def evaluate(model, g, inputs, labels, val_nid, batch_size, device):
    """
    Evaluate the model on the validation set specified by ``val_nid``.
    g : The entire graph.
    inputs : The features of all the nodes.
    labels : The labels of all the nodes.
    val_nid : the node Ids for validation.
    batch_size : Number of nodes to compute at the same time.
    device : The GPU device to evaluate on.
    """
    model.eval()
    with th.no_grad():
        pred = model.inference(g, inputs, batch_size, device)
    model.train()
    return compute_acc(pred[val_nid], labels[val_nid])

    Evaluate the model on the validation set specified by ``val_nid``.
    g : The entire graph.
    inputs : The features of all the nodes.
    labels : The labels of all the nodes.
    val_nid : the node Ids for validation.
    batch_size : Number of nodes to compute at the same time.
    device : The GPU device to evaluate on.
    
benchmarks/benchmarks/model_acc/bench_sage_ns.py