code: def test_DistributionForecast():
      forecast = DistributionForecast(
          distribution=Uniform(
              low=torch.tensor([0.0, 0.0]),
              high=torch.tensor([1.0, 2.0]),
          ),
          start_date=START_DATE,
      )
  
      def percentile(value):
          return f"p{int(round(value * 100)):02d}"
  
      for quantile in QUANTILES:
          test_cases = [quantile, str(quantile), percentile(quantile)]
          for quant_pred in map(forecast.quantile, test_cases):
              expected = quantile * np.array([1.0, 2.0])
              assert np.allclose(
                  quant_pred, expected
              ), f"Expected {percentile(quantile)} quantile {quantile}. Obtained {quant_pred}."
  
      pred_length = 2
      assert forecast.prediction_length == pred_length
      assert len(forecast.index) == pred_length
      assert forecast.index[0] == START_DATE
  
file_path: gluonts/test/torch/model/test_torch_forecast.py
imports: import numpy
  import pandas
  import pytest
  import torch
  from torch.distributions import Uniform
  from gluonts.torch.model.forecast import DistributionForecast
label_desc: Use image node_id 2 for calling a global function with example usage: test_DistributionForecast() and returns: str, int