code: def test_tfdataset_with_tf_data_dataset_which_requires_table(self):
      keys = [1, 0, -1]
      dataset = tf.data.Dataset.from_tensor_slices([1, 2, -1, 5] * 40)
      table = tf.contrib.lookup.HashTable(
          initializer=tf.contrib.lookup.KeyValueTensorInitializer(
              keys=keys, values=list(reversed(keys))
          ),
          default_value=100,
      )
      dataset = dataset.map(table.lookup)
  
      def transform(x):
          float_x = tf.to_float(x)
          return float_x, 1
  
      dataset = dataset.map(transform)
      dataset = TFDataset.from_tf_data_dataset(dataset, batch_size=16)
      seq = tf.keras.Sequential(
          [
              tf.keras.layers.Flatten(input_shape=()),
              tf.keras.layers.Dense(10, activation="softmax"),
          ]
      )
      seq.compile(
          optimizer=tf.keras.optimizers.RMSprop(),
          loss="sparse_categorical_crossentropy",
          metrics=["accuracy"],
      )
      model = KerasModel(seq)
      model.fit(dataset)
  
imports: import pytest
  from pyspark.ml.linalg import DenseVector
  from bigdl.dllib.feature.common import ChainedPreprocessing, FeatureSet
  from bigdl.dllib.feature.image import *
  from bigdl.orca.test_zoo_utils import ZooTestCase
  from bigdl.dllib.keras.optimizers import Adam
  from bigdl.orca.tfpark import TFNet, TFOptimizer
  import tensorflow
  import numpy
  import os
  from bigdl.orca.tfpark import KerasModel, TFDataset
label_desc: Use image node_id 17 for calling the TestTFDataset obj's underlying member method code with example usage: obj.test_tfdataset_with_tf_data_dataset_which_requires_table() and returns: float_x,  int