code: def test_multiple_features(self) -> None:
      eb1_config = EmbeddingBagConfig(
          name="t1",
          embedding_dim=16,
          num_embeddings=10,
          feature_names=["f1", "f2"],
      )
      eb2_config = EmbeddingBagConfig(
          name="t2",
          embedding_dim=16,
          num_embeddings=10,
          feature_names=["f3"],
      )
      features = KeyedJaggedTensor(
          keys=["f1", "f2", "f3"],
          values=torch.as_tensor([0, 1, 2]),
          lengths=torch.as_tensor([1, 1, 1]),
      )
      self._test_ebc([eb1_config, eb2_config], features)
  
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 7 for calling the EmbeddingBagCollectionTest obj's underlying member method code with example usage: obj.test_multiple_features() without return types