code: def forward(
      self,
      id_list_features: KeyedJaggedTensor,
      batch_size: Optional[int] = None,
  ) -> torch.Tensor:
      jt_dict = self.ec(id_list_features)
      padded_embeddings = [
          torch.ops.fbgemm.jagged_2d_to_dense(
              values=jt_dict[e].values(),
              offsets=jt_dict[e].offsets(),
              max_sequence_length=20,
          ).view(-1, 20 * self.embedding_dim)
          for e in self.embedding_names
      ]
  
      return _post_sparsenn_forward(padded_embeddings, batch_size)
  
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 TestSequenceSparseArch obj's underlying member method code with example usage: obj.forward(id_list_features, batch_size) and returns: _post_sparsenn_forward