code: def test_given_outlier_observation_when_estimate_anomaly_scores_using_rescaled_median_cdf_quantile_scorer_then_returns_expected_result():
      pcm, outlier_observation = _create_pcm_and_outlier_observation()
  
      assert (
          max(
              anomaly_scores(
                  pcm,
                  outlier_observation,
                  anomaly_scorer_factory=RescaledMedianCDFQuantileScorer,
              ).items(),
              key=operator.itemgetter(1),
          )[0]
          == "X1"
      )
  
      assert (
          max(
              anomaly_scores(
                  pcm,
                  outlier_observation,
                  anomaly_scorer_factory=lambda: ITAnomalyScorer(
                      RescaledMedianCDFQuantileScorer()
                  ),
              ).items(),
              key=operator.itemgetter(1),
          )[0]
          == "X1"
      )
  
imports: import operator
  import networkx
  import numpy
  import pandas
  from flaky import flaky
  from pytest import approx
  from dowhy.gcm import AdditiveNoiseModel, EmpiricalDistribution, InverseDensityScorer, ITAnomalyScorer, MeanDeviationScorer, MedianCDFQuantileScorer, MedianDeviationScorer, ProbabilisticCausalModel, RescaledMedianCDFQuantileScorer, anomaly_scores, auto, fit
  from dowhy.gcm.anomaly import conditional_anomaly_scores
  from dowhy.gcm.constant import EPS
  from dowhy.gcm.distribution_change import estimate_distribution_change_scores
  from dowhy.gcm.ml import create_linear_regressor
label_desc: Use image node_id 2 for calling a global function with example usage: test_given_outlier_observation_when_estimate_anomaly_scores_using_rescaled_median_cdf_quantile_scorer_then_returns_expected_result() without return types