code: def lognormal(label: str, mu: float, sigma: float) -> Numerical:
      """Log-normal (in the context of NNI) is defined as the exponential transformation of a normal random variable,
      with mean ``mu`` and deviation ``sigma``. That is::
  
          exp(normal(mu, sigma))
  
      In another word, the logarithm of the return value is normally distributed.
  
      Examples
      --------
      >>> nni.lognormal('x', 4., 2.)
      Numerical(-inf, inf, mu=4.0, sigma=2.0, log_distributed=True, label='x')
      >>> nni.lognormal('x', 0., 1.).random()
      2.3308575497749584
      >>> np.log(x) for x in nni.lognormal('x', 4., 2.).grid(granularity=2)]
      [2.6510204996078364, 4.0, 5.348979500392163]
      """
      return Numerical(
          mu=mu, sigma=sigma, log_distributed=True, label=label
      )
  
imports: from __future__ import annotations
  from .annotation import MutableAnnotation
  from .mutable import LabeledMutable, MutableSymbol, Categorical, Numerical
label_desc: Use image node_id 2 for calling a global function with example usage: lognormal(label, mu, sigma) and returns: Numerical