code: def compare(
      dataset_results, standalone_results, iterations=-1, num_devices=1
  ):
      if iterations == -1:
          iterations = len(standalone_results)
  
      # list [iterations] of tuple [devices] of tuple [outputs] of tensors representing batch
      assert (
          len(dataset_results) == iterations
      ), f"Got {len(dataset_results)} dataset results for {iterations} iterations"
      for it in range(iterations):
          for device_id in range(num_devices):
              for tf_data, dali_data in zip(
                  dataset_results[it][device_id],
                  standalone_results[it][device_id],
              ):
                  np.testing.assert_array_equal(
                      tf_data,
                      dali_data,
                      f"Iteration {it}, x = tf_data, y = DALI baseline",
                  )
  
imports: import math
  import numpy
  import nvidia.dali.fn
  import nvidia.dali.plugin.tf
  import nvidia.dali.types
  import os
  import tensorflow
  from contextlib import contextmanager
  from nose import SkipTest
  from nvidia.dali.pipeline import Pipeline
  from tensorflow.python.client import device_lib
  from test_utils import to_array, get_dali_extra_path
label_desc: Use image node_id 15 for calling a global function with example usage: compare(dataset_results, standalone_results, iterations, num_devices) without return types