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: fake quantize input according to scale and number of bits, dequantize
  quantize(input))
code: def forward(ctx, input, scale, bits=8):
      """fake quantize input according to scale and number of bits, dequantize
      quantize(input))"""
      return dequantize(quantize(input, scale, bits), scale)
  
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 1 for calling the FakeLinearQuantizationWithSTE obj's underlying member method code with example usage: obj.forward(ctx, input, scale, bits) and returns: dequantize