code: def tensorflow_hello_world(
      dataset_url="file:///tmp/external_dataset",
  ):
      # Example: tf_tensors will return tensors with dataset data
      with make_batch_reader(dataset_url) as reader:
          tensor = tf_tensors(reader)
          with tf.Session() as sess:
              # Because we are using make_batch_reader(), each read returns a batch of rows instead of a single row
              batched_sample = sess.run(tensor)
              print("id batch: {0}".format(batched_sample.id))
  
      # Example: use tf.data.Dataset API
      with make_batch_reader(dataset_url) as reader:
          dataset = make_petastorm_dataset(reader)
          iterator = dataset.make_one_shot_iterator()
          tensor = iterator.get_next()
          with tf.Session() as sess:
              batched_sample = sess.run(tensor)
              print("id batch: {0}".format(batched_sample.id))
  
imports: from __future__ import print_function
  import tensorflow.compat.v1
  from petastorm import make_batch_reader
  from petastorm.tf_utils import tf_tensors, make_petastorm_dataset
label_desc: Use image node_id 1 for calling a global function with example usage: tensorflow_hello_world(dataset_url) without return types