code: def test_trident_resnet_backbone():
      tridentresnet_config = dict(
          num_branch=3,
          test_branch_idx=1,
          strides=(1, 2, 2),
          dilations=(1, 1, 1),
          trident_dilations=(1, 2, 3),
          out_indices=(2,),
      )
      """Test tridentresnet backbone."""
      with pytest.raises(AssertionError):
          # TridentResNet depth should be in [50, 101, 152]
          TridentResNet(18, **tridentresnet_config)
  
      with pytest.raises(AssertionError):
          # In TridentResNet: num_stages == 3
          TridentResNet(50, num_stages=4, **tridentresnet_config)
  
      model = TridentResNet(50, num_stages=3, **tridentresnet_config)
      model.train()
  
      imgs = torch.randn(1, 3, 32, 32)
      feat = model(imgs)
      assert len(feat) == 1
      assert feat[0].shape == torch.Size([3, 1024, 2, 2])
  
imports: import pytest
  import torch
  from mmdet.models.backbones import TridentResNet
  from mmdet.models.backbones.trident_resnet import TridentBottleneck
label_desc: Use image node_id 2 for calling a global function with example usage: test_trident_resnet_backbone() without return types