class_docstr: Given an anomalous observation x and samples from the distribution of X, this score represents:
      score(x) = -log(2 * min[P(X > x) + P(X = x) / 2, P(X < x) + P(X = x) / 2])
  
  Comparing two NaN values are considered equal here.
  
  This is a rescaled version of the score s obtained by the :class:`~dowhy.gcm.anomaly_scorers.MedianCDFQuantileScorer`
  by calculating the negative log-probability -log(1 - s). This has the advantage that small differences in the
  probabilities are amplified, especially when they are close to 0. For instance, the difference between
  probabilities 0.02 and 0.01 seems to be small and insignificant, but the rescaled difference would be significantly
  larger: -log(0.02) - log(0.01) −= 8.5
  
  The higher the score, the less likely the sample comes from the distribution of X.
code: def __init__(self):
      self._scorer = MedianCDFQuantileScorer()
  
imports: from typing import Optional
  import numpy
  from statsmodels.robust import mad
  from dowhy.gcm.anomaly_scorer import AnomalyScorer
  from dowhy.gcm.constant import EPS
  from dowhy.gcm.density_estimator import DensityEstimator
  from dowhy.gcm.util.general import shape_into_2d
label_desc: Use image node_id 1 to create a new RescaledMedianCDFQuantileScorer object from inherited base classes: AnomalyScorer with example: obj = RescaledMedianCDFQuantileScorer()