code: def domain_map(  # type: ignore
      cls, concentration1: torch.Tensor, concentration0: torch.Tensor
  ):
      epsilon = np.finfo(cls._dtype).eps  # machine epsilon
      concentration1 = F.softplus(concentration1) + epsilon
      concentration0 = F.softplus(concentration0) + epsilon
      return concentration1.squeeze(dim=-1), concentration0.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 BetaOutput obj's underlying member method code with example usage: obj.domain_map(cls, concentration1, concentration0) and returns: concentration1,  concentration0