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 InferGroupedPooledEmbeddingsLookup(
          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: import math
  from typing import Any, cast, Dict, List, Optional, Tuple, TypeVar, Union
  import torch
  import torch.distributed
  from torchrec.distributed.dist_data import EmbeddingsAllToOneReduce, KJTAllToAll, KJTOneToAll, PooledEmbeddingsReduceScatter, VariableBatchPooledEmbeddingsReduceScatter
  from torchrec.distributed.embedding_lookup import GroupedPooledEmbeddingsLookup, InferGroupedPooledEmbeddingsLookup
  from torchrec.distributed.embedding_sharding import BaseEmbeddingDist, BaseEmbeddingLookup, BaseSparseFeaturesDist, bucketize_kjt_before_all2all, EmbeddingSharding, EmbeddingShardingContext, EmbeddingShardingInfo, group_tables
  from torchrec.distributed.embedding_types import BaseGroupedFeatureProcessor, EmbeddingComputeKernel, GroupedEmbeddingConfig, KJTList, ShardedEmbeddingTable
  from torchrec.distributed.types import Awaitable, CommOp, NullShardingContext, QuantizedCommCodecs, ShardedTensorMetadata, ShardingEnv, ShardMetadata
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
  from torchrec.streamable import Multistreamable
label_desc: Use image node_id 2 for calling the InferRwPooledEmbeddingSharding obj's underlying member method code with example usage: obj.create_lookup(device, fused_params, feature_processor) and returns: InferGroupedPooledEmbeddingsLookup