* formatting fixes

python/tvm/relay/frontend/pytorch.py

@@ -970,8 +970,9 @@ def convolution(self, inputs, input_types):
         kernel_size = weight_shape[2:]
         use_bias = isinstance(bias, _expr.Expr)
 
-        # We are trying to invoke various relay operations through a single conv_op variable. However the function
-        # signatures for some operations have additional attributes so we pass these in along with the standard ones.
+        # We are trying to invoke various relay operations through a single conv_op variable.
+        # However the function signatures for some operations have additional attributes so we
+        # pass these in along with the standard ones.
         additional_arguments = dict()
 
         if use_transpose:
@@ -1016,7 +1017,6 @@ def convolution(self, inputs, input_types):
             data_layout = "NCHW"
             kernel_layout = "OIHW"
 
-
         conv_out = conv_op(
             data,
             weight,
@@ -1037,8 +1037,8 @@ def convolution(self, inputs, input_types):
         else:
             res = conv_out
         if is_grouped_conv1d:
-            # Because we conducted grouped conv1d convolution through conv2d we must squeeze the output to get the
-            # correct result.
+            # Because we conducted grouped conv1d convolution through conv2d we must
+            # squeeze the output to get the correct result.
             res = _op.squeeze(res, axis=[2])
         return res
 

tests/python/frontend/pytorch/test_forward.py

@@ -952,10 +952,17 @@ def forward(self, *args):
 @pytest.mark.parametrize("output_padding", [0, 1, 2], ids=lambda x: 'output_padding=' + str(x))
 @pytest.mark.parametrize("groups", [1], ids=lambda x: 'groups=' + str(x))
 @pytest.mark.parametrize("bias", [True, False], ids=lambda x: 'bias=' + str(x))
-def test_forward_conv_transpose(in_channels, out_channels, kernel_size, output_padding, bias, groups):
-    # Note we do not test with groups  > 1 because that is not supported in tvm for conv transpose operations
-
-    # output padding must be smaller than either stride or dilation so we opt to make the stride 1 + output padding
+def test_forward_conv_transpose(in_channels,
+                                out_channels,
+                                kernel_size,
+                                output_padding,
+                                bias,
+                                groups):
+    # Note we do not test with groups  > 1 because that is not supported
+    # in tvm for conv transpose operations
+
+    # Output padding must be smaller than either stride or dilation so we
+    # opt to make the stride 1 + output padding
     stride = output_padding + 1
 
     #Conv 3D Transpose Tests