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Wrapper of lightgbm.dask.DaskLGBMRegressor that adds a model_ property that contains the fitted booster and is sent to the workers to in the forecasting step.
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DaskLGBMForecast

 DaskLGBMForecast (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, obj
                   ective:Union[str,Callable[[Optional[numpy.ndarray],nump
                   y.ndarray],Tuple[numpy.ndarray,numpy.ndarray]],Callable
                   [[Optional[numpy.ndarray],numpy.ndarray,Optional[numpy.
                   ndarray]],Tuple[numpy.ndarray,numpy.ndarray]],Callable[
                   [Optional[numpy.ndarray],numpy.ndarray,Optional[numpy.n
                   darray],Optional[numpy.ndarray]],Tuple[numpy.ndarray,nu
                   mpy.ndarray]],NoneType]=None,
                   class_weight:Union[dict,str,NoneType]=None,
                   min_split_gain:float=0.0, min_child_weight:float=0.001,
                   min_child_samples:int=20, subsample:float=1.0,
                   subsample_freq:int=0, colsample_bytree:float=1.0,
                   reg_alpha:float=0.0, reg_lambda:float=0.0, random_state
                   :Union[int,numpy.random.mtrand.RandomState,ForwardRef('
                   np.random.Generator'),NoneType]=None,
                   n_jobs:Optional[int]=None, importance_type:str='split',
                   client:Optional[distributed.client.Client]=None,
                   **kwargs:Any)
Distributed version of lightgbm.LGBMRegressor.