code: def test_resized_crop(
      self, scale, ratio, size, interpolation, antialias, device
  ):
      if antialias and interpolation in {NEAREST, NEAREST_EXACT}:
          pytest.skip(
              f"Can not resize if interpolation mode is {interpolation} and antialias=True"
          )
  
      tensor = torch.randint(
          0, 256, size=(3, 44, 56), dtype=torch.uint8, device=device
      )
      batch_tensors = torch.randint(
          0, 256, size=(4, 3, 44, 56), dtype=torch.uint8, device=device
      )
      transform = T.RandomResizedCrop(
          size=size,
          scale=scale,
          ratio=ratio,
          interpolation=interpolation,
          antialias=antialias,
      )
      s_transform = torch.jit.script(transform)
      _test_transform_vs_scripted(transform, s_transform, tensor)
      _test_transform_vs_scripted_on_batch(
          transform, s_transform, batch_tensors
      )
  
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 4 for calling the TestResize obj's underlying member method code with example usage: obj.test_resized_crop(scale, ratio, size, interpolation, antialias, device) without return types