def vector_norm(data, axis=None, out=None):
    """Return length, i.e. Euclidean norm, of ndarray along axis.

    >>> v = np.random.random(3)
    >>> n = vector_norm(v)
    >>> np.allclose(n, np.linalg.norm(v))
    True
    >>> v = np.random.rand(6, 5, 3)
    >>> n = vector_norm(v, axis=-1)
    >>> np.allclose(n, np.sqrt(np.sum(v*v, axis=2)))
    True
    >>> n = vector_norm(v, axis=1)
    >>> np.allclose(n, np.sqrt(np.sum(v*v, axis=1)))
    True
    >>> v = np.random.rand(5, 4, 3)
    >>> n = np.empty((5, 3))
    >>> vector_norm(v, axis=1, out=n)
    >>> np.allclose(n, np.sqrt(np.sum(v*v, axis=1)))
    True
    >>> vector_norm([])
    0.0
    >>> vector_norm([1])
    1.0

    """
    data = np.array(data, dtype=np.float64, copy=True)
    if out is None:
        if data.ndim == 1:
            return math.sqrt(np.dot(data, data))
        data *= data
        out = np.atleast_1d(np.sum(data, axis=axis))
        np.sqrt(out, out)
        return out
    else:
        data *= data
        np.sum(data, axis=axis, out=out)
        np.sqrt(out, out)
Return length, i.e. Euclidean norm, of ndarray along axis.

    >>> v = np.random.random(3)
    >>> n = vector_norm(v)
    >>> np.allclose(n, np.linalg.norm(v))
    True
    >>> v = np.random.rand(6, 5, 3)
    >>> n = vector_norm(v, axis=-1)
    >>> np.allclose(n, np.sqrt(np.sum(v*v, axis=2)))
    True
    >>> n = vector_norm(v, axis=1)
    >>> np.allclose(n, np.sqrt(np.sum(v*v, axis=1)))
    True
    >>> v = np.random.rand(5, 4, 3)
    >>> n = np.empty((5, 3))
    >>> vector_norm(v, axis=1, out=n)
    >>> np.allclose(n, np.sqrt(np.sum(v*v, axis=1)))
    True
    >>> vector_norm([])
    0.0
    >>> vector_norm([1])
    1.0