code: def train_eval(
      root_dir,
      env_name="CartPole-v0",
      num_iterations=1000,
      actor_fc_layers=(100,),
      value_net_fc_layers=(100,),
      use_value_network=False,
      use_tf_functions=True,
      # Params for collect
      collect_episodes_per_iteration=2,
      replay_buffer_capacity=2000,
      # Params for train
      learning_rate=1e-3,
      gamma=0.9,
      gradient_clipping=None,
      normalize_returns=True,
      value_estimation_loss_coef=0.2,
      # Params for eval
      num_eval_episodes=10,
      eval_interval=100,
      # Params for checkpoints, summaries, and logging
      log_interval=100,
      summary_interval=100,
      summaries_flush_secs=1,
      debug_summaries=True,
      summarize_grads_and_vars=False,
      eval_metrics_callback=None,
  ):
      """A simple train and eval for Reinforce."""
      root_dir = os.path.expanduser(root_dir)
      train_dir = os.path.join(root_dir, "train")
      eval_dir = os.path.join(root_dir, "eval")
  
      train_summary_writer = tf.compat.v2.summary.create_file_writer(
          train_dir, flush_millis=summaries_flush_secs * 1000
      )
      train_summary_writer.set_as_default()
  
      eval_summary_writer = tf.compat.v2.summary.create_file_writer(
          eval_dir, flush_millis=summaries_flush_secs * 1000
      )
      eval_metrics = [
          tf_metrics.AverageReturnMetric(buffer_size=num_eval_episodes),
          tf_metrics.AverageEpisodeLengthMetric(
              buffer_size=num_eval_episodes
          ),
      ]
  
      with tf.compat.v2.summary.record_if(
          lambda: tf.math.equal(global_step % summary_interval, 0)
      ):
          tf_env = tf_py_environment.TFPyEnvironment(
              suite_gym.load(env_name)
          )
          eval_tf_env = tf_py_environment.TFPyEnvironment(
              suite_gym.load(env_name)
          )
  
          actor_net = (
              actor_distribution_network.ActorDistributionNetwork(
                  tf_env.time_step_spec().observation,
                  tf_env.action_spec(),
                  fc_layer_params=actor_fc_layers,
              )
          )
  
          if use_value_network:
              value_net = value_network.ValueNetwork(
                  tf_env.time_step_spec().observation,
                  fc_layer_params=value_net_fc_layers,
              )
  
          global_step = tf.compat.v1.train.get_or_create_global_step()
          tf_agent = reinforce_agent.ReinforceAgent(
              tf_env.time_step_spec(),
              tf_env.action_spec(),
              actor_network=actor_net,
              value_network=value_net if use_value_network else None,
              value_estimation_loss_coef=value_estimation_loss_coef,
              gamma=gamma,
              optimizer=tf.compat.v1.train.AdamOptimizer(
                  learning_rate=learning_rate
              ),
              normalize_returns=normalize_returns,
              gradient_clipping=gradient_clipping,
              debug_summaries=debug_summaries,
              summarize_grads_and_vars=summarize_grads_and_vars,
              train_step_counter=global_step,
          )
  
          replay_buffer = (
              tf_uniform_replay_buffer.TFUniformReplayBuffer(
                  tf_agent.collect_data_spec,
                  batch_size=tf_env.batch_size,
                  max_length=replay_buffer_capacity,
              )
          )
  
          tf_agent.initialize()
  
          train_metrics = [
              tf_metrics.NumberOfEpisodes(),
              tf_metrics.EnvironmentSteps(),
              tf_metrics.AverageReturnMetric(),
              tf_metrics.AverageEpisodeLengthMetric(),
          ]
  
          eval_policy = tf_agent.policy
          collect_policy = tf_agent.collect_policy
  
          collect_driver = dynamic_episode_driver.DynamicEpisodeDriver(
              tf_env,
              collect_policy,
              observers=[replay_buffer.add_batch] + train_metrics,
              num_episodes=collect_episodes_per_iteration,
          )
  
          def train_step():
              experience = replay_buffer.gather_all()
              return tf_agent.train(experience)
  
          if use_tf_functions:
              # To speed up collect use TF function.
              collect_driver.run = common.function(collect_driver.run)
              # To speed up train use TF function.
              tf_agent.train = common.function(tf_agent.train)
              train_step = common.function(train_step)
  
          # Compute evaluation metrics.
          metrics = metric_utils.eager_compute(
              eval_metrics,
              eval_tf_env,
              eval_policy,
              num_episodes=num_eval_episodes,
              train_step=global_step,
              summary_writer=eval_summary_writer,
              summary_prefix="Metrics",
          )
          # TODO(b/126590894): Move this functionality into eager_compute_summaries
          if eval_metrics_callback is not None:
              eval_metrics_callback(metrics, global_step.numpy())
  
          time_step = None
          policy_state = collect_policy.get_initial_state(
              tf_env.batch_size
          )
  
          timed_at_step = global_step.numpy()
          time_acc = 0
  
          for _ in range(num_iterations):
              start_time = time.time()
              time_step, policy_state = collect_driver.run(
                  time_step=time_step,
                  policy_state=policy_state,
              )
              total_loss = train_step()
              replay_buffer.clear()
              time_acc += time.time() - start_time
  
              global_step_val = global_step.numpy()
              if global_step_val % log_interval == 0:
                  logging.info(
                      "step = %d, loss = %f",
                      global_step_val,
                      total_loss.loss,
                  )
                  steps_per_sec = (
                      global_step_val - timed_at_step
                  ) / time_acc
                  logging.info("%.3f steps/sec", steps_per_sec)
                  tf.compat.v2.summary.scalar(
                      name="global_steps_per_sec",
                      data=steps_per_sec,
                      step=global_step,
                  )
                  timed_at_step = global_step_val
                  time_acc = 0
  
              for train_metric in train_metrics:
                  train_metric.tf_summaries(
                      train_step=global_step,
                      step_metrics=train_metrics[:2],
                  )
  
              if global_step_val % eval_interval == 0:
                  metrics = metric_utils.eager_compute(
                      eval_metrics,
                      eval_tf_env,
                      eval_policy,
                      num_episodes=num_eval_episodes,
                      train_step=global_step,
                      summary_writer=eval_summary_writer,
                      summary_prefix="Metrics",
                  )
                  # TODO(b/126590894): Move this functionality into
                  # eager_compute_summaries.
                  if eval_metrics_callback is not None:
                      eval_metrics_callback(metrics, global_step_val)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import time
  from absl import app
  from absl import flags
  from absl import logging
  from six.moves import range
  import tensorflow
  from tf_agents.agents.reinforce import reinforce_agent
  from tf_agents.drivers import dynamic_episode_driver
  from tf_agents.environments import suite_gym
  from tf_agents.environments import tf_py_environment
  from tf_agents.eval import metric_utils
  from tf_agents.metrics import tf_metrics
  from tf_agents.networks import actor_distribution_network
  from tf_agents.networks import value_network
  from tf_agents.replay_buffers import tf_uniform_replay_buffer
  from tf_agents.utils import common
label_desc: Use image node_id 1 for calling a global function with example usage: train_eval(root_dir, env_name, num_iterations, actor_fc_layers, value_net_fc_layers, use_value_network, use_tf_functions, collect_episodes_per_iteration, replay_buffer_capacity, learning_rate, gamma, gradient_clipping, normalize_returns, value_estimation_loss_coef, num_eval_episodes, eval_interval, log_interval, summary_interval, summaries_flush_secs, debug_summaries, summarize_grads_and_vars, eval_metrics_callback) and returns: tf_agent