code: def l1_loss(
      pred, target, weight=None, reduction="mean", avg_factor=None
  ):
      """L1 loss.
  
      Args:
          pred (Tensor): The prediction.
          target (Tensor): The learning target of the prediction.
  
      Returns:
          Tensor: Calculated loss
      """
      if not target.astype("bool").any():
          return pred.sum() * 0
  
      assert pred.shape == target.shape
      loss = paddle.abs(pred - target)
  
      if weight is not None:
          if weight.shape != loss.shape:
              if weight.shape[0] == loss.shape[0]:
                  # For most cases, weight is of shape (num_priors, ),
                  #  which means it does not have the second axis num_class
                  weight = weight.reshape((-1, 1))
              else:
                  # Sometimes, weight per anchor per class is also needed. e.g.
                  #  in FSAF. But it may be flattened of shape
                  #  (num_priors x num_class, ), while loss is still of shape
                  #  (num_priors, num_class).
                  assert weight.numel() == loss.numel()
                  weight = weight.reshape((loss.shape[0], -1))
          assert weight.ndim == loss.ndim
          loss = loss * weight
  
      # if avg_factor is not specified, just reduce the loss
      if avg_factor is None:
          if reduction == "mean":
              loss = loss.mean()
          elif reduction == "sum":
              loss = loss.sum()
      else:
          # if reduction is mean, then average the loss by avg_factor
          if reduction == "mean":
              # Avoid causing ZeroDivisionError when avg_factor is 0.0,
              # i.e., all labels of an image belong to ignore index.
              eps = 1e-10
              loss = loss.sum() / (avg_factor + eps)
          # if reduction is 'none', then do nothing, otherwise raise an error
          elif reduction != "none":
              raise ValueError(
                  'avg_factor can not be used with reduction="sum"'
              )
  
      return loss
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from itertools import cycle, islice
  from collections import abc
  import numpy
  import paddle
  import paddle.nn
  from ppdet.core.workspace import register, serializable
label_desc: Use image node_id 5 for calling a global function with example usage: l1_loss(pred, target, weight, reduction, avg_factor) and returns: loss, unknown