code: def test_deepar_nonnegative_pred_samples(
      distr_output,
      datasets,
  ):
      estimator = DeepAREstimator(
          distr_output=distr_output,
          nonnegative_pred_samples=True,
          freq="D",
          prediction_length=3,
          trainer_kwargs={"max_epochs": 1},
      )
  
      dataset_train, dataset_test = datasets
      predictor = estimator.train(dataset_train)
      forecasts = list(predictor.predict(dataset_test))
  
      assert len(forecasts) == len(dataset_test)
  
      for forecast in forecasts:
          assert (forecast.samples >= 0).all()
  
imports: import pytest
  from gluonts.torch import DeepAREstimator
  from gluonts.torch.distributions import StudentTOutput, NormalOutput
  from gluonts.testutil.dummy_datasets import make_dummy_datasets_with_features
label_desc: Use image node_id 1 for calling a global function with example usage: test_deepar_nonnegative_pred_samples(distr_output, datasets) without return types