code: def create_module_and_freeze(
      tables: List[EmbeddingBagConfig],
      use_fp_collection: bool,
      device: torch.device,
  ) -> SparseArch:
      sparse_arch = SparseArch(tables, use_fp_collection, device)
  
      torch.manual_seed(0)
      for param in sparse_arch.parameters():
          nn.init.normal_(param, mean=0, std=0.01)
      torch.manual_seed(0)
  
      return sparse_arch
  
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 for calling a global function with example usage: create_module_and_freeze(tables, use_fp_collection, device) and returns: sparse_arch