code: def test_fit_predict_determinism(self, config):
      (model_cls, model_kwargs, _), mode = config
      # whether the first predictions of two models initiated with the same random state are the same
      model_kwargs["multi_models"] = mode
      model = model_cls(**model_kwargs)
      model.fit(self.constant_noisy_multivar_ts)
      pred1 = model.predict(n=10, num_samples=2).values()
  
      model = model_cls(**model_kwargs)
      model.fit(self.constant_noisy_multivar_ts)
      pred2 = model.predict(n=10, num_samples=2).values()
  
      assert (pred1 == pred2).all()
  
      # test whether the next prediction of the same model is different
      pred3 = model.predict(n=10, num_samples=2).values()
      assert (pred2 != pred3).any()
  
imports: import copy
  import functools
  import itertools
  import math
  from unittest.mock import patch
  import numpy
  import pandas
  import pytest
  from sklearn.ensemble import HistGradientBoostingRegressor, RandomForestRegressor
  from sklearn.linear_model import LinearRegression
  import darts
  from darts import TimeSeries
  from darts.dataprocessing.encoders import FutureCyclicEncoder, PastDatetimeAttributeEncoder
  from darts.logging import get_logger
  from darts.metrics import mae, rmse
  from darts.models import CatBoostModel, LightGBMModel, LinearRegressionModel, NotImportedModule, RandomForest, RegressionModel, XGBModel
  from darts.utils import timeseries_generation
  from darts.utils.multioutput import MultiOutputRegressor
label_desc: Use image node_id 1 for calling the TestProbabilisticRegressionModels obj's underlying member method code with example usage: obj.test_fit_predict_determinism(config) without return types