class_docstr: Shards sequence (unpooled) embedding table-wise, i.e.. a given embedding table is
  placed entirely on a selected rank.
class_docstr_tok: ["Shards","sequence","(","unpooled",")","embedding","table-wise",",","i.e","..","a","given","embedding","table","is","placed","entirely","on","a","selected","rank","."]
code: def create_output_dist(
      self,
      device: Optional[torch.device] = None,
  ) -> BaseEmbeddingDist[
      SequenceShardingContext, torch.Tensor, torch.Tensor
  ]:
      assert self._pg is not None
      return TwSequenceEmbeddingDist(
          self._pg,
          self.features_per_rank(),
          device if device is not None else self._device,
          qcomm_codecs_registry=self.qcomm_codecs_registry,
      )
  
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.sequence_sharding import InferSequenceShardingContext, SequenceShardingContext
  from torchrec.distributed.sharding.tw_sharding import BaseTwEmbeddingSharding, InferTwSparseFeaturesDist, TwSparseFeaturesDist
  from torchrec.distributed.types import Awaitable, CommOp, QuantizedCommCodecs
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
label_desc: Use image node_id 3 for calling the TwSequenceEmbeddingSharding obj's underlying member method code with example usage: obj.create_output_dist(device) and returns: TwSequenceEmbeddingDist