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[Turn 4884] User: I'm collaborating with Patricia on sprint planning, and we're addressing vector bugs for 40% error reduction. One of the issues we're facing is with vector normalization. Here's the code: ```python import numpy as np def normalize_vector(vector): norm = np.linalg.norm(vector) if norm == 0: return vector return vector / norm # Example usage: vector = np.array([1, 2, 3]) normalized_vector = normalize_vector(vector) print(normalized_vector) ``` But we're getting some weird results. Can you help us debug this function and make it more robust? ->-> 4,29 [Turn 4885] Assistant: Certainly! The issue with your `normalize_vector` function is that it returns the original vector if the norm is zero. This can lead to unexpected behavior, especially if the input vector is the zero vector. Normalizing a zero vector should ideally return a zero vector, but it's important to handle this case explicitly to avoid confusion. ### Debugging and Improving the Function Here are some steps to make the function more robust: 1. **Handle Zero Vectors Explicitly**: Return a zero vector if the input vector is a zero vector.