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# Source code for torch.nn.modules.channelshuffle

    from .module import Module
    from .. import functional as F

    from torch import Tensor

    __all__ = ['ChannelShuffle']

    class ChannelShuffle(Module):
        r"""Divide the channels in a tensor of shape :math:`(*, C , H, W)`
        into g groups and rearrange them as :math:`(*, C \frac g, g, H, W)`,
        while keeping the original tensor shape.

        Args:
            groups (int): number of groups to divide channels in.

        Examples::

            >>> # xdoctest: +IGNORE_WANT("FIXME: incorrect want")
            >>> channel_shuffle = nn.ChannelShuffle(2)
            >>> input = torch.randn(1, 4, 2, 2)
            >>> print(input)
            [[[[1, 2],
               [3, 4]],
              [[5, 6],
               [7, 8]],
              [[9, 10],
               [11, 12]],
              [[13, 14],
               [15, 16]],
             ]]
            >>> output = channel_shuffle(input)
            >>> print(output)
            [[[[1, 2],
               [3, 4]],
              [[9, 10],
               [11, 12]],
              [[5, 6],
               [7, 8]],
              [[13, 14],
               [15, 16]],
             ]]
        """
        __constants__ = ['groups']
        groups: int

        def __init__(self, groups: int) -> None:
            super().__init__()
            self.groups = groups

        def forward(self, input: Tensor) -> Tensor:
            return F.channel_shuffle(input, self.groups)

        def extra_repr(self) -> str:
            return f'groups={self.groups}'

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