code: def ppo_clip_train_eval(
      root_dir, num_iterations, reverb_port=None, eval_interval=0
  ):
      """Executes train and eval for ppo_clip.
  
      gin is used to configure parameters related to the agent and environment.
      Arguments related to the execution, e.g. number of iterations and how often to
      eval, are set directly by this method. This keeps the gin config focused on
      the agent and execution level arguments quickly changed on the command line
      without using the more verbose --gin_bindings.
  
      See the `./configs` directory for example gin configs.
  
      Args:
        root_dir: Root directory for writing logs/summaries/checkpoints.
        num_iterations: Total number train/eval iterations to perform.
        reverb_port: Port for reverb server. If None, a random unused port is used.
        eval_interval: Number of train steps between evaluations. Set to 0 to skip.
      """
      train_eval_lib.train_eval(
          root_dir,
          # Training params
          num_iterations=num_iterations,
          # Replay params
          reverb_port=reverb_port,
          # Others
          eval_interval=eval_interval,
      )
  
file_path: agents/tf_agents/examples/ppo/schulman17/ppo_clip_train_eval.py
imports: import os
  from absl import app
  from absl import flags
  from absl import logging
  import gin
  import tensorflow.compat.v2
  from tf_agents.examples.ppo.schulman17 import train_eval_lib
label_desc: Use image node_id 1 for calling a global function with example usage: ppo_clip_train_eval(root_dir, num_iterations, reverb_port, eval_interval) without return types