code: def test_variance_threshold(self):
      X = np.array(
          [
              [0.0, 0.0, 3.0],
              [1.0, -1.0, 0.0],
              [0.0, 2.0, 1.0],
              [1.0, 0.0, -2.0],
          ],
          dtype=np.float32,
      )
      selector = VarianceThreshold()
      selector.fit_transform(X)
      data_tensor = torch.from_numpy(X)
  
      torch_model = hummingbird.ml.convert(selector, "torch")
  
      self.assertIsNotNone(torch_model)
      np.testing.assert_allclose(
          selector.transform(X),
          torch_model.transform(data_tensor),
          rtol=1e-06,
          atol=1e-06,
      )
  
file_path: hummingbird/tests/test_sklearn_array_feature_extractor_converter.py
imports: import unittest
  import warnings
  import numpy
  import torch
  from sklearn.feature_selection import chi2, mutual_info_classif, SelectKBest, SelectPercentile, VarianceThreshold
  from sklearn.datasets import load_digits
  import hummingbird.ml
label_desc: Use image node_id 3 for calling the TestSklearnArrayFeatureExtractor obj's underlying member method code with example usage: obj.test_variance_threshold() without return types