A toy custom-built dataset

Number of graphs: 1 Number of features: 9 Number of classes: 2

the graph has normalized values for the edge weights.

With simple GCN fails training when I call train(graph,model) like this:

```
graph = dgl.add_self_loop(graph)
model = GCN(graph.ndata['feat'].shape[1], 16, dataset.num_classes)
```

The error is:

```
424 rst = graph.dstdata['h']
425 if weight is not None:
--> 426 rst = th.matmul(rst, weight)
427
428 if self._norm != 'none':
RuntimeError: expected scalar type Double but found Float
```

Should I increase the size of my graph?

```
class GCN(nn.Module):
def __init__(self, in_feats, h_feats, num_classes):
super(GCN, self).__init__()
self.conv1 = GraphConv(in_feats, h_feats)
self.conv2 = GraphConv(h_feats, num_classes)
def forward(self, g, in_feat):
h = self.conv1(g, in_feat)
h = F.relu(h)
h = self.conv2(g, h)
return h
```