code: def test_quantized_embedding_backward(self):
      embedding = QuantizedEmbedding(10, 3, mode="ema")
      linear = nn.Linear(3, 1)
      indices = torch.tensor([2])
      h = embedding(indices)
      y = linear(h)
      y.backward()
      grad = torch.zeros_like(embedding.weight)
      grad[indices.item(), :] = linear.weight.t().squeeze()
      self.assertTrue((embedding.weight.grad == grad).all())
      self.assertTrue((linear.weight.grad == h).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 3 for calling the QuantizedEmbeddingTest obj's underlying member method code with example usage: obj.test_quantized_embedding_backward() without return types