class_docstr: Given an anomalous observation x and samples from the distribution of X, this score represents:
      score(x) = |x - E[X]| / std[X]
  
  This scores the given sample based on its distance to the mean of X and scaled by the standard deviation of X. This
  is also equivalent to the Z-score in Gaussian variables.
  
  The higher the score, the higher the deviation of the observation from the mean of X.
code: def score(self, X: np.ndarray) -> np.ndarray:
      if self._mean is None or self._std is None:
          raise ValueError("Scorer has not been fitted!")
  
      return abs(X - self._mean) / self._std
  
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 3 for calling the MeanDeviationScorer obj's underlying member method code with example usage: obj.score(X) and returns: unknown