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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 is_subgrid(self, other, atol=0.0):
 """Return ``True`` if this grid is a subgrid of ``other``. Parameters other : `RectGrid` The other grid which is supposed to contain this grid atol : float, optional Allow deviations up to this number in absolute value per coordinate vector entry. Returns ------- is_subgrid : bool ``True`` if all coordinate vectors of ``self`` are within absolute distance ``atol`` of the other grid, else ``False``. Examples -------- (array([-2., -1., 0.]), array([-2., 0., 2., 4.])) (array([-1., 0.]), array([ 2., 4.])) True Fuzzy check is also possible. Note that the tolerance still applies to the coordinate vectors. False True """

        # Optimization for some common cases
        if other is self:
            return True
        if not isinstance(other, RectGrid):
            return False
        if not all(self.shape[i] <= other.shape[i] and
                   self.min_pt[i] >= other.min_pt[i] - atol and
                   self.max_pt[i] <= other.max_pt[i] + atol
                   for i in range(self.ndim)):
            return False
        if self.size == 0:
            return True

        if self.is_uniform and other.is_uniform:
            # For uniform grids, it suffices to show that min_pt, max_pt
            # and g[1,...,1] are contained in the other grid. For axes
            # with less than 2 points, this reduces to min_pt and max_pt,
            # and the corresponding indices in the other check point are
            # set to 0.
            minmax_contained = (
                other.approx_contains(self.min_pt, atol=atol) and
                other.approx_contains(self.max_pt, atol=atol))
            check_idx = np.zeros(self.ndim, dtype=int)
            check_idx[np.array(self.shape) >= 3] = 1
            checkpt_contained = other.approx_contains(self[tuple(check_idx)],
                                                      atol=atol)
            return minmax_contained and checkpt_contained

        else:
            # Array version of the fuzzy subgrid test, about 3 times faster
            # than the loop version.
            for vec_o, vec_s in zip(other.coord_vectors, self.coord_vectors):
                # Create array of differences of all entries in vec_o and
                # vec_s. If there is no almost zero entry in each row,
                # return False.
                vec_o_mg, vec_s_mg = sparse_meshgrid(vec_o, vec_s)
                if not np.all(np.any(np.isclose(vec_s_mg, vec_o_mg, atol=atol),
                                     axis=0)):
                    return False
            return True