code: def forward(
      self,
      input: ModelInput,
  ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
      dense_r = self.dense(input.float_features)
      # multiply the sparse output by 10 since the model output is not sensitive to the
      # embedding output. It won't catch the unexpected embedding output without this
      sparse_r = 10 * self.sparse(
          input.idlist_features, input.float_features.size(0)
      )
      over_r = self.over(dense_r, sparse_r)
      pred = torch.sigmoid(torch.mean(over_r, dim=1))
      if self.training:
          return (
              torch.nn.functional.binary_cross_entropy_with_logits(
                  pred, input.label
              ),
              pred,
          )
      else:
          return pred
  
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 2 for calling the TestSequenceSparseNN obj's underlying member method code with example usage: obj.forward(input) and returns: torch,  pred, pred