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_cdf(self):
      incomplete_cdf = (
          self.bins_prob().mul(self.bin_widths).cumsum(dim=0)
      )
      zero = 0 * incomplete_cdf[0].view(
          1
      )  # ensured to be on same device
      return torch.cat((zero, incomplete_cdf))
  
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 6 for calling the Binned obj's underlying member method code with example usage: obj.bins_cdf() and returns: torch