code: def build_batch_norm(
      beta_initializer: Text = "zeros",
      gamma_initializer: Text = "ones",
      data_format: Text = "channels_last",
      momentum: float = 0.99,
      epsilon: float = 1e-3,
      name: Text = "tpu_batch_normalization",
  ):
      """Build a batch normalization layer.
  
      Args:
        beta_initializer: `str`, beta initializer.
        gamma_initializer: `str`, gamma initializer.
        data_format: `str` either "channels_first" for `[batch, channels, height,
          width]` or "channels_last for `[batch, height, width, channels]`.
        momentum: `float`, momentume of batch norm.
        epsilon: `float`, small value for numerical stability.
        name: the name of the batch normalization layer
  
      Returns:
        A normalized `Tensor` with the same `data_format`.
      """
      axis = 1 if data_format == "channels_first" else -1
  
      bn_layer = BatchNormalization(
          axis=axis,
          momentum=momentum,
          epsilon=epsilon,
          center=True,
          scale=True,
          beta_initializer=beta_initializer,
          gamma_initializer=gamma_initializer,
          name=name,
      )
  
      return bn_layer
  
imports: from typing import Text, Tuple, Union
  import tensorflow
  import argparse
  import tensorflow.compat.v1
  from enum import Enum
  from collections import namedtuple
  from tensorflow.python.eager import tape
  from tensorflow.python.tpu import tpu_function
label_desc: Use image node_id 7 for calling a global function with example usage: build_batch_norm(beta_initializer, gamma_initializer, data_format, momentum, epsilon, name) and returns: bn_layer