code: def test_dynamic_quantized_linear_backward(self):
      x = torch.randn(1, 100, requires_grad=True)
      linear = QuantizedLinear(100, 1, bias=False, mode="DYNAMIC")
      y = linear(x)
      y.backward()
      self.assertTrue((x.grad == linear.fake_quantized_weight).all())
      with torch.no_grad():
          scale = (2 ** (8 - 1) - 1) / x.abs().max()
      self.assertTrue(
          (
              fake_quantize_np(x.detach(), scale, 8)
              == linear.weight.grad
          ).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 7 for calling the QuantizedLinearTest obj's underlying member method code with example usage: obj.test_dynamic_quantized_linear_backward() without return types