class_docstr: Feature Response Normalization layer from 'Filter Response Normalization Layer: Eliminating Batch Dependence
  in the Training of Deep Neural Networks', see :cite:`FRN2019` for more details.
  
  .. math::
      y =  \gamma \times \frac{x}{\sqrt{\mathrm{E}[x^2]} + |\epsilon|} + \beta
  
  
  Args:
      num_features: number of channels
      eps: normalization constant
      is_bias: use bias
      is_scale: use scale
      drop_rate: dropout rate,
      is_eps_leanable: if eps is learnable
  
  Returns:
      torch.Tensor: Normalized features
  
  Shape:
      - Input: :math:`(B, \text{num_features}, H, W)`
      - Output: :math:`(B, \text{num_features}, H, W)`
class_docstr_tok: ["Feature","Response","Normalization","layer","from","'Filter","Response","Normalization","Layer",":","Eliminating","Batch","Dependence","in","the","Training","of","Deep","Neural","Networks","'",",","see",":","cite",":","`","FRN2019","`","for","more","details",".","..","math",":",":","y","=","\\gamma","\\times","\\frac","{","x","}","{","\\sqrt","{","\\mathrm","{","E","}","[","x^2","]","}","+","|\\epsilon|","}","+","\\beta","Args",":","num_features",":","number","of","channels","eps",":","normalization","constant","is_bias",":","use","bias","is_scale",":","use","scale","drop_rate",":","dropout","rate",",","is_eps_leanable",":","if","eps","is","learnable","Returns",":","torch.Tensor",":","Normalized","features","Shape",":","-","Input",":",":","math",":","`","(","B",",","\\text","{","num_features","}",",","H",",","W",")","`","-","Output",":",":","math",":","`","(","B",",","\\text","{","num_features","}",",","H",",","W",")","`"]
code: def forward(self, x: Tensor) -> Tensor:
      # Compute the mean norm of activations per channel.
      nu2 = x.pow(2).mean(dim=[2, 3], keepdim=True)
  
      # Perform FRN.
      x = x * torch.rsqrt(nu2 + self.eps.abs())
  
      # Scale and Bias
      if self.is_scale:
          x = self.weight * x
      if self.is_bias:
          x = x + self.bias
      return x
  
imports: from typing import Dict
  import torch
  from torch import nn
  from kornia.core import Module, Parameter, Tensor, tensor, zeros
  from kornia.utils.helpers import map_location_to_cpu
label_desc: Use image node_id 4 for calling the FilterResponseNorm2d obj's underlying member method code with example usage: obj.forward(x) and returns: x