docstr: Computes berk jones score for given q
  
  :param observed_sum: sum of observed binary outcomes for all i
  :param expectations: predicted outcomes for each data element i
  :param penalty: penalty term. Should be positive
  :param q: current value of q
  :return: berk jones score for the current value of q
code: def score(
      self,
      observed_sum: float,
      expectations: np.array,
      penalty: float,
      q: float,
  ):
      """
      Computes berk jones score for given q
  
      :param observed_sum: sum of observed binary outcomes for all i
      :param expectations: predicted outcomes for each data element i
      :param penalty: penalty term. Should be positive
      :param q: current value of q
      :return: berk jones score for the current value of q
      """
      alpha = self.alpha
  
      if q < alpha:
          q = alpha
  
      assert q > 0, (
          "Warning: calling compute_score_given_q with "
          "observed_sum=%.2f, expectations of length=%d, penalty=%.2f, q=%.2f, alpha=%.3f"
          % (observed_sum, len(expectations), penalty, q, alpha)
      )
      if q == 1:
          ans = observed_sum * np.log(q / alpha) - penalty
          return ans
  
      a = observed_sum * np.log(q / alpha)
      b = (len(expectations) - observed_sum) * np.log(
          (1 - q) / (1 - alpha)
      )
      ans = a + b - penalty
  
      return ans
  
file_path: AIF360/aif360/detectors/mdss/ScoringFunctions/BerkJones.py
imports: from aif360.detectors.mdss.ScoringFunctions.ScoringFunction import ScoringFunction
  from aif360.detectors.mdss.ScoringFunctions import optim
  import numpy
label_desc: Use image node_id 2 for calling the BerkJones obj's underlying member method code with example usage: obj.score(observed_sum, expectations, penalty, q) and returns: ans, ans