code: def test_iou_type_loss_zeros_weight(loss_class):
      pred = torch.rand((10, 4))
      target = torch.rand((10, 4))
      weight = torch.zeros(10)
  
      loss = loss_class()(pred, target, weight)
      assert loss == 0.0
  
file_path: mmdetection/tests/test_models/test_losses/test_loss.py
imports: import json
  import pytest
  import torch
  import torch.nn.functional
  from mmengine.utils import digit_version
  from mmdet.models.losses import BalancedL1Loss, CrossEntropyLoss, DDQAuxLoss, DiceLoss, DistributionFocalLoss, EQLV2Loss, FocalLoss, GaussianFocalLoss, KnowledgeDistillationKLDivLoss, L1Loss, MarginL2Loss, MSELoss, QualityFocalLoss, SeesawLoss, SmoothL1Loss, VarifocalLoss
  from mmdet.models.losses.ghm_loss import GHMC, GHMR
  from mmdet.models.losses.iou_loss import BoundedIoULoss, CIoULoss, DIoULoss, EIoULoss, GIoULoss, IoULoss, SIoULoss
label_desc: Use image node_id 1 for calling a global function with example usage: test_iou_type_loss_zeros_weight(loss_class) without return types