code: def append(self, state, action, reward, terminal):
      state = (
          state[-1]
          .mul(255)
          .to(dtype=torch.uint8, device=torch.device("cpu"))
      )
  
      self.transitions.append(
          Transition(
              self.timestep, state, action, reward, not terminal
          ),
          self.transitions.max,
      )
      self.timestep = 0 if terminal else self.timestep + 1
  
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 4 for calling the ReplayMemory obj's underlying member method code with example usage: obj.append(state, action, reward, terminal) without return types