code: def _pad_eval_names(
      lgbm_model: LGBMModel, required_names: List[str]
  ) -> LGBMModel:
      """Append missing (key, value) pairs to a LightGBM model's evals_result_ and best_score_ OrderedDict attrs based on a set of required eval_set names.
  
      Allows users to rely on expected eval_set names being present when fitting DaskLGBM estimators with ``eval_set``.
      """
      for eval_name in required_names:
          if eval_name not in lgbm_model.evals_result_:
              lgbm_model.evals_result_[eval_name] = {}
          if eval_name not in lgbm_model.best_score_:
              lgbm_model.best_score_[eval_name] = {}
  
      return lgbm_model
  
imports: import operator
  import socket
  from collections import defaultdict
  from copy import deepcopy
  from enum import Enum, auto
  from functools import partial
  from typing import Any, Dict, Iterable, List, Optional, Tuple, Type, Union
  from urllib.parse import urlparse
  import numpy
  import scipy.sparse
  from .basic import LightGBMError, _choose_param_value, _ConfigAliases, _log_info, _log_warning
  from .compat import DASK_INSTALLED, PANDAS_INSTALLED, SKLEARN_INSTALLED, Client, Future, LGBMNotFittedError, concat, dask_Array, dask_array_from_delayed, dask_bag_from_delayed, dask_DataFrame, dask_Series, default_client, delayed, pd_DataFrame, pd_Series, wait
  from .sklearn import LGBMClassifier, LGBMModel, LGBMRanker, LGBMRegressor, _LGBM_ScikitCustomObjectiveFunction, _LGBM_ScikitEvalMetricType, _lgbmmodel_doc_custom_eval_note, _lgbmmodel_doc_fit, _lgbmmodel_doc_predict
label_desc: Use image node_id 6 for calling a global function with example usage: _pad_eval_names(lgbm_model, required_names) and returns: lgbm_model