code: def test_create_on_meta_device_without_providing_weights(
      self,
  ) -> None:
      emb_bag = EmbeddingBagConfig(
          name="t1",
          embedding_dim=16,
          num_embeddings=10,
          feature_names=["f1"],
      )
      QuantEmbeddingBagCollection(
          [emb_bag], is_weighted=False, device=torch.device("meta")
      )
      emb = EmbeddingConfig(
          name="t1",
          embedding_dim=16,
          num_embeddings=10,
          feature_names=["f1"],
      )
      QuantEmbeddingCollection([emb], device=torch.device("meta"))
  
imports: import unittest
  from typing import Dict, List, Optional, Type
  import hypothesis.strategies
  import torch
  from hypothesis import given, settings, Verbosity
  from torchrec import inference
  from torchrec.modules.embedding_configs import DataType, EmbeddingBagConfig, EmbeddingConfig, QuantConfig
  from torchrec.modules.embedding_modules import EmbeddingBagCollection, EmbeddingCollection
  from torchrec.quant.embedding_modules import EmbeddingBagCollection, EmbeddingCollection, quant_prep_enable_quant_state_dict_split_scale_bias
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor, KeyedTensor
label_desc: Use image node_id 4 for calling the EmbeddingBagCollectionTest obj's underlying member method code with example usage: obj.test_create_on_meta_device_without_providing_weights() without return types