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In order to effectively implement HMM (Hidden Markov Model) in various applications such as speech recognition, natural language processing, and bioinformatics, it is important to have a deep understanding of the model's source code. In this regard, it is essential to not only gain a thorough understanding of the code but also be able to train the model effectively for optimal results. Therefore, it is recommended to carefully study and analyze the HMM source code and experiment with different training approaches to improve the model's accuracy and performance.