class_docstr: Samples from a Pareto Type 1 distribution.
  
  Example::
  
      >>> # xdoctest: +IGNORE_WANT("non-deterministic")
      >>> m = Pareto(torch.tensor([1.0]), torch.tensor([1.0]))
      >>> m.sample()  # sample from a Pareto distribution with scale=1 and alpha=1
      tensor([ 1.5623])
  
  Args:
      scale (float or Tensor): Scale parameter of the distribution
      alpha (float or Tensor): Shape parameter of the distribution
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code: def variance(self):
      # var is inf for alpha <= 2
      a = self.alpha.clamp(min=2)
      return self.scale.pow(2) * a / ((a - 1).pow(2) * (a - 2))
  
file_path: pytorch/torch/distributions/pareto.py
imports: from torch.distributions import constraints
  from torch.distributions.exponential import Exponential
  from torch.distributions.transformed_distribution import TransformedDistribution
  from torch.distributions.transforms import AffineTransform, ExpTransform
  from torch.distributions.utils import broadcast_all
label_desc: Use image node_id 5 for calling the Pareto obj's underlying member method code with example usage: obj.variance() and returns: unknown