class_docstr: Smooth L1 Loss.
  Args:
      beta (float): controls smooth region, it becomes L1 Loss when beta=0.0
      loss_weight (float): the final loss will be multiplied by this 
class_docstr_tok: ["Smooth","L1","Loss",".","Args",":","beta","(","float",")",":","controls","smooth","region",",","it","becomes","L1","Loss","when","beta=0.0","loss_weight","(","float",")",":","the","final","loss","will","be","multiplied","by","this"]
code: def __init__(self, beta=1.0, loss_weight=1.0):
      super(SmoothL1Loss, self).__init__()
      assert beta >= 0
      self.beta = beta
      self.loss_weight = loss_weight
  
file_path: PaddleDetection/ppdet/modeling/losses/smooth_l1_loss.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register
label_desc: Use image node_id 1 to create a new SmoothL1Loss object from inherited base classes: nn with example: obj = SmoothL1Loss(beta, loss_weight)