code: def test_state_dict(
      self,
      device: torch.device,
  ) -> None:
      eb1_config = EmbeddingBagConfig(
          name="t1",
          embedding_dim=4,
          num_embeddings=2,
          feature_names=["f1"],
      )
      eb2_config = EmbeddingBagConfig(
          name="t2",
          embedding_dim=4,
          num_embeddings=2,
          feature_names=["f2"],
      )
      eb3_config = EmbeddingBagConfig(
          name="t3",
          embedding_dim=4,
          num_embeddings=2,
          feature_names=["f3"],
      )
  
      ebc = FusedEmbeddingBagCollection(
          tables=[eb1_config, eb2_config, eb3_config],
          optimizer_type=torch.optim.SGD,
          optimizer_kwargs={"lr": 0.02},
          device=device,
      )
  
      ebc.load_state_dict(ebc.state_dict())
  
imports: import unittest
  from collections import OrderedDict
  from typing import Any, Dict, List, Optional, Tuple, Type
  import hypothesis.strategies
  import torch
  import torch.fx
  import torchrec
  from fbgemm_gpu.split_table_batched_embeddings_ops_training import EmbeddingLocation
  from hypothesis import given, settings
  from torchrec.modules.embedding_configs import EmbeddingBagConfig, EmbeddingConfig
  from torchrec.modules.embedding_modules import EmbeddingBagCollection, EmbeddingCollection
  from torchrec.modules.fused_embedding_modules import fuse_embedding_optimizer, FusedEmbeddingBagCollection, FusedEmbeddingCollection
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
label_desc: Use image node_id 5 for calling the FusedEmbeddingBagCollectionTest obj's underlying member method code with example usage: obj.test_state_dict(device) without return types