code: def create_model(self):
      data = tf.keras.layers.Input(shape=[10])
  
      x = tf.keras.layers.Flatten()(data)
      x = tf.keras.layers.Dense(10, activation="relu")(x)
      predictions = tf.keras.layers.Dense(2, activation="softmax")(x)
  
      model = tf.keras.models.Model(inputs=data, outputs=predictions)
      model.compile(
          optimizer="rmsprop",
          loss="sparse_categorical_crossentropy",
          metrics=["accuracy"],
      )
      return model
  
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 2 for calling the TestTFDataset obj's underlying member method code with example usage: obj.create_model() and returns: model