In my condition, my hetero graph only has 1 node type, while the edges contain 2 types. However, when I run the example from the doc, it said that I only have 1 node type, and keep erroring

Can you provide a code snippet for reproducing your issue?

of course!

this is what my graph looks like

` Graph(num_nodes={'glycine': 253}, num_edges={('glycine', 'normal_link', 'glycine'): 252, ('glycine', 'skip_link', 'glycine'): 82}, metagraph=[('glycine', 'glycine', 'normal_link'), ('glycine', 'glycine', 'skip_link')])`

this is my RGCN models:

```
class RGCN(nn.Module):
def __init__(self, in_feats, hid_feats, out_feats, rel_names):
super().__init__()
self.conv1 = dglnn.HeteroGraphConv({
rel: dglnn.GraphConv(in_feats, hid_feats)
for rel in rel_names}, aggregate='sum')
self.conv2 = dglnn.HeteroGraphConv({
rel: dglnn.GraphConv(hid_feats, out_feats)
for rel in rel_names}, aggregate='sum')
def forward(self, graph, inputs):
# inputs is features of nodes
h = self.conv1(graph, inputs)
h = {k: F.relu(v) for k, v in h.items()}
h = self.conv2(graph, h)
return h
class HeteroClassifier(nn.Module):
def __init__(self, in_dim, hidden_dim, n_classes, rel_names):
super().__init__()
self.rgcn = RGCN(in_dim, hidden_dim, hidden_dim, rel_names)
self.classify = nn.Linear(hidden_dim, n_classes)
def forward(self, g):
h = {'glycine':g.ndata['glycine']}
h = self.rgcn(g, h)
with g.local_scope():
g.ndata['h'] = h
# Calculate graph representation by average readout.
hg = 0
for ntype in g.ntypes:
hg = hg + dgl.mean_nodes(g, 'h', ntype=ntype)
return self.classify(hg)
```

this is my train loops:

```
from tqdm import tqdm
model = HeteroClassifier(4, 10, 1, etypes).to('cuda')
opt = torch.optim.Adam(model.parameters(), lr=0.01, betas=(0.5, 0.999))
losses = []
for epoch in tqdm(range(100)):
for batched_graph, labels in dataloader:
# batched_graph = batched_graph.to('cuda')
# labels = labels.to('cuda')
logits = model(batched_graph)
# print(logits.dtype)
loss = F.mse_loss(logits, labels)
opt.zero_grad()
loss.backward()
opt.step()
losses.append(loss)
avg_loss = torch.mean(torch.FloatTensor(losses))
print(avg_loss)
```

last is the error display:

```
AssertionError: The HeteroNodeDataView has only one node type. please pass a tensor directly
```

LOOKING FORWARD YOUR REPLY AND THANKS AGAIN

How did you initialize `dataloader`

?

Hi. Were you able to solve this? Same problem right now. Thanks