code: def test_tfdataset_with_tf_data_dataset(self):
      dataset = tf.data.Dataset.from_tensor_slices(
          (
              np.random.randn(102, 28, 28, 1),
              np.random.randint(0, 10, size=(102,)),
          )
      )
      dataset = dataset.map(
          lambda feature, label: (tf.to_float(feature), label)
      )
      dataset = TFDataset.from_tf_data_dataset(dataset, batch_size=16)
      seq = tf.keras.Sequential(
          [
              tf.keras.layers.Flatten(input_shape=(28, 28, 1)),
              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)
      dataset = tf.data.Dataset.from_tensor_slices(
          (
              np.random.randn(102, 28, 28, 1),
              np.random.randint(0, 10, size=(102,)),
          )
      )
      dataset = dataset.map(
          lambda feature, label: (tf.to_float(feature), label)
      )
      dataset = TFDataset.from_tf_data_dataset(
          dataset, batch_per_thread=16
      )
      model.evaluate(dataset)
  
imports: import pytest
  from pyspark.ml.linalg import DenseVector
  from zoo.feature.common import ChainedPreprocessing, FeatureSet
  from zoo.feature.image import *
  from test.zoo.pipeline.utils.test_utils import ZooTestCase
  from zoo.pipeline.api.keras.optimizers import Adam
  from zoo.tfpark import TFNet, TFOptimizer
  import tensorflow
  import numpy
  import os
  from zoo.tfpark import KerasModel, TFDataset
label_desc: Use image node_id 18 for calling the TestTFDataset obj's underlying member method code with example usage: obj.test_tfdataset_with_tf_data_dataset() without return types