Repository: Project-MONAI/MONAI

Issue:
Document warning when make html
**Describe the bug**
Got below warining when `make html`:
```
/workspace/data/medical/MONAI/monai/data/dataset.py:docstring of monai.data.SmartCacheDataset:51: WARNING: Unexpected indentation.
/workspace/data/medical/MONAI/monai/data/dataset.py:docstring of monai.data.SmartCacheDataset:52: WARNING: Block quote ends without a blank line; unexpected unindent.
/workspace/data/medical/MONAI/monai/networks/blocks/upsample.py:docstring of monai.networks.blocks.SubpixelUpsample:11: WARNING: Definition list ends without a blank line; unexpected unindent.
/workspace/data/medical/MONAI/monai/networks/utils.py:docstring of monai.networks.utils.pixelshuffle:5: WARNING: Definition list ends without a blank line; unexpected unindent.
```



Discussion:

Resolving patch:
diff --git a/monai/data/dataset.py b/monai/data/dataset.py
--- a/monai/data/dataset.py
+++ b/monai/data/dataset.py
@@ -357,19 +357,6 @@ class SmartCacheDataset(CacheDataset):
     3. Call `update_cache()` before every epoch to replace training items.
     4. Call `shutdown()` when training ends.
 
-    Args:
-        data: input data to load and transform to generate dataset for model.
-        transform: transforms to execute operations on input data.
-        replace_rate: percentage of the cached items to be replaced in every epoch.
-        cache_num: number of items to be cached. Default is `sys.maxsize`.
-            will take the minimum of (cache_num, data_length x cache_rate, data_length).
-        cache_rate: percentage of cached data in total, default is 1.0 (cache all).
-            will take the minimum of (cache_num, data_length x cache_rate, data_length).
-        num_init_workers: the number of worker threads to initialize the cache for first epoch.
-            if 0, run in main thread, no separate thread will open.
-        num_replace_workers: the number of worker threads to prepare the replacement cache for every epoch.
-            if 0, run in main thread, no separate thread will open.
-
     """
 
     def __init__(
@@ -382,6 +369,20 @@ def __init__(
         num_init_workers: int = 0,
         num_replace_workers: int = 0,
     ) -> None:
+        """
+        Args:
+            data: input data to load and transform to generate dataset for model.
+            transform: transforms to execute operations on input data.
+            replace_rate: percentage of the cached items to be replaced in every epoch.
+            cache_num: number of items to be cached. Default is `sys.maxsize`.
+                will take the minimum of (cache_num, data_length x cache_rate, data_length).
+            cache_rate: percentage of cached data in total, default is 1.0 (cache all).
+                will take the minimum of (cache_num, data_length x cache_rate, data_length).
+            num_init_workers: the number of worker threads to initialize the cache for first epoch.
+                if 0, run in main thread, no separate thread will open.
+            num_replace_workers: the number of worker threads to prepare the replacement cache for every epoch.
+                if 0, run in main thread, no separate thread will open.
+        """
         super().__init__(data, transform, cache_num, cache_rate, num_init_workers)
         if self.cache_num >= len(data):
             raise ValueError("cache_num must be smaller than dataset length to support replacement.")
diff --git a/monai/networks/blocks/upsample.py b/monai/networks/blocks/upsample.py
--- a/monai/networks/blocks/upsample.py
+++ b/monai/networks/blocks/upsample.py
@@ -85,7 +85,8 @@ class SubpixelUpsample(nn.Module):
     default single layer.
 
     See: Shi et al., 2016, "Real-Time Single Image and Video Super-Resolution
-        Using a nEfficient Sub-Pixel Convolutional Neural Network."
+    Using a nEfficient Sub-Pixel Convolutional Neural Network."
+
     See: Aitken et al., 2017, "Checkerboard artifact free sub-pixel convolution".
 
     The idea comes from:
@@ -95,6 +96,7 @@ class SubpixelUpsample(nn.Module):
     https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/PixelShuffle.cpp
     and:
     https://github.com/pytorch/pytorch/pull/6340/files
+
     """
 
     def __init__(
diff --git a/monai/networks/utils.py b/monai/networks/utils.py
--- a/monai/networks/utils.py
+++ b/monai/networks/utils.py
@@ -195,7 +195,8 @@ def pixelshuffle(x: torch.Tensor, dimensions: int, scale_factor: int) -> torch.T
     Apply pixel shuffle to the tensor `x` with spatial dimensions `dimensions` and scaling factor `scale_factor`.
 
     See: Shi et al., 2016, "Real-Time Single Image and Video Super-Resolution
-        Using a nEfficient Sub-Pixel Convolutional Neural Network."
+    Using a nEfficient Sub-Pixel Convolutional Neural Network."
+
     See: Aitken et al., 2017, "Checkerboard artifact free sub-pixel convolution".
 
     Args:


Test patch: