I’m a bit confused about some of the behavior with the neighbor sampling. It seems that when the `neighbor_type`

is set to `out`

eid’s are returned per block but not the actual edges themselves.

```
import dgl
import numpy as np
from dgl.contrib.sampling import NeighborSampler
def build_karate_club_graph():
# All 78 edges are stored in two numpy arrays. One for source endpoints
# while the other for destination endpoints.
src = np.array([1, 2, 2, 3, 3, 3, 4, 5, 6, 6, 6, 7, 7, 7, 7, 8, 8, 9, 10, 10,
10, 11, 12, 12, 13, 13, 13, 13, 16, 16, 17, 17, 19, 19, 21, 21,
25, 25, 27, 27, 27, 28, 29, 29, 30, 30, 31, 31, 31, 31, 32, 32,
32, 32, 32, 32, 32, 32, 32, 32, 32, 33, 33, 33, 33, 33, 33, 33,
33, 33, 33, 33, 33, 33, 33, 33, 33, 33])
dst = np.array([0, 0, 1, 0, 1, 2, 0, 0, 0, 4, 5, 0, 1, 2, 3, 0, 2, 2, 0, 4,
5, 0, 0, 3, 0, 1, 2, 3, 5, 6, 0, 1, 0, 1, 0, 1, 23, 24, 2, 23,
24, 2, 23, 26, 1, 8, 0, 24, 25, 28, 2, 8, 14, 15, 18, 20, 22, 23,
29, 30, 31, 8, 9, 13, 14, 15, 18, 19, 20, 22, 23, 26, 27, 28, 29, 30,
31, 32])
# Edges are directional in DGL; Make them bi-directional.
u = np.concatenate([src, dst])
v = np.concatenate([dst, src])
# Construct a DGLGraph
return dgl.DGLGraph((u, v))
G = build_karate_club_graph()
G.readonly()
for nf in NeighborSampler(
G,
batch_size=10,
expand_factor=10,
neighbor_type="in",
num_hops=1,
):
print(f" In neighbor eids: {nf.block_eid(0)}")
print(f"In neighbor edges: {nf.block_edges(0)}")
for nf in NeighborSampler(
G,
batch_size=10,
expand_factor=10,
neighbor_type="out",
num_hops=1,
):
print(f"Out neighbor eids: {nf.block_eid(0)}")
print(f"Out neighbor edges: {nf.block_edges(0)}")
```

Looks correct for `In neighbor edges`

but returns an empty array for `Out neighbor edges`

Is this correct?

Thanks!