class_docstr: A minimal replay buffer used by Decision Transformer (DT)
  to process episodes into max_seq_len length segments and do shuffling.
class_docstr_tok: ["A","minimal","replay","buffer","used","by","Decision","Transformer","(","DT",")","to","process","episodes","into","max_seq_len","length","segments","and","do","shuffling","."]
code: def _sample_single(self) -> SampleBatch:
      # TODO: sample proportional to episode length
      # Sample a random episode from the buffer and then sample a random
      # segment from that episode.
      buffer_ind = random.randint(0, len(self._buffer) - 1)
  
      episode = self._buffer[buffer_ind]
      ep_len = episode[SampleBatch.OBS].shape[0]
  
      # ei (end index) is exclusive
      ei = random.randint(1, ep_len)
      # si (start index) is inclusive
      si = max(ei - self.max_seq_len, 0)
  
      # Slice segments from obs, actions, timesteps, and rtgs
      obs = episode[SampleBatch.OBS][si:ei]
      actions = episode[SampleBatch.ACTIONS][si:ei]
      timesteps = episode[SampleBatch.T][si:ei]
      masks = episode[SampleBatch.ATTENTION_MASKS][si:ei]
      # Note that returns-to-go needs an extra elem as the rtg target for the last
      # action token passed into the transformer.
      returns_to_go = episode[SampleBatch.RETURNS_TO_GO][si : ei + 1]
  
      # Front-pad if we're at the beginning of the episode and we need more tokens
      # to pass into the transformer. Or if the episode length is shorter
      # than max_seq_len.
      obs = front_pad_with_zero(obs, self.max_seq_len)
      actions = front_pad_with_zero(actions, self.max_seq_len)
      returns_to_go = front_pad_with_zero(
          returns_to_go, self.max_seq_len + 1
      )
      timesteps = front_pad_with_zero(timesteps, self.max_seq_len)
      masks = front_pad_with_zero(masks, self.max_seq_len)
  
      assert obs.shape[0] == self.max_seq_len
      assert actions.shape[0] == self.max_seq_len
      assert timesteps.shape[0] == self.max_seq_len
      assert masks.shape[0] == self.max_seq_len
      assert returns_to_go.shape[0] == self.max_seq_len + 1
  
      return SampleBatch(
          {
              SampleBatch.OBS: obs[None],
              SampleBatch.ACTIONS: actions[None],
              SampleBatch.RETURNS_TO_GO: returns_to_go[None],
              SampleBatch.T: timesteps[None],
              SampleBatch.ATTENTION_MASKS: masks[None],
          }
      )
  
file_path: ray/rllib_contrib/dt/src/rllib_dt/dt/segmentation_buffer.py
imports: import logging
  import random
  from collections import defaultdict
  from typing import List
  import numpy
  from ray.rllib.evaluation.postprocessing import discount_cumsum
  from ray.rllib.policy.sample_batch import MultiAgentBatch, SampleBatch, concat_samples
  from ray.rllib.utils.typing import SampleBatchType
label_desc: Use image node_id 5 for calling the SegmentationBuffer obj's underlying member method code with example usage: obj._sample_single() and returns: SampleBatch