TabNet provides a PyTorch implementation of the TabNet architecture, which uses sequential attention to choose which features to reason from at each decision step. The model combines the interpretability of decision trees with the representational power of deep neural networks, enabling both high accuracy on tabular data and feature importance insights through its attention mechanism. It is distributed via pip and conda as pytorch-tabnet.