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)
docstr: cdf values for a tensor x of shape (*batch_shape)
docstr_tok: ["cdf","values","for","a","tensor","x","of","shape","(","*","batch_shape",")"]
code: def cdf(self, x):
      """
      cdf values for a tensor x of shape (*batch_shape)
      """
      x = x.unsqueeze(dim=-1)
      x_shifted = torch.div(x, self.beta)
      u = 1 - torch.pow(
          1 + self.xi * x_shifted, -torch.reciprocal(self.xi)
      )
      return u.squeeze(dim=-1)
  
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 6 for calling the GeneralizedPareto obj's underlying member method code with example usage: obj.cdf(x) and returns: u