code: def __init__(self):
      observation_tensor_spec = tf.TensorSpec(
          shape=[1], dtype=tf.float32
      )
      action_tensor_spec = tensor_spec.BoundedTensorSpec(
          [1], tf.float32, -1, 1
      )
  
      actor_net = actor_distribution_network.ActorDistributionNetwork(
          observation_tensor_spec,
          action_tensor_spec,
          fc_layer_params=(1,),
          activation_fn=tf.nn.tanh,
          kernel_initializer=tf.keras.initializers.Orthogonal(seed=1),
          seed_stream_class=DeterministicSeedStream,
          seed=1,
      )
  
      value_net = value_network.ValueNetwork(
          observation_tensor_spec, fc_layer_params=(1,)
      )
  
      super(PPOAgentActorDist, self).__init__(
          time_step_spec=ts.time_step_spec(observation_tensor_spec),
          action_spec=action_tensor_spec,
          actor_net=actor_net,
          value_net=value_net,
          # Ensures value_prediction, return and advantage are included as parts
          # of the training_data_spec.
          compute_value_and_advantage_in_train=True,
          update_normalizers_in_train=False,
          optimizer=tf.compat.v1.train.AdamOptimizer(),
      )
      # There is an artifical call on `_train` during the initialization which
      # ensures that the variables of the optimizer are initialized. This is
      # excluded from the call count.
      self.train_called_times = -1
      self.experiences = []
  
imports: from absl import flags
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents.agents.ppo import ppo_actor_network
  from tf_agents.agents.ppo import ppo_agent
  from tf_agents.agents.ppo import ppo_policy
  from tf_agents.distributions import utils
  from tf_agents.networks import actor_distribution_network
  from tf_agents.networks import value_network
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
  from tf_agents.utils import test_utils
label_desc: Use image node_id 1 to create a new PPOAgentActorDist object from inherited base classes: ppo_agent with example: obj = PPOAgentActorDist()