code: def test_swin_transformer():
      """Test Swin Transformer backbone."""
  
      with pytest.raises(TypeError):
          # Pretrained arg must be str or None.
          SwinTransformer(pretrained=123)
  
      with pytest.raises(AssertionError):
          # Because swin uses non-overlapping patch embed, so the stride of patch
          # embed must be equal to patch size.
          SwinTransformer(strides=(2, 2, 2, 2), patch_size=4)
  
      # test pretrained image size
      with pytest.raises(AssertionError):
          SwinTransformer(pretrain_img_size=(224, 224, 224))
  
      # Test absolute position embedding
      temp = torch.randn((1, 3, 224, 224))
      model = SwinTransformer(
          pretrain_img_size=224, use_abs_pos_embed=True
      )
      model.init_weights()
      model(temp)
  
      # Test patch norm
      model = SwinTransformer(patch_norm=False)
      model(temp)
  
      # Test normal inference
      temp = torch.randn((1, 3, 32, 32))
      model = SwinTransformer()
      outs = model(temp)
      assert outs[0].shape == (1, 96, 8, 8)
      assert outs[1].shape == (1, 192, 4, 4)
      assert outs[2].shape == (1, 384, 2, 2)
      assert outs[3].shape == (1, 768, 1, 1)
  
      # Test abnormal inference size
      temp = torch.randn((1, 3, 31, 31))
      model = SwinTransformer()
      outs = model(temp)
      assert outs[0].shape == (1, 96, 8, 8)
      assert outs[1].shape == (1, 192, 4, 4)
      assert outs[2].shape == (1, 384, 2, 2)
      assert outs[3].shape == (1, 768, 1, 1)
  
      # Test abnormal inference size
      temp = torch.randn((1, 3, 112, 137))
      model = SwinTransformer()
      outs = model(temp)
      assert outs[0].shape == (1, 96, 28, 35)
      assert outs[1].shape == (1, 192, 14, 18)
      assert outs[2].shape == (1, 384, 7, 9)
      assert outs[3].shape == (1, 768, 4, 5)
  
      model = SwinTransformer(frozen_stages=4)
      model.train()
      for p in model.parameters():
          assert not p.requires_grad
  
imports: import pytest
  import torch
  from mmdet.models.backbones.swin import SwinBlock, SwinTransformer
label_desc: Use image node_id 2 for calling a global function with example usage: test_swin_transformer() without return types