class_docstr: Generalised Pareto distribution.
  
  Parameters
  ----------
  xi
      Tensor containing the xi (heaviness) shape parameters. The tensor is
      of shape (*batch_shape, 1)
  beta
      Tensor containing the beta scale parameters. The tensor is of
      shape (*batch_shape, 1)
code: def stddev(self):
      return torch.sqrt(self.variance)
  
imports: from numbers import Number
  from typing import Dict, Optional, Tuple, cast
  import numpy
  import torch
  from torch.distributions import Distribution, constraints
  from torch.distributions.utils import broadcast_all
  from gluonts.core.component import validated
  from .distribution_output import DistributionOutput
label_desc: Use image node_id 4 for calling the GeneralizedPareto obj's underlying member method code with example usage: obj.stddev() and returns: torch