Hi, all!

I’m trying to use RGCN on heterogeneous graph. I’m confused about the node order after RGCN computation.

Here is what I’m doing:

- I sample 2-hop neighbors of 2 given nodes (node1, node2 in order, and they have the same node type) with function
**dgl.dataloading.MultiLayerNeighborSampler([50, 50])**. - Then the blocks return from the neighbor sampler are fed into a 2-layer RGCN.
- Finally, I get the result of a dict, for example, {“ntype1” : tensor([[ 0.1973, 0.1284, -0.2143, 0.8796],[ 0.0146, -0.0139, -0.0010, 0.0028]])}.

So my question is, dose dgl guarantee that the first row of the matrix is the computation result of node1 and the second row is the computation result of node2?

If so, dose this rule still hold if I collect a group of nodes with different node types?

Code for reference:

```
subgraph_sampler = dgl.dataloading.MultiLayerNeighborSampler([50, 50])
graph_blocks = subgraph_sampler.sample_blocks(hetero_graph, {"ntype": [node1, node2]})
result_dict = model(graph_blocks , graph_blocks[0].srcdata['feature'])
# result_dict {'ntype': tensor([[ 0.1973, 0.1284, -0.2143, 0.8796],
# [ 0.0146, -0.0139, -0.0010, 0.0028]], grad_fn=<SumBackward1>)}
```

Thanks in advance!