class_docstr: Simulate quantize and dequantize in training time.
  
  Simulate quantize and dequantize with fixed quantization
  parameters in training time. Only per tensor quantization
  is supported.
class_docstr_tok: ["Simulate","quantize","and","dequantize","in","training","time",".","Simulate","quantize","and","dequantize","with","fixed","quantization","parameters","in","training","time",".","Only","per","tensor","quantization","is","supported","."]
code: def __init__(self, observer):
      super().__init__(observer=observer)
      assert (
          type(self.activation_post_process) == FixedQParamsObserver
      ), f"{self.__class__.__name__}'s observer must be a {FixedQParamsObserver.__name__}"
      self._observer_ctr = observer
      self.scale = self.activation_post_process.scale
      self.zero_point = self.activation_post_process.zero_point
      assert _is_per_tensor(self.qscheme), (
          "Only per tensor quantization is supported"
          + " FixedQParamsFakeQuantize module, got qscheme:"
          + str(self.qscheme)
      )
  
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 1 to create a new FixedQParamsFakeQuantize object from inherited base classes: FakeQuantize with example: obj = FixedQParamsFakeQuantize(observer)