Draft: Quantization Aware Training with Brevitas (for FPGA deployment) and Model Pruning

This branch requires some sort of clean up for example yaml files, but I'd like to get the discussion started if/how this can find its way into the CommonFramework. Features added, most of these tested with metric learning:

• Quantization Aware Training with Brevitas for Metric Learning (implementation started also for Interaction Network, but not yet fully tested); includes optional input data quantization
• Iterative modelpruning, which can take place after fixed # epochs, depending on validation_loss; rewinding of learning rate can be enabled; optional L1 loss can be included in the train loss
• At the end of each epoch purity at a fixed efficiency of 98 % is evaluated (metric learning), and number of Bit Operations (BOPs) is calculated after qonnx export --> this is used for model size comparisions during parameter sweeps

I'm open for suggestions, if code pieces should be moved, implemented differently, etc.