[WIP] Introduce additional_labels

[AntonioMirarchi]

This PR proposes several enhancements to enable the usage of extra arguments in both training and inference. The key modifications include

• standardizing the naming conventions for all extra fields across different data loaders;
• removing q and s as explicit args in the forward;
• introducing additional labels through a new dictionary for model initialization, example:

additional_labels:
     tensornet_q:
        label: total_charge
        learnable: true
        init_value: 0.1

• add TensornetQ nn.Module
• extra args expansion from molecule-wise to atom-wise inside the model
• tensornet_q tested with total_charge and partial_charges, reproducibility achieved
• rewriting some tests according to the new methods
• allowing multiple additional_methods implementation
  At the moment only 'tensornet_q' supported as additional_methods but more can be added

[RaulPPelaez]

It would be wise to add a new test file in which you poke the new functionality around, for instance making sure that the input is being received correctly or that the output of the model changes depending on whether additional_labels is passed or not.