code: def convert_gluonts_result_to_multiindex(gluonts_result):
      """Back Convert from Gluonts to sktime.
  
      Convert the output of Gluonts's prediction to a multiindex
      dataframe compatible with sktime.
  
      Parameters
      ----------
      gluonts_result: The first element of the tuple resulting
      from running `make_evaluation_predictions`.
          For example in Eg:
          forecast_it, ts_it = make_evaluation_predictions()
          gluonts_result = forecast_it
  
      Returns
      -------
      A MultiIndex DF mtype type compatible with sktime.
      """
      import pandas as pd
  
      from sktime.datatypes import convert_to
  
      instance_no = len(gluonts_result)
      global_ls = []
      per_instance_ls = []
      columns = []
  
      for i in range(instance_no):
          validation_no = gluonts_result[i].samples.shape[0]
          period = gluonts_result[i].samples.shape[1]
          start_date = pd.to_datetime(gluonts_result[i].start_date)
          freq = gluonts_result[i].freq
          ts_index = pd.date_range(
              start=start_date, periods=period, freq=freq
          )
          per_instance_ls = [
              pd.Series(
                  data=gluonts_result[i].samples[j], index=ts_index
              )
              for j in range(validation_no)
          ]
          global_ls.append(per_instance_ls)
  
      for k in range(validation_no):
          columns.append("validation_" + str(k))
  
      nested_univ = pd.DataFrame(global_ls, columns=columns)
  
      return convert_to(nested_univ, to_type="pd-multiindex")
  
label_desc: Use image node_id 1 for calling a global function with example usage: convert_gluonts_result_to_multiindex(gluonts_result) and returns: convert_to