code: def get_prediction(self, states):
      tf_input_state = self.prepare_batch_for_inference(states, "main")
      return self.networks["main"].online_network.predict(
          tf_input_state
      )[1:]
  
imports: from typing import Union
  import numpy
  import scipy.signal
  from rl_coach.agents.policy_optimization_agent import PolicyOptimizationAgent, PolicyGradientRescaler
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.architectures.head_parameters import PolicyHeadParameters, VHeadParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters
  from rl_coach.exploration_policies.categorical import CategoricalParameters
  from rl_coach.exploration_policies.continuous_entropy import ContinuousEntropyParameters
  from rl_coach.logger import screen
  from rl_coach.memories.episodic.single_episode_buffer import SingleEpisodeBufferParameters
  from rl_coach.spaces import DiscreteActionSpace, BoxActionSpace
  from rl_coach.utils import last_sample
label_desc: Use image node_id 6 for calling the ActorCriticAgent obj's underlying member method code with example usage: obj.get_prediction(states) and returns: self