docstr: Maps raw tensors to valid arguments for constructing a Gamma
  distribution.
  
  Parameters
  ----------
  F
  alpha
      Tensor of shape `(*batch_shape, 1)`
  beta
      Tensor of shape `(*batch_shape, 1)`
  
  Returns
  -------
  Tuple[Tensor, Tensor]
      Two squeezed tensors, of shape `(*batch_shape)`: both have entries
      mapped to the positive orthant.
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code: def domain_map(cls, F, alpha, beta):
      r"""
      Maps raw tensors to valid arguments for constructing a Gamma
      distribution.
  
      Parameters
      ----------
      F
      alpha
          Tensor of shape `(*batch_shape, 1)`
      beta
          Tensor of shape `(*batch_shape, 1)`
  
      Returns
      -------
      Tuple[Tensor, Tensor]
          Two squeezed tensors, of shape `(*batch_shape)`: both have entries
          mapped to the positive orthant.
      """
      alpha = F.maximum(softplus(F, alpha), cls.eps())
      beta = F.maximum(softplus(F, beta), cls.eps())
      return alpha.squeeze(axis=-1), beta.squeeze(axis=-1)
  
imports: 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 GammaOutput obj's underlying member method code with example usage: obj.domain_map(cls, F, alpha, beta) and returns: alpha,  beta