class_docstr: Normalize layer described in https://arxiv.org/abs/1512.02325.
  
  Parameters
  ----------
  n_channel : int
      Number of channels of input.
  initial : float
      Initial value for the rescaling factor.
  eps : float
      Small value to avoid division by zero.
class_docstr_tok: ["Normalize","layer","described","in","https",":","\/\/arxiv.org\/abs\/1512.02325",".","Parameters","--","--","--","--","--","n_channel",":","int","Number","of","channels","of","input",".","initial",":","float","Initial","value","for","the","rescaling","factor",".","eps",":","float","Small","value","to","avoid","division","by","zero","."]
code: def hybrid_forward(self, F, x, scale):
      x = F.L2Normalization(x, mode="channel", eps=self.eps)
      return F.broadcast_mul(x, scale)
  
imports: from __future__ import division
  import os
  import mxnet
  from mxnet import gluon
  from mxnet.gluon import nn
  from mxnet.initializer import Xavier
label_desc: Use image node_id 2 for calling the Normalize obj's underlying member method code with example usage: obj.hybrid_forward(F, x, scale) and returns: F