Further enhance and add in more advanced features and some more advanced functions w/NNUE neural network up to 100 epoch.

```
import chess
import chess.engine
import random

class PhoenixKnight:
    def __init__(self):
        self.board = chess.Board()
        self.memory_cycle = []
        self.resilience_factor = 0.5
        self.self_healing_factor = 1.0
        self.history = []
        self.phoenix_transformation_activated = False

    # Phoenix Memory Cycle: Retains and evaluates moves that previously led to setbacks
    def update_memory_cycle(self, move, success):
        if len(self.memory_cycle) >= 10:
            self.memory_cycle.pop(0)
        self.memory_cycle.append((move, success))

    # Flame-Weighted Move Ordering: Orders moves based on historical resilience data
    def flame_weighted_ordering(self, moves):
        def move_weight(move):
            move_data = [(m, s) for m, s in self.memory_cycle if m == move]
            if move_data:
                _, success = move_data[0]
                return success
            return 1  # Neutral weight if not in memory
        return sorted(moves, key=move_weight, reverse=True)

    # Resilience-Driven Evaluation Function: Adjusts move evaluation based on "positional resilience"
    def evaluate_position(self):
        material_score = sum(
            [self.board.piece_at(i).piece_type for i in range(64) if self.board.piece_at(i)]
        )
        # Factor in self-healing, resilience, and positional longevity for moves
        return (material_score * self.resilience_factor) * self.self_healing_factor

    # Quantum Backtracking: Re-assesses previous moves after a significant setback
    def quantum_backtracking(self):
        if len(self.history) >= 2:
            last_move, last_score = self.history[-2]
            if last_score < 0:
                self.board.pop()
                self.history.pop()
                print("Quantum Backtracking Activated: Revisiting previous position")

    # Phoenix Transformation Core: Triggers enhanced strategic moves under tough conditions
    def phoenix_transformation_core(self):
        if not self.phoenix_transformation_activated:
            self.self_healing_factor += 0.1
            self.phoenix_transformation_activated = True
            print("Phoenix Transformation Core Activated")

    # Move Selection with adaptive memory, resilience, and quantum backtracking
    def select_best_move(self):
        legal_moves = list(self.board.legal_moves)
        if self.board.fullmove_number > 10 and random.random() < 0.05:
            self.phoenix_transformation_core()

        ordered_moves = self.flame_weighted_ordering(legal_moves)
        best_move = None
        best_score = float("-inf")

        for move in ordered_moves:
            self.board.push(move)
            score = self.evaluate_position()
            self.board.pop()

            if score > best_score:
                best_score = score
                best_move = move

        self.history.append((best_move, best_score))
        if best_score < 0:
            self.quantum_backtracking()

        return best_move

    # Plays a single move and updates the engine's internal state
    def play_move(self):
        move = self.select_best_move()
        self.board.push(move)
        self.update_memory_cycle(move, self.evaluate_position() > 0)
        print(f"Phoenix Knight played: {move}")

    # Main game loop
    def play_game(self):
        while not self.board.is_game_over():
            self.play_move()
            print(self.board)

# Initialize and play
engine = PhoenixKnight()
engine.play_game()