docs: def sel_points(self, dim='points', method=None, tolerance=None,
                     **indexers):
          """Returns a new dataset with each array indexed pointwise by tick
          labels along the specified dimension(s).
  
          In contrast to `Dataset.isel_points`, indexers for this method should
          use labels instead of integers.
  
          In contrast to `Dataset.sel`, this method selects points along the
          diagonal of multi-dimensional arrays, not the intersection.
  
          Parameters
          ----------
          dim : str or DataArray or pandas.Index or other list-like object, optional
              Name of the dimension to concatenate along. If dim is provided as a
              string, it must be a new dimension name, in which case it is added
              along axis=0. If dim is provided as a DataArray or Index or
              list-like object, its name, which must not be present in the
              dataset, is used as the dimension to concatenate along and the
              values are added as a coordinate.
          method : {None, 'nearest', 'pad'/'ffill', 'backfill'/'bfill'}, optional
              Method to use for inexact matches (requires pandas>=0.16):
  
              * None (default): only exact matches
              * pad / ffill: propagate last valid index value forward
              * backfill / bfill: propagate next valid index value backward
              * nearest: use nearest valid index value
          tolerance : optional
              Maximum distance between original and new labels for inexact
              matches. The values of the index at the matching locations must
              satisfy the equation ``abs(index[indexer] - target) <= tolerance``.
              Requires pandas>=0.17.
          **indexers : {dim: indexer, ...}
              Keyword arguments with names matching dimensions and values given
              by array-like objects. All indexers must be the same length and
              1 dimensional.
  
          Returns
          -------
          obj : Dataset
              A new Dataset with the same contents as this dataset, except each
              array and dimension is indexed by the appropriate indexers. With
              pointwise indexing, the new Dataset will always be a copy of the
              original.
  
          See Also
          --------
          Dataset.sel
          Dataset.isel
          Dataset.isel_points
          DataArray.sel_points
          """  # noqa
          warnings.warn('Dataset.sel_points is deprecated: use Dataset.sel()'
                        'instead.', DeprecationWarning, stacklevel=2)
  
          pos_indexers, _ = indexing.remap_label_indexers(
              self, indexers, method=method, tolerance=tolerance
          )
          return self.isel_points(dim=dim, **pos_indexers)