code: def run_tf_dataset_eager_mode(
      device,
      device_id=0,
      get_pipeline_desc=get_image_pipeline,
      to_dataset=to_image_dataset,
      to_stop_iter=False,
  ):
      batch_size = 12
      num_threads = 4
      iterations = 10
  
      standalone_pipeline, dali_dataset = get_pipe_dataset(
          batch_size,
          num_threads,
          device,
          device_id,
          get_pipeline_desc=get_pipeline_desc,
          to_dataset=to_dataset,
      )
  
      dataset_results = run_dataset_eager_mode(
          dali_dataset, iterations, to_stop_iter=to_stop_iter
      )
      standalone_results = run_pipeline(
          standalone_pipeline,
          iterations,
          device,
          to_stop_iter=to_stop_iter,
      )
  
      compare(dataset_results, standalone_results)
  
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 17 for calling a global function with example usage: run_tf_dataset_eager_mode(device, device_id, get_pipeline_desc, to_dataset, to_stop_iter) without return types