class_docstr: Implementation of the scikit-learn API for LightGBM.
class_docstr_tok: ["Implementation","of","the","scikit-learn","API","for","LightGBM","."]
docstr: Set the parameters of this estimator.
  
  Parameters
  ----------
  **params
      Parameter names with their new values.
  
  Returns
  -------
  self : object
      Returns self.
code: def set_params(self, **params: Any) -> "LGBMModel":
      """Set the parameters of this estimator.
  
      Parameters
      ----------
      **params
          Parameter names with their new values.
  
      Returns
      -------
      self : object
          Returns self.
      """
      for key, value in params.items():
          setattr(self, key, value)
          if hasattr(self, f"_{key}"):
              setattr(self, f"_{key}", value)
          self._other_params[key] = value
      return self
  
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 5 for calling the LGBMModel obj's underlying member method code with example usage: obj.set_params() and returns: self