code: def domain_map(self, F, *args: Tensor) -> Tuple[Tensor, ...]:
      lambda_1, lambda_2 = args
      if self.fix_lambda_2:
          lambda_2 = -self.lb_obs * F.ones_like(lambda_2)
      else:
          # This makes sure that :math:`z +  \lambda_2 > 0`, where :math:`z >
          # lb_obs`
          lambda_2 = softplus(F, lambda_2) - self.lb_obs * F.ones_like(
              lambda_2
          )
  
      # we squeeze the output since event_shape is ()
      return lambda_1.squeeze(axis=-1), lambda_2.squeeze(axis=-1)
  
imports: from typing import Dict, List, Tuple
  import mxnet
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from .bijection import Bijection, InverseBijection
  from .bijection_output import BijectionOutput
  from .distribution import getF, softplus
label_desc: Use image node_id 2 for calling the BoxCoxTransformOutput obj's underlying member method code with example usage: obj.domain_map(F) and returns: lambda_1,  lambda_2