class_docstr: _BatchedFusedEmbeddingLookups is a thin wrapper we have around SplitTableBatchedEmbeddingBagsCodegen.
  This is not meant to be directly used. Instead use FusedEmbeddingBagCollection/FusedEmbeddingCollection (which in turn utilizes this).
  
  Example
  -------
  >>> See usage in FusedEmbeddingBagCollection and FusedEmbeddingCollection
code: def flush(self) -> None:
      self._emb_module.flush()
  
imports: import copy
  import itertools
  from collections import defaultdict
  from typing import Any, cast, Dict, Iterator, List, Optional, Set, Tuple, Type
  import torch
  import torch.nn
  import torchrec.optim
  from fbgemm_gpu.split_embedding_configs import EmbOptimType
  from fbgemm_gpu.split_table_batched_embeddings_ops_training import ComputeDevice, EmbeddingLocation, SplitTableBatchedEmbeddingBagsCodegen
  from torchrec.modules.embedding_configs import BaseEmbeddingConfig, DataType, EmbeddingBagConfig, EmbeddingConfig, pooling_type_to_pooling_mode, PoolingType
  from torchrec.modules.embedding_modules import EmbeddingBagCollection, EmbeddingBagCollectionInterface, EmbeddingCollection, EmbeddingCollectionInterface, get_embedding_names_by_table
  from torchrec.optim.fused import FusedOptimizer, FusedOptimizerModule
  from torchrec.optim.keyed import CombinedOptimizer, KeyedOptimizer
  from torchrec.sparse.jagged_tensor import JaggedTensor, KeyedJaggedTensor, KeyedTensor
label_desc: Use image node_id 10 for calling the _BatchedFusedEmbeddingLookups obj's underlying member method code with example usage: obj.flush() without return types