code: def test_fit_predict_determinism(self, config):
      model_cls, model_kwargs, _, _ = config
      if TORCH_AVAILABLE and issubclass(
          model_cls, TorchForecastingModel
      ):
          fit_kwargs = {"epochs": 1, "max_samples_per_ts": 3}
      else:
          fit_kwargs = {}
      # whether the first predictions of two models initiated with the same random state are the same
      model = model_cls(**model_kwargs)
      model.fit(self.constant_noisy_ts_short, **fit_kwargs)
      pred1 = model.predict(n=10, num_samples=2).values()
  
      model = model_cls(**model_kwargs)
      model.fit(self.constant_noisy_ts_short, **fit_kwargs)
      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()
  
file_path: darts/darts/tests/models/forecasting/test_probabilistic_models.py
imports: import platform
  import numpy
  import pytest
  from darts import TimeSeries
  from darts.logging import get_logger
  from darts.metrics import mae
  from darts.models import ARIMA, BATS, TBATS, CatBoostModel, ExponentialSmoothing, LightGBMModel, LinearRegressionModel, NotImportedModule, XGBModel
  from darts.tests.conftest import tfm_kwargs
  from darts.utils import timeseries_generation
label_desc: Use image node_id 1 for calling the TestProbabilisticModels obj's underlying member method code with example usage: obj.test_fit_predict_determinism(config) without return types