class_docstr: Logistic distribution.
  
  This is a smooth distribution with symmetric asymptotically exponential
  tails and a concave log density. For standard ``loc=0``, ``scale=1``, the
  density is given by
  
  .. math::
  
      p(x) = \frac {e^{-x}} {(1 + e^{-x})^2}
  
  Like the :class:`~pyro.distributions.Laplace` density, this density has the
  heaviest possible tails (asymptotically) while still being log-convex.
  Unlike the :class:`~pyro.distributions.Laplace` distribution, this
  distribution is infinitely differentiable everywhere, and is thus suitable
  for constructing Laplace approximations.
  
  :param loc: Location parameter.
  :param scale: Scale parameter.
code: def expand(self, batch_shape, _instance=None):
      new = self._get_checked_instance(Logistic, _instance)
      batch_shape = torch.Size(batch_shape)
      new.loc = self.loc.expand(batch_shape)
      new.scale = self.scale.expand(batch_shape)
      super(Logistic, 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 Logistic obj's underlying member method code with example usage: obj.expand(batch_shape, _instance) and returns: new