code: def lognormal(
      mean=0.0, sigma=1.0, size=None, dtype=None, device=None, out=None
  ):
      r"""Draw samples from a log-normal distribution.
  
      Draw samples from a `log-normal distribution` [1]_ with specified mean,
      standard deviation, and array shape. Note that the mean and standard
      deviation are not the values for the distribution itself, but of the
      underlying normal distribution it is derived from.
  
      Parameters
      ----------
      mean : float or array_like of floats, optional
          Mean value of the underlying normal distribution. Default is 0.
      sigma : float or array_like of floats, optional
          Standard deviation of the underlying normal distribution. Must be
          non-negative. Default is 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 if ``mean`` and ``sigma`` are both scalars.
          Otherwise, ``np.broadcast(mean, sigma).size`` samples are drawn.
      dtype : {'float16', 'float32', 'float64'}, optional
          Data type of output samples. Default is 'float32'
      device : Device, optional
          Device context of output. Default is current device.
      out : ``ndarray``, optional
          Store output to an existing ``ndarray``.
  
      Returns
      -------
      out : ndarray or scalar
          Drawn samples from the parameterized log-normal distribution.
  
      Notes
      -----
      A variable `x` has a log-normal distribution if `log(x)` is normally
      distributed.  The `probability density function for the log-normal
      distribution` [2]_ is:
  
      .. math:: p(x) = \frac{1}{\sigma x \sqrt{2\pi}}
                      e^{(-\frac{(ln(x)-\mu)^2}{2\sigma^2})}
  
      where :math:`\mu` is the mean and :math:`\sigma` is the standard
      deviation of the normally distributed logarithm of the variable.
      A log-normal distribution results if a random variable is the *product*
      of a large number of independent, identically-distributed variables in
      the same way that a normal distribution results if the variable is the
      *sum* of a large number of independent, identically-distributed
      variables.
  
      References
      ----------
      .. [1] Limpert, E., Stahel, W. A., and Abbt, M., "Log-normal
             Distributions across the Sciences: Keys and Clues,"
             BioScience, Vol. 51, No. 5, May, 2001.
             http://www.statlit.org/pdf/2001-Limpert-Bioscience2.pdf
      .. [2] Reiss, R.D. and Thomas, M., "Statistical Analysis of Extreme
             Values," Basel: Birkhauser Verlag, 2001, pp. 31-32.
  
      Examples
      --------
      Draw samples from the distribution:
      >>> mu, sigma = 3., 1. # mean and standard deviation
      >>> s = np.random.lognormal(mu, sigma, 1000)
      """
      return _mx_nd_np.random.lognormal(
          mean, sigma, size, dtype, device, out
      )
  
imports: from ..ndarray import numpy
  from ..random import seed
  from ..util import wrap_ctx_to_device_func
label_desc: Use image node_id 4 for calling a global function with example usage: lognormal(mean, sigma, size, dtype, device, out) and returns: _mx_nd_np