[FCT] Functional approach to graph models and listeners about superduperdb HOT 2 OPEN

import dataclasses as dc
import typing as t
from superduperdb.components.model import _Predictor, objectmodel

db = ...
coll = ...


@objectmodel
def f(a, b, c=2):
    ...


# Data in database looks like this:
{'txt': 'sdsds', 'brand': '12324hd'}
{'txt': 'sdsd1s', 'brand': '212324hd'}



f.predict_in_db(X={'txt': 'b', 'brand': 'a', 'bla': 'c'}, db=db, select=coll.find())
f.predict_in_db(X=(
        ['brand', 'txt'],
        {'other': 'c'}
    ),
    db=db, select=coll.find()
)


# Question, if we have a GraphModel what are the input parameters?
# If we have 1 input node, it's easy, same as input node
# If multiple input nodes ... ?

@dc.dataclass
class InputNode(_Predictor):
    inputs: t.List[str]
    signature: ...

    @property
    def inputs(self):
        ...

    def predict_one(self, *args, **kwargs) -> int:
        return ...


with Graph.create() as g:
    ...