code: def create_n_step_transition_config(
      step_spec: Step,
      n_step: int,
      table: str = reverb_base.DEFAULT_PRIORITY_TABLE,
  ) -> List[sw.Config]:
      """Generates configs that replicates the behaviour of NStepTransitionAdder.
  
      Please see the docstring of NStepTransitionAdder for more details.
  
      NOTE! In contrast to NStepTransitionAdder, the trajectories written by the
      `StructuredWriter` does not include the precomputed cumulative reward and
      discounts. Instead the trajectory includes the raw rewards and discounts
      required to comptute these values.
  
      Args:
        step_spec: The full structure of the data which will be appended to the
          Reverb `StructuredWriter` in each step. Please use `create_step_spec` to
          create `step_spec`.
        n_step: The "N" in N-step transition. See the class docstring for the
          precise definition of what an N-step transition is. `n_step` must be at
          least 1, in which case we use the standard one-step transition, i.e. (s_t,
          a_t, r_t, d_t, s_t+1, e_t).
        table: Name of the Reverb table to write items to. Defaults to the default
          Acme table.
  
      Returns:
        A list of configs for `StructuredAdder` to produce the described behaviour.
      """
  
      def _make_pattern(n: int):
          ref_step = sw.create_reference_step(step_spec)
  
          get_first = lambda x: x[-(n + 1) : -n]
          get_all = lambda x: x[-(n + 1) : -1]
          get_first_and_last = lambda x: x[-(n + 1) :: n]
  
          tmap = tree.map_structure
  
          # We use the exact same structure as we done when writing sequences except
          # we trim the number of steps in each sub tree. This has the benefit that
          # the postprocessing used to transform these items into N-step transition
          # structures (cumulative rewards and discounts etc.) can be applied on
          # full sequence items as well. The only difference being that the latter is
          # more wasteful than the trimmed down version we write here.
          return Trajectory(
              observation=tmap(
                  get_first_and_last, ref_step.observation
              ),
              action=tmap(get_first, ref_step.action),
              reward=tmap(get_all, ref_step.reward),
              discount=tmap(get_all, ref_step.discount),
              start_of_episode=tmap(
                  get_first, ref_step.start_of_episode
              ),
              extras=tmap(get_first, ref_step.extras),
          )
  
      # At the start of the episodes we'll add shorter transitions.
      start_of_episode_configs = []
      for n in range(1, n_step):
          config = sw.create_config(
              pattern=_make_pattern(n),
              table=table,
              conditions=[
                  sw.Condition.step_index() == n,
              ],
          )
          start_of_episode_configs.append(config)
  
      # During all other steps we'll add a full N-step transition.
      base_config = sw.create_config(
          pattern=_make_pattern(n_step), table=table
      )
  
      # When the episode ends we'll add shorter transitions.
      end_of_episode_configs = []
      for n in range(n_step - 1, 0, -1):
          config = sw.create_config(
              pattern=_make_pattern(n),
              table=table,
              conditions=[
                  sw.Condition.is_end_episode(),
                  # If the entire episode is shorter than n_step then the episode
                  # start configs will already create an item that covers all the
                  # steps so we add this filter here to avoid adding it again.
                  sw.Condition.step_index() != n,
              ],
          )
          end_of_episode_configs.append(config)
  
      return (
          start_of_episode_configs
          + [base_config]
          + end_of_episode_configs
      )
  
imports: import itertools
  import time
  from typing import Callable, List, Optional, Sequence, Sized
  from absl import logging
  from acme import specs
  from acme import types
  from acme.adders import base
  from acme.adders.reverb import base
  from acme.adders.reverb import sequence
  from acme.utils import tree_utils
  import dm_env
  import numpy
  import reverb
  from reverb import structured_writer
  import tensorflow
  import tree
label_desc: Use image node_id 4 for calling a global function with example usage: create_n_step_transition_config(step_spec, n_step, table) and returns: unknown, Trajectory