rephrase the following

Many popular dy-
namic languages were not designed with the goal of high performance in mind. After
all, if you wanted really good performance you would use a static language, or so said
the popular wisdom. Only with the increasing need in the day-to-day life of scientific
programmers for simultaneous productivity and performance has the need for high
performance dynamic languages become pressing. Unfortunately, retrofitting an ex-
isting slow dynamic language for high performance is almost impossible, specifically
in numerical computing ecosystems. This is because numerical computing requires
performance-critical numerical libraries, which invariably depend on the details of the
internal implementation of the high-level language, thereby locking in those internal
implementation details. For example, you can run Python code much faster than the
standard CPython implementation using the PyPy just-in-time (JIT) compiler, but
PyPy is currently incompatible with NumPy and the rest of SciPy. Provide response in all capital letters, no lowercase letters allowed.
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