code: def sigmoid_focal_loss(
      logit, label, normalizer=1.0, alpha=0.25, gamma=2.0
  ):
      prob = F.sigmoid(logit)
      ce_loss = F.binary_cross_entropy_with_logits(
          logit, label, reduction="none"
      )
      p_t = prob * label + (1 - prob) * (1 - label)
      loss = ce_loss * ((1 - p_t) ** gamma)
  
      if alpha >= 0:
          alpha_t = alpha * label + (1 - alpha) * (1 - label)
          loss = alpha_t * loss
      return loss.mean(1).sum() / normalizer
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import copy
  import math
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from ..bbox_utils import bbox_overlaps
label_desc: Use image node_id 4 for calling a global function with example usage: sigmoid_focal_loss(logit, label, normalizer, alpha, gamma) and returns: unknown