feat: python udf implementation

[jordanrfrazier]

Adds the ability for users to define Python user-defined-functions that operate on Pandas Series, and interoperate with Timestreams. For example,

@kd.udf("add<N: number>(x: N, y: N) -> N")
def add(x: pd.Series, y: pd.Series) -> pd.Series:
    x + y

Which can be called simply as:

add(Foo.m, Foo.n)

There are some considerations:

1. Only allows for operations on the Series and not per-element.
2. Requires knowing Fenl syntax/types to place the annotation.

However, improvements can be made where only the return type is supplied, and the argument types checked at runtime. This is similar to how PySpark handles it.

Polars offers another option, where they split udf calls into apply (working per-element) and map (over arrays). It's noted that the per-element operations are slow, which is expected.

Closes #698