LocalResponseNorm
class torch.nn.LocalResponseNorm(size, alpha=0.0001, beta=0.75, k=1.0)
Applies local response normalization over an input signal composed
   of several input planes, where channels occupy the second
   dimension. Applies normalization across channels.
  b_{c} = a_{c}\left(k + \frac{\alpha}{n} \sum_{c'=\max(0,
  c-n/2)}^{\min(N-1,c+n/2)}a_{c'}^2\right)^{-\beta}

Parameters:
      * size (int) -- amount of neighbouring channels used for
        normalization
  * **alpha** (*float*) -- multiplicative factor. Default: 0.0001

  * **beta** (*float*) -- exponent. Default: 0.75

  * **k** (*float*) -- additive factor. Default: 1

Shape:
      * Input: (N, C, *)
  * Output: (N, C, *) (same shape as input)

Examples:
  >>> lrn = nn.LocalResponseNorm(2)
  >>> signal_2d = torch.randn(32, 5, 24, 24)
  >>> signal_4d = torch.randn(16, 5, 7, 7, 7, 7)
  >>> output_2d = lrn(signal_2d)
  >>> output_4d = lrn(signal_4d)
