code: def sample(self, batch_size):
      priority_total = self.transitions.total()
  
      segment = priority_total / batch_size
      batch = [
          self._get_sample_from_segment(segment, i)
          for i in range(batch_size)
      ]
  
      (
          probabilities,
          indices,
          tree_indices,
          states,
          actions,
          returns,
          next_states,
          nonterminals,
      ) = zip(*batch)
      probabilities = (
          torch.tensor(
              probabilities, dtype=torch.float32, device=self.device
          )
          / priority_total
      )
  
      (
          states,
          next_states,
      ) = torch.stack(
          states
      ), torch.stack(next_states)
      actions, returns, nonterminals = (
          torch.cat(actions),
          torch.cat(returns),
          torch.stack(nonterminals),
      )
  
      capacity = (
          self.capacity
          if self.transitions.full
          else self.transitions.index
      )
  
      weights = (capacity * probabilities) ** -self.priority_weight
      weights = weights / weights.max()
  
      return (
          tree_indices,
          states,
          actions,
          returns,
          next_states,
          nonterminals,
          weights,
      )
  
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 5 for calling the ReplayMemory obj's underlying member method code with example usage: obj.sample(batch_size) and returns: tree_indices,  states,  actions,  returns,  next_states,  nonterminals,  weights