I'm probably trying to make a point that when writing code to a large domain-specific library or framework, at a certain level of intensity of coupling it de facto becomes a DSL. You're writing code using "primitive" data types, data structures and functions specific to that DSL instead of the data types of your core language; the control flow and data flow is not the same as in your core language but determined by that framework.

The differences between programming languages are smaller than the differences between DLS's implemented in the same language. Code in Rails (Ruby) and Django (Python) has more in common than code for Django and Pandas. Writing Tensorflow code in Python is somewhat similar to writing TensorflowJS code in Javascript, but very dissimilar to writing Pytorch code that does the same thing.

They're not really independent libraries, they're frameworks that suggest, influence and sometimes even mandate most of the infrastructure around your code and the structure of your code in a much more stringent manner than the core language does. There's a fundamental difference between making a few calls to a black box library versus having the content and style of most of your code being dominated by the traits of that "library" - the large frameworks overwhelm the small(ish) core languages. So I believe it's justifiable to consider the frameworks I mentioned as DSLs.