class_docstr: Binned univariate distribution designed as an nn.Module
  
  Arguments
  ----------
  bins_lower_bound: The lower bound of the bin edges
  bins_upper_bound: The upper bound of the bin edges
  nbins: The number of equidistance bins to allocate between `bins_lower_bound` and `bins_upper_bound`. Default value is 100.
  smoothing_indicator: The method of smoothing to perform on the bin probabilities
docstr: Inverse cdf of a single percentile `value`
docstr_tok: ["Inverse","cdf","of","a","single","percentile","`","value","`"]
code: def inverse_cdf(self, value):
      """
      Inverse cdf of a single percentile `value`
      """
      return self.inverse_binned_cdf(value)
  
imports: from typing import Optional
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from .genpareto import GenPareto
label_desc: Use image node_id 14 for calling the Binned obj's underlying member method code with example usage: obj.inverse_cdf(value) and returns: self