code: def forward(self, kjt: KeyedJaggedTensor) -> torch.Tensor:
      ec_output = self.ec.forward(kjt)
      sparse_features = []
      for key in kjt.keys():
          sparse_features.extend(ec_output[key].to_dense())
      sparse_features = torch.Tensor(sparse_features)
      sparse_features = torch.sum(sparse_features)
      return self.over_arch(sparse_features)
  
imports: import unittest
  from collections import OrderedDict
  from typing import Any, Dict, List, Optional, Tuple, Type
  import hypothesis.strategies
  import torch
  import torch.fx
  import torchrec
  from fbgemm_gpu.split_table_batched_embeddings_ops_training import EmbeddingLocation
  from hypothesis import given, settings
  from torchrec.modules.embedding_configs import EmbeddingBagConfig, EmbeddingConfig
  from torchrec.modules.embedding_modules import EmbeddingBagCollection, EmbeddingCollection
  from torchrec.modules.fused_embedding_modules import fuse_embedding_optimizer, FusedEmbeddingBagCollection, FusedEmbeddingCollection
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
label_desc: Use image node_id 2 for calling the TestSequentialModel obj's underlying member method code with example usage: obj.forward(kjt) and returns: self