code: def sample_forward_transitions(self, batch_size, batch_info,
                                     forward_steps: int, discount_factor: float) -> Transitions:
          
          probs, indexes, tree_idxs = self.backend.sample_batch_transitions(batch_size, forward_steps)
  
          return self._get_transitions(
              probs, indexes, tree_idxs, batch_info
          )
docs: Sample transitions from replay buffer with _forward steps_.
          That is, instead of getting a transition s_t -> s_t+1 with reward r,
          get a transition s_t -> s_t+n with sum of intermediate rewards.
  
          Used in a variant of Deep Q-Learning