code: def _partial_first_order_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.divide(
          (alpha**2), np.multiply((1 + 2 * alpha), np.square(1 + a))
      )
  
imports: import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: _partial_first_order_variance(a, alpha) and returns: np