docs: def _pypsa_load_timeseries(network, timesteps, mode=None):
      """
      Time series in PyPSA compatible format for load instances
  
      Parameters
      ----------
      network : Network
          The eDisGo grid topology model overall container
      timesteps : array_like
          Timesteps is an array-like object with entries of type
          :pandas:`pandas.Timestamp<timestamp>` specifying which time steps
          to export to pypsa representation and use in power flow analysis.
      mode : str, optional
          Specifically retrieve load time series for MV or LV grid level or both.
          Either choose 'mv' or 'lv'.
          Defaults to None, which returns both timeseries for MV and LV in a
          single DataFrame.
  
      Returns
      -------
      :pandas:`pandas.DataFrame<dataframe>`
          Time series table in PyPSA format
      """
      mv_load_timeseries_p = []
      lv_load_timeseries_p = []
  
      # add MV grid loads
      if mode is 'mv' or mode is None:
          for load in network.mv_grid.graph.nodes_by_attribute('load'):
              mv_load_timeseries_p.append(load.pypsa_timeseries('p').rename(
                  repr(load)).to_frame().loc[timesteps])
  
      # add LV grid's loads
      if mode is 'lv' or mode is None:
          for lv_grid in network.mv_grid.lv_grids:
              for load in lv_grid.graph.nodes_by_attribute('load'):
                  for sector in list(load.consumption.keys()):
                      # for sector in list(list(load.consumption.keys())[0]):
                      # ToDo: remove consideration of only industrial sector
                      # now, if a load object has consumption in multiple sectors
                      # (like currently only industrial/retail) the consumption is
                      # implicitly assigned to the industrial sector when being
                      # exported to pypsa.
                      # ToDo: resolve this in the importer
                      if sector != 'retail':
                          # lv_load_timeseries_q.append(
                          #     load.pypsa_timeseries('q').rename(
                          #         repr(load)).to_frame().loc[timesteps])
                          lv_load_timeseries_p.append(
                              load.pypsa_timeseries('p').rename(
                                  repr(load)).to_frame().loc[timesteps])
  
      load_df_p = pd.concat(mv_load_timeseries_p + lv_load_timeseries_p, axis=1)
  
      return load_df_p