code: def learn(
      self,
      states,
      actions,
      rewards,
      dones,
      rtg,
      timesteps,
      attention_mask,
  ):
      action_target = torch.clone(actions)
  
      state_preds, action_preds, reward_preds = self.model.forward(
          states,
          actions,
          rewards,
          rtg[:, :-1],
          timesteps,
          attention_mask=attention_mask,
      )
  
      act_dim = action_preds.shape[2]
      action_preds = action_preds.reshape(-1, act_dim)[
          attention_mask.reshape(-1) > 0
      ]
      action_target = action_target.reshape(-1, act_dim)[
          attention_mask.reshape(-1) > 0
      ]
  
      loss = torch.mean((action_preds - action_target) ** 2)
  
      self.optimizer.zero_grad()
      loss.backward()
      torch.nn.utils.clip_grad_norm_(self.model.parameters(), 0.25)
      self.optimizer.step()
      self.scheduler.step()
  
      return loss
  
file_path: PARL/parl/algorithms/torch/dt.py
imports: import torch
  import numpy
  import parl
  import time
label_desc: Use image node_id 3 for calling the DecisionTransformer obj's underlying member method code with example usage: obj.learn(states, actions, rewards, dones, rtg, timesteps, attention_mask) and returns: loss