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 if device is not None else self._device,
      )
  
imports: from typing import Any, Dict, List, Optional
  import torch
  from torchrec.distributed.dist_data import SeqEmbeddingsAllToOne
  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.cw_sharding import BaseCwEmbeddingSharding
  from torchrec.distributed.sharding.sequence_sharding import InferSequenceShardingContext, SequenceShardingContext
  from torchrec.distributed.sharding.tw_sequence_sharding import TwSequenceEmbeddingDist
  from torchrec.distributed.sharding.tw_sharding import InferTwSparseFeaturesDist, TwSparseFeaturesDist
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
label_desc: Use image node_id 2 for calling the InferCwSequenceEmbeddingSharding obj's underlying member method code with example usage: obj.create_lookup(device, fused_params, feature_processor) and returns: InferGroupedEmbeddingsLookup