code: def forward(
      self,
      states,
      actions,
      rewards,
      returns_to_go,
      timesteps,
      attention_mask=None,
  ):
      batch_size, seq_length = states.shape[0], states.shape[1]
  
      if attention_mask is None:
          # attention mask for GPT: 1 if can be attended to, 0 if not
          attention_mask = torch.ones(
              (batch_size, seq_length), dtype=torch.long
          )
  
      # embed each modality with a different head
      state_embeddings = self.embed_state(states)
      action_embeddings = self.embed_action(actions)
      returns_embeddings = self.embed_return(returns_to_go)
      time_embeddings = self.embed_timestep(timesteps)
  
      # time embeddings are treated similar to positional embeddings
      state_embeddings = state_embeddings + time_embeddings
      action_embeddings = action_embeddings + time_embeddings
      returns_embeddings = returns_embeddings + time_embeddings
  
      # this makes the sequence look like (R_1, s_1, a_1, R_2, s_2, a_2, ...)
      # which works nice in an autoregressive sense since states predict actions
      stacked_inputs = (
          torch.stack(
              (returns_embeddings, state_embeddings, action_embeddings),
              dim=1,
          )
          .permute(0, 2, 1, 3)
          .reshape(batch_size, 3 * seq_length, self.hidden_size)
      )
      # (B, R, s, a) -> (B, T, s-R-a)
      stacked_inputs = self.embed_ln(stacked_inputs)
  
      # to make the attention mask fit the stacked inputs, have to stack it as well
      # （B, T, 3) -> (B, 3, T)
      stacked_attention_mask = (
          torch.stack(
              (attention_mask, attention_mask, attention_mask), dim=1
          )
          .permute(0, 2, 1)
          .reshape(batch_size, 3 * seq_length)
      )
  
      # we feed in the input embeddings (not word indices as in NLP) to the model
      transformer_outputs = self.transformer(
          inputs_embeds=stacked_inputs,
          attention_mask=stacked_attention_mask,
      )
      x = transformer_outputs["last_hidden_state"]
  
      # reshape x so that the second dimension corresponds to the original
      # returns (0), states (1), or actions (2); i.e. x[:,1,t] is the token for s_t
      x = x.reshape(
          batch_size, seq_length, 3, self.hidden_size
      ).permute(0, 2, 1, 3)
  
      # get predictions
      return_preds = self.predict_return(
          x[:, 2]
      )  # predict next return given state and action
      state_preds = self.predict_state(
          x[:, 2]
      )  # predict next state given state and action
      action_preds = self.predict_action(
          x[:, 1]
      )  # predict next action given state
  
      return state_preds, action_preds, return_preds
  
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 2 for calling the TrajectoryModel obj's underlying member method code with example usage: obj.forward(states, actions, rewards, returns_to_go, timesteps, attention_mask) and returns: state_preds,  action_preds,  return_preds