code: def forward(
      self,
      local_embs: List[torch.Tensor],
      sharding_ctx: Optional[InferSequenceShardingContext] = None,
  ) -> List[torch.Tensor]:
      return self._dist(local_embs)
  
imports: from typing import Any, Dict, List, Optional
  import torch
  import torch.distributed
  from torchrec.distributed.dist_data import SeqEmbeddingsAllToOne, SequenceEmbeddingsAllToAll
  from torchrec.distributed.embedding_lookup import GroupedEmbeddingsLookup, InferGroupedEmbeddingsLookup
  from torchrec.distributed.embedding_sharding import BaseEmbeddingDist, BaseEmbeddingLookup, BaseSparseFeaturesDist
  from torchrec.distributed.embedding_types import BaseGroupedFeatureProcessor, KJTList
  from torchrec.distributed.sharding.rw_sharding import BaseRwEmbeddingSharding, InferRwSparseFeaturesDist, RwSparseFeaturesDist
  from torchrec.distributed.sharding.sequence_sharding import InferSequenceShardingContext, SequenceShardingContext
  from torchrec.distributed.types import Awaitable, CommOp, QuantizedCommCodecs
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
label_desc: Use image node_id 2 for calling the InferRwSequenceEmbeddingDist obj's underlying member method code with example usage: obj.forward(local_embs, sharding_ctx) and returns: self