code: def __next__(self):
      if self.current_index == self.capacity:
          raise StopIteration
  
      state_stack = [None] * self.history
      state_stack[-1] = self.transitions.data[self.current_index].state
  
      previous_timestep = self.transitions.data[
          self.current_index
      ].timestep
  
      for t in reversed(range(self.history - 1)):
          if previous_timestep == 0:
              state_stack[t] = blank_transition.state
          else:
              state_stack[t] = self.transitions.data[
                  self.current_index + t - self.history + 1
              ].state
              previous_timestep -= 1
  
      state = (
          torch.stack(state_stack, 0)
          .to(dtype=torch.float32, device=self.device)
          .div_(255)
      )
  
      self.current_index += 1
  
      return state
  
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 3 for calling the ReplayMemory obj's underlying member method code with example usage: obj.__next__() and returns: state