code: def _get_univar_exo_data_with_index_index():
      """Generates multiple datasets of univariate data with exogenous variables
      where the index has been set to various types of indices.
      """
      # TODO: Find a better source of data ----
      data1 = pd.read_csv(
          "https://raw.githubusercontent.com/ngupta23/DS6373_TimeSeries/2b40f0071c3b7ec6a05dc0106f64e041f8cbaaef/Projects/gdp_prediction/data/economic_indicators_all_ex_3mo_china_inc_treas3mo.csv"
      )
      data1["date"] = pd.to_datetime(
          data1["date"].str.replace(" ", "-")
      )
      data1.set_index("date", inplace=True)
  
      # PeriodIndex
      data1.index = data1.index.to_period()
  
      # DatetimeIndex
      data2 = data1.copy()
      data2 = data2.to_timestamp()
  
      # Int64Index
      data3 = data1.copy()
      data3.reset_index(drop=True, inplace=True)
  
      ids = ["Period", "Datetime", "Int"]
  
      # DateTimeIndex is coerced and returned as PeriodIndex in PyCaret
      return [
          [
              (data1, pd.PeriodIndex),
              (data2, pd.PeriodIndex),
              (data3, pd.Index),
          ],
          ids,
      ]
  
file_path: pycaret/tests/test_time_series_indices.py
imports: import os
  from typing import Any
  import pandas
  import pytest
  from pycaret.datasets import get_data
  from pycaret.time_series import TSForecastingExperiment
label_desc: Use image node_id 3 for calling a global function with example usage: _get_univar_exo_data_with_index_index() and returns: list