docstr: Forward function.
  
  Args:
      pred (Tensor): The prediction.
      target (Tensor): The learning target of the prediction.
      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.
  Returns:
      Tensor: The calculated loss
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code: def forward(self, pred, target, weight=None, avg_factor=None):
      """Forward function.
  
      Args:
          pred (Tensor): The prediction.
          target (Tensor): The learning target of the prediction.
          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.
      Returns:
          Tensor: The calculated loss
      """
      loss = self.loss_weight * varifocal_loss(
          pred,
          target,
          alpha=self.alpha,
          gamma=self.gamma,
          iou_weighted=self.iou_weighted,
          use_sigmoid=self.use_sigmoid,
      )
  
      if weight is not None:
          loss = loss * weight
      if avg_factor is None:
          if self.reduction == "none":
              return loss
          elif self.reduction == "mean":
              return loss.mean()
          elif self.reduction == "sum":
              return loss.sum()
      else:
          # if reduction is mean, then average the loss by avg_factor
          if self.reduction == "mean":
              loss = loss.sum() / avg_factor
          # if reduction is 'none', then do nothing, otherwise raise an error
          elif self.reduction != "none":
              raise ValueError(
                  'avg_factor can not be used with reduction="sum"'
              )
      return loss
  
file_path: PaddleDetection/ppdet/modeling/losses/varifocal_loss.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register, serializable
  from ppdet.modeling import ops
label_desc: Use image node_id 2 for calling the VarifocalLoss obj's underlying member method code with example usage: obj.forward(pred, target, weight, avg_factor) and returns: loss, loss, loss, loss