code: def test_regnet_backbone(arch_name, arch, out_channels):
      with pytest.raises(AssertionError):
          # ResNeXt depth should be in [50, 101, 152]
          RegNet(arch_name + "233")
  
      # Test RegNet with arch_name
      model = RegNet(arch_name)
      model.train()
  
      imgs = torch.randn(1, 3, 32, 32)
      feat = model(imgs)
      assert len(feat) == 4
      assert feat[0].shape == torch.Size([1, out_channels[0], 8, 8])
      assert feat[1].shape == torch.Size([1, out_channels[1], 4, 4])
      assert feat[2].shape == torch.Size([1, out_channels[2], 2, 2])
      assert feat[3].shape == torch.Size([1, out_channels[3], 1, 1])
  
      # Test RegNet with arch
      model = RegNet(arch)
      assert feat[0].shape == torch.Size([1, out_channels[0], 8, 8])
      assert feat[1].shape == torch.Size([1, out_channels[1], 4, 4])
      assert feat[2].shape == torch.Size([1, out_channels[2], 2, 2])
      assert feat[3].shape == torch.Size([1, out_channels[3], 1, 1])
  
imports: import pytest
  import torch
  from mmdet.models.backbones import RegNet
label_desc: Use image node_id 1 for calling a global function with example usage: test_regnet_backbone(arch_name, arch, out_channels) without return types