code: def prepare_batch_norm_deeplift_model(self, axis):
      self.input_layer = layers.Input(batch_shape=(None, 2, 2, 2))
      self.batch_norm_layer = layers.BatchNormalization(
          gamma=self.gamma,
          beta=self.beta,
          axis=axis,
          mean=self.mean,
          var=self.var,
          epsilon=self.epsilon,
      )
      self.batch_norm_layer.set_inputs(self.input_layer)
      self.flatten_layer = layers.Flatten()
      self.flatten_layer.set_inputs(self.batch_norm_layer)
      self.dense_layer = layers.Dense(
          kernel=np.ones((1, 8)).astype("float32").T,
          bias=np.zeros(1).astype("float32"),
          dense_mxts_mode=DenseMxtsMode.Linear,
      )
      self.dense_layer.set_inputs(self.flatten_layer)
      self.dense_layer.build_fwd_pass_vars()
      self.dense_layer.set_scoring_mode(layers.ScoringMode.OneAndZeros)
      self.dense_layer.set_active()
      self.dense_layer.update_task_index(0)
      self.input_layer.update_mxts()
  
imports: from __future__ import division
  from __future__ import print_function
  from __future__ import absolute_import
  import unittest
  from unittest import skip
  import sys
  import os
  import numpy
  from deeplift.conversion import kerasapi_conversion
  import deeplift.layers
  from deeplift.layers import NonlinearMxtsMode, DenseMxtsMode
  from deeplift.util import compile_func
  import tensorflow
  import keras
  from keras import models
  from keras import backend
label_desc: Use image node_id 2 for calling the TestBatchNorm obj's underlying member method code with example usage: obj.prepare_batch_norm_deeplift_model(axis) without return types