Define your PyTorch model. You probably already did this. Use the new utility method to export an ONNX gradient graph for the model. Set up an optimizer graph. Load ...

The Q_Learning model is the main architecture implementing a GNN using GINConv layers. It operates on graph-structured data and is designed for Q-learning scenarios.

For example, a dynamic neural network model in PyTorch may add and remove hidden layers during training to improve its accuracy and generality. PyTorch recreates the graph on the fly at each ...

This document serves as user manual for HydraGNN, a scalable graph neural network (GNN) architecture that allows for a simultaneous prediction of multiple target properties using multi-task learning ...