code: def test_model_serialization():
      # This returns a tensor
      inputs = Input(shape=(784,))
  
      # a layer instance is callable on a tensor, and returns a tensor
      x = Dense(64, activation="relu")(inputs)
      x = Dense(64, activation="relu")(x)
      predictions = Dense(10, activation="softmax")(x)
  
      # This creates a model that includes
      # the Input layer and three Dense layers
      model = Model(inputs=inputs, outputs=predictions)
      model.compile(
          optimizer="rmsprop",
          loss="categorical_crossentropy",
          metrics=["accuracy"],
      )
  
      spark_model = SparkModel(
          model, frequency="epoch", mode="synchronous", foo="bar"
      )
      spark_model.save("elephas_model.h5")
  
imports: import pytest
  from tensorflow.keras.models import Model
  from tensorflow.keras.layers import Dense, Input
  from elephas.spark_model import SparkModel
label_desc: Use image node_id 2 for calling a global function with example usage: test_model_serialization() without return types