class_docstr: Shards sequence (unpooled) embedding table-wise, i.e.. a given embedding table is
  placed entirely on a selected rank, for inference.
class_docstr_tok: ["Shards","sequence","(","unpooled",")","embedding","table-wise",",","i.e","..","a","given","embedding","table","is","placed","entirely","on","a","selected","rank",",","for","inference","."]
code: def create_output_dist(
      self,
      device: Optional[torch.device] = None,
  ) -> BaseEmbeddingDist[
      InferSequenceShardingContext,
      List[torch.Tensor],
      List[torch.Tensor],
  ]:
      device = device if device is not None else self._device
      return InferTwSequenceEmbeddingDist(
          # pyre-fixme [6]
          device,
          self._world_size,
      )
  
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 InferTwSequenceEmbeddingSharding obj's underlying member method code with example usage: obj.create_output_dist(device) and returns: InferTwSequenceEmbeddingDist