code: def to_transition_spec(trajectory_spec: Trajectory) -> Transition:
      """Create a transition spec from a trajectory spec.
  
      Note: since trajectories do not include the policy step's state (except
      in special cases where the policy chooses to store this in the info field),
      the returned `transition.action_spec.state` field will be an empty tuple.
  
      Args:
        trajectory_spec: An instance of `Trajectory` representing trajectory specs.
  
      Returns:
        A tuple `(time_steps, policy_steps, next_time_steps)` specs.
      """
      policy_step_spec = policy_step.PolicyStep(
          action=trajectory_spec.action,
          state=(),
          info=trajectory_spec.policy_info,
      )
      time_step_spec = ts.TimeStep(
          trajectory_spec.step_type,
          reward=trajectory_spec.reward,
          discount=trajectory_spec.discount,
          observation=trajectory_spec.observation,
      )
      return Transition(
          time_step_spec, policy_step_spec, time_step_spec
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import pprint
  from typing import NamedTuple, Optional
  import numpy
  import tensorflow
  from tf_agents.trajectories import policy_step
  from tf_agents.trajectories import time_step
  from tf_agents.typing import types
  from tf_agents.utils import composite
  from tf_agents.utils import nest_utils
  from tf_agents.utils import value_ops
label_desc: Use image node_id 12 for calling a global function with example usage: to_transition_spec(trajectory_spec) and returns: Transition