code: def _get_transition(self, index):
      transition = [None] * (self.history + self.multi_step)
      transition[self.history - 1] = self.transitions.get(index)
  
      for t in range(self.history - 2, -1, -1):
          if transition[t + 1].timestep == 0:
              transition[t] = blank_transition
          else:
              transition[t] = self.transitions.get(
                  index - self.history + 1 + t
              )
  
      for t in range(self.history, self.history + self.multi_step):
          if transition[t - 1].nonterminal:
              transition[t] = self.transitions.get(
                  index - self.history + 1 + t
              )
          else:
              transition[t] = blank_transition
  
      return transition
  
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 7 for calling the ReplayMemory obj's underlying member method code with example usage: obj._get_transition(index) and returns: transition