code: def test_crop(device):
      fn_kwargs = {"top": 2, "left": 3, "height": 4, "width": 5}
      # Test transforms.RandomCrop with size and padding as tuple
      meth_kwargs = {
          "size": (4, 5),
          "padding": (4, 4),
          "pad_if_needed": True,
      }
      _test_op(
          F.crop,
          T.RandomCrop,
          device=device,
          fn_kwargs=fn_kwargs,
          meth_kwargs=meth_kwargs,
      )
  
      # Test transforms.functional.crop including outside the image area
      fn_kwargs = {"top": -2, "left": 3, "height": 4, "width": 5}  # top
      _test_functional_op(F.crop, fn_kwargs=fn_kwargs, device=device)
  
      fn_kwargs = {
          "top": 1,
          "left": -3,
          "height": 4,
          "width": 5,
      }  # left
      _test_functional_op(F.crop, fn_kwargs=fn_kwargs, device=device)
  
      fn_kwargs = {
          "top": 7,
          "left": 3,
          "height": 4,
          "width": 5,
      }  # bottom
      _test_functional_op(F.crop, fn_kwargs=fn_kwargs, device=device)
  
      fn_kwargs = {
          "top": 3,
          "left": 8,
          "height": 4,
          "width": 5,
      }  # right
      _test_functional_op(F.crop, fn_kwargs=fn_kwargs, device=device)
  
      fn_kwargs = {
          "top": -3,
          "left": -3,
          "height": 15,
          "width": 15,
      }  # all
      _test_functional_op(F.crop, fn_kwargs=fn_kwargs, device=device)
  
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 9 for calling a global function with example usage: test_crop(device) without return types