code: def test_quantization_backward(self):
      fake_quantize = FakeLinearQuantizationWithSTE().apply
      x = torch.randn(1, 100, requires_grad=True)
      with torch.no_grad():
          scale = (2 ** (8 - 1) - 1) / x.abs().max()
      y = torch.sum(fake_quantize(x, scale, 8))
      y.backward()
      self.assertTrue((x.grad == torch.ones_like(x)).all())
  
imports: import unittest
  import numpy
  import torch
  from torch import nn
  from torch.nn import functional
  from nlp_architect.nn.torch.quantization import FakeLinearQuantizationWithSTE, QuantizedLinear, get_dynamic_scale, get_scale, QuantizedEmbedding
label_desc: Use image node_id 2 for calling the FakeLinearQuantizationWithSTETester obj's underlying member method code with example usage: obj.test_quantization_backward() without return types