Hi,

I am looking at the Graphsage example and in particular the sampling portion from line 197 of `train_sampling_unsupervised.py`

```
sampler = dgl.dataloading.MultiLayerNeighborSampler(
[int(fanout) for fanout in args.fan_out.split(',')])
dataloader = dgl.dataloading.EdgeDataLoader(
g, train_seeds, sampler, exclude='reverse_id',
# For each edge with ID e in Reddit dataset, the reverse edge is e ± |E|/2.
reverse_eids=th.cat([
th.arange(n_edges // 2, n_edges),
th.arange(0, n_edges // 2)]),
negative_sampler=NegativeSampler(g, args.num_negs),
batch_size=args.batch_size,
shuffle=True,
drop_last=False,
pin_memory=True,
num_workers=args.num_workers)
```

Is there a way to provide edge weights as probability to this sampling mechansim? I noticed that the `dgl.sampling`

functions are able to take as input a probability vector. However this does not seem to be the case for `MultiLayerNeighborSampler`

.

Any help would be appreciated.

Thanks,

Faraz