(\text{kernel_size}[d] - 1) - 1}{\text{stride}[d]} +
      1\right\rfloor,
where d is over all spatial dimensions.

"output_size" describes the spatial shape of the large containing
     tensor of the sliding local blocks. It is useful to resolve the
     ambiguity when multiple input shapes map to same number of
     sliding blocks, e.g., with "stride > 0".

The "padding", "stride" and "dilation" arguments specify how the
   sliding blocks are retrieved.


"stride" controls the stride for the sliding blocks.


"padding" controls the amount of implicit zero-paddings on both
     sides for "padding" number of points for each dimension before
     reshaping.


"dilation" controls the spacing between the kernel points; also
     known as the Ã  trous algorithm. It is harder to describe, but
     this link has a nice visualization of what "dilation" does.


Parameters:
      * output_size (int or tuple) -- the shape of the