code: def _update_embedding_configs(
      embedding_configs: List[BaseEmbeddingConfig],
      quant_config: Union[QuantConfig, torch.quantization.QConfig],
  ) -> None:
      per_table_weight_dtype = (
          quant_config.per_table_weight_dtype
          if isinstance(quant_config, QuantConfig)
          and quant_config.per_table_weight_dtype
          else {}
      )
      for config in embedding_configs:
          config.data_type = dtype_to_data_type(
              per_table_weight_dtype[config.name]
              if config.name in per_table_weight_dtype
              else quant_config.weight().dtype
          )
  
imports: import copy
  import itertools
  from collections import defaultdict
  from typing import Callable, cast, Dict, List, Optional, Tuple, Type, Union
  import torch
  import torch.nn
  from fbgemm_gpu.split_table_batched_embeddings_ops_inference import EmbeddingLocation, IntNBitTableBatchedEmbeddingBagsCodegen, PoolingMode
  from torch import Tensor
  from torchrec.distributed.utils import none_throws
  from torchrec.modules.embedding_configs import BaseEmbeddingConfig, DATA_TYPE_NUM_BITS, data_type_to_sparse_type, DataType, dtype_to_data_type, EmbeddingBagConfig, EmbeddingConfig, pooling_type_to_pooling_mode, PoolingType, QuantConfig
  from torchrec.modules.embedding_modules import EmbeddingBagCollection, EmbeddingBagCollectionInterface, EmbeddingCollection, EmbeddingCollectionInterface, get_embedding_names_by_table
  from torchrec.modules.feature_processor_ import FeatureProcessorsCollection
  from torchrec.modules.fp_embedding_modules import FeatureProcessedEmbeddingBagCollection
  from torchrec.sparse.jagged_tensor import JaggedTensor, KeyedJaggedTensor, KeyedTensor
  from torchrec.tensor_types import UInt2Tensor, UInt4Tensor
  from torchrec.types import ModuleNoCopyMixin
label_desc: Use image node_id 9 for calling a global function with example usage: _update_embedding_configs(embedding_configs, quant_config) without return types