class_docstr: Base fake quantize module.
  
  Base fake quantize module
  Any fake quantize implementation should derive from this class.
  
  Concrete fake quantize module should follow the same API. In forward, they will update
  the statistics of the observed Tensor and fake quantize the input. They should also provide a
  `calculate_qparams` function that computes the quantization parameters given
  the collected statistics.
class_docstr_tok: ["Base","fake","quantize","module",".","Base","fake","quantize","module","Any","fake","quantize","implementation","should","derive","from","this","class",".","Concrete","fake","quantize","module","should","follow","the","same","API",".","In","forward",",","they","will","update","the","statistics","of","the","observed","Tensor","and","fake","quantize","the","input",".","They","should","also","provide","a","`","calculate_qparams","`","function","that","computes","the","quantization","parameters","given","the","collected","statistics","."]
code: def with_args(cls, **kwargs):
      fake_quant_constructor = _with_args(cls, **kwargs)
      # need to assign the correct module to fake_quantize
      # constructors to satisfy public v private requirements
      fake_quant_constructor.__module__ = (
          "torch.ao.quantization.fake_quantize"
      )
      return fake_quant_constructor
  
imports: import torch
  from torch.nn import Module
  from torch.ao.quantization.observer import MovingAverageMinMaxObserver, HistogramObserver, MovingAveragePerChannelMinMaxObserver, FixedQParamsObserver, default_fixed_qparams_range_0to1_observer, default_fixed_qparams_range_neg1to1_observer, _with_args
  import re
  from abc import ABC, abstractmethod
  from typing import Any, Tuple
label_desc: Use image node_id 8 for calling the FakeQuantizeBase obj's underlying member method code with example usage: obj.with_args(cls) and returns: fake_quant_constructor