code: def lognormal(
      mean=0.0, sigma=1.0, size=None, dtype=None, ctx=None, out=None
  ):
      r"""Draw samples from a log-normal distribution.
  
      Draw samples from a log-normal distribution 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, optional
          Mean value of the underlying normal distribution. Default is 0.
      sigma : float, 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'
      ctx : Context, optional
          Device context of output. Default is current context.
  
      Returns
      -------
      out : _Symbol (symbol representing `mxnet.numpy.ndarray` in computational graphs)
          Drawn samples from the parameterized lognormal distribution.
      """
      from . import _symbol as _mx_np_symbol
  
      return _mx_np_symbol.exp(
          normal(
              loc=mean,
              scale=sigma,
              size=size,
              dtype=dtype,
              ctx=ctx,
              out=out,
          )
      )
  
imports: import numpy
  from ...context import current_context
  from ...util import is_np_default_dtype
  from .None import _internal
label_desc: Use image node_id 5 for calling a global function with example usage: lognormal(mean, sigma, size, dtype, ctx, out) and returns: _mx_np_symbol