code: def create_sequence_config(
      step_spec: Step,
      sequence_length: int,
      period: int,
      table: str = reverb_base.DEFAULT_PRIORITY_TABLE,
      end_of_episode_behavior: EndBehavior = EndBehavior.TRUNCATE,
      sequence_pattern: Callable[[int, Step], Trajectory] = _last_n,
  ) -> List[sw.Config]:
      """Generates configs that produces the same behaviour as `SequenceAdder`.
  
      NOTE! ZERO_PAD is not supported as the same behaviour can be achieved by
      writing with TRUNCATE and then adding padding in the dataset pipeline on the
      learner.
  
      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`.
        sequence_length: The number of steps that each trajectory should span.
        period: The period with which we add sequences. If less than
          sequence_length, overlapping sequences are added. If equal to
          sequence_length, sequences are exactly non-overlapping.
        table: Name of the Reverb table to write items to. Defaults to the default
          Acme table.
        end_of_episode_behavior: Determines how sequences at the end of the episode
          are handled (default `EndOfEpisodeBehavior.TRUNCATE`). See the docstring
          of `EndOfEpisodeBehavior` for more information.
        sequence_pattern: Transformation to obtain a sequence given the length and
          the shape of the step.
  
      Returns:
        A list of configs for `StructuredAdder` to produce the described behaviour.
  
      Raises:
        ValueError: If sequence_length is <= 0.
        NotImplementedError: If `end_of_episod_behavior` is `ZERO_PAD`.
      """
      if sequence_length <= 0:
          raise ValueError(
              f"sequence_length must be > 0 but got {sequence_length}."
          )
  
      if end_of_episode_behavior == EndBehavior.ZERO_PAD:
          raise NotImplementedError(
              "Zero-padding is not supported. Please use TRUNCATE instead."
          )
  
      if end_of_episode_behavior == EndBehavior.CONTINUE:
          raise NotImplementedError(
              "Merging episodes is not supported."
          )
  
      def _sequence_pattern(n: int) -> sw.Pattern:
          return sw.pattern_from_transform(
              step_spec, lambda step: sequence_pattern(n, step)
          )
  
      # The base config is considered for all but the last step in the episode. No
      # trajectories are created for the first `sequence_step-1` steps and then a
      # new trajectory is inserted every `period` steps.
      base_config = sw.create_config(
          pattern=_sequence_pattern(sequence_length),
          table=table,
          conditions=[
              sw.Condition.step_index() >= sequence_length - 1,
              sw.Condition.step_index() % period
              == (sequence_length - 1) % period,
          ],
      )
  
      end_of_episode_configs = []
      if end_of_episode_behavior == EndBehavior.WRITE:
          # Simply write a trajectory in exactly the same way as the base config. The
          # only difference here is that we ALWAYS create a trajectory even if it
          # doesn't align with the `period`. The exceptions to the rule are episodes
          # that are shorter than `sequence_length` steps which are completely
          # ignored.
          config = sw.create_config(
              pattern=_sequence_pattern(sequence_length),
              table=table,
              conditions=[
                  sw.Condition.is_end_episode(),
                  sw.Condition.step_index() >= sequence_length - 1,
              ],
          )
          end_of_episode_configs.append(config)
      elif end_of_episode_behavior == EndBehavior.TRUNCATE:
          # The first trajectory is written at step index `sequence_length - 1` and
          # then written every `period` step. This means that the
          # `step_index % period` will always be equal to the below value everytime a
          # trajectory is written.
          target = (sequence_length - 1) % period
  
          # When the episode ends we still want to capture the steps that has been
          # appended since the last item was created. We do this by creating a config
          # for all `step_index % period`, except `target`, and condition these
          # configs so that they only are triggered when `end_episode` is called.
          for x in range(period):
              # When the last step is aligned with the period of the inserts then no
              # action is required as the item was already generated by `base_config`.
              if x == target:
                  continue
  
              # If we were to pad the trajectory then we'll need to continue adding
              # padding until `step_index % period` is equal to `target` again. We can
              # exploit this relation by conditioning the config to only be applied for
              # a single value of `step_index % period`. This constraint means that we
              # can infer the number of padding steps required until the next write
              # would have occurred if the episode didn't end.
              #
              # Now if we assume that the padding instead is added on the dataset (or
              # the trajectory is simply truncated) then we can infer from the above
              # that the number of real steps in this padded trajectory will be the
              # difference between `sequence_length` and number of pad steps.
              num_pad_steps = (target - x) % period
              unpadded_length = sequence_length - num_pad_steps
  
              config = sw.create_config(
                  pattern=_sequence_pattern(unpadded_length),
                  table=table,
                  conditions=[
                      sw.Condition.is_end_episode(),
                      sw.Condition.step_index() % period == x,
                      sw.Condition.step_index() >= sequence_length,
                  ],
              )
              end_of_episode_configs.append(config)
  
          # The above configs will capture the "remainder" of any episode that is at
          # least `sequence_length` steps long. However, if the entire episode is
          # shorter than `sequence_length` then data might still be lost. We avoid
          # this by simply creating `sequence_length-1` configs that capture the last
          # `x` steps iff the entire episode is `x` steps long.
          for x in range(1, sequence_length):
              config = sw.create_config(
                  pattern=_sequence_pattern(x),
                  table=table,
                  conditions=[
                      sw.Condition.is_end_episode(),
                      sw.Condition.step_index() == x - 1,
                  ],
              )
              end_of_episode_configs.append(config)
      else:
          raise ValueError(
              f"Unexpected `end_of_episod_behavior`: {end_of_episode_behavior}"
          )
  
      return [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 3 for calling a global function with example usage: create_sequence_config(step_spec, sequence_length, period, table, end_of_episode_behavior, sequence_pattern) and returns: unknown, sw