code: def learn(self, replay_memory):
      (
          idxs,
          states,
          actions,
          returns,
          next_states,
          nonterminals,
          weights,
      ) = replay_memory.sample(self.batch_size)
  
      log_ps = self.online_net(states, log=True)
      log_ps_a = log_ps[range(self.batch_size), actions]
  
      with torch.no_grad():
          pns = self.online_net(next_states)
          dns = self.support.expand_as(pns) * pns
  
          argmax_indices_ns = dns.sum(2).argmax(1)
  
          self.target_net.reset_noise()
  
          pns = self.target_net(next_states)
          pns_a = pns[range(self.batch_size), argmax_indices_ns]
  
          Tz = returns.unsqueeze(1) + nonterminals * (
              self.discount**self.multi_step
          ) * self.support.unsqueeze(0)
          Tz = Tz.clamp(min=self.Vmin, max=self.Vmax)
  
          b = (Tz - self.Vmin) / self.delta_z
          l, u = b.floor().to(torch.int64), b.ceil().to(torch.int64)
  
          l[(u > 0) * (l == u)] -= 1
          u[(l < (self.atoms - 1)) * (l == u)] += 1
  
          m = states.new_zeros(self.batch_size, self.atoms)
  
          offset = (
              torch.linspace(
                  0,
                  ((self.batch_size - 1) * self.atoms),
                  self.batch_size,
              )
              .unsqueeze(1)
              .expand(self.batch_size, self.atoms)
              .to(actions)
          )
  
          m.view(-1).index_add_(
              0,
              (l + offset).view(-1),
              (pns_a * (u.float() - b)).view(-1),
          )
          m.view(-1).index_add_(
              0,
              (u + offset).view(-1),
              (pns_a * (b - l.float())).view(-1),
          )
  
      loss = -torch.sum(m * log_ps_a, 1)
      loss = weights * loss
  
      self.online_net.zero_grad()
      loss.mean().backward()
      self.optimiser.step()
  
      torch.nn.utils.clip_grad_norm_(
          self.online_net.parameters(), self.max_grad_norm
      )
  
      replay_memory.update_priorities(idxs, loss.detach())
  
file_path: SerpentAI/serpent/machine_learning/reinforcement_learning/rainbow_dqn/rainbow_agent.py
imports: import os
  import random
  import torch
  from torch import nn, optim
  from serpent.machine_learning.reinforcement_learning.rainbow_dqn.dqn import DQN3, DQN4
label_desc: Use image node_id 5 for calling the RainbowAgent obj's underlying member method code with example usage: obj.learn(replay_memory) without return types