code: def batch_norm_relu(
      inputs,
      is_training,
      relu=True,
      init_zero=False,
      data_format="channels_first",
  ):
      """Performs a batch normalization followed by a ReLU.
  
      Args:
        inputs: `Tensor` of shape `[batch, channels, ...]`.
        is_training: `bool` for whether the model is training.
        relu: `bool` if False, omits the ReLU operation.
        init_zero: `bool` if True, initializes scale parameter of batch
            normalization with 0 instead of 1 (default).
        data_format: `str` either "channels_first" for `[batch, channels, height,
            width]` or "channels_last for `[batch, height, width, channels]`.
  
      Returns:
        A normalized `Tensor` with the same `data_format`.
      """
      if init_zero:
          gamma_initializer = tf.zeros_initializer()
      else:
          gamma_initializer = tf.ones_initializer()
  
      if data_format == "channels_first":
          axis = 1
      else:
          axis = 3
  
      inputs = layers().BatchNormalization(
          axis=axis,
          momentum=BATCH_NORM_DECAY,
          epsilon=BATCH_NORM_EPSILON,
          center=True,
          scale=True,
          fused=True,
          gamma_initializer=gamma_initializer,
      )(inputs, training=is_training)
  
      if relu:
          inputs = tf.nn.relu(inputs)
      return inputs
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_layers
  from tensor2tensor.utils import hparam
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 2 for calling a global function with example usage: batch_norm_relu(inputs, is_training, relu, init_zero, data_format) and returns: inputs