code: def test_multirocket_on_gunpoint():
      """Test of MultiRocket on gun point."""
      # load training data
      X_training, Y_training = load_gunpoint(
          split="train", return_X_y=True
      )
  
      # 'fit' MultiRocket -> infer data dimensions, generate random kernels
      multirocket = MultiRocket(random_state=0)
      multirocket.fit(X_training)
  
      # transform training data
      X_training_transform = multirocket.transform(X_training)
  
      # test shape of transformed training data -> (number of training
      # examples, nearest multiple of 4*84=336 < 50,000 (2*4*6_250))
      np.testing.assert_equal(
          X_training_transform.shape, (len(X_training), 49_728)
      )
  
      # fit classifier
      classifier = make_pipeline(
          StandardScaler(with_mean=False),
          RidgeClassifierCV(alphas=np.logspace(-3, 3, 10)),
      )
      classifier.fit(X_training_transform, Y_training)
  
      # load test data
      X_test, Y_test = load_gunpoint(split="test", return_X_y=True)
  
      # transform test data
      X_test_transform = multirocket.transform(X_test)
  
      # test shape of transformed test data -> (number of test examples,
      # nearest multiple of 4*84=336 < 50,000 (2*4*6_250))
      np.testing.assert_equal(
          X_test_transform.shape, (len(X_test), 49_728)
      )
  
      # predict (alternatively: 'classifier.score(X_test_transform, Y_test)')
      predictions = classifier.predict(X_test_transform)
      accuracy = accuracy_score(predictions, Y_test)
  
      # test predictions (on Gunpoint, should be > 99% accurate)
      assert accuracy > 0.99
  
file_path: sktime/sktime/transformations/panel/rocket/tests/test_MultiRocket.py
imports: import numpy
  import pytest
  from sklearn.linear_model import RidgeClassifierCV
  from sklearn.metrics import accuracy_score
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import StandardScaler
  from sktime.datasets import load_gunpoint
  from sktime.tests.test_switch import run_test_for_class
  from sktime.transformations.panel.rocket import MultiRocket
label_desc: Use image node_id 1 for calling a global function with example usage: test_multirocket_on_gunpoint() without return types