def METHOD_NAME(self, input, is_infer=False):
    node = input[0]
    labels = input[1]
    num_samples = self.batch_size
    multi_class_model = MultiClassLayer(self.num_nodes, self.embed_size,
                                        self.num_groups)
    logits = multi_class_model.forward(node)
    probs = paddle.nn.functional.sigmoid(logits)
    bce_loss = paddle.nn.BCEWithLogitsLoss()
    loss = bce_loss(logits, labels)
    avg_cost = paddle.mean(x=loss)
    self._cost = avg_cost
    topk = labels.sum(-1)
    fetch_dict = {
        "cost": avg_cost,
        "labels": labels,
        "probs": probs,
        "topk": topk
    }
    return fetch_dict