How to generalize linear interpolation between two numbers into higher dimension tensors?

Question

Linear interpolation between two values is rather simple:

def lerp(v, d):
    return v[0] * (1 - d) + v[1] * d

print lerp(np.array([3, 5]), 0.75)
> 4.5

Let generalize it to arbitrary tensors of shape (2, 2, …), i.e.:

def lerp(v, d):
    assert len(v.shape) >= 1 and d.shape == (len(v.shape),)
    if len(v.shape) == 1:
        assert v.shape[0] == 2
        dd = np.array([1 - d[0], d[0]], dtype=v.dtype)
        return sum(v * dd)
    else:
        v = [lerp(submatrix, d[1:]) for submatrix in v]
        return lerp(np.array(v), d[:1])

assert lerp(np.array([3.0, 4.0]), np.array([0.75])) == 3.75
assert lerp(
    np.array(xrange(8), dtype='float64').reshape((2,2,2)),
    np.array([0.25, 0.5, 0.75])
) == 2.75

It works when every value is a scalar, but does not when the individual values are tensors and the shapes are not like asserted above. For instance:

assert all(lerp(
    np.array([[1.0, 2.0], [3.0, 4.0]]),
    np.array([0.75])
) == np.array([ 2.5,  3.5]))

How to implement that with pure numpy, without python recursion, playing with array indices etc. so that it work also with tensor values? Is there any numpy function for that?