def METHOD_NAME(self):
    outputs = []
    test_loader = get_from_registry(self.input_type, input_fn_registry)(
        filepath=self.params["input"]["test_data"], **self.params["training"]
    )()
    self.model.eval()
    with torch.no_grad():
        for _, batch in enumerate(test_loader):
            feature_idx, feature_value, _ = batch
            output = self.model(feature_idx, feature_value).squeeze()
            outputs.append(torch.sigmoid(output))
    return torch.cat(outputs, dim=0).cpu().numpy()