code: def pareto(a, size=None, device=None, out=None):
      r"""Draw samples from a Pareto II or Lomax distribution with specified shape a.
  
      Parameters
      ----------
      a : float or array_like of floats
              Shape of the distribution. Must 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 ``a`` is a scalar. Otherwise,
          ``np.array(a).size`` samples are drawn.
  
      Returns
      -------
      out : ndarray or scalar
          Drawn samples from the Pareto distribution.
  
      Examples
      --------
      >>> np.random.pareto(a=5)
      array(0.12749612)
      >>> mx.numpy.random.pareto(a=5, size=[2,3])
      array([[0.06933999, 0.0344373 , 0.10654891],
              [0.0311172 , 0.12911797, 0.03370714]])
      >>> np.random.pareto(a=np.array([2,3])
      array([0.26636696, 0.15685666])
  
      The probability density for the Pareto distribution is f(x) = \frac{am^a}{x^{a+1}}
      where a is the shape and m the scale. Here m is assumed 1. The Pareto distribution
      is a power law distribution. Pareto created it to describe the wealth in the economy.
      """
      if device is None:
          device = str(current_device())
      else:
          device = str(device)
      if size == ():
          size = None
      return _api_internal.pareto(a, size, device, out)
  
imports: import numpy
  from ...util import is_np_default_dtype
  from ...device import current_device
  from .None import _internal
  from .None import _api_internal
  from ...util import wrap_ctx_to_device_func
  from ..ndarray import get_dtype_name
label_desc: Use image node_id 13 for calling a global function with example usage: pareto(a, size, device, out) and returns: _api_internal