code: def load_japanese_vowels(
      split=None, return_X_y=True, return_type=None
  ):
      """Load the JapaneseVowels time series classification problem.
  
      Example of a multivariate problem with unequal length series.
  
      Parameters
      ----------
      split: None or one of "TRAIN", "TEST", optional (default=None)
          Whether to load the train or test instances of the problem.
          By default it loads both train and test instances (in a single container).
      return_X_y: bool, optional (default=True)
          If True, returns (features, target) separately instead of a single
          dataframe with columns for features and the target.
      return_type: valid Panel mtype str or None, optional (default=None="nested_univ")
          Memory data format specification to return X in, None = "nested_univ" type.
          str can be any supported sktime Panel mtype,
              for list of mtypes, see datatypes.MTYPE_REGISTER
              for specifications, see examples/AA_datatypes_and_datasets.ipynb
          commonly used specifications:
              "nested_univ: nested pd.DataFrame, pd.Series in cells
              "numpy3D"/"numpy3d"/"np3D": 3D np.ndarray (instance, variable, time index)
              "numpy2d"/"np2d"/"numpyflat": 2D np.ndarray (instance, time index)
              "pd-multiindex": pd.DataFrame with 2-level (instance, time) MultiIndex
          Exception is raised if the data cannot be stored in the requested type.
  
      Returns
      -------
      X: pd.DataFrame with m rows and c columns
          The time series data for the problem with m cases and c dimensions
      y: numpy array
          The class labels for each case in X
  
      Examples
      --------
      >>> from sktime.datasets import load_japanese_vowels
      >>> X, y = load_japanese_vowels()
  
      Notes
      -----
      Dimensionality:     multivariate, 12
      Series length:      7-29
      Train cases:        270
      Test cases:         370
      Number of classes:  9
  
      A UCI Archive dataset. 9 Japanese-male speakers were recorded saying
      the vowels 'a' and 'e'. A '12-degree
      linear prediction analysis' is applied to the raw recordings to
      obtain time-series with 12 dimensions and series lengths between 7 and 29.
      The classification task is to predict the speaker. Therefore,
      each instance is a transformed utterance,
      12*29 values with a single class label attached, [1...9]. The given
      training set is comprised of 30
      utterances for each speaker, however the test set has a varied
      distribution based on external factors of
      timing and experimental availability, between 24 and 88 instances per
      speaker. Reference: M. Kudo, J. Toyama
      and M. Shimbo. (1999). "Multidimensional Curve Classification Using
      Passing-Through Regions". Pattern
      Recognition Letters, Vol. 20, No. 11--13, pages 1103--1111.
      Dataset details: http://timeseriesclassification.com/description.php
      ?Dataset=JapaneseVowels
      """
      name = "JapaneseVowels"
      return _load_dataset(
          name, split, return_X_y, return_type=return_type
      )
  
imports: import os
  import zipfile
  from urllib.error import HTTPError, URLError
  from warnings import warn
  import numpy
  import pandas
  from sktime.datasets._data_io import _download_and_extract, _list_available_datasets, _load_dataset, _load_provided_dataset
  from sktime.datasets._readers_writers.tsf import load_tsf_to_dataframe
  from sktime.datasets.tsf_dataset_names import tsf_all, tsf_all_datasets
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 8 for calling a global function with example usage: load_japanese_vowels(split, return_X_y, return_type) and returns: _load_dataset