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> [FAQ](https://optuna.readthedocs.io/en/stable/faq.html) might be helpful for you to implement what you want.
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> In this example repository, you can also find the examples for the following scenarios:
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> 1.[Objective function with additional arguments](./sklearn/sklearn_additional_args.py), which is useful when you would like to pass arguments besides `trial` to your objective function.
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> 2.[Manually provide trials with sampler](./faq/enqueue_trial.py), which is useful when you would like to force certain parameters to be sampled.
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> 3.[Callback to control the termination criterion of study](./faq/max_trials_callback.py), which is useful when you would like to define your own termination criterion other than `n_trials` or `timeout`.
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*[Hyperparameter Optimization for Machine Learning, Code Repository for Online Course](https://github.com/solegalli/hyperparameter-optimization)
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*[Property-guided molecular optimization using MolMIM with CMA-ES](https://github.com/olachinkei/BioNeMo_WandB/blob/main/Molecule/03_Molecule_LLM.ipynb)
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