def eval_step(self, state):
        ''' Given a state, predict action based on average policy

        Args:
            state (numpy.array): State representation

        Returns:
            action (int): Predicted action
            info (dict): A dictionary containing information
        '''
        probs = self.action_probs(state['obs'].tostring(), list(state['legal_actions'].keys()), self.average_policy)
        action = np.random.choice(len(probs), p=probs)

        info = {}
        info['probs'] = {state['raw_legal_actions'][i]: float(probs[list(state['legal_actions'].keys())[i]]) for i in range(len(state['legal_actions']))}

        return action, info
 Given a state, predict action based on average policy

        Args:
            state (numpy.array): State representation

        Returns:
            action (int): Predicted action
            info (dict): A dictionary containing information
        
rlcard/agents/cfr_agent.py