put back error on warnings for sphinx (#3671)

docs/Makefile

@@ -2,7 +2,7 @@
 #
 
 # You can set these variables from the command line.
-SPHINXOPTS    = # -W  # turn warnings into errors
+SPHINXOPTS    = -W  # turn warnings into errors
 SPHINXBUILD   = sphinx-build
 SPHINXPROJ    = torchvision
 SOURCEDIR     = source

torchvision/ops/deform_conv.py

@@ -29,24 +29,21 @@ def deform_conv2d(
 
     Args:
         input (Tensor[batch_size, in_channels, in_height, in_width]): input tensor
-        offset (Tensor[batch_size, 2 * offset_groups * kernel_height * kernel_width,
-            out_height, out_width]): offsets to be applied for each position in the
-            convolution kernel.
-        weight (Tensor[out_channels, in_channels // groups, kernel_height, kernel_width]):
-            convolution weights, split into groups of size (in_channels // groups)
+        offset (Tensor[batch_size, 2 * offset_groups * kernel_height * kernel_width, out_height, out_width]):
+            offsets to be applied for each position in the convolution kernel.
+        weight (Tensor[out_channels, in_channels // groups, kernel_height, kernel_width]): convolution weights,
+            split into groups of size (in_channels // groups)
         bias (Tensor[out_channels]): optional bias of shape (out_channels,). Default: None
         stride (int or Tuple[int, int]): distance between convolution centers. Default: 1
         padding (int or Tuple[int, int]): height/width of padding of zeroes around
             each image. Default: 0
         dilation (int or Tuple[int, int]): the spacing between kernel elements. Default: 1
-        mask (Tensor[batch_size, offset_groups * kernel_height * kernel_width,
-            out_height, out_width]): masks to be applied for each position in the
-            convolution kernel. Default: None
+        mask (Tensor[batch_size, offset_groups * kernel_height * kernel_width, out_height, out_width]):
+            masks to be applied for each position in the convolution kernel. Default: None
 
     Returns:
         Tensor[batch_sz, out_channels, out_h, out_w]: result of convolution
 
-
     Examples::
         >>> input = torch.rand(4, 3, 10, 10)
         >>> kh, kw = 3, 3