code: def load_uschange(y_name="Consumption"):
      """Load MTS dataset for forecasting Growth rates of personal consumption and income.
  
      Returns
      -------
      y : pd.Series
          selected column, default consumption
      X : pd.DataFrame
          columns with explanatory variables
  
      Examples
      --------
      >>> from sktime.datasets import load_uschange
      >>> y, X = load_uschange()
  
      Notes
      -----
      Percentage changes in quarterly personal consumption expenditure,
      personal disposable income, production, savings and the
      unemployment rate for the US, 1960 to 2016.
  
  
      Dimensionality:     multivariate
      Columns:            ['Quarter', 'Consumption', 'Income', 'Production',
                           'Savings', 'Unemployment']
      Series length:      188
      Frequency:          Quarterly
      Number of cases:    1
  
      This data shows an increasing trend, non-constant (increasing) variance
      and periodic, seasonal patterns.
  
      References
      ----------
      .. [1] Data for "Forecasting: Principles and Practice" (2nd Edition)
      """
      name = "Uschange"
      fname = name + ".csv"
      path = os.path.join(MODULE, DIRNAME, name, fname)
      data = pd.read_csv(path, index_col=0).squeeze("columns")
  
      # Sort by Quarter then set simple numeric index
      # TODO add support for period/datetime indexing
      # data.index = pd.PeriodIndex(data.index, freq='Y')
      data = data.sort_values("Quarter")
      data = data.reset_index(drop=True)
      data.index = pd.Index(data.index, dtype=int)
      data.name = name
      y = data[y_name]
      if y_name != "Quarter":
          data = data.drop("Quarter", axis=1)
      X = data.drop(y_name, axis=1)
      return y, X
  
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 16 for calling a global function with example usage: load_uschange(y_name) and returns: y,  X