class_docstr: Simulates error caused by quantization. Uses Straight-Through Estimator for Back prop
class_docstr_tok: ["Simulates","error","caused","by","quantization",".","Uses","Straight-Through","Estimator","for","Back","prop"]
docstr: Calculate estimated gradients for fake quantization using
  Straight-Through Estimator (STE) according to:
  https://openreview.net/pdf?id=B1ae1lZRb
code: def backward(ctx, grad_output):
      """Calculate estimated gradients for fake quantization using
      Straight-Through Estimator (STE) according to:
      https://openreview.net/pdf?id=B1ae1lZRb"""
      return grad_output, None, None
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  from enum import Enum, auto
  import logging
  from abc import ABC, abstractmethod
  import torch
  from torch import nn
  from torch.nn import functional
  from nlp_architect.common import Config
label_desc: Use image node_id 2 for calling the FakeLinearQuantizationWithSTE obj's underlying member method code with example usage: obj.backward(ctx, grad_output) and returns: grad_output,  None,  None