Complete this chess engine;

```
import chess
import chess.engine
import chess.syzygy
import chess.polyglot
import random
import time
import threading
import concurrent.futures
from typing import Optional, Tuple, List, Dict
import math
import hashlib
import logging
from collections import defaultdict
import copy

import torch
import torch.nn as nn
import torch.nn.functional as F

# --- Neural Network Definition ---

class ResidualBlock(nn.Module):
    def __init__(self, features):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Conv2d(features, features, kernel_size=3, padding=1)
        self.bn1 = nn.BatchNorm2d(features)
        self.conv2 = nn.Conv2d(features, features, kernel_size=3, padding=1)
        self.bn2 = nn.BatchNorm2d(features)

    def forward(self, x):
        residual = x
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += residual
        out = F.relu(out)
        return out

class ChessNet(nn.Module):
    def __init__(self, num_residual_blocks=20):
        super(ChessNet, self).__init__()
        self.conv1 = nn.Conv2d(14, 256, kernel_size=3, padding=1)  # 14 feature planes
        self.bn1 = nn.BatchNorm2d(256)
        self.residual_blocks = nn.Sequential(
            *[ResidualBlock(256) for _ in range(num_residual_blocks)]
        )
        self.policy_head = nn.Conv2d(256, 2, kernel_size=1)
        self.policy_bn = nn.BatchNorm2d(2)
        self.policy_fc = nn.Linear(2 * 8 * 8, 1968)  # Adjust based on possible moves
        self.value_head = nn.Conv2d(256, 1, kernel_size=1)
        self.value_bn = nn.BatchNorm2d(1)
        self.value_fc1 = nn.Linear(1 * 8 * 8, 256)
        self.value_fc2 = nn.Linear(256, 1)

    def forward(self, x):
        x = F.relu(self.bn1(self.conv1(x)))
        x = self.residual_blocks(x)
        
        # Policy Head
        p = F.relu(self.policy_bn(self.policy_head(x)))
        p = p.view(-1, 2 * 8 * 8)
        p = self.policy_fc(p)
        p = F.log_softmax(p, dim=1)
        
        # Value Head
        v = F.relu(self.value_bn(self.value_head(x)))
        v = v.view(-1, 1 * 8 * 8)
        v = F.relu(self.value_fc1(v))
        v = torch.tanh(self.value_fc2(v))
        
        return p, v

# --- Zobrist Hashing Implementation ---

import random
random.seed(0)  # For reproducibility
ZOBRIST_TABLE = [[[random.getrandbits(64) for _ in range(12)] for _ in range(64)] for _ in range(2)]  # [color][square][piece]
ZOBRIST_CASTLING = [random.getrandbits(64) for _ in range(16)]
ZOBRIST_ENPASSANT = [random.getrandbits(64) for _ in range(8)]
ZOBRIST_BLACK_TO_MOVE = random.getrandbits(64)

def compute_zobrist_hash(board: chess.Board) -> int:
    h = 0
    for square in chess.SQUARES:
        piece = board.piece_at(square)
        if piece:
            piece_index = piece.piece_type - 1 + (0 if piece.color == chess.WHITE else 6)
            h ^= ZOBRIST_TABLE[piece.color][square][piece_index]
    # Castling rights
    castling = board.castling_rights
    h ^= ZOBRIST_CASTLING[castling]
    # En passant
    if board.ep_square:
        h ^= ZOBRIST_ENPASSANT[chess.square_file(board.ep_square)]
    # Black to move
    if board.turn == chess.BLACK:
        h ^= ZOBRIST_BLACK_TO_MOVE
    return h

# --- Transposition Table Entry ---

class TranspositionTableEntry:
    def __init__(self, depth: int, value: float, flag: str, move: Optional[chess.Move]):
        self.depth = depth
        self.value = value
        self.flag = flag  # "EXACT", "LOWERBOUND", "UPPERBOUND"
        self.move = move

# --- MCTS Node for Monte Carlo Tree Search (Optional) ---

class MCTSNode:
    def __init__(self, board: chess.Board, parent=None, move=None):
        self.board = board
        self.parent = parent
        self.move = move
        self.children = {}
        self.visits = 0
        self.wins = 0
        self.potential_moves = list(board.legal_moves)

    def is_leaf(self):
        return len(self.children) == 0

    def expand(self):
        if not self.potential_moves:
            return self
        move = self.potential_moves.pop()
        new_board = copy.deepcopy(self.board)
        new_board.push(move)
        child_node = MCTSNode(new_board, parent=self, move=move)
        self.children[move] = child_node
        return child_node

    def best_child(self, c_param=1.4):
        choices_weights = [
            (child.wins / child.visits) + c_param * math.sqrt((2 * math.log(self.visits) / child.visits))
            for child in self.children.values()
        ]
        return list(self.children.values())[choices_weights.index(max(choices_weights))]

    def backpropagate(self, result):
        self.visits += 1
        self.wins += result
        if self.parent:
            self.parent.backpropagate(result)

# --- DevilGambit Chess Engine Implementation ---

class DevilGambitChessEngine:
    def __init__(self, 
                 max_depth: int = 5, 
                 iterative_depth_limit: int = 20, 
                 stockfish_path: str = r"stockfish",  # Ensure Stockfish is in PATH or provide full path
                 tablebase_path: str = r"path_to_syzygy_tablebases",  # Update with actual path
                 opening_book_path: str = r"path_to_opening_book.bin",  # Update with actual path
                 time_per_move: float = 5.0):
        self.board = chess.Board()
        self.max_depth = max_depth
        self.iterative_depth_limit = iterative_depth_limit
        self.stockfish_path = stockfish_path
        self.tablebase_path = tablebase_path
        self.opening_book_path = opening_book_path
        self.time_per_move = time_per_move
        self.gambit_moves = []
        self.passive_moves = []
        self.history = defaultdict(list)  # For Smooth Transposition
        self.transposition_table: Dict[int, TranspositionTableEntry] = {}
        self.engine = chess.engine.SimpleEngine.popen_uci(self.stockfish_path)
        self.tablebase = chess.syzygy.open_tablebase(self.tablebase_path)
        self.opening_book = None
        self.load_opening_book()
        self.piece_values = PIECE_VALUES
        self.positional_weights = PIECE_SQUARE_TABLES
        self.lock = threading.Lock()  # For thread-safe operations
        self.move_order_history = defaultdict(int)  # For History Heuristic

        # Load neural network
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        self.model = ChessNet(num_residual_blocks=20).to(self.device)
        try:
            self.model.load_state_dict(torch.load('path_to_pretrained_model.pth'))
            self.model.eval()
            logging.info("Neural network model loaded successfully.")
        except Exception as e:
            logging.error(f"Failed to load neural network model: {e}")
            # Handle missing model appropriately

    def load_opening_book(self):
        """Load an opening book from a file."""
        try:
            self.opening_book = chess.polyglot.MemoryMappedReader(self.opening_book_path)
            logging.info("Opening book loaded successfully.")
        except Exception as e:
            logging.error(f"Failed to load opening book: {e}")
            self.opening_book = None

    def board_to_tensor(self, board: chess.Board) -> torch.Tensor:
        """
        Convert a chess.Board instance to a tensor suitable for the neural network.
        """
        board_tensor = torch.zeros(14, 8, 8)  # 14 feature planes

        # Piece planes
        piece_map = board.piece_map()
        for square, piece in piece_map.items():
            row = 7 - chess.square_rank(square)
            col = chess.square_file(square)
            piece_type = piece.piece_type
            color = 0 if piece.color == chess.WHITE else 1
            plane = (piece_type - 1) + (6 * color)  # 0-5 for white, 6-11 for black
            if plane < 14:
                board_tensor[plane, row, col] = 1

        # Additional planes can include castling rights, en passant, etc.
        # Example: Castling rights
        if board.has_kingside_castling_rights(chess.WHITE):
            board_tensor[12, :, :] = 1
        if board.has_queenside_castling_rights(chess.WHITE):
            board_tensor[13, :, :] = 1
        # Similarly for Black castling rights, en passant, etc.

        return board_tensor.unsqueeze(0)  # Add batch dimension

    def evaluate_board_nn(self) -> float:
        """Evaluate the board using the neural network."""
        with torch.no_grad():
            board_tensor = self.board_to_tensor(self.board).to(self.device)
            policy, value = self.model(board_tensor)
            eval_score = value.item() * 1000  # Scale appropriately
        return eval_score

    def evaluate_board(self) -> float:
        """Override the traditional evaluation with neural network evaluation."""
        return self.evaluate_board_nn()

    def is_aggressive_move(self, move: chess.Move) -> bool:
        """Determine if a move is aggressive based on captures and checks."""
        return self.board.is_capture(move) or self.board.gives_check(move)

    def order_moves(self, moves: List[chess.Move]) -> List[chess.Move]:
        """Order moves to improve alpha-beta pruning efficiency using MVV-LVA and History Heuristic."""
        def move_score(move):
            score = 0
            if self.is_aggressive_move(move):
                score += 100
            # MVV-LVA heuristic
            if self.board.is_capture(move):
                captured_piece = self.board.piece_at(move.to_square)
                if captured_piece:
                    score += self.piece_values[captured_piece.piece_type] * 10
                    score += 1 / self.piece_values[self.board.piece_at(move.from_square).piece_type]
            # History heuristic
            score += self.move_order_history[move]
            return score

        return sorted(moves, key=lambda move: move_score(move), reverse=True)

    def gambit_pruning(self, depth: int, alpha: float, beta: float, is_maximizing: bool) -> Tuple[float, Optional[chess.Move]]:
        """Minimax with alpha-beta pruning, quiescence search, transposition tables, and advanced pruning."""
        current_time = time.time()
        if current_time - self.start_time > self.time_limit:
            logging.debug("Time limit exceeded during search.")
            return self.evaluate_board(), None

        zobrist_hash = compute_zobrist_hash(self.board)
        if zobrist_hash in self.transposition_table:
            entry = self.transposition_table[zobrist_hash]
            if entry.depth >= depth:
                if entry.flag == "EXACT":
                    return entry.value, entry.move
                elif entry.flag == "LOWERBOUND":
                    alpha = max(alpha, entry.value)
                elif entry.flag == "UPPERBOUND":
                    beta = min(beta, entry.value)
                if alpha >= beta:
                    return entry.value, entry.move

        # Endgame tablebase check
        if self.is_endgame():
            tablebase_result = self.query_tablebase()
            if tablebase_result is not None:
                return tablebase_result, None

        if depth == 0:
            return self.quiescence_search(alpha, beta), None

        if self.board.is_game_over():
            result = self.get_game_result()
            return result, None

        legal_moves = list(self.board.legal_moves)

        # Move ordering: prioritize aggressive moves using Deadly Moves Heuristics and MVV-LVA
        legal_moves = self.order_moves(legal_moves)

        best_move = None

        if is_maximizing:
            max_eval = float('-inf')
            for move in legal_moves:
                self.board.push(move)
                eval, _ = self.gambit_pruning(depth - 1, alpha, beta, False)
                self.board.pop()
                if eval > max_eval:
                    max_eval = eval
                    best_move = move
                alpha = max(alpha, eval)
                if beta <= alpha:
                    break  # Beta cutoff
            # Store in transposition table
            flag = "EXACT"
            if max_eval <= alpha:
                flag = "UPPERBOUND"
            elif max_eval >= beta:
                flag = "LOWERBOUND"
            self.transposition_table[zobrist_hash] = TranspositionTableEntry(depth, max_eval, flag, best_move)
            return max_eval, best_move
        else:
            min_eval = float('inf')
            for move in legal_moves:
                self.board.push(move)
                eval, _ = self.gambit_pruning(depth - 1, alpha, beta, True)
                self.board.pop()
                if eval < min_eval:
                    min_eval = eval
                    best_move = move
                beta = min(beta, eval)
                if beta <= alpha:
                    break  # Alpha cutoff
            # Store in transposition table
            flag = "EXACT"
            if min_eval <= alpha:
                flag = "UPPERBOUND"
            elif min_eval >= beta:
                flag = "LOWERBOUND"
            self.transposition_table[zobrist_hash] = TranspositionTableEntry(depth, min_eval, flag, best_move)
            return min_eval, best_move

    def quiescence_search(self, alpha: float, beta: float) -> float:
        """Extend search in volatile positions to avoid horizon effect."""
        stand_pat = self.evaluate_board()
        if stand_pat >= beta:
            return beta
        if stand_pat > alpha:
            alpha = stand_pat

        # Explore only aggressive moves (captures and checks)
        aggression_moves = [move for move in self.board.legal_moves if self.is_aggressive_move(move)]
        aggression_moves = self.order_moves(aggression_moves)

        for move in aggression_moves:
            self.board.push(move)
            score = -self.quiescence_search(-beta, -alpha)
            self.board.pop()

            if score >= beta:
                return beta
            if score > alpha:
                alpha = score
        return alpha

    def get_game_result(self) -> float:
        """Assign evaluation based on the game result."""
        if self.board.is_checkmate():
            if self.board.turn == chess.WHITE:
                return -math.inf  # Black wins
            else:
                return math.inf   # White wins
        elif self.board.is_stalemate() or self.board.is_insufficient_material() or \
             self.board.can_claim_fifty_moves() or self.board.can_claim_threefold_repetition():
            return 0  # Draw
        return 0  # Default

    def iterated_deepening(self) -> Optional[chess.Move]:
        """Iterative deepening with advanced features."""
        best_move = None
        self.start_time = time.time()
        self.time_limit = self.time_per_move

        with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:
            future = executor.submit(self.iterative_deepening_search)
            try:
                best_move = future.result(timeout=self.time_limit)
            except concurrent.futures.TimeoutError:
                logging.info("Iterative deepening search timed out.")
                # best_move remains None or last best_move
        return best_move

    def iterative_deepening_search(self) -> Optional[chess.Move]:
        """Perform iterated deepening with dynamic time management and PVS."""
        best_move = None
        for depth in range(1, self.iterative_depth_limit + 1):
            logging.debug(f"Searching at depth {depth}")
            eval, move = self.pvs(depth, float('-inf'), float('inf'))
            if move:
                best_move = move
                logging.debug(f"Best move at depth {depth}: {move} with eval {eval}")
            current_time = time.time()
            if current_time - self.start_time > self.time_limit:
                break
        return best_move

    def pvs(self, depth: int, alpha: float, beta: float) -> Tuple[float, Optional[chess.Move]]:
        """Principal Variation Search (PVS) implementation."""
        legal_moves = list(self.board.legal_moves)
        legal_moves = self.order_moves(legal_moves)
        best_move = None

        for i, move in enumerate(legal_moves):
            self.board.push(move)
            if i == 0:
                score, _ = self.gambit_pruning(depth-1, alpha, beta, False)
            else:
                score, _ = self.gambit_pruning(depth-1, alpha, alpha + 1, False)
                if alpha < score < beta:
                    score, _ = self.gambit_pruning(depth-1, score, beta, False)
            self.board.pop()

            if score > alpha:
                alpha = score
                best_move = move
                if alpha >= beta:
                    break  # Beta cutoff

        return alpha, best_move

    def is_endgame(self) -> bool:
        """Determine if the current position is in the endgame."""
        # Simple criterion: total material below a threshold
        total_material = self.calculate_material()
        return abs(total_material) < 1500  # Adjust threshold as needed

    def query_tablebase(self) -> Optional[float]:
        """Query the endgame tablebase for a perfect evaluation."""
        try:
            result = self.tablebase.probe_wdl(self.board)
            if result is not None:
                if result == chess.syzygy.WDL_WIN:
                    return math.inf if self.board.turn == chess.WHITE else -math.inf
                elif result == chess.syzygy.WDL_DRAW:
                    return 0
                elif result == chess.syzygy.WDL_LOSS:
                    return -math.inf if self.board.turn == chess.WHITE else math.inf
        except Exception as e:
            logging.error(f"Tablebase query failed: {e}")
        return None

    def make_gambit_move(self) -> Optional[chess.Move]:
        """Attempt to make a gambit move early in the game."""
        # Attempt to play from the opening book
        if self.opening_book:
            opening_move = self.play_from_opening_book()
            if opening_move:
                self.gambit_moves.append(opening_move)
                self.move_order_history[opening_move] += 1  # Update history heuristic
                logging.info(f"Played opening book move: {opening_move}")
                return opening_move

        # If not in opening book, proceed with gambit strategy
        legal_moves = list(self.board.legal_moves)
        gambit_moves = [move for move in legal_moves if self.is_aggressive_move(move)]

        if gambit_moves:
            move = random.choice(gambit_moves)
            self.gambit_moves.append(move)
            self.move_order_history[move] += 1  # Update history heuristic
            logging.info(f"Made gambit move: {move}")
            return move

        if self.board.fullmove_number < 10:
            return self.gambit_variation()

        return random.choice(legal_moves) if legal_moves else None

    def play_from_opening_book(self) -> Optional[chess.Move]:
        """Attempt to select a move from the opening book."""
        try:
            entry = self.opening_book.choice(self.board)
            if entry and entry.move:
                return entry.move
        except Exception as e:
            logging.error(f"Opening book error: {e}")
        return None

    def gambit_variation(self) -> Optional[chess.Move]:
        """Implement specific gambit variations."""
        # Example: Always try to sacrifice a pawn for initiative
        sacrificial_moves = [move for move in self.board.legal_moves 
                             if self.board.is_capture(move) and 
                             self.board.piece_at(move.to_square).piece_type == chess.PAWN]
        if sacrificial_moves:
            move = random.choice(sacrificial_moves)
            self.gambit_moves.append(move)
            self.move_order_history[move] += 1  # Update history heuristic
            logging.info(f"Made sacrificial gambit move: {move}")
            return move
        return None

    def passive_tactic_play(self) -> Optional[chess.Move]:
        """Fallback strategy that emphasizes defense and solid play."""
        legal_moves = list(self.board.legal_moves)
        if not legal_moves:
            return None

        # Evaluate moves and choose one that minimizes opponent's opportunities
        best_move = max(legal_moves, key=lambda move: self.evaluate_move(move))
        self.passive_moves.append(best_move)
        self.move_order_history[best_move] += 1  # Update history heuristic
        logging.info(f"Made passive tactic move: {best_move}")
        return best_move

    def evaluate_move(self, move: chess.Move) -> float:
        """Evaluate a single move."""
        self.board.push(move)
        evaluation = self.evaluate_board()
        self.board.pop()
        return evaluation

    def stockfish_helper(self) -> Optional[chess.Move]:
        """Use Stockfish engine for assistance in complex positions."""
        try:
            result = self.engine.play(self.board, chess.engine.Limit(time=0.1))
            if result:
                logging.info(f"Stockfish suggests move: {result.move}")
                return result.move
        except Exception as e:
            logging.error(f"Stockfish error: {e}")
        return None

    def play_engine_move(self, use_gambit: bool = True) -> Optional[chess.Move]:
        """Main engine move logic integrating all strategies."""
        move = None

        # Use Gambit move if conditions met
        if use_gambit and len(self.gambit_moves) < 5:
            move = self.make_gambit_move()

        # If opening book and gambit move not made, try opening book
        if move is None and self.opening_book:
            move = self.play_from_opening_book()

        # If no gambit move made, proceed with deep search
        if move is None:
            move = self.iterated_deepening()

        # If deep search fails or time runs out, use passive tactic play
        if move is None:
            move = self.passive_tactic_play()

        # If still no move, fallback to Stockfish
        if move is None and len(self.board.piece_map()) < 10:
            move = self.stockfish_helper()

        if move:
            self.board.push(move)
            self.smooth_transposition(move)
            # Update history heuristic for killer moves or commonly good moves
            self.move_order_history[move] += 1
            logging.info(f"Engine played move: {move}")
        return move

    def smooth_transposition(self, move: chess.Move):
        """Adjust move selection to avoid transposition repetition."""
        move_key = compute_zobrist_hash(self.board)
        self.history[move_key].append(move)
        # Potentially deprioritize moves that lead to repeated positions

    def display_board(self) -> str:
        """Returns the current board state as a Unicode string."""
        return self.board.unicode()

    def close(self):
        """Close the Stockfish engine and tablebase to release resources."""
        self.engine.quit()
        self.tablebase.close()

    def __del__(self):
        """Destructor to ensure resources are freed."""
        self.close()

# --- Main Function to Run the Engine ---

def main():
    # Initialize the engine with appropriate paths
    engine = DevilGambitChessEngine(
        max_depth=5, 
        iterative_depth_limit=20, 
        stockfish_path=r"stockfish",  # Update with actual path
        tablebase_path=r"path_to_syzygy_tablebases",  # Update with actual path
        opening_book_path=r"path_to_opening_book.bin",  # Update with actual path
        time_per_move=5.0
    )

    print("Welcome to The DEVIL's Gambit Chess Engine!")
    print("This advanced engine plays aggressively with gambit variations and passive tactics when needed.")
    print("It leverages a deep neural network for board evaluation, Stockfish's assistance, Syzygy tablebases for endgames, and an opening book.")
    print("Enter your moves in standard algebraic notation (e.g., e4, Nf3). Type 'quit' to exit.")

    while not engine.board.is_game_over():
        print("\nCurrent Board:")
        print(engine.display_board())
        print(f"FEN: {engine.board.fen()}")
        user_move = input("Your move: ").strip()
        if user_move.lower() == 'quit':
            break
        try:
            move = engine.board.parse_san(user_move)
            engine.board.push(move)
        except ValueError:
            try:
                move = chess.Move.from_uci(user_move)
                if move in engine.board.legal_moves:
                    engine.board.push(move)
                else:
                    print("Move is not legal. Please try again.")
                    continue
            except Exception:
                print("Invalid move format. Please try again.")
                continue

        if engine.board.is_game_over():
            break

        print("Engine is thinking...")
        start_time = time.time()
        engine_move = engine.play_engine_move()
        end_time = time.time()
        if engine_move:
            print(f"Engine move: {engine_move} (computed in {end_time - start_time:.2f} seconds)")
        else:
            print("Engine did not make a move.")

    print("\nGame over!")
    result = engine.board.result()
    print(f"Result: {result}")
    print(engine.display_board())
    engine.close()

if __name__ == "__main__":
    main()
```