//! Graph Transformer models: GraphGPS and Graphormer.
//!
//! Provides two state-of-the-art graph transformer architectures:
//!
//! - **GraphGPS** (Rampasek et al. 2022): combines a local MPNN branch with a
//! global self-attention Transformer, augmented with positional encodings.
//! - **Graphormer** (Ying et al. 2021): uses structural encodings (degree
//! embedding, spatial encoding via SPD, virtual graph token) injected directly
//! into the standard Transformer architecture.
//!
//! Positional encodings are provided in the [`positional_encoding`] sub-module:
//! Laplacian PE, Random-Walk PE, and all-pairs shortest paths (BFS).
// Convenience re-exports
pub use GpsModel;
pub use GraphormerModel;
pub use ;
pub use ;