python-package/lightgbm/dask.py [1095:1142]:
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    def __init__(
        self,
        boosting_type: str = 'gbdt',
        num_leaves: int = 31,
        max_depth: int = -1,
        learning_rate: float = 0.1,
        n_estimators: int = 100,
        subsample_for_bin: int = 200000,
        objective: Optional[Union[str, _LGBM_ScikitCustomObjectiveFunction]] = None,
        class_weight: Optional[Union[dict, str]] = None,
        min_split_gain: float = 0.,
        min_child_weight: float = 1e-3,
        min_child_samples: int = 20,
        subsample: float = 1.,
        subsample_freq: int = 0,
        colsample_bytree: float = 1.,
        reg_alpha: float = 0.,
        reg_lambda: float = 0.,
        random_state: Optional[Union[int, np.random.RandomState]] = None,
        n_jobs: int = -1,
        importance_type: str = 'split',
        client: Optional[Client] = None,
        **kwargs: Any
    ):
        """Docstring is inherited from the lightgbm.LGBMClassifier.__init__."""
        self.client = client
        super().__init__(
            boosting_type=boosting_type,
            num_leaves=num_leaves,
            max_depth=max_depth,
            learning_rate=learning_rate,
            n_estimators=n_estimators,
            subsample_for_bin=subsample_for_bin,
            objective=objective,
            class_weight=class_weight,
            min_split_gain=min_split_gain,
            min_child_weight=min_child_weight,
            min_child_samples=min_child_samples,
            subsample=subsample,
            subsample_freq=subsample_freq,
            colsample_bytree=colsample_bytree,
            reg_alpha=reg_alpha,
            reg_lambda=reg_lambda,
            random_state=random_state,
            n_jobs=n_jobs,
            importance_type=importance_type,
            **kwargs
        )
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python-package/lightgbm/dask.py [1267:1314]:
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    def __init__(
        self,
        boosting_type: str = 'gbdt',
        num_leaves: int = 31,
        max_depth: int = -1,
        learning_rate: float = 0.1,
        n_estimators: int = 100,
        subsample_for_bin: int = 200000,
        objective: Optional[Union[str, _LGBM_ScikitCustomObjectiveFunction]] = None,
        class_weight: Optional[Union[dict, str]] = None,
        min_split_gain: float = 0.,
        min_child_weight: float = 1e-3,
        min_child_samples: int = 20,
        subsample: float = 1.,
        subsample_freq: int = 0,
        colsample_bytree: float = 1.,
        reg_alpha: float = 0.,
        reg_lambda: float = 0.,
        random_state: Optional[Union[int, np.random.RandomState]] = None,
        n_jobs: int = -1,
        importance_type: str = 'split',
        client: Optional[Client] = None,
        **kwargs: Any
    ):
        """Docstring is inherited from the lightgbm.LGBMRegressor.__init__."""
        self.client = client
        super().__init__(
            boosting_type=boosting_type,
            num_leaves=num_leaves,
            max_depth=max_depth,
            learning_rate=learning_rate,
            n_estimators=n_estimators,
            subsample_for_bin=subsample_for_bin,
            objective=objective,
            class_weight=class_weight,
            min_split_gain=min_split_gain,
            min_child_weight=min_child_weight,
            min_child_samples=min_child_samples,
            subsample=subsample,
            subsample_freq=subsample_freq,
            colsample_bytree=colsample_bytree,
            reg_alpha=reg_alpha,
            reg_lambda=reg_lambda,
            random_state=random_state,
            n_jobs=n_jobs,
            importance_type=importance_type,
            **kwargs
        )
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