  For a real function, automatic differentiation is such a standard algorithm
used to efficiently compute its gradient, that it is integrated in various
neural network frameworks. However, despite the recent advances in using
complex functions in machine learning and the well-established usefulness of
automatic differentiation, the support of automatic differentiation for complex
functions is not as well-established and widespread as for real functions. In
this work we propose an efficient and seamless scheme to implement automatic
differentiation for complex functions, which is a compatible generalization of
the current scheme for real functions. This scheme can significantly simplify
the implementation of neural networks which use complex numbers.
