#!/usr/bin/env python3
"""
Advanced MetaLearner Module for Adaptive Updates
---------------------------------------------------
This module is a placeholder for future advanced meta-learning updates.
In future iterations, this module will integrate advanced techniques such as:
- MAML/Reptile: leveraging gradient-based meta-learning methods
- Quantum-inspired optimization: incorporating techniques inspired by quantum computing
- Adaptive strategies combining historical gradient data and loss trajectory analysis

For now, the advanced_meta_update function delegates to the basic meta_update function.

Author: Quantum Pioneers
Date: 2023-10-XX
"""

import numpy as np
from meta_learner import meta_update


def advanced_meta_update(noisy_grad, learning_rate):
    """ 
    Computes the parameter update using an advanced meta-learning strategy.
    This is a placeholder function that currently delegates to the basic meta_update function.
    Future updates may include:
      - Integration of MAML/Reptile inspired adaptation
      - Incorporation of second-order gradient information
      - Use of quantum-inspired optimization techniques
    
    Args:
        noisy_grad (np.array): The noisy gradient for the parameters.
        learning_rate (float): The scaling factor for parameter updates.
      
    Returns:
        np.array: The computed update for the parameters.
    """
    # Currently, delegate to the basic meta_update as a fallback
    update = meta_update(noisy_grad, learning_rate)
    return update 