code: def __init__(
      self,
      tables: List[EmbeddingConfig],
      device: Optional[torch.device] = None,
  ) -> None:
      super().__init__()
      if device is None:
          device = torch.device("cpu")
      in_features = 8 + sum(
          [
              table.embedding_dim * len(table.feature_names) * 20
              for table in tables
          ]
      )
      self.linear: nn.modules.Linear = nn.Linear(
          in_features=in_features, out_features=16, device=device
      )
  
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 TestSequenceOverArch object from inherited base classes: nn with example: obj = TestSequenceOverArch(tables, device)