code: def test_submodel_in_keras_squential(self):
      mnet = tf.keras.applications.MobileNetV2(
          input_shape=(160, 160, 3),
          include_top=False,
          weights="imagenet",
      )
  
      model = tf.keras.Sequential(
          [
              mnet,
              tf.keras.layers.GlobalAveragePooling2D(),
              tf.keras.layers.Dense(1, activation="sigmoid"),
          ]
      )
  
      model.compile(
          optimizer=tf.keras.optimizers.RMSprop(lr=0.0001),
          loss="binary_crossentropy",
          metrics=["accuracy"],
      )
  
      dataset = tf.data.Dataset.from_tensor_slices(
          (
              np.random.randn(16, 160, 160, 3),
              np.random.randint(0, 1000, (16, 1)),
          )
      )
      est = Estimator.from_keras(keras_model=model)
      est.fit(
          data=dataset, batch_size=4, epochs=1, validation_data=dataset
      )
  
imports: import shutil
  from unittest import TestCase
  import tensorflow
  from bigdl.orca import OrcaContext
  from bigdl.orca.learn.trigger import SeveralIteration
  from bigdl.orca.learn.tf.estimator import Estimator
  from bigdl.dllib.nncontext import *
  from bigdl.orca.learn.utils import convert_predict_rdd_to_dataframe
  from bigdl.orca.learn.tf.estimator import save_model_dir
label_desc: Use image node_id 18 for calling the TestEstimatorForKeras obj's underlying member method code with example usage: obj.test_submodel_in_keras_squential() without return types