code: def _smooth_l1_loss(x, t, in_weight, sigma):
      sigma2 = sigma**2
      diff = in_weight * (x - t)
      abs_diff = F.absolute(diff)
      flag = (abs_diff.array < (1.0 / sigma2)).astype(np.float32)
  
      y = flag * (sigma2 / 2.0) * F.square(diff) + (1 - flag) * (
          abs_diff - 0.5 / sigma2
      )
  
      return F.sum(y)
  
file_path: chainercv/chainercv/links/model/faster_rcnn/faster_rcnn_train_chain.py
imports: import numpy
  import chainer
  from chainer.backends import cuda
  import chainer.functions
  from chainercv.links.model.faster_rcnn.utils.anchor_target_creator import AnchorTargetCreator
  from chainercv.links.model.faster_rcnn.utils.proposal_target_creator import ProposalTargetCreator
label_desc: Use image node_id 1 for calling a global function with example usage: _smooth_l1_loss(x, t, in_weight, sigma) and returns: F