extracts the values in the local blocks by copying from the large
     tensor. So, if the blocks overlap, they are not inverses of each
     other.In general, folding and unfolding operations are related as
     follows. Consider "Fold" and "Unfold" instances created with the
     same parameters:
 >>> fold_params = dict(kernel_size=..., dilation=..., padding=..., stride=...)
 >>> fold = nn.Fold(output_size=..., **fold_params)
 >>> unfold = nn.Unfold(**fold_params)

 Then for any (supported) "input" tensor the following equality
 holds:

    fold(unfold(input)) == divisor * input

 where "divisor" is a tensor that depends only on the shape and
 dtype of the "input":

 >>> input_ones = torch.ones(input.shape, dtype=input.dtype)
 >>> divisor = fold(unfold(input_ones))

 When the "divisor" tensor contains no zero elements, then "fold"
 and "unfold" operations are inverses of each other (up to
 constant divisor).

Warning: