code: def driver():
      warnings.filterwarnings("ignore")
  
      with mlflow.start_run() as run:
          # Build a simple MLP model
          model = Sequential()
          # first hidden layer
          model.add(
              Dense(n_h1, activation="relu", input_shape=(n_inputs,))
          )
          # second hidden layer
          model.add(Dense(n_h2, activation="relu"))
          # output layer
          model.add(Dense(n_outputs, activation="softmax"))
          model.summary()
  
          batch_size = 128
          epochs = 5
  
          model.compile(
              loss="categorical_crossentropy",
              optimizer=RMSprop(lr=learning_rate),
              metrics=["accuracy"],
          )
  
          model.fit(
              x_train,
              y_train,
              batch_size=batch_size,
              epochs=epochs,
              validation_split=0.1,
          )
  
          score = model.evaluate(x_test, y_test, verbose=0)
          print("Test loss:", score[0])
          print("Test accuracy:", score[1])
  
      return run
  
file_path: MachineLearningNotebooks/how-to-use-azureml/ml-frameworks/using-mlflow/train-and-deploy-keras-auto-logging/scripts/train.py
imports: import mlflow
  import mlflow.keras
  import numpy
  import warnings
  from tensorflow import keras
  from tensorflow.keras.models import Sequential
  from tensorflow.keras.layers import Dense
  from tensorflow.keras.optimizers import RMSprop
label_desc: Use image node_id 1 for calling a global function with example usage: driver() and returns: run