code: def domain_map(cls, concentration: torch.Tensor, rate: torch.Tensor):  # type: ignore
      epsilon = np.finfo(cls._dtype).eps  # machine epsilon
      concentration = F.softplus(concentration) + epsilon
      rate = F.softplus(rate) + epsilon
      return concentration.squeeze(dim=-1), rate.squeeze(dim=-1)
  
imports: from typing import Dict, Optional, Tuple
  import numpy
  import torch
  import torch.nn.functional
  from torch.distributions import Beta, Distribution, Gamma, Laplace, Normal, Poisson
  from gluonts.core.component import validated
  from gluonts.model.forecast_generator import DistributionForecastGenerator, ForecastGenerator
  from gluonts.torch.distributions import AffineTransformed
  from .output import Output
label_desc: Use image node_id 1 for calling the GammaOutput obj's underlying member method code with example usage: obj.domain_map(cls, concentration, rate) and returns: concentration,  rate