docstr: Maps raw tensors to valid arguments for constructing a log-logistic
  distribution.
  
  Parameters
  ----------
  F
      MXNet backend.
  mu
      Mean of the underlying logistic distribution.
      Shape `(*batch_shape, 1)`
  sigma
      Scale of the underlying logistic distribution.
      Shape `(*batch_shape, 1)`
  
  Returns
  -------
  Tuple[Tensor, Tensor]
      Two squeezed tensors of shape `(*batch_shape)`. The sigma parameter
      is strictly positive.
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code: def domain_map(cls, F, mu, sigma):
      r"""
      Maps raw tensors to valid arguments for constructing a log-logistic
      distribution.
  
      Parameters
      ----------
      F
          MXNet backend.
      mu
          Mean of the underlying logistic distribution.
          Shape `(*batch_shape, 1)`
      sigma
          Scale of the underlying logistic distribution.
          Shape `(*batch_shape, 1)`
  
      Returns
      -------
      Tuple[Tensor, Tensor]
          Two squeezed tensors of shape `(*batch_shape)`. The sigma parameter
          is strictly positive.
      """
      sigma = F.Activation(sigma, "softrelu")
      return mu.squeeze(axis=-1), sigma.squeeze(axis=-1)
  
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 for calling the LoglogisticOutput obj's underlying member method code with example usage: obj.domain_map(cls, F, mu, sigma) and returns: mu,  sigma