code: def _create_config(
      self, compute_kernel: EmbeddingComputeKernel
  ) -> GroupedEmbeddingConfig:
      num_embedding_tables = 2
      embedding_tables = []
      for i in range(num_embedding_tables):
          rows = (i + 1) * 10
          cols = 16
          local_metadata = ShardMetadata(
              shard_offsets=[0, 0],
              shard_sizes=[rows, cols],
              placement=torch.distributed._remote_device(
                  "rank:0/cuda:0"
              ),
          )
          embedding_tables.append(
              ShardedEmbeddingTable(
                  num_embeddings=rows,
                  embedding_dim=cols,
                  name="table_" + str(i),
                  feature_names=["feature_" + str(i)],
                  pooling=PoolingType.MEAN,
                  is_weighted=False,
                  has_feature_processor=False,
                  local_rows=rows,
                  local_cols=cols,
                  compute_kernel=compute_kernel,
                  local_metadata=local_metadata,
                  global_metadata=ShardedTensorMetadata(
                      shards_metadata=[local_metadata],
                      size=torch.Size([rows, cols]),
                  ),
                  weight_init_max=1.0,
                  weight_init_min=0.0,
              )
          )
      return GroupedEmbeddingConfig(
          data_type=DataType.FP32,
          pooling=PoolingType.MEAN,
          is_weighted=False,
          has_feature_processor=False,
          compute_kernel=compute_kernel,
          embedding_tables=embedding_tables,
      )
  
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 3 for calling the QuantizeKernelTest obj's underlying member method code with example usage: obj._create_config(compute_kernel) and returns: GroupedEmbeddingConfig