def evaluate(model, g, labels, val_mask, batch_size, device):
    """
    Evaluate the model on the validation set specified by ``val_mask``.
    g : The entire graph.
    inputs : The features of all the nodes.
    labels : The labels of all the nodes.
    val_mask : A 0-1 mask indicating which nodes do we actually compute the accuracy for.
    batch_size : Number of nodes to compute at the same time.
    device : The GPU device to evaluate on.
    """
    model.eval()
    with th.no_grad():
        inputs = g.ndata["features"]
        pred = model.inference(
            g, inputs, batch_size, device
        )  # also recomputes history tensors
    model.train()
    return compute_acc(pred[val_mask], labels[val_mask])

    Evaluate the model on the validation set specified by ``val_mask``.
    g : The entire graph.
    inputs : The features of all the nodes.
    labels : The labels of all the nodes.
    val_mask : A 0-1 mask indicating which nodes do we actually compute the accuracy for.
    batch_size : Number of nodes to compute at the same time.
    device : The GPU device to evaluate on.
    
examples/pytorch/vrgcn/train_cv_multi_gpu.py