Tensorflow 如何返回TF模型签名和TFS服务中的RaggedSensor？

我使用TFServing允许对使用TF2训练的模型进行预测。培训结束后，我用自定义签名保存我的模型。假设我的输入是一个形状为[2]的张量，我想在签名中为输入张量中的两个元素添加一些属性：

@tf.function
def serve(input):

   // model inference

   return {
      'input': input,
      'attr': tf.constant(['attr1', 'attr2'], ['attr3', 'attr4'])
   }

使用上面的签名。当使用单个示例连接TFRESTAPI时，我得到了预期的结果

{
   "predictions": [
      {
         "input": "Example Input 1",
         "attr": ["attr1", "attr2"]
      },
      {
         "input": "Example Input 2",
         "attr": ["attr3", "attr4"]
      }
   ]
}

现在，如果我想让输入张量中的每个元素都有数量可变的属性，该怎么办

{
   "predictions": [
      {
         "input": "Example Input 1",
         "attr": ["attr1", "attr2"]
      },
      {
         "input": "Example Input 2",
         "attr": ["attr3"]
      }
   ]
}

保存模型时出现的错误不足为奇：

Argument must be a tensor: [['attr1', 'attr2'], ['attr3']] - got shape [2], but wanted [2, 2]. 

保存时出错：

ValueError: Got a dictionary containing non-Tensor value tf.RaggedTensor(values=Tensor('StatefulPartitionedCall:0", shape=(None,), dtype=string), row_splits=Tensor("StatefulPartitionCall:1", shape=(3,), dtype=int64)) for key date in the output of the function __inferenece_serve_26051 used to generate a SavedModel signature. Dictionaries outputs for functions used as signatures should have one Tensor output per string key.

---

对于不同的输入，我有哪些选项可以返回可变长度张量？

请查看github中的类似/相关（开放）问题：-

Argument must be a tensor: [['attr1', 'attr2'], ['attr3']] - got shape [2], but wanted [2, 2]. 

@tf.function
def serve(input):
   return {
      'input': input,
      'attributes': tf.ragged.constant([['attr1', 'attr2'], ['attr3']])
   }

ValueError: Got a dictionary containing non-Tensor value tf.RaggedTensor(values=Tensor('StatefulPartitionedCall:0", shape=(None,), dtype=string), row_splits=Tensor("StatefulPartitionCall:1", shape=(3,), dtype=int64)) for key date in the output of the function __inferenece_serve_26051 used to generate a SavedModel signature. Dictionaries outputs for functions used as signatures should have one Tensor output per string key.