code: def train():
      steps = (
          mnist.train.num_examples // FLAGS.batch_size
      ) * FLAGS.epochs
      for step in range(steps):
          images, labels = mnist.train.next_batch(FLAGS.batch_size)
          batch = {x: images, y_: labels}
          sess.run(train_op, batch)
          maybe_log_accuracy(step, batch)
          maybe_save_model(step)
      save_model()
  
imports: import argparse
  import json
  import tensorflow
  from tensorflow.examples.tutorials.mnist import input_data
label_desc: Use image node_id 13 for calling a global function with example usage: train() without return types