code: def _handle_frequency_grouper(self, by):
      # if `by` is a time frequency grouper, we bin the key column
      # using bin intervals specified by `by.freq`, then use *that*
      # as the groupby key
  
      freq = by.freq
      label = by.label
      closed = by.closed
  
      if isinstance(freq, (cudf.DateOffset, pd.DateOffset)):
          raise NotImplementedError(
              "Resampling by DateOffset objects is not yet supported."
          )
      if not isinstance(freq, str):
          raise TypeError(
              f"Unsupported type for freq: {type(freq).__name__}"
          )
      # convert freq to a pd.DateOffset:
      offset = pd.tseries.frequencies.to_offset(freq)
  
      if offset.freqstr == "M" or offset.freqstr.startswith("W-"):
          label = "right" if label is None else label
          closed = "right" if closed is None else closed
      else:
          label = "left" if label is None else label
          closed = "left" if closed is None else closed
  
      # determine the key column
      if by.key is None and by.level is None:
          # then assume that the key is the index of `self._obj`:
          self._handle_index(self._obj.index)
      elif by.key:
          self._handle_label(by.key)
      elif by.level:
          self._handle_level(by.level)
  
      if not len(self._key_columns) == 1:
          raise ValueError("Must resample on exactly one column")
  
      key_column = self._key_columns[0]
  
      if not isinstance(key_column, cudf.core.column.DatetimeColumn):
          raise TypeError(
              f"Can only resample on a DatetimeIndex or datetime column, "
              f"got column of type {key_column.dtype}"
          )
  
      # get the start and end values that will be used to generate
      # the bin labels
      min_date, max_date = key_column._minmax()
      start, end = _get_timestamp_range_edges(
          pd.Timestamp(min_date.value),
          pd.Timestamp(max_date.value),
          offset,
          closed=closed,
      )
  
      # in some cases, an extra time stamp is required in order to
      # bin all the values. It's OK if we generate more labels than
      # we need, as we remove any unused labels below
      end += offset
  
      # generate the labels for binning the key column:
      bin_labels = cudf.date_range(
          start=start,
          end=end,
          freq=freq,
      )
  
      # We want the (resampled) column of timestamps in the result
      # to have a resolution closest to the resampling
      # frequency. For example, if resampling from '1T' to '1s', we
      # want the resulting timestamp column to by of dtype
      # 'datetime64[s]'.  libcudf requires the bin labels and key
      # column to have the same dtype, so we compute a `result_type`
      # and cast them both to that type.
      try:
          result_type = np.dtype(
              _unit_dtype_map[_offset_alias_to_code[offset.name]]
          )
      except KeyError:
          # unsupported resolution (we don't support resolutions >s)
          # fall back to using datetime64[s]
          result_type = np.dtype("datetime64[s]")
  
      # TODO: Ideally, we can avoid one cast by having `date_range`
      # generate timestamps of a given dtype.  Currently, it can
      # only generate timestamps with 'ns' precision
      key_column = key_column.astype(result_type)
      bin_labels = bin_labels.astype(result_type)
  
      # bin the key column:
      bin_numbers = cudf._lib.labeling.label_bins(
          key_column,
          left_edges=bin_labels[:-1]._column,
          left_inclusive=(closed == "left"),
          right_edges=bin_labels[1:]._column,
          right_inclusive=(closed == "right"),
      )
  
      if label == "right":
          bin_labels = bin_labels[1:]
      else:
          bin_labels = bin_labels[:-1]
  
      # if we have more labels than bins, remove the extras labels:
      nbins = bin_numbers.max() + 1
      if len(bin_labels) > nbins:
          bin_labels = bin_labels[:nbins]
  
      bin_labels.name = self.names[0]
      self.bin_labels = bin_labels
  
      # replace self._key_columns with the binned key column:
      self._key_columns = [
          bin_labels._gather(
              bin_numbers, check_bounds=False
          )._column.astype(result_type)
      ]
  
imports: import pickle
  import numpy
  import pandas
  import cudf
  import cudf._lib.labeling
  import cudf.core.index
  from cudf._typing import DataFrameOrSeries
  from cudf.core.groupby.groupby import DataFrameGroupBy, GroupBy, SeriesGroupBy, _Grouping
  from cudf.core.tools.datetimes import _offset_alias_to_code, _unit_dtype_map
label_desc: Use image node_id 6 for calling the _ResampleGrouping obj's underlying member method code with example usage: obj._handle_frequency_grouper(by) without return types