class_docstr: Implement ChannelNorm.
  
  Based on this paper and keras' InstanceNorm layer:
    Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton.
    "Layer normalization."
    arXiv preprint arXiv:1607.06450 (2016).
class_docstr_tok: ["Implement","ChannelNorm",".","Based","on","this","paper","and","keras","'","InstanceNorm","layer",":","Ba",",","Jimmy","Lei",",","Jamie","Ryan","Kiros",",","and","Geoffrey","E.","Hinton",".","``","Layer","normalization",".","''","arXiv","preprint","arXiv:1607.06450","(","2016",")","."]
code: def call(self, inputs, modulation=None):
      mean, variance = self._get_moments(inputs)
      # inputs = tf.Print(inputs, [mean, variance, self.beta, self.gamma], "NORM")
      return tf.nn.batch_normalization(
          inputs,
          mean,
          variance,
          self.beta,
          self.gamma,
          self.epsilon,
          name="normalize",
      )
  
imports: import collections
  from compare_gan.architectures import abstract_arch
  from compare_gan.architectures import arch_ops
  import numpy
  import tensorflow.compat.v1
  import tensorflow_compression
  from hific.helpers import ModelMode
label_desc: Use image node_id 3 for calling the ChannelNorm obj's underlying member method code with example usage: obj.call(inputs, modulation) and returns: tf