code: def test_quantized_embedding(self, input_dtype):
      pytest.xfail(
          "rdar://106152706 gather: Required param 'validate_indices' is missing"
      )
  
      num_embeddings = 4
      embedding_size = 10
      B = 2
      dim = 5
      converter_input_type = TensorType(
          shape=(B, dim), dtype=input_dtype
      )
  
      # input shape: (B, dim)
      # output shape : (B, dim, embedding_size)
      # shape of weights : (num_embeddings, embedding_size)
      class EmbeddingModel(torch.nn.Module):
          def __init__(self):
              super().__init__()
              self.embedding = torch.nn.quantized.Embedding(
                  num_embeddings, embedding_size
              )
  
          def forward(self, x):
              return self.embedding(x)
  
      input_data = np.random.randint(
          low=0, high=num_embeddings, size=(B, dim)
      )
      input_data = torch.from_numpy(input_data)
      model = EmbeddingModel()
      self.run_compare_torch(
          [input_data],
          model,
          input_as_shape=False,
          converter_input_type=converter_input_type,
      )
  
imports: import itertools
  from typing import Optional
  import numpy
  import pytest
  import torch
  import torchvision
  import coremltools
  from coremltools import TensorType
  from coremltools._deps import _HAS_TORCH, _HAS_TORCH_VISION, MSG_TORCH_NOT_FOUND, MSG_TORCH_VISION_NOT_FOUND
  from coremltools.converters.mil.testing_utils import get_op_types_in_program
  from .testing_utils import TorchBaseTest
label_desc: Use image node_id 3 for calling the TestPytorchQuantizedOps obj's underlying member method code with example usage: obj.test_quantized_embedding(input_dtype) and returns: self