code: def create_ppo_agent_and_dataset_fn(
      action_spec, time_step_spec, train_step, batch_size
  ):
      """Builds and returns a dummy PPO Agent, dataset and dataset function."""
      del action_spec  # Unused.
      del time_step_spec  # Unused.
      del batch_size  # Unused.
  
      # No arbitrary spec supported.
      obs_spec = tensor_spec.TensorSpec([2], tf.float32)
      ts_spec = ts.time_step_spec(obs_spec)
      act_spec = tensor_spec.BoundedTensorSpec([1], tf.float32, -1, 1)
      actor_net = actor_distribution_network.ActorDistributionNetwork(
          obs_spec,
          act_spec,
          fc_layer_params=(100,),
          activation_fn=tf.keras.activations.tanh,
      )
  
      value_net = value_network.ValueNetwork(
          obs_spec,
          fc_layer_params=(100,),
          activation_fn=tf.keras.activations.tanh,
      )
  
      agent = ppo_clip_agent.PPOClipAgent(
          ts_spec,
          act_spec,
          optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
          actor_net=actor_net,
          value_net=value_net,
          entropy_regularization=0.0,
          importance_ratio_clipping=0.2,
          normalize_observations=False,
          normalize_rewards=False,
          use_gae=False,
          use_td_lambda_return=False,
          num_epochs=1,
          debug_summaries=False,
          summarize_grads_and_vars=False,
          train_step_counter=train_step,
          compute_value_and_advantage_in_train=False,
      )
  
      def _create_experience(_):
          observations = tf.constant(
              [
                  [[1, 2], [3, 4], [5, 6]],
                  [[1, 2], [3, 4], [5, 6]],
              ],
              dtype=tf.float32,
          )
          mid_time_step_val = ts.StepType.MID.tolist()
          time_steps = ts.TimeStep(
              step_type=tf.constant(
                  [[mid_time_step_val] * 3] * 2, dtype=tf.int32
              ),
              reward=tf.constant([[1] * 3] * 2, dtype=tf.float32),
              discount=tf.constant([[1] * 3] * 2, dtype=tf.float32),
              observation=observations,
          )
          actions = tf.constant(
              [[[0], [1], [1]], [[0], [1], [1]]], dtype=tf.float32
          )
  
          action_distribution_parameters = {
              "loc": tf.constant([[[0.0]] * 3] * 2, dtype=tf.float32),
              "scale": tf.constant([[[1.0]] * 3] * 2, dtype=tf.float32),
          }
          value_preds = tf.constant(
              [[9.0, 15.0, 21.0], [9.0, 15.0, 21.0]], dtype=tf.float32
          )
  
          policy_info = {
              "dist_params": action_distribution_parameters,
          }
          policy_info["value_prediction"] = value_preds
          experience = trajectory.Trajectory(
              time_steps.step_type,
              observations,
              actions,
              policy_info,
              time_steps.step_type,
              time_steps.reward,
              time_steps.discount,
          )
          return agent._preprocess(
              experience
          )  # pylint: disable=protected-access
  
      dataset = tf.data.Dataset.from_tensor_slices(
          [[i] for i in range(100)]
      ).map(_create_experience)
      dataset = tf.data.Dataset.zip(
          (dataset, tf.data.experimental.Counter())
      )
      dataset_fn = lambda: dataset
  
      return agent, dataset, dataset_fn, agent.training_data_spec
  
imports: import functools
  from absl import logging
  import numpy
  import reverb
  import tensorflow
  from tf_agents.agents.dqn import dqn_agent
  from tf_agents.agents.ppo import ppo_clip_agent
  from tf_agents.environments import suite_gym
  from tf_agents.experimental.distributed import reverb_variable_container
  from tf_agents.networks import actor_distribution_network
  from tf_agents.networks import sequential
  from tf_agents.networks import value_network
  from tf_agents.policies import py_tf_eager_policy
  from tf_agents.replay_buffers import reverb_replay_buffer
  from tf_agents.specs import tensor_spec
  from tf_agents.train import actor
  from tf_agents.train.utils import replay_buffer_utils
  from tf_agents.train.utils import spec_utils
  from tf_agents.train.utils import train_utils
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
label_desc: Use image node_id 4 for calling a global function with example usage: create_ppo_agent_and_dataset_fn(action_spec, time_step_spec, train_step, batch_size) and returns: agent,  dataset,  dataset_fn,  agent, agent