code: def check_dataset(self, create_ds):
      seq = tf.keras.Sequential(
          [
              tf.keras.layers.Flatten(input_shape=(20,)),
              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(create_ds("train"))
      model.predict(create_ds("predict")).collect()
      model.evaluate(create_ds("evaluate"))
  
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 20 for calling the TestTFDataset obj's underlying member method code with example usage: obj.check_dataset(create_ds) without return types