pecify
    how the sliding blocks are retrieved.

    * :attr:`stride` controls the stride for the sliding blocks.

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

    * :attr:`dilation` controls the spacing between the kernel points; also known as the à trous algorithm.
      It is harder to describe, but this `link`_ has a nice visualization of what :attr:`dilation` does.

    Args:
        kernel_size (int or tuple): the size of the sliding blocks
        dilation (int or tuple, optional): a parameter that controls the
                                           stride of elements within the
                                           neighborhood. Default: 1
        padding (int or tuple, optional): implicit zero padding to be added on
                                          both sides of input. Default: 0
        stride (int or tuple, optional): the stride of the sliding blocks in the input
                                         spatial dimensions. Default: 1

    * If :attr:`kernel_size`, :attr:`dilation`, :attr:`padding` or
      :attr:`stride` is an int or a tuple of length 1, their values will be
      replicated across all spatial dimensions.

    * For the case of two input spatial dimensions this operation is sometimes
      called ``im2col``.

    .. note::
        :class:`~torch.nn.Fold` calculates each combined value in the resulting
        large tensor by summing all values from all containing blocks.
        :class:`~torch.nn.Unfold` 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 :class:`~torch.nn.Fold` and
        :class:`~torch.nn.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``:

        >>> # xdoctest: +SKIP
        >>> 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::
        Currently, only 4-D input tensors (batched image-like tensors) are
        supported.

    Shape:
        - Input: :math:`(N, C, *)`
        - Output: :math:`(N, C \times \prod(\text{kernel\_size}), L)` as described above

    Examples::

        >>> unfold = nn.Unfold(kernel_size=(2, 3))
        >>> input = torch.randn(2, 5, 3, 4)
        >>> output = unfold(input)
        >>> # each patch contains 30 values (2x3=6 vectors, each of 5 channels)
        >>> # 4 blocks (2x3 kernels) in total in the 3x4 input
        >>> output.size()
        torch.Size([2, 30, 4])

        >>> # xdoctest: +IGNORE_WANT
        >>> # Convolution is equivalent with Unfold + Matrix Multiplication + Fold (or view to output shape)
        >>> inp = torch.randn(1, 3, 10, 12)
        >>> w = torch.randn(2, 3, 4, 5)
        >>> inp_unf = torch.nn.functional.unfold(inp, (4, 5))
        >>> out_unf = inp_unf.transpose(1, 2).matmul(w.view(w.size(0), -1).t()).transpose(1, 2)
        >>> out = torch.nn.functional.fold(out_unf, (7, 8), (1, 1))
        >>> # or equivalently (and avoiding a copy),
        >>> # out = out_unf.view(1, 2, 7, 8)
        >>> (torch.nn.functional.conv2d(inp, w) - out).abs().max()
        tensor(1.9073e-06)

    .. _link:
        https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md

    )