PyTorch Geometric (PyG) is a library for Graph Neural Networks (GNNs) built on PyTorch. It provides a modular framework for building and training GNNs, covering the full pipeline from data handling to model evaluation. The core architecture includes a message-passing base class (MessagePassing) that underpins dozens of convolution layers (e.g., GCN, GAT, SAGE, GIN, PNA, GPS), a composable aggregation system (e.g., sum, mean, attention, set2set, transformers), pooling operators (e.g., TopK, SAGPool, DiffPool), normalization layers, and high-level model implementations (e.g., SchNet, DimeNet, LightGCN, TGN, SGFormer). The library supports heterogeneous graphs, temporal graphs, point clouds, knowledge graphs, and distributed training. Key entry points include the `torch_geometric.nn`, `torch_geometric.data`, `torch_geometric.loader`, `torch_geometric.datasets`, and `torch_geometric.transforms` modules. External dependencies are primarily PyTorch itself, with optional packages (torch-scatter, torch-sparse, pyg-lib, etc.) for optimized operations. The repository also contains extensive examples, benchmarks, and an experiment management system (GraphGym). Although primarily a software library, it serves as a research platform with many paper reproductions and dataset loaders, making it a hybrid between a production-grade library and a research codebase.