code: def sample_as_dataset(replay_buffer, num_steps=None, batch_size=None):
      ds = replay_buffer.as_dataset(
          num_steps=num_steps, sample_batch_size=batch_size
      )
      # Note, we don't use tf.contrib.data.get_single_element because the dataset
      # contains more than one element.
      if tf.executing_eagerly():
          itr = iter(ds)
          return next(itr)
      else:
          itr = tf.compat.v1.data.make_initializable_iterator(ds)
          with tf.control_dependencies([itr.initializer]):
              return itr.get_next()
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents import specs
  from tf_agents.replay_buffers import episodic_replay_buffer
  from tf_agents.utils import common
  from tf_agents.utils import test_utils
  from tensorflow.python.ops import list_ops
label_desc: Use image node_id 1 for calling a global function with example usage: sample_as_dataset(replay_buffer, num_steps, batch_size) and returns: next, itr