class_docstr: Config class for `LGBMForecaster`.
class_docstr_tok: ["Config","class","for","`","LGBMForecaster","`","."]
docstr: :param learning_rate: learning rate for boosting
  :param n_jobs: num of threading, -1 or 0 indicates device default, positive int indicates num of threads
docstr_tok: [":","param","learning_rate",":","learning","rate","for","boosting",":","param","n_jobs",":","num","of","threading",",","-1","or","0","indicates","device","default",",","positive","int","indicates","num","of","threads"]
code: def __init__(
      self, learning_rate: float = 0.1, n_jobs: int = -1, **kwargs
  ):
      """
      :param learning_rate: learning rate for boosting
      :param n_jobs: num of threading, -1 or 0 indicates device default, positive int indicates num of threads
      """
      super().__init__(**kwargs)
      self.learning_rate = learning_rate
      self.n_jobs = n_jobs
  
imports: import logging
  from lightgbm import LGBMRegressor
  from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor
  from sklearn.multioutput import MultiOutputRegressor
  from merlion.models.forecast.sklearn_base import SKLearnForecaster, SKLearnForecasterConfig
label_desc: Use image node_id 1 to create a new LGBMForecasterConfig object from inherited base classes: _TreeEnsembleForecasterConfig with example: obj = LGBMForecasterConfig(learning_rate, n_jobs)