code: def _get_sample_from_segment(self, segment, i):
      valid = False
  
      while not valid:
          sample = random.uniform(i * segment, (i + 1) * segment)
          probability, index, tree_index = self.transitions.find(sample)
  
          if (
              (self.transitions.index - index) % self.capacity
              > self.multi_step
              and (index - self.transitions.index) % self.capacity
              >= self.history
              and probability != 0
          ):
              valid = True
  
      transition = self._get_transition(index)
  
      state = (
          torch.stack(
              [trans.state for trans in transition[: self.history]]
          )
          .to(dtype=torch.float32, device=self.device)
          .div_(255)
      )
      next_state = (
          torch.stack(
              [
                  trans.state
                  for trans in transition[
                      self.multi_step : self.multi_step + self.history
                  ]
              ]
          )
          .to(dtype=torch.float32, device=self.device)
          .div_(255)
      )
  
      action = torch.tensor(
          [transition[self.history - 1].action],
          dtype=torch.int64,
          device=self.device,
      )
  
      R = torch.tensor(
          [
              sum(
                  self.discount**n
                  * transition[self.history + n - 1].reward
                  for n in range(self.multi_step)
              )
          ],
          dtype=torch.float32,
          device=self.device,
      )
  
      nonterminal = torch.tensor(
          [transition[self.history + self.multi_step - 1].nonterminal],
          dtype=torch.float32,
          device=self.device,
      )
  
      return (
          probability,
          index,
          tree_index,
          state,
          action,
          R,
          next_state,
          nonterminal,
      )
  
file_path: SerpentAI/serpent/machine_learning/reinforcement_learning/rainbow_dqn/replay_memory.py
imports: import collections
  import random
  import torch
  from serpent.machine_learning.reinforcement_learning.rainbow_dqn.segment_tree import SegmentTree
label_desc: Use image node_id 8 for calling the ReplayMemory obj's underlying member method code with example usage: obj._get_sample_from_segment(segment, i) and returns: probability,  index,  tree_index,  state,  action,  R,  next_state,  nonterminal