import tensorflow as tf

def init_gpu():
    """
    Verifies Nvidia GPU presence, configures virtual memory limits, 
    and handles dynamic memory growth to maximize processing performance.
    """
    gpus = tf.config.list_physical_devices('GPU')
    if gpus:
        print(f"✅ Nvidia GPU Detected: {gpus}")
        try:
            for gpu in gpus:
                # Set memory growth so TensorFlow only allocates GPU VRAM as needed, 
                # rather than aggressively pre-allocating 100% of it up front.
                tf.config.experimental.set_memory_growth(gpu, True)
            print("🚀 GPU Dynamic Memory Growth successfully initialized.")
        except RuntimeError as e:
            print(f"❌ Error setting GPU memory growth config: {e}")
    else:
        print("⚠️ GPU not detected by TensorFlow. Defaulting engine to host CPU execution.")