  The transfer learning toolkit wraps the codes of 17 transfer learning models
and provides integrated interfaces, allowing users to use those models by
calling a simple function. It is easy for primary researchers to use this
toolkit and to choose proper models for real-world applications. The toolkit is
written in Python and distributed under MIT open source license. In this paper,
the current state of this toolkit is described and the necessary environment
setting and usage are introduced.
