code: def check_pytorch_operator_batch_processing(device):
      pipe = BasicPipeline()
      pt_pipe = TorchPythonFunctionPipeline(
          torch_batch_operation, device, True
      )
      pipe.build()
      pt_pipe.build()
      for it in range(ITERS):
          (preprocessed_output,) = pipe.run()
          tensors = [
              torch.from_numpy(preprocessed_output.at(i))
              for i in range(BATCH_SIZE)
          ]
          exp1, exp2 = torch_batch_operation(tensors)
          output1, output2 = pt_pipe.run()
          if device == "gpu":
              output1 = output1.as_cpu()
              output2 = output2.as_cpu()
          for i in range(len(output1)):
              res1 = output1.at(i)
              res2 = output2.at(i)
              assert numpy.allclose(res1, exp1[i].numpy())
              assert numpy.allclose(res2, exp2[i].numpy())
  
imports: import numpy
  import nvidia.dali.ops
  import nvidia.dali.plugin.pytorch
  import nvidia.dali.types
  import os
  import torch
  from nvidia.dali.pipeline import Pipeline
  from test_utils import get_dali_extra_path
label_desc: Use image node_id 5 for calling a global function with example usage: check_pytorch_operator_batch_processing(device) without return types