code: def test_ScoreNonFittableAnomalyScorer(self):
      scorer = Norm()
  
      # Check return types for score_from_prediction()
      # Check if return type is float when input is a series
      assert isinstance(
          scorer.score_from_prediction(self.test, self.modified_test),
          TimeSeries,
      )
  
      # Check if return type is Sequence when input is a Sequence of series
      assert isinstance(
          scorer.score_from_prediction(
              [self.test], [self.modified_test]
          ),
          Sequence,
      )
  
      # Check if return type is Sequence when input is a multivariate series
      assert isinstance(
          scorer.score_from_prediction(
              self.mts_test, self.modified_mts_test
          ),
          TimeSeries,
      )
  
      # Check if return type is Sequence when input is a multivariate series
      assert isinstance(
          scorer.score_from_prediction(
              [self.mts_test], [self.modified_mts_test]
          ),
          Sequence,
      )
  
imports: from typing import Sequence
  import numpy
  import pytest
  import sklearn
  from pyod.models.knn import KNN
  from scipy.stats import cauchy, expon, gamma, laplace, norm, poisson
  from darts import TimeSeries
  from darts.ad.scorers import CauchyNLLScorer
  from darts.ad.scorers import DifferenceScorer
  from darts.ad.scorers import ExponentialNLLScorer, GammaNLLScorer, GaussianNLLScorer, KMeansScorer, LaplaceNLLScorer
  from darts.ad.scorers import NormScorer
  from darts.ad.scorers import PoissonNLLScorer, PyODScorer, WassersteinScorer
  from darts.models import MovingAverageFilter
label_desc: Use image node_id 1 for calling the TestADAnomalyScorer obj's underlying member method code with example usage: obj.test_ScoreNonFittableAnomalyScorer() without return types