Documentation Improvements (#313)

Summary:
This fixes all docstring formatting to avoid unnecessary indents / spacing in website documentation. The list of tutorials is also updated appropriately, and additional details are added to feature permutation documentation.
Pull Request resolved: #313

Reviewed By: NarineK

Differential Revision: D20250642

Pulled By: vivekmig

fbshipit-source-id: 95d5e726d193c5e0e0022751bcc408c735eab36f

Author: vivekmig

captum/attr/_core/deep_lift.py

@@ -145,7 +145,7 @@ def attribute(  # type: ignore
     ) -> Union[
         TensorOrTupleOfTensorsGeneric, Tuple[TensorOrTupleOfTensorsGeneric, Tensor]
     ]:
-        r""""
+        r"""
         Args:
 
             inputs (tensor or tuple of tensors):  Input for which
@@ -165,23 +165,25 @@ def attribute(  # type: ignore
                         Baselines can be provided as:
 
                         - a single tensor, if inputs is a single tensor, with
-                            exactly the same dimensions as inputs or the first
-                            dimension is one and the remaining dimensions match
-                            with inputs.
+                          exactly the same dimensions as inputs or the first
+                          dimension is one and the remaining dimensions match
+                          with inputs.
 
                         - a single scalar, if inputs is a single tensor, which will
-                            be broadcasted for each input value in input tensor.
+                          be broadcasted for each input value in input tensor.
 
                         - a tuple of tensors or scalars, the baseline corresponding
-                            to each tensor in the inputs' tuple can be:
-                            - either a tensor with matching dimensions to
-                                corresponding tensor in the inputs' tuple
-                                or the first dimension is one and the remaining
-                                dimensions match with the corresponding
-                                input tensor.
-                            - or a scalar, corresponding to a tensor in the
-                                inputs' tuple. This scalar value is broadcasted
-                                for corresponding input tensor.
+                          to each tensor in the inputs' tuple can be:
+
+                          - either a tensor with matching dimensions to
+                            corresponding tensor in the inputs' tuple
+                            or the first dimension is one and the remaining
+                            dimensions match with the corresponding
+                            input tensor.
+
+                          - or a scalar, corresponding to a tensor in the
+                            inputs' tuple. This scalar value is broadcasted
+                            for corresponding input tensor.
 
                         In the cases when `baselines` is not provided, we internally
                         use zero scalar corresponding to each input tensor.
@@ -195,21 +197,21 @@ def attribute(  # type: ignore
                         For general 2D outputs, targets can be either:
 
                         - a single integer or a tensor containing a single
-                            integer, which is applied to all input examples
+                          integer, which is applied to all input examples
 
                         - a list of integers or a 1D tensor, with length matching
-                            the number of examples in inputs (dim 0). Each integer
-                            is applied as the target for the corresponding example.
+                          the number of examples in inputs (dim 0). Each integer
+                          is applied as the target for the corresponding example.
 
                         For outputs with > 2 dimensions, targets can be either:
 
                         - A single tuple, which contains #output_dims - 1
-                            elements. This target index is applied to all examples.
+                          elements. This target index is applied to all examples.
 
                         - A list of tuples with length equal to the number of
-                            examples in inputs (dim 0), and each tuple containing
-                            #output_dims - 1 elements. Each tuple is applied as the
-                            target for the corresponding example.
+                          examples in inputs (dim 0), and each tuple containing
+                          #output_dims - 1 elements. Each tuple is applied as the
+                          target for the corresponding example.
 
                         Default: None
             additional_forward_args (any, optional): If the forward function
@@ -232,9 +234,11 @@ def attribute(  # type: ignore
                         computing final attribution scores. This function can take
                         at least one and at most three arguments with the
                         following signature:
-                            - custom_attribution_func(multipliers)
-                            - custom_attribution_func(multipliers, inputs)
-                            - custom_attribution_func(multipliers, inputs, baselines)
+
+                        - custom_attribution_func(multipliers)
+                        - custom_attribution_func(multipliers, inputs)
+                        - custom_attribution_func(multipliers, inputs, baselines)
+
                         In case this function is not provided, we use the default
                         logic defined as: multipliers * (inputs - baselines)
                         It is assumed that all input arguments, `multipliers`,
@@ -608,21 +612,21 @@ def attribute(  # type: ignore
                         corresponding references. Baselines can be provided as:
 
                         - a single tensor, if inputs is a single tensor, with
-                            the first dimension equal to the number of examples
-                            in the baselines' distribution. The remaining dimensions
-                            must match with input tensor's dimension starting from
-                            the second dimension.
+                          the first dimension equal to the number of examples
+                          in the baselines' distribution. The remaining dimensions
+                          must match with input tensor's dimension starting from
+                          the second dimension.
 
                         - a tuple of tensors, if inputs is a tuple of tensors,
-                            with the first dimension of any tensor inside the tuple
-                            equal to the number of examples in the baseline's
-                            distribution. The remaining dimensions must match
-                            the dimensions of the corresponding input tensor
-                            starting from the second dimension.
+                          with the first dimension of any tensor inside the tuple
+                          equal to the number of examples in the baseline's
+                          distribution. The remaining dimensions must match
+                          the dimensions of the corresponding input tensor
+                          starting from the second dimension.
 
                         - callable function, optionally takes `inputs` as an
-                            argument and either returns a single tensor
-                            or a tuple of those.
+                          argument and either returns a single tensor
+                          or a tuple of those.
 
                         It is recommended that the number of samples in the baselines'
                         tensors is larger than one.
@@ -634,21 +638,21 @@ def attribute(  # type: ignore
                         For general 2D outputs, targets can be either:
 
                         - a single integer or a tensor containing a single
-                            integer, which is applied to all input examples
+                          integer, which is applied to all input examples
 
                         - a list of integers or a 1D tensor, with length matching
-                            the number of examples in inputs (dim 0). Each integer
-                            is applied as the target for the corresponding example.
+                          the number of examples in inputs (dim 0). Each integer
+                          is applied as the target for the corresponding example.
 
                         For outputs with > 2 dimensions, targets can be either:
 
                         - A single tuple, which contains #output_dims - 1
-                            elements. This target index is applied to all examples.
+                          elements. This target index is applied to all examples.
 
                         - A list of tuples with length equal to the number of
-                            examples in inputs (dim 0), and each tuple containing
-                            #output_dims - 1 elements. Each tuple is applied as the
-                            target for the corresponding example.
+                          examples in inputs (dim 0), and each tuple containing
+                          #output_dims - 1 elements. Each tuple is applied as the
+                          target for the corresponding example.
 
                         Default: None
             additional_forward_args (any, optional): If the forward function
@@ -671,9 +675,11 @@ def attribute(  # type: ignore
                         computing final attribution scores. This function can take
                         at least one and at most three arguments with the
                         following signature:
-                            - custom_attribution_func(multipliers)
-                            - custom_attribution_func(multipliers, inputs)
-                            - custom_attribution_func(multipliers, inputs, baselines)
+
+                        - custom_attribution_func(multipliers)
+                        - custom_attribution_func(multipliers, inputs)
+                        - custom_attribution_func(multipliers, inputs, baselines)
+
                         In case this function is not provided we use the default
                         logic defined as: multipliers * (inputs - baselines)
                         It is assumed that all input arguments, `multipliers`,