class_docstr: Redistributes sequence embedding tensor in hierarchical fashion with an AlltoOne
  operation.
  
  Args:
      device (torch.device): device on which the tensors will be communicated to.
      world_size (int): number of devices in the topology.
class_docstr_tok: ["Redistributes","sequence","embedding","tensor","in","hierarchical","fashion","with","an","AlltoOne","operation",".","Args",":","device","(","torch.device",")",":","device","on","which","the","tensors","will","be","communicated","to",".","world_size","(","int",")",":","number","of","devices","in","the","topology","."]
docstr: Performs AlltoOne operation on sequence embeddings tensor.
  
  Args:
      local_embs (List[orch.Tensor]): tensor of values to distribute.
      sharding_ctx (InferSequenceShardingContext): shared context from KJTAllToOne
          operation.
  
  
  Returns:
      Awaitable[torch.Tensor]: awaitable of sequence embeddings.
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code: def forward(
      self,
      local_embs: List[torch.Tensor],
      sharding_ctx: Optional[InferSequenceShardingContext] = None,
  ) -> List[torch.Tensor]:
      """
      Performs AlltoOne operation on sequence embeddings tensor.
  
      Args:
          local_embs (List[orch.Tensor]): tensor of values to distribute.
          sharding_ctx (InferSequenceShardingContext): shared context from KJTAllToOne
              operation.
  
  
      Returns:
          Awaitable[torch.Tensor]: awaitable of sequence embeddings.
      """
      return self._dist(local_embs)
  
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 InferTwSequenceEmbeddingDist obj's underlying member method code with example usage: obj.forward(local_embs, sharding_ctx) and returns: self