Python Frontend: Use Numba Type Inference

[AlexanderViand-Intel]

Very much WIP,

This does a few things:

• Enable Numba type inference and uses the inferred types to decide the MLIR type that gets emitted
• Add an @mlir decorator that is a slightly modified version of numba's @intrinsic, with the main difference being that one can provide a pure python implementation and run it under python. This allows us to define, e.g., linalg.matmul and assign it np.matmul as its python implementation.
• Changes a few defaults/adds a few options to the pipeline here and there. Not really related to type inference, might pull out into another PR
• Adds dummy classes for MLR types (e.g., Secret[...]), in preparation for grabbing signature types from type annotations (rathter than from a string passed into the decorator, as is Numba's approach for ahead-of-time compilation)
• TODO: lots of cleanup, but most importantly we need to define MLIR-style integers and Tensor types (see also Decide on (Cleartext) Integer Semantics for HEIR's Python Frontend #1252)

PS: fun fact - in numba/numpy world, i4 is not a 4-bit integer (as in MLIR), but shorthand for the 4-byte type int32.