How to use the rlcard.models.load function in rlcard

def test_load(self):
        register(model_id='test_load', entry_point='rlcard.models.pretrained_models:LeducHoldemNFSPModel')
        models.load('test_load')
        with self.assertRaises(ValueError):
            load('test_random_make')

# The paths for saving the logs and learning curves
root_path = './experiments/leduc_holdem_cfr_result/'
log_path = root_path + 'log.txt'
csv_path = root_path + 'performance.csv'
figure_path = root_path + 'figures/'

# Set a global seed
set_global_seed(0)

# Initilize CFR Agent
agent = CFRAgent(env)
agent.load()  # If we have saved model, we first load the model

# Evaluate CFR against pre-trained NFSP
eval_env.set_agents([agent, models.load('leduc-holdem-nfsp').agents[0]])

# Init a Logger to plot the learning curve
logger = Logger(xlabel='iteration', ylabel='reward', legend='CFR on Leduc Holdem', log_path=log_path, csv_path=csv_path)

for episode in range(episode_num):
    agent.train()
    print('\rIteration {}'.format(episode), end='')
    # Evaluate the performance. Play with NFSP agents.
    if episode % evaluate_every == 0:
        agent.save() # Save model

        reward = 0
        for eval_episode in range(evaluate_num):
            _, payoffs = eval_env.run(is_training=False)

            reward += payoffs[0]

def load_model(self):
        ''' Load pretrained/rule model

        Returns:
            model (Model): A Model object
        '''
        return models.load('uno-rule-v1')

def load_model(self):
        ''' Load pretrained/rule model

        Returns:
            model (Model): A Model object
        '''
        return models.load('leduc-holdem-nfsp')