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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 contains_all(self, other, atol=0.0):
 """Return ``True`` if all points defined by ``other`` are contained. Parameters other : Collection of points to be tested. Can be given as a single point, a ``(d, N)`` array-like where ``d`` is the number of dimensions, or a length-``d`` `meshgrid` tuple. atol : float, optional The maximum allowed distance in 'inf'-norm between the other set and this interval product. Returns ------- contains : bool ``True`` if all points are contained, ``False`` otherwise. Examples -------- Arrays are expected in ``(ndim, npoints)`` shape: True Implicit meshgrids defined by coordinate vectors: True Works also with an arbitrary iterable: True Grids are also accepted as input: True """

        atol = float(atol)

        # First try optimized methods
        if other in self:
            return True
        if hasattr(other, 'meshgrid'):
            return self.contains_all(other.meshgrid, atol=atol)
        elif is_valid_input_meshgrid(other, self.ndim):
            vecs = tuple(vec.squeeze() for vec in other)
            mins = np.fromiter((np.min(vec) for vec in vecs), dtype=float)
            maxs = np.fromiter((np.max(vec) for vec in vecs), dtype=float)
            return (np.all(mins >= self.min_pt - atol) and
                    np.all(maxs <= self.max_pt + atol))

        # Convert to array and check each element
        other = np.asarray(other)
        if is_valid_input_array(other, self.ndim):
            if self.ndim == 1:
                mins = np.min(other)
                maxs = np.max(other)
            else:
                mins = np.min(other, axis=1)
                maxs = np.max(other, axis=1)
            return np.all(mins >= self.min_pt) and np.all(maxs <= self.max_pt)
        else:
            return False