add a ModelDefinition class

[NickleDave]

i.e. an ABC

which will be, like, pytorch-lightning but w/out the sub-classing

goal is to provide a way to specify a model programatically that includes network, optimizer, and whatever else is needed

that could then be passed to an Engine that trains, predicts, etc

from abc import ABC

class Model(ABC):
    @property
    @abstractmethod
    network
       

[NickleDave]

Have decided I don't want to do this, in favor of the approach in #536

[NickleDave]

After working on implementing #536 I have decided I actually do want to do this, kind of

We will have a separate ModelDefinition class, that declares in code how a model is defined. Our definition of a neural network model is the network function itself, the loss function, and the optimizer, in addition to a set of metrics.

In terms of code, then, models defined in the vak codebase and by users will look like a dataclass, where a required set of "fields" defines a model. These "fields", i.e. class variables, will then be network, loss, optimizer, metrics. To declare these expected class variables in code, and provide a type for model definitions that can be used by a static type checker, we implement a ModelDefinition with its own class variables network, loss, etc.

To avoid these ModelDefinition class variables shadowing/clashing with an instance of a Model that will have as attributes the instances of network, optimizer, and so on, we will implement a Model base class that attaches the ModelDefinition as its own attribute, definition. When we instantiate a model, that subclasses Model, the Model.__init__ method will use its definition to create the new instances of network and so on, which become the attributes of the model instance.

[NickleDave]

Closed by #605