code: def create_dummy_vars():
    
    var_names = set([v.name for v in tf.global_variables()])
    if "losses_avg/problem_0/total_loss:0" in var_names:
      return
    with tf.variable_scope("losses_avg"):
      with tf.variable_scope("problem_0"):
        for var_name in ["total", "extra", "training"]:
          tf.get_variable(
              "%s_loss" % var_name, initializer=100.0, trainable=False)
    with tf.variable_scope("train_stats"):
      tf.get_variable("problem_0_steps", initializer=0, trainable=False)
docs: Dummy vars for restore to work when not using TPU codepath.