code: def test_torch_model_on_gpu(
      model: PyTorchLightningEstimator,
  ):
      n = 100
      date = "2015-04-07 00:00:00"
  
      df = pd.DataFrame(
          np.random.randn(n).astype(np.float32),
          index=pd.period_range(date, periods=n, freq=FREQ),
      )
      dataset = PandasDataset(df, target=0)
  
      predictor = model.train(dataset)
  
      forecast_it, ts_it = make_evaluation_predictions(
          dataset, predictor=predictor, num_samples=100
      )
  
      evaluator = Evaluator(quantiles=[0.1, 0.5, 0.9])
      agg_metrics, item_metrics = evaluator(
          ts_it,
          forecast_it,
      )
  
file_path: gluonts/test/torch/model/test_on_gpu.py
imports: import pandas
  import numpy
  import torch
  import pytest
  from gluonts.dataset.pandas import PandasDataset
  from gluonts.evaluation import make_evaluation_predictions
  from gluonts.torch.model.estimator import PyTorchLightningEstimator
  from gluonts.torch.model.simple_feedforward import SimpleFeedForwardEstimator
  from gluonts.torch.model.deepar import DeepAREstimator
  from gluonts.evaluation import Evaluator
label_desc: Use image node_id 1 for calling a global function with example usage: test_torch_model_on_gpu(model) without return types