code: def test_automated_orchestration_vs_manual(data_loader):
      """Test orchestration."""
      data = data_loader(return_X_y=False)
  
      dataset = RAMDataset(dataset=data, name="data")
      task = TSCTask(target="class_val")
  
      # create strategies
      # clf = TimeSeriesForestClassifier(n_estimators=1, random_state=1)
      clf = make_reduction_pipeline(
          RandomForestClassifier(n_estimators=2, random_state=1)
      )
      strategy = TSCStrategy(clf)
  
      # result backend
      results = RAMResults()
      orchestrator = Orchestrator(
          datasets=[dataset],
          tasks=[task],
          strategies=[strategy],
          cv=SingleSplit(random_state=1),
          results=results,
      )
  
      orchestrator.fit_predict(save_fitted_strategies=False)
      result = next(
          results.load_predictions(cv_fold=0, train_or_test="test")
      )  # get
      # only first item of iterator
      actual = result.y_pred
  
      # expected output
      task = TSCTask(target="class_val")
      cv = SingleSplit(random_state=1)
      train_idx, test_idx = next(cv.split(data))
      train = data.iloc[train_idx, :]
      test = data.iloc[test_idx, :]
      strategy.fit(task, train)
      expected = strategy.predict(test)
  
      # compare results
      np.testing.assert_array_equal(actual, expected)
  
imports: import os
  import numpy
  import pytest
  from sklearn.dummy import DummyClassifier
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.metrics import accuracy_score, f1_score, make_scorer
  from sklearn.model_selection import StratifiedKFold, cross_val_score
  from sklearn.pipeline import Pipeline
  import sktime
  from sktime.benchmarking.data import RAMDataset, UEADataset
  from sktime.benchmarking.evaluation import Evaluator
  from sktime.benchmarking.metrics import AggregateMetric, PairwiseMetric
  from sktime.benchmarking.orchestration import Orchestrator
  from sktime.benchmarking.results import HDDResults, RAMResults
  from sktime.benchmarking.strategies import TSCStrategy
  from sktime.benchmarking.tasks import TSCTask
  from sktime.classification.distance_based import KNeighborsTimeSeriesClassifier
  from sktime.classification.ensemble import ComposableTimeSeriesForestClassifier
  from sktime.datasets import load_arrow_head, load_gunpoint
  from sktime.series_as_features.model_selection import SingleSplit
  from sktime.transformations.panel.reduce import Tabularizer
label_desc: Use image node_id 2 for calling a global function with example usage: test_automated_orchestration_vs_manual(data_loader) without return types