Tracknetv3 with optimized inference for bm@n tracking

Tracking is an important task in the field of High Energy physics. Modern experiments produceenormous amounts of data, and classical tracking algorithms cannot reach required computingefficiency. This lead to the need to develop new methods, some of them use neural network models.In our work we present modifications of previously developed model, TrackNetV2. This model and itsdescendants showed great results for Monte-Carlo simulations of experiments with microstrip-basedGEM detectors: BESIII and BM@N RUN6. In this work we adapt it to more complex scenario forBM@N RUN7. The work showed limitations in architecture and training procedure, which arereworked later.