code: def _test_transform_vs_scripted_on_batch(
      transform, s_transform, batch_tensors, msg=None
  ):
      torch.manual_seed(12)
      transformed_batch = transform(batch_tensors)
  
      for i in range(len(batch_tensors)):
          img_tensor = batch_tensors[i, ...]
          torch.manual_seed(12)
          transformed_img = transform(img_tensor)
          assert_equal(
              transformed_img, transformed_batch[i, ...], msg=msg
          )
  
      torch.manual_seed(12)
      s_transformed_batch = s_transform(batch_tensors)
      assert_equal(transformed_batch, s_transformed_batch, msg=msg)
  
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 2 for calling a global function with example usage: _test_transform_vs_scripted_on_batch(transform, s_transform, batch_tensors, msg) without return types