parameters: (self, image, means)
return_statement: return tf.concat(axis=2, values=channels)
docstring: 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`.
docstring_summary: Subtracts the given means from each image channel.
docstring_tokens: Subtracts the given means from each image channel .
function: def _mean_image_subtraction(self, 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)
function_tokens: def _mean_image_subtraction ( self , image , 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 )