code: def combined_loss_given_predictions(log_probab_actions_new,
                                      log_probab_actions_old,
                                      value_prediction,
                                      padded_actions,
                                      padded_rewards,
                                      reward_mask,
                                      gamma=0.99,
                                      lambda_=0.95,
                                      epsilon=0.2,
                                      c1=1.0,
                                      c2=0.01):
    
    loss_value = value_loss_given_predictions(
        value_prediction, padded_rewards, reward_mask, gamma=gamma)
    loss_ppo = ppo_loss_given_predictions(log_probab_actions_new,
                                          log_probab_actions_old,
                                          value_prediction,
                                          padded_actions,
                                          padded_rewards,
                                          reward_mask,
                                          gamma=gamma,
                                          lambda_=lambda_,
                                          epsilon=epsilon)
    # TODO(afrozm): Add the entropy bonus, but since we don't do that in T2T
    # we'll skip if for now.
    entropy_bonus = 0.0
    return (loss_ppo + (c1 * loss_value) - (c2 * entropy_bonus), loss_ppo,
            loss_value, entropy_bonus)
docs: Computes the combined (clipped loss + value loss) given predictions.