code: def get_test_input(
      supported_hierarchical_methods: List[int],
      supported_base_forecast_methods: List[int],
      nonnegative_options: List[bool],
      levels: List[int],
      covariance_options: List[str],
      algorithm_options: List[str],
  ) -> List[Tuple[Union[str, int]]]:
      """
      We compute the combinations that we want for testing.
      """
  
      test_input = []
      test_input_constant = {
          "supported_base_forecast_methods": supported_base_forecast_methods,
          "nonnegative_options": nonnegative_options,
      }
      for hierarchical_method in supported_hierarchical_methods:
          if "mint" in hierarchical_method:
              test_input_method_dependant = {
                  "levels": [None],
                  "covariance_options": covariance_options,
                  "algorithm_options": algorithm_options,
              }
          elif "middle_out" in hierarchical_method:
              test_input_method_dependant = {
                  "levels": levels,
                  "covariance_options": [None],
                  "algorithm_options": [None],
              }
          else:
              test_input_method_dependant = {
                  "levels": [None],
                  "covariance_options": [None],
                  "algorithm_options": [None],
              }
  
          test_input_params = {
              "hierarchical_method": [hierarchical_method],
              **test_input_constant,
              **test_input_method_dependant,
          }
          test_input.extend(
              list(itertools.product(*test_input_params.values()))
          )
  
      return test_input
  
file_path: gluonts/test/ext/r_forecast/test_r_hierarchical_predictor.py
imports: from typing import List, Tuple, Union
  import itertools
  import pytest
  from gluonts.core import serde
  from gluonts.dataset.util import forecast_start
  from gluonts.evaluation import MultivariateEvaluator, backtest_metrics
  from gluonts.model import SampleForecast
  from gluonts.ext.r_forecast import RHierarchicalForecastPredictor, R_IS_INSTALLED, RPY2_IS_INSTALLED, SUPPORTED_HIERARCHICAL_METHODS
label_desc: Use image node_id 1 for calling a global function with example usage: get_test_input(supported_hierarchical_methods, supported_base_forecast_methods, nonnegative_options, levels, covariance_options, algorithm_options) and returns: test_input