code: def _post_sparsenn_forward(
      padded_embeddings: List[torch.Tensor],
      batch_size: Optional[int] = None,
  ) -> torch.Tensor:
      if (
          batch_size is None
          or padded_embeddings[0].size(0) == batch_size
      ):
          return torch.cat(
              padded_embeddings,
              dim=1,
          )
      else:
          seq_emb = torch.cat(padded_embeddings, dim=1)
          ec_values = torch.zeros(
              batch_size,
              seq_emb.size(1),
              dtype=seq_emb.dtype,
              device=seq_emb.device,
          )
          ec_values[: seq_emb.size(0), :] = seq_emb
          return ec_values
  
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 1 for calling a global function with example usage: _post_sparsenn_forward(padded_embeddings, batch_size) and returns: torch, ec_values