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 event_shape(self) -> Tuple:
      return ()
  
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 3 for calling the Loglogistic obj's underlying member method code with example usage: obj.event_shape() and returns: 