code: def __init__(
      self,
      embedding_names: List[str],
      embedding_dim: int,
      device: Optional[torch.device] = None,
  ) -> None:
      super().__init__()
      if device is None:
          device = torch.device("cpu")
      self.embedding_names = embedding_names
      self.embedding_dim: int = embedding_dim
      self.max_sequence_length = 20
      self.linear = nn.Linear(
          in_features=self.max_sequence_length
          * self.embedding_dim
          * len(embedding_names),
          out_features=8,
          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 TestSequenceTowerInteraction object from inherited base classes: nn with example: obj = TestSequenceTowerInteraction(embedding_names, embedding_dim, device)