text: import tensorflow as tf
  from stable_baselines.common.policies import ActorCriticPolicy
  from tensorflow.keras.layers import Lambda
  
  
  class AslaugPolicy(ActorCriticPolicy):
      def __init__(self, sess, ob_space, ac_space, n_env, n_steps, n_batch,
                   reuse=False, **kwargs):
          super(AslaugPolicy, self).__init__(sess, ob_space, ac_space,
                                             n_env, n_steps, n_batch,
                                             reuse=reuse, scale=False)
  
          # Scale observation
          with tf.variable_scope("observation_scaling", reuse=reuse):
              obs_avg = tf.constant((self.ob_space.high+self.ob_space.low)/2.0,
                                    name="obs_avg")
              obs_dif = tf.constant((self.ob_space.high - self.ob_space.low),
                                    name="obs_diff")
              shifted = tf.math.subtract(self.processed_obs, obs_avg)
              proc_obs = tf.math.divide(shifted, obs_dif)
  
          if "obs_slicing" in kwargs and kwargs["obs_slicing"] is not None:
              obs_slicing = kwargs["obs_slicing"]
          else:
              obs_slicing = [0, 6, 9, 57, 64, 71, 78]
          # Create network
          with tf.variable_scope("model/inputs", reuse=reuse):
              lrelu = tf.nn.leaky_relu
              o = obs_slicing
              in_sp = self.crop(1, o[0], o[1])(proc_obs)
              in_mb = self.crop(1, o[1], o[2])(proc_obs)
              in_lp = self.crop(1, o[2], o[3])(proc_obs)
              in_jp = self.crop(1, o[3], o[4])(proc_obs)
              in_jv = self.crop(1, o[4], o[5])(proc_obs)
              in_cp = self.crop(1, o[5], o[6])(proc_obs)
              in_cp_d = self.crop(1, o[6], o[7])(proc_obs)
              in_cp_d_chg = self.crop(1, o[7], o[8])(proc_obs)
              
          with tf.variable_scope("model/scan_block", reuse=reuse):
  
              sc_0 = tf.keras.layers.Concatenate(name="c_0")([in_cp, in_cp_d, in_cp_d_chg])
              sc_1 = tf.layers.Dense(16, activation=lrelu, name="sc_1")(sc_0)
              sc_2 = tf.layers.Dense(8, activation=lrelu, name="sc_2")(sc_1)
              sc_out = tf.layers.Dense(8, activation=lrelu, name="sc_out")(sc_2)
  
          with tf.variable_scope("model/combination_block", reuse=reuse):
              c_0 = tf.keras.layers.Concatenate(name="c_0")([sc_out, in_sp,
                                                             in_mb, in_lp, in_jp,
                                                             in_jv])
              c_1 = tf.layers.Dense(512, activation=lrelu, name="c_1")(c_0)
              c_2 = tf.layers.Dense(256, activation=lrelu, name="c_2")(c_1)
              c_3 = tf.layers.Dense(256, activation=lrelu, name="c_3")(c_2)
              c_4 = tf.layers.Dense(128, activation=lrelu, name="c_4")(c_3)
              c_5 = tf.layers.Dense(128, activation=lrelu, name="c_5")(c_4)
              c_6 = tf.layers.Dense(64, activation=lrelu, name="c_6")(c_5)
              c_7 = tf.layers.Dense(64, activation=lrelu, name="c_7")(c_6)
              c_out = tf.layers.Dense(32, activation=lrelu, name="c_out")(c_7)
  
          with tf.variable_scope("model/actor_critic_block", reuse=reuse):
              vf_0 = tf.layers.Dense(64, activation=lrelu,
                                     name="vf_0")(c_out)
              vf_1 = tf.layers.Dense(32, activation=lrelu,
                                     name="vf_1")(vf_0)
              vf_latent = tf.layers.Dense(16, activation=lrelu,
                                          name="vf_latent")(vf_1)
              pi_0 = tf.layers.Dense(64, activation=lrelu,
                                     name="vf_0")(c_out)
              pi_latent = tf.layers.Dense(32, activation=lrelu,
                                          name="vf_f_1")(pi_0)
  
              value_fn = tf.layers.Dense(1, name='vf')(vf_latent)
  
              self._proba_distribution, self._policy, self.q_value = \
                  self.pdtype.proba_distribution_from_latent(pi_latent,
                                                             vf_latent,
                                                             init_scale=1.0)
  
  
          self._value_fn = value_fn
          # self._initial_state = None
          self._setup_init()
  
      def step(self, obs, state=None, mask=None, deterministic=False):
          if deterministic:
              action, value, neglogp = self.sess.run([self.deterministic_action,
                                                      self.value_flat,
                                                      self.neglogp],
                                                     {self.obs_ph: obs})
          else:
              action, value, neglogp = self.sess.run([self.action,
                                                      self.value_flat,
                                                      self.neglogp],
                                                     {self.obs_ph: obs})
          return action, value, self.initial_state, neglogp
  
      def proba_step(self, obs, state=None, mask=None):
          return self.sess.run(self.policy_proba, {self.obs_ph: obs})
  
      def value(self, obs, state=None, mask=None):
          return self.sess.run(self.value_flat, {self.obs_ph: obs})
  
      def crop(self, dimension, start, end):
          # Crops (or slices) a Tensor on a given dimension from start to end
          def func(x):
              if dimension == 0:
                  return x[start: end]
              if dimension == 1:
                  return x[:, start: end]
              if dimension == 2:
                  return x[:, :, start: end]
              if dimension == 3:
                  return x[:, :, :, start: end]
              if dimension == 4:
                  return x[:, :, :, :, start: end]
          return Lambda(func)
  