code: def _build_activation_vars(self, input_act_vars):
      new_shape = [
          (
              1
              if (
                  i != self.axis and i != (len(self._shape) + self.axis)
              )  # neg self.axis
              else self._shape[i]
          )
          for i in range(len(self._shape))
      ]
      self.reshaped_mean = self.mean.reshape(new_shape)
      self.reshaped_var = self.var.reshape(new_shape)
      self.reshaped_gamma = self.gamma.reshape(new_shape)
      self.reshaped_beta = self.beta.reshape(new_shape)
      return tf.nn.batch_normalization(
          input_act_vars,
          scale=self.reshaped_gamma,
          offset=self.reshaped_beta,
          mean=self.reshaped_mean,
          variance=self.reshaped_var,
          variance_epsilon=self.epsilon,
      )
  
imports: from __future__ import division
  from __future__ import print_function
  from __future__ import absolute_import
  import sys
  import os
  import numpy
  from collections import namedtuple
  from collections import OrderedDict
  from collections import defaultdict
  import deeplift.util
  from .helper_functions import pseudocount_near_zero, add_val_to_col
  from .None import helper_functions
  from .core import SingleInputMixin, Node
  import tensorflow
label_desc: Use image node_id 3 for calling the BatchNormalization obj's underlying member method code with example usage: obj._build_activation_vars(input_act_vars) and returns: tf