Doc: fix broken links (#1798)

docs/en_US/NAS/NasInterface.md

@@ -55,7 +55,7 @@ def forward(self, x):
     out = self.input_switch([in_tensor1, in_tensor2, in_tensor3])
     ...
 ```
-`InputChoice` is a PyTorch module, in init, it needs meta information, for example, from how many input candidates to choose how many inputs, the name of this initialized `InputChoice`. The real candidate input tensors can only be obtained in `forward` function. In `forward`, `InputChoice` instance is called with real candidate input tensors.
+`InputChoice` is a PyTorch module, in init, it needs meta information, for example, from how many input candidates to choose how many inputs, and the name of this initialized `InputChoice`. The real candidate input tensors can only be obtained in `forward` function. In the `forward` function, the `InputChoice` module you create in `__init__` (e.g., `self.input_switch`) is called with real candidate input tensors.
 
 Some [NAS trainers](#one-shot-training-mode) need to know the source layer the input tensors, thus, we add one input argument `choose_from` in `InputChoice` to indicate the source layer of each candidate input. `choose_from` is a list of string, each element is `key` of `LayerChoice` and `InputChoice` or the name of a module (refer to [the code](https://github.com/microsoft/nni/blob/master/src/sdk/pynni/nni/nas/pytorch/mutables.py) for more details).
 
@@ -102,8 +102,6 @@ Different trainers could have different input arguments depending on their algor
 
 The supported trainers can be found [here](./Overview.md#supported-one-shot-nas-algorithms). A very simple example using NNI NAS API can be found [here](https://github.com/microsoft/nni/tree/master/examples/nas/simple/train.py).
 
-The complete example code can be found [here]().
-
 ### Classic distributed search
 
 Neural architecture search is originally executed by running each child model independently as a trial job. We also support this searching approach, and it naturally fits in NNI hyper-parameter tuning framework, where tuner generates child model for next trial and trials run in training service.
@@ -174,4 +172,4 @@ Correspondingly, the generate architecture is in the following format:
         "_idex": [1]
     }
 }
-```
\ No newline at end of file
+```

docs/en_US/NAS/Overview.md

@@ -6,11 +6,11 @@ However, it takes great efforts to implement NAS algorithms, and it is hard to r
 
 With this motivation, our ambition is to provide a unified architecture in NNI, to accelerate innovations on NAS, and apply state-of-art algorithms on real world problems faster.
 
-With [the unified interface](.NasInterface.md), there are two different modes for the architecture search. [The one](#supported-one-shot-nas-algorithms) is the so-called one-shot NAS, where a super-net is built based on search space, and using one shot training to generate good-performing child model. [The other](.ClassicNas.md) is the traditional searching approach, where each child model in search space runs as an independent trial, the performance result is sent to tuner and the tuner generates new child model.
+With [the unified interface](./NasInterface.md), there are two different modes for the architecture search. [The one](#supported-one-shot-nas-algorithms) is the so-called one-shot NAS, where a super-net is built based on search space, and using one shot training to generate good-performing child model. [The other](./NasInterface.md#classic-distributed-search) is the traditional searching approach, where each child model in search space runs as an independent trial, the performance result is sent to tuner and the tuner generates new child model.
 
 * [Supported One-shot NAS Algorithms](#supported-one-shot-nas-algorithms)
-* [Classic Distributed NAS with NNI experiment](.NasInterface.md#classic-distributed-search)
-* [NNI NAS Programming Interface](.NasInterface.md)
+* [Classic Distributed NAS with NNI experiment](./NasInterface.md#classic-distributed-search)
+* [NNI NAS Programming Interface](./NasInterface.md)
 
 ## Supported One-shot NAS Algorithms
 

docs/en_US/feature_engineering.rst

-#################
+###################
 Feature Engineering
-#################
+###################
 
 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 usage feedback.

docs/en_US/nas.rst

-#################
+##############
 NAS Algorithms
-#################
+##############
 
 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 designed and tuned models.