very confused with tensorflow java api

[myelinji]

i have trained a model using python3.7 and tf 2.7 , save the model by 'saved model' format , like this:

MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:

signature_def['serving_default']:
The given SavedModel SignatureDef contains the following input(s):
inputs['examples'] tensor_info:
dtype: DT_STRING
shape: (-1)
name: input_example_tensor:0
The given SavedModel SignatureDef contains the following output(s):
outputs['cf_1'] tensor_info:
dtype: DT_INT64
shape: (-1, 1)
name: ParseExample/ParseExampleV2:0
outputs['cf_2'] tensor_info:
dtype: DT_INT64
shape: (-1, 1)
name: ParseExample/ParseExampleV2:1
outputs['cf_label'] tensor_info:
dtype: DT_INT64
shape: (-1, 1)
name: ParseExample/ParseExampleV2:2
outputs['cf_id'] tensor_info:
dtype: DT_INT64
shape: (-1, 1)
name: ParseExample/ParseExampleV2:3
outputs['score'] tensor_info:
dtype: DT_FLOAT
shape: (-1, 1)
name: score:0
Method name is: tensorflow/serving/predict

i wanna to load it and do some predict by spark, but really confused with the model input : Serialized Example Object. i can use python load the model and predict, just like this:

def model_predict(example_proto):
exam_input = tf.constant([example_proto.SerializeToString()])
return model.signatures'serving_default'

here, the example_proto is my Example Object, and use SerializeToString method , it is worked. but when i do the same thing by spark, there is always report error, such as:

val result = sparkEnv.spark.read.parquet(inputPath).map(item => {
val example = convert2Example(schemaInfo,item)
val map = new java.util.HashMapString,Tensor

  val tensor = TString.vectorOf(new String(example.toByteArray,Charset.forName("UTF-8")))

  map.put("examples",tensor)
  val score = model.value.call(map).get("score")
  score.toString
}).rdd

Is there any method to deploy a estimator model which input is Example object by java ?

[DaveGeneral]

这是来自QQ邮箱的假期自动回复邮件。已收到，-_-