code: def test_minirocket_multivariate_variable_on_japanese_vowels():
      """Test of MiniRocketMultivariate on japanese vowels."""
      # load training data
      X_training, Y_training = load_japanese_vowels(
          split="train", return_X_y=True
      )
  
      # 'fit' MINIROCKET -> infer data dimensions, generate random kernels
      num_kernels = 10_000
      minirocket_mv_var = MiniRocketMultivariateVariable(
          num_kernels=num_kernels,
          pad_value_short_series=0,
          reference_length="max",
          max_dilations_per_kernel=32,
          n_jobs=1,
          random_state=42,
      )
      minirocket_mv_var.fit(X_training)
  
      # transform training data
      X_training_transform = minirocket_mv_var.transform(X_training)
  
      # test shape of transformed training data -> (number of training
      # examples, nearest multiple of 84 < 1000)
      np.testing.assert_equal(
          X_training_transform.shape,
          (len(X_training), 84 * (num_kernels // 84)),
      )
  
      # 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_japanese_vowels(
          split="test", return_X_y=True
      )
  
      # transform test data
      X_test_transform = minirocket_mv_var.transform(X_test)
  
      # test shape of transformed test data -> (number of test examples,
      # nearest multiple of 84 < 10,000)
      np.testing.assert_equal(
          X_test_transform.shape,
          (len(X_test), 84 * (num_kernels // 84)),
      )
  
      # predict (alternatively: 'classifier.score(X_test_transform, Y_test)')
      predictions = classifier.predict(X_test_transform)
      accuracy = accuracy_score(predictions, Y_test)
  
      # test accuracy, mean usually .987, and minimum .983
      assert (
          accuracy > 0.97
      ), "Test accuracy should be greater than 0.97"
  
file_path: sktime/sktime/transformations/panel/rocket/tests/test_MiniRocketMultivariateVariable.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_japanese_vowels
  from sktime.tests.test_switch import run_test_for_class
  from sktime.transformations.panel.rocket import MiniRocketMultivariateVariable
label_desc: Use image node_id 1 for calling a global function with example usage: test_minirocket_multivariate_variable_on_japanese_vowels() without return types