ConvTranspose1d.patch

--- /opt/conda/lib/python3.6/site-packages/torch/nn/modules/conv.py
+++ /opt/conda/lib/python3.6/site-packages/torch/nn/modules/conv.py
@@ -75,9 +75,8 @@
 
     Attributes:
         weight (Tensor): the learnable weights of the module of shape
-                         :math:`(\text{in\_channels}, \frac{\text{out\_channels}}{\text{groups}},`
-                         :math:`\text{kernel\_size})`.
-                         The values of these weights are sampled from
+                         :math:`(\text{in\_channels}, \frac{\text{out\_channels}}{\text{groups}},
+                               \text{kernel\_size})`. The values of these weights are sampled from
                          :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
                          :math:`k = \frac{1}{C_\text{in} * \text{kernel\_size}}`
         bias (Tensor):   the learnable bias of the module of shape (out_channels).
@@ -87,8 +86,7 @@
     """
 
     def __init__(self, in_channels, out_channels, kernel_size, stride=1,
-                 padding=0, output_padding=0, groups=1, bias=True,
-                 dilation=1, padding_mode='zeros'):
+                 padding=0, output_padding=0, groups=1, bias=True, dilation=1):
         kernel_size = _single(kernel_size)
         stride = _single(stride)
         padding = _single(padding)
@@ -96,14 +94,11 @@
         output_padding = _single(output_padding)
         super(ConvTranspose1d, self).__init__(
             in_channels, out_channels, kernel_size, stride, padding, dilation,
-            True, output_padding, groups, bias, padding_mode)
+            True, output_padding, groups, bias)
 
     @weak_script_method
     def forward(self, input, output_size=None):
         # type: (Tensor, Optional[List[int]]) -> Tensor
-        if self.padding_mode != 'zeros':
-            raise ValueError('Only `zeros` padding mode is supported for ConvTranspose1d')
-
         output_padding = self._output_padding(input, output_size, self.stride, self.padding, self.kernel_size)
         return F.conv_transpose1d(
             input, self.weight, self.bias, self.stride, self.padding,