code: def load_italy_power_demand(
      split=None, return_X_y=True, return_type=None
  ):
      """Load ItalyPowerDemand time series classification problem.
  
      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: sktime data container, following mtype specification `return_type`
          The time series data for the problem, with n instances
      y: 1D numpy array of length n, only returned if return_X_y if True
          The class labels for each time series instance in X
          If return_X_y is False, y is appended to X instead.
  
      Examples
      --------
      >>> from sktime.datasets import load_italy_power_demand
      >>> X, y = load_italy_power_demand()
  
      Notes
      -----
      Dimensionality:     univariate
      Series length:      24
      Train cases:        67
      Test cases:         1029
      Number of classes:  2
  
      The data was derived from twelve monthly electrical power demand time series from
      Italy and first used in the paper "Intelligent Icons: Integrating Lite-Weight Data
      Mining and Visualization into GUI Operating Systems". The classification task is to
      distinguish days from Oct to March (inclusive) from April to September.
      Dataset details:
      http://timeseriesclassification.com/description.php?Dataset=ItalyPowerDemand
      """
      name = "ItalyPowerDemand"
      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 6 for calling a global function with example usage: load_italy_power_demand(split, return_X_y, return_type) and returns: _load_dataset