code: def test_random_erasing_with_invalid_data():
      img = torch.rand(3, 60, 60)
      # Test Set 0: invalid value
      random_erasing = T.RandomErasing(
          value=(0.1, 0.2, 0.3, 0.4), p=1.0
      )
      with pytest.raises(
          ValueError,
          match="If value is a sequence, it should have either a single value or 3",
      ):
          random_erasing(img)
  
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 38 for calling a global function with example usage: test_random_erasing_with_invalid_data() without return types