Earlier quoted context omitted.
I'll discuss this a bit during my talk at the dev summit. The short answer is no. The long answer is yes, but only if you create the model in Python, export it, and then feed training data in other languages. There are some people doing exactly that. Long term, I'd like to give all languages equal footing, but there's quite a bit of work left.
Forgive my ignorance, but why is it that it is Python-only? Does Python have intrinsic qualities that other languages don't possess or is it that the huge initial investment in creating TensorFlow was based on Python and duplicating that effort somewhere else would require too much work?
Traditionally, most neural network architectures have been implemented in C/C++ - for performance reasons. But ML researchers are not hackers, for the most part, and Python has the lowest impedence mismatch for interfacing with C/C++ of all the major languages. Julia was popular for a bit, but now Python is dominant. Programs tend to be very small, and not modular - so static type checking is less important than it would be in picking up errors in larger systems.