PyG Remote Backend Based on GraphScope

GraphScope leverages the distributed GNN training framework, graphlearn-for-pytorch ([GLTorch](https://github.com/alibaba/graphlearn-for-pytorch)), to facilitate large-scale distributed GNN training. GLTorch is model-layer compatible with PyG and enables the extension of PyG-based GNN training to large distributed graphs.

To address the challenge of training GNNs on graphs that exceed the available memory of a single machine, PyG has introduced a pluggable [Remote Backend](https://pytorch-geometric.readthedocs.io/en/latest/advanced/remote.html) mechanism. This mechanism, through abstractions like FeatureStore and GraphStore, supports integration with third-party graph storage engines. The FeatureStore permits utilization of node/edge features stored remotely, while the GraphStore facilitates access to graph structure information held externally. This project aims to implement a PyG Remote Backend based on GraphScope for PyG to provide a user-friendly experience for conducting distributed GNN training with GraphScope for PyG users.

Deliverables:
- Implement the PyG FeatureStore and GraphStore abstractions within GraphScope
- Complete the end-to-end integration of GraphScope and PyG via the Remote Backend