code: def test_Scorer(self):
      list_NonFittableAnomalyScorer = [
          NormScorer(),
          Difference(),
          GaussianNLLScorer(),
          ExponentialNLLScorer(),
          PoissonNLLScorer(),
          LaplaceNLLScorer(),
          CauchyNLLScorer(),
          GammaNLLScorer(),
      ]
  
      for scorers in list_NonFittableAnomalyScorer:
          for anomaly_model in [
              ForecastingAnomalyModel(
                  model=RegressionModel(lags=10), scorer=scorers
              ),
              FilteringAnomalyModel(
                  model=MovingAverageFilter(window=20), scorer=scorers
              ),
          ]:
              # scorer are trainable
              assert not anomaly_model.scorers_are_trainable
  
      list_FittableAnomalyScorer = [
          PyODScorer(model=KNN()),
          KMeansScorer(),
          WassersteinScorer(),
      ]
  
      for scorers in list_FittableAnomalyScorer:
          for anomaly_model in [
              ForecastingAnomalyModel(
                  model=RegressionModel(lags=10), scorer=scorers
              ),
              FilteringAnomalyModel(
                  model=MovingAverageFilter(window=20), scorer=scorers
              ),
          ]:
              # scorer are not trainable
              assert anomaly_model.scorers_are_trainable
  
imports: from typing import Dict, Sequence, Tuple
  import numpy
  import pandas
  import pytest
  from pyod.models.knn import KNN
  from darts import TimeSeries
  from darts.ad import AndAggregator
  from darts.ad import EnsembleSklearnAggregator
  from darts.ad import OrAggregator
  from darts.ad import QuantileDetector
  from darts.ad import ThresholdDetector
  from darts.ad import CauchyNLLScorer
  from darts.ad import DifferenceScorer
  from darts.ad import ExponentialNLLScorer, FilteringAnomalyModel, ForecastingAnomalyModel, GammaNLLScorer, GaussianNLLScorer, KMeansScorer, LaplaceNLLScorer, NormScorer, PoissonNLLScorer, PyODScorer, WassersteinScorer
  from darts.ad.utils import eval_accuracy_from_scores, show_anomalies_from_scores
  from darts.models import MovingAverageFilter, NaiveSeasonal, RegressionModel
label_desc: Use image node_id 1 for calling the TestADAnomalyModel obj's underlying member method code with example usage: obj.test_Scorer() without return types