code: def test_RFModel(self) -> None:
      datasets = [
          SupervisedDataset(
              X=torch.rand(10, 2),
              Y=torch.rand(10, 1),
              Yvar=torch.rand(10, 1),
              feature_names=["x1", "x2"],
              outcome_names=[f"y{i}"],
          )
          for i in range(2)
      ]
  
      m = RandomForest(num_trees=5)
      m.fit(
          datasets=datasets,
          metric_names=["y1", "y2"],
          search_space_digest=SearchSpaceDigest(
              feature_names=["x1", "x2"],
              # pyre-fixme[6]: For 2nd param expected `List[Tuple[Union[float,
              #  int], Union[float, int]]]` but got `List[Tuple[int, int]]`.
              bounds=[(0, 1)] * 2,
          ),
      )
      self.assertEqual(len(m.models), 2)
      # pyre-fixme[16]: `RandomForestRegressor` has no attribute `estimators_`.
      self.assertEqual(len(m.models[0].estimators_), 5)
  
      f, cov = m.predict(torch.rand(5, 2))
      self.assertEqual(f.shape, torch.Size((5, 2)))
      self.assertEqual(cov.shape, torch.Size((5, 2, 2)))
  
      f, cov = m.cross_validate(
          datasets=datasets, X_test=torch.rand(3, 2)
      )
      self.assertEqual(f.shape, torch.Size((3, 2)))
      self.assertEqual(cov.shape, torch.Size((3, 2, 2)))
  
imports: import torch
  from ax.core.search_space import SearchSpaceDigest
  from ax.models.torch.randomforest import RandomForest
  from ax.utils.common.testutils import TestCase
  from botorch.utils.datasets import SupervisedDataset
label_desc: Use image node_id 1 for calling the RandomForestTest obj's underlying member method code with example usage: obj.test_RFModel() without return types