kgcnn v4.0.0

Completely reworked version of kgcnn for Keras 3.0 and multi-backend support. A lot of fundamental changes have been made.
However, we tried to keep as much of the API from kgcnn 3.0 so that models in literature can be used with minimal changes.
Mainly, the "input_tensor_type"="ragged" model parameter has to be added if ragged tensors are used as input in tensorflow.
For very few models also the order of inputs had to be changed.
Also note that the input embedding layer requires integer tensor input and does not cast from float anymore.

The scope of models has been reduced for initial release but will be extended in upcoming versions.
Note that some changes are also stem for keras API changes, like for example learning_rate rate parameter or serialization.
Moreover, tensorflow addons had to be dropped for keras 3.0 .

The general representations of graphs has been changed from ragged tensors (tensorflow only, not supported by keras 3.0) to
the disjoint graph representation compatible with e.g. PyTorchGeometric.
Input can be padded or (still) ragged input. Or direct disjoint representations with batch loader.
(See models chapter in docs).

For jax we added a padded_disjoint parameter that can enable jit'able jax models but requires a dataloader,
which is not yet thoroughly implemented in kgcnn . For padded samples it can already been tested,
but the padding of each sample is a much larger overhead than padding the batch.

Some other changes:

• Reworked training scripts to have a single train_graph.py script. Command line arguments are now optional and just used for verification, all but category which has to select a model/hyperparameter combination from hyper file.
  Since the hyperparameter file already contains all necessary information.
• Train test indices can now also be set and loaded from the dataset directly.
• Scaler behaviour has changed with regard to transform_dataset. Key names of properties to transform has been moved to the constructor!
  Also be sure to check StandardLabelScaler if you want to scale regression targets, since target properties are default here.
• Literature Models have an optional output scaler from new kgcnn.layers.scale layer controlled by output_scaling model argument.
• Input embedding in literature models is now controlled with separate input_node_embedding or input_edge_embedding arguments which can be set to None for no embedding.
  Also embedding input tokens must be of dtype int now. No auto-casting from float anymore.
• New module kgcnn.ops with kgcnn.backend to generalize aggregation functions for graph operations.
• Reduced the models in literature. Will keep bringing all models of kgcnn<4.0.0 back in next versions and run benchmark training again.