gops_func_list = [
"""def evaluate_state(state) -> tuple[tuple[float, float], dict]:
    score_cards = state[0]
    player_0_played_cards = state[1]
    player_1_played_cards = state[2]
    is_turn = state[3]
    player_0_score = state[4]
    player_1_score = state[5]
    score_deck = list(state[6])
    player_0_hand = list(state[7])
    player_1_hand = list(state[8])
    
    def calculate_expected_scores():
        player_0_future_score = player_0_score
        player_1_future_score = player_1_score
        
        player_0_hand_copy = player_0_hand.copy()
        player_1_hand_copy = player_1_hand.copy()
        
        # Incorporate the feature of relative position of player's hand cards compared to revealed score cards
        player_0_hand_sum = sum(player_0_hand_copy)
        player_1_hand_sum = sum(player_1_hand_copy)
        score_cards_sum = sum(score_cards)
        
        # Adjust future scores based on relative card values
        if player_0_hand_sum > player_1_hand_sum and score_cards_sum < player_0_hand_sum:
            player_0_future_score += 2 * player_0_hand_sum
        elif player_1_hand_sum > player_0_hand_sum and score_cards_sum < player_1_hand_sum:
            player_1_future_score += 2 * player_1_hand_sum
        
        # Simulate potential future moves based on player strategies
        for i in range(len(score_deck)):
            if player_0_hand_copy and player_1_hand_copy:
                if max(player_0_hand_copy) >= player_1_hand_copy[0]:
                    player_0_future_score += score_deck[i]
                    player_0_hand_copy.remove(max(player_0_hand_copy))
                else:
                    player_1_future_score += score_deck[i]
                    player_1_hand_copy.remove(player_1_hand_copy[0])
            elif player_0_hand_copy:
                player_0_future_score += score_deck[i]
            elif player_1_hand_copy:
                player_1_future_score += score_deck[i]
        
        # Consider the specific score cards revealed in the game and their values
        for card in score_cards:
            if card % 2 == 0:  # Even score cards favor player 0
                player_0_future_score += card
            else:  # Odd score cards favor player 1
                player_1_future_score += card
        
        return player_0_future_score, player_1_future_score
    
    player_0_expected_score, player_1_expected_score = calculate_expected_scores()
    
    intermediate_values = {
        'player_0_expected_score': player_0_expected_score,
        'player_1_expected_score': player_1_expected_score
    }
    
    return (player_0_expected_score, player_1_expected_score), intermediate_values""",
"""def evaluate_state(state):
    # Extracting the game state information
    score_cards = state[0]
    player_0_played_cards = state[1]
    player_1_played_cards = state[2]
    is_turn = state[3]
    player_0_score = state[4]
    player_1_score = state[5]
    score_deck = state[6]
    player_0_hand = state[7]
    player_1_hand = state[8]
    
    # Calculating the potential scores for each player
    player_0_potential_score = sum(player_0_hand)
    player_1_potential_score = sum(player_1_hand)
    
    # Calculating the remaining score potential in the deck
    remaining_score_potential = sum(score_deck)
    
    # Calculating the potential final scores for each player
    player_0_final_score = player_0_score + player_0_potential_score
    player_1_final_score = player_1_score + player_1_potential_score
    
    # Determining the expected final scores based on the current game state
    if remaining_score_potential > abs(player_0_final_score - player_1_final_score):
        if player_0_final_score > player_1_final_score:
            player_scores = (player_0_final_score + remaining_score_potential/2, player_1_final_score + remaining_score_potential/2)
        else:
            player_scores = (player_0_final_score + remaining_score_potential/2, player_1_final_score + remaining_score_potential/2)
    else:
        if player_0_final_score > player_1_final_score:
            player_scores = (player_0_final_score, player_1_final_score)
        else:
            player_scores = (player_0_final_score, player_1_final_score)
    
    # Storing the intermediate values used to calculate the scores
    intermediate_values = {
        'player_0_potential_score': player_0_potential_score,
        'player_1_potential_score': player_1_potential_score,
        'remaining_score_potential': remaining_score_potential,
        'player_0_final_score': player_0_final_score,
        'player_1_final_score': player_1_final_score
    }
    
    return player_scores, intermediate_values""",
"""def evaluate_state(state) -> tuple[tuple[float, float], dict]:
    score_cards = state[0]
    player_0_played_cards = state[1]
    player_1_played_cards = state[2]
    is_turn = state[3]
    player_0_score = state[4]
    player_1_score = state[5]
    score_deck = state[6]
    player_0_hand = state[7]
    player_1_hand = state[8]

    # Function to recursively simulate future rounds with updated card weights
    def simulate_rounds(player_0_score, player_1_score, player_0_hand, player_1_hand, score_deck, depth):
        if depth == 0:
            return player_0_score, player_1_score

        # Convert player hands and score deck to lists to allow indexing
        player_0_hand = list(player_0_hand)
        player_1_hand = list(player_1_hand)
        score_deck = list(score_deck)

        # Check for empty hands or score deck
        if not player_0_hand or not player_1_hand or not score_deck:
            return player_0_score, player_1_score

        # Simulate all possible plays for both players
        player_0_future_scores = []
        player_1_future_scores = []

        for card_0 in player_0_hand:
            for card_1 in player_1_hand:
                # Calculate the adjusted card weights based on expected future scores
                card_0_weight = card_0 + sum(score_deck) / len(player_0_hand)
                card_1_weight = card_1 + sum(score_deck) / len(player_1_hand)

                new_player_0_score = player_0_score + score_deck[0] if card_0_weight > card_1_weight else player_0_score
                new_player_1_score = player_1_score + score_deck[0] if card_1_weight > card_0_weight else player_1_score
                new_player_0_hand = player_0_hand.copy()
                new_player_0_hand.remove(card_0)
                new_player_1_hand = player_1_hand.copy()
                new_player_1_hand.remove(card_1)
                new_score_deck = score_deck.copy()
                new_score_deck.pop(0)

                player_0_future, player_1_future = simulate_rounds(new_player_0_score, new_player_1_score, new_player_0_hand, new_player_1_hand, new_score_deck, depth-1)
                player_0_future_scores.append(player_0_future)
                player_1_future_scores.append(player_1_future)

        # Calculate expected scores based on simulations
        expected_player_0_score = sum(player_0_future_scores) / len(player_0_future_scores)
        expected_player_1_score = sum(player_1_future_scores) / len(player_1_future_scores)

        return expected_player_0_score, expected_player_1_score

    # Call the recursive function to get expected scores
    player_0_expected_score, player_1_expected_score = simulate_rounds(player_0_score, player_1_score, player_0_hand, player_1_hand, score_deck, 3)

    # Define any intermediate values used in the calculation
    intermediate_values = {
        'player_0_expected_score': player_0_expected_score,
        'player_1_expected_score': player_1_expected_score,
        'depth': 3
    }

    return (player_0_expected_score, player_1_expected_score), intermediate_values""",
"""def evaluate_state(state) -> tuple[tuple[float, float], dict]:
    score_cards = state[0]
    player_0_played_cards = state[1]
    player_1_played_cards = state[2]
    is_turn = state[3]
    player_0_score = state[4]
    player_1_score = state[5]
    score_deck = state[6]
    player_0_hand = state[7]
    player_1_hand = state[8]

    def calculate_weighted_score(hand, score_card):
        if not hand:
            return 0.0
        average_card_value = sum(hand) / len(hand)
        weights = {1: 0.1, 2: 0.2, 3: 0.3, 4: 0.4, 5: 0.5, 6: 0.6, 7: 0.7, 8: 0.8, 9: 0.9, 10: 1.0}
        card_weight = weights.get(average_card_value, 1.0)
        return card_weight * score_card

    player_0_future_score = player_0_score
    player_1_future_score = player_1_score

    player_0_hand = list(player_0_hand)
    player_1_hand = list(player_1_hand)

    remaining_rounds = len(score_deck)

    # Dynamic adjustment of the weight based on the remaining rounds
    if remaining_rounds <= 5:
        deck_score_weight = 1.0
    elif 5 < remaining_rounds <= 10:
        deck_score_weight = 0.8
    else:
        deck_score_weight = 0.5

    player_0_best_score = player_0_score
    player_1_best_score = player_1_score
    player_0_worst_score = player_0_score
    player_1_worst_score = player_1_score

    for idx, score_card in enumerate(score_deck):
        score_card *= deck_score_weight

        if player_0_hand and player_1_hand:
            if player_0_hand[0] >= player_1_hand[0]:
                player_0_future_score += calculate_weighted_score(player_0_hand, score_card)
                player_1_worst_score += calculate_weighted_score(player_1_hand, score_card)
                player_0_hand = player_0_hand[1:]
                player_1_hand = player_1_hand[1:]
            else:
                player_1_future_score += calculate_weighted_score(player_1_hand, score_card)
                player_0_worst_score += calculate_weighted_score(player_0_hand, score_card)
                player_0_hand = player_0_hand[1:]
                player_1_hand = player_1_hand[1:]

    player_scores = (player_0_future_score, player_1_future_score)
    intermediate_values = {
        'player_0_future_score': player_0_future_score,
        'player_1_future_score': player_1_future_score,
        'deck_score_weight': deck_score_weight,
        'player_0_best_score': player_0_best_score,
        'player_1_best_score': player_1_best_score,
        'player_0_worst_score': player_0_worst_score,
        'player_1_worst_score': player_1_worst_score
    }
    return player_scores, intermediate_values""",
"""def evaluate_state(state) -> tuple[tuple[float, float], dict]:
    score_cards = state[0]
    player_0_played_cards = state[1]
    player_1_played_cards = state[2]
    is_turn = state[3]
    player_0_score = state[4]
    player_1_score = state[5]
    score_deck = list(state[6])
    player_0_hand = list(state[7])
    player_1_hand = list(state[8])
    
    # Function to simulate the game outcome based on current state
    def simulate_game(player_0_hand, player_1_hand, score_deck, score_cards):
        player_0_score_sim = player_0_score
        player_1_score_sim = player_1_score
        
        while score_deck:
            score_card = score_deck.pop()
            player_0_card = player_0_hand.pop(0)
            player_1_card = player_1_hand.pop(0)
            
            if player_0_card > player_1_card:
                player_0_score_sim += score_card
            elif player_1_card > player_0_card:
                player_1_score_sim += score_card
            else:  
                if score_deck:
                    score_card_tie = score_deck.pop()
                    player_0_score_sim += score_card_tie
                else:  
                    break
                    
        return player_0_score_sim, player_1_score_sim
    
    # Run multiple simulations to estimate expected scores
    num_simulations = 1000
    player_0_expected_score = 0
    player_1_expected_score = 0
    
    card_values = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]  # Possible card values
    
    for _ in range(num_simulations):
        player_0_hand_sim = player_0_hand[:]
        player_1_hand_sim = player_1_hand[:]
        score_deck_sim = score_deck.copy()
        
        player_0_std = np.std(player_0_hand_sim)  # Calculate standard deviation of player 0's hand
        player_1_std = np.std(player_1_hand_sim)  # Calculate standard deviation of player 1's hand
        
        player_0_sim, player_1_sim = simulate_game(player_0_hand_sim, player_1_hand_sim, score_deck_sim, score_cards[:])
        
        player_0_expected_score += player_0_sim + player_0_std
        player_1_expected_score += player_1_sim + player_1_std
    
    player_0_expected_score /= num_simulations
    player_1_expected_score /= num_simulations
    
    intermediate_values = {
        'num_simulations': num_simulations,
        'player_0_std': player_0_std,
        'player_1_std': player_1_std
    }
    
    player_scores = (player_0_expected_score, player_1_expected_score)
    
    return player_scores, intermediate_values""",
"""def evaluate_state(state) -> tuple[tuple[float, float], dict]:
    score_cards = state[0]
    player_0_played_cards = state[1]
    player_1_played_cards = state[2]
    is_turn = state[3]
    player_0_score = state[4]
    player_1_score = state[5]
    score_deck = state[6]
    player_0_hand = state[7]
    player_1_hand = state[8]

    def adjust_expected_scores(player_0_score, player_1_score, score_deck, player_0_hand, player_1_hand):
        # Calculate the number of rounds left based on the remaining cards in the deck and hands
        rounds_left = min(len(score_deck), len(player_0_hand), len(player_1_hand))

        # Initialize variables to track the potential scores for each player
        player_0_potential = player_0_score
        player_1_potential = player_1_score

        # Convert sets to lists to avoid 'set' object is not subscriptable error
        score_deck_list = list(score_deck)
        player_0_hand_list = list(player_0_hand)
        player_1_hand_list = list(player_1_hand)

        # Adjust the expected scores dynamically based on the remaining cards and potential outcomes
        for round_num in range(rounds_left):
            player_0_avg_card = sum(player_0_hand_list) / len(player_0_hand_list) if len(player_0_hand_list) > 0 else 0
            player_1_avg_card = sum(player_1_hand_list) / len(player_1_hand_list) if len(player_1_hand_list) > 0 else 0

            # Determine the winner of the round based on average card values
            if player_0_avg_card > player_1_avg_card:
                player_0_potential += score_deck_list[round_num]
            elif player_1_avg_card > player_0_avg_card:
                player_1_potential += score_deck_list[round_num]
            else:
                player_0_potential += score_deck_list[round_num] / 2
                player_1_potential += score_deck_list[round_num] / 2

        return player_0_potential, player_1_potential

    player_0_expected_score, player_1_expected_score = adjust_expected_scores(player_0_score, player_1_score, score_deck.copy(), player_0_hand.copy(), player_1_hand.copy())

    intermediate_values = {
        'player_0_expected_score': player_0_expected_score,
        'player_1_expected_score': player_1_expected_score
    }

    return (player_0_expected_score, player_1_expected_score), intermediate_values""",
"""def evaluate_state(state) -> tuple[tuple[float, float], dict]:
    score_cards = state[0]
    player_0_played_cards = state[1]
    player_1_played_cards = state[2]
    is_turn = state[3]
    player_0_score = state[4]
    player_1_score = state[5]
    score_deck = state[6]
    player_0_hand = state[7]
    player_1_hand = state[8]

    def generate_strategic_combinations(player_hand, opponent_played_cards):
        strategic_combinations = []
        for card in player_hand:
            if not any(card >= opp_card for opp_card in opponent_played_cards):
                strategic_combinations.append([card])
        return strategic_combinations

    player_0_strategic_combinations = generate_strategic_combinations(player_0_hand, player_1_played_cards)
    player_1_strategic_combinations = generate_strategic_combinations(player_1_hand, player_0_played_cards)

    player_0_potential = sum(player_0_hand)
    player_1_potential = sum(player_1_hand)
    score_potential = sum(score_deck)

    # New calculation for player potentials
    total_score = player_0_score + player_1_score
    if total_score != 0:
        player_0_potential += score_potential * (player_0_score / total_score)
        player_1_potential += score_potential * (player_1_score / total_score)

    def determine_card_advantage(player_hand):
        high_cards = [card for card in player_hand if card > 7]
        return len(high_cards) / len(player_hand) if player_hand else 0

    player_0_card_advantage = determine_card_advantage(player_0_hand)
    player_1_card_advantage = determine_card_advantage(player_1_hand)

    player_0_expected_scores = [player_0_score + player_0_potential + sum(max(score_card, p0_card) for score_card, p0_card in zip(score_cards, combo)) + player_0_card_advantage for combo in player_0_strategic_combinations]
    player_1_expected_scores = [player_1_score + player_1_potential + sum(max(score_card, p1_card) for score_card, p1_card in zip(score_cards, combo)) + player_1_card_advantage for combo in player_1_strategic_combinations]

    player_0_expected_score = sum(player_0_expected_scores) / len(player_0_expected_scores) if player_0_expected_scores else player_0_score
    player_1_expected_score = sum(player_1_expected_scores) / len(player_1_expected_scores) if player_1_expected_scores else player_1_score

    intermediate_values = {
        'player_0_potential': player_0_potential,
        'player_1_potential': player_1_potential,
        'score_potential': score_potential,
        'player_0_expected_scores': player_0_expected_scores,
        'player_1_expected_scores': player_1_expected_scores,
        'player_0_card_advantage': player_0_card_advantage,
        'player_1_card_advantage': player_1_card_advantage,
        'player_0_strategic_combinations': player_0_strategic_combinations,
        'player_1_strategic_combinations': player_1_strategic_combinations
    }

    return (player_0_expected_score, player_1_expected_score), intermediate_values""",
"""def evaluate_state(state) -> tuple[tuple[float, float], dict]:
    score_cards = state[0]
    player_0_played_cards = state[1]
    player_1_played_cards = state[2]
    is_turn = state[3]
    player_0_score = state[4]
    player_1_score = state[5]
    score_deck = state[6]
    player_0_hand = state[7]
    player_1_hand = state[8]
    
    # Function to evaluate the potential value of each card in the players' hands
    def evaluate_card_value(player_hand, player_played_cards, score_cards):
        card_values = {}  # Dictionary to store potential values of each card in the hand
        
        # Loop through each card in the player's hand
        for card in player_hand:
            total_value = card  # Start with the base value of the card
            # Consider the impact of playing this card on the game state
            for played_card in player_played_cards:
                # Simple logic to calculate the potential value of the card based on previously played cards
                if card > played_card:
                    total_value += 1  # Increment value if the player has a higher card
                elif card == played_card:
                    total_value += 0.5  # Increment value if the player has an equal card
                
            # Consider the impact of score cards on the card's value
            for score_card in score_cards:
                total_value += score_card  # Add the value of score cards to the card's potential value
            
            card_values[card] = total_value  # Store the potential value of the card
        
        return card_values
    
    # Evaluate potential values of cards for each player
    player_0_card_values = evaluate_card_value(player_0_hand, player_0_played_cards, score_cards)
    player_1_card_values = evaluate_card_value(player_1_hand, player_1_played_cards, score_cards)
    
    # Calculate the expected final scores for each player based on potential card values
    player_0_expected_score = player_0_score + sum(player_0_card_values.values())
    player_1_expected_score = player_1_score + sum(player_1_card_values.values())
    
    # Calculate the value of remaining score cards in the deck
    remaining_score_cards_value = sum(score_deck)
    
    # Storing the intermediate values used to calculate the scores
    intermediate_values = {
        'player_0_card_values': player_0_card_values,
        'player_1_card_values': player_1_card_values,
        'remaining_score_cards_value': remaining_score_cards_value
    }
    
    return (player_0_expected_score, player_1_expected_score), intermediate_values""",
"""def evaluate_state(state) -> tuple[tuple[float, float], dict]:
    score_cards = state[0]
    player_0_played_cards = state[1]
    player_1_played_cards = state[2]
    is_turn = state[3]
    player_0_score = state[4]
    player_1_score = state[5]
    score_deck = state[6]
    player_0_hand = state[7]
    player_1_hand = state[8]

    # Initialize variables for potential scores
    player_0_potential = sum(player_0_hand)
    player_1_potential = sum(player_1_hand)
    score_potential = sum(score_deck)

    # Determine the number of rounds left
    rounds_left = len(score_deck)

    # Update potential scores based on the cards played so far
    for i in range(len(player_0_played_cards)):
        if player_0_played_cards[i] > player_1_played_cards[i]:
            player_0_potential += score_cards[i]
        elif player_1_played_cards[i] > player_0_played_cards[i]:
            player_1_potential += score_cards[i]

    # Add half of the score potential to the player who has the turn
    if is_turn:
        player_0_potential += score_potential / 2
    else:
        player_1_potential += score_potential / 2

    # Calculate expected scores
    player_0_expected_score = player_0_score + player_0_potential
    player_1_expected_score = player_1_score + player_1_potential

    # Create a factor based on the composition of the remaining deck
    if len(score_deck) > 0:
        high_value_cards = [card for card in score_deck if card > 5]  # Assuming high-value cards are those above 5
        low_value_cards = [card for card in score_deck if card <= 5]  # Assuming low-value cards are those 5 or below

        high_value_factor = len(high_value_cards) / len(score_deck)
        low_value_factor = len(low_value_cards) / len(score_deck)

        # Adjust potential scores based on the deck composition factors
        player_0_potential *= high_value_factor
        player_1_potential *= low_value_factor

        # Store intermediate values in a dictionary
        intermediate_values = {
            'player_0_potential': player_0_potential,
            'player_1_potential': player_1_potential,
            'score_potential': score_potential,
            'rounds_left': rounds_left,
            'high_value_factor': high_value_factor,
            'low_value_factor': low_value_factor
        }
    else:
        # Store intermediate values in a dictionary without deck composition factors (to avoid division by zero)
        intermediate_values = {
            'player_0_potential': player_0_potential,
            'player_1_potential': player_1_potential,
            'score_potential': score_potential,
            'rounds_left': rounds_left
        }

    return (player_0_expected_score, player_1_expected_score), intermediate_values""",
"""def evaluate_state(state) -> tuple[tuple[float, float], dict]:
    score_cards = state[0]
    player_0_played_cards = state[1]
    player_1_played_cards = state[2]
    is_turn = state[3]
    player_0_score = state[4]
    player_1_score = state[5]
    score_deck = state[6]
    player_0_hand = state[7]
    player_1_hand = state[8]

    # Function to calculate the weighted potential value for a player's hand
    def calculate_weighted_potential(hand, win_prob):
        total_weighted_value = 0
        for card in hand:
            card_weight = card * win_prob
            total_weighted_value += card_weight
        return total_weighted_value

    # Calculate the win probability for player 0 and player 1 based on card distribution
    def calculate_win_probability(hand, opponent_hand):
        total_outcomes = 0
        favorable_outcomes = 0
        for card in hand:
            for opponent_card in opponent_hand:
                total_outcomes += 1
                if card > opponent_card:
                    favorable_outcomes += 1
        if total_outcomes == 0:
            return 0
        return favorable_outcomes / total_outcomes

    # Calculate initial potentials
    player_0_potential = calculate_weighted_potential(player_0_hand, calculate_win_probability(player_0_hand, player_1_hand)) + sum(score_deck)
    player_1_potential = calculate_weighted_potential(player_1_hand, calculate_win_probability(player_1_hand, player_0_hand)) + sum(score_deck)

    # Add half of the score potential to the player who has the turn
    if is_turn:
        player_0_potential += sum(score_deck) / 2
    else:
        player_1_potential += sum(score_deck) / 2

    # Calculate expected scores
    player_0_expected_score = player_0_score + player_0_potential
    player_1_expected_score = player_1_score + player_1_potential

    # Calculate intermediate values
    intermediate_values = {
        'player_0_potential': player_0_potential,
        'player_1_potential': player_1_potential,
        'player_0_win_probability': calculate_win_probability(player_0_hand, player_1_hand),
        'player_1_win_probability': calculate_win_probability(player_1_hand, player_0_hand)
    }

    return (player_0_expected_score, player_1_expected_score), intermediate_values"""
]
