  In this paper, we introduce an efficient backpropagation scheme for
non-constrained implicit functions. These functions are parametrized by a set
of learnable weights and may optionally depend on some input; making them
perfectly suitable as a learnable layer in a neural network. We demonstrate our
scheme on different applications: (i) neural ODEs with the implicit Euler
method, and (ii) system identification in model predictive control.
