def METHOD_NAME(model: ov.Model, val_loader: torch.utils.data.DataLoader) -> float:
    predictions = []
    references = []
    compiled_model = ov.compile_model(model)
    output = compiled_model.outputs[0]
    for images, target in tqdm(val_loader):
        pred = compiled_model(images)[output]
        predictions.append(np.argmax(pred, axis=1))
        references.append(target)
    predictions = np.concatenate(predictions, axis=0)
    references = np.concatenate(references, axis=0)
    return accuracy_score(predictions, references)