How to use the rlcard.utils.utils.remove_illegal function in rlcard

def step(self, state):
        ''' Predict the action for genrating training data

        Args:
            state (numpy.array): current state

        Returns:
            action (int): an action id
        '''
        A = self.predict(state['obs'])
        A = remove_illegal(A, state['legal_actions'])
        action = np.random.choice(np.arange(len(A)), p=A)
        return action

def eval_step(self, state):
        ''' Predict the action for evaluation purpose.

        Args:
            state (numpy.array): current state

        Returns:
            action (int): an action id
        '''
        q_values = self.q_estimator.predict(self.sess, np.expand_dims(self.normalizer.normalize(state['obs']), 0))[0]
        probs = remove_illegal(np.exp(q_values), state['legal_actions'])
        best_action = np.argmax(probs)
        return best_action

Args:
            state (dict): The current state

        Returns:
            action (int): An action id
        '''
        obs = state['obs']
        legal_actions = state['legal_actions']
        if self._mode == MODE.best_response:
            probs = self._rl_agent.predict(obs)
            self._add_transition(obs, probs)

        elif self._mode == MODE.average_policy:
            probs = self._act(obs)

        probs = remove_illegal(probs, legal_actions)
        action = np.random.choice(len(probs), p=probs)

        return action

def eval_step(self, state):
        ''' Predict the action given state for evaluation

        args:
            state (dict): current state

        returns:
            action (int): an action id
        '''
        obs = state['obs']
        legal_actions = state['legal_actions']
        action_prob = self.action_probabilities(obs)
        action_prob = remove_illegal(action_prob, legal_actions)
        action_prob /= action_prob.sum()
        action = np.random.choice(np.arange(len(action_prob)), p=action_prob)
        return action

def eval_step(self, state):
        ''' Use the average policy for evaluation purpose

        Args:
            state (dict): The current state.

        Returns:
            action (int): An action id.
        '''
        if self.evaluate_with == 'best_response':
            action = self._rl_agent.eval_step(state)
        elif self.evaluate_with == 'average_policy':
            obs = state['obs']
            legal_actions = state['legal_actions']
            probs = self._act(obs)
            probs = remove_illegal(probs, legal_actions)
            action = np.random.choice(len(probs), p=probs)
        else:
            raise ValueError("'evaluate_with' should be either 'average_policy' or 'best_response'.")
             

        return action

Args:
            state (dict): The current state

        Returns:
            action (int): An action id
        '''
        obs = state['obs']
        legal_actions = state['legal_actions']
        if self._mode == MODE.best_response:
            probs = self._rl_agent.predict(obs)
            self._add_transition(obs, probs)

        elif self._mode == MODE.average_policy:
            probs = self._act(obs)

        probs = remove_illegal(probs, legal_actions)
        action = np.random.choice(len(probs), p=probs)

        return action

def eval_step(self, state):
        ''' Predict the action for evaluation purpose.

        Args:
            state (numpy.array): current state

        Returns:
            action (int): an action id
        '''
        q_values = self.q_estimator.predict_nograd(np.expand_dims(self.normalizer.normalize(state['obs']), 0))[0]
        probs = remove_illegal(np.exp(q_values), state['legal_actions'])
        best_action = np.argmax(probs)
        return best_action

def step(self, state):
        ''' Predict the action for genrating training data but
            have the predictions disconnected from the computation graph

        Args:
            state (numpy.array): current state

        Returns:
            action (int): an action id
        '''
        A = self.predict(state['obs'])
        A = remove_illegal(A, state['legal_actions'])
        action = np.random.choice(np.arange(len(A)), p=A)
        return action