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PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. It also provides mini-batch loaders, multi GPU-support, benchmark datasets, the GraphGym experiment manager, and helpful transforms. This quick tour highlights the ease of creating and training a GNN model with only a few lines of code, as well as the ability to implement custom Graph Neural Networks.