code: def domain_map(cls, F, mu, sigma, nu):
      sigma = F.maximum(softplus(F, sigma), cls.eps())
      nu = 2.0 + F.maximum(softplus(F, nu), cls.eps())
      return (
          mu.squeeze(axis=-1),
          sigma.squeeze(axis=-1),
          nu.squeeze(axis=-1),
      )
  
imports: import math
  from typing import Dict, List, Optional, Tuple
  import numpy
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from .distribution import Distribution, _sample_multiple, getF, nans_like, softplus
  from .distribution_output import DistributionOutput
label_desc: Use image node_id 1 for calling the StudentTOutput obj's underlying member method code with example usage: obj.domain_map(cls, F, mu, sigma, nu) and returns: mu,  sigma,  nu