code: def test_center_crop(device, tmpdir):
      fn_kwargs = {"output_size": (4, 5)}
      meth_kwargs = {"size": (4, 5)}
      _test_op(
          F.center_crop,
          T.CenterCrop,
          device=device,
          fn_kwargs=fn_kwargs,
          meth_kwargs=meth_kwargs,
      )
      fn_kwargs = {"output_size": (5,)}
      meth_kwargs = {"size": (5,)}
      _test_op(
          F.center_crop,
          T.CenterCrop,
          device=device,
          fn_kwargs=fn_kwargs,
          meth_kwargs=meth_kwargs,
      )
      tensor = torch.randint(
          0, 256, (3, 10, 10), dtype=torch.uint8, device=device
      )
      # Test torchscript of transforms.CenterCrop with size as int
      f = T.CenterCrop(size=5)
      scripted_fn = torch.jit.script(f)
      scripted_fn(tensor)
  
      # Test torchscript of transforms.CenterCrop with size as [int, ]
      f = T.CenterCrop(size=[5])
      scripted_fn = torch.jit.script(f)
      scripted_fn(tensor)
  
      # Test torchscript of transforms.CenterCrop with size as tuple
      f = T.CenterCrop(size=(6, 6))
      scripted_fn = torch.jit.script(f)
      scripted_fn(tensor)
  
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 12 for calling a global function with example usage: test_center_crop(device, tmpdir) without return types