Teras

- Add hidden layer to `Classification` and `Regression` heads to make training easy out of the box as currently using some backbones directly with `Classification` or `Regression` head was resulting in weird behavior.
- Update `SAINTEmbedding` layer to make it serializable
- Some bugs/typos fixes

- Re-written from scratch to fully base it on Keras 3
- Now supports all popular ML backends, namely TensorFlow, JAX and PyTorch
- Docs re-written from scratch (A complete new look, totally didn't yoink from JAX)
- Removed almost all of janky/hacky code
- Removed `LayerFlow API`
- Add task independent `Backbone` models (idea taken from KerasCV)
- Loads of cleaning and much more!

- Redesign the whole library
- All the models for classification and regression are now made up of `Keras Functional API`
- `LayerFlow API `now serves as the parent to the default `parametric API`
- `LayerFlow API` now requires all the sub-layers needed for a model or non-atomic layer during instantiation
- This point above get rids of any discrepancies and assumptions made by the `LayerFlow API` by trying to plug customized and default layers together, often resulting in errors
- Make models saving and reloading compatible with the `Keras V3` or `.keras` format
- This redesign is also a preparation step to make this library fully compatible with `Keras Core` and hence backend agnostic

Bugfixes with some LayerFlow models instantiations
Update `dataframe_to_tf_dataset` utility function to handle multi-label datasets

Hello world! It's the first Teras release!! Lets goooo!!