Conv1d
class torch.nn.Conv1d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros', device=None, dtype=None)
Applies a 1D convolution over an input signal composed of several
   input planes.
In the simplest case, the output value of the layer with input size
   (N, C_{\text{in}}, L) and output (N, C_{\text{out}},
   L_{\text{out}}) can be precisely described as:
  \text{out}(N_i, C_{\text{out}_j}) =
  \text{bias}(C_{\text{out}_j}) + \sum_{k = 0}^{C_{in} - 1}
  \text{weight}(C_{\text{out}_j}, k) \star \text{input}(N_i, k)

where \star is the valid cross-correlation operator, N is a batch
   size, C denotes a number of channels, L is a length of signal
   sequence.
This module supports TensorFloat32.
On certain ROCm devices, when using float16 inputs this module will
   use different precision for backward.

"stride" controls the stride for the cross-correlation, a single
