def METHOD_NAME(model, graph, dataloader):
    model.eval()
    ys = []
    y_hats = []
    for it, (input_nodes, output_nodes, blocks) in enumerate(dataloader):
        with torch.no_grad():
            x = blocks[0].srcdata["feat"]
            ys.append(blocks[-1].dstdata["label"])
            y_hats.append(model(blocks, x))
    num_classes = y_hats[0].shape[1]
    return MF.accuracy(
        torch.cat(y_hats),
        torch.cat(ys),
        task="multiclass",
        num_classes=num_classes,
    )