MaxPool1d
class torch.nn.MaxPool1d(kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False)
Applies a 1D max pooling over an input signal composed of several
   input planes.
In the simplest case, the output value of the layer with input size
   (N, C, L) and output (N, C, L_{out}) can be precisely described as:
  out(N_i, C_j, k) = \max_{m=0, \ldots, \text{kernel\_size} - 1}
  input(N_i, C_j, stride \times k + m)

If "padding" is non-zero, then the input is implicitly padded with
   negative infinity on both sides for "padding" number of points.
   "dilation" is the stride between the elements within the sliding
   window. This link has a nice visualization of the pooling
   parameters.
Note:
 When ceil_mode=True, sliding windows are allowed to go off-bounds
 if they start within the left padding or the input. Sliding
 windows that would start in the right padded region are ignored.

Parameters: