class_docstr: Base class for column-wise sharding.
class_docstr_tok: ["Base","class","for","column-wise","sharding","."]
code: def embedding_names(self) -> List[str]:
      return (
          self._combined_embedding_names
          if self._permute_embeddings
          else self.uncombined_embedding_names()
      )
  
imports: from typing import Any, Callable, Dict, List, Optional, Set, Tuple, TypeVar
  import torch
  import torch.distributed
  from fbgemm_gpu.permute_pooled_embedding_modules_split import PermutePooledEmbeddingsSplit
  from torchrec.distributed.dist_data import EmbeddingsAllToOne
  from torchrec.distributed.embedding_lookup import GroupedPooledEmbeddingsLookup, InferGroupedPooledEmbeddingsLookup
  from torchrec.distributed.embedding_sharding import BaseEmbeddingDist, BaseEmbeddingLookup, BaseSparseFeaturesDist, EmbeddingShardingContext, EmbeddingShardingInfo
  from torchrec.distributed.embedding_types import BaseGroupedFeatureProcessor, EmbeddingComputeKernel, KJTList, ShardedEmbeddingTable
  from torchrec.distributed.sharding.tw_sharding import BaseTwEmbeddingSharding, InferTwSparseFeaturesDist, TwPooledEmbeddingDist, TwSparseFeaturesDist
  from torchrec.distributed.types import NullShardingContext, QuantizedCommCodecs, ShardedTensorMetadata, ShardingEnv, ShardMetadata
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
  from torchrec.streamable import Multistreamable
label_desc: Use image node_id 5 for calling the BaseCwEmbeddingSharding obj's underlying member method code with example usage: obj.embedding_names() and returns: unknown