code: def build_classifiers():
      """Examples of building a classifier.
  
      1. Directly from 2D numpy arrays.
      2. Directly from 3D numpy arrays.
      3. From a nested pandas.
      4. From a baked in dataset.
      5. From any UCR/UEA dataset downloaded from timeseriesclassification.com.
      """
      # Create an array
      # Random forest, rocket and HC2.
      randf = RandomForestClassifier()
      trainX, train_y, testX, test_y = make_toy_2d_problem()
      X = trainX.reshape(trainX.shape[0], 1, trainX.shape[1])
      train_y = pd.Series(train_y)
      test_y = pd.Series(test_y)
      # randf.fit(trainX, train_y)
      cls1 = ContractableBOSS(time_limit_in_minutes=1)
      # cls2 = BOSSEnsemble()
      cls1.fit(trainX, train_y)
      print(" CBOSS acc = ", cls1.score(testX, test_y))
  
file_path: sktime/sktime/_contrib/classification_examples.py
imports: import numpy
  import pandas
  from sklearn.ensemble import RandomForestClassifier
  from sktime.classification.dictionary_based import BOSSEnsemble, ContractableBOSS
  from sktime.datasets import load_unit_test
label_desc: Use image node_id 1 for calling a global function with example usage: build_classifiers() without return types