code: def domain_map(cls, rate: torch.Tensor):  # type: ignore
      rate_pos = F.softplus(rate).clone()
      return (rate_pos.squeeze(-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 PoissonOutput obj's underlying member method code with example usage: obj.domain_map(cls, rate) and returns: rate_pos