with its own set of filters (of size
          \frac{\text{out_channels}}{\text{in_channels}}).
Note:
 The "padding" argument effectively adds "dilation * (kernel_size
 - 1) - padding" amount of zero padding to both sizes of the
 input. This is set so that when a "Conv1d" and a
 "ConvTranspose1d" are initialized with same parameters, they are
 inverses of each other in regard to the input and output shapes.
 However, when "stride > 1", "Conv1d" maps multiple input shapes
 to the same output shape. "output_padding" is provided to resolve
 this ambiguity by effectively increasing the calculated output
 shape on one side. Note that "output_padding" is only used to
 find output shape, but does not actually add zero-padding to
 output.

Note:
 In some circumstances when using the CUDA backend with CuDNN,
 this operator may select a nondeterministic algorithm to increase
 performance. If this is undesirable, you can try to make the
