code: def absolute_difference_error(output, target, is_mean=False, name="absolute_difference_error_loss"):
      
      # with tf.name_scope("absolute_difference_error_loss"):
      if output.get_shape().ndims == 2:  # [batch_size, n_feature]
          if is_mean:
              loss = tf.reduce_mean(tf.reduce_mean(tf.abs(output - target), 1), name=name)
          else:
              loss = tf.reduce_mean(tf.reduce_sum(tf.abs(output - target), 1), name=name)
      elif output.get_shape().ndims == 3:  # [batch_size, w, h]
          if is_mean:
              loss = tf.reduce_mean(tf.reduce_mean(tf.abs(output - target), [1, 2]), name=name)
          else:
              loss = tf.reduce_mean(tf.reduce_sum(tf.abs(output - target), [1, 2]), name=name)
      elif output.get_shape().ndims == 4:  # [batch_size, w, h, c]
          if is_mean:
              loss = tf.reduce_mean(tf.reduce_mean(tf.abs(output - target), [1, 2, 3]), name=name)
          else:
              loss = tf.reduce_mean(tf.reduce_sum(tf.abs(output - target), [1, 2, 3]), name=name)
      else:
          raise Exception("Unknow dimension")
      return loss
docs: Return the TensorFlow expression of absolute difference error (L1) of two batch of data.
  
      Parameters
      ----------
      output : Tensor
          2D, 3D or 4D tensor i.e. [batch_size, n_feature], [batch_size, height, width] or [batch_size, height, width, channel].
      target : Tensor
          The target distribution, format the same with `output`.
      is_mean : boolean
          Whether compute the mean or sum for each example.
              - If True, use ``tf.reduce_mean`` to compute the loss between one target and predict data.
              - If False, use ``tf.reduce_sum`` (default).
      name : str
          An optional name to attach to this function.