class_docstr: Loss function for knowledge distilling using KL divergence.
  
  Args:
      reduction (str): Options are `'none'`, `'mean'` and `'sum'`.
      loss_weight (float): Loss weight of current loss.
      T (int): Temperature for distillation.
class_docstr_tok: ["Loss","function","for","knowledge","distilling","using","KL","divergence",".","Args",":","reduction","(","str",")",":","Options","are","`","'none","'","`",",","`","'mean","'","`","and","`","'sum","'","`",".","loss_weight","(","float",")",":","Loss","weight","of","current","loss",".","T","(","int",")",":","Temperature","for","distillation","."]
docstr: Forward function.
  
  Args:
      pred (Tensor): Predicted logits with shape (N, n + 1).
      soft_label (Tensor): Target logits with shape (N, N + 1).
      weight (Tensor, optional): The weight of loss for each
          prediction. Defaults to None.
      avg_factor (int, optional): Average factor that is used to average
          the loss. Defaults to None.
      reduction_override (str, optional): The reduction method used to
          override the original reduction method of the loss.
          Defaults to None.
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code: def forward(
      self,
      pred,
      soft_label,
      weight=None,
      avg_factor=None,
      reduction_override=None,
  ):
      """Forward function.
  
      Args:
          pred (Tensor): Predicted logits with shape (N, n + 1).
          soft_label (Tensor): Target logits with shape (N, N + 1).
          weight (Tensor, optional): The weight of loss for each
              prediction. Defaults to None.
          avg_factor (int, optional): Average factor that is used to average
              the loss. Defaults to None.
          reduction_override (str, optional): The reduction method used to
              override the original reduction method of the loss.
              Defaults to None.
      """
      assert reduction_override in (None, "none", "mean", "sum")
  
      reduction = (
          reduction_override if reduction_override else self.reduction
      )
  
      loss_kd_out = self.knowledge_distillation_kl_div_loss(
          pred, soft_label, T=self.T
      )
  
      if weight is not None:
          loss_kd_out = weight * loss_kd_out
  
      if avg_factor is None:
          if reduction == "none":
              loss = loss_kd_out
          elif reduction == "mean":
              loss = loss_kd_out.mean()
          elif reduction == "sum":
              loss = loss_kd_out.sum()
      else:
          # if reduction is mean, then average the loss by avg_factor
          if reduction == "mean":
              loss = loss_kd_out.sum() / avg_factor
          # if reduction is 'none', then do nothing, otherwise raise an error
          elif reduction != "none":
              raise ValueError(
                  'avg_factor can not be used with reduction="sum"'
              )
  
      loss_kd = self.loss_weight * loss
      return loss_kd
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr
  from ppdet.core.workspace import register
  from ppdet.modeling import ops
  from ppdet.modeling.losses.iou_loss import GIoULoss
  from ppdet.utils.logger import setup_logger
label_desc: Use image node_id 3 for calling the KnowledgeDistillationKLDivLoss obj's underlying member method code with example usage: obj.forward(pred, soft_label, weight, avg_factor, reduction_override) and returns: loss_kd