code: def named_buffers(
      self,
      prefix: str = "",
      recurse: bool = True,
      remove_duplicate: bool = True,
  ) -> Iterator[Tuple[str, torch.Tensor]]:
      assert (
          remove_duplicate
      ), "remove_duplicate=False not supported in QuantBatchedEmbeddingBag.named_split_embedding_weights"
      for config, (weight, weight_qscale, weight_qbias) in zip(
          self._config.embedding_tables,
          self.emb_module.split_embedding_weights_with_scale_bias(
              split_scale_bias_mode=2
              if self._quant_state_dict_split_scale_bias
              else 0
          ),
      ):
          yield append_prefix(prefix, f"{config.name}.weight"), weight
          if self._quant_state_dict_split_scale_bias:
              yield append_prefix(
                  prefix, f"{config.name}.weight_qscale"
              ), weight_qscale
              yield append_prefix(
                  prefix, f"{config.name}.weight_qbias"
              ), weight_qbias
  
imports: import copy
  import logging
  from typing import Any, Dict, Iterator, List, Optional, Tuple
  import torch
  import torch.distributed
  from fbgemm_gpu.split_embedding_configs import SparseType
  from fbgemm_gpu.split_table_batched_embeddings_ops_inference import EmbeddingLocation, IntNBitTableBatchedEmbeddingBagsCodegen, PoolingMode, rounded_row_size_in_bytes
  from torchrec.distributed.batched_embedding_kernel import BaseBatchedEmbedding, BaseBatchedEmbeddingBag, BatchedDenseEmbedding, BatchedDenseEmbeddingBag
  from torchrec.distributed.embedding_kernel import BaseEmbedding
  from torchrec.distributed.embedding_types import compute_kernel_to_embedding_location, GroupedEmbeddingConfig
  from torchrec.distributed.fused_params import is_fused_param_quant_state_dict_split_scale_bias, is_fused_param_register_tbe, tbe_fused_params, TBEToRegisterMixIn
  from torchrec.distributed.utils import append_prefix
  from torchrec.modules.embedding_configs import DATA_TYPE_NUM_BITS, data_type_to_sparse_type, DataType, dtype_to_data_type
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
label_desc: Use image node_id 6 for calling the QuantBatchedEmbeddingBag obj's underlying member method code with example usage: obj.named_buffers(prefix, recurse, remove_duplicate) without return types