class_docstr: Generalised Pareto distribution.
  
  Parameters
  ----------
  xi
      Tensor containing the xi shape parameters, of shape
      `(*batch_shape, *event_shape)`.
  beta
      Tensor containing the beta scale parameters, of shape
      `(*batch_shape, *event_shape)`.
class_docstr_tok: ["Generalised","Pareto","distribution",".","Parameters","--","--","--","--","--","xi","Tensor","containing","the","xi","shape","parameters",",","of","shape","`","(","*","batch_shape",",","*","event_shape",")","`",".","beta","Tensor","containing","the","beta","scale","parameters",",","of","shape","`","(","*","batch_shape",",","*","event_shape",")","`","."]
code: def event_dim(self) -> int:
      return 0
  
imports: from typing import Dict, List, Optional, Tuple
  import numpy
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.distribution import box_cox_transform, uniform
  from gluonts.mx.distribution.distribution import MAX_SUPPORT_VAL, _sample_multiple, getF, softplus
  from gluonts.mx.distribution.distribution_output import DistributionOutput
  from .distribution import Distribution
label_desc: Use image node_id 6 for calling the GenPareto obj's underlying member method code with example usage: obj.event_dim() and returns: int