From the code in here: transformers/src/transformers/models/decision_transformer/modeling_decision_transformer.py at v4.35.2 路 huggingface/transformers 路 GitHub

```
# 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
```

I鈥檓 not sure I understand why ` self.predict_return(x[:, 2])`

or `self.predict_state(x[:, 2])`

is predicting the return/next state given the state and action. From the comment on the top, `x[:, 2]`

is only the action? Am I missing something?

And if this code is correct, what is the use of `x[:, 0]`

?