class_docstr: Generalised Pareto distribution.
  
  Parameters
  ----------
  xi
      Tensor containing the xi (heaviness) shape parameters
  beta
      Tensor containing the beta scale parameters
code: def mean(self):
      mu = torch.where(
          self.xi < 1,
          torch.div(self.beta, 1 - self.xi),
          np.nan * torch.ones_like(self.xi),
      )
      return mu
  
file_path: gluonts/src/gluonts/nursery/spliced_binned_pareto/genpareto.py
imports: from numbers import Number
  import torch
  from torch.distributions import constraints
  from torch.distributions.distribution import Distribution
  from torch.distributions.utils import broadcast_all
  import numpy
label_desc: Use image node_id 1 for calling the GenPareto obj's underlying member method code with example usage: obj.mean() and returns: mu