code: def __init__(
      self,
      tables: List[EmbeddingBagConfig],
      use_fp_collection: bool,
      device: torch.device,
  ) -> None:
      super().__init__()
  
      self._fp_ebc: FeatureProcessedEmbeddingBagCollection = (
          FeatureProcessedEmbeddingBagCollection(
              EmbeddingBagCollection(
                  tables=tables,
                  device=device,
                  is_weighted=True,
              ),
              cast(
                  Dict[str, FeatureProcessor],
                  {
                      "feature_0": PositionWeightedModule(
                          max_feature_length=10
                      ),
                      "feature_1": PositionWeightedModule(
                          max_feature_length=10
                      ),
                      "feature_2": PositionWeightedModule(
                          max_feature_length=12
                      ),
                      "feature_3": PositionWeightedModule(
                          max_feature_length=12
                      ),
                  },
              )
              if not use_fp_collection
              else PositionWeightedModuleCollection(
                  max_feature_lengths={
                      "feature_0": 10,
                      "feature_1": 10,
                      "feature_2": 12,
                      "feature_3": 12,
                  }
              ),
          ).to(device)
      )
  
file_path: torchrec/torchrec/distributed/tests/test_fp_embeddingbag_utils.py
imports: from typing import cast, Dict, List, Tuple
  import torch
  from torch import nn
  from torchrec.modules.embedding_configs import EmbeddingBagConfig
  from torchrec.modules.embedding_modules import EmbeddingBagCollection
  from torchrec.modules.feature_processor_ import FeatureProcessor, PositionWeightedModule, PositionWeightedModuleCollection
  from torchrec.modules.fp_embedding_modules import FeatureProcessedEmbeddingBagCollection
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
label_desc: Use image node_id 1 to create a new SparseArch object from inherited base classes: nn with example: obj = SparseArch(tables, use_fp_collection, device)