#!/usr/bin/env python3
"""
MetaLearner Module for Adaptive Updates
----------------------------------------
This module provides a placeholder function for computing parameter updates using a meta-learning strategy.
Currently, it applies a simple transformation to the noisy gradient using a tanh activation and scales it by the learning rate.
In the future, this function will be extended to incorporate advanced meta-learning techniques (e.g., MAML/Reptile) or quantum-inspired updates.

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

import numpy as np


def meta_update(noisy_grad, learning_rate):
    """
    Computes the parameter update using a baseline meta-learning strategy.

    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.
    """
    # Apply tanh to stabilize the noisy gradient and scale by the learning rate.
    update = learning_rate * np.tanh(noisy_grad)
    return update 