class_docstr: L1 loss.
  
  Args:
      reduction (str, optional): The method to reduce the loss.
          Options are "none", "mean" and "sum".
      loss_weight (float, optional): The weight of loss.
class_docstr_tok: ["L1","loss",".","Args",":","reduction","(","str",",","optional",")",":","The","method","to","reduce","the","loss",".","Options","are","``","none","''",",","``","mean","''","and","``","sum","''",".","loss_weight","(","float",",","optional",")",":","The","weight","of","loss","."]
code: def __init__(self, reduction="mean", loss_weight=1.0):
      super(L1Loss, self).__init__()
      self.reduction = reduction
      self.loss_weight = loss_weight
  
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 1 to create a new L1Loss object from inherited base classes: nn with example: obj = L1Loss(reduction, loss_weight)