code: def concat_state_and_timestep(self, dataset):
      current_states_with_timestep = [
          np.append(
              transition.state["observation"],
              transition.info["timestep"],
          )
          for transition in dataset
      ]
      current_states_with_timestep = np.expand_dims(
          current_states_with_timestep, -1
      )
      return current_states_with_timestep
  
imports: import copy
  from collections import OrderedDict
  from typing import Union
  import numpy
  from rl_coach.agents.actor_critic_agent import ActorCriticAgent
  from rl_coach.agents.policy_optimization_agent import PolicyGradientRescaler
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.architectures.head_parameters import PPOHeadParameters, VHeadParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters, DistributedTaskParameters
  from rl_coach.core_types import EnvironmentSteps, Batch
  from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
  from rl_coach.exploration_policies.categorical import CategoricalParameters
  from rl_coach.logger import screen
  from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
  from rl_coach.spaces import DiscreteActionSpace, BoxActionSpace
  from rl_coach.utils import force_list
label_desc: Use image node_id 5 for calling the PPOAgent obj's underlying member method code with example usage: obj.concat_state_and_timestep(dataset) and returns: current_states_with_timestep