"""
Attention Mechanisms for Hybrid Recommendation Systems

This module provides implementations of attention mechanisms tailored for hybrid
recommendation systems. Attention mechanisms allow the model to focus on relevant
parts of the input data, improving the quality of recommendations by considering
contextual and sequential information.

Key Features:
- Implements self-attention and multi-head attention mechanisms.
- Enhances hybrid models by integrating attention layers.
- Supports attention-based feature extraction and representation learning.

Classes:
- ATTENTION_MECHANISMS: Core class for implementing attention mechanisms in hybrid
  recommendation systems.

Usage:
Instantiate the ATTENTION_MECHANISMS class to integrate attention layers into your
hybrid recommendation model. Use the provided methods to train and apply attention
mechanisms to your data.

Example:
    attention_model = ATTENTION_MECHANISMS()
    attention_model.train(user_item_matrix, attention_features)
    enhanced_recommendations = attention_model.recommend(user_id, top_n=10)
"""
pass
