class_docstr: Tests fake quantize calculate_qparams() method
  by comparing with result from observer calculate_qparams.
  Uses hard-coded values: alpha=1.0, b=4, k=2.
class_docstr_tok: ["Tests","fake","quantize","calculate_qparams","(",")","method","by","comparing","with","result","from","observer","calculate_qparams",".","Uses","hard-coded","values",":","alpha=1.0",",","b=4",",","k=2","."]
code: def test_backward(self):
      input = torch.randn(20, dtype=torch.double, requires_grad=True)
  
      observer = APoTObserver(b=4, k=2)
      observer(input)
      (
          alpha,
          gamma,
          quantization_levels,
          level_indices,
      ) = observer.calculate_qparams(signed=False)
  
      test = gradcheck(
          fake_quantize_function.apply,
          (input, alpha, gamma, quantization_levels, level_indices),
          atol=1e-4,
      )
  
file_path: pytorch/test/quantization/core/experimental/test_fake_quantize.py
imports: import torch
  import unittest
  from torch.ao.quantization.experimental.observer import APoTObserver
  from torch.ao.quantization.experimental.quantizer import quantize_APoT, dequantize_APoT
  from torch.ao.quantization.experimental.fake_quantize import APoTFakeQuantize
  from torch.ao.quantization.experimental.fake_quantize_function import fake_quantize_function
  from torch.autograd import gradcheck
label_desc: Use image node_id 4 for calling the TestFakeQuantize obj's underlying member method code with example usage: obj.test_backward() without return types