code: def batch_normalization(
      x, mean, variance, axis, offset=None, scale=None, epsilon=1e-3
  ):
      if axis != -1:
          shape = [1] * len(x.shape)
          shape[axis] = mean.shape[0]
          mean = tf.reshape(mean, shape)
          variance = tf.reshape(variance, shape)
          if offset is not None:
              offset = tf.reshape(offset, shape)
          if scale is not None:
              scale = tf.reshape(scale, shape)
  
      return tf.nn.batch_normalization(
          x=x,
          mean=mean,
          variance=variance,
          offset=offset,
          scale=scale,
          variance_epsilon=epsilon,
      )
  
imports: import warnings
  import tensorflow
  from keras.backend import standardize_data_format
  from keras.backend import standardize_dtype
  from keras.backend.common.backend_utils import compute_conv_transpose_output_shape
  from keras.backend.config import epsilon
  from keras.backend.tensorflow.core import cast
  from keras.backend.tensorflow.core import convert_to_tensor
label_desc: Use image node_id 35 for calling a global function with example usage: batch_normalization(x, mean, variance, axis, offset, scale, epsilon) and returns: tf