class_docstr: Soft Actor-Critic (SAC).
  
  See https://arxiv.org/abs/1812.05905
  
  Args:
      policy (Policy): Policy.
      q_func1 (Module): First Q-function that takes state-action pairs as input
          and outputs predicted Q-values.
      q_func2 (Module): Second Q-function that takes state-action pairs as
          input and outputs predicted Q-values.
      policy_optimizer (Optimizer): Optimizer setup with the policy
      q_func1_optimizer (Optimizer): Optimizer setup with the first
          Q-function.
      q_func2_optimizer (Optimizer): Optimizer setup with the second
          Q-function.
      replay_buffer (ReplayBuffer): Replay buffer
      gamma (float): Discount factor
      gpu (int): GPU device id if not None nor negative.
      replay_start_size (int): if the replay buffer's size is less than
          replay_start_size, skip update
      minibatch_size (int): Minibatch size
      update_interval (int): Model update interval in step
      phi (callable): Feature extractor applied to observations
      soft_update_tau (float): Tau of soft target update.
      logger (Logger): Logger used
      batch_states (callable): method which makes a batch of observations.
          default is `pfrl.utils.batch_states.batch_states`
      burnin_action_func (callable or None): If not None, this callable
          object is used to select actions before the model is updated
          one or more times during training.
      initial_temperature (float): Initial temperature value. If
          `entropy_target` is set to None, the temperature is fixed to it.
      entropy_target (float or None): If set to a float, the temperature is
          adjusted during training to match the policy's entropy to it.
      temperature_optimizer_lr (float): Learning rate of the temperature
          optimizer. If set to None, Adam with default hyperparameters
          is used.
      act_deterministically (bool): If set to True, choose most probable
          actions in the act method instead of sampling from distributions.
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code: def _batch_act_eval(self, batch_obs):
      assert not self.training
      return self.batch_select_greedy_action(
          batch_obs, deterministic=self.act_deterministically
      )
  
imports: import collections
  import copy
  from logging import getLogger
  import numpy
  import torch
  from torch import nn
  from torch.nn import functional
  import pfrl
  from pfrl.agent import AttributeSavingMixin, BatchAgent
  from pfrl.replay_buffer import ReplayUpdater, batch_experiences
  from pfrl.utils import clip_l2_grad_norm_
  from pfrl.utils.batch_states import batch_states
  from pfrl.utils.copy_param import synchronize_parameters
  from pfrl.utils.mode_of_distribution import mode_of_distribution
label_desc: Use image node_id 11 for calling the SoftActorCritic obj's underlying member method code with example usage: obj._batch_act_eval(batch_obs) and returns: self