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
code: def bins_prob(self):
      bins_prob = self.log_bins_prob().exp()
      return bins_prob
  
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 5 for calling the Binned obj's underlying member method code with example usage: obj.bins_prob() and returns: bins_prob