docstr: Creates a LightGBM model.
  
  Parameters
  ----------
  **kwargs : dict, optional
      A dictionary containing the hyperparameters for the FairGBM model.
code: def __init__(self, **kwargs):
      """Creates a LightGBM model.
  
      Parameters
      ----------
      **kwargs : dict, optional
          A dictionary containing the hyperparameters for the FairGBM model.
      """
      super().__init__(**kwargs)
      kwargs.pop("unawareness", None)
      self.logger = create_logger("methods.base_estimator.LightGBM")
      self.logger.info("Instantiating LightGBM model.")
      self.model = LGBMClassifier(**kwargs, verbose=-1)
      self.logger.debug(
          f"Instantiating LightGBM with following kwargs: {kwargs}"
      )
  
file_path: aequitas/src/aequitas/fairflow/methods/base_estimator/lightgbm.py
imports: from typing import Optional
  import pandas
  from lightgbm import LGBMClassifier
  from ...utils import create_logger
  from .base_estimator import BaseEstimator
label_desc: Use image node_id 1 to create a new LightGBM object from inherited base classes: BaseEstimator with example: obj = LightGBM()