class_docstr: Base class for table wise row wise sharding.
class_docstr_tok: ["Base","class","for","table","wise","row","wise","sharding","."]
code: def embedding_names(self) -> List[str]:
      embedding_names = []
      for (
          grouped_embedding_configs
      ) in self._grouped_embedding_configs_per_node:
          for grouped_config in grouped_embedding_configs:
              embedding_names.extend(grouped_config.embedding_names())
      return embedding_names
  
imports: import itertools
  import math
  from typing import Any, cast, Dict, List, Optional, Tuple, TypeVar, Union
  import torch
  import torch.distributed
  from torchrec.distributed.comm import get_local_size, intra_and_cross_node_pg
  from torchrec.distributed.dist_data import KJTAllToAll, PooledEmbeddingsAllToAll, PooledEmbeddingsReduceScatter, VariableBatchPooledEmbeddingsAllToAll, VariableBatchPooledEmbeddingsReduceScatter
  from torchrec.distributed.embedding_lookup import GroupedPooledEmbeddingsLookup
  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, ShardedEmbeddingTable
  from torchrec.distributed.types import Awaitable, CommOp, QuantizedCommCodecs, ShardedTensorMetadata, ShardingEnv, ShardMetadata
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
  from torchrec.streamable import Multistreamable
label_desc: Use image node_id 4 for calling the BaseTwRwEmbeddingSharding obj's underlying member method code with example usage: obj.embedding_names() and returns: embedding_names