code: def load_acsf1(split=None, return_X_y=True, return_type=None):
      """Load dataset on power consumption of typical appliances.
  
      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_acsf1
      >>> X, y = load_acsf1()
  
      Notes
      -----
      Dimensionality:     univariate
      Series length:      1460
      Train cases:        100
      Test cases:         100
      Number of classes:  10
  
      The dataset contains the power consumption of typical appliances.
      The recordings are characterized by long idle periods and some high bursts
      of energy consumption when the appliance is active.
      The classes correspond to 10 categories of home appliances;
      mobile phones (via chargers), coffee machines, computer stations
      (including monitor), fridges and freezers, Hi-Fi systems (CD players),
      lamp (CFL), laptops (via chargers), microwave ovens, printers, and
      televisions (LCD or LED)."
  
      Dataset details: http://www.timeseriesclassification.com/description.php?Dataset
      =ACSF1
      """
      name = "ACSF1"
      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 10 for calling a global function with example usage: load_acsf1(split, return_X_y, return_type) and returns: _load_dataset