code: def test_predict_likelihood_parameters_multivariate_naive_ensemble(
      self,
  ):
      m_proba_quantile1 = LinearRegressionModel(
          lags=2,
          output_chunk_length=2,
          likelihood="quantile",
          quantiles=[0.05, 0.5, 0.95],
      )
      m_proba_quantile2 = LinearRegressionModel(
          lags=3,
          output_chunk_length=2,
          likelihood="quantile",
          quantiles=[0.05, 0.5, 0.95],
      )
      m_proba_quantile3 = DLinearModel(
          input_chunk_length=4,
          output_chunk_length=2,
          likelihood=QuantileRegression([0.05, 0.5, 0.95]),
          **tfm_kwargs,
      )
  
      multivariate_series = self.series1.stack(self.series2)
  
      naive_ensemble = NaiveEnsembleModel(
          [m_proba_quantile1, m_proba_quantile2]
      )
      naive_ensemble.fit(multivariate_series)
      pred_ens = naive_ensemble.predict(
          n=1, predict_likelihood_parameters=True
      )
      naive_ensemble = NaiveEnsembleModel(
          [
              m_proba_quantile2.untrained_model(),
              m_proba_quantile3.untrained_model(),
          ]
      )
      naive_ensemble.fit(multivariate_series)
      pred_mix_ens = naive_ensemble.predict(
          n=1, predict_likelihood_parameters=True
      )
      assert pred_ens.time_index == pred_mix_ens.time_index
      assert all(
          pred_ens.components
          == [
              "sine_q0.05",
              "sine_q0.50",
              "sine_q0.95",
              "linear_q0.05",
              "linear_q0.50",
              "linear_q0.95",
          ]
      )
      assert all(pred_ens.components == pred_mix_ens.components)
      assert (
          pred_ens["sine_q0.05"].values()
          < pred_ens["sine_q0.50"].values()
          < pred_ens["sine_q0.95"].values()
      )
      assert (
          pred_ens["linear_q0.05"].values()
          < pred_ens["linear_q0.50"].values()
          < pred_ens["linear_q0.95"].values()
      )
  
imports: import numpy
  import pandas
  import pytest
  from darts import TimeSeries
  from darts.logging import get_logger
  from darts.models import ExponentialSmoothing, LinearRegressionModel, NaiveDrift, NaiveEnsembleModel, NaiveSeasonal, StatsForecastAutoARIMA, Theta
  from darts.tests.conftest import tfm_kwargs
  from darts.utils import timeseries_generation
label_desc: Use image node_id 12 for calling the TestEnsembleModels obj's underlying member method code with example usage: obj.test_predict_likelihood_parameters_multivariate_naive_ensemble() without return types