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: Cumulative density for one datapoint `xx`, where `cum_density` is the
  cdf up to bin_edges `idx` which must be lower than `xx`
docstr_tok: ["Cumulative","density","for","one","datapoint","`","xx","`",",","where","`","cum_density","`","is","the","cdf","up","to","bin_edges","`","idx","`","which","must","be","lower","than","`","xx","`"]
code: def cdf_components(self, xx, idx=0, cum_density=torch.tensor([0.0])):
      """
      Cumulative density for one datapoint `xx`, where `cum_density` is the
      cdf up to bin_edges `idx` which must be lower than `xx`
      """
      return self.cdf_binned_components(xx, idx, cum_density)
  
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 11 for calling the Binned obj's underlying member method code with example usage: obj.cdf_components(xx, idx, cum_density) and returns: self