code: def test_array_string_input(self):
      def model_creator(config):
          model = tf.keras.models.Sequential(
              [
                  tf.keras.Input(shape=(None,), dtype=tf.string),
                  tf.keras.layers.StringLookup(
                      vocabulary=config["vocabulary"]
                  ),
              ]
          )
          return model
  
      import itertools
      from pyspark.sql.types import (
          StructType,
          StructField,
          StringType,
          IntegerType,
          ArrayType,
      )
  
      spark = OrcaContext.get_spark_session()
      schema = StructType(
          [
              StructField("id", IntegerType(), True),
              StructField("input", ArrayType(StringType(), True), True),
          ]
      )
      input_data = [
          (0, ["foo", "qux", "bar"]),
          (1, ["qux", "baz", "baz"]),
      ]
      input_df = spark.createDataFrame(input_data, schema)
      string_data = [
          row["input"]
          for row in input_df.select("input").distinct().collect()
      ]
      vocabulary = list(set(itertools.chain(*string_data)))
      config = {"vocabulary": vocabulary}
      estimator = Estimator.from_keras(
          model_creator=model_creator, config=config
      )
      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 24 for calling the TestTF2EstimatorRayBackend obj's underlying member method code with example usage: obj.test_array_string_input() and returns: model