code: def __init__(
      self,
      tables: List[EmbeddingConfig],
      weighted_tables: Optional[List[EmbeddingConfig]] = None,
      num_float_features: int = 10,
      embedding_groups: Optional[Dict[str, List[str]]] = None,
      dense_device: Optional[torch.device] = None,
      sparse_device: Optional[torch.device] = None,
      feature_processor_modules: Optional[
          Dict[str, torch.nn.Module]
      ] = None,
  ) -> None:
      super().__init__(
          tables=cast(List[BaseEmbeddingConfig], tables),
          weighted_tables=cast(
              Optional[List[BaseEmbeddingConfig]], weighted_tables
          ),
          embedding_groups=embedding_groups,
          dense_device=dense_device,
          sparse_device=sparse_device,
      )
      if embedding_groups is None:
          embedding_groups = {}
  
      self.dense = TestDenseArch(
          device=dense_device, num_float_features=num_float_features
      )
      self.sparse = TestSequenceSparseArch(
          tables,
          list(embedding_groups.values())[0]
          if embedding_groups.values()
          else [],
          device=sparse_device,
      )
      self.over = TestSequenceOverArch(
          tables=tables, device=dense_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 TestSequenceSparseNN object from inherited base classes: TestSparseNNBase with example: obj = TestSequenceSparseNN(tables, weighted_tables, num_float_features, embedding_groups, dense_device, sparse_device, feature_processor_modules)