Subtracts the given means from each image channel.

  For example:
    means = [123.68, 116.779, 103.939]
    image = _mean_image_subtraction(image, means)

  Note that the rank of `image` must be known.

  Args:
    image: a tensor of size [height, width, C].
    means: a C-vector of values to subtract from each channel.

  Returns:
    the centered image.

  Raises:
    ValueError: If the rank of `image` is unknown, if `image` has a rank other
      than three or if the number of channels in `image` doesn't match the
      number of values in `means`.

def _mean_image_subtraction(image, means):
  """Subtracts the given means from each image channel.

  For example:
    means = [123.68, 116.779, 103.939]
    image = _mean_image_subtraction(image, means)

  Note that the rank of `image` must be known.

  Args:
    image: a tensor of size [height, width, C].
    means: a C-vector of values to subtract from each channel.

  Returns:
    the centered image.

  Raises:
    ValueError: If the rank of `image` is unknown, if `image` has a rank other
      than three or if the number of channels in `image` doesn't match the
      number of values in `means`.
  """
  if image.get_shape().ndims != 3:
    raise ValueError("Input must be of size [height, width, C>0]")
  num_channels = image.get_shape().as_list()[-1]
  if len(means) != num_channels:
    raise ValueError("len(means) must match the number of channels")

  channels = tf.split(axis=2, num_or_size_splits=num_channels, value=image)
  for i in range(num_channels):
    channels[i] -= means[i]
  return tf.concat(axis=2, values=channels)