class_docstr: Generalised Pareto distribution.
  
  Parameters
  ----------
  xi
      Tensor containing the xi (heaviness) shape parameters
  beta
      Tensor containing the beta scale parameters
code: def icdf(self, value):
      x_shifted = torch.div(torch.pow(1 - value, -self.xi) - 1, self.xi)
      x = torch.mul(x_shifted, self.beta)
      return x
  
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 7 for calling the GenPareto obj's underlying member method code with example usage: obj.icdf(value) and returns: x