code: def load_UCR_UEA_dataset(
      name,
      split=None,
      return_X_y=True,
      return_type=None,
      extract_path=None,
  ):
      """Load dataset from UCR UEA time series archive.
  
      Downloads and extracts dataset if not already downloaded. Data is assumed to be
      in the standard .ts format: each row is a (possibly multivariate) time series.
      Each dimension is separated by a colon, each value in a series is comma
      separated. For examples see sktime.datasets.data.tsc. ArrowHead is an example of
      a univariate equal length problem, BasicMotions an equal length multivariate
      problem.
  
      Parameters
      ----------
      name : str
          Name of data set. If a dataset that is listed in tsc_dataset_names is given,
          this function will look in the extract_path first, and if it is not present,
          attempt to download the data from www.timeseriesclassification.com, saving it to
          the extract_path.
      split : None or str{"train", "test"}, optional (default=None)
          Whether to load the train or test partition of the problem. By default it
          loads both into a single dataset, otherwise it looks only for files of the
          format <name>_TRAIN.ts or <name>_TEST.ts.
      return_X_y : bool, optional (default=False)
          it returns two objects, if False, it appends the class labels to the dataframe.
      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.
      extract_path : str, optional (default=None)
          the path to look for the data. If no path is provided, the function
          looks in `sktime/datasets/data/`. If a path is given, it can be absolute,
          e.g. C:/Temp or relative, e.g. Temp or ./Temp.
  
      Returns
      -------
      X: pd.DataFrame
          The time series data for the problem with n_cases rows and either
          n_dimensions or n_dimensions+1 columns. Columns 1 to n_dimensions are the
          series associated with each case. If return_X_y is False, column
          n_dimensions+1 contains the class labels/target variable.
      y: numpy array, optional
          The class labels for each case in X, returned separately if return_X_y is
          True, or appended to X if False
  
      Examples
      --------
      >>> from sktime.datasets import load_UCR_UEA_dataset
      >>> X, y = load_UCR_UEA_dataset(name="ArrowHead")
      """
      return _load_dataset(
          name, split, return_X_y, return_type, extract_path
      )
  
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 1 for calling a global function with example usage: load_UCR_UEA_dataset(name, split, return_X_y, return_type, extract_path) and returns: _load_dataset