code: def transition_iterator_from_spec(
      spec: specs.EnvironmentSpec,
  ) -> Callable[[int], Iterator[types.Transition]]:
      """Constructs fake iterator of transitions.
  
      Args:
        spec: Constructed fake transitions match the provided specification..
  
      Returns:
        A callable that given a batch_size returns an iterator of transitions.
      """
  
      observation = _generate_from_spec(spec.observations)
      action = _generate_from_spec(spec.actions)
      reward = _generate_from_spec(spec.rewards)
      discount = _generate_from_spec(spec.discounts)
      data = types.Transition(
          observation, action, reward, discount, observation
      )
  
      dataset = tf.data.Dataset.from_tensors(data).repeat()
  
      return lambda batch_size: dataset.batch(
          batch_size
      ).as_numpy_iterator()
  
imports: import threading
  from typing import List, Mapping, Optional, Sequence, Callable, Iterator
  from acme import core
  from acme import specs
  from acme import types
  from acme import wrappers
  import dm_env
  import numpy
  import reverb
  from rlds import rlds_types
  import tensorflow
  import tree
label_desc: Use image node_id 6 for calling a global function with example usage: transition_iterator_from_spec(spec) and returns: function