the size of the window
  * **stride** (*Union**[**int**, **Tuple**[**int**]**]*) -- the
    stride of the window. Default value is "kernel_size"

  * **padding** (*Union**[**int**, **Tuple**[**int**]**]*) --
    implicit zero padding to be added on both sides

  * **ceil_mode** (*bool*) -- when True, will use *ceil* instead
    of *floor* to compute the output shape

  * **count_include_pad** (*bool*) -- when True, will include the
    zero-padding in the averaging calculation

Shape:
      * Input: (N, C, L_{in}) or (C, L_{in}).
  * Output: (N, C, L_{out}) or (C, L_{out}), where

       L_{out} = \left\lfloor \frac{L_{in} + 2 \times
       \text{padding} - \text{kernel\_size}}{\text{stride}} +
       1\right\rfloor

Examples:
  >>> # pool with window of size=3, stride=2
  >>> m = nn.AvgPool1d(3, stride=2)
  >>> m(torch.tensor([[[1., 2, 3, 4, 5, 6, 7]]]))
  tensor([[[2., 4., 6.]]])
