class_docstr: Class to construct a distribution given the output of a network.
docstr: Construct the associated distribution, given the collection of
  constructor arguments and, optionally, a scale tensor.
  
  Parameters
  ----------
  distr_args
      Constructor arguments for the underlying Distribution type.
  loc
      Optional tensor, of the same shape as the
      batch_shape+event_shape of the resulting distribution.
  scale
      Optional tensor, of the same shape as the
      batch_shape+event_shape of the resulting distribution.
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code: def distribution(
      self,
      distr_args,
      loc: Optional[torch.Tensor] = None,
      scale: Optional[torch.Tensor] = None,
  ) -> Distribution:
      r"""
      Construct the associated distribution, given the collection of
      constructor arguments and, optionally, a scale tensor.
  
      Parameters
      ----------
      distr_args
          Constructor arguments for the underlying Distribution type.
      loc
          Optional tensor, of the same shape as the
          batch_shape+event_shape of the resulting distribution.
      scale
          Optional tensor, of the same shape as the
          batch_shape+event_shape of the resulting distribution.
      """
      distr = self._base_distribution(distr_args)
      if loc is None and scale is None:
          return distr
      else:
          return AffineTransformed(distr, loc=loc, scale=scale)
  
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 3 for calling the DistributionOutput obj's underlying member method code with example usage: obj.distribution(distr_args, loc, scale) and returns: distr, AffineTransformed