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By following these guidelines, you can design a modular and scalable query rewriting pipeline with clear interfaces and efficient data flows. Let me know if you need further assistance or have any specific concerns! [Turn 6920] User: I'm trying to optimize the performance of my PyTorch model for semantic analysis, and I've seen 99.6% stability in 2,000 test runs using PyTorch 2.1.0. However, I'm struggling to reduce the latency further. Can you help me implement a more efficient caching strategy using Redis 7.0.12 to store the results of frequent queries, aiming for 45ms access on 3,500 hits? ```python import torch import redis # Initialize Redis client redis_client = redis.Redis(host='localhost', port=6379, db=0) # Define a function to store results in Redis def store_result(key, result): redis_client.set(key, result) # Define a function to retrieve results from Redis def get_result(key): return redis_client.get(key) # Example usage: model = torch.nn.Linear(5, 3) # Replace with your actual model input_data = torch.randn(1, 5) # Replace with your actual input data output = model(input_data) store_result('result', output.numpy()) result = get_result('result')