{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "_Ip8kp4TfBLZ"
   },
   "outputs": [],
   "source": [
    "# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kuXHJv44fBLe"
   },
   "source": [
    "# Render a textured mesh\n",
    "\n",
    "This tutorial shows how to:\n",
    "- load a mesh and textures from an `.obj` file. \n",
    "- set up a renderer \n",
    "- render the mesh \n",
    "- vary the rendering settings such as lighting and camera position\n",
    "- use the batching features of the pytorch3d API to render the mesh from different viewpoints"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Bnj3THhzfBLf"
   },
   "source": [
    "## 0. Install and Import modules"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "okLalbR_g7NS"
   },
   "source": [
    "If `torch`, `torchvision` and `pytorch3d` are not installed, run the following cell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 717
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "musUWTglgxSB",
    "outputId": "16d1a1b2-3f7f-43ed-ca28-a4d236cc0572"
   },
   "outputs": [],
   "source": [
    "!pip install torch torchvision\n",
    "!pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "nX99zdoffBLg"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "from skimage.io import imread\n",
    "\n",
    "# Util function for loading meshes\n",
    "from pytorch3d.io import load_objs_as_meshes\n",
    "\n",
    "# Data structures and functions for rendering\n",
    "from pytorch3d.structures import Meshes, Textures\n",
    "from pytorch3d.renderer import (\n",
    "    look_at_view_transform,\n",
    "    OpenGLPerspectiveCameras, \n",
    "    PointLights, \n",
    "    DirectionalLights, \n",
    "    Materials, \n",
    "    RasterizationSettings, \n",
    "    MeshRenderer, \n",
    "    MeshRasterizer,  \n",
    "    TexturedSoftPhongShader\n",
    ")\n",
    "\n",
    "# add path for demo utils functions \n",
    "import sys\n",
    "import os\n",
    "sys.path.append(os.path.abspath(''))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Lxmehq6Zhrzv"
   },
   "source": [
    "If using **Google Colab**, fetch the utils file for plotting image grids:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "HZozr3Pmho-5",
    "outputId": "be5eb60d-5f65-4db1-cca0-44ee68c8f5fd"
   },
   "outputs": [],
   "source": [
    "!wget https://raw.githubusercontent.com/facebookresearch/pytorch3d/master/docs/tutorials/utils/plot_image_grid.py\n",
    "from plot_image_grid import image_grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "g4B62MzYiJUM"
   },
   "source": [
    "OR if running **locally** uncomment and run the following cell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "paJ4Im8ahl7O"
   },
   "outputs": [],
   "source": [
    "# from utils import image_grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "collapsed": true,
    "id": "5jGq772XfBLk"
   },
   "source": [
    "### 1. Load a mesh and texture file\n",
    "\n",
    "Load an `.obj` file and it's associated `.mtl` file and create a **Textures** and **Meshes** object. \n",
    "\n",
    "**Meshes** is a unique datastructure provided in PyTorch3D for working with batches of meshes of different sizes. \n",
    "\n",
    "**Textures** is an auxillary datastructure for storing texture information about meshes. \n",
    "\n",
    "**Meshes** has several class methods which are used throughout the rendering pipeline."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "a8eU4zo5jd_H"
   },
   "source": [
    "If running this notebook using **Google Colab**, run the following cell to fetch the mesh obj and texture files and save it at the path `data/cow_mesh`:\n",
    "If running locally, the data is already available at the correct path. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 578
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "tTm0cVuOjb1W",
    "outputId": "6cd7e2ec-65e1-4dcc-99e8-c347bc504f0a"
   },
   "outputs": [],
   "source": [
    "!mkdir -p data/cow_mesh\n",
    "!wget -P data/cow_mesh https://dl.fbaipublicfiles.com/pytorch3d/data/cow_mesh/cow.obj\n",
    "!wget -P data/cow_mesh https://dl.fbaipublicfiles.com/pytorch3d/data/cow_mesh/cow.mtl\n",
    "!wget -P data/cow_mesh https://dl.fbaipublicfiles.com/pytorch3d/data/cow_mesh/cow_texture.png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "gi5Kd0GafBLl"
   },
   "outputs": [],
   "source": [
    "# Setup\n",
    "device = torch.device(\"cuda:0\")\n",
    "torch.cuda.set_device(device)\n",
    "\n",
    "# Set paths\n",
    "DATA_DIR = \"./data\"\n",
    "obj_filename = os.path.join(DATA_DIR, \"cow_mesh/cow.obj\")\n",
    "\n",
    "# Load obj file\n",
    "mesh = load_objs_as_meshes([obj_filename], device=device)\n",
    "texture_image=mesh.textures.maps_padded()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5APAQs6-fBLp"
   },
   "source": [
    "#### Let's visualize the texture map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 428
    },
    "colab_type": "code",
    "id": "YipUhrIHfBLq",
    "outputId": "48987b1d-5cc1-4c2a-cb3c-713d64f6a38d"
   },
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 25132 characters]\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "bento_obj_id": "140631044165712",
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "plt.imshow(texture_image.squeeze().cpu().numpy())\n",
    "plt.grid(\"off\");\n",
    "plt.axis('off');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GcnG6XJ6fBLu"
   },
   "source": [
    "## 2. Create a renderer\n",
    "\n",
    "A renderer in PyTorch3D is composed of a **rasterizer** and a **shader** which each have a number of subcomponents such as a **camera** (orthographic/perspective). Here we initialize some of these components and use default values for the rest.\n",
    "\n",
    "In this example we will first create a **renderer** which uses a **perspective camera**, a **point light** and applies **phong shading**. Then we learn how to vary different components using the modular API.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "dX466mWnfBLv"
   },
   "outputs": [],
   "source": [
    "# Initialize an OpenGL perspective camera.\n",
    "# With world coordinates +Y up, +X left and +Z in, the front of the cow is facing the -Z direction. \n",
    "# So we move the camera by 180 in the azimuth direction so it is facing the front of the cow. \n",
    "R, T = look_at_view_transform(2.7, 0, 180) \n",
    "cameras = OpenGLPerspectiveCameras(device=device, R=R, T=T)\n",
    "\n",
    "# Define the settings for rasterization and shading. Here we set the output image to be of size\n",
    "# 512x512. As we are rendering images for visualization purposes only we will set faces_per_pixel=1\n",
    "# and blur_radius=0.0. We also set bin_size and max_faces_per_bin to None which ensure that \n",
    "# the faster coarse-to-fine rasterization method is used. Refer to rasterize_meshes.py for \n",
    "# explanations of these parameters. Refer to docs/notes/renderer.md for an explanation of \n",
    "# the difference between naive and coarse-to-fine rasterization. \n",
    "raster_settings = RasterizationSettings(\n",
    "    image_size=512, \n",
    "    blur_radius=0.0, \n",
    "    faces_per_pixel=1, \n",
    "    bin_size = None,  # this setting controls whether naive or coarse-to-fine rasterization is used\n",
    "    max_faces_per_bin = None  # this setting is for coarse rasterization\n",
    ")\n",
    "\n",
    "# Place a point light in front of the object. As mentioned above, the front of the cow is facing the \n",
    "# -z direction. \n",
    "lights = PointLights(device=device, location=[[0.0, 0.0, -3.0]])\n",
    "\n",
    "# Create a phong renderer by composing a rasterizer and a shader. The textured phong shader will \n",
    "# interpolate the texture uv coordinates for each vertex, sample from a texture image and \n",
    "# apply the Phong lighting model\n",
    "renderer = MeshRenderer(\n",
    "    rasterizer=MeshRasterizer(\n",
    "        cameras=cameras, \n",
    "        raster_settings=raster_settings\n",
    "    ),\n",
    "    shader=TexturedSoftPhongShader(\n",
    "        device=device, \n",
    "        cameras=cameras,\n",
    "        lights=lights\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KyOY5qXvfBLz"
   },
   "source": [
    "## 3. Render the mesh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8VkRA4qJfBL0"
   },
   "source": [
    "The light is in front of the object so it is bright and the image has specular highlights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 592
    },
    "colab_type": "code",
    "id": "gBLZH8iUfBL1",
    "outputId": "cc3cd3f0-189e-4497-ce47-e64b4da542e8"
   },
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 48716 characters]\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "bento_obj_id": "140630998081360",
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "images = renderer(mesh)\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.imshow(images[0, ..., :3].cpu().numpy())\n",
    "plt.grid(\"off\");\n",
    "plt.axis(\"off\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "k161XF3sfBL5"
   },
   "source": [
    "## 4. Move the light behind the object and re-render\n",
    "\n",
    "We can pass arbirary keyword arguments to the `rasterizer`/`shader` via the call to the `renderer` so the renderer does not need to be reinitialized if any of the settings change/\n",
    "\n",
    "In this case, we can simply update the location of the lights and pass them into the call to the renderer. \n",
    "\n",
    "The image is now dark as there is only ambient lighting, and there are no specular highlights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "BdWkkeibfBL6"
   },
   "outputs": [],
   "source": [
    "# Now move the light so it is on the +Z axis which will be behind the cow. \n",
    "lights.location = torch.tensor([0.0, 0.0, +1.0], device=device)[None]\n",
    "images = renderer(mesh, lights=lights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 592
    },
    "colab_type": "code",
    "id": "UmV3j1YffBL9",
    "outputId": "2e8edca0-5bd8-4a2f-a160-83c4b0520123"
   },
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 33552 characters]\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "bento_obj_id": "140630596295504",
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "plt.imshow(images[0, ..., :3].cpu().numpy())\n",
    "plt.grid(\"off\");\n",
    "plt.axis(\"off\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "t93aVotMfBMB"
   },
   "source": [
    "## 5. Rotate the object, modify the material properties or light properties\n",
    "\n",
    "We can also change many other settings in the rendering pipeline. Here we:\n",
    "\n",
    "- change the **viewing angle** of the camera\n",
    "- change the **position** of the point light\n",
    "- change the **material reflectance** properties of the mesh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "4mYXYziefBMB"
   },
   "outputs": [],
   "source": [
    "# Rotate the object by increasing the elevation and azimuth angles\n",
    "R, T = look_at_view_transform(dist=2.7, elev=10, azim=-150)\n",
    "cameras = OpenGLPerspectiveCameras(device=device, R=R, T=T)\n",
    "\n",
    "# Move the light location so the light is shining on the cow's face.  \n",
    "lights.location = torch.tensor([[2.0, 2.0, -2.0]], device=device)\n",
    "\n",
    "# Change specular color to green and change material shininess \n",
    "materials = Materials(\n",
    "    device=device,\n",
    "    specular_color=[[0.0, 1.0, 0.0]],\n",
    "    shininess=10.0\n",
    ")\n",
    "\n",
    "# Re render the mesh, passing in keyword arguments for the modified components.\n",
    "images = renderer(mesh, lights=lights, materials=materials, cameras=cameras)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 592
    },
    "colab_type": "code",
    "id": "rHIxIfh5fBME",
    "outputId": "1ca2d337-2983-478f-b3c9-d64b84ba1a31"
   },
   "outputs": [
    {
     "data": {
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kapHlVQER7PtdpoUIWW8oWsiWJ7cphCRNgQLQ0FUaCAqQbbqejJXTxVYJpJvi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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "bento_obj_id": "139721054514064"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "plt.imshow(images[0, ..., :3].cpu().numpy())\n",
    "plt.grid(\"off\");\n",
    "plt.axis(\"off\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "17c4xmtyfBMH"
   },
   "source": [
    "## 6. Batched Rendering\n",
    "\n",
    "One of the core design choices of the PyTorch3D API is to support **batched inputs for all components**. \n",
    "The renderer and associated components can take batched inputs and **render a batch of output images in one forward pass**. We will now use this feature to render the mesh from many different viewpoints.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "CDQKebNNfBMI"
   },
   "outputs": [],
   "source": [
    "# Set batch size - this is the number of different viewpoints from which we want to render the mesh.\n",
    "batch_size = 20\n",
    "\n",
    "# Create a batch of meshes by repeating the cow mesh and associated textures. \n",
    "# Meshes has a useful `extend` method which allows us do this very easily. \n",
    "# This also extends the textures. \n",
    "meshes = mesh.extend(batch_size)\n",
    "\n",
    "# Get a batch of viewing angles. \n",
    "elev = torch.linspace(0, 180, batch_size)\n",
    "azim = torch.linspace(-180, 180, batch_size)\n",
    "\n",
    "# All the cameras helper methods support mixed type inputs and broadcasting. So we can \n",
    "# view the camera from the same distance and specify dist=2.7 as a float,\n",
    "# and then specify elevation and azimuth angles for each viewpoint as tensors. \n",
    "R, T = look_at_view_transform(dist=2.7, elev=elev, azim=azim)\n",
    "cameras = OpenGLPerspectiveCameras(device=device, R=R, T=T)\n",
    "\n",
    "# Move the light back in front of the cow which is facing the -z direction.\n",
    "lights.location = torch.tensor([[0.0, 0.0, -3.0]], device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "gyYJCwEDfBML"
   },
   "outputs": [],
   "source": [
    "# We can pass arbirary keyword arguments to the rasterizer/shader via the renderer\n",
    "# so the renderer does not need to be reinitialized if any of the settings change.\n",
    "images = renderer(meshes, cameras=cameras, lights=lights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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34uc//zlYfRhKGjEaDMedpFPleMEz0WzpxAdm/JcY9zKhIxK0f0gBpVIRBc+z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\nbkBbx21k+pxAFOALC3XHwYOoKGlepkD0uPqU6vkXXIQv/vMXcPrJx6G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v3Nt+LjMQuBxgJv6j5wnCBzTgiB3t5eTJo4AYWCGSxl0NtBs+BbTgenvndWPzgZGBzCDYvuxDU3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l+EEA13Xh+X5UpicQAr7v4Z5pV8XV5BCLtOEkJfxMSFUPuDhiDHY7OBtXpmkiJAWei2f/OiM/\nr8JJUzPKcDMSF9hU1ZINndgmzqYlyw2cTdq+Lv/iR1EatzNGjt0WHaUiHMdRWXEBnHL2eSnvgXnX\n/18cQyoBP/Dhui5c19PeB0HkhRAIlSvB9wP4gQ/fD+DpMncxCSPL2Jtlmlo1Vni+h8APmiRVTZL8\nfIua8JB9DSMeu/oylGv1uPSpLtuIRPIotu3eYJYNMIbjzpiCsVtvjet+/2sgYWNCCLieF4kE/f39\nqFRVgsTQNVuIbPWSTB37cMGScxyw33uVTfo+fF1B5fv//euG2trh49+mnglPexltQbaUB9nXMOT+\n3/aiXKuh7ikvHM/z4fo+PD+IHr72gvMCAWHYgOkAVkH9ZWxQrv4P3DobQggsX7kK/3XF/zS1m2YP\nDCIRXEiqqkLiOk899VTtkevjwP32xdjRo1GpVnHL3+5DtVrDE088MajfciND9rWFcv8ts+D7Pv7f\nf16mSq4mqn+1s6GB2lE4vBy79WgcdtAB6OroyIgBsmHyX63V0V8uRw7nnMVhTMViQS9stZ7vDaM+\ns+lVbhECQsi86/8M13Phez5cz1M1soWA5/koV8r4+5xrEpEGDJwhisWWCcUqEBLbH3QMSj2jGhpO\n04YlJV586K+orF3VstF84KSpg0teIzMCQuIahlEDfKdQBzEM+dXX/g12Rze6tt8TxaISEEzTABwH\nx5+iVkO7u7tx1VVXYYQpEjdtBsM0IKVEpVyB63nwfT9KuBg+YgFBPVw3ISLIpLqrmgfTrte2ZcOy\nzMi/INADwUBPeloLCZutCNcKsq9hxoNX9qLu+QgCEa2KIhH3yThXIoJhNYxDwr5KCAHP81Cr11Gp\nVNFfLqNcqWhbk1EIUWrHnMieow47BLV6HRdd9pOmA7PwfQA4+7RT0FEqRkL9ZtQPrS9kX8OUu//n\nOyhX63A9H3XPQ91T1UdiEUE/z4TSgakSjDBMIPAGXM/+/ltmwQ8CXPrTXzSUmxuogJD824xRI0fg\n1OM/0mDQWVs8+uijAV269cEHHxz4Dze8IPvagrl37gzc98jjmDHvNrUw3KSPWl8BIcuuO+2Awz90\nQG6zk1LiLzPnwPeD3H1DRo3owanHH9M419Pj2WtumIHXXnttva5vCGie9EYWAAAgAElEQVRqX5t1\nCEOWCVM/hkkf+xQ6Okro7OhAV2cnSsWiSvhmmOgpWuhyLHQWLHQVbBQtE45pwDG5fhhwdAbrUs/o\nxgybDRP/ZJIdhsq61W1vdVEFiIGSXCkakAcDQbw7mAaHjQCMc0gpohAclrgxH3nkkRg9ejR+/Itf\nq0zwOhabASgVS9h2/LYYP348Ro8apbwYwmSLlkq0aOkwB8tS9bot08yUx4kbvOOoTPIFJwyJUMnf\nbMeG49hwdM3vVFhE08RYZEjExqPg2LBMQ4UyhOXfQrT9sLdfBmNSZR1hjf2PFBJCxMID50aUQDi3\ndec0/d4f/RSGYaBYKOCnvd/GsUceHttw4rp6urtx8jFH4fOfOA/dXZ3ae4LEA2J4c8yXLoOjQ4YM\ng0eadG7VhZQDnFAi81XTBiweAMARp5wBzjku/uoXGmyoFQN10bYsE+dOOk1NThIG3fujn+ba4j33\n3IN77rlnUxYPiC0cwzBw2EH761X9gbO+yUtfff1NJRA0jB0lyuVKW/EAAEaPGpU/xtTiwabCFuWB\nkMfNf/kT+stlLF+5CuVyBasenqXcRiEhopQG6rmQEn2uwK5HnxEru7LZpL0xjuGFO6er0FWp3FJ9\n0fznVZnkWx+v1Ueb2cBtc53NbdL2dfW3P4eibcEeNQ7W6PFRLhHOOY6fNDXabtq0aTjnnHNw58zr\ndfESNTjjnKOjowPd3d3o6OwEA7Bs2XIsffttlMtluJ6HIHKXDuD7ys207tbheX7ix5PgjKOjo4SO\nUgmmacI0DEggUqSFEGo1V6jSXJ4+luu6uf+Ezcx+2kH2NUy5/7e98LR3jhAqlpIzDkOH6XDGIbfZ\nA5KbmbauvG3CNl6r11GrqYoLdddV4UOep07SomRbaAeP330rXNfTNqlXZrUXX7VeR71WR61eQ931\ntH16W1oYUCvIvoY5t11xEfqrNVTrLmquh1rdi8IX/EDA8/3Ulx2MaJDHvfNmwvd8zLrtr7j/kcda\nul1nV085Zxg1YgT22WN3bLftuDh/V4YtyPbIvrZw7pk7A269jt/9+Xq88MqrAwphwAA8efKQAD46\n6bRUmKw+GMAYHnvqabzwyqtN9z/njFNh21Zjs5USM265HbZt46m/zx/0dQ0hTe1rs67CMBBOO/cT\nmP7Hq1RN+SBA54ET1AcSqP/9FggpEUgJNwC2OvhUjEnt3UY8yOQocEzl8CEkothWNxC5QgIDolKT\ngRD5/8Ns0gb90rK3+H8rsQEw9Uok+lYAtgM5YqzKWJ2pMHLOOUoMY4yl8xYKiXJ/WU1wajV0d/dg\n3Lhx6O7pxpIlS7F82TK4nIMHAQzuRyXohBSQEvD1oI4xDtNUoQsFx0GhUIBpmlFG7SASDXwEemXU\n8A2Yhg/HsZUt6omXZZn42ncu3Qi/JkE0UnJsVCAhPQnwcIyivBJM00TBcSDXLQLG7wNPT94jESHh\nzomwKgnCGteJkzTJDJ2agEjAtm0wxuDCi/rL0JtBQmo7CuB7PiAZen+8xUxgiE2ck/7jR7jtiovg\nej5Mw4BtqaSKfpDOEfJOhYMQ27IghcDpJx6LE446HH+cdhNeWvBaw4Sm4DjYevQo7LrTDhg/bhwM\n04htusma0hYkHBAEoPNe+Z6HT0w9E7+95lq88dYiBNmwo/UUDLIwIF+00wkPDvrgfvB8H6++/kbq\n42M+fBi222ZcTlWGMNEQw+QJJw15VZZ3E5ppAvjXiy9hRE9P+k0GOPufAuiV0sa46czdOxmTjTCR\nAlLbcKsAS3qqXJChPBFMg6PuBXADkZIbTdNULnWMx+Umw3hTrVZnjw8ApVIB37jksnfrpyGIppiG\nodyrwYC1b0N6FYixu4IJhr/OvD7lhdB70YWRyxgDIMMyjFLCdV2sWbMGlUoFnZ1dKJVKGDtGSXWr\nV69CrVaHz1hkVKEgAEBNYgwO0zBQKDgqVMFRIRBh8ppAT2pCrwPf95W7W0F1KIFOJGeZJv7ti/9v\no/yWBJHHAZ/8Nh7/w/chJeD6frTKEdaSt2wbhUIBqK9EuTRGJyf1lFCmkwQLGXsmSO1Wlz9EaZ4Q\nTkLC0EKdsluBQBjwfR5VeAg9HyRA4gGxyXHSf/wo9fq3X/8UAOAbP7+uyR7rz6EnnBolVbQtG5+Y\neib6yv2o1+qoVFXSU8exIQKVCDW0LREEUTWVbL4SEg6ILZXDTzodd8+eDs4Zzj97Mvr6+/HSqwvw\n1DPPYW1fHzzfR6VSBd4FEWHH7cc3D39gqpD54QcdgH322A19/WVsO26M9lbIJAFPln1MHmI9Qys2\nBlu8gNB70YWAZCpuJZV5OlaFVHmrvPwGibt3Tkx2lh0PPwXLHpoDcBaFMphcwuQMViDgBQKBUOES\nhmHAMk0YhqEuKZu0SsarpkyvRpmGgf938SVD8jsRRBbbMiEh47FMtR9y0fMQ4/cGY8BfZ16PBx97\nAv3lCj588EFApKklhIRE0rdX33wLf//70zhr0uko2A5KxSKCoBucl1Gr1SIPBiGCKHu8lBIGN8AN\nrgZbrocXI/cxhuOPOgJ1z0OtVoMVmPBtFbumhIMAgR9gyqcu2Fg/IUG05aBPXYzHrlaisOspEYEl\nRATbtsHgo75mEZg1IuV9ENqK0EICIJXGLRs1cEC9lzf5/+Gvfo/vfPkCVZnIMKJHsvRbIIS6PoLY\nDPj3y/8wpMe3LEtVAQuCqPSwsCUK2kPI94PIhgMtBgLxSicJBwQRww0DAANjgGPb2GOXnbHD+PHR\nAlKtXkcQCNTdOirVKpavXI1Vq1dj+cpVWNvXr8PuoD308udx++65Ow7Y732NH2T7UqYSmY4a2ZOf\nXCjhibspB+Bs8QICoP6BQRCosAHGIKASU0V6UTLPgcy4nbQi8kZg0X47H3cOFv7tBjCEuRCAUV1d\nqNU99JfLEFJit2OnwLYt2JYNw+CJw0nE4pmMxAPLsmAYBj79ZapdT2w4io6lslMnhFXhe2Bv/QNi\nqx3AOkfh8A8dqF2nG+6vceI3xvCxz3weS5ctBwD8+uo/4osXfAZnnHwCOGNwHDuuwa29Dnx93kAP\nvpiur71k2bLUmQ498bQN8lsQxFDyoU9/B4/87nuqUolejeScR0kRgyBAUF6HwO+DZ3RBBHHej7iS\nQ2iqOgY01X2Fgnn++WfPno0n/nabisxLrOAwMF0KWeVckFLShIYgBoChPecC09SJ19Q4UYq4skkQ\n2nCq3KoaS5KdEUSMwbmqApZYlJLQ9qSFb8YYDKOIguNgZE8PhNwxql4nhETdrWPZipV4+bU3sHKV\nSnrfUSxh1512wF677QLGk3UHchaQG8jmOUh4pkce6yzz2aYDCQiaMKlbmHcgVKDyyn2kSlelP8mo\nTNnnEuVyFdt9ZCqW3z8dDEAgJbxqGVwCXY6J0YedoV2wbdi2FXkghJm4o3KS+lS2ZcE0TUylVVRi\nA3Pq1y/H3Mu/oZOMxpYgpARWvAm+ahHENruDO6XoMxaKBjoGjAFYu3ZdJB4AwK233or+/n649bra\nB4BpGgiEul35vg/fNOMYbKZKNfbVyvHFyU3wbkwQLTjks/+JB6+8FK7O5VGv19DT3Q3GOILAhed5\nqK9ZA99dhGDk9ggkiyYhMhE3nRShs6sjrSYlP/if3+Giz386EiDCRI2+LsPqui5NaghigDBoLyLG\no7wmYZ6SMEdWaL8xkpKTEkQOhmHoIFclxImU17ZIeeIpUSHhAsAAbjAUi0XsuN147Lj9+OZ55/JT\nBjX5LONmkFx7ltk5Ija58uEkIOiENoypMnCMQd/UWfRfjupwJzLIJxOvhUltIrfq8MDZMkC6EZUr\nFZQOmJB7LQbnsC0bnR0lFIsF2JadKPeDqLFbpomTzvrokP4uBNGOU7/+E9z5y4uVu6W2k9B7R4gA\nbPGLAOfg2+wJ2AUdHSSjUnQAsGj58tQxTdNET08P7rp9Hj5yyMGADumxLYBzplZbdVZ63/f1KmvW\ndZqSuBGbH4dfcAmu/upUvPbaazjw4ENgmia2MgxVbUGH6viui2Dh8wgK3QhKo6NKJkHgIxBBQhQP\nVz4kUF2H3l/+vuW5Z82aBQA4/fTT8aV/+yh8XY3htbcW4bLLr9gQX58gNivC8pGGwSPvnmQ+Ed/X\n/Vpi5ZLEA4JoxDQMFcaQXPwVMvbi0Qm0k/5zesN4Rp+TWyRG5iwKJ2hwsU0qBeEhEsdoEB82vUWv\nLV5ACN1eLMuCrUMBDG7oT2W04p9MYhgnkUqoWkKm4rKzf4VescmNfUm0GxXTasFxHHR2dOKUKedt\n2B+EIAbJcV/+Pr52wn74wP4HYtRWW8UeO4kbuVjyIphpg43bHTDi2w5jDPvuvlv6eMcdh9GjR+PA\n978Xxxx2iLpvC6bDeQxYlgnfN1FnLgAW2VeKTes+TBAD5tP/fT0+d8jOmDdnNo474SQwxlAsllCp\nVFQSw3AS0r8KwdoVEJJBMAvSLEJKBul5EG4NqFeBWj8gg0Fll58zZ86Qfj+C2BKQkKm5RSip53of\nUH9GEC2Jp1EyWuhNeh+kPXkSO0WhewlvvIbwvvYhBllh767Z07Fo8RKsWbcOruvB87xUaFKjmLDp\nGfkWLyCYpgHTNFEqFKOQAc6zeQe0t4GUkNFfmREQRBRzE/ULMt2Qk3XoU5OeSDzQajQ3okRwJCAQ\nmwI/u+MZfObgnTBh4iQ4jpNo2/FNUfousOifkMURYFvtADAOHXwWZXIPWblyJfY/4IPK8wYAOFd2\nxVXSRJXEjceVHZqUoiOIzZGrHnkNnzt0Z9x5x20457yPo1arw3Nd3Q+FQyio0IUggPSrkMFqSD+I\nHuHA6N0qTUcQxMBReQ8SY8zMolQQ5hdKdGfkfUAQ+cjI45slFpZie0psGOexC/PTpSrqJT0N4tXe\nwXq01l0Xo0eNgh8E8CwPnu+j7tbhuqqqimSZc26CXrNbvIBgQMA0TRUuYNs6CUeY9DCZuFAnoUpU\nP4jcp3Wm69jjIL19LCAEukGnwx98ncDR4AYggb5yuc1VE8Tw4/ePvo7PHbYLJp81NSUIhCE4UdhC\nbS3w1j8gu7cGRmwDALhz5vU49owpqeMddcjBgL7RMgCMM3DwKFeJaZiwLBOMAfW6CzUcax/LTRCb\nA1c9/BouOHRnLHj5Jeyw8y5RQlGZ6HuQyOUb9V8yjv8k8YAgNg4/+e0f8PULPhl5p4aP5Ngw6dZM\nfRpBtEHqhdvEQm8yvDy/fGLz8sXvhKuunY5xW4/GUQcfoOd5PhhYVE0MIumBwFLeSJsKW7yAYK5b\nCqNrBEzTRMFxtJt0rD4JEXsVgAGcGyrkwbTAOEe9XkelUkGtVou9EBKlFlOvo1wJcYxbIASY6+py\neKpkZLmvbyP/KgSxflz10AJc/ZUp2HX3PeAHAUTkiBALCIwpDx/ZvwKyshYYtwfAOe6ceT2EBO64\n734cfcThyWxvETJxPMZV+VLf8zep2rkE8W5x5cOv4befPwOWaehycIDBOASTidw5CS+gaH2G4ZKr\nb9yo104QWzKzZs3CxIkT8eVPnpfwUI0XpISUgGRYvW4tfvHb1vlJCIJAwosnFhGC0JaAjKdB1BkO\niTh388034+STT8Z1s27BxBM+Ep82SoIfX4sfBLjs8p+/69cw1GzxAoLBAF5bB85GgDHANMy4AgMA\ncIYlK1bCsS30FIuQUmV8l0KgUChizJit0dnZCSklfM9H3XVR7u9HtVZDtVqF67pqIhUEqTCIIEqS\nE0QlHX3f10qZbtnlVRv75yGIQfPpX9yAJ//3B6jUXXi6TUspwJiRmtRwbqoB0+J/QRa6gFHbgzGO\nE488AgAghURSlg3j2aIkcOEqayZ6gVZqiC2Jf//NLDx01aVAmLJJ+lHonM+CTG4nZXskHhDExmf2\n7Nk49dRT8dlzzwQQe7iG3gh33v8wHn3ssY19mQQx/JFJz+5A560TehyZwwZYcwpLG183ax6CIIBt\nmhjR04Xx48bCdT0sfvttLFm2Ak888cTQX8wQwLIlCjNsgk4Vg+PXF34StmmitMt+sG0LxUJBV2VQ\nWXGPnzQ12vaOO+7AJz/5SfzhF5dnEioijsdOuKKoGqQJscDzoxi30FUtCAJUa3X4vodqtabEBh3W\nQBOhiM11eXmzty8AeODKXrier715dP1rbWM8sULqeR58ISG22hHSLDRUPgkC5QbmeR5cV9lL3a2j\nVnfhui5czwVWvEFu2YOH7Gsz4s5fXIya66Lmeqi7PuqeB88P4AcBvvizP27sy9sSIfsi2nL00UfD\n81SytcdINBgMZF8EAOCOGdOwYuVKlMtVlKsV1PXYsCHJdoINMc869NBDASDObQJsSqJBU/siDwRd\nR55VVgP2WKX8cgOGwXQpxxjXdfGnP/0JJ510Em6dfm1qcoNQTNBJrIBYXAAkOFPu1kayTI8WExhj\ncD1VisswDdTrLqAbGUFs6nz4gl48dNWl0SRGBAKucGHZFkxdphSMweYcRhDAXfE6fAmI0TtDcjOu\nO68TkPp+EHnqRLYHCax4k8QDYovnuK98f2NfAkEQg+See+7Z2JdAEJs0alzIovx1IhW+kEB7qz7z\nzxc2yHU9/PDDG+Q8G5ot3gMBAP548QVwbAuWacDaZg+YpU5wrqohcIOnvBAAwDJN3DbzekCIsNxC\nLBpAudL89FdX4a/33Bvtc/jBB+Hb//HlTE4E1cDrrotavY5qtRqvqi5ZgN7fXb+hf4rhCinMmwkP\nXNkL3w/g66Q2pmmhVCoBjClvnUDA9z24nqeUY9MBRu8AwUwEQoX8uK4Lz/NQq9eV50G9Bnfpa/B8\njwSE9YPsiyCGDrIvghg6yL4IAMCECRNwzmknor9cQX+5rKoT+X7DdrVaHXc/9Oim5AWwMWlqXyQg\nAPjLpV8CAJgGh2kYMA0DvNgJNnoHGLYDzjkY51i09G2MHDUSXY7T9FgSwEWX/gBPzn8aADBixAhM\nnz4dAPCvJx/FXnuomvfJpIqup9yx+8tlVGs11Go1fLOXVpESUAexmfHAlb3wfJ1ElHFYloVCsYgg\nCOC5HuquC89zVWIpKSEkIOwS/OIIuDBRX7EIXq0Mr15DvVaF63r4zu+mb+yvtalC9kUQQwfZF0EM\nHWRfRMSxxx4Lz/Ow4/ht0NVRQrVWg8ENLFr6NpavWh1tR+LBgCEBoR3TL/uqymDNucpnAAbGoErG\nOSWw7jHgpR4wbqjkmWFJurAOfXQkhkVLl+LjF3yp4Ryf+fhHcfbEU3Wlkbi0Y73uolKtolKtolyp\nwF34Ar7x679syK8/3KEOYjPk8T98H9W6C88PdBUSDsNUSUxDLwMlIAhdLlXADwRcz4cXBPB8H54X\nwPV9uL6Pi6+8YWN/pU0Vsi+CGDrIvghi6CD7Iho49NBDoySGSUg4GDQkIAyEOT+5MPrGKlM8UmWw\nePQeB7NsYOR4sEJXrpgQcI4TTj8rOnaxUMDN067R8dqIxAPP81CtKVfsct86uG8vQL1Ww9d/dd1G\n+Q2GKdRBbMY8dNWlWL58BSRnKBSK4IYRJU0MdFiDEAK+fh4KCH4QqOdegAt/de3G/hqbMmRfBDF0\nkH0RxNBB9kUQQwcJCIPh9p9/K1Ktkgk4QpEg+2uypBcCA2A6YN1bA4VOgFuQYVnIhvwHahW1XndR\nX/Qi3GoFfhDgCz/93w32XTcRqIPYQvjsITtDSIn3vf8D2GPPPRGIsPpCoB8i+uv5Pj7346s39iVv\nDpB9EcTQQfZFEEMH2RdBDB0kIKwPnz54Jxx59DEYM3ZMorLCwPdPbRp5HgCSMQjDRlCvwvc8uL6v\nEssFAp/+4VXv/hfZ9KEOYgvj3w/bBZPOmgLGOeqeDz8IonwIoYBwXu+vNvZlbi6QfRHE0EH2RRBD\nB9kXQQwdJCC8E75y9F6YOPlMAExlj9flGWVCUJCqpmOKsCKD1h6iko8yTAqnSzm6ng8hJBhnNCnK\nhzqILZy//eq7EELg2C9TctEhgOyLIIYOsi+CGDrIvghi6CAB4d3moasuha9jswMhIkEBMg57kFKm\nRIQ4hEHnQNA1SoWQMDjD5G//fGN/reEKdRAEMXSQfRHE0EH2RRBDB9kXQQwdJCBsKB688lIlDETC\nghII/CAAYwyc84QXggRjDKd87ccb+7KHO9RBEMTQQfZFEEMH2RdBDB1kXwQxdJCAQGzSUAdBEEMH\n2RdBDB1kXwQxdJB9EcTQ0dS++Ia9DoIgCIIgCIIgCIIgNkVIQCAIgiAIgiAIgiAIoi0kIBAEQRAE\nQRAEQRAE0RYSEAiCIAiCIAiCIAiCaAsJCARBEARBEARBEARBtIUEBIIgCIIgCIIgCIIg2kICAkEQ\nBEEQBEEQBEEQbSEBgSAIgiAIgiAIgiCItpCAQBAEQRAEQRAEQRBEW0hAIAiCIAiCIAiCIAiiLSQg\nEARBEARBEARBEATRFhIQCIIgCIIgCIIgCIJoCwkIBEEQBEEQBEEQBEG0hQQEgiAIgiAIgiAIgiDa\nQgICQRAEQRAEQRAEQRBtIQGBIAiCIAiCIAiCIIi2kIBAEARBEARBEARBEERbSEAgCIIgCIIgCIIg\nCKItJCAQBEEQBEEQBEEQBNEWEhAIgiAIgiAIgiAIgmgLCQgEQRAEQRAEQRAEQbSFBASCIAiCIAiC\nIAiCINpCAgJBEARBEARBEARBEG0hAYEgCIIgCIIgCIIgiLaQgEAQBEEQBEEQBEEQRFtIQCAIgiAI\ngiAIgiAIoi0kIBAEQRAEQRAEQRAE0RYSEAiCIAiCIAiCIAiCaAsJCARBEARBEARBEARBtIUEBIIg\nCIIgCIIgCIIg2kICAkEQBEEQBEEQBPH/2XvzOCmOK9/3F1lr79BsTbPvCAnQwiIJCQES2pCEEJLx\nzrz3/N59n5m3eSyPuM++M32v7bnII8+Mx5659oxHY+RNSAIZ7UIyaLEsAZKQACH2HZqt6b27uqoy\n4/6RlVmRkZGZVb1BV53v5yN3ZWRmZBaOUxHxi3NOEEQgJCAQBEEQBEEQBEEQBBEICQgEQRAEQRAE\nQRAEQQRCAgJBEARBEARBEARBEIGQgEAQBEEQBEEQBEEQRCAkIBAEQRAEQRAEQRAEEQgJCARBEARB\nEARBEARBBEICAkEQBEEQBEEQBEEQgZCAQBAEQRAEQRAEQRBEICQgEARBEARBEARBEAQRCAkIBEEQ\nBEEQBEEQBEEEQgICQRAEQRAEQRAEQRCBkIBAEARBEARBEARBEEQgJCAQBEEQBEEQBEEQBBEICQgE\nQRAEQRAEQRAEQQRCAgJBEARBEARBEARBEIGQgEAQBEEQBEEQBEEQRCAkIBAEQRAEQRAEQRAEEQgJ\nCARBEARBEARBEARBBEICAkEQBEEQBEEQBEEQgZCAQBAEQRAEQRAEQRBEICQgEARBEARBEARBEAQR\nCAkIBEEQBEEQBEEQBEEEQgICQRAEQRAEQRAEQRCBkIBAEARBEARBEARBEEQgJCAQBEEQBEEQBEEQ\nBBEICQgEQRAEQRAEQRAEQQRCAgJBEARBEARBEARBEIGQgEAQBEEQBEEQBEEQRCAkIBAEQRAEQRAE\nQRAEEQgJCARBEARBEARBEARBBEICAkEQBEEQBEEQBEEQgZCAQBAEQRAEQRAEQRBEICQgEARBEARB\nEARBEAQRCAkIBEEQBEEQBEEQBEEEQgICQRAEQRAEQRAEQRCBkIBAEARBEARBEARBEEQgJCDkyPJZ\no7B81qjL/RoEUVSQ3REEQRAEQRDElQMJCDnwpRvG2p9pMkMQ/cMj142xP5PdEQRBEARBEMTlJ3y5\nX+BK5/9YMAmMscv9GgRRdCR143K/AkEMOJbPHAVkuqxNu05f7tchCIIgCKLAIAEhAM45DM4dZctn\njaKBGUHkgexBEGQ/37h5Yh+/EUEUHstnZcUDdLOvEm2V+jmCIAiCIGQohCEAzgHwHC4kCELJitmj\nswcZW1o+0z8kIZkWvA/I/gii2wTZmgvB3ih0iCAIgiAIGRIQAjAAGDSDIYhu4/DgYdm/fpOTlBi+\nQBFEBNF98rCfLwh5RwiCCMZK9EtiG0EQxQQJCD785ZJp4JyTFwJB9IB85/9/futk6JwMjiC6jWQ+\nuU5uunTDNFgyP4IIRLarnogIJEIQBDGQIAHBB+XKKUEQeRHWtLwmJCndQFrnNIkhiDxhrg89rYgg\niHzojggg3kMiAkEQAwFKoujxg71p12kYBofBM3kQJH7w3TVIpdPuE5yj7vEf9dGbEsTAo7qqAuca\nm01BQFzdzIQxXHfvlxzXj1r4EGpSKXQ0N+D0vk/Q2dxIExqCyIFxI0fgWP25bAHPigFBCRUduUqk\newmCyBGeX/JSV44gsjmCIAYARS8gfGXuOGX58lmjMLQshq60Dh0MsbIK6Mkk0qkuAEAqnXL/0nMA\ntOUjQTiYNGEczjXucpqL+FkxaApFIqgYWoPpt9wtXEQQhB8/fv3jrCCusKu6NY8q76tb+wQGjRyH\ndKoLLRfqzULqyggiGNnO8rQbw+BkawRBDDgY9481LvhR+5duGIuOlG4eSB3Bdfd+KVOWjyxsXlu3\n9ok+ed8ipVC714K3LwD4i4WTcaqp0/P8dfd80fzAmMcKTKYw493jngRxgDOAgeyue5B9FRArZo02\nw+8c/68yXHfvKsEFSCV+O4vqD+zCzzdu7o9XLnTIvgoYr5CDXD0QvDxgiZwh+yoyRJF80+7u2cry\nmdktj8nefPG0r6LOgfD/LpoKJnoMyP9M9qBKHmyZdv1fXaEK5H9GEDLDbn4Q0ZD6pyYSKzGFA8sO\nWabPdHSdzPbuYUoPH2abXd2ab/Xy2xPEwGLy+NG2TnDdvV8y/7tnVdaOHGQKLLMSFhRGTp3Vb+9M\nEMUI5TsgiB4QsJuXF8tnjaKpWi9Q1B4I31w8Fedbu9CWFHIZcOciTTY+WxYHPMSCTDGthPYqhWrq\nBW1fEFymj7y1Ec0dXY5zcu4DTxvz0+U8zpH95QXZV4FQt+ZRtCU1fRgAACAASURBVJ48gIrRUySP\nHsFQuLcjAuAuJ1vqMWRfBYy4kgkOxMoqMGPRfdkLOHeI3BZ1a5/Amr/433F89zZ0NDY4ztOKaF6Q\nfRURveGxs3zWKFc/Rzbniad9FW0OhCd/8g/QGEM4pCHEmHvbONc/mTTi4sxdTA4IBGHzvf//r+zP\n0UgUQBcY03DtPas87hCMR7SlnGwqewNNeIhixBLrKkZPVehwCk87WViAVJ65z6zXTzDnqFtLiYOJ\n4mTT7tNYOWciZt7xkBDyKmDZnh1WlOmnHvsW4hVVmHbTnbZp7XzldzSRIYh8yDNpqS1A0FytxxSl\nB8Iv//kf0djcjNbWNpx+9/do6uhCIq0rrzVXSfNTBmgC0+sUqqkXpH0hIx7ohpEtsAZWXFyJkY/F\na4OeIF7kvIHsL2/IvgYIdY9lQnTyTdYr5vJxeSV42KF9L8/5eWR7Ssi+ChxnXh7JznLpz4RryIby\nhuyryOhu3pGe5ispUsgDweK5X/4bWtva0dHRCcPgYOAIaUG/P0LsqN+l5IFAEFj7N9+BoRIPXG6c\nQeIBd3r6OFClvSYDJAqT73/3MaTTusdEPustIBa5TUS0JcnFx89s5HphCQruTrFuzaM0ASIGNF47\nlbiQ+yovl7lcuiQxZIhsiCDyQtNCGDNzHgCg7rFHPcNax86cj/oDu5Dq8k7qTeRO0QkIl5qa0JlI\nIJVOAwwIaUA4SEDIaQWGxEKC+PHa7yGdToMxhpCmIa2nM7aTZxIDILsqqjIthwNC1j5p4EUUIul0\n2icfiGUnQj8VKHR75+/J/lX0e0w1USLhjihQbI1MauMu21C0/+6YROZ5dWsezcmsqL8jipVrlixH\nJF7q9N5ZoxYPrHNDxkzAkDETHeU7X/5dP7xtYVJ0uzAkEl1IJpP2saZpCGlMKSLYSd4cHYWXUGAN\n4mggRRQviUQX0roOg3OkdUVYkCtkSsoML4/JvAZRYpwpkyc0BFFgeHri2Be48x7I5y286rBsKnN+\n3TMbzdvkHVHcN9p2TRMaoqBgzrwF2XLmtg3uKnD8ye95PCviWZXY9WQrrFvzqLniShBFxKZdpxGJ\nlZoHoo6NIHtz91/d3QaSKDIPhHX/8mMkuhKO2OyaWx7CwTfXQ3O4ZPpliPf19XQn0CGIIsGV9wDw\n9zywPAyYVC7e53H7uvUbsXrVSkcZTV6IQqXu8SeyrtVy+g8oPApUXgrWqqnnhkJuY1v3zAaXnSlF\nPdLuiEKEM7f3gYVY7vLqEdx5ZJvL7MqgGzp+/dzvASBjY7J3D3eK43b13QiRIIgBiG84ERNszLYP\nP29xxU5E8jMcnkWUHDiIohIQ2to7kE7r4EZ2tSaVTmP8bQ/h/MFP0dJ4CZPm3gamhRR3+7h8wn8Q\nRhCFzt//oM4tHqgSt6mywdvXK1Z7RHdqoZOYM3sm1q1XTG4IokDRNM3MLSLmMHCJb4JQIM/0vRx1\nFF3WpcZG+3PWzuRnOgdwJOARhUDd2idQt+ZbQltn7pAFaaLhzj8iGZvDs848sMQD2Db2kPQmeSZi\nJIgBiksoEO3LM4Q8l1A6jt88twlfWblcMfZU7UDEHOPWbCiRaIggYSFD0YQwrPuXHyOZTJqxpA7X\nMLPRDJ96LSbPX+IhHlgo3NPsOrIfJ0yY0HsvThADgHgsnvkku0qrPAkU8aSAOleC2EfYv98Mx0+Z\nbmfr1m8AyPuAKAL++m9/KBxlJjUqhzd58sIFAc7Gx1OOAy9u3qKqWLo3+xyyP6KwUEws4OXx4+MR\n4GFmVr/lulER4RcMeb0SAxd7m2BHKCtTfwZySIplHnODY936jUjruhmO58LDuByh6MKiFoc9EM05\n0WqBUxTbOD7/qydRf+4cWlrb0JVMwvWdHQqTjDBYCkymmK1j3foNOHr0aO99ieKmULX3grAvAPjT\nm69i85t/6MaKiSwmSPd71CMOwFZ/4SHUPU6KcA8g+xpA1D32LeeExpXgTfX/qP81yWQSb7+/HafP\nnlM2hknjx+GW+XMUZ8jNMwfIvgYo9goklIubHn2U2wCff+V1tLS2+T7L9ECQbdSvIyXPnwxkXwMc\n7wm5T7/lYx9qgQ5Zj1UvPV0Z2ud+pyLr8zztqyhCGNra25FMpgBwMAYwZjpeRMJhpNNpaJoG3TCc\nW8/ZqJQwjyBSKdHVhAkTSEQgioKTJ04gFo2iK9nljEcLCv3x8zhQnAaAiw2XHMcbXn4NYDSQIoqD\nusd/JA24AmKibZdqwfCEa8TBlt3DcQ4miOVu8aDoBlFEEWKGMyhyj8Cvj8q6XqfSOn67cVPgc778\n0APZAzHdlq8QT30eUcC48oqo7C9rJJxzrHtmo6e5hMNh571WyCyXw/IyFzCu7jspUb5NUYQwtHd0\nIJVOQ9cNhMNhRMJhVJSXobSkBBUV5WCMwVBljFdi7U3P3bKV7YHNMWTwoD76NgRx5XH0+AlUVw92\n/8JzOIX0oAUV+7NbfP/o091Yt34DXn5zq6O8rb2jh29PEAOLurVPmBN8W6gTcGRuF3dncBvex7v2\nKOtnXp529m4LJB4QxYE5Sec+E3kLOayV5SQeAEAkEhHGkc7wWtc6dGboSeIBUSgo27Kvs7fQx2VE\n8Kd8xIPVq1aaeRAc9bPsYhcX53KCMqjoO+seJ7uzKHgBYf2TP4Ou60in0wiFQhnxoBxVFZWIRMLo\n7EwgmUq5k+DA3R/YMTC28uXVXBmqMwIC5UMgCp2Nv/p3aBrDhQsX3RMPMQ8CAjoFh/bgtMd16zdg\nz74DPjdziksjioaf/f0PwQ3Dw8VTHABlV0Md12SYPnlSfg/2DeEjiMKkbu2PTG8E30m72zZumnN9\nYN3XTJ+WPbDjrL2rrXv8CZrEEAVH1ra4YvIFZ5ngFbBuvSq/QZavPfyg/4NVng4ecUsk2jkpeAGh\nK5lCoisJxhgYA8pKSzGoqhLpdBrtHR3oSiazF3Ppg+jWYo/TuDvhh4Ijx0/an2liQxQy7R2dCIcj\nMAwDI0cM97nSw2i8ksFlaGppzuEtaGJDFA+RSMSZdDQjEIQjGTdNVzI2pujXgNLSktwe6BDVSawj\nCAce/dfUScELSDfMvsajTu4Q/oIFDIIY2Jjt22txVirjCu8cicqKcmihkH86CVf4gt+7ESIFLyB0\ndnZC1/WMgKAhFotB0zS0d3agvaMze6G4kKPaz9fhyiJvn+XkuRdfhS6EROzc/VnvfimCuJLgHLqu\nwzAMnDl7DsOGVAvnxAs9kpRartgev9/xWCznV6GJDVEMtLW3K1ZkYO4yBC89Td1pBW2FGo/FpHhv\nEusIAvBcqHSwetVKhEPq3b0evGeps0DsB1nWg4gmL0Sx4BTKfBaXmLmYW15WqjwdCoWw4t67rIuz\nJ1QbB7g8f7LCPAl33hR0EsXnf/0kOjo6YRgGDG4gEg5j6JBqXLzYkBloCWl0xYGRI7tnTmk5bX75\n9HMuN+5de/ehbs2j1AiJgqSyqgonz5yxBzsXxCSHLnPxykQlevg4b4rH45g8fhwOHTvu/yKBu6QQ\nRGHQ2tqqzrKW8+4nTlavWolEIoH1m152lC+55SaMqa3thTcmiALEjp/2W1Ti+IroRu3Y4x5uo6Uu\njCAc8yVzNxTVPI1h5X33AADOnjuPRFcXxo8Z47sgpUQOE6K5Wk4UtIAwbPgIHD563MwoDQbGGDRb\n1ZWSZQBObwMxZIGLeQ+sY/dILZlM+iSf6stvShCXh1/9/Ke41NgIbvAcJy8ekx37s1qwWzB/DpKp\nFE6cPuOq0TYtEg+IIuDJn/4j0npa7eoZKNgBjmVT4XQ8HsfqVSvtbY6VfZl9K0fdmm9RMkWiqFB6\nuPn1O6qtHpksFph22NDYiHg8hrJSaUXVf6t1gih8bNFNGGdKdlcjh88Kp5taWpBOpTF0SLVHvgOF\nGE8EUtACQnNTMzg3wDkXtmhkmD1rFj7Ytg2JRAK6Y+tG8YddVLkyZVx0t3YrzvXnL3i+y7pnNlDi\nG6LgqB5cjbNnz5mTjlx/ey0Pn8CBl/P84ltuQiLRhfWbXnKU/5nggv3Ca28C/Fuoe5wmNkRh0tTc\nnMdAxzvRbxbnwImpMr9L2z2GNA1ffWRFrq9MEAWMaCwqgVxelJLuNgw89ezzrhrNfo286gjC3Lr4\nW8p5l4kgAgjXnD1/Aa9vfcd19ZSJ43Hz3BsyR8JCMqddFvKhYHMgbPrNL9HQ0OAQb1OpFD7btx9H\njx5FZWUl4vF4cEUe3taqRZ1xo5yunlx4+IhhQyk+mygonvof/4SOzg7ohiEIdH6ISdwUK6eWvXgJ\nwRyIx2NYvWollt52C+5avNCM387c9sqbW9HY3EziAVHQtLW1OQt6skCpskVFfR9+sstxrBsGXtvi\nHpgRRFHgSHAoebRysZ8T8OjTZPHAunTd+g3kuUoQtuePKleWIgmJMJZUiQcAcPDIMYcgLt5L87Tc\nKVgBweAGupJdCIVCMDJeCBwcqVTK3FrXMFASj3kmt8kiKsryqo10GWNYetst2SJBOb57yUIcOHyk\nx9+LIK4UYvE46s+ehW7oMFxulqqRj5crtXjepx7h9tqaEagZPsxR7si9QBAFyL/++EeCrcm7AwWQ\njfVxu0XLi6iSqX62/6CrunMXvD3uCKKgERIcKs/Z+NhZxrXaj3XP+G9RRxBFh6O/81DpGMPmt94N\nrMopIpBaly8FKyDEYnHouo6QpkFjWkZEAHTdwJ7P9yMajSIWi6GkxPJC4NJfGdlFWx0fWlszAmNH\nqZJOMbz/4c4efiuCuDJ46q//HPF4CTo7EzD0PLwPRPtybPcrJMhh8j3CcS46BUEUKBcbGoQjeXcg\ngSD9zpHEzSvSwTxp7+xAEEVM91Ym/XOUvPzm1j58NkEUIJx7dVgOzp47L9ziPVDM9m8UKpQvBZkD\n4eWnf4VRY8fiTH09dL0DgORtBmD/wcOYPHE8DMNAOq2jM5FwXiAjJk70yvaeKVq84KbMZRzpdNrc\ns5smOkQBMWTYCOw/fBi6rkM39Bz8NeWEpdJKjXIio8hD4lGtXwdBEIXAs3/3n5FMpswDZQJSZAvk\n/sl2q/ZIYioXCDlIfrNhk/J9jh492rMvRBADCcfuXFaZlBk+14oycdq5iXOckpUShJw8MUBHcDjV\n+eQR+c2GTcJWxpRIMR8K0gMhlU7hfGZLD3MLRyG22oIBh44eQ/2586goL4fzAsVkROwk/FzUHJcx\nRMJhu4wGXEQh8Pzav8Tp+rNIppLQdd09oEKOngGiuTHmnBCJMB6oOq97ZiOJCERBc6GhEVDlGnFF\nBqnEbYXLtd99NIYiCJu6x76ltglrRy/PrkfyuJMmKKHAEFqQMRKEavgnJreH+/P8669VVyWNEzWt\nIKfB/ULB/ctt+ucfgIPh9OnTSKfT4ODqpDb2jznH+YsXMaiqMjuAUnUG3PUB7koV5ymDLlFghONl\naGtvh97WJO1iIrR3h61Jwpxsj3IMtyumO2iQZgnTjEQ6omBpbmkBOprMA79uRWl7XqhyjPjfs+yO\nxcKKDUEUAczRWalFblW5nRle7TH04N1LXY/inGP21Vfh6194CKtXPURxekRRU7fmUcU0S+y3hNxZ\nLGtk06dMUtbHGMPgqiosunk+KsrL8DXFbkJ1j1HIUC4UVAjDS0/8FVJDJmHsqFE4d/48DN2AYXD1\nz6/kdtbU3CJsteOz546dgMprOxGPVLukIhMFQs3IWnx24DD0RAMwqMQs9BXK5PiDIFtQ2Iss/smu\npARR4KTTOsBbgfJqs0C1xTAEcdz2OvCxFWvLK2VYnulqfeuNc/HuBzsAwBYO6tbSVldEEeMyJ4Vw\nLp5jqs9AeXkZvvrwg3jpjS24b+kSySPBCpnt3VcniIGFajxox66an+XtHTPHDqFbUc240aPVMbKM\njC4XCkpASKbSGFQ9BBcvXkQ6bcZm88wODEqs+DWumgAxRbuyhANFwirPuQxNdIjC4vSZM0jrOoy0\nDiQ7gFhZ5ozc1j22bQwS3gJNRm3PtCpKFDJmrhEA6SQQjnonRRTtTSV2q3KKMOa+N3PfxHFjMHHc\nWKkCgigijDSghfxzYOVFNg9CKBTCcoUngjU4pW2JieLGZ2yo8nh1HYt9oOQKZIUgOS7vDfsuDgpG\nQHjhh9+GbnCEGHDh4kVzazmDmx4IrrhRSQhQJaNSxKsJakPmGu6O3WZcUsPMD8o9RxXQyg5xpXOm\n/mxWlGu9CERL1T/Erh9mjx92r07B6z7Fj7u9MrrmUYADdY+THRGFhbWbEJrqgaHj1J44ASZol6mS\nlno5z9nnSAwnipTGM8AQS0RjattzEGQrzDmcdFzulzWYIIoM0dZyNgenDX20aw/2fL4fyOQ8MMMW\nPCoj8SBnWEDisQGz1PDsD76JYSNG4PT5BjRHqtHZlUAymUI6nXZniVf9tit/yGUk8QD5NDbV6mxu\nyheJCgXbiw4Y+xL5529/A5eaW7L5D6KlQOUwycaE7NSBiq6c/d03xbx/BnrXsySRgsQFFWRfA4B/\n/Mv/BU0treYB04QJDRQ25C5WXZrjCf8OMmOzlCneE7KvAU7dNx4Ghozz6MeC7UZcQPJ1q7bNjMaC\neUD2VcCIW5iue3oDVn8xD09Tyb42vfYGmppbwAB8fdVK3/El2ZqNp30VhIDw4t99G5F4KXQOnDx9\nBl2VtUikdKT1NNJpPY/s7LlO6rupQHhNbnJ5j+LuPKiDuIL42WPfwIVGQUDgAMoGA6WVzpmLHJdm\nT+YlO/DcCsvDQyHvCZC3TRaxTYmQfQ0Q6r7xcPYgWgpUDldcpXIvUCHFWOfUCnK3MbItG7KvAY5t\nd0PHe1yhMCLOsWnzFjQ1NdlFX1n5AMLhiMf9fqqfc/xItuWA7KuAsbxK1z2zQQpT7abg7bqM5lsB\neP4DFkQIQyqto3b4cOz9fD9SqRTS7U0wwmXgBneLB6rBEpdiYcQJjlJxlmYz1u4N4iRI5ZLG5A7C\nYyDm0eYtJY4aNnE5CVvb3ojNuKMRiJcBmvCT4nLR9LIpr98n63oPN22XnXipDN4DM7IpYsCS7DBd\nqwfXSieEPgiKfkjOdZD5c/jYcfxx24eOmirLy/HgvXdmLg9wyZYg2yIKjovHzfAhF+72/9If3naI\nB9defZUpHji0ApV47hxDbnjxVbR1dAAA7rl9EYYPHZKxLfL6IYoBcz7mLR6oJkyKsZ+4oCWHn8vP\nU22FTLgoiG0cGWNI6zo6Ep3QDQNGawMMXYdhGO5trOx5OxcGWFKmW083NeXTM3UyV7HHgTCjEqsV\n9rr3GKjpuo6Dh486XHoIor/5xn//V0TDYXezvnRaOMi0ccvWHJOYzB/b/gTbUnoLefyY+4nPcjgF\n4OwUpMfUPfYo2RVxxVP3i+ecBXoSuHRKfTFzdjVOQS5bzgA89cxGvLf9I1cVLW1t+M2GTYK55NM3\nZt6Z7IoY4GTtjnvbm0RDQ4P9ORaNYvY1M8wDh1bAnF5AIplQhpX332MXvfqHt7LnaZJDFAOSbaxb\nvwHr1m9EsxXOF2QHjkTCfsl/OI4cP2EeN5/ttdcvZApCQADTcPbsOehpM3GirhswWi+aAoItDnCp\nIUrqU+C2HTn8WHNZFHBdIPxRuGYHDM5CmoY/ffgx1q3fgLo1NOEhLh8l8aiilAMNJwURQNjy1DWJ\nEYQ8h3dB7oOig0eO4fevvI5nNr2MN97+Izo7E45HWK/kzDDvfA1byMjYP9kUcaXjEhGMNNB4Wt3x\nuIq4wwaj0Qh+s/EFaJr3UEDX9exgTYnaZk+cOo3NW981V0rJrogBTt0vnjPNx0ibnggygq3JSbO/\n+OB92c5GJZIrh35ZW129aqW9X/269Rtw6sxZM06b7IoocNo7OwFbOMja1e9f3YwPP90tLU5J8zzu\nMaZkQGtrm12nWS/DxLFjzc+Davr4WxUGBZED4bm//SYSKR2Nza3oSqWQTKWRSuvgFUOFLeYy5BMm\nHRA+4xc/4w/3ntj4PgswDAO/evZ5u3jI4EH48OOdeT5/wFGoUvuAsC8/HDHZNgyoHp3Z9iqDKqTH\n4zCo/I/bduDwsROe73T7rQswutajA/B4D8t9e9qkCbhxzvXF5nZN9jVAqfvfHnb+v1c9FtA0t2u0\n3dSzbb6stAQvbN6ChkuNSCaTMAzvLY+X370Ug6oqpboEFGXvbf8Ih44eA8RdUorLrizIvgoIR583\ndLwrp8+O3Z9j79699vGSW27CmNpapzdQTmM+j+TDnOO3G1/AqJE1uO3m+eY7FaddWZB9FTB1ax71\n3cXuSyvuRzSqWtDygQPvvL8NR0+6vYlWr1qJdes34OjRo9153ULE074KQkB49gf/H1o7utDa0YGu\nZBrJdBppPbNndsUwYZu5DMKKYzYuJudMbQGokumYh6lUCq1t7QiHQojHY0KjD+hVlPl5OPZ8vh+N\nzS2Yf/21iEUjAGOF2pFQB3EFoxQROIDyaqCkMrdK7FwJ3js2aMzcjmd3ZjseZOxARXlZKVbed4/y\nnNczrE4qEg7jyyuXF6otqSD7GuC4bDBSAlSN8Lw+Hovh2mtn42//4adoaGhAZ2enb7Lhrz78IEKh\nkLPQ8uyThXAh78mm182s15xz/NkXzXcsIruyIPsqMBz2Ztua2fDFyU48FsWqB+/PIZkbx6bX30R7\nRwdmTp+OmTOm+TxdURfnqHu8aPMhkH0VKEHiAQBUVpRjxb135Vkzx/pNLyOR6PK9ikQEoOAFBAB4\n8jv/J1raO5BM6U4BAQBKBwOlVebnXHQCv996z3OCV4HE9p2fYu/+Q57e2VdNmYx518/O52F5UQAD\nNuogBghKMSEcAwaN9L4poJlHImFEwhG88uZWnKo/a090OFckSRUIhUL46srl7nAl+6FQPvgP775n\nuohm1OgCsJ8gyL6KhLV/8x1omoZ5c+fiJ//2H7jQ0ID6+nqk02nXtZxzMMZQPagK9991h7pCz1VS\nuZ/leOrZ58E5LxabEiH7KmDEPm/dH7J5RGZeNQ3Xz7rGfYNkG9s+/gT7Dh52nF500zyMHztGuF5I\nAuexm1dnZwKP//invf31BgJkXwXKhAkTcrrOmWAxN5594RV0ZMIjvCABASgKAeGpv/5ztLR3oiOR\ndAsIAFBSBZQOym2wozoXKDyoVeGNr7yO1rb24C/AOW67eb7ZaeTl5tYNAQRA3eMDagBHHcQARRxc\nuWK3rXIrjlOytUg4jGgkglA4DE1j+Ocnf+USD4K2aL1z8a0YOWy4t+gXsCp0qv4cRtfWFPqEh+yr\niPj3n/w9Kisr8ctnnsepU6fQ2trqsCsm9ZF3LV6ImuHDhBIPu/EK6fOY8BS4TYmQfRUBy5Yts0MX\nGGP4+hcecl4g2oFkJr/dsAmpjIi3eMGNGDt6lOIJ8mBUyC8kHBaRXVmQfRUguYoHSluDtEak6IN0\n3cCvn/u9b92DKiux89NP83ntQqTwBQQA+Plj30BLeye6UgoBATDzIVQMU93qQZ6uCEJROBTC86+8\njsbmFuclAROeWVdNw3WWap1nfoTcTzjLB0CHQx1EEbD2b74DzjkMw0AoFEIsFkUoFEIoFEJnZyf+\n9VdPO4SDXASEyRPGYcG8G7qXpwRCAtbC3n+b7KuIWPcvP0YkEsG/P70BJ06cUHofWDDG8PVHVnhO\nfBz4hB9lb7bCBotKRCD7KgKsCY/p+fagR2isG845Nrz4Kh5+4F7VWadIYB8He/4UiW2B7KvwyFU8\nAIB771iMYUOqc7tYYY6n689i1959OH+xQXlLEXrMyRSHgAAAP/nLP0NrRwJJr0FR6WDTG8HLm0D8\noXa4jCGn3ymNMUSjURw+ehxb//SBY4IjrvJ4wQE8cOftqB48KDcvh1xRuJTKHdAVbCTUQRQJP/3h\n38IwdHAOxGIxxGJRgAPv7fgI736w3U70lquAUFszAktvu0UoydfdSH39FWwr3YHsq4j49c9/il8/\n/xJOnjwZmPtgyS03YcyoWo+zHnbCeSaaz8+7p6gmOmRfBc7ixYtx7NgxhDQNX83sluAmH9dSxeWy\niJBjHQPM27Q7kH0VEPmIB2AMq1XeBy4km1GaEEdbewc2vPSao7QkHscXli8rhn7Ki+IREADg7/+f\nr6Glo9Pn95oBg2uBUMT7R92xd6iMrAqbWyxGImGEw2FoWgi/+PXTSKXTynjtoElPSTyOLzywLOBn\nMSh2wctDwv3uA8AjgTqIIuG3//YvSCQS0A0DI2tqYBgcia4E/uXJp1B/7rxDQDAMI7C+saNqsfiW\nm7IFrj2Bma9radBg7Qq0le5A9lVE/OpnP8HG17fg008/tW1JNdkfM2okltxys7sC216ctsENA08J\nOwQBMAd3XkKCZFoFYksqyL4KHGvS44rFVobA5rkQxBWJSgOvh+tisq8BR9HZV17igWhv8tQHfq0i\nQEzgwItvvIlLjc0AgKHV1Vi2dDFQ2Dbkh+e/ZLh/36N/+Mt/+hV++BdfRkdX0ixQTQoaT5uFVSOA\nSFxRC3Motz9e+z00NjU7r9AYQqEQIuEQwuEwQloIWkiDxpgdQsEYcwzQfL0PMtd1JhLQDd2d9Rri\n4C1rLc+9+CraOzozW9BdJ3hOSPE/Yqdie1m4Ve26NY8Wq6EQl5mSuGmL8ZI4RtTUIJVK4Wx9PS40\nXMo5eaJIZUV59sAeWFkw6a+EuYzqE/Pt3N+ebIYYCDy/eSsOHz5s25BKPBg7uhaLFwjCmzgqY2q7\nef8j93bC2z7+FPNvuFaqR+yDspAtEQORpUuXAp6J3ETPHCgm9s6+5ejxE9jxyW6k0iloWggjhw/D\nosxWjdkhH1fsKiZ+VosNdWu+hbq1RbtTA1FgrF71kNAtSQ1e4V0QON4TLrv/zjuQTqdx5PgJTBo/\nLnOu6PScQArSA0GFer9678RuKt7Y+DTOnD2H1vZ2cMOArhvQNIZQKAxNM4UDc0GT4x9+/qTL6yBX\nt2sAWHTzfIwbM9rH0wAAmL1/vXhm4tgxmHvtTJSUlHi6IZkGXwAAIABJREFU6rg7NevUFRnvTQpz\nkfDas79FW0cHysvLMWbMGDQ1NeHoseP4/t//E3Rdtz0PchHiAODh++9BWWlpTs/mnGPdMxvtxnbt\nNTMw++qrpHAmn/HfwF1RJfsqEhYsWADDMHD+/HkPDx6Gr6x8AOGwsLbg4XGgIpHowvpNLzlrZAxf\nf+Qh75VY5SptlgFkR16QfRUwEyZMMPeij0QDJi7e9hO0VR0HcNWUSZh//bUeVWUWgzy8Zi9euoSX\n39gKFGZmebKvAmDihIkYO7oWJ0+fgeEzvisrLcHD92fyhXgk6PU6zA+xY0J2gXXg90f5UlwhDH3J\n73/9H+jo7EQymURa1+2EbwwwGz3nMDjHP/78SaQzEx50Q0C47poZmHX1dPOAe4RScCCtp/GbDZuU\ndTj37pYHa0EJr0yuEGOhDqJIeH3D79DW3o6SklKMGzcO5y9cwLf/+ntoyWSKNwwjp9AFACgvK8XK\nZXfn1M7BgcbmJrzw+h8cxXOvm4UZUyabB7nUo6h4AKz6kH0VCY888gh27dqFZDLpOldZXo777lyC\nSCSSLVSPoZyILtMc2Pz2u6g/d9683CtDdi4MXEFOhuyrQJk7dy4uXrzos42c9/be5mlTtM6HL624\nH9Fo1PkMz0lT9kAUKQpMRCD7GuD8l29/056rfPDhx9h/2N0+Y7Eovvjg/d1/iCxU55SShCvnXwO4\nL+oOxRXC0JeUlpaAw1zhjGRWQg3DgME5mCUQ6DqYpgGKnSBydb02B3EqV89Mg86Uh8NhPHDXHXjz\nnT+iozPhqOPYydOYNH4s7IsdXjyWB4IoJEjGwimcgehfGGOmbUUiOHbyFH7x1G/Q3tGRl/hmce/t\ni4In/ZZNMHPLHpkdO3dhx85duGXeHEyaMM55j2/uhKw7qeWaTXZEXE4WL16M9vZ2pFIp1zn1nvVC\n+/YzI9HGGHDHwgXoSiZREovltnuD+Dwf926yI+JK4+LFi84CVzv3yqNljgWfylM8AIDfPf8i7l5y\nG0YMG6oWJ5g8UzIpKytFe3sHkPGaKDARgRjAiOHa866/Fqfqz6KtvQOMMVw1ZTLmXT/bvwJLxBZ2\n+PEcn+WUk0QYGBaqPNULaJf7BQYaoVAI0UgEsWgUkUgEoZCGUDiEcCiEkKYJYWrZiY5X8kS/ydBY\nO/O1fA2z27XF4EFVeOSBZa6VnonjxohPUxuCo/NRG4sYm0oQfQljDMlUCmt/8jP8fN1vcPT4CTt0\nIVfxQNM03H/n7WYIT+ADM385B9M0rF61EsvvXuq67OKlRvc95o3ZMoWpipAdEZeTsrIyNDU1uco9\nxQMxzE0sdhU60TTNzGVixWJDoQvIj+KqQZwgcgsXkx0RVxKOELmcRDKT7ogHFq9tedv08mFWfaKN\nqGO9Vy6723Gcb8I6gugzhOaraRoevu8e/NkXV2L1qpU5iAeQJvuW6O2TE4Gp7AUBZZQHQYYEhDyJ\nhM18B6GQKRrEojGUxEuyClomgaIueR+oJj9i8irxfFlZKcrKrE7JK/+B/BlgYFh2x2IsmDcHq7/w\nUMZ+hNg49y3iC7rLHAodQfQ96XQa//3H/wNVVVVoampCIpHIayvUeDyGh5bdZW6Dmr0r+1c1eYGz\nsxlUVYmKsjLHZfOVnRjLKt22QOdvK3WPPUoDN6LfWbRoEerr620xzkJjDNfPvNpf/ZJDuK3+RLRB\nDnXbVwkHKp1A5SkkestlwgPtOFQSEYjLzI033ggA2bGel7jmOHSHEwTh1edtfutd7+QhAeNNC+qL\niMtN3ZpHHQs5gNUfWG2b+4+rmPd836wT/v0bVOdV5UJ+OOp/ABIQ8kfTmLnbgqYhGo2irLwcmmb+\nM7LMisvLb2xRehv4raBaP+6MMdy9aKF4o9QZ+CTkYcDQIYMxefzYrAFy5pzYMMkgsi/g860ZGQzR\nL/zDz59ELBZDKBRCU1OTY9tG0X7kwRAHMHnCOHzhgWXqpIl2gikuHKs6HvPEQ/fdjXtuX4Qvr3jA\njG+1k8gJOIrUqz6uuhmw+gsraeBG9CvhcBitmTwiIqNrR0q7+vggN3HXCo/Xqo10n12eozBtuWlL\nmefJhojLyblz5wBRQLCbp+Q5w4RJDAdOnTmb13PEsaHM6fqzCvEgaDwnXMo52RFx+XEIB5A6DblM\ndb9znciN4A2nWkjyROxzKJZBhpIodoM3f/8MkskkNC2ElvZ2/Ptv1uP8hYtIJrug684JTz7JE6PR\nKB65/x6Ew1Y8UKCslsM13udTqRRe3fJ2dntKzsE0hrLSUsy7bjbGjKp1x6FenvjTQrVcsi+Ja665\nBjU1NUin06ivr7cFBCtxosqGIuEw7l5ym+R14IGXSfiWc+egMKfBmUeFQvG69RuulDhUsq8CZubM\nmeCco7Oz05WAdOrECbhpzvWKsJw8moScE8TjdkPX8clnn+PQ0eMwuIFIOIzRI2sw++qrEI/H/W3L\nrpM7BpTr1ptu4FeIHXlB9lWAiBNvRxLFAPPJx/sgF/J5Njhw8OhRHDp6HA2NTbb4cYXbTxBkXwMU\nc2HSp9H6jrdUnjc+rcF30ci73/Krv0jy8Xj+q1ASxW5gJnrTcOjYcTz74qvozOzKYBhZV7N8k76V\nlZZi+T1LndtneeGrMIt+ph5GwTk2vf4mmppb5C8GzoG29g5s+eP7AIA7F92KkcOHZ87n9FUIotsw\nxmAYBhobG1025CUerFh2lxlzLZKLvuZ4sHAfE5KJ+q2s+vY4smspF2L0TLwzdxNE7xEOh9GRSUQq\nc+7CRacnjW97F+BSgkX5WMDQdTy96WVX8sauriT2HTqCfYeOAACGDB6M++5con6evLqbsaevrFyO\n32zYREnhiH7luuuusz+7rMoR+tmPgyauOJAzyDNgysTxmDIxK36sW78BU6ZMwcGDB/vtVQkii4+N\nSF5nzkulRIkIWNxhHvbI3XnlPB/qErGLGwph6AamgAD88unnoOs6UqmUr2AQJCSMGDYUK+69ExGH\neCDHl4ovAPV1qosU4sHvnn/RLR54sPmtd/G7379gGw6FMhB9xaxZs8AYQ1tbG5LJZE7i2/WzrnaL\nB/DwfHPEcHvApIm+fa/gYucKW5DCIiCfV1yb4ab5c31ehiB6TiqVcuU+sGhpa0NXV9Js90G24UAO\nn7M8dJzFyWQSv3ru98qdH2QaGhuxbv0GvLf9Q0XonroTDIfCee3MQhC9gZiMlAGCZ48q3DRbnEh0\n9d1LOSZBzCVY2+8huWKvXrUSs6+eTmM74vLg+vn2iEVQzdllQcG3XkUH59l1CIq6q38h8cCCPBC6\nAQOw7+BhGIaBZDLpSpiYDyXxOO5atBBMk92lRdXLz8VH3LZEHMip3Ul3fLILyRwGcyLJZCoTN0SG\nQ/QdnJvbo1qu1rl48dTWjBBrUNuJLLjJq6xWfKoqFpwJwoGjPg5dN/CnHR/h+Kkz9m8AYwzVg6pw\n05zrMaR6sOKNnTZ51+LbMHHiRBw5csTzOxJEd5kxY4avDXHOsWffftwwe2aOLpwZO3GMxUTvA2cl\nv3v+xbzf+dDR4zh09Djuu3MJhgwepPa4E7rJP/viw1i3fgN5IRCXDY1Za3EecXCZ9tva3tarzzUT\noOaK9R5uQ79m+rRi8JgnrkRcJsOy843AKYd4Ac9O+JnXvfL4Dh6inzjeY9LcjDwQLMgDoTswhv94\n+jkYhoF0Om1PdixyDWFgjOGuRbeCaaJa7CezKZQ5+5S0TaOHO+neA4cc75kr69ZvtAdspFQTfQHn\nHLquI51O57zlaXt7p3DkYScu92zm7jNcyXssrwOW7ZQE3nl/O3793O9x5PhJh4DIOUdDYxNeemML\n1q3fkE2oyhU2m+Hrj6zw/kchiB5gGEagwL33wCHTQ8A1mBKwbUgS0lx9VvbzvoOH0BNe2rwFL23e\n4rNDA7cTYllbFlNCOOKy4OW9w+GwicGVlWZxL3nNzJwx3eOMyitOMTly3ELJsonLiNxWmWQjXNWm\npXsgi83WPb5up8Hv5ejnhEXaIocEhG5gNSNZQMg154F1zZDBg1BVVWGVZldCA58srp5aLtcqV5vM\n+UxxZyLhrC3HTL0E0V8YhuES5PzY64rbFLxx4JMYx7PpC67YjslSVnR4afMfcPTEKc93Et/94qVG\nPPXMRvx6wybFhdm6aeBG9AW59Eu6YWDHzl2Zoxz7BA9P7cxDwTnHto8/zf+FJRoam/CrZ5/3eAFm\nDzRvvXEeGGN44K47evxMgugWVi4Qh1jstLtwJIIvrbgf9y1dgoU3zsOc2TMxcdxYlJaU5P24SePG\nqk+IYQoeDhEeBwRx+VB6ImTgHBxAe3sHUum0RwWqtiyJCl7NnXskQfBNc0XzJwph6CZWZnjV4Czo\n2Jq4p9Jpp7rlF6pgCQzySqk8yYGc0Cp7vuFSY3e+qpK6NY8WSwZSop/IJ+moxcnT9fhs3wFcPX1q\npkTlomY9INf5kXxhNi/CW+99gIbGJp97ncKcFZah6zrWrd8ABuDrVvJE0ZRltZ0gegGNMegBNsUA\nHDh6DNOnTMrsZCKKcIqQn8BkVcDHn+7pra8AwzCw6bU3sPzupU7hwPENgK9/4SGA+iainwmFQo7j\ndc9sxF2LF6Jm2FBpvGZ+jkajGDqkGkOHVDvue+f97Th64mROz/zyigcQiUSkUil/j+IUIPeBcmge\n2Q/Rj3B5HGT+3jc2NePIiZP4/MAhGJwrm/TqVQ9lPlnzHiu0zn83IOXzPUWCoJcubsgDoRtoWvaf\nzUssyGUi1NzSisbmZqFEEecpnmPyoMnjeo/BnZnLoPegVVOiN5FtJxdBgTGGj3ftwaXGpszKD/ee\nkFudi+Oh8ktAMQjLHhw/dTqv7yR7+XDVNl6ZCRrZE9Hb3HHbLbldyDneeX+7ELaTwcoL4tCtvVyg\n7Ztw6Ojxnry2i6bmFhi6kcOzTciWiL5kiLBl8HXXzMh84riU2RJ781vvSk0yeMJx641zMWxIta9T\nwD1LbsPqVSsRiYb9V2xzSUCngnRsoj8R2mU6nca+g4exftNLeOmNLdi7/6C5AONx6979h5QCWPBO\nDOrnB+OIhSVjIQGheyxctkLp/p9r3LbIm+/8CVzam9u8WYrzhNBeXdVa3gjcWcad5ysrygPfJ6d3\n93wPgug+jLFuxYYanON1e8Am5xIRXErlsATrMjF0yKdD6c1s7x/v2kP2Q/Q5k8aNxZ2Lbg30tmSM\nobm1FYmuTJZ4OaeOfaHwWd4lwbIlzvNK1JurXT3zwsvO54kffeZOBNHb3HP7IvvztEkTM58Yjp00\nQ9s459i5+7NMeW4DJsYY7r1jMZbcehNGDBtqL1SNGz0KX1m5HKtXrcTwYUPtZ/lXmZvQli0L7v8I\noq84XX8Wz770KrZ9/EnOC50f7fLzcuPOeb4gbncbVxJ5MhYSELrJoptvtD93x/XaoqOzE1veex9p\nOa6HiR+keDZP5VnM5CNmyjY/DKqqzPm9fPMjBOTiIYjucMv8OfbnfO0pmUzig492ZhIW2pU4PXfs\n9qqafDCfwVVmUpRM5vVOfuz+fL8idrzXqicIAIBu6BhTOxJLbl2Q0/WNTS1qoc0Fd4bMSVnkutsf\n+t3XZdmf0KcJBVmoXyL6kO9/dw1CoZA9LwlHspHAza2t9udde/dlPqnGZ96MGVWLu5fchq89sgKr\nv/AQFi2Yj7AUJmGTb1tXLZ66QpQIop/gHOlUGm++8549vsq17zDEhVfXLcwxFfIM6/F7lCiswUNQ\nL3JIQOgmy+5Y3Gt1nTpzFpteewOdnZmM8pLnQJagY6hjwDMDrnA4hKgrbi43rPhS8dEUJ0f0Jgvm\nzcFVUyZ3O7nngcNHsHvvPnV+EAfC6o09oOLu8Z0jbpShvaNTrqiXyHhJME6u10SvYiUlHVs70iHQ\nedGV7BI83/xw5yDI5vEBwuHc0yuJ9h5o+w5vA3eeH4Loa6xdTWZfNQ21w4c5zo0bNcr+bG5JnJDu\nVrRVx0RF+myHEPVyG1d67JCCTfQzDHj5za3Oohzbupx7xF13QHtmXC2oCe/mv1pK9kICQjfRNA0h\nrff++draO/D8K5tx5PgJd8P0c7/x63ysY2HAtWjBjegOolHXrX0CdY+TeED0PrffejMevHspaoSB\nWe6rmQw79+zFyTP1mRvh/Ou+XP05+2Tn5Vpwxxb0rurzLDtQJIhe4ul//xl03Uz0a3COSePH2dsd\nehG2BeYgF+kMcqgDN9ty0HO6A2PMw6hpIEf0H9Zv+HUzr8btty7IjsE4UDtyhOM3/lJTkzvvDiCJ\nBHIMTj/3A7IgRxD9BkNTS0v37pS3FFbUHfTs7Ee/kG0v+yV7IQGhm2gaw1cfWWGv6KsmBn5Kmur6\nVDqNdz/YgT99+DG44VgedfzJHsjx3vJnuOoYOWI4pkwYr3ynaDSCmVdNw6KbbzQz/HIgpGlYbWWN\nJ68Dog8xDANgQM3wYbhv6RJcc9U034SlXrzz/nZ0Jbry/H1XbIMq2W9pPB5cS4B67jgfMK4kiJ7Q\n3tEhbYvKccv8uSgr9d4yrrqqSjiyvHI4OjsT9sqreYo7wxjgbLtzZs9Eqc9z/PCy83tuXyRspyr3\nedxlO+TNQ/QJtms0M1dBrTEY44jHYo7f+KqKCo+JhseYzfP3n6snMr5leXQmroUogugPut/mQqFc\npq/uXHBqRBvkzjmXPKZz9T/FC23j2E2WLH8El5qa8M3/9L/i088+x9b3PkBHZ4fj99xvwuM30Th4\n5BiOnTyF+5fejoqKcncOBCC7ZYkIV225ZZVnt+W6ed4NuHnu9ebAEqZIIO5zD8YxbkytKy6cvA6I\nvkRP6+CZCQ8AzL9uNm6YeTV27d2HTz773DmB8SGt63hvx0dYcuvNZoHsrukZ2cDUF2SKYtFYt76X\nJx7jStpGi+gN2traoes6DLsfYmDgePj+e/Hci6+ivaPDcX0sGkVpiSWScbuPaWlrw/OvbAYATJk4\nHvOvvxYhLZS9TtE/hcMhPHL/vUgkEjhx+gwuNFxCW3sHOjo70dWVRFcy6WmKqr4xHA6ZGeqhuMnq\n3xyhFzTAI/oIz6blPlFeVqq4Tl4ckldSVZbB1OdU48i8wh28FqIIoj9g3U6eXTtihLOAi205W7+n\nh413BySEwSK3e4oUEhB6AjdDGaZPmYTRtTW4cPESDhw+gj37D7qTIuZJKpXGu9s+xL13LJIarEd8\nt1fiK6tcTBaXuV+TY4iEfAn29WQsRD+wYd0vwLkBwzDdra2VU8YYZs6YjgnjxuCNt/7oSFLlx4WG\nS+YWQPLAyK89e9mQNXBjwJjakdkQiR4QccWIU89E9C4GN6DLiaYy7Xv53XfgtxtfcJyaNnmiU0TL\nuHWKfdmBw8dw5ux53H7rzRg8qMpnUGbWE4/HMXXiREy1M9Wb6LqOXXv3CYnm/PnKygfVJ1w2Kycy\nIYg+xG562d/vUCgEXddRWV6eu2s1F0MZ/PoBD+HB/+V8hDWaIRGXl6umTMbeAwdd5eb4zbstxqJR\nZ0G+whmT7MOeK8nnxHvgnB8VORTC0EN4pqGHw2EMHzYEN829Hl9ecT/uXHQLamtGOFyw8+ViQ4PC\nRS3rFaDrOs6ev4DzFy4irSsEC0cSuBwHVcqcIdy9dz1B9CKcc+gZV2tLPDAysducc5SVluKBu27H\npPFjc6rL3I1BEQbkh2MwKH7MDuwWL7gRI+yttLrPl1culx7lnPiQ+zXRU8wwOAHhNz0SieCuxQtR\nUV6OUCiE4UOrcc30qcKFWdspLcmuojJmhka8uPkP2PP5fvFp0l/hgYqBVkgL4bqZV+OWecGJHVdb\nCXy55LLtNYjjmT6SdASir3C1rWxDvPf2RWCM4Z7bb3P2P8r2KNqIlHfE/svV7Vk873o/xbhRFeqj\n+h69uF0xQQQx86ppyvKgcNBwWFgA9Wqznk3ZsjVBWLN36/J0Uc3aEIkHAHkg9BxrwsM5BzfMz9Fo\nFGNqazFqZA0SiS4cOnocn+0/gNa29rwyzDOX+MCzDZ1zbHrtDbS2tWfmOBwlJXFMHj8Ok8aPQ1VF\nuXS/x0qRNUGSY1klN6CjR4/m889CEHlhcG67W3Nu2ZPh+P3XQiHcMn8uUqk0jp867WlLjDEMHlQl\nnXeqzfsOHkH9ufOYedU0DKke7H2t5MHANA13L16IZCqFM2fP43xDAzjniEYippAYCqOlrQ3HTp7y\nDLn42sMPWq/hnKtxpkioRRDdw+CG98Ij56gZNhTL774DHZ2dKC0pyWa1dnitMcRiUblqcM7x0a49\nOHvhIu5YuMAlOjgvVrhmZ/5MmjAONcOH4bWtb6OtvcNe/AmHw1h+1x0oLy+TvoOPbUjflcKAiD7D\nJyyuevAg3Hv7IsQdOXNUIac5Ohso+znmGg86rvObBHl1i35lBNFHRCLOaWiQ54FFPCaElHpdrxSY\nhVAHT69tvyeTgViQgNAL2OIB56YLtiUocI5IJILpUyZh2uSJOHriJLbv3IWurq6c6i0vKxX22nYP\nwqy4IXNcxZBIdGHPvgPYs+8AGGOoqqzAnNkzMapmhHvw5fA0kHpDyajWrd9AgzGiT8kKcUbWhpwX\n2O7Vi2+5CZ/s3otP937uWd+Esd5Z4HXdwLaPPwEAnDh9BpUV5Zh//bUYOXwYGNOyduYZ0sAQjUYx\nfuxojB87StmhLJh3A17f+g7OXbjoKP/aIyuyXkmidxCX41BpFYjoGYbBnYIUd09KQqEQKsrLnecl\nLzQGoKqyAs0t7vCh0/Vn0dLahsryMiEPgfQwl2u2c4RWVlaKlffd4xDjzX3vvT0YXOWebqcE0Yc4\nPNSsMo6hVr4Oz/tU7VUVwy3dkxHbDcNwbpWaU9v3mRk53ofsiOg/GHPmQch1kbWivNynSXv0e5Bt\nRVGB5+NpcUeGBIQewIFM0jfudL0WBATLO4ABmDhuLMaPGY2Tp89g+85P0d7R6WksHMCMqZOzBfYK\nZbZg6qSJ+HjXHvX9nKOpuQVvvPMeYpEIvrji/hz6GLkjBNY9s4G8D4g+xwphUIoHEH70MzZw7cwZ\nqK4ehPe2f4RkMpmtB8CgygpBQHB3JBpj0DTNTtbY0tqGzW/9EaUlccy7bjbGjal1CgnS4NBz4Od4\nXYa7l9yGtrZ2dCQ6UVVRgVjMJwnj5d7Kiyg4ODfUorEXnhN1hrnXzsKb77ynPP361rfxyAP3uiuT\n8+4EPIhZWe1lRC85z1Bushein7GTUzPnMfPoLxwCnqrCbKGh6+hMdKG9swPvf7gTzS2tMAzuauYP\n3rMUVZWV7vqVeCoTZD/EZUNjDPFYDJ2JhPK8yiOBc47y0hKFJq3KI6IyDLkj8bpGHpNxdQL7IoUE\nhB5hCQc6DG6tnhrZCZDdRrMDH41pGDdmNMaNGY1UKoVPP9uH/YePIJ1O2Y21orwcC2+ch6FDBns/\nmgEzp0/FhYYGnDztndSNAUimUuhMJFBaUmK9tpATwduASDwg+ous8JbdhcFE8gQQmujYUbUYu6IW\nnYkETp2px6XGZtQMH4YxtTXZBKHiPvVWFZqG0bU1OHHqjF0XY0BnIoG3398GvA9cM30arp85w+y4\nVOq15+TISXlZmdMNW+XOnemQyMuH6E0MgwfMKnxWaaSCUSNrPJ/TmeiCoRtCUl5xJVU14/d7rgLR\nzlQeDgRxOZBDB5g0lrL7LcEeHOFq5nFaT6P+/AUcOHQEjc3NaO/oVE6aVHP837/6hpkjROyn8vbG\nITsi+h87zxNjKC8r9RQQVIusmqaZ4yr7IvEDd9hXdsym8LLxHcd52IVQXOw7ZpGA0BMycxsrdEHX\nLfFAdkUTO5nswCcSiWDOtTMx59qZSKVSSKd1RCJh031T7ozEOqxJEWNYeOM8PPviq45VWJlBVZWC\neKBS6OAWDyhpItGPGHYCRZ7Zdk6ceMhZn5y2URKLY8rECeqKRTsSlOP5113rEBBk9uzbj6MnT2Ll\nvXd7h6Aq38njOmsy5bHLQzF3QkTfYCYS7e5qifum8rJStLV3uMoHVVU6kwU7cnv4GY9PtmuH3TP3\nKbGAe4ROEERf4zVRVwnFcApgjU0t2HvgIE6erkeXNH7LJ1eWbhhOzx3Xc3MUEYXDYp8YEf3LmFG1\nON9wKedf7kFVlZkwNxFLuBNySYm24LnDllQFFKYjCg3UxdjQLgw9gCOT6M0KV+DW5Eee9Ahql+Nz\ntqZIJIKSkrgZ16ZK6Ca6yQkCQDgcxoN3L0U0GnG9XyQcxsIb52H5XXcI1VnKHJfew/yQSCRs8YC8\nD4j+QhfsyER0V1bYgyIhm40c/8DdF5WWxDF65Ejfd2pv78Bb73+QzYLtwsOf2nWtaiVYtGeC6H24\n6H0jNkq7PYurMMHt8J7bFynLF8y9Qb3K47JL7rYlX1tlkr3LK02QhHrVQwmij+Cq33pIbdLdHlOp\nNP64/SO89MYWHDp63CUe5EvIlWybZXcisY+93lW+lfojop8QmtrokTWIRSM5NdHSkhJcNWVSxuNN\nMc9yCcziA6UnyA9UrqtyhZ5NdgLyQOgZ1mqpUzywUKyaeArCTFWYvRYqN7nsLSUlcXzxwfvR3NKK\npuYWlJbEMbS6GlpI827n4iAvc83GV15Da1t7N/4lCKJnmEJcNjGo9+qlhYcxqcxIpUYDuHX+HPz2\n+Rd8V3saLjU6X4WrJjEer+b3PTjw1vsfYNHNN9JKD9EncPt/mbOPkdsj8/FUEFZSS0tKsOLeO/Hu\nBzvQ2t6B0ngMM2dMx5DBg5x1q1yolXageKanZ4/PCqrq3UmYI/oa1Y45fnvIZ649cfoMDh873sfv\nJj1b2Qdx99DTWqgiHY7oa4Q2VllRjlE1NTh99iySyZTycg6gNB7HjKmTMW70KHclyptEkTwHDwRA\nEd6tsgkyEJCA0DPEhImGYYA7YrcFxFUg11ZtXiEFQfE5YhVmvNygqkoMqqp0q2OqFVqhrLWtDRtf\neT1TVW5bqBBEbyJ68QCqSbgsFFiDInkyJNwDxTVlrzwRAAAZc0lEQVTWZIMB0VgU1YOq0Njc4vle\nM6ZOydqDSpwIcmdTTGyOnTyNt9/fhoeW3U3iAdGHyJNrr4YaEOYgTIoqKyq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      "text/plain": [
       "<Figure size 1080x648 with 20 Axes>"
      ]
     },
     "metadata": {
      "bento_obj_id": "139721051438480"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_grid(images.cpu().numpy(), rows=4, cols=5, rgb=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "t3qphI1ElUb5"
   },
   "source": [
    "## 7. Conclusion\n",
    "In this tutorial we learnt how to **load** a textured mesh from an obj file, initialize a PyTorch3D datastructure called **Meshes**, set up an **Renderer** consisting of a **Rasterizer** and a **Shader**, and modify several components of the rendering pipeline. "
   ]
  }
 ],
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  "colab": {
   "name": "render_textured_meshes.ipynb",
   "provenance": []
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  "kernelspec": {
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   "language": "python",
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