code: def _build_critic_network(
      self,
      obs_n,
      act_n,
      obs_space_n,
      act_space_n,
      use_state_preprocessor,
      hiddens,
      activation=None,
      scope=None,
  ):
      with tf1.variable_scope(scope, reuse=tf1.AUTO_REUSE) as scope:
          if use_state_preprocessor:
              model_n = [
                  ModelCatalog.get_model_v2(
                      obs_space,
                      act_space,
                      1,
                      self.config["model"],
                  )
                  for obs, obs_space, act_space in zip(
                      obs_n, obs_space_n, act_space_n
                  )
              ]
              out_n = [model.last_layer for model in model_n]
              out = tf.concat(out_n + act_n, axis=1)
          else:
              model_n = [None] * len(obs_n)
              out = tf.concat(obs_n + act_n, axis=1)
  
          for hidden in hiddens:
              out = tf1.layers.dense(
                  out, units=hidden, activation=activation
              )
          feature = out
          out = tf1.layers.dense(feature, units=1, activation=None)
  
      return out, feature, model_n, tf1.global_variables(scope.name)
  
imports: import logging
  import numpy
  from gymnasium.spaces import Box, Discrete
  from ray.rllib.algorithms.dqn.dqn_tf_policy import minimize_and_clip
  from ray.rllib.evaluation.postprocessing import adjust_nstep
  from ray.rllib.models import ModelCatalog
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.tf_policy import TFPolicy
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.error import UnsupportedSpaceException
  from ray.rllib.utils.framework import try_import_tf, try_import_tfp
  from ray.rllib.utils.metrics.learner_info import LEARNER_STATS_KEY
label_desc: Use image node_id 11 for calling the MADDPGTFPolicy obj's underlying member method code with example usage: obj._build_critic_network(obs_n, act_n, obs_space_n, act_space_n, use_state_preprocessor, hiddens, activation, scope) and returns: out,  feature,  model_n,  tf1