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: Takes input x as new logits.
docstr_tok: ["Takes","input","x","as","new","logits","."]
code: def forward(self, x):
      """
      Takes input x as new logits.
      """
      self.logits = x
      return self.logits
  
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 3 for calling the Binned obj's underlying member method code with example usage: obj.forward(x) and returns: self