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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def differentiate(self, coord, edge_order=1, datetime_unit=None):
 """ Differentiate the array with the second order accurate central differences. .. note:: This feature is limited to simple cartesian geometry, i.e. coord must be one dimensional. Parameters coord: str The coordinate to be used to compute the gradient. edge_order: 1 or 2. Default 1 N-th order accurate differences at the boundaries. datetime_unit: None or any of {'Y', 'M', 'W', 'D', 'h', 'm', 's', 'ms', 'us', 'ns', 'ps', 'fs', 'as'} Unit to compute gradient. Only valid for datetime coordinate. Returns ------- differentiated: DataArray See also -------- numpy.gradient: corresponding numpy function Examples -------- <xarray.DataArray (x: 4, y: 3)> array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11]]) Coordinates: * x (x) float64 0.0 0.1 1.1 1.2 Dimensions without coordinates: y <xarray.DataArray (x: 4, y: 3)> array([[30. , 30. , 30. ], [27.545455, 27.545455, 27.545455], [27.545455, 27.545455, 27.545455], [30. , 30. , 30. ]]) Coordinates: * x (x) float64 0.0 0.1 1.1 1.2 Dimensions without coordinates: y """

        ds = self._to_temp_dataset().differentiate(
            coord, edge_order, datetime_unit)
        return self._from_temp_dataset(ds)