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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 create_dummy_vars():
 """Dummy vars for restore to work when not using TPU codepath."""

  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)