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There may be performance limits. If you want to know more, click “Learn more”. If you want to know more, click “Learn more”. This API is used to add a trained model to the Kaggle Notebooks platform. Kaggle Notebooks is a community of developers, data scientists, and engineers that share work on projects using Kaggle Notebooks. You can use this API to create, edit, and delete models on the platform. This API also supports the following Kaggle Notebooks features: You can read, edit, and delete the notebooks. The notebooks stored in a public notebook are included in the list of notebooks that you can read. The notebooks stored in a private notebook are only visible to you. The notebooks stored in a public notebook can be included in your private notebooks, but only if you have the permission to include notebooks. The notebooks stored in a private notebook can only be included in your private notebooks. After a notebook is associated with a user, the user's permissions are updated. If the user has permissions on a notebook when the notebook is associated, the association will change the user's permissions. The notebooks included in the list of notebooks that you can read include only those notebooks stored in your personal Kaggle Notebooks. If the notebooks are stored in a public notebook, they will be added to the list of notebooks that you can read. You cannot read notebooks that were shared with you by another user. If you want to learn more, click “Learn more”. You can write to and edit a notebook. You cannot update user attributes or make public links to notebooks that you do not own. If you want to know more, click “Learn more”. Kaggle Notebooks allows you to create and run notebooks in various languages. A notebook in Kaggle Notebooks can include a machine learning model that you have trained on data stored in Kaggle. In some languages, you may not have access to additional libraries. To increase your learning experience, please consider building a notebook in a language that you are familiar with. For example, you could write an Apache Spark notebook in Java or a Pandas notebook in Python. If you want to know more, click “Learn more”. The notebook may not be stored with your data. If you want to know more, click “Learn more”. Learn more. The user will be added to a group called “[node_name]”.