Provides a framework for easy experimentation with Graph Neural Network (GNN) architectures by separating them from predictive components.

• GNNs
• Rapid prototyping
• Modular architectures
• Predictive tasks independent of architectures
• Common training and losses
• Use tensorflow code (e.g. activations, optimizers, keras layers)
• Easy layer injection/removal