code: def batch_norm_relu(inputs,
                      is_training,
                      relu=True,
                      init_zero=False,
                      data_format="channels_first"):
    
    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
docs: 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`.