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[Tensor] = None,
      scale: Optional[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.
      """
      if loc is None and scale is None:
          return self.distr_cls(*distr_args)
      else:
          distr = self.distr_cls(*distr_args)
          return AffineTransformedDistribution(
              distr, loc=loc, scale=scale
          )
  
imports: from typing import Callable, Dict, Optional, Tuple, Type
  import numpy
  from mxnet import gluon
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from .distribution import Distribution
  from .transformed_distribution import AffineTransformedDistribution
label_desc: Use image node_id 2 for calling the DistributionOutput obj's underlying member method code with example usage: obj.distribution(distr_args, loc, scale) and returns: self, AffineTransformedDistribution