channels into
  * **num_channels** (*int*) -- number of channels expected in
    input

  * **eps** (*float*) -- a value added to the denominator for
    numerical stability. Default: 1e-5

  * **affine** (*bool*) -- a boolean value that when set to
    "True", this module has learnable per-channel affine
    parameters initialized to ones (for weights) and zeros (for
    biases). Default: "True".

Shape:
      * Input: (N, C, *) where C=\text{num_channels}
  * Output: (N, C, *) (same shape as input)

Examples:
  >>> input = torch.randn(20, 6, 10, 10)
  >>> # Separate 6 channels into 3 groups
  >>> m = nn.GroupNorm(3, 6)
  >>> # Separate 6 channels into 6 groups (equivalent with InstanceNorm)
  >>> m = nn.GroupNorm(6, 6)
  >>> # Put all 6 channels into a single group (equivalent with LayerNorm)
  >>> m = nn.GroupNorm(1, 6)
  >>> # Activating the module
  >>> output = m(input)
