docstr: get the mean and stdev of the policy distribution given 'states'
  :param states: the states for which we need to sample actions from the policy
  :return: mean and stdev
docstr_tok: ["get","the","mean","and","stdev","of","the","policy","distribution","given","'states'",":","param","states",":","the","states","for","which","we","need","to","sample","actions","from","the","policy",":","return",":","mean","and","stdev"]
code: def get_prediction(self, states):
      """
      get the mean and stdev of the policy distribution given 'states'
      :param states: the states for which we need to sample actions from the policy
      :return: mean and stdev
      """
      tf_input_state = self.prepare_batch_for_inference(
          states, "policy"
      )
      return self.networks["policy"].online_network.predict(
          tf_input_state
      )
  
imports: from typing import Union
  import copy
  import numpy
  from collections import OrderedDict
  from rl_coach.agents.agent import Agent
  from rl_coach.agents.policy_optimization_agent import PolicyOptimizationAgent
  from rl_coach.architectures.head_parameters import SACQHeadParameters, SACPolicyHeadParameters, VHeadParameters
  from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
  from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters, EmbedderScheme, MiddlewareScheme
  from rl_coach.core_types import ActionInfo, EnvironmentSteps, RunPhase
  from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
  from rl_coach.memories.non_episodic.experience_replay import ExperienceReplayParameters
  from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
  from rl_coach.spaces import BoxActionSpace
label_desc: Use image node_id 4 for calling the SoftActorCriticAgent obj's underlying member method code with example usage: obj.get_prediction(states) and returns: self