docstr: Maps raw tensors to valid arguments for constructing a Gaussian
  distribution.
  
  Parameters
  ----------
  F
  mu
      Tensor of shape `(*batch_shape, 1)`
  sigma
      Tensor of shape `(*batch_shape, 1)`
  
  Returns
  -------
  Tuple[Tensor, Tensor]
      Two squeezed tensors, of shape `(*batch_shape)`: the first has the
      same entries as `mu` and the second has entries mapped to the
      positive orthant.
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code: def domain_map(cls, F, mu, sigma):
      r"""
      Maps raw tensors to valid arguments for constructing a Gaussian
      distribution.
  
      Parameters
      ----------
      F
      mu
          Tensor of shape `(*batch_shape, 1)`
      sigma
          Tensor of shape `(*batch_shape, 1)`
  
      Returns
      -------
      Tuple[Tensor, Tensor]
          Two squeezed tensors, of shape `(*batch_shape)`: the first has the
          same entries as `mu` and the second has entries mapped to the
          positive orthant.
      """
      sigma = F.maximum(softplus(F, sigma), cls.eps())
      return mu.squeeze(axis=-1), sigma.squeeze(axis=-1)
  
imports: import math
  from functools import partial
  from typing import Dict, List, Optional, Tuple
  import numpy
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from .distribution import Distribution, _sample_multiple, getF, softplus
  from .distribution_output import DistributionOutput
label_desc: Use image node_id 1 for calling the GaussianOutput obj's underlying member method code with example usage: obj.domain_map(cls, F, mu, sigma) and returns: mu,  sigma