class_docstr: Bucketizes sparse features in RW fashion and then redistributes with an AlltoAll
  collective operation.
  
  Args:
      pg (dist.ProcessGroup): ProcessGroup for AlltoAll communication.
      intra_pg (dist.ProcessGroup): ProcessGroup within single host group for AlltoAll
          communication.
      num_features (int): total number of features.
      feature_hash_sizes (List[int]): hash sizes of features.
      device (Optional[torch.device]): device on which buffers will be allocated.
      is_sequence (bool): if this is for a sequence embedding.
      has_feature_processor (bool): existence of feature processor (ie. position
          weighted features).
code: def __init__(
      self,
      pg: dist.ProcessGroup,
      num_features: int,
      feature_hash_sizes: List[int],
      device: Optional[torch.device] = None,
      is_sequence: bool = False,
      has_feature_processor: bool = False,
      need_pos: bool = False,
  ) -> None:
      super().__init__()
      self._world_size: int = pg.size()
      self._num_features = num_features
      feature_block_sizes = [
          (hash_size + self._world_size - 1) // self._world_size
          for hash_size in feature_hash_sizes
      ]
      self.register_buffer(
          "_feature_block_sizes_tensor",
          torch.tensor(
              feature_block_sizes,
              device=device,
              dtype=torch.int64,
          ),
      )
      self._dist = KJTAllToAll(
          pg=pg,
          splits=[self._num_features] * self._world_size,
      )
      self._is_sequence = is_sequence
      self._has_feature_processor = has_feature_processor
      self._need_pos = need_pos
      self.unbucketize_permute_tensor: Optional[torch.Tensor] = None
  
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 1 to create a new RwSparseFeaturesDist object from inherited base classes: BaseSparseFeaturesDist with example: obj = RwSparseFeaturesDist(pg, num_features, feature_hash_sizes, device, is_sequence, has_feature_processor, need_pos)