class_docstr: Distributed version of lightgbm.LGBMClassifier.
class_docstr_tok: ["Distributed","version","of","lightgbm.LGBMClassifier","."]
docstr: Create regular version of lightgbm.LGBMClassifier from the distributed version.
  
  Returns
  -------
  model : lightgbm.LGBMClassifier
      Local underlying model.
code: def to_local(self) -> LGBMClassifier:
      """Create regular version of lightgbm.LGBMClassifier from the distributed version.
  
      Returns
      -------
      model : lightgbm.LGBMClassifier
          Local underlying model.
      """
      return self._lgb_dask_to_local(LGBMClassifier)
  
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 the DaskLGBMClassifier obj's underlying member method code with example usage: obj.to_local() and returns: self