code: def _test_meta_to_cpu(
      self,
      tables: List[EmbeddingBagConfig],
      features: KeyedJaggedTensor,
      quant_type: torch.dtype = torch.qint8,
      output_type: torch.dtype = torch.float,
  ) -> None:
      ebc = EmbeddingBagCollection(
          tables=tables, device=torch.device("meta")
      )
  
      # test forward
      ebc.qconfig = torch.quantization.QConfig(
          activation=torch.quantization.PlaceholderObserver.with_args(
              dtype=output_type
          ),
          weight=torch.quantization.PlaceholderObserver.with_args(
              dtype=quant_type
          ),
      )
  
      qebc = QuantEmbeddingBagCollection.from_float(ebc)
      model = TestSparseArch(qebc)
      dmp = DistributedModelParallel(
          model,
          env=ShardingEnv.from_local(1, 0),
          sharders=[],
          init_data_parallel=False,
      )
      meta_to_cpu_placement(dmp)
      # assert device is cpu from meta
      self.assertEqual(dmp.module.ebc.device.type, "cpu")
      # successful cpu execution
      _ = dmp(features)
  
imports: import unittest
  from typing import List
  import torch
  from hypothesis import given, settings, strategies, Verbosity
  from torch import nn
  from torchrec.distributed.model_parallel import DistributedModelParallel
  from torchrec.distributed.types import ShardingEnv
  from torchrec.modules.embedding_configs import DataType, EmbeddingBagConfig
  from torchrec.modules.embedding_modules import EmbeddingBagCollection
  from torchrec.quant.embedding_modules import EmbeddingBagCollection
  from torchrec.quant.utils import meta_to_cpu_placement
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor, KeyedTensor
label_desc: Use image node_id 1 for calling the QuantUtilsTest obj's underlying member method code with example usage: obj._test_meta_to_cpu(tables, features, quant_type, output_type) without return types