code: def test_tree_ensemble_regressor_2_targets(self):
      X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
      Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None])
      node1 = make_node(
          "TreeEnsembleRegressor",
          ["X"],
          ["Y"],
          domain="ai.onnx.ml",
          n_targets=2,
          nodes_falsenodeids=[4, 3, 0, 0, 6, 0, 0, 4, 3, 0, 0, 6, 0, 0],
          nodes_featureids=[0, 2, 0, 0, 2, 0, 0, 0, 1, 0, 0, 2, 0, 0],
          nodes_hitrates=[
              1.0,
              1.0,
              1.0,
              1.0,
              1.0,
              1.0,
              1.0,
              1.0,
              1.0,
              1.0,
              1.0,
              1.0,
              1.0,
              1.0,
          ],
          nodes_missing_value_tracks_true=[
              0,
              0,
              0,
              0,
              0,
              0,
              0,
              0,
              0,
              0,
              0,
              0,
              0,
              0,
          ],
          nodes_modes=[
              "BRANCH_LEQ",
              "BRANCH_LEQ",
              "LEAF",
              "LEAF",
              "BRANCH_LEQ",
              "LEAF",
              "LEAF",
              "BRANCH_LEQ",
              "BRANCH_LEQ",
              "LEAF",
              "LEAF",
              "BRANCH_LEQ",
              "LEAF",
              "LEAF",
          ],
          nodes_nodeids=[0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6],
          nodes_treeids=[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1],
          nodes_truenodeids=[1, 2, 0, 0, 5, 0, 0, 1, 2, 0, 0, 5, 0, 0],
          nodes_values=[
              -0.3367232382297516,
              1.5326381921768188,
              0.0,
              0.0,
              -0.24646544456481934,
              0.0,
              0.0,
              -0.3367232382297516,
              0.6671845316886902,
              0.0,
              0.0,
              -0.24646544456481934,
              0.0,
              0.0,
          ],
          post_transform="NONE",
          target_ids=[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1],
          target_nodeids=[
              2,
              2,
              3,
              3,
              5,
              5,
              6,
              6,
              2,
              2,
              3,
              3,
              5,
              5,
              6,
              6,
          ],
          target_treeids=[
              0,
              0,
              0,
              0,
              0,
              0,
              0,
              0,
              1,
              1,
              1,
              1,
              1,
              1,
              1,
              1,
          ],
          target_weights=[
              0.0,
              2.5,
              0.5,
              3.0,
              0.15000000596046448,
              2.6500000953674316,
              0.5,
              3.0,
              0.02777777798473835,
              2.527777671813965,
              0.5,
              3.0,
              0.20000000298023224,
              2.700000047683716,
              0.5,
              3.0,
          ],
      )
      graph = make_graph([node1], "ml", [X], [Y])
      onx = make_model_gen_version(graph, opset_imports=OPSETS)
      check_model(onx)
      x = np.arange(9).reshape((-1, 3)).astype(np.float32) / 10 - 0.5
      expected = np.array(
          [[0.027778, 5.027778], [1.0, 6.0], [1.0, 6.0]],
          dtype=np.float32,
      )
      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 25 for calling the TestReferenceEvaluatorAiOnnxMl obj's underlying member method code with example usage: obj.test_tree_ensemble_regressor_2_targets() without return types