code: def get_action(
      self, states, actions, rewards, returns_to_go, timesteps, **kwargs
  ):
      # we don't care about the past rewards in this model
  
      states = states.reshape(1, -1, self.state_dim)
      actions = actions.reshape(1, -1, self.act_dim)
      returns_to_go = returns_to_go.reshape(1, -1, 1)
      timesteps = timesteps.reshape(1, -1)
  
      if self.max_length is not None:
          states = states[:, -self.max_length :]
          actions = actions[:, -self.max_length :]
          returns_to_go = returns_to_go[:, -self.max_length :]
          timesteps = timesteps[:, -self.max_length :]
  
          # pad all tokens to sequence length
          attention_mask = torch.cat(
              [
                  torch.zeros(self.max_length - states.shape[1]),
                  torch.ones(states.shape[1]),
              ]
          )
          attention_mask = attention_mask.to(
              dtype=torch.long, device=states.device
          ).reshape(1, -1)
          states = torch.cat(
              [
                  torch.zeros(
                      (
                          states.shape[0],
                          self.max_length - states.shape[1],
                          self.state_dim,
                      ),
                      device=states.device,
                  ),
                  states,
              ],
              dim=1,
          ).to(dtype=torch.float32)
          actions = torch.cat(
              [
                  torch.zeros(
                      (
                          actions.shape[0],
                          self.max_length - actions.shape[1],
                          self.act_dim,
                      ),
                      device=actions.device,
                  ),
                  actions,
              ],
              dim=1,
          ).to(dtype=torch.float32)
          returns_to_go = torch.cat(
              [
                  torch.zeros(
                      (
                          returns_to_go.shape[0],
                          self.max_length - returns_to_go.shape[1],
                          1,
                      ),
                      device=returns_to_go.device,
                  ),
                  returns_to_go,
              ],
              dim=1,
          ).to(dtype=torch.float32)
          timesteps = torch.cat(
              [
                  torch.zeros(
                      (
                          timesteps.shape[0],
                          self.max_length - timesteps.shape[1],
                      ),
                      device=timesteps.device,
                  ),
                  timesteps,
              ],
              dim=1,
          ).to(dtype=torch.long)
      else:
          attention_mask = None
  
      _, action_preds, return_preds = self.forward(
          states,
          actions,
          None,
          returns_to_go,
          timesteps,
          attention_mask=attention_mask,
          **kwargs,
      )
  
      return action_preds[0, -1]
  
file_path: PARL/benchmark/torch/DT/model.py
imports: import numpy
  import torch
  import torch.nn
  import transformers
  from trajectory_gpt2 import GPT2Model
  import parl
label_desc: Use image node_id 3 for calling the TrajectoryModel obj's underlying member method code with example usage: obj.get_action(states, actions, rewards, returns_to_go, timesteps) and returns: action_preds