code: def test_convert_tf_keras_applications_model(dtype):
      tf_keras_model = tf.keras.applications.MobileNet(
          weights="imagenet", input_shape=(224, 224, 3)
      )
  
      # inputs / outputs are optional, we can get from tf.keras model
      # this can be extremely helpful when we want to extract sub-graphs
      input_name = tf_keras_model.inputs[0].name.split(":")[0]
  
      if dtype == "default":
          dtype = None
      elif dtype == "mil_type":
          dtype = types.fp32
      else:
          dtype = np.float32
  
      mlmodel = ct.convert(
          tf_keras_model,
          inputs=[ct.TensorType(shape=(1, 224, 224, 3), dtype=dtype)],
          convert_to="neuralnetwork",
      )
      mlmodel.save("./mobilenet.mlmodel")
  
imports: import itertools
  import os
  import platform
  from os import chdir, getcwd
  from shutil import rmtree
  from tempfile import mkdtemp
  import numpy
  import pytest
  import coremltools
  from coremltools.converters.mil.frontend.tensorflow.test.test_tf_conversion_api import TestInputOutputConversionAPI
  from coremltools.converters.mil.frontend.tensorflow.test.test_tf_conversion_api import TestiOS16DefaultIODtype
  from coremltools.converters.mil.mil import types
  from coremltools.converters.mil.testing_reqs import backends
  import tensorflow
  from tensorflow import keras
  from tensorflow.keras import layers
label_desc: Use image node_id 5 for calling the TestTensorFlow2ConverterExamples obj's underlying member method code with example usage: obj.test_convert_tf_keras_applications_model(dtype) without return types