code: def stack_reverb_observation(
      sample: reverb.ReplaySample, stack_size: int
  ) -> reverb.ReplaySample:
      """Stacks observations in a Reverb sample.
  
      This function is meant to be used on the dataset creation side as a
      post-processing function before batching.
  
      Warnings!
        * Only works if SequenceAdder is in end_of_episode_behavior=CONTINUE mode.
        * Only tested on RGB and scalar (shape = (1,)) observations.
        * At episode starts, this function repeats the first observation to form a
          stack. Could consider using zeroed observations instead.
        * At episode starts, this function always selects the latest possible
          stacked trajectory. Could consider randomizing the start index of the
          sequence.
  
      Args:
        sample: A sample coming from a Reverb replay table. Should be an unbatched
          sequence so that sample.data.observation is a nested structure of
          time-major tensors.
        stack_size: Number of observations to stack.
  
      Returns:
        A new sample where sample.data.observation has the same nested structure as
        the incoming sample but with every tensor having its final dimension
        multiplied by `stack_size`.
      """
  
      def _repeat_first(sequence: tf.Tensor) -> tf.Tensor:
          repeated_first_step = tf_utils.tile_tensor(
              sequence[0], stack_size - 1
          )
          return tf.concat([repeated_first_step, sequence], 0)[
              : -(stack_size - 1)
          ]
  
      def _stack_observation(observation: tf.Tensor) -> tf.Tensor:
          stack = [
              tf.roll(observation, i, axis=0) for i in range(stack_size)
          ]
          stack.reverse()  # Reverse stack order to be chronological.
          return tf.concat(stack, axis=-1)
  
      # Maybe repeat the first observation, if at the start of an episode.
      data = tf.cond(
          sample.data.start_of_episode[0],
          lambda: tree.map_structure(_repeat_first, sample.data),
          lambda: sample.data,
      )
  
      # Stack observation in the sample's data.
      data_with_stacked_obs = data._replace(
          observation=tree.map_structure(
              _stack_observation, data.observation
          )
      )
  
      # Truncate the start of the sequence due to the first stacks containing the
      # final observations that were rolled over to the start.
      data = tree.map_structure(
          lambda x: x[stack_size - 1 :], data_with_stacked_obs
      )
  
      return reverb.ReplaySample(info=sample.info, data=data)
  
imports: from typing import Any, Mapping, NamedTuple, Tuple
  from acme import specs
  from acme import types
  from acme.agents.jax import actor_core
  from acme.jax import networks
  from acme.jax import types
  from acme.jax import utils
  from acme.tf import utils
  import jax
  import jax.numpy
  import reverb
  import tensorflow
  import tree
label_desc: Use image node_id 4 for calling a global function with example usage: stack_reverb_observation(sample, stack_size) and returns: reverb, tf, tf