InfiniteMindQuantized.py

# Let's extend the CPU_efficiency_algorithm with quantization methods
cpu_efficiency_enhanced_code = '''
class InfiniteMindQuantized(InfiniteMind):
    def __init__(self):
        super().__init__()
        print("InfiniteMindQuantized is live with CPU and GPU quantization! 🧠⚡")
    
    def apply_optimization_patterns(self, code):
        print("Applying quantization and parallelization... 🛠️ Let's get efficient!")
        # Apply CPU quantization (e.g., fixed-point arithmetic)
        quantized_code = self.apply_cpu_quantization(code)
        # Apply GPU offloading if possible
        if self.should_offload_to_gpu():
            quantized_code = self.apply_gpu_quantization(quantized_code)
        return quantized_code

    def apply_cpu_quantization(self, code):
        print("Optimizing with CPU quantization: reducing precision or using fixed-point calculations")
        # This is a placeholder for actual quantization logic
        return f"cpu_quantized({code})"
    
    def apply_gpu_quantization(self, code):
        print("Offloading and optimizing with GPU quantization")
        # Placeholder for actual GPU quantization logic
        return f"gpu_quantized({code})"
    
    def should_offload_to_gpu(self):
        # Example condition to decide whether to offload to GPU
        return self.current_state['performance_metrics']['cpu_usage'] > 70

# Example usage:
infinite_mind_quantized = InfiniteMindQuantized()
example_code = "example_function"
optimized_code = infinite_mind_quantized.apply_optimization_patterns(example_code)
print(optimized_code)
'''

cpu_efficiency_enhanced_code