code: def test_center_crop(device):
      script_center_crop = torch.jit.script(F.center_crop)
  
      img_tensor, pil_img = _create_data(32, 34, device=device)
  
      cropped_pil_image = F.center_crop(pil_img, [10, 11])
  
      cropped_tensor = F.center_crop(img_tensor, [10, 11])
      _assert_equal_tensor_to_pil(cropped_tensor, cropped_pil_image)
  
      cropped_tensor = script_center_crop(img_tensor, [10, 11])
      _assert_equal_tensor_to_pil(cropped_tensor, cropped_pil_image)
  
      batch_tensors = _create_data_batch(
          16, 18, num_samples=4, device=device
      )
      _test_fn_on_batch(
          batch_tensors, F.center_crop, output_size=[10, 11]
      )
  
imports: import colorsys
  import itertools
  import math
  import os
  from functools import partial
  from typing import Sequence
  import numpy
  import PIL.Image
  import pytest
  import torch
  import torchvision.transforms
  import torchvision.transforms._functional_pil
  import torchvision.transforms._functional_tensor
  import torchvision.transforms.functional
  from common_utils import _assert_approx_equal_tensor_to_pil, _assert_equal_tensor_to_pil, _create_data, _create_data_batch, _test_fn_on_batch, assert_equal, cpu_and_cuda, needs_cuda
  from torchvision.transforms import InterpolationMode
label_desc: Use image node_id 36 for calling a global function with example usage: test_center_crop(device) without return types