Improve docs for Sphinx (#550)

* Update common.py

* Update reducer.py

* Update __init__.py

Author: ProGamerGov

captum/optim/__init__.py

@@ -1,6 +1,6 @@
 """optim submodule."""
 
-from captum.optim import models
+from captum.optim import models  # noqa: F401
 from captum.optim._core import loss, optimization  # noqa: F401
 from captum.optim._core.optimization import InputOptimization  # noqa: F401
 from captum.optim._param.image import images, transforms  # noqa: F401

captum/optim/_utils/image/common.py

@@ -70,7 +70,7 @@ def make_grid_image(
 
 def show(
     x: torch.Tensor,
-    figsize: Optional[Tuple[int, int]] = (10, 10),
+    figsize: Optional[Tuple[int, int]] = (8, 8),
     scale: float = 255.0,
     images_per_row: Optional[int] = None,
     padding: int = 2,
@@ -84,7 +84,7 @@ def show(
         x (torch.Tensor): The tensor you want to display as an image.
         figsize (Tuple[int, int], optional): height & width to use
             for displaying the image figure.
-            Default: ``(10, 10)``
+            Default: ``(8, 8)``
         scale (float, optional): Value to multiply the input tensor by so that
             it's value range is [0-255] for display.
             Default: ``255.0``

captum/optim/_utils/reducer.py

@@ -28,7 +28,7 @@ class ChannelReducer:
         reduction_alg (str or callable, optional): The desired dimensionality
             reduction algorithm to use. The default ``reduction_alg`` is set to NMF
             from sklearn, which requires users to put inputs on CPU before passing them
-            to ``fit_transform``.
+            to :func:`ChannelReducer.fit_transform`.
             Default: ``NMF``
         **kwargs (optional): Arbitrary keyword arguments used by the specified
             reduction_alg.
@@ -76,14 +76,14 @@ def fit_transform(
 
         Args:
 
-            tensor (tensor): A tensor to perform dimensionality reduction on.
+            tensor (torch.Tensor): A tensor to perform dimensionality reduction on.
             swap_2nd_and_last_dims (bool, optional): If ``True``, input channels are
                 expected to be in the second dimension unless the input tensor has a
                 shape of CHW.
                 Default: ``True``.
 
         Returns:
-            *tensor*:  A tensor with one of it's dimensions reduced.
+            tensor: A tensor with one of it's dimensions reduced.
         """
 
         if x.dim() == 3 and swap_2nd_and_last_dims:
@@ -138,12 +138,12 @@ def posneg(x: torch.Tensor, dim: int = 0) -> torch.Tensor:
 
     Args:
 
-        x (tensor): A tensor to make positive.
+        x (torch.Tensor): A tensor to make positive.
         dim (int, optional): The dimension to concatinate the two tensor halves at.
             Default: ``0``
 
     Returns:
-        tensor (torch.tensor): A positive tensor for one-sided dimensionality
+        tensor (torch.Tensor): A positive tensor for one-sided dimensionality
             reduction.
     """