def METHOD_NAME(
    random_state, shape, size=None, chunk_size=None, gpu=None, dtype=None
):
    r"""
    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 or array_like of floats
        Parameter, should be > 0.
    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 if ``shape`` is a scalar.  Otherwise,
        ``mt.array(shape).size`` samples are drawn.
    chunk_size : int or tuple of int or tuple of ints, optional
        Desired chunk size on each dimension
    gpu : bool, optional
        Allocate the tensor on GPU if True, False as default
    dtype : data-type, optional
      Data-type of the returned tensor.
    Returns
    -------
    out : Tensor or scalar
        Drawn samples from the parameterized standard gamma distribution.
    See Also
    --------
    scipy.stats.gamma : probability density function, distribution or
        cumulative density function, etc.
    Notes
    -----
    The probability density for the Gamma distribution is
    .. math:: p(x) = x^{k-1}\frac{e^{-x/\theta}}{\theta^k\Gamma(k)},
    where :math:`k` is the shape and :math:`\theta` the scale,
    and :math:`\Gamma` is the Gamma function.
    The Gamma distribution is often used to model the times to failure of
    electronic components, and arises naturally in processes for which the
    waiting times between Poisson distributed events are relevant.
    References
    ----------
    .. [1] Weisstein, Eric W. "Gamma Distribution." From MathWorld--A
           Wolfram Web Resource.
           http://mathworld.wolfram.com/GammaDistribution.html
    .. [2] Wikipedia, "Gamma distribution",
           http://en.wikipedia.org/wiki/Gamma_distribution
    Examples
    --------
    Draw samples from the distribution:
    >>> import mars.tensor as mt
    >>> shape, scale = 2., 1. # mean and width
    >>> s = mt.random.standard_gamma(shape, 1000000)
    Display the histogram of the samples, along with
    the probability density function:
    >>> import matplotlib.pyplot as plt
    >>> import scipy.special as sps
    >>> count, bins, ignored = plt.hist(s.execute(), 50, normed=True)
    >>> y = bins**(shape-1) * ((mt.exp(-bins/scale))/ \
    ...                       (sps.gamma(shape) * scale**shape))
    >>> plt.plot(bins, y.execute(), linewidth=2, color='r')
    >>> plt.show()
    """
    if dtype is None:
        dtype = (
            np.random.RandomState().METHOD_NAME(handle_array(shape), size=(0,)).dtype
        )
    size = random_state._handle_size(size)
    seed = gen_random_seeds(1, random_state.to_numpy())[0]
    op = TensorStandardGamma(size=size, seed=seed, gpu=gpu, dtype=dtype)
    return op(shape, chunk_size=chunk_size)