code: def __init__(
      self,
      tables: List[EmbeddingConfig],
      embedding_names: List[str],
      device: Optional[torch.device] = None,
  ) -> None:
      super().__init__()
      if device is None:
          device = torch.device("cpu")
      self.ec: EmbeddingCollection = EmbeddingCollection(
          tables=tables, device=device, need_indices=True
      )
      self.embedding_names = embedding_names
      self.embedding_dim: int = self.ec.embedding_dim()
  
imports: from typing import cast, Dict, List, Optional, Tuple, Union
  import torch
  import torch.nn
  from torchrec.distributed.embedding import EmbeddingCollectionSharder
  from torchrec.distributed.fbgemm_qcomm_codec import get_qcomm_codecs_registry, QCommsConfig
  from torchrec.distributed.test_utils.test_model import ModelInput, TestDenseArch, TestSparseNNBase
  from torchrec.modules.embedding_configs import BaseEmbeddingConfig, EmbeddingConfig
  from torchrec.modules.embedding_modules import EmbeddingCollection
  from torchrec.modules.embedding_tower import EmbeddingTower
  from torchrec.sparse.jagged_tensor import JaggedTensor, KeyedJaggedTensor
label_desc: Use image node_id 1 to create a new TestSequenceSparseArch object from inherited base classes: nn with example: obj = TestSequenceSparseArch(tables, embedding_names, device)