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