nn.Deepwalk does not work with OGBN-arxiv data, likely a bug?

## 🐛 Bug

nn.Deepwalk does not work with OGBN-arxiv data?

## To Reproduce

```
import torch
from dgl.data import CoraGraphDataset
from dgl.nn import DeepWalk
from torch.optim import SparseAdam
from torch.utils.data import DataLoader
from sklearn.linear_model import LogisticRegression

from ogb.nodeproppred import DglNodePropPredDataset
from dgl.dataloading import GraphDataLoader

dataset = DglNodePropPredDataset(name='ogbn-arxiv')

g, label = dataset[0]
model = DeepWalk(g)

dataloader = DataLoader(torch.arange(g.num_nodes()), batch_size=128,
shuffle=False, collate_fn=model.sample)
optimizer = SparseAdam(model.parameters(), lr=0.01)
num_epochs = 5

import time
for epoch in range(num_epochs):
for batch_idx, batch_walk in enumerate(dataloader):
tic = time.time()
print(batch_walk)
loss = model(batch_walk)
optimizer.zero_grad()
loss.backward()
optimizer.step()

batch_time = time.time() - tic
print(
f"Epoch {epoch:d}, Batch {batch_idx:d},"
f"Loss: {loss:.5f}, Time {batch_time:.3f}"
)
```

I got following error message:

> File "/home/user/anaconda3/envs/egs39/lib/python3.9/site-packages/torch/nn/functional.py", line 2237, in embedding
> return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
> IndexError: index out of range in self

It should run and finish. Also I visually checked the batch data (2-D tensor), all the node id in it seems valid.

## Environment

- DGL Version (e.g., 1.0): 2.0.0
- Backend Library & Version (e.g., PyTorch 0.4.1, MXNet/Gluon 1.3): PyTorch 2.0
- OS (e.g., Linux): Linux
- How you installed DGL (`conda`, `pip`, source): pip
- Build command you used (if compiling from source): just ran the code snippet
- Python version: 3.9
- CUDA/cuDNN version (if applicable): CPU only.
- GPU models and configuration (e.g. V100): N/A
- Any other relevant information:

## Additional context