date this, you can provide the needed output size
              as an additional argument :attr:`output_size` in the forward call.
              See the Inputs and Example below.

    Args:
        kernel_size (int or tuple): Size of the max pooling window.
        stride (int or tuple): Stride of the max pooling window.
            It is set to :attr:`kernel_size` by default.
        padding (int or tuple): Padding that was added to the input

    Inputs:
        - `input`: the input Tensor to invert
        - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d`
        - `output_size` (optional): the targeted output size

    Shape:
        - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`.
        - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where

          .. math::
            H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]}

          .. math::
            W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]}

          or as given by :attr:`output_size` in the call operator

    Example::

        >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True)
        >>> unpool = nn.MaxUnpool2d(2, stride=2)
        >>> input = torch.tensor([[[[ 1.,  2.,  3.,  4.],
                                    [ 5.,  6.,  7.,  8.],
                                    [ 9., 10., 11., 12.],
                                    [13., 14., 15., 16.]]]])
        >>> output, indices = pool(input)
        >>> unpool(output, indices)
        tensor([[[[  0.,   0.,   0.,   0.],
                  [  0.,   6.,   0.,   8.],
                  [  0.,   0.,   0.,   0.],
                  [  0.,  14.,   0.,  16.]]]])
        >>> # Now using output_size to resolve an ambiguous size for the inverse
        >>> input = torch.torch.tensor([[[[ 1.,  2.,  3., 4., 5.],
                                          [ 6.,  7.,  8., 9., 10.],
                                          [11., 12., 13., 14., 15.],
                                          [16., 17., 18., 19., 20.]]]])
        >>> output, indices = pool(input)
        >>> # This call will not work without specifying output_size
        >>> unpool(output, indices, output_size=input.size())
        tensor([[[[ 0.,  0.,  0.,  0.,  0.],
                  [ 0.,  7.,  0.,  9.,  0.],
                  [ 0.,  0.,  0.,  0.,  0.],
                  [ 0., 17.,  0., 19.,  0.]]]])


    r)