code: def _mean_image_subtraction(image, means, num_channels):
      if image.get_shape().ndims != 3:
          raise ValueError("Input must be of size [height, width, C>0]")
  
      if len(means) != num_channels:
          raise ValueError(
              "len(means) must match the number of channels"
          )
  
      # We have a 1-D tensor of means; convert to 3-D.
      # means = tf.expand_dims(tf.expand_dims(means, 0), 0)
  
      means = tf.broadcast_to(means, tf.shape(image))
  
      return image - means
  
file_path: analytics-zoo/pyzoo/zoo/examples/orca/learn/tf/inception/inception_preprocessing.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow.compat.v1
  from tensorflow.python.ops import control_flow_ops
label_desc: Use image node_id 2 for calling a global function with example usage: _mean_image_subtraction(image, means, num_channels) and returns: unknown