code: def varifocal_loss_with_logits(
      pred_logits,
      gt_score,
      label,
      normalizer=1.0,
      alpha=0.75,
      gamma=2.0,
  ):
      pred_score = F.sigmoid(pred_logits)
      weight = (
          alpha * pred_score.pow(gamma) * (1 - label) + gt_score * label
      )
      loss = F.binary_cross_entropy_with_logits(
          pred_logits, gt_score, weight=weight, reduction="none"
      )
      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 12 for calling a global function with example usage: varifocal_loss_with_logits(pred_logits, gt_score, label, normalizer, alpha, gamma) and returns: unknown