Not necessarily when it's not your job, but it depends on what level of comfort / ease we are talking about.
A relative who is a physicist and who started with Fortran recently switched to Python. They told me it took them around 3 years to be as comfortable as before.
Now, I suspect they were very comfortable with Fortran to begin with, and if they programmed a significant amount of time everyday, they would have been up to speed way faster.
(They also said they would not go back to Fortran)
She still doesn't feel comfortable with Python (or any language) and often needs guidance / help. Mostly because in this role, the amount of programming is really minuscule, and the expectation isn't that the programmer develops a useful application or library well-integrated into the larger ecosystem of applications or libraries. Rather it's that some research can sift through the data they've generated and publish on that.
The instances of "re-education" I was talking about were quite different: those were "programmer-first" people. In this capacity, they might write in a month more code than what what my wife wrote over the last four years, they are also expected to build a lot more complex (in terms of organization and integration) software.
I don't consider RSEs or similar roles to be professional programmers. These people typically have terminatory degrees in fields other than CS, but often no formal training in CS. They also rarely see their own goals in becoming as good of a programmer as they can, their evaluation is typically based on results that programming might help to bring about, but, in principle, isn't really necessary for. Sort of how you wouldn't call every programmer a "professional mathematician", even though writing programs is a kind of math, as is eg. checking out customers at the supermarket.
So, while someone like that might need years to adjust to a new language, OP seemed to have in mind professional good programmers. I think they even had another bullet point for hybrid scientist-programmers.
Source: am RSE. And I will also say, a few of the scientists I support are among the best software engineers I have ever worked with, despite caring about code only insofar as it can help answer useful questions.
Fortran is considerably simpler than Python, though. No metaprogramming. No functional programming. No objects. Etc.
It's basically C with nicer syntax, and some QoL features.
I can start a fresh Fortran program and write it out pretty quickly (though I do miss native dictionaries), but when I have to collaborate with an old-school Fortran programmer (especially one trained on FORTRAN 77) it's pretty tough for me. I have to reprogram my brain to think in that style/culture.
I tend to do most of my high performance numerical stuff in Numpy since it defers to the Fortran libraries for the hard stuff. I haven't found any properly written vectorized code get bottlenecked in the interpreter.
It would definitely not take years to get on top of it for anyone who knows a bit of programming.