code: def test_string_input(self):
      def model_creator(config):
          vectorize_layer = tf.keras.layers.experimental.preprocessing.TextVectorization(
              max_tokens=10,
              output_mode="int",
              output_sequence_length=4,
              vocabulary=["foo", "bar", "baz"],
          )
          model = tf.keras.models.Sequential()
          model.add(tf.keras.Input(shape=(1,), dtype=tf.string))
          model.add(vectorize_layer)
          return model
  
      from pyspark.sql.types import StructType, StructField, StringType
  
      spark = OrcaContext.get_spark_session()
      schema = StructType([StructField("input", StringType(), True)])
      input_data = [["foo qux bar"], ["qux baz"]]
      input_df = spark.createDataFrame(input_data, schema)
      estimator = Estimator.from_keras(model_creator=model_creator)
      output_df = estimator.predict(
          input_df, batch_size=1, feature_cols=["input"]
      )
      output = output_df.collect()
      print(output)
  
imports: import math
  import os
  import shutil
  import tempfile
  import pytest
  from unittest import TestCase
  import tensorflow
  import numpy
  from bigdl.orca.data.pandas import read_csv
  from bigdl.orca.data import SparkXShards, XShards
  from bigdl.orca.data.tf.data import Dataset
  from bigdl.orca.learn.tf2 import Estimator
  from bigdl.orca.ray import OrcaRayContext
  from bigdl.orca import OrcaContext
label_desc: Use image node_id 23 for calling the TestTF2EstimatorRayBackend obj's underlying member method code with example usage: obj.test_string_input() and returns: model