code: def _construct_jagged_tensors_rw(
      embeddings: List[torch.Tensor],
      features_before_input_dist: KeyedJaggedTensor,
      need_indices: bool,
      unbucketize_tensor: torch.Tensor,
  ) -> Dict[str, JaggedTensor]:
      ret: Dict[str, JaggedTensor] = {}
      unbucketized_embs = torch.concat(embeddings, dim=0).index_select(
          0, unbucketize_tensor
      )
      embs_split_per_key = unbucketized_embs.split(
          features_before_input_dist.length_per_key(), dim=0
      )
      stride = features_before_input_dist.stride()
      lengths_list = torch.unbind(
          features_before_input_dist.lengths().view(-1, stride), dim=0
      )
      values_list: List[torch.Tensor] = []
      if need_indices:
          # pyre-ignore
          values_list = torch.split(
              features_before_input_dist.values(),
              features_before_input_dist.length_per_key(),
          )
      for i, key in enumerate(features_before_input_dist.keys()):
          ret[key] = JaggedTensor(
              values=embs_split_per_key[i],
              lengths=lengths_list[i],
              weights=values_list[i] if need_indices else None,
          )
      return ret
  
imports: from collections import defaultdict, deque
  from dataclasses import dataclass
  from typing import Any, cast, Dict, List, Optional, Tuple, Type
  import torch
  from fbgemm_gpu.split_table_batched_embeddings_ops_inference import IntNBitTableBatchedEmbeddingBagsCodegen
  from torch import nn
  from torchrec.distributed.embedding import create_sharding_infos_by_sharding, EmbeddingShardingInfo
  from torchrec.distributed.embedding_sharding import EmbeddingSharding
  from torchrec.distributed.embedding_types import BaseQuantEmbeddingSharder, FeatureShardingMixIn, GroupedEmbeddingConfig, KJTList, ListOfKJTList, ShardingType
  from torchrec.distributed.fused_params import FUSED_PARAM_QUANT_STATE_DICT_SPLIT_SCALE_BIAS, FUSED_PARAM_REGISTER_TBE_BOOL, get_tbes_to_register_from_iterable, is_fused_param_quant_state_dict_split_scale_bias, is_fused_param_register_tbe
  from torchrec.distributed.quant_state import ShardedQuantEmbeddingModuleState
  from torchrec.distributed.sharding.cw_sequence_sharding import InferCwSequenceEmbeddingSharding
  from torchrec.distributed.sharding.rw_sequence_sharding import InferRwSequenceEmbeddingSharding
  from torchrec.distributed.sharding.rw_sharding import InferRwSparseFeaturesDist
  from torchrec.distributed.sharding.sequence_sharding import InferSequenceShardingContext
  from torchrec.distributed.sharding.tw_sequence_sharding import InferTwSequenceEmbeddingSharding
  from torchrec.distributed.types import ParameterSharding, ShardingEnv, ShardMetadata
  from torchrec.modules.embedding_configs import data_type_to_sparse_type, dtype_to_data_type, EmbeddingConfig
  from torchrec.quant.embedding_modules import EmbeddingCollection, MODULE_ATTR_QUANT_STATE_DICT_SPLIT_SCALE_BIAS
  from torchrec.sparse.jagged_tensor import JaggedTensor, KeyedJaggedTensor
  from torchrec.streamable import Multistreamable
label_desc: Use image node_id 4 for calling a global function with example usage: _construct_jagged_tensors_rw(embeddings, features_before_input_dist, need_indices, unbucketize_tensor) and returns: ret