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

        Args:
            state (dict): An dictionary that represents the current state

        Returns:
            action (int): The action predicted (randomly chosen) by the random agent
            probs (list): The list of action probabilities
        '''
        probs = [0 for _ in range(self.num_actions)]
        for i in state['legal_actions']:
            probs[i] = 1/len(state['legal_actions'])

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

        return self.step(state), info
 Predict the action given the current state for evaluation.
            Since the random agents are not trained. This function is equivalent to step function

        Args:
            state (dict): An dictionary that represents the current state

        Returns:
            action (int): The action predicted (randomly chosen) by the random agent
            probs (list): The list of action probabilities
        
rlcard/agents/random_agent.py