code: def _register_scalers(self):
      # scalers are used to support different sparse/quant granularity
      register_scalers(
          self._target_spaces, self._set_default_sparse_granularity
      )
  
imports: from __future__ import annotations
  from collections import defaultdict
  from copy import deepcopy
  import logging
  from typing import Any, Dict, List, Literal, Callable
  import torch
  from torch.utils._pytree import tree_map
  from .config import trans_legacy_config_list, default_config_schema
  from .target_space import TargetSpace, TargetType, DistillationTargetSpace, PruningTargetSpace, QuantizationTargetSpace
  from .wrapper import ModuleWrapper, register_wrappers
  from ..utils import Evaluator, Scaling, _EVALUATOR_DOCSTRING
label_desc: Use image node_id 2 for calling the Quantizer obj's underlying member method code with example usage: obj._register_scalers() without return types