code: def load_unit_test(split=None, return_X_y=True, return_type=None):
      """Load UnitTest data.
  
      This is an equal length univariate time series classification problem. It is a
      stripped down version of the ChinaTown problem that is used in correctness tests
      for classification. It loads a two class classification problem with number of
      cases, n, where n = 42 (if split is None) or 20/22 (if split is "train"/"test")
      of series length m = 24
  
      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:  The time series data for the problem. If return_type is either
          "numpy2d"/"numpyflat", it returns 2D numpy array of shape (n,m), if "numpy3d" it
          returns 3D numpy array of shape (n,1,m) and if "nested_univ" or None it returns
          a nested pandas DataFrame of shape (n,1), where each cell is a pd.Series of
          length m.
      y: (optional) numpy array shape (n,1). The class labels for each case in X.
          If return_X_y is False, y is appended to X.
  
      Examples
      --------
      >>> from sktime.datasets import load_unit_test
      >>> X, y = load_unit_test()
  
      Details
      -------
      This is the Chinatown problem with a smaller test set, useful for rapid tests.
      Dimensionality:     univariate
      Series length:      24
      Train cases:        20
      Test cases:         22 (full dataset has 345)
      Number of classes:  2
  
       See
      http://timeseriesclassification.com/description.php?Dataset=Chinatown
      for the full dataset
      """
      name = "UnitTest"
      return _load_provided_dataset(
          name, split, return_X_y, 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 7 for calling a global function with example usage: load_unit_test(split, return_X_y, return_type) and returns: _load_provided_dataset