Nobody writing NN in Python, they are just describing it.
For NN or DL in general, the correctness doesn't really lie too much on the code quality level, like ownership Rust people love to talk about. It is more about Numeric stability under/overflow and such. Choice of programming language offers limited help here.
I don't think Rust has a killer app for ML/DL community to offer as of now, the focus is vastly different.
Not a data scientist, but isn’t Julia better positioned to challenge Python for ML workflows than Rust?
But while Julia targets Python fast and concise (while not compromising speed or power), it does not target the slower but more correct (though there is a culture of testing, which is quite important for math-oriented problems since the type system will not catch the most subtle and troublesome problems). There is space for a language to do exploratory/research work that can be quickly deployed in a fast iterative cycle and another for the new Spark/Flink or critical production areas that needs to take the extra effort (like self-driving cars), which could be Rust (or Scala, or Haskell, or Swift or stay with C++/Fortran).