\text{K}, ..., \text{groups}=C_\text{in}).
Note:
 In some circumstances when given tensors on a CUDA device and
 using CuDNN, this operator may select a nondeterministic
 algorithm to increase performance. If this is undesirable, you
 can try to make the operation deterministic (potentially at a
 performance cost) by setting "torch.backends.cudnn.deterministic
 = True". See Reproducibility for more information.

Note:
 "padding='valid'" is the same as no padding. "padding='same'"
 pads the input so the output has the shape as the input. However,
 this mode doesn't support any stride values other than 1.

Note:
 This module supports complex data types i.e. "complex32,
 complex64, complex128".

Parameters:
      * in_channels (int) -- Number of channels in the input
        image
  * **out_channels** (*int*) -- Number of channels produced by the
    convolution

  * **kernel_size** (*int** or **tuple*) -- Size of the convolving
