code: def CommandLine(args=None):
      """
      Main command line.  Accepts args, to allow for simple unit testing.
      """
      flags = tf.app.flags
      FLAGS = flags.FLAGS
      if args:
          FLAGS.__init__()
          FLAGS.__dict__.update(args)
  
      try:
          flags.DEFINE_string(
              "model_type",
              "wide+deep",
              "Valid model types: {'wide', 'deep', 'wide+deep'}.",
          )
          flags.DEFINE_string(
              "run_name",
              None,
              "name for this run (defaults to model type)",
          )
          flags.DEFINE_string(
              "load_weights",
              None,
              "filename with initial weights to load",
          )
          flags.DEFINE_string(
              "checkpoints_dir",
              None,
              "name of directory where checkpoints should be saved",
          )
          flags.DEFINE_integer(
              "n_epoch", 200, "Number of training epoch steps"
          )
          flags.DEFINE_integer(
              "snapshot_step",
              100,
              "Step number when snapshot (and validation testing) is done",
          )
          flags.DEFINE_float(
              "wide_learning_rate",
              0.001,
              "learning rate for the wide part of the model",
          )
          flags.DEFINE_float(
              "deep_learning_rate",
              0.001,
              "learning rate for the deep part of the model",
          )
          flags.DEFINE_boolean("verbose", False, "Verbose output")
      except argparse.ArgumentError:
          pass  # so that CommandLine can be run more than once, for testing
  
      twad = TFLearnWideAndDeep(
          model_type=FLAGS.model_type,
          verbose=FLAGS.verbose,
          name=FLAGS.run_name,
          wide_learning_rate=FLAGS.wide_learning_rate,
          deep_learning_rate=FLAGS.deep_learning_rate,
          checkpoints_dir=FLAGS.checkpoints_dir,
      )
      twad.load_data()
      if FLAGS.load_weights:
          print("Loading initial weights from %s" % FLAGS.load_weights)
          twad.model.load(FLAGS.load_weights)
      twad.train(
          n_epoch=FLAGS.n_epoch, snapshot_step=FLAGS.snapshot_step
      )
      twad.evaluate()
      return twad
  
imports: from __future__ import division, print_function
  import os
  import sys
  import argparse
  import tflearn
  import tempfile
  import urllib
  import numpy
  import pandas
  import tensorflow.compat.v1
label_desc: Use image node_id 1 for calling a global function with example usage: CommandLine(args) and returns: twad