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_lookup(
      self,
      device: Optional[torch.device] = None,
      fused_params: Optional[Dict[str, Any]] = None,
      feature_processor: Optional[BaseGroupedFeatureProcessor] = None,
  ) -> BaseEmbeddingLookup[KJTList, List[torch.Tensor]]:
      return InferGroupedEmbeddingsLookup(
          grouped_configs_per_rank=self._grouped_embedding_configs_per_rank,
          world_size=self._world_size,
          fused_params=fused_params,
          device=device,
      )
  
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 2 for calling the InferTwSequenceEmbeddingSharding obj's underlying member method code with example usage: obj.create_lookup(device, fused_params, feature_processor) and returns: InferGroupedEmbeddingsLookup