Fix bugs in doc (#3414)

docs/en_US/CommunitySharings/NNI_AutoFeatureEng.rst

@@ -137,5 +137,6 @@ Conclusion: NNI could offer users some inspirations of design and it is a good o
 
 Tips: Because the scripts of open source projects are compiled based on gcc7, Mac system may encounter problems of gcc (GNU Compiler Collection). The solution is as follows:
 
-brew install libomp
-===================
+.. code-block:: bash
+
+   brew install libomp

docs/en_US/FeatureEngineering/GBDTSelector.rst

@@ -22,7 +22,7 @@ Then
 
 .. code-block:: python
 
-   from nni.feature_engineering.gbdt_selector import GBDTSelector
+   from nni.algorithms.feature_engineering.gbdt_selector import GBDTSelector
 
    # load data
    ...

docs/en_US/FeatureEngineering/GradientFeatureSelector.rst

@@ -18,7 +18,7 @@ Usage
 
 .. code-block:: python
 
-   from nni.feature_engineering.gradient_selector import FeatureGradientSelector
+   from nni.algorithms.feature_engineering.gradient_selector import FeatureGradientSelector
 
    # load data
    ...

docs/en_US/NAS/Overview.rst

@@ -60,8 +60,8 @@ NNI currently supports the one-shot NAS algorithms listed below and is adding mo
      - `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.
    * - `TextNAS <TextNAS.rst>`__
      - `TextNAS: A Neural Architecture Search Space tailored for Text Representation <https://arxiv.org/pdf/1912.10729.pdf>`__. It is a neural architecture search algorithm tailored for text representation.
-   * - `Cream </NAS/Cream.html>`__
-     - `Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search  <https://papers.nips.cc/paper/2020/file/d072677d210ac4c03ba046120f0802ec-Paper.pdf>`__. It is a new NAS algorithm distilling prioritized paths in search space, without using evolutionary algorithms. Achieving competitive performance on ImageNet, especially for small models (e.g. <200 M Flops).
+   * - `Cream <Cream.rst>`__
+     - `Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search <https://papers.nips.cc/paper/2020/file/d072677d210ac4c03ba046120f0802ec-Paper.pdf>`__. It is a new NAS algorithm distilling prioritized paths in search space, without using evolutionary algorithms. Achieving competitive performance on ImageNet, especially for small models (e.g. <200 M FLOPs).
 
 
 One-shot algorithms run **standalone without nnictl**. NNI supports both PyTorch and Tensorflow 2.X.

docs/en_US/Tuner/BuiltinTuner.rst

@@ -188,7 +188,7 @@ SMAC
    Built-in Tuner Name: **SMAC**
 
 
-**Please note that SMAC doesn't support running on Windows currently. For the specific reason, please refer to this `GitHub issue <https://github.com/automl/SMAC3/issues/483>`__.**
+**Please note that SMAC doesn't support running on Windows currently**. For the specific reason, please refer to this `GitHub issue <https://github.com/automl/SMAC3/issues/483>`__.
 
 **Installation**