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 __init__(self, n_channel, initial=1, eps=1e-5):
      super(Normalize, self).__init__()
      self.eps = eps
      with self.name_scope():
          self.scale = self.params.get(
              "normalize_scale",
              shape=(1, n_channel, 1, 1),
              init=mx.init.Constant(initial),
          )
  
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 1 to create a new Normalize object from inherited base classes: gluon with example: obj = Normalize(n_channel, initial, eps)