code: def test_tree_ensemble_regressor(self, name, base_values, agg):
      self.assertTrue(ONNX_ML)
      del name  # variable only used to print test name
      x = np.arange(9).reshape((-1, 3)).astype(np.float32) / 10 - 0.5
      expected_agg = {
          "SUM": np.array(
              [[0.576923], [0.576923], [0.576923]], dtype=np.float32
          ),
          "AVERAGE": np.array(
              [[0.288462], [0.288462], [0.288462]], dtype=np.float32
          ),
          "MIN": np.array(
              [[0.076923], [0.076923], [0.076923]], dtype=np.float32
          ),
          "MAX": np.array([[0.5], [0.5], [0.5]], dtype=np.float32),
      }
  
      expected = expected_agg[agg]
      if base_values is not None:
          expected += base_values[0]
      with self.subTest(aggregate_function=agg):
          onx = self._get_test_tree_ensemble_regressor(
              agg, base_values=base_values
          )
          self._check_ort(onx, {"X": x}, equal=True)
          sess = ReferenceEvaluator(onx)
          got = sess.run(None, {"X": x})
          assert_allclose(expected, got[0], atol=1e-6)
  
imports: import unittest
  from functools import wraps
  from os import getenv
  import numpy
  from numpy.testing import assert_allclose
  from parameterized import parameterized
  from onnx import ONNX_ML, TensorProto, TypeProto, ValueInfoProto
  from onnx.checker import check_model
  from onnx.defs import onnx_opset_version
  from onnx.helper import make_graph, make_model_gen_version, make_node, make_opsetid, make_tensor, make_tensor_value_info
  from onnx.reference import ReferenceEvaluator
label_desc: Use image node_id 23 for calling the TestReferenceEvaluatorAiOnnxMl obj's underlying member method code with example usage: obj.test_tree_ensemble_regressor(name, base_values, agg) without return types