code: def forward(self, logits, labels):
      scores = F.softmax(logits, dim=1)
      factor = paddle.pow(1.0 - scores, self.gamma)
      log_score = F.log_softmax(logits, dim=1)
      log_score = factor * log_score
      loss = self.nll(log_score, labels)
      return loss
  
file_path: PaddleDetection/ppdet/modeling/losses/clrnet_loss.py
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register
  from ppdet.modeling.clrnet_utils import accuracy
  from ppdet.modeling.assigners.clrnet_assigner import assign
  from ppdet.modeling.losses.clrnet_line_iou_loss import liou_loss
label_desc: Use image node_id 2 for calling the SoftmaxFocalLoss obj's underlying member method code with example usage: obj.forward(logits, labels) and returns: loss