public-child/PanelMonacoEditor.tsx
+++ b/ts/webui/src/components/common/PanelMonacoEditor.tsx
@@ -1,16 +1,16 @@
 import * as React from 'react';
 import { Stack, Panel, PrimaryButton } from '@fluentui/react';
 import MonacoEditor from 'react-monaco-editor';
-import { caclMonacoEditorHeight } from '../../static/function';
-import '../../static/style/logDrawer.scss';
+import { caclMonacoEditorHeight } from '@static/function';
+import '@style/logPanel.scss';
 
-interface LogDrawerProps {
+interface LogPanelProps {
     hideConfigPanel: () => void;
     panelName: string;
     panelContent: string;
 }
 
-interface LogDrawerState {
+interface LogPanelState {
     panelInnerHeight: number;
 }
 
@@ -21,8 +21,8 @@ interface LogDrawerState {
  * panel
  */
 
-class PanelMonacoEditor extends React.Component<LogDrawerProps, LogDrawerState> {
-    constructor(props: LogDrawerProps) {
+class PanelMonacoEditor extends React.Component<LogPanelProps, LogPanelState> {
+    constructor(props: LogPanelProps) {
         super(props);
 
         this.state = {
@@ -31,16 +31,16 @@ class PanelMonacoEditor extends React.Component<LogDrawerProps, LogDrawerState>
     }
 
     // use arrow function for change window size met error: this.setState is not a function
-    setLogDrawerHeight = (): void => {
+    setLogPanelHeight = (): void => {
         this.setState(() => ({ panelInnerHeight: window.innerHeight, innerWidth: window.innerWidth }));
     };
 
     async componentDidMount(): Promise<void> {
-        window.addEventListener('resize', this.setLogDrawerHeight);
+        window.addEventListener('resize', this.setLogPanelHeight);
     }
 
     componentWillUnmount(): void {
-        window.removeEventListener('resize', this.setLogDrawerHeight);
+        window.removeEventListener('resize', this.setLogPanelHeight);
     }
 
     render(): React.ReactNode {
diff --git a/ts/webui/src/components/public-child/TrialLog.tsx b/ts/webui/src/components/common/TrialLog.tsx
similarity index 100%
rename from ts/webui/src/components/public-child/TrialLog.tsx
rename to ts/webui/src/components/common/TrialLog.tsx
diff --git a/ts/webui/src/components/Overview.tsx b/ts/webui/src/components/experiment/overview/Overview.tsx
similarity index 91%
rename from ts/webui/src/components/Overview.tsx
rename to ts/webui/src/components/experiment/overview/Overview.tsx
index f8575636a..49d37ade9 100644
--- a/ts/webui/src/components/Overview.tsx
+++ b/ts/webui/src/components/experiment/overview/Overview.tsx
@@ -1,22 +1,27 @@
 import * as React from 'react';
 import { Stack, Icon, Dropdown, DefaultButton } from '@fluentui/react';
-import { EXPERIMENT, TRIALS } from '../static/datamodel';
-import { Trial } from '../static/model/trial';
-import { AppContext } from '../App';
-import { Title } from './overview/Title';
-import SuccessTable from './overview/table/SuccessTable';
-import DefaultPoint from './trial-detail/DefaultMetricPoint';
-import { BasicInfo } from './overview/params/BasicInfo';
-import { ExpDuration } from './overview/count/ExpDuration';
-import { ExpDurationContext } from './overview/count/ExpDurationContext';
-import { TrialCount } from './overview/count/TrialCount';
-import { Command1 } from './overview/command/Command1';
-import { Command2 } from './overview/command/Command2';
-import { TitleContext } from './overview/TitleContext';
-import { itemStyleSucceed, entriesOption } from './overview/overviewConst';
-import '../static/style/overview/overview.scss';
-import '../static/style/overview/topTrial.scss';
-import '../static/style/logPath.scss';
+import { EXPERIMENT, TRIALS } from '@static/datamodel';
+import { Trial } from '@static/model/trial';
+import { AppContext } from '@/App';
+import { Title } from './Title';
+import SuccessTable from './table/SuccessTable';
+import DefaultPoint from '../trialdetail/chart/DefaultMetricPoint';
+import { BasicInfo } from './params/BasicInfo';
+import { ExpDuration } from './count/ExpDuration';
+import { ExpDurationContext } from './count/ExpDurationContext';
+import { TrialCount } from './count/TrialCount';
+import { Command1 } from './command/Command1';
+import { Command2 } from './command/Command2';
+import { TitleContext } from './TitleContext';
+import { itemStyleSucceed, entriesOption } from './overviewConst';
+import '@style/experiment/overview/overview.scss';
+import '@style/experiment/overview/topTrial.scss';
+import '@style/logPath.scss';
+
+/**
+ * single experiment
+ * overview page
+ */
 
 interface OverviewState {
     trialConcurrency: number;
diff --git a/ts/webui/src/components/overview/Title.tsx b/ts/webui/src/components/experiment/overview/Title.tsx
similarity index 73%
rename from ts/webui/src/components/overview/Title.tsx
rename to ts/webui/src/components/experiment/overview/Title.tsx
index 06367c049..b97038eb3 100644
--- a/ts/webui/src/components/overview/Title.tsx
+++ b/ts/webui/src/components/experiment/overview/Title.tsx
@@ -1,8 +1,7 @@
 import React from 'react';
-import { Stack, Icon, initializeIcons } from '@fluentui/react';
+import { Stack, Icon } from '@fluentui/react';
 import { TitleContext } from './TitleContext';
-import '../../static/style/overview/overviewTitle.scss';
-initializeIcons();
+import '@style/experiment/overview/overviewTitle.scss';
 
 export const Title = (): any => (
     <TitleContext.Consumer>
diff --git a/ts/webui/src/components/overview/TitleContext.tsx b/ts/webui/src/components/experiment/overview/TitleContext.tsx
similarity index 100%
rename from ts/webui/src/components/overview/TitleContext.tsx
rename to ts/webui/src/components/experiment/overview/TitleContext.tsx
diff --git a/ts/webui/src/components/overview/command/Command1.tsx b/ts/webui/src/components/experiment/overview/command/Command1.tsx
similarity index 91%
rename from ts/webui/src/components/overview/command/Command1.tsx
rename to ts/webui/src/components/experiment/overview/command/Command1.tsx
index e6264defd..aa917b6c5 100644
--- a/ts/webui/src/components/overview/command/Command1.tsx
+++ b/ts/webui/src/components/experiment/overview/command/Command1.tsx
@@ -1,7 +1,7 @@
 import React from 'react';
-import { EXPERIMENT } from '../../../static/datamodel';
+import { EXPERIMENT } from '@static/datamodel';
 import { rightEidtParam } from '../count/commonStyle';
-import '../../../static/style/overview/command.scss';
+import '@style/experiment/overview/command.scss';
 
 export const Command1 = (): any => {
     const tuner = EXPERIMENT.profile.params.tuner;
diff --git a/ts/webui/src/components/overview/command/Command2.tsx b/ts/webui/src/components/experiment/overview/command/Command2.tsx
similarity index 92%
rename from ts/webui/src/components/overview/command/Command2.tsx
rename to ts/webui/src/components/experiment/overview/command/Command2.tsx
index 5235a707c..90712031c 100644
--- a/ts/webui/src/components/overview/command/Command2.tsx
+++ b/ts/webui/src/components/experiment/overview/command/Command2.tsx
@@ -1,9 +1,9 @@
 import React from 'react';
 import { TooltipHost, DirectionalHint } from '@fluentui/react';
-import { EXPERIMENT } from '../../../static/datamodel';
+import { EXPERIMENT } from '@static/datamodel';
 import { leftProgress } from '../count/commonStyle';
-import { TOOLTIP_BACKGROUND_COLOR } from '../../../static/const';
-import '../../../static/style/overview/command.scss';
+import { TOOLTIP_BACKGROUND_COLOR } from '@static/const';
+import '@style/experiment/overview/command.scss';
 
 export const Command2 = (): any => {
     return (
diff --git a/ts/webui/src/components/overview/count/EditExperimentParam.tsx b/ts/webui/src/components/experiment/overview/count/EditExperimentParam.tsx
similarity index 95%
rename from ts/webui/src/components/overview/count/EditExperimentParam.tsx
rename to ts/webui/src/components/experiment/overview/count/EditExperimentParam.tsx
index 480e85993..5554dc2a8 100644
--- a/ts/webui/src/components/overview/count/EditExperimentParam.tsx
+++ b/ts/webui/src/components/experiment/overview/count/EditExperimentParam.tsx
@@ -1,15 +1,15 @@
 import React, { useState, useCallback, useContext } from 'react';
 import axios from 'axios';
 import { Dropdown } from '@fluentui/react';
-import { EXPERIMENT } from '../../../static/datamodel';
-import { toSeconds } from '../../../static/experimentConfig';
-import { AppContext } from '../../../App';
+import { AppContext } from '@/App';
+import { MANAGER_IP, MAX_TRIAL_NUMBERS } from '@static/const';
+import { EXPERIMENT } from '@static/datamodel';
+import { toSeconds } from '@static/experimentConfig';
 import { EditExpeParamContext } from './context';
 import { durationUnit } from '../overviewConst';
-import { MANAGER_IP, MAX_TRIAL_NUMBERS } from '../../../static/const';
-import { Edit, CheckMark, Cancel } from '../../buttons/Icon';
-import MessageInfo from '../../modals/MessageInfo';
-import '../../../static/style/overview/count.scss';
+import { Edit, CheckMark, Cancel } from '@components/fluent/Icon';
+import MessageInfo from '@components/common/MessageInfo';
+import '@style/experiment/overview/count.scss';
 
 const DurationInputRef = React.createRef<HTMLInputElement>();
 
diff --git a/ts/webui/src/components/overview/count/ExpDuration.tsx b/ts/webui/src/components/experiment/overview/count/ExpDuration.tsx
similarity index 92%
rename from ts/webui/src/components/overview/count/ExpDuration.tsx
rename to ts/webui/src/components/experiment/overview/count/ExpDuration.tsx
index 362c648f4..c32f09de8 100644
--- a/ts/webui/src/components/overview/count/ExpDuration.tsx
+++ b/ts/webui/src/components/experiment/overview/count/ExpDuration.tsx
@@ -1,13 +1,13 @@
 import React from 'react';
 import { Stack, ProgressIndicator, TooltipHost, DirectionalHint } from '@fluentui/react';
-import { EXPERIMENT } from '../../../static/datamodel';
-import { CONTROLTYPE, TOOLTIP_BACKGROUND_COLOR } from '../../../static/const';
-import { convertDuration, convertTimeAsUnit } from '../../../static/function';
+import { EXPERIMENT } from '@static/datamodel';
+import { CONTROLTYPE, TOOLTIP_BACKGROUND_COLOR } from '@static/const';
+import { convertDuration, convertTimeAsUnit } from '@static/function';
 import { EditExperimentParam } from './EditExperimentParam';
 import { ExpDurationContext } from './ExpDurationContext';
 import { EditExpeParamContext } from './context';
 import { leftProgress, rightEidtParam, progressHeight } from './commonStyle';
-import '../../../static/style/overview/count.scss';
+import '@style/experiment/overview/count.scss';
 
 export const ExpDuration = (): any => (
     <ExpDurationContext.Consumer>
diff --git a/ts/webui/src/components/overview/count/ExpDurationContext.tsx b/ts/webui/src/components/experiment/overview/count/ExpDurationContext.tsx
similarity index 100%
rename from ts/webui/src/components/overview/count/ExpDurationContext.tsx
rename to ts/webui/src/components/experiment/overview/count/ExpDurationContext.tsx
diff --git a/ts/webui/src/components/overview/count/TrialCount.tsx b/ts/webui/src/components/experiment/overview/count/TrialCount.tsx
similarity index 98%
rename from ts/webui/src/components/overview/count/TrialCount.tsx
rename to ts/webui/src/components/experiment/overview/count/TrialCount.tsx
index 9cdf62e3b..44e644dc2 100644
--- a/ts/webui/src/components/overview/count/TrialCount.tsx
+++ b/ts/webui/src/components/experiment/overview/count/TrialCount.tsx
@@ -1,7 +1,7 @@
 import * as React from 'react';
 import { Stack, TooltipHost, ProgressIndicator, DirectionalHint, IStackTokens } from '@fluentui/react';
-import { EXPERIMENT, TRIALS } from '../../../static/datamodel';
-import { CONTROLTYPE, TOOLTIP_BACKGROUND_COLOR, MAX_TRIAL_NUMBERS } from '../../../static/const';
+import { EXPERIMENT, TRIALS } from '@static/datamodel';
+import { CONTROLTYPE, TOOLTIP_BACKGROUND_COLOR, MAX_TRIAL_NUMBERS } from '@static/const';
 import { EditExperimentParam } from './EditExperimentParam';
 import { EditExpeParamContext } from './context';
 import { ExpDurationContext } from './ExpDurationContext';
diff --git a/ts/webui/src/components/overview/count/commonStyle.ts b/ts/webui/src/components/experiment/overview/count/commonStyle.ts
similarity index 100%
rename from ts/webui/src/components/overview/count/commonStyle.ts
rename to ts/webui/src/components/experiment/overview/count/commonStyle.ts
diff --git a/ts/webui/src/components/overview/count/context.tsx b/ts/webui/src/components/experiment/overview/count/context.tsx
similarity index 100%
rename from ts/webui/src/components/overview/count/context.tsx
rename to ts/webui/src/components/experiment/overview/count/context.tsx
diff --git a/ts/webui/src/components/overview/overviewConst.ts b/ts/webui/src/components/experiment/overview/overviewConst.ts
similarity index 100%
rename from ts/webui/src/components/overview/overviewConst.ts
rename to ts/webui/src/components/experiment/overview/overviewConst.ts
diff --git a/ts/webui/src/components/overview/params/BasicInfo.tsx b/ts/webui/src/components/experiment/overview/params/BasicInfo.tsx
similarity index 91%
rename from ts/webui/src/components/overview/params/BasicInfo.tsx
rename to ts/webui/src/components/experiment/overview/params/BasicInfo.tsx
index fedc8d3d3..74e5d610a 100644
--- a/ts/webui/src/components/overview/params/BasicInfo.tsx
+++ b/ts/webui/src/components/experiment/overview/params/BasicInfo.tsx
@@ -1,13 +1,13 @@
 import React, { useState, useCallback } from 'react';
 import { Stack, Callout, Link, IconButton } from '@fluentui/react';
-import LogDrawer from '../../modals/LogPanel';
-import { EXPERIMENT } from '../../../static/datamodel';
-import { formatTimestamp } from '../../../static/function';
 import { useId } from '@uifabric/react-hooks';
-import { BestMetricContext } from '../../Overview';
+import { EXPERIMENT } from '@static/datamodel';
+import { formatTimestamp } from '@static/function';
+import LogPanel from '@components/nav/slideNav/LogPanel';
+import { BestMetricContext } from '../Overview';
 import { styles } from './basicInfoStyles';
-import '../../../static/style/overview/probar.scss';
-import '../../../static/style/overview/basic.scss';
+import '@style/common/experimentStatusColor.scss';
+import '@style/experiment/overview/basic.scss';
 
 export const BasicInfo = (): any => {
     const labelId: string = useId('callout-label');
@@ -92,7 +92,7 @@ export const BasicInfo = (): any => {
                 </div>
             </Stack>
             {/* learn about click -> default active key is dispatcher. */}
-            {isShowLogDrawer ? <LogDrawer closeDrawer={closeLogDrawer} activeTab='dispatcher' /> : null}
+            {isShowLogDrawer ? <LogPanel closePanel={closeLogDrawer} activeTab='dispatcher' /> : null}
         </div>
     );
 };
diff --git a/ts/webui/src/components/overview/params/basicInfoStyles.ts b/ts/webui/src/components/experiment/overview/params/basicInfoStyles.ts
similarity index 90%
rename from ts/webui/src/components/overview/params/basicInfoStyles.ts
rename to ts/webui/src/components/experiment/overview/params/basicInfoStyles.ts
index 05c9f819b..0a5adfff1 100644
--- a/ts/webui/src/components/overview/params/basicInfoStyles.ts
+++ b/ts/webui/src/components/experiment/overview/params/basicInfoStyles.ts
@@ -48,5 +48,10 @@ export const styles = mergeStyleSets({
         {
             color: theme.palette.neutralPrimary
         }
-    ]
+    ],
+    buttons: {
+        display: 'flex',
+        justifyContent: 'flex-end',
+        padding: '0 24px 24px'
+    }
 });
diff --git a/ts/webui/src/components/public-child/DefaultMetric.tsx b/ts/webui/src/components/experiment/overview/table/DefaultMetric.tsx
similarity index 83%
rename from ts/webui/src/components/public-child/DefaultMetric.tsx
rename to ts/webui/src/components/experiment/overview/table/DefaultMetric.tsx
index 9ad82533b..f5d48f80d 100644
--- a/ts/webui/src/components/public-child/DefaultMetric.tsx
+++ b/ts/webui/src/components/experiment/overview/table/DefaultMetric.tsx
@@ -1,6 +1,6 @@
 import * as React from 'react';
-import { TRIALS } from '../../static/datamodel';
-import { formatAccuracy } from '../../static/function';
+import { TRIALS } from '@static/datamodel';
+import { formatAccuracy } from '@static/function';
 
 // oview page table: default metric column render
 interface DefaultMetricProps {
diff --git a/ts/webui/src/components/overview/table/SuccessTable.tsx b/ts/webui/src/components/experiment/overview/table/SuccessTable.tsx
similarity index 93%
rename from ts/webui/src/components/overview/table/SuccessTable.tsx
rename to ts/webui/src/components/experiment/overview/table/SuccessTable.tsx
index 5a64fde07..64a445714 100644
--- a/ts/webui/src/components/overview/table/SuccessTable.tsx
+++ b/ts/webui/src/components/experiment/overview/table/SuccessTable.tsx
@@ -13,16 +13,16 @@ import {
     ScrollablePane,
     ScrollbarVisibility
 } from '@fluentui/react';
-import DefaultMetric from '../../public-child/DefaultMetric';
-import OpenRow from '../../public-child/OpenRow';
-import CopyButton from '../../public-child/CopyButton';
-import { convertDuration, copyAndSort } from '../../../static/function';
-import { TRIALS } from '../../../static/datamodel';
-import { SortInfo } from '../../../static/interface';
-import { DETAILTABS } from '../../stateless-component/NNItabs';
-import '../../../static/style/succTable.scss';
-import '../../../static/style/tableStatus.css';
-import '../../../static/style/openRow.scss';
+import DefaultMetric from './DefaultMetric';
+import OpenRow from '@components/common/OpenRow';
+import CopyButton from '@components/common/CopyButton';
+import { convertDuration, copyAndSort } from '@static/function';
+import { TRIALS } from '@static/datamodel';
+import { SortInfo } from '@static/interface';
+import { DETAILTABS } from '@components/nav/slideNav/NNItabs';
+import '@style/experiment/overview/succTable.scss';
+import '@style/common/trialStatus.css';
+import '@style/openRow.scss';
 
 interface SuccessTableProps {
     trialIds: string[];
diff --git a/ts/webui/src/components/modals/ChangeColumnComponent.tsx b/ts/webui/src/components/experiment/trialdetail/ChangeColumnComponent.tsx
similarity index 96%
rename from ts/webui/src/components/modals/ChangeColumnComponent.tsx
rename to ts/webui/src/components/experiment/trialdetail/ChangeColumnComponent.tsx
index 65f945ba5..6c50f73ca 100644
--- a/ts/webui/src/components/modals/ChangeColumnComponent.tsx
+++ b/ts/webui/src/components/experiment/trialdetail/ChangeColumnComponent.tsx
@@ -1,6 +1,11 @@
 import * as React from 'react';
 import { Dialog, DialogType, DialogFooter, Checkbox, PrimaryButton, DefaultButton } from '@fluentui/react';
 
+/**
+ * changeColumnComponent file is for [customized table column, customized hyper-parameter graph yAxis]
+ * and currently it uses localstorage to store the customized results
+ */
+
 interface ChangeColumnState {
     // buffer, not saved yet
     currentSelected: string[];
diff --git a/ts/webui/src/components/TrialsDetail.tsx b/ts/webui/src/components/experiment/trialdetail/TrialsDetail.tsx
similarity index 84%
rename from ts/webui/src/components/TrialsDetail.tsx
rename to ts/webui/src/components/experiment/trialdetail/TrialsDetail.tsx
index 1161dac4a..6bdbd56a2 100644
--- a/ts/webui/src/components/TrialsDetail.tsx
+++ b/ts/webui/src/components/experiment/trialdetail/TrialsDetail.tsx
@@ -1,13 +1,26 @@
 import * as React from 'react';
 import { Stack, Pivot, PivotItem } from '@fluentui/react';
-import { EXPERIMENT, TRIALS } from '../static/datamodel';
-import { AppContext } from '../App';
-import DefaultPoint from './trial-detail/DefaultMetricPoint';
-import Duration from './trial-detail/Duration';
-import Para from './trial-detail/Para';
-import Intermediate from './trial-detail/Intermediate';
-import TableList from './trial-detail/TableList';
-import '../static/style/search.scss';
+import { EXPERIMENT, TRIALS } from '@static/datamodel';
+import { AppContext } from '@/App';
+import DefaultPoint from './chart/DefaultMetricPoint';
+import Duration from './chart/Duration';
+import Para from './chart/Para';
+import Intermediate from './chart/Intermediate';
+import TableList from './table/TableList';
+import '@style/button.scss';
+import '@style/logPath.scss';
+import '@style/openRow.scss';
+import '@style/pagination.scss';
+import '@style/experiment/overview/overviewTitle.scss';
+import '@style/experiment/trialdetail/search.scss';
+import '@style/experiment/trialdetail/tensorboard.scss';
+import '@style/table.scss';
+import '@style/common/trialStatus.css';
+
+/**
+ * single experiment
+ * trial detail page
+ */
 
 interface TrialDetailState {
     whichChart: string;
diff --git a/ts/webui/src/components/trial-detail/DefaultMetricPoint.tsx b/ts/webui/src/components/experiment/trialdetail/chart/DefaultMetricPoint.tsx
similarity index 96%
rename from ts/webui/src/components/trial-detail/DefaultMetricPoint.tsx
rename to ts/webui/src/components/experiment/trialdetail/chart/DefaultMetricPoint.tsx
index ceed84b97..c444bb8bb 100644
--- a/ts/webui/src/components/trial-detail/DefaultMetricPoint.tsx
+++ b/ts/webui/src/components/experiment/trialdetail/chart/DefaultMetricPoint.tsx
@@ -1,10 +1,10 @@
 import * as React from 'react';
 import { Toggle, Stack } from '@fluentui/react';
 import ReactEcharts from 'echarts-for-react';
-import { EXPERIMENT, TRIALS } from '../../static/datamodel';
-import { Trial } from '../../static/model/trial';
-import { TooltipForAccuracy, EventMap } from '../../static/interface';
-import { reformatRetiariiParameter } from '../../static/function';
+import { Trial } from '@model/trial';
+import { EXPERIMENT, TRIALS } from '@static/datamodel';
+import { TooltipForAccuracy, EventMap } from '@static/interface';
+import { reformatRetiariiParameter } from '@static/function';
 import 'echarts/lib/chart/scatter';
 import 'echarts/lib/component/tooltip';
 import 'echarts/lib/component/title';
diff --git a/ts/webui/src/components/trial-detail/Duration.tsx b/ts/webui/src/components/experiment/trialdetail/chart/Duration.tsx
similarity index 97%
rename from ts/webui/src/components/trial-detail/Duration.tsx
rename to ts/webui/src/components/experiment/trialdetail/chart/Duration.tsx
index 3f3b50fde..83df4d22a 100644
--- a/ts/webui/src/components/trial-detail/Duration.tsx
+++ b/ts/webui/src/components/experiment/trialdetail/chart/Duration.tsx
@@ -1,7 +1,7 @@
 import * as React from 'react';
 import ReactEcharts from 'echarts-for-react';
-import { TableObj, EventMap } from '../../static/interface';
-import { filterDuration, convertDuration } from '../../static/function';
+import { TableObj, EventMap } from '@static/interface';
+import { filterDuration, convertDuration } from '@static/function';
 import 'echarts/lib/chart/bar';
 import 'echarts/lib/component/tooltip';
 import 'echarts/lib/component/title';
diff --git a/ts/webui/src/components/trial-detail/Intermediate.tsx b/ts/webui/src/components/experiment/trialdetail/chart/Intermediate.tsx
similarity index 99%
rename from ts/webui/src/components/trial-detail/Intermediate.tsx
rename to ts/webui/src/components/experiment/trialdetail/chart/Intermediate.tsx
index 8822279b0..ce38d278a 100644
--- a/ts/webui/src/components/trial-detail/Intermediate.tsx
+++ b/ts/webui/src/components/experiment/trialdetail/chart/Intermediate.tsx
@@ -1,7 +1,7 @@
 import * as React from 'react';
 import { Stack, PrimaryButton, Toggle, IStackTokens } from '@fluentui/react';
-import { TooltipForIntermediate, TableObj, Intermedia, EventMap } from '../../static/interface';
-import { reformatRetiariiParameter } from '../../static/function';
+import { TooltipForIntermediate, TableObj, Intermedia, EventMap } from '@static/interface';
+import { reformatRetiariiParameter } from '@static/function';
 import ReactEcharts from 'echarts-for-react';
 import 'echarts/lib/component/tooltip';
 import 'echarts/lib/component/title';
diff --git a/ts/webui/src/components/trial-detail/Para.tsx b/ts/webui/src/components/experiment/trialdetail/chart/Para.tsx
similarity index 96%
rename from ts/webui/src/components/trial-detail/Para.tsx
rename to ts/webui/src/components/experiment/trialdetail/chart/Para.tsx
index e39434142..b09bea597 100644
--- a/ts/webui/src/components/trial-detail/Para.tsx
+++ b/ts/webui/src/components/experiment/trialdetail/chart/Para.tsx
@@ -1,15 +1,16 @@
+import * as React from 'react';
 import * as d3 from 'd3';
 import { Dropdown, IDropdownOption, Stack, DefaultButton } from '@fluentui/react';
 import ParCoords from 'parcoord-es';
+import { SearchSpace } from '@model/searchspace';
+import { filterByStatus } from '@static/function';
+import { EXPERIMENT, TRIALS } from '@static/datamodel';
+import { TableObj, SingleAxis, MultipleAxes } from '@static/interface';
+import ChangeColumnComponent from '../ChangeColumnComponent';
+
 import 'parcoord-es/dist/parcoords.css';
-import * as React from 'react';
-import { EXPERIMENT, TRIALS } from '../../static/datamodel';
-import { SearchSpace } from '../../static/model/searchspace';
-import { filterByStatus } from '../../static/function';
-import { TableObj, SingleAxis, MultipleAxes } from '../../static/interface';
-import '../../static/style/button.scss';
-import '../../static/style/para.scss';
-import ChangeColumnComponent from '../modals/ChangeColumnComponent';
+import '@style/button.scss';
+import '@style/experiment/trialdetail/para.scss';
 
 interface ParaState {
     dimName: string[];
diff --git a/ts/webui/src/components/trial-detail/TableList.tsx b/ts/webui/src/components/experiment/trialdetail/table/TableList.tsx
similarity index 94%
rename from ts/webui/src/components/trial-detail/TableList.tsx
rename to ts/webui/src/components/experiment/trialdetail/table/TableList.tsx
index e2a69b8d9..63f61c266 100644
--- a/ts/webui/src/components/trial-detail/TableList.tsx
+++ b/ts/webui/src/components/experiment/trialdetail/table/TableList.tsx
@@ -10,31 +10,22 @@ import {
     DirectionalHint,
     Checkbox
 } from '@fluentui/react';
-import { EXPERIMENT, TRIALS } from '../../static/datamodel';
-import { TOOLTIP_BACKGROUND_COLOR } from '../../static/const';
-import { convertDuration, formatTimestamp, copyAndSort, parametersType, parseMetrics } from '../../static/function';
-import { TableObj, SortInfo, SearchItems } from '../../static/interface';
-import { getTrialsBySearchFilters } from './search/searchFunction';
-import { blocked, copy, LineChart, tableListIcon } from '../buttons/Icon';
-import ChangeColumnComponent from '../modals/ChangeColumnComponent';
-import Compare from '../modals/Compare';
-import Customize from '../modals/CustomizedTrial';
-import TensorboardUI from '../modals/tensorboard/TensorboardUI';
-import Search from './search/Search';
-import KillJob from '../modals/Killjob';
-import ExpandableDetails from '../public-child/ExpandableDetails';
-import PaginationTable from '../public-child/PaginationTable';
-import CopyButton from '../public-child/CopyButton';
-import { Trial } from '../../static/model/trial';
-import '../../static/style/button.scss';
-import '../../static/style/logPath.scss';
-import '../../static/style/openRow.scss';
-import '../../static/style/pagination.scss';
-import '../../static/style/search.scss';
-import '../../static/style/table.scss';
-import '../../static/style/tableStatus.css';
-import '../../static/style/tensorboard.scss';
-import '../../static/style/overview/overviewTitle.scss';
+import { EXPERIMENT, TRIALS } from '@static/datamodel';
+import { TOOLTIP_BACKGROUND_COLOR } from '@static/const';
+import { convertDuration, formatTimestamp, copyAndSort, parametersType, parseMetrics } from '@static/function';
+import { TableObj, SortInfo, SearchItems } from '@static/interface';
+import { getTrialsBySearchFilters } from './tableFunction/search/searchFunction';
+import { blocked, copy, LineChart, tableListIcon } from '@components/fluent/Icon';
+import ChangeColumnComponent from '../ChangeColumnComponent';
+import Compare from './tableFunction/Compare';
+import Customize from './tableFunction/CustomizedTrial';
+import TensorboardUI from './tableFunction/tensorboard/TensorboardUI';
+import Search from './tableFunction/search/Search';
+import KillJob from './tableFunction/killTrial/Killjob';
+import ExpandableDetails from '@components/common/ExpandableDetails';
+import PaginationTable from '@components/common/PaginationTable';
+import CopyButton from '@components/common/CopyButton';
+import { Trial } from '@model/trial';
 
 require('echarts/lib/chart/line');
 require('echarts/lib/component/tooltip');
diff --git a/ts/webui/src/components/modals/Compare.tsx b/ts/webui/src/components/experiment/trialdetail/table/tableFunction/Compare.tsx
similarity index 95%
rename from ts/webui/src/components/modals/Compare.tsx
rename to ts/webui/src/components/experiment/trialdetail/table/tableFunction/Compare.tsx
index 6d9248d2c..535a9c08f 100644
--- a/ts/webui/src/components/modals/Compare.tsx
+++ b/ts/webui/src/components/experiment/trialdetail/table/tableFunction/Compare.tsx
@@ -2,15 +2,16 @@ import * as React from 'react';
 import { renderToString } from 'react-dom/server';
 import { Stack, Modal, IconButton, IDragOptions, ContextualMenu, Dropdown, IDropdownOption } from '@fluentui/react';
 import ReactEcharts from 'echarts-for-react';
-import { TooltipForIntermediate, TableObj, SingleAxis } from '../../static/interface';
-import { contentStyles, iconButtonStyles } from '../buttons/ModalTheme';
-import { convertDuration, parseMetrics } from '../../static/function';
-import { EXPERIMENT, TRIALS } from '../../static/datamodel';
-import '../../static/style/compare.scss';
+import { TooltipForIntermediate, TableObj, SingleAxis } from '@static/interface';
+import { contentStyles, iconButtonStyles } from '@components/fluent/ModalTheme';
+import { convertDuration, parseMetrics } from '@static/function';
+import { EXPERIMENT, TRIALS } from '@static/datamodel';
+import '@style/experiment/trialdetail/compare.scss';
 
 /***
- * Compare file is design for [each trial intermediate result and trials compare function]
- * if trial has dict intermediate result, graph support shows all keys that type is number
+ * Compare file is designed for [each trial intermediate result, trials compare model]
+ * if trial has dict intermediate result,
+ * graph only supports to show all keys that type is number
  */
 
 function _getWebUIWidth(): number {
diff --git a/ts/webui/src/components/modals/CustomizedTrial.tsx b/ts/webui/src/components/experiment/trialdetail/table/tableFunction/CustomizedTrial.tsx
similarity index 96%
rename from ts/webui/src/components/modals/CustomizedTrial.tsx
rename to ts/webui/src/components/experiment/trialdetail/table/tableFunction/CustomizedTrial.tsx
index cdd523f2e..a06673dc5 100644
--- a/ts/webui/src/components/modals/CustomizedTrial.tsx
+++ b/ts/webui/src/components/experiment/trialdetail/table/tableFunction/CustomizedTrial.tsx
@@ -2,10 +2,16 @@ import * as React from 'react';
 import axios from 'axios';
 import { Stack, StackItem, PrimaryButton, DefaultButton } from '@fluentui/react';
 import { Dialog, DialogType, DialogFooter } from '@fluentui/react/lib/Dialog';
-import { MANAGER_IP } from '../../static/const';
-import { EXPERIMENT, TRIALS } from '../../static/datamodel';
-import { warining, errorBadge, completed } from '../buttons/Icon';
-import './customized.scss';
+import { MANAGER_IP } from '@static/const';
+import { EXPERIMENT, TRIALS } from '@static/datamodel';
+import { warining, errorBadge, completed } from '@components/fluent/Icon';
+import '@style/experiment/trialdetail/customized.scss';
+
+/**
+ * customized trial file is for
+ * [rerun failed trial,
+ * change trial parameters and add this customized trial into the experiment]
+ */
 
 interface CustomizeProps {
     visible: boolean;
diff --git a/ts/webui/src/components/modals/Killjob.tsx b/ts/webui/src/components/experiment/trialdetail/table/tableFunction/killTrial/Killjob.tsx
similarity index 67%
rename from ts/webui/src/components/modals/Killjob.tsx
rename to ts/webui/src/components/experiment/trialdetail/table/tableFunction/killTrial/Killjob.tsx
index 0a567815a..96e072305 100644
--- a/ts/webui/src/components/modals/Killjob.tsx
+++ b/ts/webui/src/components/experiment/trialdetail/table/tableFunction/killTrial/Killjob.tsx
@@ -1,63 +1,8 @@
 import * as React from 'react';
-import {
-    Stack,
-    FocusTrapCallout,
-    DefaultButton,
-    FocusZone,
-    PrimaryButton,
-    getTheme,
-    mergeStyleSets,
-    FontWeights
-} from '@fluentui/react';
-import { killJob } from '../../static/function';
-import { blocked } from '../buttons/Icon';
-
-const theme = getTheme();
-const styles = mergeStyleSets({
-    buttonArea: {
-        verticalAlign: 'top',
-        display: 'inline-block',
-        textAlign: 'center',
-        height: 32
-    },
-    callout: {
-        maxWidth: 300
-    },
-    header: {
-        padding: '18px 24px 12px'
-    },
-    title: [
-        theme.fonts.xLarge,
-        {
-            margin: 0,
-            color: theme.palette.neutralPrimary,
-            fontWeight: FontWeights.semilight
-        }
-    ],
-    inner: {
-        height: '100%',
-        padding: '0 24px 20px'
-    },
-    actions: {
-        position: 'relative',
-        marginTop: 20,
-        width: '100%',
-        whiteSpace: 'nowrap'
-    },
-    buttons: {
-        display: 'flex',
-        justifyContent: 'flex-end',
-        padding: '0 24px 24px'
-    },
-    subtext: [
-        theme.fonts.small,
-        {
-            margin: 0,
-            color: theme.palette.neutralPrimary,
-            fontWeight: FontWeights.semilight
-        }
-    ]
-});
+import { Stack, FocusTrapCallout, DefaultButton, FocusZone, PrimaryButton } from '@fluentui/react';
+import { killJob } from '@static/function';
+import { blocked } from '@components/fluent/Icon';
+import { styles } from '@components/experiment/overview/params/basicInfoStyles';
 
 interface KillJobState {
     isCalloutVisible: boolean;
@@ -74,23 +19,6 @@ class KillJob extends React.Component<KillJobProps, KillJobState> {
         this.state = { isCalloutVisible: false };
     }
 
-    onDismiss = (): void => {
-        this.setState(() => ({ isCalloutVisible: false }));
-    };
-
-    onKill = (): void => {
-        this.setState({ isCalloutVisible: false }, () => {
-            const { trial } = this.props;
-            killJob(trial.key, trial.id, trial.status);
-        });
-    };
-
-    openPromot = (event: React.SyntheticEvent<EventTarget>): void => {
-        event.preventDefault();
-        event.stopPropagation();
-        this.setState({ isCalloutVisible: true });
-    };
-
     render(): React.ReactNode {
         const { isCalloutVisible } = this.state;
         const prompString = 'Are you sure to cancel this trial?';
@@ -135,6 +63,23 @@ class KillJob extends React.Component<KillJobProps, KillJobState> {
             </div>
         );
     }
+
+    private onDismiss = (): void => {
+        this.setState(() => ({ isCalloutVisible: false }));
+    };
+
+    private onKill = (): void => {
+        this.setState({ isCalloutVisible: false }, () => {
+            const { trial } = this.props;
+            killJob(trial.key, trial.id, trial.status);
+        });
+    };
+
+    private openPromot = (event: React.SyntheticEvent<EventTarget>): void => {
+        event.preventDefault();
+        event.stopPropagation();
+        this.setState({ isCalloutVisible: true });
+    };
 }
 
 export default KillJob;
diff --git a/ts/webui/src/components/trial-detail/search/GeneralSearch.tsx b/ts/webui/src/components/experiment/trialdetail/table/tableFunction/search/GeneralSearch.tsx
similarity index 95%
rename from ts/webui/src/components/trial-detail/search/GeneralSearch.tsx
rename to ts/webui/src/components/experiment/trialdetail/table/tableFunction/search/GeneralSearch.tsx
index 86501ab20..7c96c559e 100644
--- a/ts/webui/src/components/trial-detail/search/GeneralSearch.tsx
+++ b/ts/webui/src/components/experiment/trialdetail/table/tableFunction/search/GeneralSearch.tsx
@@ -1,7 +1,7 @@
 import React, { useState } from 'react';
 import PropTypes from 'prop-types';
 import { Stack, PrimaryButton } from '@fluentui/react';
-import { searchConditonsGap } from '../../modals/ChildrenGap';
+import { gap10 } from '@components/fluent/ChildrenGap';
 import { getSearchInputValueBySearchList } from './searchFunction';
 
 // This file is for search trial ['Trial id', 'Trial No.']
@@ -62,7 +62,7 @@ function GeneralSearch(props): any {
 
     return (
         // Trial id & Trial No.
-        <Stack horizontal className='filterConditions' tokens={searchConditonsGap}>
+        <Stack horizontal className='filterConditions' tokens={gap10}>
             <span>{searchName === 'Trial id' ? 'Includes' : 'Equals to'}</span>
             <input type='text' className='input input-padding' onChange={updateFirstInputVal} value={firstInputVal} />
             <PrimaryButton text='Apply' className='btn-vertical-middle' onClick={startFilterTrial} />
diff --git a/ts/webui/src/components/trial-detail/search/Search.tsx b/ts/webui/src/components/experiment/trialdetail/table/tableFunction/search/Search.tsx
similarity index 98%
rename from ts/webui/src/components/trial-detail/search/Search.tsx
rename to ts/webui/src/components/experiment/trialdetail/table/tableFunction/search/Search.tsx
index b984fd8ac..c7f518e5f 100644
--- a/ts/webui/src/components/trial-detail/search/Search.tsx
+++ b/ts/webui/src/components/experiment/trialdetail/table/tableFunction/search/Search.tsx
@@ -8,8 +8,8 @@ import {
     DirectionalHint,
     SearchBox
 } from '@fluentui/react';
-import { EXPERIMENT } from '../../../static/datamodel';
-import { SearchItems } from '../../../static/interface';
+import { EXPERIMENT } from '@static/datamodel';
+import { SearchItems } from '@static/interface';
 import SearchParameterConditions from './SearchParameterConditions';
 import GeneralSearch from './GeneralSearch';
 import { classNames, isChoiceType } from './searchFunction';
diff --git a/ts/webui/src/components/trial-detail/search/SearchParameterConditions.tsx b/ts/webui/src/components/experiment/trialdetail/table/tableFunction/search/SearchParameterConditions.tsx
similarity index 97%
rename from ts/webui/src/components/trial-detail/search/SearchParameterConditions.tsx
rename to ts/webui/src/components/experiment/trialdetail/table/tableFunction/search/SearchParameterConditions.tsx
index 7d3b94650..59671fcfc 100644
--- a/ts/webui/src/components/trial-detail/search/SearchParameterConditions.tsx
+++ b/ts/webui/src/components/experiment/trialdetail/table/tableFunction/search/SearchParameterConditions.tsx
@@ -1,9 +1,9 @@
 import React, { useState } from 'react';
 import PropTypes from 'prop-types';
 import { Stack, PrimaryButton, Dropdown, IDropdownOption } from '@fluentui/react';
-import { EXPERIMENT } from '../../../static/datamodel';
+import { EXPERIMENT } from '@static/datamodel';
 import { getDropdownOptions, getSearchInputValueBySearchList } from './searchFunction';
-import { searchConditonsGap } from '../../modals/ChildrenGap';
+import { gap10 } from '@components/fluent/ChildrenGap';
 
 // This file is for filtering trial parameters and trial status
 
@@ -131,7 +131,7 @@ function SearchParameterConditions(props): any {
 
     return (
         // for trial parameters & Status
-        <Stack horizontal className='filterConditions' tokens={searchConditonsGap}>
+        <Stack horizontal className='filterConditions' tokens={gap10}>
             <Dropdown
                 selectedKey={operatorVal}
                 options={operatorList.map(item => ({
diff --git a/ts/webui/src/components/trial-detail/search/searchFunction.ts b/ts/webui/src/components/experiment/trialdetail/table/tableFunction/search/searchFunction.ts
similarity index 97%
rename from ts/webui/src/components/trial-detail/search/searchFunction.ts
rename to ts/webui/src/components/experiment/trialdetail/table/tableFunction/search/searchFunction.ts
index e836f406f..da45e1704 100644
--- a/ts/webui/src/components/trial-detail/search/searchFunction.ts
+++ b/ts/webui/src/components/experiment/trialdetail/table/tableFunction/search/searchFunction.ts
@@ -1,7 +1,7 @@
 import { mergeStyleSets } from '@fluentui/react';
-import { trialJobStatus } from '../../../static/const';
-import { EXPERIMENT } from '../../../static/datamodel';
-import { TableObj, SearchItems } from '../../../static/interface';
+import { trialJobStatus } from '@static/const';
+import { EXPERIMENT } from '@static/datamodel';
+import { TableObj, SearchItems } from '@static/interface';
 
 const classNames = mergeStyleSets({
     menu: {
diff --git a/ts/webui/src/components/modals/tensorboard/TensorboardDialog.tsx b/ts/webui/src/components/experiment/trialdetail/table/tableFunction/tensorboard/TensorboardDialog.tsx
similarity index 100%
rename from ts/webui/src/components/modals/tensorboard/TensorboardDialog.tsx
rename to ts/webui/src/components/experiment/trialdetail/table/tableFunction/tensorboard/TensorboardDialog.tsx
diff --git a/ts/webui/src/components/modals/tensorboard/TensorboardUI.tsx b/ts/webui/src/components/experiment/trialdetail/table/tableFunction/tensorboard/TensorboardUI.tsx
similarity index 96%
rename from ts/webui/src/components/modals/tensorboard/TensorboardUI.tsx
rename to ts/webui/src/components/experiment/trialdetail/table/tableFunction/tensorboard/TensorboardUI.tsx
index afb6c8207..528d429fa 100644
--- a/ts/webui/src/components/modals/tensorboard/TensorboardUI.tsx
+++ b/ts/webui/src/components/experiment/trialdetail/table/tableFunction/tensorboard/TensorboardUI.tsx
@@ -2,9 +2,9 @@ import React, { useState, useEffect } from 'react';
 import PropTypes from 'prop-types';
 import axios from 'axios';
 import { DefaultButton, IContextualMenuProps } from '@fluentui/react';
-import { MANAGER_IP } from '../../../static/const';
-import { disableTensorboard, getTensorboardMenu } from '../../../static/function';
-import { Tensorboard } from '../../../static/interface';
+import { MANAGER_IP } from '@static/const';
+import { disableTensorboard, getTensorboardMenu } from '@static/function';
+import { Tensorboard } from '@static/interface';
 import TensorboardDialog from './TensorboardDialog';
 
 function TensorboardUI(props): any {
diff --git a/ts/webui/src/components/managementExp/ExperimentManager.tsx b/ts/webui/src/components/experimentManagement/ExperimentManagerIndex.tsx
similarity index 96%
rename from ts/webui/src/components/managementExp/ExperimentManager.tsx
rename to ts/webui/src/components/experimentManagement/ExperimentManagerIndex.tsx
index 3dfad16c3..83d84baaf 100644
--- a/ts/webui/src/components/managementExp/ExperimentManager.tsx
+++ b/ts/webui/src/components/experimentManagement/ExperimentManagerIndex.tsx
@@ -1,22 +1,22 @@
 import * as React from 'react';
 import { Stack, DetailsList, DefaultButton, Icon, SearchBox, IColumn, IStackTokens } from '@fluentui/react';
-import { ExperimentsManager } from '../../static/model/experimentsManager';
-import { expformatTimestamp, copyAndSort } from '../../static/function';
-import { AllExperimentList, SortInfo } from '../../static/interface';
-import MessageInfo from '../modals/MessageInfo';
+import { ExperimentsManager } from '@model/experimentsManager';
+import { expformatTimestamp, copyAndSort } from '@static/function';
+import { AllExperimentList, SortInfo } from '@static/interface';
+import MessageInfo from '@components/common/MessageInfo';
 import { compareDate, filterByStatusOrPlatform, getSortedSource } from './expFunction';
 import { MAXSCREENCOLUMNWIDHT, MINSCREENCOLUMNWIDHT } from './experimentConst';
 import { Hearder } from './Header';
 import TrialIdColumn from './TrialIdColumn';
 import FilterBtns from './FilterBtns';
-import { TitleContext } from '../overview/TitleContext';
-import { Title } from '../overview/Title';
-import '../../App.scss';
-import '../../static/style/common.scss';
-import '../../static/style/nav/nav.scss';
-import '../../static/style/experiment/experiment.scss';
-import '../../static/style/overview/probar.scss';
-import '../../static/style/tableStatus.css';
+import { TitleContext } from '../experiment/overview/TitleContext';
+import { Title } from '../experiment/overview/Title';
+import '@style/App.scss';
+import '@style/nav/nav.scss';
+import '@style/common/common.scss';
+import '@style/common/experimentStatusColor.scss';
+import '@style/common/trialStatus.css';
+import '@style/experimentManagement/experiment.scss';
 
 const expTokens: IStackTokens = {
     childrenGap: 25
diff --git a/ts/webui/src/components/managementExp/FilterBtns.tsx b/ts/webui/src/components/experimentManagement/FilterBtns.tsx
similarity index 98%
rename from ts/webui/src/components/managementExp/FilterBtns.tsx
rename to ts/webui/src/components/experimentManagement/FilterBtns.tsx
index 91d64b47c..98f1ea698 100644
--- a/ts/webui/src/components/managementExp/FilterBtns.tsx
+++ b/ts/webui/src/components/experimentManagement/FilterBtns.tsx
@@ -1,6 +1,6 @@
 import * as React from 'react';
 import { DefaultButton, Icon, Dropdown, DatePicker, DayOfWeek } from '@fluentui/react';
-import { EXPERIMENTSTATUS } from '../../static/const';
+import { EXPERIMENTSTATUS } from '@static/const';
 import { fillOptions } from './expFunction';
 
 interface FilterBtnsProps {
diff --git a/ts/webui/src/components/managementExp/Header.tsx b/ts/webui/src/components/experimentManagement/Header.tsx
similarity index 83%
rename from ts/webui/src/components/managementExp/Header.tsx
rename to ts/webui/src/components/experimentManagement/Header.tsx
index d32fc9854..7e2a52e47 100644
--- a/ts/webui/src/components/managementExp/Header.tsx
+++ b/ts/webui/src/components/experimentManagement/Header.tsx
@@ -1,9 +1,9 @@
 import React from 'react';
 import { Link } from 'react-router-dom';
 import { Stack, StackItem, CommandBarButton } from '@fluentui/react';
-import { RevToggleKey } from '../buttons/Icon';
-import { NNILOGO } from '../stateless-component/NNItabs';
-import { stackTokens, stackStyle } from '../NavConst';
+import { RevToggleKey } from '../fluent/Icon';
+import { NNILOGO } from '../nav/slideNav/NNItabs';
+import { gap15, stackStyle } from '../fluent/ChildrenGap';
 
 export const Hearder = (): any => (
     <div className='header'>
@@ -14,7 +14,7 @@ export const Hearder = (): any => (
                     <span className='logoTitle'>Neural Network Intelligence</span>
                 </StackItem>
                 <StackItem grow={70} className='navOptions'>
-                    <Stack horizontal horizontalAlign='end' tokens={stackTokens} styles={stackStyle}>
+                    <Stack horizontal horizontalAlign='end' tokens={gap15} styles={stackStyle}>
                         <Link to='/oview' className='experiment'>
                             <CommandBarButton iconProps={RevToggleKey} text='Back to the experiment' />
                         </Link>
diff --git a/ts/webui/src/components/managementExp/TrialIdColumn.tsx b/ts/webui/src/components/experimentManagement/TrialIdColumn.tsx
similarity index 93%
rename from ts/webui/src/components/managementExp/TrialIdColumn.tsx
rename to ts/webui/src/components/experimentManagement/TrialIdColumn.tsx
index a0954ca7c..144528164 100644
--- a/ts/webui/src/components/managementExp/TrialIdColumn.tsx
+++ b/ts/webui/src/components/experimentManagement/TrialIdColumn.tsx
@@ -1,7 +1,7 @@
 import * as React from 'react';
 import { Stack } from '@fluentui/react';
-import { AllExperimentList } from '../../static/interface';
-import CopyButton from '../public-child/CopyButton';
+import { AllExperimentList } from '@static/interface';
+import CopyButton from '../common/CopyButton';
 
 interface TrialIdColumnProps {
     item: AllExperimentList;
diff --git a/ts/webui/src/components/managementExp/expFunction.ts b/ts/webui/src/components/experimentManagement/expFunction.ts
similarity index 90%
rename from ts/webui/src/components/managementExp/expFunction.ts
rename to ts/webui/src/components/experimentManagement/expFunction.ts
index 193c79f2c..8ba1c46e7 100644
--- a/ts/webui/src/components/managementExp/expFunction.ts
+++ b/ts/webui/src/components/experimentManagement/expFunction.ts
@@ -1,5 +1,5 @@
-import { AllExperimentList, SortInfo } from '../../static/interface';
-import { copyAndSort } from '../../static/function';
+import { AllExperimentList, SortInfo } from '@static/interface';
+import { copyAndSort } from '@static/function';
 
 function compareDate(date1: Date, date2: Date): boolean {
     if (date1 !== undefined && date2 !== undefined) {
diff --git a/ts/webui/src/components/managementExp/experimentConst.ts b/ts/webui/src/components/experimentManagement/experimentConst.ts
similarity index 100%
rename from ts/webui/src/components/managementExp/experimentConst.ts
rename to ts/webui/src/components/experimentManagement/experimentConst.ts
diff --git a/ts/webui/src/components/NavConst.ts b/ts/webui/src/components/fluent/ChildrenGap.ts
similarity index 60%
rename from ts/webui/src/components/NavConst.ts
rename to ts/webui/src/components/fluent/ChildrenGap.ts
index 9ebb93db7..3781e6f70 100644
--- a/ts/webui/src/components/NavConst.ts
+++ b/ts/webui/src/components/fluent/ChildrenGap.ts
@@ -1,8 +1,15 @@
 import { IStackTokens, IStackStyles } from '@fluentui/react';
 
-const stackTokens: IStackTokens = {
+// name: gap + specific gap number
+
+const gap10: IStackTokens = {
+    childrenGap: 10
+};
+
+const gap15: IStackTokens = {
     childrenGap: 15
 };
+
 const stackStyle: IStackStyles = {
     root: {
         minWidth: 400,
@@ -12,4 +19,4 @@ const stackStyle: IStackStyles = {
     }
 };
 
-export { stackTokens, stackStyle };
+export { gap10, gap15, stackStyle };
diff --git a/ts/webui/src/components/buttons/Icon.tsx b/ts/webui/src/components/fluent/Icon.tsx
similarity index 100%
rename from ts/webui/src/components/buttons/Icon.tsx
rename to ts/webui/src/components/fluent/Icon.tsx
diff --git a/ts/webui/src/components/buttons/ModalTheme.tsx b/ts/webui/src/components/fluent/ModalTheme.tsx
similarity index 100%
rename from ts/webui/src/components/buttons/ModalTheme.tsx
rename to ts/webui/src/components/fluent/ModalTheme.tsx
diff --git a/ts/webui/src/components/modals/ChildrenGap.ts b/ts/webui/src/components/modals/ChildrenGap.ts
deleted file mode 100644
index 2471bbe58..000000000
--- a/ts/webui/src/components/modals/ChildrenGap.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-import { IStackTokens } from '@fluentui/react';
-
-const searchConditonsGap: IStackTokens = {
-    childrenGap: 10
-};
-
-export { searchConditonsGap };
diff --git a/ts/webui/src/components/NavCon.tsx b/ts/webui/src/components/nav/Nav.tsx
similarity index 92%
rename from ts/webui/src/components/NavCon.tsx
rename to ts/webui/src/components/nav/Nav.tsx
index b07aa031d..736159ed6 100644
--- a/ts/webui/src/components/NavCon.tsx
+++ b/ts/webui/src/components/nav/Nav.tsx
@@ -1,15 +1,22 @@
 import * as React from 'react';
 import axios from 'axios';
-import { WEBUIDOC, MANAGER_IP } from '../static/const';
 import { Stack, StackItem, CommandBarButton, IContextualMenuProps } from '@fluentui/react';
 import { Link } from 'react-router-dom';
-import { infoIconAbout, timeIcon, disableUpdates, requency, closeTimer, ChevronRightMed } from './buttons/Icon';
-import ExperimentSummaryPanel from './modals/ExperimentSummaryPanel';
-import { OVERVIEWTABS, DETAILTABS, NNILOGO } from './stateless-component/NNItabs';
-import { EXPERIMENT } from '../static/datamodel';
-import { stackTokens, stackStyle } from './NavConst';
-import '../static/style/nav/nav.scss';
-import '../static/style/icon.scss';
+import { WEBUIDOC, MANAGER_IP } from '@static/const';
+import ExperimentSummaryPanel from './slideNav/ExperimentSummaryPanel';
+import { OVERVIEWTABS, DETAILTABS, NNILOGO } from './slideNav/NNItabs';
+import { EXPERIMENT } from '@static/datamodel';
+import { gap15, stackStyle } from '@components/fluent/ChildrenGap';
+import {
+    infoIconAbout,
+    timeIcon,
+    disableUpdates,
+    requency,
+    closeTimer,
+    ChevronRightMed
+} from '@components/fluent/Icon';
+import '@style/nav/nav.scss';
+import '@style/icon.scss';
 
 interface NavState {
     version: string;
@@ -121,7 +128,7 @@ class NavCon extends React.Component<NavProps, NavState> {
                         <span>{DETAILTABS}</span>
                     </StackItem>
                     <StackItem grow={70} className='navOptions'>
-                        <Stack horizontal horizontalAlign='end' tokens={stackTokens} styles={stackStyle}>
+                        <Stack horizontal horizontalAlign='end' tokens={gap15} styles={stackStyle}>
                             {/* refresh button danyi*/}
                             {/* TODO: fix bug */}
                             {/* <CommandBarButton
diff --git a/ts/webui/src/components/modals/ExperimentSummaryPanel.tsx b/ts/webui/src/components/nav/slideNav/ExperimentSummaryPanel.tsx
similarity index 93%
rename from ts/webui/src/components/modals/ExperimentSummaryPanel.tsx
rename to ts/webui/src/components/nav/slideNav/ExperimentSummaryPanel.tsx
index 2d6d51d93..1cda612d0 100644
--- a/ts/webui/src/components/modals/ExperimentSummaryPanel.tsx
+++ b/ts/webui/src/components/nav/slideNav/ExperimentSummaryPanel.tsx
@@ -1,11 +1,11 @@
 import * as React from 'react';
-import { downFile } from '../../static/function';
 import { Stack, PrimaryButton, DefaultButton, Panel, StackItem } from '@fluentui/react';
-import { DRAWEROPTION } from '../../static/const';
-import { EXPERIMENT, TRIALS } from '../../static/datamodel';
-import { caclMonacoEditorHeight } from '../../static/function';
 import MonacoEditor from 'react-monaco-editor';
-import '../../static/style/logDrawer.scss';
+import { downFile } from '@static/function';
+import { DRAWEROPTION } from '@static/const';
+import { EXPERIMENT, TRIALS } from '@static/datamodel';
+import { caclMonacoEditorHeight } from '@static/function';
+import '@style/logPanel.scss';
 
 interface ExpDrawerProps {
     closeExpDrawer: () => void;
diff --git a/ts/webui/src/components/slideNav/IconButtonTemplet.tsx b/ts/webui/src/components/nav/slideNav/IconButtonTemplet.tsx
similarity index 100%
rename from ts/webui/src/components/slideNav/IconButtonTemplet.tsx
rename to ts/webui/src/components/nav/slideNav/IconButtonTemplet.tsx
diff --git a/ts/webui/src/components/modals/LogPanel.tsx b/ts/webui/src/components/nav/slideNav/LogPanel.tsx
similarity index 83%
rename from ts/webui/src/components/modals/LogPanel.tsx
rename to ts/webui/src/components/nav/slideNav/LogPanel.tsx
index 7c29c1a4a..35a4b48e2 100644
--- a/ts/webui/src/components/modals/LogPanel.tsx
+++ b/ts/webui/src/components/nav/slideNav/LogPanel.tsx
@@ -1,35 +1,35 @@
 import * as React from 'react';
 import axios from 'axios';
 import { Stack, StackItem, Panel, PrimaryButton, DefaultButton, Pivot, PivotItem } from '@fluentui/react';
-import { infoIcon } from '../buttons/Icon';
-import { DOWNLOAD_IP } from '../../static/const';
-import { downFile } from '../../static/function';
-import MonacoHTML from '../public-child/MonacoEditor';
-import '../../static/style/logDrawer.scss';
-
-interface LogDrawerProps {
-    closeDrawer: () => void;
+import { DOWNLOAD_IP } from '@static/const';
+import { downFile } from '@static/function';
+import { infoIcon } from '@components/fluent/Icon';
+import MonacoHTML from '@components/common/MonacoEditor';
+import '@style/logPanel.scss';
+
+interface LogPanelProps {
+    closePanel: () => void;
     activeTab?: string;
 }
 
-interface LogDrawerState {
+interface LogPanelState {
     nniManagerLogStr: string | null;
     dispatcherLogStr: string | null;
     isLoading: boolean;
-    logDrawerHeight: number;
+    logPanelHeight: number;
 }
 
-class LogDrawer extends React.Component<LogDrawerProps, LogDrawerState> {
+class LogPanel extends React.Component<LogPanelProps, LogPanelState> {
     private timerId: number | undefined;
 
-    constructor(props: LogDrawerProps) {
+    constructor(props: LogPanelProps) {
         super(props);
 
         this.state = {
             nniManagerLogStr: null,
             dispatcherLogStr: null,
             isLoading: true,
-            logDrawerHeight: window.innerHeight
+            logPanelHeight: window.innerHeight
         };
     }
 
@@ -63,25 +63,25 @@ class LogDrawer extends React.Component<LogDrawerProps, LogDrawerState> {
         </div>
     );
 
-    setLogDrawerHeight = (): void => {
-        this.setState(() => ({ logDrawerHeight: window.innerHeight }));
+    setLogPanelHeight = (): void => {
+        this.setState(() => ({ logPanelHeight: window.innerHeight }));
     };
 
     async componentDidMount(): Promise<void> {
         this.refresh();
-        window.addEventListener('resize', this.setLogDrawerHeight);
+        window.addEventListener('resize', this.setLogPanelHeight);
     }
 
     componentWillUnmount(): void {
         window.clearTimeout(this.timerId);
-        window.removeEventListener('resize', this.setLogDrawerHeight);
+        window.removeEventListener('resize', this.setLogPanelHeight);
     }
 
     render(): React.ReactNode {
-        const { closeDrawer, activeTab } = this.props;
-        const { nniManagerLogStr, dispatcherLogStr, isLoading, logDrawerHeight } = this.state;
+        const { closePanel, activeTab } = this.props;
+        const { nniManagerLogStr, dispatcherLogStr, isLoading, logPanelHeight } = this.state;
         // tab[height: 56] + tab[margin-bottom: 20] + button[32] + button[margin-top: 45, -bottom: 7] + fluent-panel own paddingBottom[20] + title-border[2]
-        const monacoHeight = logDrawerHeight - 182;
+        const monacoHeight = logPanelHeight - 182;
         return (
             <Stack>
                 <Panel
@@ -89,7 +89,7 @@ class LogDrawer extends React.Component<LogDrawerProps, LogDrawerState> {
                     hasCloseButton={false}
                     isFooterAtBottom={true}
                     isLightDismiss={true}
-                    onLightDismissClick={closeDrawer}
+                    onLightDismissClick={closePanel}
                     className='logPanel'
                 >
                     <Pivot selectedKey={activeTab} style={{ minHeight: 190 }}>
@@ -105,7 +105,7 @@ class LogDrawer extends React.Component<LogDrawerProps, LogDrawerState> {
                                         <PrimaryButton text='Download' onClick={this.downloadDispatcher} />
                                     </StackItem>
                                     <StackItem grow={12} className='close'>
-                                        <DefaultButton text='Close' onClick={closeDrawer} />
+                                        <DefaultButton text='Close' onClick={closePanel} />
                                     </StackItem>
                                 </Stack>
                             </div>
@@ -122,7 +122,7 @@ class LogDrawer extends React.Component<LogDrawerProps, LogDrawerState> {
                                         <PrimaryButton text='Download' onClick={this.downloadNNImanager} />
                                     </StackItem>
                                     <StackItem grow={12} className='close'>
-                                        <DefaultButton text='Close' onClick={closeDrawer} />
+                                        <DefaultButton text='Close' onClick={closePanel} />
                                     </StackItem>
                                 </Stack>
                             </div>
@@ -159,4 +159,4 @@ class LogDrawer extends React.Component<LogDrawerProps, LogDrawerState> {
     };
 }
 
-export default LogDrawer;
+export default LogPanel;
diff --git a/ts/webui/src/components/stateless-component/NNItabs.tsx b/ts/webui/src/components/nav/slideNav/NNItabs.tsx
similarity index 91%
rename from ts/webui/src/components/stateless-component/NNItabs.tsx
rename to ts/webui/src/components/nav/slideNav/NNItabs.tsx
index f7ca44fe3..cb7283007 100644
--- a/ts/webui/src/components/stateless-component/NNItabs.tsx
+++ b/ts/webui/src/components/nav/slideNav/NNItabs.tsx
@@ -1,7 +1,7 @@
 import * as React from 'react';
 import { NavLink } from 'react-router-dom';
 
-import { getPrefix } from '../../static/function';
+import { getPrefix } from '@static/function';
 
 const OVERVIEWTABS = (
     <NavLink to='/oview' activeClassName='selected' className='common-tabs'>
diff --git a/ts/webui/src/components/slideNav/SlideNavBtns.tsx b/ts/webui/src/components/nav/slideNav/SlideNavBtns.tsx
similarity index 90%
rename from ts/webui/src/components/slideNav/SlideNavBtns.tsx
rename to ts/webui/src/components/nav/slideNav/SlideNavBtns.tsx
index 3f5658b14..8d177b96a 100644
--- a/ts/webui/src/components/slideNav/SlideNavBtns.tsx
+++ b/ts/webui/src/components/nav/slideNav/SlideNavBtns.tsx
@@ -2,10 +2,14 @@ import React, { useState, useCallback } from 'react';
 import { Stack, DefaultButton, Icon } from '@fluentui/react';
 import MediaQuery from 'react-responsive';
 import TrialConfigPanel from './TrialConfigPanel';
-import LogPanel from '../modals/LogPanel';
+import LogPanel from './LogPanel';
 import IconButtonTemplate from './IconButtonTemplet';
-import '../../static/style/overview/panel.scss';
+import '@style/nav/slideNavBtns.scss';
 
+/***
+ * this file is the container of [config, search space, dispatcher/nnimanager log]
+ * these three button is in the right of page
+ */
 export const SlideNavBtns = (): any => {
     const [isShowConfigPanel, setShowConfigPanle] = useState(false);
     const [isShowLogPanel, setShowLogPanel] = useState(false);
@@ -57,7 +61,7 @@ export const SlideNavBtns = (): any => {
             </Stack>
             {isShowConfigPanel && <TrialConfigPanel panelName={panelName} hideConfigPanel={hideConfigPanel} />}
             {/* the panel for dispatcher & nnimanager log message */}
-            {isShowLogPanel && <LogPanel closeDrawer={hideLogPanel} />}
+            {isShowLogPanel && <LogPanel closePanel={hideLogPanel} />}
         </React.Fragment>
     );
 };
diff --git a/ts/webui/src/components/slideNav/TrialConfigPanel.tsx b/ts/webui/src/components/nav/slideNav/TrialConfigPanel.tsx
similarity index 93%
rename from ts/webui/src/components/slideNav/TrialConfigPanel.tsx
rename to ts/webui/src/components/nav/slideNav/TrialConfigPanel.tsx
index 481729259..5abee45fe 100644
--- a/ts/webui/src/components/slideNav/TrialConfigPanel.tsx
+++ b/ts/webui/src/components/nav/slideNav/TrialConfigPanel.tsx
@@ -1,12 +1,12 @@
 import * as React from 'react';
 import { Stack, Panel, PrimaryButton } from '@fluentui/react';
-import { EXPERIMENT } from '../../static/datamodel';
-import MonacoEditor from 'react-monaco-editor';
-import { MONACO } from '../../static/const';
-import { convertDuration, caclMonacoEditorHeight } from '../../static/function';
-import { prettyStringify } from '../../static/json_util';
 import lodash from 'lodash';
-import '../../static/style/logDrawer.scss';
+import MonacoEditor from 'react-monaco-editor';
+import { EXPERIMENT } from '@static/datamodel';
+import { MONACO } from '@static/const';
+import { convertDuration, caclMonacoEditorHeight } from '@static/function';
+import { prettyStringify } from '@static/jsonutil';
+import '@static/style/logPanel.scss';
 
 interface LogDrawerProps {
     hideConfigPanel: () => void;
diff --git a/ts/webui/src/components/public-child/IntermediateVal.tsx b/ts/webui/src/components/public-child/IntermediateVal.tsx
deleted file mode 100644
index 1d41b1b41..000000000
--- a/ts/webui/src/components/public-child/IntermediateVal.tsx
+++ /dev/null
@@ -1,18 +0,0 @@
-import * as React from 'react';
-import { TRIALS } from '../../static/datamodel';
-
-interface IntermediateValProps {
-    trialId: string;
-}
-
-class IntermediateVal extends React.Component<IntermediateValProps, {}> {
-    constructor(props: IntermediateValProps) {
-        super(props);
-    }
-
-    render(): React.ReactNode {
-        return <div>{TRIALS.getTrial(this.props.trialId).formatLatestAccuracy()}</div>;
-    }
-}
-
-export default IntermediateVal;
diff --git a/ts/webui/src/index.tsx b/ts/webui/src/index.tsx
index c0ed77370..9c690f929 100644
--- a/ts/webui/src/index.tsx
+++ b/ts/webui/src/index.tsx
@@ -3,11 +3,11 @@ import ReactDOM from 'react-dom';
 import App from './App';
 import { getPrefix } from './static/function';
 import { BrowserRouter as Router, Route, Switch } from 'react-router-dom';
-const Overview = lazy(() => import('./components/Overview'));
-const TrialsDetail = lazy(() => import('./components/TrialsDetail'));
-const Experiment = lazy(() => import('./components/managementExp/ExperimentManager'));
-import './index.css';
-import './static/style/loading.scss';
+const Overview = lazy(() => import('./components/experiment/overview/Overview'));
+const TrialsDetail = lazy(() => import('./components/experiment/trialdetail/TrialsDetail'));
+const ExperimentManagerIndex = lazy(() => import('./components/experimentManagement/ExperimentManagerIndex'));
+import '@style/index.css';
+import '@style/loading.scss';
 import * as serviceWorker from './serviceWorker';
 
 const path = getPrefix();
@@ -21,7 +21,7 @@ ReactDOM.render(
                 </div>
             }
         >
-            <Route path='/experiment' component={Experiment} exact />
+            <Route path='/experiment' component={ExperimentManagerIndex} exact />
             <Switch>
                 <App>
                     <Route path='/' component={Overview} exact />
diff --git a/ts/webui/src/static/const.ts b/ts/webui/src/static/const.ts
index c55dd9d6e..982b63ab2 100644
--- a/ts/webui/src/static/const.ts
+++ b/ts/webui/src/static/const.ts
@@ -16,7 +16,7 @@ const RESTAPI = '/api/v1/nni';
 const MANAGER_IP = prefix === undefined ? RESTAPI : `${prefix}${RESTAPI}`;
 const DOWNLOAD_IP = `/logs`;
 
-const WEBUIDOC = 'https://nni.readthedocs.io/en/latest/Tutorial/WebUI.html';
+const WEBUIDOC = 'https://nni.readthedocs.io/en/latest/experiment/webui.html';
 
 const trialJobStatus = [
     'UNKNOWN',
diff --git a/ts/webui/src/static/json_util.ts b/ts/webui/src/static/jsonutil.ts
similarity index 100%
rename from ts/webui/src/static/json_util.ts
rename to ts/webui/src/static/jsonutil.ts
diff --git a/ts/webui/src/App.scss b/ts/webui/src/static/style/App.scss
similarity index 100%
rename from ts/webui/src/App.scss
rename to ts/webui/src/static/style/App.scss
diff --git a/ts/webui/src/static/style/common.scss b/ts/webui/src/static/style/common/common.scss
similarity index 100%
rename from ts/webui/src/static/style/common.scss
rename to ts/webui/src/static/style/common/common.scss
diff --git a/ts/webui/src/static/style/overview/probar.scss b/ts/webui/src/static/style/common/experimentStatusColor.scss
similarity index 93%
rename from ts/webui/src/static/style/overview/probar.scss
rename to ts/webui/src/static/style/common/experimentStatusColor.scss
index e05f460b1..8f5da7139 100644
--- a/ts/webui/src/static/style/overview/probar.scss
+++ b/ts/webui/src/static/style/common/experimentStatusColor.scss
@@ -36,6 +36,7 @@ $error: #a4262c;
     }
 }
 
+/* set color for experiment best-metric value in overview page */
 .bestMetric {
     .DONE,
     .STOPPED,
diff --git a/ts/webui/src/static/style/tableStatus.css b/ts/webui/src/static/style/common/trialStatus.css
similarity index 84%
rename from ts/webui/src/static/style/tableStatus.css
rename to ts/webui/src/static/style/common/trialStatus.css
index 58f134672..f46d49247 100644
--- a/ts/webui/src/static/style/tableStatus.css
+++ b/ts/webui/src/static/style/common/trialStatus.css
@@ -1,4 +1,4 @@
-/* all table status font-color */
+/* all trial status font-color in the table */
 .RUNNING,
 .USER_CANCELED {
     color: #0071bc;
diff --git a/ts/webui/src/static/style/overview/basic.scss b/ts/webui/src/static/style/experiment/overview/basic.scss
similarity index 100%
rename from ts/webui/src/static/style/overview/basic.scss
rename to ts/webui/src/static/style/experiment/overview/basic.scss
diff --git a/ts/webui/src/static/style/overview/command.scss b/ts/webui/src/static/style/experiment/overview/command.scss
similarity index 100%
rename from ts/webui/src/static/style/overview/command.scss
rename to ts/webui/src/static/style/experiment/overview/command.scss
diff --git a/ts/webui/src/static/style/overview/count.scss b/ts/webui/src/static/style/experiment/overview/count.scss
similarity index 100%
rename from ts/webui/src/static/style/overview/count.scss
rename to ts/webui/src/static/style/experiment/overview/count.scss
diff --git a/ts/webui/src/static/style/overview/overview.scss b/ts/webui/src/static/style/experiment/overview/overview.scss
similarity index 100%
rename from ts/webui/src/static/style/overview/overview.scss
rename to ts/webui/src/static/style/experiment/overview/overview.scss
diff --git a/ts/webui/src/static/style/overview/overviewTitle.scss b/ts/webui/src/static/style/experiment/overview/overviewTitle.scss
similarity index 100%
rename from ts/webui/src/static/style/overview/overviewTitle.scss
rename to ts/webui/src/static/style/experiment/overview/overviewTitle.scss
diff --git a/ts/webui/src/static/style/succTable.scss b/ts/webui/src/static/style/experiment/overview/succTable.scss
similarity index 100%
rename from ts/webui/src/static/style/succTable.scss
rename to ts/webui/src/static/style/experiment/overview/succTable.scss
diff --git a/ts/webui/src/static/style/overview/topTrial.scss b/ts/webui/src/static/style/experiment/overview/topTrial.scss
similarity index 100%
rename from ts/webui/src/static/style/overview/topTrial.scss
rename to ts/webui/src/static/style/experiment/overview/topTrial.scss
diff --git a/ts/webui/src/static/style/compare.scss b/ts/webui/src/static/style/experiment/trialdetail/compare.scss
similarity index 100%
rename from ts/webui/src/static/style/compare.scss
rename to ts/webui/src/static/style/experiment/trialdetail/compare.scss
diff --git a/ts/webui/src/components/modals/customized.scss b/ts/webui/src/static/style/experiment/trialdetail/customized.scss
similarity index 100%
rename from ts/webui/src/components/modals/customized.scss
rename to ts/webui/src/static/style/experiment/trialdetail/customized.scss
diff --git a/ts/webui/src/static/style/para.scss b/ts/webui/src/static/style/experiment/trialdetail/para.scss
similarity index 100%
rename from ts/webui/src/static/style/para.scss
rename to ts/webui/src/static/style/experiment/trialdetail/para.scss
diff --git a/ts/webui/src/static/style/search.scss b/ts/webui/src/static/style/experiment/trialdetail/search.scss
similarity index 99%
rename from ts/webui/src/static/style/search.scss
rename to ts/webui/src/static/style/experiment/trialdetail/search.scss
index b4dfc0dd5..8291e99ed 100644
--- a/ts/webui/src/static/style/search.scss
+++ b/ts/webui/src/static/style/experiment/trialdetail/search.scss
@@ -13,7 +13,6 @@
     &-button-gap {
         margin-right: 10px;
     }
-
 }
 
 /* each row's Intermediate btn -> Modal */
diff --git a/ts/webui/src/static/style/tensorboard.scss b/ts/webui/src/static/style/experiment/trialdetail/tensorboard.scss
similarity index 100%
rename from ts/webui/src/static/style/tensorboard.scss
rename to ts/webui/src/static/style/experiment/trialdetail/tensorboard.scss
diff --git a/ts/webui/src/static/style/trialsDetail.scss b/ts/webui/src/static/style/experiment/trialdetail/trialsDetail.scss
similarity index 100%
rename from ts/webui/src/static/style/trialsDetail.scss
rename to ts/webui/src/static/style/experiment/trialdetail/trialsDetail.scss
diff --git a/ts/webui/src/static/style/experiment/experiment.scss b/ts/webui/src/static/style/experimentManagement/experiment.scss
similarity index 100%
rename from ts/webui/src/static/style/experiment/experiment.scss
rename to ts/webui/src/static/style/experimentManagement/experiment.scss
diff --git a/ts/webui/src/index.css b/ts/webui/src/static/style/index.css
similarity index 100%
rename from ts/webui/src/index.css
rename to ts/webui/src/static/style/index.css
diff --git a/ts/webui/src/static/style/logDrawer.scss b/ts/webui/src/static/style/logPanel.scss
similarity index 100%
rename from ts/webui/src/static/style/logDrawer.scss
rename to ts/webui/src/static/style/logPanel.scss
index e0c8842a3..576acac25 100644
--- a/ts/webui/src/static/style/logDrawer.scss
+++ b/ts/webui/src/static/style/logPanel.scss
@@ -55,8 +55,8 @@ $navBarMarginBottom: 18px;
 
 .panelName {
     line-height: $navBarHeight;
-    font-size: 14px;
     margin-bottom: $navBarMarginBottom;
+    font-size: 14px;
     font-weight: 500;
 }
 
diff --git a/ts/webui/src/static/style/overview/panel.scss b/ts/webui/src/static/style/nav/slideNavBtns.scss
similarity index 100%
rename from ts/webui/src/static/style/overview/panel.scss
rename to ts/webui/src/static/style/nav/slideNavBtns.scss
diff --git a/ts/webui/src/static/style/openRow.scss b/ts/webui/src/static/style/openRow.scss
index 656b2e92c..c0363df17 100644
--- a/ts/webui/src/static/style/openRow.scss
+++ b/ts/webui/src/static/style/openRow.scss
@@ -54,7 +54,7 @@ $bgColor: #f2f2f2;
             outline: none;
         }
     }
-    
+
     .ms-MessageBar {
         width: 70%;
         margin-left: 15px;
diff --git a/ts/webui/tsconfig.json b/ts/webui/tsconfig.json
index f2e91db87..751293a70 100644
--- a/ts/webui/tsconfig.json
+++ b/ts/webui/tsconfig.json
@@ -6,6 +6,14 @@
       "dom.iterable",
       "esnext"
     ],
+    "baseUrl": ".",
+    "paths": {
+      "@/*": ["src/*"],
+      "@components/*": ["src/components/*"],
+      "@static/*": ["src/static/*"],
+      "@style/*": ["src/static/style/*"],
+      "@model/*": ["src/static/model/*"]
+    },
     "allowJs": true,
     "skipLibCheck": true,
     "esModuleInterop": true,
diff --git a/ts/webui/yarn.lock b/ts/webui/yarn.lock
index 994191717..61d2fa608 100644
--- a/ts/webui/yarn.lock
+++ b/ts/webui/yarn.lock
@@ -2141,7 +2141,7 @@
     "@types/qs" "*"
     "@types/range-parser" "*"
 
-"@types/express@*":
+"@types/express@*", "@types/express@^4.17.13":
   version "4.17.13"
   resolved "https://registry.yarnpkg.com/@types/express/-/express-4.17.13.tgz#a76e2995728999bab51a33fabce1d705a3709034"
   integrity sha512-6bSZTPaTIACxn48l50SR+axgrqm6qXFIxrdAKaG6PaJk3+zuUr35hBlgT7vOmJcum+OEaIBLtHV/qloEAFITeA==
@@ -2771,11 +2771,6 @@ alphanum-sort@^1.0.2:
   resolved "https://registry.yarnpkg.com/alphanum-sort/-/alphanum-sort-1.0.2.tgz#97a1119649b211ad33691d9f9f486a8ec9fbe0a3"
   integrity sha1-l6ERlkmyEa0zaR2fn0hqjsn74KM=
 
-amdefine@>=0.0.4:
-  version "1.0.1"
-  resolved "https://registry.yarnpkg.com/amdefine/-/amdefine-1.0.1.tgz#4a5282ac164729e93619bcfd3ad151f817ce91f5"
-  integrity sha1-SlKCrBZHKek2Gbz9OtFR+BfOkfU=
-
 ansi-colors@^4.1.1:
   version "4.1.1"
   resolved "https://registry.yarnpkg.com/ansi-colors/-/ansi-colors-4.1.1.tgz#cbb9ae256bf750af1eab344f229aa27fe94ba348"
@@ -3572,10 +3567,10 @@ character-reference-invalid@^1.0.0:
   resolved "https://registry.yarnpkg.com/character-reference-invalid/-/character-reference-invalid-1.1.4.tgz#083329cda0eae272ab3dbbf37e9a382c13af1560"
   integrity sha512-mKKUkUbhPpQlCOfIuZkvSEgktjPFIsZKRRbC6KWVEMvlzblj3i3asQv5ODsrwt0N3pHAEvjP8KTQPHkp0+6jOg==
 
-chokidar@^3.5.2:
-  version "3.5.2"
-  resolved "https://registry.yarnpkg.com/chokidar/-/chokidar-3.5.2.tgz#dba3976fcadb016f66fd365021d91600d01c1e75"
-  integrity sha512-ekGhOnNVPgT77r4K/U3GDhu+FQ2S8TnK/s2KbIGXi0SZWuwkZ2QNyfWdZW+TVfn84DpEP7rLeCt2UI6bJ8GwbQ==
+chokidar@^3.5.3:
+  version "3.5.3"
+  resolved "https://registry.yarnpkg.com/chokidar/-/chokidar-3.5.3.tgz#1cf37c8707b932bd1af1ae22c0432e2acd1903bd"
+  integrity sha512-Dr3sfKRP6oTcjf2JmUmFJfeVMvXBdegxB0iVQ5eb2V10uFJUCAS8OByZdVAyVb8xXNz3GjjTgj9kLWsZTqE6kw==
   dependencies:
     anymatch "~3.1.2"
     braces "~3.0.2"
@@ -7545,7 +7540,7 @@ jest@^27.0.4:
     import-local "^3.0.2"
     jest-cli "^27.4.5"
 
-js-base64@^2.1.8:
+js-base64@^2.4.3:
   version "2.6.4"
   resolved "https://registry.yarnpkg.com/js-base64/-/js-base64-2.6.4.tgz#f4e686c5de1ea1f867dbcad3d46d969428df98c4"
   integrity sha512-pZe//GGmwJndub7ZghVHz7vjb2LgC1m8B07Au3eYqeqv9emhESByMXxaEgkUkEqJe87oBbSniGYoQNIBklc7IQ==
@@ -8048,7 +8043,7 @@ lodash.uniq@^4.5.0:
   resolved "https://registry.yarnpkg.com/lodash.uniq/-/lodash.uniq-4.5.0.tgz#d0225373aeb652adc1bc82e4945339a842754773"
   integrity sha1-0CJTc662Uq3BvILklFM5qEJ1R3M=
 
-lodash@^4.0.0, lodash@^4.17.14, lodash@^4.17.15, lodash@^4.17.19, lodash@^4.17.20, lodash@^4.17.21, lodash@^4.7.0, lodash@~4.17.10:
+lodash@^4.17.11, lodash@^4.17.14, lodash@^4.17.15, lodash@^4.17.19, lodash@^4.17.20, lodash@^4.17.21, lodash@^4.7.0, lodash@~4.17.10:
   version "4.17.21"
   resolved "https://registry.yarnpkg.com/lodash/-/lodash-4.17.21.tgz#679591c564c3bffaae8454cf0b3df370c3d6911c"
   integrity sha512-v2kDEe57lecTulaDIuNTPy3Ry4gLGJ6Z1O3vE1krgXZNrsQ+LFTGHVxVjcXPs17LhbZVGedAJv8XZ1tvj5FvSg==
@@ -8202,10 +8197,10 @@ media-typer@0.3.0:
   resolved "https://registry.yarnpkg.com/media-typer/-/media-typer-0.3.0.tgz#8710d7af0aa626f8fffa1ce00168545263255748"
   integrity sha1-hxDXrwqmJvj/+hzgAWhUUmMlV0g=
 
-memfs@^3.2.2:
-  version "3.4.0"
-  resolved "https://registry.yarnpkg.com/memfs/-/memfs-3.4.0.tgz#8bc12062b973be6b295d4340595736a656f0a257"
-  integrity sha512-o/RfP0J1d03YwsAxyHxAYs2kyJp55AFkMazlFAZFR2I2IXkxiUTXRabJ6RmNNCQ83LAD2jy52Khj0m3OffpNdA==
+memfs@^3.4.1:
+  version "3.4.1"
+  resolved "https://registry.yarnpkg.com/memfs/-/memfs-3.4.1.tgz#b78092f466a0dce054d63d39275b24c71d3f1305"
+  integrity sha512-1c9VPVvW5P7I85c35zAdEr1TD5+F11IToIHIlrVIcflfnzPkJa0ZoYEoEdYDP8KgPFoSZ/opDrUsAoZWym3mtw==
   dependencies:
     fs-monkey "1.0.3"
 
@@ -8353,9 +8348,9 @@ minimist-options@4.1.0:
     kind-of "^6.0.3"
 
 minimist@^1.1.1, minimist@^1.2.0, minimist@^1.2.5:
-  version "1.2.5"
-  resolved "https://registry.yarnpkg.com/minimist/-/minimist-1.2.5.tgz#67d66014b66a6a8aaa0c083c5fd58df4e4e97602"
-  integrity sha512-FM9nNUYrRBAELZQT3xeZQ7fmMOBg6nWNmJKTcgsJeaLstP/UODVpGsr5OhXhhXg6f+qtJ8uiZ+PUxkDWcgIXLw==
+  version "1.2.6"
+  resolved "https://registry.yarnpkg.com/minimist/-/minimist-1.2.6.tgz#8637a5b759ea0d6e98702cfb3a9283323c93af44"
+  integrity sha512-Jsjnk4bw3YJqYzbdyBiNsPWHPfO++UGG749Cxs6peCu5Xg4nrena6OVxOYxrQTqww0Jmwt+Ref8rggumkTLz9Q==
 
 minipass-collect@^1.0.2:
   version "1.0.2"
@@ -8548,10 +8543,10 @@ no-case@^3.0.4:
     lower-case "^2.0.2"
     tslib "^2.0.3"
 
-node-forge@^0.10.0:
-  version "0.10.0"
-  resolved "https://registry.yarnpkg.com/node-forge/-/node-forge-0.10.0.tgz#32dea2afb3e9926f02ee5ce8794902691a676bf3"
-  integrity sha512-PPmu8eEeG9saEUvI97fm4OYxXVB6bFvyNTyiUOBichBpFG8A1Ljw3bY62+5oOjDEMHRnd0Y7HQ+x7uzxOzC6JA==
+node-forge@^1:
+  version "1.3.1"
+  resolved "https://registry.yarnpkg.com/node-forge/-/node-forge-1.3.1.tgz#be8da2af243b2417d5f646a770663a92b7e9ded3"
+  integrity sha512-dPEtOeMvF9VMcYV/1Wb8CPoVAXtp6MKMlcbAt4ddqmGqUJ6fQZFXkNZNkNlfevtNkGtaSoXf/vNNNSvgrdXwtA==
 
 node-gyp@^7.1.0:
   version "7.1.2"
@@ -8595,10 +8590,10 @@ node-releases@^2.0.1:
   resolved "https://registry.yarnpkg.com/node-releases/-/node-releases-2.0.1.tgz#3d1d395f204f1f2f29a54358b9fb678765ad2fc5"
   integrity sha512-CqyzN6z7Q6aMeF/ktcMVTzhAHCEpf8SOarwpzpf8pNBY2k5/oM34UHldUwp8VKI7uxct2HxSRdJjBaZeESzcxA==
 
-node-sass@^7.0.0:
-  version "7.0.0"
-  resolved "https://registry.yarnpkg.com/node-sass/-/node-sass-7.0.0.tgz#33ee7c2df299d51f682f13d79f3d2a562225788e"
-  integrity sha512-6yUnsD3L8fVbgMX6nKQqZkjRcG7a/PpmF0pEyeWf+BgbTj2ToJlCYrnUifL2KbjV5gIY22I3oppahBWA3B+jUg==
+node-sass@^7.0.1:
+  version "7.0.1"
+  resolved "https://registry.yarnpkg.com/node-sass/-/node-sass-7.0.1.tgz#ad4f6bc663de8acc0a9360db39165a1e2620aa72"
+  integrity sha512-uMy+Xt29NlqKCFdFRZyXKOTqGt+QaKHexv9STj2WeLottnlqZEEWx6Bj0MXNthmFRRdM/YwyNo/8Tr46TOM0jQ==
   dependencies:
     async-foreach "^0.1.3"
     chalk "^4.1.2"
@@ -8609,10 +8604,10 @@ node-sass@^7.0.0:
     lodash "^4.17.15"
     meow "^9.0.0"
     nan "^2.13.2"
-    node-gyp "^7.1.0"
+    node-gyp "^8.4.1"
     npmlog "^5.0.0"
     request "^2.88.0"
-    sass-graph "2.2.5"
+    sass-graph "4.0.0"
     stdout-stream "^1.4.0"
     "true-case-path" "^1.0.2"
 
@@ -10227,11 +10222,6 @@ pump@^3.0.0:
     end-of-stream "^1.1.0"
     once "^1.3.1"
 
-punycode@1.3.2:
-  version "1.3.2"
-  resolved "https://registry.yarnpkg.com/punycode/-/punycode-1.3.2.tgz#9653a036fb7c1ee42342f2325cceefea3926c48d"
-  integrity sha1-llOgNvt8HuQjQvIyXM7v6jkmxI0=
-
 punycode@^2.1.0, punycode@^2.1.1:
   version "2.1.1"
   resolved "https://registry.yarnpkg.com/punycode/-/punycode-2.1.1.tgz#b58b010ac40c22c5657616c8d2c2c02c7bf479ec"
@@ -10257,11 +10247,6 @@ qs@~6.5.2:
   resolved "https://registry.yarnpkg.com/qs/-/qs-6.5.2.tgz#cb3ae806e8740444584ef154ce8ee98d403f3e36"
   integrity sha512-N5ZAX4/LxJmF+7wN74pUD6qAh9/wnvdQcjq9TZjevvXzSUo7bfmw91saqMjzGS2xq91/odN2dW/WOl7qQHNDGA==
 
-querystring@0.2.0:
-  version "0.2.0"
-  resolved "https://registry.yarnpkg.com/querystring/-/querystring-0.2.0.tgz#b209849203bb25df820da756e747005878521620"
-  integrity sha1-sgmEkgO7Jd+CDadW50cAWHhSFiA=
-
 queue-microtask@^1.2.2:
   version "1.2.3"
   resolved "https://registry.yarnpkg.com/queue-microtask/-/queue-microtask-1.2.3.tgz#4929228bbc724dfac43e0efb058caf7b6cfb6243"
@@ -10935,15 +10920,15 @@ sanitize.css@*:
   resolved "https://registry.yarnpkg.com/sanitize.css/-/sanitize.css-13.0.0.tgz#2675553974b27964c75562ade3bd85d79879f173"
   integrity sha512-ZRwKbh/eQ6w9vmTjkuG0Ioi3HBwPFce0O+v//ve+aOq1oeCy7jMV2qzzAlpsNuqpqCBjjriM1lbtZbF/Q8jVyA==
 
-sass-graph@2.2.5:
-  version "2.2.5"
-  resolved "https://registry.yarnpkg.com/sass-graph/-/sass-graph-2.2.5.tgz#a981c87446b8319d96dce0671e487879bd24c2e8"
-  integrity sha512-VFWDAHOe6mRuT4mZRd4eKE+d8Uedrk6Xnh7Sh9b4NGufQLQjOrvf/MQoOdx+0s92L89FeyUUNfU597j/3uNpag==
+sass-graph@4.0.0:
+  version "4.0.0"
+  resolved "https://registry.yarnpkg.com/sass-graph/-/sass-graph-4.0.0.tgz#fff8359efc77b31213056dfd251d05dadc74c613"
+  integrity sha512-WSO/MfXqKH7/TS8RdkCX3lVkPFQzCgbqdGsmSKq6tlPU+GpGEsa/5aW18JqItnqh+lPtcjifqdZ/VmiILkKckQ==
   dependencies:
     glob "^7.0.0"
-    lodash "^4.0.0"
-    scss-tokenizer "^0.2.3"
-    yargs "^13.3.2"
+    lodash "^4.17.11"
+    scss-tokenizer "^0.3.0"
+    yargs "^17.2.1"
 
 sass-loader@^12.1.0:
   version "12.4.0"
@@ -11003,25 +10988,25 @@ schema-utils@^4.0.0:
     ajv-formats "^2.1.1"
     ajv-keywords "^5.0.0"
 
-scss-tokenizer@^0.2.3:
-  version "0.2.3"
-  resolved "https://registry.yarnpkg.com/scss-tokenizer/-/scss-tokenizer-0.2.3.tgz#8eb06db9a9723333824d3f5530641149847ce5d1"
-  integrity sha1-jrBtualyMzOCTT9VMGQRSYR85dE=
+scss-tokenizer@^0.3.0:
+  version "0.3.0"
+  resolved "https://registry.yarnpkg.com/scss-tokenizer/-/scss-tokenizer-0.3.0.tgz#ef7edc3bc438b25cd6ffacf1aa5b9ad5813bf260"
+  integrity sha512-14Zl9GcbBvOT9057ZKjpz5yPOyUWG2ojd9D5io28wHRYsOrs7U95Q+KNL87+32p8rc+LvDpbu/i9ZYjM9Q+FsQ==
   dependencies:
-    js-base64 "^2.1.8"
-    source-map "^0.4.2"
+    js-base64 "^2.4.3"
+    source-map "^0.7.1"
 
 select-hose@^2.0.0:
   version "2.0.0"
   resolved "https://registry.yarnpkg.com/select-hose/-/select-hose-2.0.0.tgz#625d8658f865af43ec962bfc376a37359a4994ca"
   integrity sha1-Yl2GWPhlr0Psliv8N2o3NZpJlMo=
 
-selfsigned@^1.10.11:
-  version "1.10.11"
-  resolved "https://registry.yarnpkg.com/selfsigned/-/selfsigned-1.10.11.tgz#24929cd906fe0f44b6d01fb23999a739537acbe9"
-  integrity sha512-aVmbPOfViZqOZPgRBT0+3u4yZFHpmnIghLMlAcb5/xhp5ZtB/RVnKhz5vl2M32CLXAqR4kha9zfhNg0Lf/sxKA==
+selfsigned@^2.0.0:
+  version "2.0.1"
+  resolved "https://registry.yarnpkg.com/selfsigned/-/selfsigned-2.0.1.tgz#8b2df7fa56bf014d19b6007655fff209c0ef0a56"
+  integrity sha512-LmME957M1zOsUhG+67rAjKfiWFox3SBxE/yymatMZsAx+oMrJ0YQ8AToOnyCm7xbeg2ep37IHLxdu0o2MavQOQ==
   dependencies:
-    node-forge "^0.10.0"
+    node-forge "^1"
 
 "semver@2 || 3 || 4 || 5", semver@^5.5.0, semver@^5.6.0:
   version "5.7.1"
@@ -11291,13 +11276,6 @@ source-map-url@^0.4.0:
   resolved "https://registry.yarnpkg.com/source-map-url/-/source-map-url-0.4.1.tgz#0af66605a745a5a2f91cf1bbf8a7afbc283dec56"
   integrity sha512-cPiFOTLUKvJFIg4SKVScy4ilPPW6rFgMgfuZJPNoDuMs3nC1HbMUycBoJw77xFIp6z1UJQJOfx6C9GMH80DiTw==
 
-source-map@^0.4.2:
-  version "0.4.4"
-  resolved "https://registry.yarnpkg.com/source-map/-/source-map-0.4.4.tgz#eba4f5da9c0dc999de68032d8b4f76173652036b"
-  integrity sha1-66T12pwNyZneaAMti092FzZSA2s=
-  dependencies:
-    amdefine ">=0.0.4"
-
 source-map@^0.5.0, source-map@^0.5.6:
   version "0.5.7"
   resolved "https://registry.yarnpkg.com/source-map/-/source-map-0.5.7.tgz#8a039d2d1021d22d1ea14c80d8ea468ba2ef3fcc"
@@ -11308,7 +11286,7 @@ source-map@^0.6.0, source-map@^0.6.1, source-map@~0.6.0, source-map@~0.6.1:
   resolved "https://registry.yarnpkg.com/source-map/-/source-map-0.6.1.tgz#74722af32e9614e9c287a8d0bbde48b5e2f1a263"
   integrity sha512-UjgapumWlbMhkBgzT7Ykc5YXUT46F0iKu8SGXq0bcwP5dz/h0Plj6enJqjz1Zbq2l5WaqYnrVbwWOWMyF3F47g==
 
-source-map@^0.7.3, source-map@~0.7.2:
+source-map@^0.7.1, source-map@^0.7.3, source-map@~0.7.2:
   version "0.7.3"
   resolved "https://registry.yarnpkg.com/source-map/-/source-map-0.7.3.tgz#5302f8169031735226544092e64981f751750383"
   integrity sha512-CkCj6giN3S+n9qrYiBTX5gystlENnRW5jZeNLHpe6aue+SrHcG5VYwujhW9s4dY31mEGsxBDrHR6oI69fTXsaQ==
@@ -11981,11 +11959,16 @@ treeverse@^1.0.4:
   resolved "https://registry.yarnpkg.com/treeverse/-/treeverse-1.0.4.tgz#a6b0ebf98a1bca6846ddc7ecbc900df08cb9cd5f"
   integrity sha512-whw60l7r+8ZU8Tu/Uc2yxtc4ZTZbR/PF3u1IPNKGQ6p8EICLb3Z2lAgoqw9bqYd8IkgnsaOcLzYHFckjqNsf0g==
 
-trim-newlines@>=4.0.2, trim-newlines@^3.0.0:
+trim-newlines@>=4.0.2:
   version "4.0.2"
   resolved "https://registry.yarnpkg.com/trim-newlines/-/trim-newlines-4.0.2.tgz#d6aaaf6a0df1b4b536d183879a6b939489808c7c"
   integrity sha512-GJtWyq9InR/2HRiLZgpIKv+ufIKrVrvjQWEj7PxAXNc5dwbNJkqhAUoAGgzRmULAnoOM5EIpveYd3J2VeSAIew==
 
+trim-newlines@^3.0.0:
+  version "3.0.1"
+  resolved "https://registry.yarnpkg.com/trim-newlines/-/trim-newlines-3.0.1.tgz#260a5d962d8b752425b32f3a7db0dcacd176c144"
+  integrity sha512-c1PTsA3tYrIsLGkJkzHF+w9F2EyxfXGo4UyJc4pFL++FMjnq0HJS69T3M7d//gKrFKwy429bouPescbjecU+Zw==
+
 trough@^1.0.0:
   version "1.0.5"
   resolved "https://registry.yarnpkg.com/trough/-/trough-1.0.5.tgz#b8b639cefad7d0bb2abd37d433ff8293efa5f406"
@@ -12265,14 +12248,6 @@ url-loader@^4.1.1:
     mime-types "^2.1.27"
     schema-utils "^3.0.0"
 
-url@^0.11.0:
-  version "0.11.0"
-  resolved "https://registry.yarnpkg.com/url/-/url-0.11.0.tgz#3838e97cfc60521eb73c525a8e55bfdd9e2e28f1"
-  integrity sha1-ODjpfPxgUh63PFJajlW/3Z4uKPE=
-  dependencies:
-    punycode "1.3.2"
-    querystring "0.2.0"
-
 use@^3.1.0:
   version "3.1.1"
   resolved "https://registry.yarnpkg.com/use/-/use-3.1.1.tgz#d50c8cac79a19fbc20f2911f56eb973f4e10070f"
@@ -12452,30 +12427,31 @@ webidl-conversions@^6.1.0:
   resolved "https://registry.yarnpkg.com/webidl-conversions/-/webidl-conversions-6.1.0.tgz#9111b4d7ea80acd40f5270d666621afa78b69514"
   integrity sha512-qBIvFLGiBpLjfwmYAaHPXsn+ho5xZnGvyGvsarywGNc8VyQJUMHJ8OBKGGrPER0okBeMDaan4mNBlgBROxuI8w==
 
-webpack-dev-middleware@^5.3.0:
-  version "5.3.0"
-  resolved "https://registry.yarnpkg.com/webpack-dev-middleware/-/webpack-dev-middleware-5.3.0.tgz#8fc02dba6e72e1d373eca361623d84610f27be7c"
-  integrity sha512-MouJz+rXAm9B1OTOYaJnn6rtD/lWZPy2ufQCH3BPs8Rloh/Du6Jze4p7AeLYHkVi0giJnYLaSGDC7S+GM9arhg==
+webpack-dev-middleware@^5.3.1:
+  version "5.3.1"
+  resolved "https://registry.yarnpkg.com/webpack-dev-middleware/-/webpack-dev-middleware-5.3.1.tgz#aa079a8dedd7e58bfeab358a9af7dab304cee57f"
+  integrity sha512-81EujCKkyles2wphtdrnPg/QqegC/AtqNH//mQkBYSMqwFVCQrxM6ktB2O/SPlZy7LqeEfTbV3cZARGQz6umhg==
   dependencies:
     colorette "^2.0.10"
-    memfs "^3.2.2"
+    memfs "^3.4.1"
     mime-types "^2.1.31"
     range-parser "^1.2.1"
     schema-utils "^4.0.0"
 
-webpack-dev-server@>=4.7.0:
-  version "4.7.0"
-  resolved "https://registry.yarnpkg.com/webpack-dev-server/-/webpack-dev-server-4.7.0.tgz#658aae4902dc920e90dbb8ad8b4eaf6491ac33af"
-  integrity sha512-ldR+a54iygMxUawTzMlWD/JblePhNRVGHxTHQz9EAvsbH7HZbX53OxV6Y092x+tgN5umv885i2X4wfdo/ynEQA==
+webpack-dev-server@>=4.7.4:
+  version "4.7.4"
+  resolved "https://registry.yarnpkg.com/webpack-dev-server/-/webpack-dev-server-4.7.4.tgz#d0ef7da78224578384e795ac228d8efb63d5f945"
+  integrity sha512-nfdsb02Zi2qzkNmgtZjkrMOcXnYZ6FLKcQwpxT7MvmHKc+oTtDsBju8j+NMyAygZ9GW1jMEUpy3itHtqgEhe1A==
   dependencies:
     "@types/bonjour" "^3.5.9"
     "@types/connect-history-api-fallback" "^1.3.5"
+    "@types/express" "^4.17.13"
     "@types/serve-index" "^1.9.1"
     "@types/sockjs" "^0.3.33"
     "@types/ws" "^8.2.2"
     ansi-html-community "^0.0.8"
     bonjour "^3.5.0"
-    chokidar "^3.5.2"
+    chokidar "^3.5.3"
     colorette "^2.0.10"
     compression "^1.7.4"
     connect-history-api-fallback "^1.6.0"
@@ -12490,14 +12466,13 @@ webpack-dev-server@>=4.7.0:
     p-retry "^4.5.0"
     portfinder "^1.0.28"
     schema-utils "^4.0.0"
-    selfsigned "^1.10.11"
+    selfsigned "^2.0.0"
     serve-index "^1.9.1"
     sockjs "^0.3.21"
     spdy "^4.0.2"
     strip-ansi "^7.0.0"
-    url "^0.11.0"
-    webpack-dev-middleware "^5.3.0"
-    ws "^8.1.0"
+    webpack-dev-middleware "^5.3.1"
+    ws "^8.4.2"
 
 webpack-manifest-plugin@^3.1.1:
   version "3.2.0"
@@ -12867,10 +12842,10 @@ ws@^7.4.6:
   resolved "https://registry.yarnpkg.com/ws/-/ws-7.5.6.tgz#e59fc509fb15ddfb65487ee9765c5a51dec5fe7b"
   integrity sha512-6GLgCqo2cy2A2rjCNFlxQS6ZljG/coZfZXclldI8FB/1G3CCI36Zd8xy2HrFVACi8tfk5XrgLQEk+P0Tnz9UcA==
 
-ws@^8.1.0:
-  version "8.4.0"
-  resolved "https://registry.yarnpkg.com/ws/-/ws-8.4.0.tgz#f05e982a0a88c604080e8581576e2a063802bed6"
-  integrity sha512-IHVsKe2pjajSUIl4KYMQOdlyliovpEPquKkqbwswulszzI7r0SfQrxnXdWAEqOlDCLrVSJzo+O1hAwdog2sKSQ==
+ws@^8.4.2:
+  version "8.5.0"
+  resolved "https://registry.yarnpkg.com/ws/-/ws-8.5.0.tgz#bfb4be96600757fe5382de12c670dab984a1ed4f"
+  integrity sha512-BWX0SWVgLPzYwF8lTzEy1egjhS4S4OEAHfsO8o65WOVsrnSRGaSiUaa9e0ggGlkMTtBlmOpEXiie9RUcBO86qg==
 
 xml-name-validator@^3.0.0:
   version "3.0.0"
@@ -12907,7 +12882,7 @@ yargs-parser@^21.0.0:
   resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-21.0.0.tgz#a485d3966be4317426dd56bdb6a30131b281dc55"
   integrity sha512-z9kApYUOCwoeZ78rfRYYWdiU/iNL6mwwYlkkZfJoyMR1xps+NEBX5X7XmRpxkZHhXJ6+Ey00IwKxBBSW9FIjyA==
 
-yargs@>=17.0.1, yargs@^13.3.2, yargs@^16.2.0:
+yargs@>=17.0.1, yargs@^16.2.0, yargs@^17.2.1:
   version "17.3.0"
   resolved "https://registry.yarnpkg.com/yargs/-/yargs-17.3.0.tgz#295c4ffd0eef148ef3e48f7a2e0f58d0e4f26b1c"
   integrity sha512-GQl1pWyDoGptFPJx9b9L6kmR33TGusZvXIZUT+BOz9f7X2L94oeAskFYLEg/FkhV06zZPBYLvLZRWeYId29lew==
-- 
GitLab