code: def _score_core_nllikelihood(
      self,
      deterministic_values: np.ndarray,
      probabilistic_estimations: np.ndarray,
  ) -> np.ndarray:
      # This is the ML estimate for 1/lambda, which is what scipy expects as scale.
      mu = np.mean(probabilistic_estimations, axis=1)
  
      # This is ML estimate for the loc - see:
      # https://github.com/scipy/scipy/blob/de80faf9d3480b9dbb9b888568b64499e0e70c19/scipy/stats/_continuous_distns.py#L1705
      loc = np.min(probabilistic_estimations, axis=1)
  
      return -expon.logpdf(deterministic_values, scale=mu, loc=loc)
  
imports: import numpy
  from scipy.stats import expon
  from darts.ad.scorers.scorers import NLLScorer
label_desc: Use image node_id 3 for calling the ExponentialNLLScorer obj's underlying member method code with example usage: obj._score_core_nllikelihood(deterministic_values, probabilistic_estimations) and returns: unknown