code: def forward(
      self,
      pred,
      target,
      weight=None,
      avg_factor=None,
      reduction_override=None,
      **kwargs,
  ):
      if weight is not None and not torch.any(weight > 0):
          return (pred * weight).sum()  # 0
      assert reduction_override in (None, "none", "mean", "sum")
      reduction = (
          reduction_override if reduction_override else self.reduction
      )
      if weight is not None and weight.dim() > 1:
          # TODO: remove this in the future
          # reduce the weight of shape (n, 4) to (n,) to match the
          # giou_loss of shape (n,)
          assert weight.shape == pred.shape
          weight = weight.mean(-1)
      loss = self.loss_weight * diou_loss(
          pred,
          target,
          weight,
          eps=self.eps,
          reduction=reduction,
          avg_factor=avg_factor,
          **kwargs,
      )
      return loss
  
imports: import math
  import torch
  import torch.nn
  from mmdet.core import bbox_overlaps
  from ..builder import LOSSES
  from .utils import weighted_loss
label_desc: Use image node_id 2 for calling the DIoULoss obj's underlying member method code with example usage: obj.forward(pred, target, weight, avg_factor, reduction_override) and returns: loss, unknown