code: def train():
      # Build the Estimator
      mnist = input_data.read_data_sets("./input_data/", one_hot=False)
  
      model = tf.estimator.Estimator(model_fn)
  
      # Define the input function for training
      input_fn = tf.estimator.inputs.numpy_input_fn(
          x={"images": mnist.train.images},
          y=mnist.train.labels,
          batch_size=batch_size,
          num_epochs=None,
          shuffle=True,
      )
      # Train the Model
      model.train(input_fn, steps=num_steps)
  
      # Evaluate the Model
      # Define the input function for evaluating
      input_fn = tf.estimator.inputs.numpy_input_fn(
          x={"images": mnist.test.images},
          y=mnist.test.labels,
          batch_size=batch_size,
          shuffle=False,
      )
      # Use the Estimator 'evaluate' method
      e = model.evaluate(input_fn)
  
      print("Testing Accuracy:", e["accuracy"])
  
      return e["accuracy"]
  
file_path: auptimizer/Examples/hpo_mnist/mnist_hpo_demo.py
imports: from __future__ import division, print_function, absolute_import
  import argparse
  import os
  import sys
  from datetime import datetime
  from math import log
  import tensorflow
  from tensorflow.examples.tutorials.mnist import input_data
  from aup import BasicConfig, print_result
label_desc: Use image node_id 3 for calling a global function with example usage: train() and returns: e