class_docstr: Skewed generalization of the Logistic distribution (Type I in [1]).
  
  This is a smooth distribution with asymptotically exponential tails and a
  concave log density. For standard ``loc=0``, ``scale=1``, ``asymmetry=α``
  the density is given by
  
  .. math::
  
      p(x;\alpha) = \frac {\alpha e^{-x}} {(1 + e^{-x})^{\alpha+1}}
  
  Like the :class:`~pyro.distributions.AsymmetricLaplace` density, this
  density has the heaviest possible tails (asymptotically) while still being
  log-convex. Unlike the :class:`~pyro.distributions.AsymmetricLaplace`
  distribution, this distribution is infinitely differentiable everywhere,
  and is thus suitable for constructing Laplace approximations.
  
  **References**
  
  [1] Generalized logistic distribution
      https://en.wikipedia.org/wiki/Generalized_logistic_distribution
  
  :param loc: Location parameter.
  :param scale: Scale parameter.
  :param asymmetry: Asymmetry parameter (positive). The distribution skews
      right when ``asymmetry > 1`` and left when ``asymmetry < 1``. Defaults
      to ``asymmetry = 1`` corresponding to the standard Logistic
      distribution.
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code: def expand(self, batch_shape, _instance=None):
      new = self._get_checked_instance(SkewLogistic, _instance)
      batch_shape = torch.Size(batch_shape)
      new.loc = self.loc.expand(batch_shape)
      new.scale = self.scale.expand(batch_shape)
      new.asymmetry = self.asymmetry.expand(batch_shape)
      super(SkewLogistic, new).__init__(
          batch_shape, validate_args=False
      )
      new._validate_args = self._validate_args
      return new
  
imports: import math
  import torch
  from torch.distributions import constraints
  from torch.distributions.utils import broadcast_all
  from torch.nn.functional import logsigmoid
  from .torch_distribution import TorchDistribution
label_desc: Use image node_id 2 for calling the SkewLogistic obj's underlying member method code with example usage: obj.expand(batch_shape, _instance) and returns: new