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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 where(self, cond, other=dtypes.NA, drop: bool = False):
 """Filter elements from this object according to a condition. This operation follows the normal broadcasting and alignment rules that xarray uses for binary arithmetic. Parameters cond : DataArray or Dataset with boolean dtype Locations at which to preserve this object's values. other : scalar, DataArray or Dataset, optional Value to use for locations in this object where ``cond`` is False. By default, these locations filled with NA. drop : boolean, optional If True, coordinate labels that only correspond to False values of the condition are dropped from the result. Mutually exclusive with ``other``. Returns ------- Same type as caller. Examples -------- <xarray.DataArray (x: 5, y: 5)> array([[ 0., 1., 2., 3., nan], [ 5., 6., 7., nan, nan], [ 10., 11., nan, nan, nan], [ 15., nan, nan, nan, nan], [ nan, nan, nan, nan, nan]]) Dimensions without coordinates: x, y <xarray.DataArray (x: 5, y: 5)> array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, -1], [10, 11, 12, -1, -1], [15, 16, -1, -1, -1], [20, -1, -1, -1, -1]]) Dimensions without coordinates: x, y <xarray.DataArray (x: 4, y: 4)> array([[ 0., 1., 2., 3.], [ 5., 6., 7., nan], [ 10., 11., nan, nan], [ 15., nan, nan, nan]]) Dimensions without coordinates: x, y See also -------- numpy.where : corresponding numpy function where : equivalent function """

        from .alignment import align
        from .dataarray import DataArray
        from .dataset import Dataset

        if drop:
            if other is not dtypes.NA:
                raise ValueError('cannot set `other` if drop=True')

            if not isinstance(cond, (Dataset, DataArray)):
                raise TypeError("cond argument is %r but must be a %r or %r" %
                                (cond, Dataset, DataArray))

            # align so we can use integer indexing
            self, cond = align(self, cond)

            # get cond with the minimal size needed for the Dataset
            if isinstance(cond, Dataset):
                clipcond = cond.to_array().any('variable')
            else:
                clipcond = cond

            # clip the data corresponding to coordinate dims that are not used
            nonzeros = zip(clipcond.dims, np.nonzero(clipcond.values))
            indexers = {k: np.unique(v) for k, v in nonzeros}

            self = self.isel(**indexers)
            cond = cond.isel(**indexers)

        return ops.where_method(self, cond, other)