code: def get_config():
      """Get the default configuration.
  
      The default hyperparameters originate from PPO paper arXiv:1707.06347
      and openAI baselines 2::
      https://github.com/openai/baselines/blob/master/baselines/ppo2/defaults.py
      """
      config = ml_collections.ConfigDict()
      # The Atari game used.
      config.game = "Pong"
      # Total number of frames seen during training.
      config.total_frames = 40000000
      # The learning rate for the Adam optimizer.
      config.learning_rate = 2.5e-4
      # Batch size used in training.
      config.batch_size = 256
      # Number of agents playing in parallel.
      config.num_agents = 8
      # Number of steps each agent performs in one policy unroll.
      config.actor_steps = 128
      # Number of training epochs per each unroll of the policy.
      config.num_epochs = 3
      # RL discount parameter.
      config.gamma = 0.99
      # Generalized Advantage Estimation parameter.
      config.lambda_ = 0.95
      # The PPO clipping parameter used to clamp ratios in loss function.
      config.clip_param = 0.1
      # Weight of value function loss in the total loss.
      config.vf_coeff = 0.5
      # Weight of entropy bonus in the total loss.
      config.entropy_coeff = 0.01
      # Linearly decay learning rate and clipping parameter to zero during
      # the training.
      config.decaying_lr_and_clip_param = True
      return config
  
label_desc: Use image node_id 1 for calling a global function with example usage: get_config() and returns: config