code: def test_quantize_embedding_kernels(
      self, compute_kernel: EmbeddingComputeKernel, dtype: torch.dtype
  ) -> None:
      config = self._create_config(compute_kernel)
      sharded = GroupedEmbeddingsLookup(
          grouped_configs=[config],
          device=torch.device("cuda:0"),
      )
  
      quantized = quantize_sharded_embeddings(sharded, dtype=dtype)
  
      for _, buffer in quantized.named_buffers():
          self.assertEqual(buffer.dtype, torch.uint8)
  
imports: import os
  import unittest
  import hypothesis.strategies
  import torch
  import torch.distributed
  import torch.quantization
  from hypothesis import given, settings, Verbosity
  from torchrec.distributed.embedding_lookup import BatchedDenseEmbedding, BatchedDenseEmbeddingBag, BatchedFusedEmbedding, BatchedFusedEmbeddingBag, GroupedEmbeddingsLookup, GroupedPooledEmbeddingsLookup
  from torchrec.distributed.embedding_types import EmbeddingComputeKernel, GroupedEmbeddingConfig, ShardedEmbeddingTable, ShardedTensorMetadata, ShardMetadata
  from torchrec.distributed.quant_embedding_kernel import QuantBatchedEmbedding, QuantBatchedEmbeddingBag
  from torchrec.modules.embedding_configs import DataType, PoolingType
  from torchrec.test_utils import get_free_port
label_desc: Use image node_id 5 for calling the QuantizeKernelTest obj's underlying member method code with example usage: obj.test_quantize_embedding_kernels(compute_kernel, dtype) without return types