class_docstr: Define a fused module to observe the tensor.
  
  Fused module that is used to observe the input tensor (compute min/max), compute
  scale/zero_point and fake_quantize the tensor.
  This module uses calculation similar MovingAverageMinMaxObserver for the inputs,
  to compute the min/max values in order to compute the scale/zero_point.
  The qscheme input in the observer is used to differentiate between symmetric/affine
  quantization scheme.
  
  The output of this module is given by
  x_out = (clamp(round(x/scale + zero_point), quant_min, quant_max)-zero_point)*scale
  
  Similar to :class:`~torch.ao.quantization.FakeQuantize`, and accepts the same attributes as the
  base class.
class_docstr_tok: ["Define","a","fused","module","to","observe","the","tensor",".","Fused","module","that","is","used","to","observe","the","input","tensor","(","compute","min\/max",")",",","compute","scale\/zero_point","and","fake_quantize","the","tensor",".","This","module","uses","calculation","similar","MovingAverageMinMaxObserver","for","the","inputs",",","to","compute","the","min\/max","values","in","order","to","compute","the","scale\/zero_point",".","The","qscheme","input","in","the","observer","is","used","to","differentiate","between","symmetric\/affine","quantization","scheme",".","The","output","of","this","module","is","given","by","x_out","=","(","clamp","(","round","(","x\/scale","+","zero_point",")",",","quant_min",",","quant_max",")","-zero_point",")","*","scale","Similar","to",":","class",":","`","~torch.ao.quantization.FakeQuantize","`",",","and","accepts","the","same","attributes","as","the","base","class","."]
code: def calculate_qparams(self) -> Tuple[torch.Tensor, torch.Tensor]:
      return self.activation_post_process.calculate_qparams()
  
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 2 for calling the FusedMovingAvgObsFakeQuantize obj's underlying member method code with example usage: obj.calculate_qparams() and returns: self