code: def quantile(self, q, dim=None, interpolation='linear', keep_attrs=None):
          
  
          ds = self._to_temp_dataset().quantile(
              q, dim=dim, keep_attrs=keep_attrs, interpolation=interpolation)
          return self._from_temp_dataset(ds)
docs: Compute the qth quantile of the data along the specified dimension.
  
          Returns the qth quantiles(s) of the array elements.
  
          Parameters
          ----------
          q : float in range of [0,1] (or sequence of floats)
              Quantile to compute, which must be between 0 and 1 inclusive.
          dim : str or sequence of str, optional
              Dimension(s) over which to apply quantile.
          interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
              This optional parameter specifies the interpolation method to
              use when the desired quantile lies between two data points
              ``i < j``:
  
                  - linear: ``i + (j - i) * fraction``, where ``fraction`` is
                    the fractional part of the index surrounded by ``i`` and
                    ``j``.
                  - lower: ``i``.
                  - higher: ``j``.
                  - nearest: ``i`` or ``j``, whichever is nearest.
                  - midpoint: ``(i + j) / 2``.
          keep_attrs : bool, optional
              If True, the dataset's attributes (`attrs`) will be copied from
              the original object to the new one.  If False (default), the new
              object will be returned without attributes.
  
          Returns
          -------
          quantiles : DataArray
              If `q` is a single quantile, then the result
              is a scalar. If multiple percentiles are given, first axis of
              the result corresponds to the quantile and a quantile dimension
              is added to the return array. The other dimensions are the
               dimensions that remain after the reduction of the array.
  
          See Also
          --------
          numpy.nanpercentile, pandas.Series.quantile, Dataset.quantile