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

    Draw samples from a standard Gamma distribution.

    Samples are drawn from a Gamma distribution with specified parameters,
    shape (sometimes designated "k") and scale=1.

    Parameters
    ----------
    shape : float
        Parameter, 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.  Default is None, in which case a
        single value is returned.

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

    See Also
    --------
    numpy.random.standard_gamma

    Availability
    --------
    Multiple GPUs, Multiple CPUs
    """
    return get_static_generator().standard_gamma(shape, size, dtype)

    standard_gamma(shape, size=None)

    Draw samples from a standard Gamma distribution.

    Samples are drawn from a Gamma distribution with specified parameters,
    shape (sometimes designated "k") and scale=1.

    Parameters
    ----------
    shape : float
        Parameter, 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.  Default is None, in which case a
        single value is returned.

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

    See Also
    --------
    numpy.random.standard_gamma

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