class_docstr: Thresholdd binarizer.
  Computes a binary mask M from a real value matrix S such that `M_{i,j} = 1` if and only if `S_{i,j} >       au`
  where `     au` is a real value threshold.
  
  Implementation is inspired from:
      https://github.com/arunmallya/piggyback
      Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights
      Arun Mallya, Dillon Davis, Svetlana Lazebnik
class_docstr_tok: ["Thresholdd","binarizer",".","Computes","a","binary","mask","M","from","a","real","value","matrix","S","such","that","`","M_","{","i",",","j","}","=","1","`","if","and","only","if","`","S_","{","i",",","j","}",">","au","`","where","`","au","`","is","a","real","value","threshold",".","Implementation","is","inspired","from",":","https",":","\/\/github.com\/arunmallya\/piggyback","Piggyback",":","Adapting","a","Single","Network","to","Multiple","Tasks","by","Learning","to","Mask","Weights","Arun","Mallya",",","Dillon","Davis",",","Svetlana","Lazebnik"]
code: def backward(ctx, gradOutput):
      return gradOutput, None, None
  
file_path: transformers/examples/research_projects/movement-pruning/emmental/modules/binarizer.py
imports: import torch
  from torch import autograd
label_desc: Use image node_id 2 for calling the ThresholdBinarizer obj's underlying member method code with example usage: obj.backward(ctx, gradOutput) and returns: gradOutput,  None,  None