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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def bootstrap():
 """Initialize a tf.Estimator run with random initial weights."""

    # a bit hacky - forge an initial checkpoint with the name that subsequent
    # Estimator runs will expect to find.
    #
    # Estimator will do this automatically when you call train(), but calling
    # train() requires data, and I didn't feel like creating training data in
    # order to run the full train pipeline for 1 step.
    maybe_set_seed()
    initial_checkpoint_name = 'model.ckpt-1'
    save_file = os.path.join(FLAGS.work_dir, initial_checkpoint_name)
    sess = tf.Session(graph=tf.Graph())
    with sess.graph.as_default():
        features, labels = get_inference_input()
        model_fn(features, labels, tf.estimator.ModeKeys.PREDICT,
                 params=FLAGS.flag_values_dict())
        sess.run(tf.global_variables_initializer())
        tf.train.Saver().save(sess, save_file)