Fix transforms.Pad and transforms.LinearTransformation doc strings (#511)

torchvision/transforms/transforms.py

@@ -227,17 +227,24 @@ class Pad(object):
             on left/right and top/bottom respectively. If a tuple of length 4 is provided
             this is the padding for the left, top, right and bottom borders
             respectively.
-        fill: Pixel fill value for constant fill. Default is 0. If a tuple of
+        fill (int or tuple): Pixel fill value for constant fill. Default is 0. If a tuple of
             length 3, it is used to fill R, G, B channels respectively.
             This value is only used when the padding_mode is constant
-        padding_mode: Type of padding. Should be: constant, edge, reflect or symmetric. Default is constant.
-            constant: pads with a constant value, this value is specified with fill
-            edge: pads with the last value at the edge of the image
-            reflect: pads with reflection of image (without repeating the last value on the edge)
-                padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode
+        padding_mode (str): Type of padding. Should be: constant, edge, reflect or symmetric.
+            Default is constant.
+
+            - constant: pads with a constant value, this value is specified with fill
+
+            - edge: pads with the last value at the edge of the image
+
+            - reflect: pads with reflection of image without repeating the last value on the edge
+
+                For example, padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode
                 will result in [3, 2, 1, 2, 3, 4, 3, 2]
-            symmetric: pads with reflection of image (repeating the last value on the edge)
-                padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode
+
+            - symmetric: pads with reflection of image repeating the last value on the edge
+
+                For example, padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode
                 will result in [2, 1, 1, 2, 3, 4, 4, 3]
     """
 
@@ -655,7 +662,7 @@ class LinearTransformation(object):
     original shape.
 
     Applications:
-    - whitening: zero-center the data, compute the data covariance matrix
+        - whitening: zero-center the data, compute the data covariance matrix
                  [D x D] with np.dot(X.T, X), perform SVD on this matrix and
                  pass it as transformation_matrix.