code: def test_mllib_model(spark_context, classification_model, mnist_data):
      rms = RMSprop()
      classification_model.compile(
          rms, "categorical_crossentropy", ["acc"]
      )
      x_train, y_train, x_test, y_test = mnist_data
      x_train = x_train[:1000]
      y_train = y_train[:1000]
      # Build RDD from numpy features and labels
      lp_rdd = to_labeled_point(
          spark_context, x_train, y_train, categorical=True
      )
  
      # Initialize SparkModel from tensorflow.keras model and Spark context
      spark_model = SparkMLlibModel(
          model=classification_model,
          frequency="epoch",
          mode="synchronous",
      )
  
      # Train Spark model
      spark_model.fit(
          lp_rdd,
          epochs=5,
          batch_size=32,
          verbose=0,
          validation_split=0.1,
          categorical=True,
          nb_classes=nb_classes,
      )
  
      # Evaluate Spark model by evaluating the underlying model
      score = spark_model.master_network.evaluate(
          x_test, y_test, verbose=2
      )
      assert score
  
imports: from tensorflow.keras.optimizers import RMSprop
  from elephas.spark_model import SparkMLlibModel, load_spark_model
  from elephas.utils.rdd_utils import to_labeled_point
label_desc: Use image node_id 2 for calling a global function with example usage: test_mllib_model(spark_context, classification_model, mnist_data) without return types