以下是一个绑定根节点的Alpha Beta剪枝算法的代码示例：

import math

# 评估函数，用于评估当前局面的分数
def evaluate(board):
    # 这里使用一个简单的评估函数，返回玩家1的棋子数减去玩家2的棋子数
    count1 = sum([row.count(1) for row in board])
    count2 = sum([row.count(2) for row in board])
    return count1 - count2

# Alpha Beta剪枝算法
def alphabeta(board, depth, alpha, beta, maximizingPlayer):
    if depth == 0 or is_terminal(board):
        return evaluate(board)

    if maximizingPlayer:
        max_eval = -math.inf
        for move in generate_moves(board):
            board_copy = make_move(board, move)
            eval = alphabeta(board_copy, depth - 1, alpha, beta, False)
            max_eval = max(max_eval, eval)
            alpha = max(alpha, eval)
            if alpha >= beta:
                break
        return max_eval
    else:
        min_eval = math.inf
        for move in generate_moves(board):
            board_copy = make_move(board, move)
            eval = alphabeta(board_copy, depth - 1, alpha, beta, True)
            min_eval = min(min_eval, eval)
            beta = min(beta, eval)
            if alpha >= beta:
                break
        return min_eval

# 生成所有可能的合法移动
def generate_moves(board):
    moves = []
    # 通过遍历棋盘的每个位置，找到可以下棋的空位
    for i in range(len(board)):
        for j in range(len(board[i])):
            if board[i][j] == 0:
                moves.append((i, j))
    return moves

# 执行一个合法移动
def make_move(board, move):
    i, j = move
    player = 1 if sum([row.count(1) for row in board]) == sum([row.count(2) for row in board]) else 2
    board_copy = [row.copy() for row in board]
    board_copy[i][j] = player
    return board_copy

# 判断是否游戏结束
def is_terminal(board):
    # 游戏结束条件：棋盘上没有空位或任一玩家已经赢得比赛
    if not any(0 in row for row in board) or evaluate(board) == math.inf or evaluate(board) == -math.inf:
        return True
    return False

# 示例使用
board = [[0, 0, 0],
         [0, 0, 0],
         [0, 0, 0]]
depth = 3
alpha = -math.inf
beta = math.inf
maximizingPlayer = True
best_move = None
max_eval = -math.inf

for move in generate_moves(board):
    board_copy = make_move(board, move)
    eval = alphabeta(board_copy, depth - 1, alpha, beta, False)
    if eval > max_eval:
        max_eval = eval
        best_move = move
    alpha = max(alpha, eval)

print("最佳移动：", best_move)
print("最佳分数：", max_eval)

这个代码示例是一个简化的井字棋游戏，通过Alpha Beta剪枝算法找到最优解。在示例中，使用一个评估函数来评估当前局面的分数，然后使用Alpha Beta剪枝算法来搜索最佳移动。代码中使用了递归来模拟棋局，同时使用了alpha和beta来进行剪枝，从而提高搜索效率。最终输出最佳移动和对应的分数。注意，此示例仅用于说明Alpha Beta剪枝算法的基本原理，实际应用中可能需要根据具体情况进行修改和优化。