code: def get_probabilistic_global_model(
      lags: Union[int, List[int]],
      output_chunk_length: int = 1,
      likelihood: str = "quantile",
      quantiles: Union[None, List[float]] = [0.05, 0.5, 0.95],
      random_state: int = 42,
  ) -> LinearRegressionModel:
      return LinearRegressionModel(
          lags=lags,
          likelihood=likelihood,
          quantiles=quantiles,
          random_state=random_state,
      )
  
imports: from typing import List, Union
  import numpy
  import pandas
  import pytest
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.linear_model import LinearRegression
  from darts import TimeSeries
  from darts.logging import get_logger
  from darts.metrics import mape, rmse
  from darts.models import LinearRegressionModel, NaiveDrift, NaiveSeasonal, RandomForest, RegressionEnsembleModel, RegressionModel, Theta
  from darts.tests.conftest import tfm_kwargs
  from darts.tests.models.forecasting.test_ensemble_models import _make_ts
  from darts.tests.models.forecasting.test_regression_models import train_test_split
  from darts.utils import timeseries_generation
label_desc: Use image node_id 26 for calling the TestRegressionEnsembleModels obj's underlying member method code with example usage: obj.get_probabilistic_global_model(lags, output_chunk_length, likelihood, quantiles, random_state) and returns: LinearRegressionModel