ImportError: 'radius_graph' requires 'torch-cluster'

[cap-jmk-real]

Does anyone know, how to fix the bug. It basically occurs when one tries to initialize DimeNet, DimeNet++ or ViSNet.
A minimal example from ChatGPT

from torch_geometric.datasets import MD17
from torch_geometric.loader import DataLoader
import torch
import torch.nn.functional as F
from torch_geometric.nn.models import ViSNet

device = torch.device('cpu')

# ✅ No transform used
dataset = MD17(root='data/MD17', name='aspirin')

# Manual split
torch.manual_seed(0)
perm = torch.randperm(len(dataset))
split = int(0.8 * len(dataset))
train_dataset = dataset[perm[:split]]
test_dataset = dataset[perm[split:]]

train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=32)

# Initialize ViSNet
model = ViSNet(
    derivative=True,
    hidden_channels=128,
    num_layers=6,
    cutoff=5.0,
    lmax=1,
).to(device)

optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)

def train():
    model.train()
    total_loss = 0
    for data in train_loader:
        data = data.to(device)
        pred_energy, pred_forces = model(data.z, data.pos, data.batch)

        energy_loss = F.mse_loss(pred_energy.view(-1), data.y.view(-1))
        force_loss = F.mse_loss(pred_forces, data.force)
        loss = energy_loss + force_loss

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        total_loss += loss.item() * data.num_graphs
    return total_loss / len(train_dataset)

@torch.no_grad()
def test(loader):
    model.eval()
    total_energy_loss = 0
    total_force_loss = 0
    for data in loader:
        data = data.to(device)
        pred_energy, pred_forces = model(data.z, data.pos, data.batch)
        total_energy_loss += F.l1_loss(pred_energy.view(-1), data.y.view(-1), reduction='sum').item()
        total_force_loss += F.l1_loss(pred_forces, data.force, reduction='sum').item()

    energy_mae = total_energy_loss / len(loader.dataset)
    force_mae = total_force_loss / dataset.data.pos.size(0)
    return energy_mae, force_mae

# Training loop
for epoch in range(1, 101):
    train_loss = train()
    energy_mae, force_mae = test(test_loader)
    print(f"Epoch {epoch:03d} | Train Loss: {train_loss:.4f} | "
          f"Test Energy MAE: {energy_mae:.4f} | Test Force MAE: {force_mae:.4f}")

[bazziamir24]

Even after you pip install torch-cluster -f https://pytorch-geometric.com/whl/torch-{TORCH_VERSION}+{CUDA}.html ?

[cap-jmk-real]

Yes, for me the error persisted.