docstr: Parameters
  ----------
  fields : list of str
     'open', 'high', 'low', 'close', or 'volume'
  start_dt: Timestamp
     Beginning of the window range.
  end_dt: Timestamp
     End of the window range.
  sids : list of int
     The asset identifiers in the window.
  
  Returns
  -------
  list of np.ndarray
      A list with an entry per field of ndarrays with shape
      (minutes in range, sids) with a dtype of float64, containing the
      values for the respective field over start and end dt range.
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code: def load_raw_arrays(self, columns, start_date, end_date, assets):
      """
      Parameters
      ----------
      fields : list of str
         'open', 'high', 'low', 'close', or 'volume'
      start_dt: Timestamp
         Beginning of the window range.
      end_dt: Timestamp
         End of the window range.
      sids : list of int
         The asset identifiers in the window.
  
      Returns
      -------
      list of np.ndarray
          A list with an entry per field of ndarrays with shape
          (minutes in range, sids) with a dtype of float64, containing the
          values for the respective field over start and end dt range.
      """
      rolls_by_asset = {}
  
      tc = self.trading_calendar
      start_session = tc.minute_to_session_label(start_date)
      end_session = tc.minute_to_session_label(end_date)
  
      for asset in assets:
          rf = self._roll_finders[asset.roll_style]
          rolls_by_asset[asset] = rf.get_rolls(
              asset.root_symbol,
              start_session,
              end_session,
              asset.offset,
          )
  
      sessions = tc.sessions_in_range(start_date, end_date)
  
      minutes = tc.minutes_in_range(start_date, end_date)
      num_minutes = len(minutes)
      shape = num_minutes, len(assets)
  
      results = []
  
      # Get partitions
      partitions_by_asset = {}
      for asset in assets:
          partitions = []
          partitions_by_asset[asset] = partitions
          rolls = rolls_by_asset[asset]
          start = start_date
          for roll in rolls:
              sid, roll_date = roll
              start_loc = minutes.searchsorted(start)
              if roll_date is not None:
                  _, end = tc.open_and_close_for_session(
                      roll_date - sessions.freq
                  )
                  end_loc = minutes.searchsorted(end)
              else:
                  end = end_date
                  end_loc = len(minutes) - 1
              partitions.append((sid, start, end, start_loc, end_loc))
              if roll[-1] is not None:
                  start, _ = tc.open_and_close_for_session(
                      tc.minute_to_session_label(minutes[end_loc + 1])
                  )
  
      for column in columns:
          if column != "volume":
              out = np.full(shape, np.nan)
          else:
              out = np.zeros(shape, dtype=np.uint32)
          for i, asset in enumerate(assets):
              partitions = partitions_by_asset[asset]
              for sid, start, end, start_loc, end_loc in partitions:
                  if column != "sid":
                      result = self._bar_reader.load_raw_arrays(
                          [column], start, end, [sid]
                      )[0][:, 0]
                  else:
                      result = int(sid)
                  out[start_loc : end_loc + 1, i] = result
          results.append(out)
      return results
  
imports: import numpy
  import pandas
  from catalyst.data.session_bars import SessionBarReader
label_desc: Use image node_id 2 for calling the ContinuousFutureMinuteBarReader obj's underlying member method code with example usage: obj.load_raw_arrays(columns, start_date, end_date, assets) and returns: results