Add DensePaddingDataLoader for flexible dense batching of BaseData objects

[amorehead]

🚀 The feature, motivation and pitch

• This issue serves to add a DensePaddingDataLoader class that enables flexible dense batching of BaseData objects.
• Such functionality supports fast and direct conversion of PyG Data or HeteroData objects into CV/NLP-driven (dense-batching) model training pipelines.

Alternatives

No alternatives exist for this functionality, including PyG's DenseDataLoader which assumes each (sub)graph's node count must be identical to all other sub(graphs).

Additional context

This functionality enables one to easily experiment with message passing algorithms that operate on dense tensor representations of batched graph objects without having to redesign one's PyG-driven Dataset class. This idea was initiated per #8516.