def eval_step(self, state):
        ''' Predict the action given the current state for evaluation.
            Since the agents is not trained, this function is equivalent to step function.

        Args:
            state (numpy.array): an numpy array that represents the current state

        Returns:
            action_id (int): the action_id predicted by the agent
            probabilities (list): The list of action probabilities
        '''
        probabilities = []
        return self.step(state), probabilities
 Predict the action given the current state for evaluation.
            Since the agents is not trained, this function is equivalent to step function.

        Args:
            state (numpy.array): an numpy array that represents the current state

        Returns:
            action_id (int): the action_id predicted by the agent
            probabilities (list): The list of action probabilities
        
rlcard/models/bridge_rule_models.py