def noncentral_chisquare(
    df: float,
    nonc: float,
    size: NdShapeLike | None = None,
    dtype: npt.DTypeLike = np.float64,
) -> ndarray:
    """
    noncentral_chisquare(df, nonc, size=None)

    Draw samples from a noncentral chi-square distribution.

    The noncentral :math:`\\chi^2` distribution is a generalization of
    the :math:`\\chi^2` distribution.

    Parameters
    ----------
    df : float
        Degrees of freedom, must be > 0.
    nonc : float
        Non-centrality, must be non-negative.
    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.  If size is ``None`` (default),
        a single value is returned.

    Returns
    -------
    out : ndarray or scalar
        Drawn samples from the parameterized noncentral chi-square
        distribution.

    See Also
    --------
    numpy.random.noncentral_chisquare

    Availability
    --------
    Multiple GPUs, Multiple CPUs
    """
    return get_static_generator().noncentral_chisquare(df, nonc, size, dtype)

    noncentral_chisquare(df, nonc, size=None)

    Draw samples from a noncentral chi-square distribution.

    The noncentral :math:`\chi^2` distribution is a generalization of
    the :math:`\chi^2` distribution.

    Parameters
    ----------
    df : float
        Degrees of freedom, must be > 0.
    nonc : float
        Non-centrality, must be non-negative.
    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.  If size is ``None`` (default),
        a single value is returned.

    Returns
    -------
    out : ndarray or scalar
        Drawn samples from the parameterized noncentral chi-square
        distribution.

    See Also
    --------
    numpy.random.noncentral_chisquare

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