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

    Draw samples from a Pareto II or Lomax distribution with
    specified shape.

    The Lomax or Pareto II distribution is a shifted Pareto
    distribution. The classical Pareto distribution can be
    obtained from the Lomax distribution by adding 1 and
    multiplying by the scale parameter ``m``.  The
    smallest value of the Lomax distribution is zero while for the
    classical Pareto distribution it is ``mu``, where the standard
    Pareto distribution has location ``mu = 1``.  Lomax can also
    be considered as a simplified version of the Generalized
    Pareto distribution (available in SciPy), with the scale set
    to one and the location set to zero.

    The Pareto distribution must be greater than zero, and is
    unbounded above.  It is also known as the "80-20 rule".  In
    this distribution, 80 percent of the weights are in the lowest
    20 percent of the range, while the other 20 percent fill the
    remaining 80 percent of the range.

    Parameters
    ----------
    a : float
        Shape of the distribution. Must be positive.
    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 Pareto distribution.

    See Also
    --------
    numpy.random.pareto

    Availability
    --------
    Multiple GPUs, Multiple CPUs
    """
    return get_static_generator().pareto(a, size, dtype)

    pareto(a, size=None)

    Draw samples from a Pareto II or Lomax distribution with
    specified shape.

    The Lomax or Pareto II distribution is a shifted Pareto
    distribution. The classical Pareto distribution can be
    obtained from the Lomax distribution by adding 1 and
    multiplying by the scale parameter ``m``.  The
    smallest value of the Lomax distribution is zero while for the
    classical Pareto distribution it is ``mu``, where the standard
    Pareto distribution has location ``mu = 1``.  Lomax can also
    be considered as a simplified version of the Generalized
    Pareto distribution (available in SciPy), with the scale set
    to one and the location set to zero.

    The Pareto distribution must be greater than zero, and is
    unbounded above.  It is also known as the "80-20 rule".  In
    this distribution, 80 percent of the weights are in the lowest
    20 percent of the range, while the other 20 percent fill the
    remaining 80 percent of the range.

    Parameters
    ----------
    a : float
        Shape of the distribution. Must be positive.
    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 Pareto distribution.

    See Also
    --------
    numpy.random.pareto

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