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 __init__(self, loc, scale, *, validate_args=None):
      self.loc, self.scale = broadcast_all(loc, scale)
      super().__init__(self.loc.shape, validate_args=validate_args)
  
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 1 to create a new Logistic object from inherited base classes: TorchDistribution with example: obj = Logistic(loc, scale)