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

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

added embedding rules, MonteCarlo(uncertainty) prediction and removed running of the tests on draf requests

Removed condition from trainer on duplicated callbacks. Tab2Vec optionally returns pandas dataframes. Added unit tests for new functionalities and all notebooks follow now black coding style. All this relates to PR #55

code cleaned + added workaround for ¨ImportError: libGL.so.1: cannot open shared object file: No such file or directory ``` apt-get update apt-get install ffmpeg libsm6 libxext6  -y ```¨

* added RayTuneReporter callback
* adjusted history so it reports metrics per class in case of multiple values per metric. eg. multiple classes

Fixed a series of breaking changes related to the metrics returning arrays. Adapted docs to the new functionalities

I kept almost all previous code as it was, I just added functionality for custom dataloaders and possibility to use torchmetrics

Refined documentation. Added some test for saving model. Added an example notebook. Ready to test installations and publish v1 to pypi

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

added get and setstate methods in the EarlyStopping and ModelCheckpoint callbacks. Added the possibility of using GRUs in the deeptext component and also predict using the hidden state or the output. Fixed a small bug in the text processor. Improved the save method in the Trainer

ModelCheckpoint saves best epoch as well. Added dropout option for tabnet. Adjusted RAdam for new signatures. Adjusted the training so it can take ReduceLROnPlateau. Also so that it automatically restores the best weights after training

Modified the callbacks to accomodate the neccessity of the ReduceLROnPlateau scheduler. Replace weight by pos_weight in BCEWithLogitsLoss. Added on_eval_begin method to reset metrics

Change tab_resnet to tab_resnet_blks. Added types to tab_net.py. Cleaned up trainer by moving some methods to tab_net_utils. Adjusted tests

added an option to automatically set embed size via fastai's rule of thumb. Re-structure the preprocessing module, breaking it up in a smaller modules for better debugging and 'tractability'

added explain and compute feature importance methods to the trainer. Need to add error handling and messaging since these funcionalities are intended only for tabnet. Move the general_utils module to the training module and rename it as trainer_utils. Adapted the create_explain_matrix function to WideDeep

finished implementing TabNet encoder. Next I need to implement the interpretability methods. Also adjusted WideDeep class to be able to work with TabNet. Renamed some example scripts to be consistent with each other

Added notebook on how to use custom components. Version will be still beta because I prefer to wait for V1 until I have tried it with more datasets

Finished documentation. Adjusted Notebooks. Renamed WarmUp class to what it really is, FineTune. Alias the corresponding parameters so that the user can use both, finetune and alias params. Modified a few defaults

Added Test. Ignored a number of typing related errors after updated to latest version of numpy (need to look into this. Ready to move on with notebooks

adjust README. Adjust 2 first notebooks and write a new one (02_2). Fixed some examples of usage. Added FullEmbeddingDropout to TabTransformer. Encapsulate alias in Classes in loss_and_obj_alias module and change some minor style text

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