class_docstr: Log-logistic distribution.
  
  A very heavy-tailed distribution over positive real numbers.
  https://en.wikipedia.org/wiki/Log-logistic_distribution
  
  Drawing :math:`x \sim \operatorname{Loglogistic}(\mu, \sigma)` is
  equivalent to:
  
  .. math::
  
      y &\sim \operatorname{Logistic}(\mu, \sigma)\\
      x &= \exp(y)
code: def __init__(self, mu: Tensor, sigma: Tensor) -> None:
      self.mu = mu
      self.sigma = sigma
  
imports: from typing import Dict, Optional, Tuple
  import numpy
  from mxnet import autograd, nd
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.distribution.distribution import getF
  from .base import TPPDistribution, TPPDistributionOutput
label_desc: Use image node_id 1 to create a new Loglogistic object from inherited base classes: TPPDistribution with example: obj = Loglogistic(mu, sigma)