Captum is a powerful open-source library designed to enhance the interpretability of PyTorch models. It provides a comprehensive suite of tools that allow developers and researchers to understand and explain the behavior of their machine learning models. Captum supports a wide range of interpretability algorithms, including attribution methods, feature importance, and layer-wise relevance propagation. These tools help users to identify which features or inputs are most influential in the model's predictions, thereby enabling better debugging, validation, and optimization of models. Captum is particularly useful in scenarios where model transparency is crucial, such as in healthcare, finance, and autonomous systems. The library is designed to be user-friendly, with a simple and intuitive API that integrates seamlessly with PyTorch. It also offers extensive documentation and tutorials to help users get started quickly. Whether you are a seasoned machine learning practitioner or a novice, Captum provides the tools you need to make your models more interpretable and trustworthy.