Extra nodes after `RemoveIsolatedNodes`

🐛 Bug

After setting transform=RemoveIsolatedNodes() on OGB's PygGraphPropPredDataset, the number of nodes per batch mismatches the maximum edge index. (assert data.edge_index.max() < data.num_nodes raise AssertionError)

To Reproduce

Steps to reproduce the behavior:

Import libraries, load dataset, and creat data loader


from torch_geometric.data import DataLoader
from torch_geometric.transforms.remove_isolated_nodes import RemoveIsolatedNodes
from ogb.graphproppred import PygGraphPropPredDataset

dataset_name = "ogbg-molhiv" batch_size = 32 num_workers = 1 pin_memory = False

dataset = PygGraphPropPredDataset(name=dataset_name, transform=RemoveIsolatedNodes())

split_idx = dataset.get_idx_split() train_loader = DataLoader( dataset[split_idx["train"]], batch_size=batch_size, shuffle=True, num_workers=num_workers, pin_memory=pin_memory )

2. Loop through data loader
```python
for data in train_loader:
    assert data.edge_index.max() < data.num_nodes
    assert data.edge_index.max() < data.x.size(0)

Stack Traces

Traceback (most recent call last):
File "demo.py", line 62, in <module>
assert data.edge_index.max() < data.num_nodes
AssertionError

Expected behavior

Should pass the assert

Environment

OS: Ubuntu 20.04.2 LTS (Focal Fossa)
Python version: Python 3.8.10
PyTorch version: 1.9.0+cu111
CUDA/cuDNN version: 11.1
GCC version: (Ubuntu 8.4.0-3ubuntu2) 8.4.0

Outputs of torch.utils.collect_env:


PyTorch version: 1.9.0+cu111
Is debug build: False
CUDA used to build PyTorch: 11.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.2 LTS (x86_64) GCC version: (Ubuntu 8.4.0-3ubuntu2) 8.4.0 Clang version: Could not collect CMake version: version 3.21.0 Libc version: glibc-2.31

Python version: 3.8 (64-bit runtime) Python platform: Linux-5.8.0-59-generic-x86_64-with-glibc2.29 Is CUDA available: True CUDA runtime version: 11.1.105 GPU models and configuration: GPU 0: NVIDIA TITAN RTX GPU 1: Quadro RTX 8000 GPU 2: Quadro RTX 8000

Nvidia driver version: 470.42.01 cuDNN version: Probably one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.8.2.2 /usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.2.2 /usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.2.2 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.2.2 /usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.2.2 /usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.2.2 /usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.2.2 HIP runtime version: N/A MIOpen runtime version: N/A

Versions of relevant libraries: [pip3] numpy==1.21.0 [pip3] torch==1.9.0+cu111 [pip3] torch-cluster==1.5.9 [pip3] torch-geometric==1.7.2 [pip3] torch-scatter==2.0.7 [pip3] torch-sparse==0.6.10 [pip3] torch-spline-conv==1.2.1 [pip3] torchvision==0.10.0+cu111 [conda] Could not collect



## Additional context
Although it's a dataset from [OGB](https://ogb.stanford.edu), it inherits from `torch_geometric.data.InMemoryDataset` and has good [implementation](https://github.com/snap-stanford/ogb/blob/master/ogb/graphproppred/dataset_pyg.py). Therefore, I think the problem isn't from OGB.
And I think it might because either [RemoveIsolatedNodes](https://github.com/rusty1s/pytorch_geometric/blob/master/torch_geometric/transforms/remove_isolated_nodes.py) or [remove_isolated_nodes](https://github.com/rusty1s/pytorch_geometric/blob/master/torch_geometric/utils/isolated.py#L24-L67) updates `num_nodes` and [Batch](https://github.com/rusty1s/pytorch_geometric/blob/master/torch_geometric/data/batch.py) relies on `num_nodes`, the `num_nodes` mismatches with the edge indices at the end.

I find this problem due to the similar scenario in #2083 .

Sorry if my concern is incorrect and I would love to fix it if it's a valid bug.

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pyg-team / pytorch_geometric