def quantile(
        self,
        q: ArrayLike,
        dim: Dims = None,
        *,
        method: QuantileMethods = "linear",
        keep_attrs: bool | None = None,
        skipna: bool | None = None,
        interpolation: QuantileMethods | None = None,
    ) -> Self:
        """Compute the qth quantile of the data along the specified dimension.

        Returns the qth quantiles(s) of the array elements.

        Parameters
        ----------
        q : float or array-like of float
            Quantile to compute, which must be between 0 and 1 inclusive.
        dim : str or Iterable of Hashable, optional
            Dimension(s) over which to apply quantile.
        method : str, default: "linear"
            This optional parameter specifies the interpolation method to use when the
            desired quantile lies between two data points. The options sorted by their R
            type as summarized in the H&F paper [1]_ are:

                1. "inverted_cdf"
                2. "averaged_inverted_cdf"
                3. "closest_observation"
                4. "interpolated_inverted_cdf"
                5. "hazen"
                6. "weibull"
                7. "linear"  (default)
                8. "median_unbiased"
                9. "normal_unbiased"

            The first three methods are discontiuous. The following discontinuous
            variations of the default "linear" (7.) option are also available:

                * "lower"
                * "higher"
                * "midpoint"
                * "nearest"

            See :py:func:`numpy.quantile` or [1]_ for details. The "method" argument
            was previously called "interpolation", renamed in accordance with numpy
            version 1.22.0.

        keep_attrs : bool or None, 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.
        skipna : bool or None, optional
            If True, skip missing values (as marked by NaN). By default, only
            skips missing values for float dtypes; other dtypes either do not
            have a sentinel missing value (int) or skipna=True has not been
            implemented (object, datetime64 or timedelta64).

        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.nanquantile, numpy.quantile, pandas.Series.quantile, Dataset.quantile

        Examples
        --------
        >>> da = xr.DataArray(
        ...     data=[[0.7, 4.2, 9.4, 1.5], [6.5, 7.3, 2.6, 1.9]],
        ...     coords={"x": [7, 9], "y": [1, 1.5, 2, 2.5]},
        ...     dims=("x", "y"),
        ... )
        >>> da.quantile(0)  # or da.quantile(0, dim=...)
        <xarray.DataArray ()> Size: 8B
        array(0.7)
        Coordinates:
            quantile  float64 8B 0.0
        >>> da.quantile(0, dim="x")
        <xarray.DataArray (y: 4)> Size: 32B
        array([0.7, 4.2, 2.6, 1.5])
        Coordinates:
          * y         (y) float64 32B 1.0 1.5 2.0 2.5
            quantile  float64 8B 0.0
        >>> da.quantile([0, 0.5, 1])
        <xarray.DataArray (quantile: 3)> Size: 24B
        array([0.7, 3.4, 9.4])
        Coordinates:
          * quantile  (quantile) float64 24B 0.0 0.5 1.0
        >>> da.quantile([0, 0.5, 1], dim="x")
        <xarray.DataArray (quantile: 3, y: 4)> Size: 96B
        array([[0.7 , 4.2 , 2.6 , 1.5 ],
               [3.6 , 5.75, 6.  , 1.7 ],
               [6.5 , 7.3 , 9.4 , 1.9 ]])
        Coordinates:
          * y         (y) float64 32B 1.0 1.5 2.0 2.5
          * quantile  (quantile) float64 24B 0.0 0.5 1.0

        References
        ----------
        .. [1] R. J. Hyndman and Y. Fan,
           "Sample quantiles in statistical packages,"
           The American Statistician, 50(4), pp. 361-365, 1996
        """

        ds = self._to_temp_dataset().quantile(
            q,
            dim=dim,
            keep_attrs=keep_attrs,
            method=method,
            skipna=skipna,
            interpolation=interpolation,
        )
        return self._from_temp_dataset(ds)
Compute the qth quantile of the data along the specified dimension.

        Returns the qth quantiles(s) of the array elements.

        Parameters
        ----------
        q : float or array-like of float
            Quantile to compute, which must be between 0 and 1 inclusive.
        dim : str or Iterable of Hashable, optional
            Dimension(s) over which to apply quantile.
        method : str, default: "linear"
            This optional parameter specifies the interpolation method to use when the
            desired quantile lies between two data points. The options sorted by their R
            type as summarized in the H&F paper [1]_ are:

                1. "inverted_cdf"
                2. "averaged_inverted_cdf"
                3. "closest_observation"
                4. "interpolated_inverted_cdf"
                5. "hazen"
                6. "weibull"
                7. "linear"  (default)
                8. "median_unbiased"
                9. "normal_unbiased"

            The first three methods are discontiuous. The following discontinuous
            variations of the default "linear" (7.) option are also available:

                * "lower"
                * "higher"
                * "midpoint"
                * "nearest"

            See :py:func:`numpy.quantile` or [1]_ for details. The "method" argument
            was previously called "interpolation", renamed in accordance with numpy
            version 1.22.0.

        keep_attrs : bool or None, 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.
        skipna : bool or None, optional
            If True, skip missing values (as marked by NaN). By default, only
            skips missing values for float dtypes; other dtypes either do not
            have a sentinel missing value (int) or skipna=True has not been
            implemented (object, datetime64 or timedelta64).

        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.nanquantile, numpy.quantile, pandas.Series.quantile, Dataset.quantile

        Examples
        --------
        >>> da = xr.DataArray(
        ...     data=[[0.7, 4.2, 9.4, 1.5], [6.5, 7.3, 2.6, 1.9]],
        ...     coords={"x": [7, 9], "y": [1, 1.5, 2, 2.5]},
        ...     dims=("x", "y"),
        ... )
        >>> da.quantile(0)  # or da.quantile(0, dim=...)
        <xarray.DataArray ()> Size: 8B
        array(0.7)
        Coordinates:
            quantile  float64 8B 0.0
        >>> da.quantile(0, dim="x")
        <xarray.DataArray (y: 4)> Size: 32B
        array([0.7, 4.2, 2.6, 1.5])
        Coordinates:
          * y         (y) float64 32B 1.0 1.5 2.0 2.5
            quantile  float64 8B 0.0
        >>> da.quantile([0, 0.5, 1])
        <xarray.DataArray (quantile: 3)> Size: 24B
        array([0.7, 3.4, 9.4])
        Coordinates:
          * quantile  (quantile) float64 24B 0.0 0.5 1.0
        >>> da.quantile([0, 0.5, 1], dim="x")
        <xarray.DataArray (quantile: 3, y: 4)> Size: 96B
        array([[0.7 , 4.2 , 2.6 , 1.5 ],
               [3.6 , 5.75, 6.  , 1.7 ],
               [6.5 , 7.3 , 9.4 , 1.9 ]])
        Coordinates:
          * y         (y) float64 32B 1.0 1.5 2.0 2.5
          * quantile  (quantile) float64 24B 0.0 0.5 1.0

        References
        ----------
        .. [1] R. J. Hyndman and Y. Fan,
           "Sample quantiles in statistical packages,"
           The American Statistician, 50(4), pp. 361-365, 1996
        
xarray/core/dataarray.py