code: def _mean_image_subtraction(image, means, num_channels):
      if image.get_shape().ndims != 3:
          invalidInputError(
              False, "Input must be of size [height, width, C>0]"
          )
  
      if len(means) != num_channels:
          invalidInputError(
              False, "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
  
imports: import os
  import argparse
  import tensorflow
  from bigdl.dllib.utils.log4Error import invalidInputError
  from bigdl.orca import init_orca_context, stop_orca_context
label_desc: Use image node_id 3 for calling a global function with example usage: _mean_image_subtraction(image, means, num_channels) and returns: unknown