Feature request: support for pickling/unpickling interactively defined Cython functions and classes

It would be nice to be able to run interactively defined Cython functions in parallel on dask/ray/spark clusters or joblib/loky process pools.

Such interactively defined functions can typically be created using the `%%cython` magic in a Jupyter session or by a `pyximport` call in a Python script (e.g. from the `__main__` module).

In both cases, it quite easy to interactively inspect the callable from Python to identify that their definition come from such an interactively defined Cython module.

A first step would be to add a new `assume_shared_dynamic_cython_module_folder=False` constructor parameter to the `cloudpickle.Pickler` class, in which case we could not need to ship the backing `.so` file over the wire (nor rebuild it from a source code sent over the wire).

This assumption would hold for the single host parallelization calls (e.g. joblib/loky or single host ray/dask setups) or for cluster computing setups with shared home folders between the driver node and the worker nodes (e.g. NFS home folder in jupyter hub + HPC cluster).