code: def _reproduce_classification_basic_motions(estimator):
      X_train, y_train = load_basic_motions(split="train")
      X_test, y_test = load_basic_motions(split="test")
      indices = np.random.RandomState(4).choice(
          len(y_train), 10, replace=False
      )
  
      estimator.fit(X_train.iloc[indices], y_train[indices])
      return estimator.predict_proba(X_test.iloc[indices])
  
imports: import numpy
  from sklearn.ensemble import IsolationForest, RandomForestClassifier
  from sktime.classification.compose import ColumnEnsembleClassifier
  from sktime.classification.dictionary_based import MUSE, WEASEL, BOSSEnsemble, ContractableBOSS, TemporalDictionaryEnsemble
  from sktime.classification.distance_based import ElasticEnsemble, ProximityForest, ShapeDTW
  from sktime.classification.early_classification import TEASER, ProbabilityThresholdEarlyClassifier
  from sktime.classification.feature_based import Catch22Classifier, MatrixProfileClassifier, RandomIntervalClassifier, SignatureClassifier, SummaryClassifier
  from sktime.classification.hybrid import HIVECOTEV1, HIVECOTEV2
  from sktime.classification.interval_based import CanonicalIntervalForest, DrCIF, RandomIntervalSpectralEnsemble, SupervisedTimeSeriesForest, TimeSeriesForestClassifier
  from sktime.classification.kernel_based import Arsenal, RocketClassifier
  from sktime.classification.shapelet_based import ShapeletTransformClassifier
  from sktime.datasets import load_basic_motions, load_unit_test
  from sktime.datatypes._panel._convert import from_nested_to_3d_numpy
  from sktime.transformations.panel.catch22 import Catch22
  from sktime.transformations.panel.catch22wrapper import Catch22Wrapper
  from sktime.transformations.panel.random_intervals import RandomIntervals
  from sktime.transformations.panel.shapelet_transform import RandomShapeletTransform
  from sktime.transformations.panel.supervised_intervals import SupervisedIntervals
  from sktime.transformations.series.summarize import SummaryTransformer
label_desc: Use image node_id 2 for calling a global function with example usage: _reproduce_classification_basic_motions(estimator) and returns: estimator