code: def test_random_crop(
      size, padding, pad_if_needed, padding_config, device
  ):
      config = dict(padding_config)
      config["size"] = size
      config["padding"] = padding
      config["pad_if_needed"] = pad_if_needed
      _test_class_op(T.RandomCrop, device, meth_kwargs=config)
  
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 10 for calling a global function with example usage: test_random_crop(size, padding, pad_if_needed, padding_config, device) without return types