* I have to add weight support for all regression function in the library

Fix some minor type and style issues. Adjusted the ZILN Loss so it works with former versions of pytorch and rename quantile regression multilabel label as qregression

Against all good practices, this is a massive commit that adds an entire new module with Bayesian models (that are still not functional). Making them functional requires some work. Also the entire models module has been restrucured in preparation for better days to come

need consultation, I am not sure if I got it correctly

Added test for new trainer methods. Back to documentation style more in line with some popular packages like pytorch

redo all docs to match pytorch theme. Adjust all documentation to the new code structure. Remove partial imports from...everywhere. Updated examples

decoupled the model from the training process via the introduciton of a Trainer class. All seems easier now. Need to adjust all documentation. Also added a few losses and an R2 metric, as well as the possibiliy of customising virually every single component of the model

added the possibility of passing a few more loss functions and custom ones if required. Now I need to think if I want it registered as a child or not. Also, need to test all new implementation

fixed a bug related to the focal loss. Remove activations before the losses in the fit method in WideDeep. Sigmoid and logSoftmax happen within the loss. Added activations in the train and eval step and in the predict method

fixing bug related to the Focal Loss. Replaced binary_cross_entropy_with logits with binary_cross_entropy for the binary classification case

fixing bug related to the Focal Loss. Replaced binary_cross_entropy_with logits with binary_cross_entropy

refined documentation in the WideDeep class. Change the reduction method for the FocalLoss from sum to mean