code: def _prefill_with_demonstrations(
      adder: adders.Adder,
      demonstrations: Sequence[types.Transition],
      reward: Optional[float],
      min_num_transitions: int = 0,
  ) -> None:
      """Fill the adder's replay buffer with expert transitions.
  
      Assumes that the demonstrations dataset stores transitions in order.
  
      Args:
        adder: the agent which adds the demonstrations.
        demonstrations: the expert demonstrations to iterate over.
        reward: if non-None, populates the environment reward entry of transitions.
        min_num_transitions: the lower bound on transitions processed, the dataset
          will be iterated over multiple times if needed. Once at least
          min_num_transitions are added, the processing is interrupted at the
          nearest episode end.
      """
      if not demonstrations:
          return
  
      reward = np.float32(reward) if reward is not None else reward
      remaining_transitions = min_num_transitions
      step_type = None
      action = None
      ts = dm_env.TimeStep(None, None, None, None)  # Unused.
      while remaining_transitions > 0:
          # In case we share the adder or demonstrations don't end with
          # end-of-episode, reset the adder prior to add_first.
          adder.reset()
          for transition_num, transition in enumerate(demonstrations):
              remaining_transitions -= 1
              discount = np.float32(1.0)
              ts_reward = (
                  reward if reward is not None else transition.reward
              )
              if (
                  step_type == dm_env.StepType.LAST
                  or transition_num == 0
              ):
                  ts = dm_env.TimeStep(
                      dm_env.StepType.FIRST,
                      ts_reward,
                      discount,
                      transition.observation,
                  )
                  adder.add_first(ts)
  
              observation = transition.next_observation
              action = transition.action
              if (
                  transition.discount == 0.0
                  or transition_num == len(demonstrations) - 1
              ):
                  step_type = dm_env.StepType.LAST
                  discount = np.float32(0.0)
              else:
                  step_type = dm_env.StepType.MID
              ts = dm_env.TimeStep(
                  step_type, ts_reward, discount, observation
              )
              adder.add(action, ts)
              if remaining_transitions <= 0:
                  # Note: we could check `step_type == dm_env.StepType.LAST` to stop at an
                  # episode end if possible.
                  break
  
      # Explicitly finalize the Reverb client writes.
      adder.reset()
  
imports: import threading
  from typing import Callable, Generic, Iterator, List, Optional, Sequence
  from acme import adders
  from acme import core
  from acme import specs
  from acme import types
  from acme.agents.jax import builders
  from acme.agents.jax.pwil import adder
  from acme.agents.jax.pwil import config
  from acme.agents.jax.pwil import rewarder
  from acme.jax import networks
  from acme.jax.imitation_learning_types import DirectPolicyNetwork, DirectRLNetworks
  from acme.jax.types import PRNGKey
  from acme.utils import counting
  from acme.utils import loggers
  import dm_env
  import numpy
  import reverb
label_desc: Use image node_id 1 for calling a global function with example usage: _prefill_with_demonstrations(adder, demonstrations, reward, min_num_transitions) without return types