class_docstr: Implementation of the scikit-learn API for LightGBM.
class_docstr_tok: ["Implementation","of","the","scikit-learn","API","for","LightGBM","."]
docstr: Get parameters for this estimator.
  
  Parameters
  ----------
  deep : bool, optional (default=True)
      If True, will return the parameters for this estimator and
      contained subobjects that are estimators.
  
  Returns
  -------
  params : dict
      Parameter names mapped to their values.
code: def get_params(self, deep: bool = True) -> Dict[str, Any]:
      """Get parameters for this estimator.
  
      Parameters
      ----------
      deep : bool, optional (default=True)
          If True, will return the parameters for this estimator and
          contained subobjects that are estimators.
  
      Returns
      -------
      params : dict
          Parameter names mapped to their values.
      """
      params = super().get_params(deep=deep)
      params.update(self._other_params)
      return params
  
imports: import copy
  from inspect import signature
  from pathlib import Path
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import scipy.sparse
  from .basic import Booster, Dataset, LightGBMError, _choose_param_value, _ConfigAliases, _LGBM_BoosterBestScoreType, _LGBM_CategoricalFeatureConfiguration, _LGBM_EvalFunctionResultType, _LGBM_FeatureNameConfiguration, _LGBM_GroupType, _LGBM_InitScoreType, _LGBM_LabelType, _LGBM_WeightType, _log_warning
  from .callback import _EvalResultDict, record_evaluation
  from .compat import SKLEARN_INSTALLED, LGBMNotFittedError, _LGBMAssertAllFinite, _LGBMCheckArray, _LGBMCheckClassificationTargets, _LGBMCheckSampleWeight, _LGBMCheckXY, _LGBMClassifierBase, _LGBMComputeSampleWeight, _LGBMCpuCount, _LGBMLabelEncoder, _LGBMModelBase, _LGBMRegressorBase, dt_DataTable, np_random_Generator, pd_DataFrame
  from .engine import train
label_desc: Use image node_id 4 for calling the LGBMModel obj's underlying member method code with example usage: obj.get_params(deep) and returns: params