def sample_trajectories(self, rollout_length, batch_info) -> Trajectories:
indexes = self.backend.sample_batch_trajectories(rollout_length)<EOL>transition_tensors = self.backend.get_trajectories(indexes, rollout_length)<EOL>return Trajectories(<EOL>num_steps=rollout_length,<EOL>num_envs=self.backend.num_envs,<EOL>environment_information=None,<EOL>transition_tensors={k: torch.from_numpy(v) for k, v in transition_tensors.items()},<EOL>rollout_tensors={}<EOL>)<EOL>
Sample batch of trajectories and return them