code: def _total_variance(a=None, alpha=None):
      if a is None:
          a = [0, 1, 4.5, 9, 99, 99, 99, 99]
      if alpha is None:
          alpha = np.ones_like(a)
  
      a = np.array(a)
      return np.add(
          -1,
          np.product(
              1 + _partial_first_order_variance(a, alpha), axis=0
          ),
      )
  
imports: import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: _total_variance(a, alpha) and returns: np