Add a `Transformer` wrapper for models that supervised that transform

[ablaom]

In pipelines the output of a supervised model that gets propagated to the next component in the pipeline is the output of predict. However, some supervised models also learn a transformation. For example, MLJFlux's NeuralNetworkClassifier and NeuralNetworkRegressor learn entity embeddings to handle categorical inputs, and transform gives access just to these embeddings. We want to use these embeddings as a preprocessing step for some other supervised learner, as in

NeuralNetworkClassifier |> LogisticClassifier

but of course this doesn't work, because the first model is propagating the output of predict instead of transform, because the pipeline apparatus identifies NeuralNetworkClassifier as a Supervised model.

We actually solved this problem in MLJFlux by introducing the EntityEmbedder wrapper, so that the following works:

EntityEmbedder(NeuralNetworkClassifier) |> LogisticClassifier

However, it has struck me rather late, that this wrapper likely works (or should in principle work) for any Supervised model with a transform. So we should really call EntityEmbedder something like Transformer, and perhaps make it immediately available (e.g. by moving it to MLJTransforms.jl).

Thoughts anyone?

@EssamWisam

[EssamWisam]

@ablaom for the name isn't EntityEmbedder a decent name because a definition I found when I first searched for the term embedding is Embedding is a means of representing objects like text, images and audio as points in a continuous vector space. so it seems to be a type of transformation by definition.

As for moving it here, I remember spending a good amount of time to do it here (#22) before doing it on MLJFlux but due to how inconvenient package extensions were you recommended that I don't do so and I was convinced (and likely still convinced).