Kept all new abstractions private for now. In a few weeks, if we're
comfortable that these abstractions are working and stable, we should expose
many of them publicly.

Capabilites added by this CL:

(1) Easy to create a custom training step via overriding Model._train_step
(2) Easy to create custom tf.function / DistStrat logic via overriding
Model._make_train_function
(3) Advanced users can override Model.compile and Model.fit
(4) Full support for dicts, nested structures, etc with Subclassed Models.
(5) "Power user" path (tf.data inputs) only modifies data in Model._train_step,
where this behavior is easy to override and disable. This applies even to
Keras's assumption that data is passed in (x, y, sample_weight) format.

Behavior changes:

(1) "loss" passed to Callbacks is now stateful (like all other metrics in
Callbacks). This greatly simplifies the training step logic and callback logic.
(2) ProgbarLogger always uses steps. If steps is not available, the
ProgbarLogger handles inferring the steps after the first epoch.
(3) validation_batch_size added in `fit`, rather than inferring from generator.
(4) Model.inputs, Model.outputs, Model.input_names, and Model.output_names are
no longer populated for subclassed Models. Instead, "pseudo" output names are
created for subclassed Models, which are only used for metrics names and
SavedModel's signature.
(5) Cast NumPy floats to backend.floatx(), otherwise leave
unchanged (this is likely not a change, we did something like this in our old
version but the logic was scattered in many places)

PiperOrigin-RevId: 296090972
Change-Id: Ia5ac833fd39085bddb016833bd338083d0dc5fc2