code: def test_compose(device):
      tensor, _ = _create_data(26, 34, device=device)
      tensor = tensor.to(dtype=torch.float32) / 255.0
      transforms = T.Compose(
          [
              T.CenterCrop(10),
              T.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
          ]
      )
      s_transforms = torch.nn.Sequential(*transforms.transforms)
  
      scripted_fn = torch.jit.script(s_transforms)
      torch.manual_seed(12)
      transformed_tensor = transforms(tensor)
      torch.manual_seed(12)
      transformed_tensor_script = scripted_fn(tensor)
      assert_equal(
          transformed_tensor,
          transformed_tensor_script,
          msg=f"{transforms}",
      )
  
      t = T.Compose(
          [
              lambda x: x,
          ]
      )
      with pytest.raises(
          RuntimeError, match="cannot call a value of type 'Tensor'"
      ):
          torch.jit.script(t)
  
imports: import os
  import sys
  import numpy
  import PIL.Image
  import pytest
  import torch
  from common_utils import _assert_approx_equal_tensor_to_pil, _assert_equal_tensor_to_pil, _create_data, _create_data_batch, assert_equal, cpu_and_cuda, float_dtypes, get_tmp_dir, int_dtypes
  from torchvision import transforms
  from torchvision.transforms import functional, InterpolationMode
  from torchvision.transforms.autoaugment import _apply_op
label_desc: Use image node_id 41 for calling a global function with example usage: test_compose(device) without return types