e max-pooling operation is applied in :math:`kH \times kW` regions by a stochastic
    step size determined by the target output size.
    The number of output features is equal to the number of input planes.

    Args:
        kernel_size: the size of the window to take a max over.
                     Can be a single number k (for a square kernel of k x k) or a tuple `(kh, kw)`
        output_size: the target output size of the image of the form `oH x oW`.
                     Can be a tuple `(oH, oW)` or a single number oH for a square image `oH x oH`
        output_ratio: If one wants to have an output size as a ratio of the input size, this option can be given.
                      This has to be a number or tuple in the range (0, 1)
        return_indices: if ``True``, will return the indices along with the outputs.
                        Useful to pass to :meth:`nn.MaxUnpool2d`. Default: ``False``

    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}, W_{out})=\text{output\_size}` or
          :math:`(H_{out}, W_{out})=\text{output\_ratio} \times (H_{in}, W_{in})`.

    Examples:
        >>> # pool of square window of size=3, and target output size 13x12
        >>> m = nn.FractionalMaxPool2d(3, output_size=(13, 12))
        >>> # pool of square window and target output size being half of input image size
        >>> m = nn.FractionalMaxPool2d(3, output_ratio=(0.5, 0.5))
        >>> input = torch.randn(20, 16, 50, 32)
        >>> output = m(input)

    .. _Fractional MaxPooling:
        https://arxiv.org/abs/1412.6071
    ©