def rayleigh(
    scale: float = 1.0,
    size: NdShapeLike | None = None,
    dtype: npt.DTypeLike = np.float64,
) -> ndarray:
    """
    rayleigh(scale=1.0, size=None)

    Draw samples from a Rayleigh distribution.

    The :math:`\\chi` and Weibull distributions are generalizations of the
    Rayleigh.

    Parameters
    ----------
    scale : float, optional
        Scale, also equals the mode. Must be non-negative. Default is 1.
    size : int or tuple of ints, optional
        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
        ``m * n * k`` samples are drawn.  Default is None, in which case a
        single value is returned.

    Returns
    -------
    out : ndarray or scalar
        Drawn samples from the parameterized Rayleigh distribution.

    See Also
    --------
    numpy.random.rayleigh

    Availability
    --------
    Multiple GPUs, Multiple CPUs
    """
    return get_static_generator().rayleigh(scale, size, dtype)

    rayleigh(scale=1.0, size=None)

    Draw samples from a Rayleigh distribution.

    The :math:`\chi` and Weibull distributions are generalizations of the
    Rayleigh.

    Parameters
    ----------
    scale : float, optional
        Scale, also equals the mode. Must be non-negative. Default is 1.
    size : int or tuple of ints, optional
        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
        ``m * n * k`` samples are drawn.  Default is None, in which case a
        single value is returned.

    Returns
    -------
    out : ndarray or scalar
        Drawn samples from the parameterized Rayleigh distribution.

    See Also
    --------
    numpy.random.rayleigh

    Availability
    --------
    Multiple GPUs, Multiple CPUs
    
cupynumeric/random/_random.py