docstr: Returns
  -------
  Dict[str, str]
      {original_parameter_name: wrapped_parameter_name}.
      i.e., {'model.fc.weight': 'model.fc._nni_wrapper.weight'}.
docstr_tok: ["Returns","--","--","--","-","Dict","[","str",",","str","]","{","original_parameter_name",":","wrapped_parameter_name","}",".","i.e.",",","{","'model.fc.weight","'",":","'model.fc._nni_wrapper.weight","'","}","."]
code: def _get_param_names_map(self) -> Dict[str, str]:
      """
      Returns
      -------
      Dict[str, str]
          {original_parameter_name: wrapped_parameter_name}.
          i.e., {'model.fc.weight': 'model.fc._nni_wrapper.weight'}.
      """
      param_names_map = {}
      if self._is_wrapped is True:
          for param_name, _ in self.bound_model.named_parameters():
              origin_param_name = (
                  "".join(param_name.split("_nni_wrapper."))
                  if "_nni_wrapper" in param_name
                  else param_name
              )
              param_names_map[origin_param_name] = param_name
      else:
          raise RuntimeError(
              "Only can get param_names_map when the model is wrapped."
          )
      return param_names_map
  
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 7 for calling the Compressor obj's underlying member method code with example usage: obj._get_param_names_map() and returns: param_names_map