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

    Draw samples from a Gamma distribution.

    Samples are drawn from a Gamma distribution with specified parameters,
    `shape` (sometimes designated "k") and `scale` (sometimes designated
    "theta"), where both parameters are > 0.

    Parameters
    ----------
    shape : float
        The shape of the gamma distribution. Must be non-negative.
    scale : float, optional
        The scale of the gamma distribution. Must be non-negative.
        Default is equal to 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.  If size is ``None`` (default),
        a single value is returned.

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

    See Also
    --------
    numpy.random.gamma

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

    gamma(shape, scale=1.0, size=None)

    Draw samples from a Gamma distribution.

    Samples are drawn from a Gamma distribution with specified parameters,
    `shape` (sometimes designated "k") and `scale` (sometimes designated
    "theta"), where both parameters are > 0.

    Parameters
    ----------
    shape : float
        The shape of the gamma distribution. Must be non-negative.
    scale : float, optional
        The scale of the gamma distribution. Must be non-negative.
        Default is equal to 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.  If size is ``None`` (default),
        a single value is returned.

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

    See Also
    --------
    numpy.random.gamma

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