code: def _get_test_tree_ensemble_classifier_multi(post_transform):
      X = make_tensor_value_info("X", TensorProto.FLOAT, [None, None])
      In = make_tensor_value_info("I", TensorProto.INT64, [None])
      Y = make_tensor_value_info("Y", TensorProto.FLOAT, [None, None])
      node1 = make_node(
          "TreeEnsembleClassifier",
          ["X"],
          ["I", "Y"],
          domain="ai.onnx.ml",
          class_ids=[
              0,
              1,
              2,
              0,
              1,
              2,
              0,
              1,
              2,
              0,
              1,
              2,
              0,
              1,
              2,
              0,
              1,
              2,
          ],
          class_nodeids=[
              2,
              2,
              2,
              3,
              3,
              3,
              4,
              4,
              4,
              1,
              1,
              1,
              3,
              3,
              3,
              4,
              4,
              4,
          ],
          class_treeids=[
              0,
              0,
              0,
              0,
              0,
              0,
              0,
              0,
              0,
              1,
              1,
              1,
              1,
              1,
              1,
              1,
              1,
              1,
          ],
          class_weights=[
              0.46666666865348816,
              0.0,
              0.03333333507180214,
              0.20000000298023224,
              0.23999999463558197,
              0.05999999865889549,
              0.0,
              0.5,
              0.0,
              0.5,
              0.0,
              0.0,
              0.44999998807907104,
              0.0,
              0.05000000074505806,
              0.10294117778539658,
              0.19117647409439087,
              0.20588235557079315,
          ],
          classlabels_int64s=[0, 1, 2],
          nodes_falsenodeids=[4, 3, 0, 0, 0, 2, 0, 4, 0, 0],
          nodes_featureids=[1, 0, 0, 0, 0, 1, 0, 0, 0, 0],
          nodes_hitrates=[
              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,
          ],
          nodes_modes=[
              "BRANCH_LEQ",
              "BRANCH_LEQ",
              "LEAF",
              "LEAF",
              "LEAF",
              "BRANCH_LEQ",
              "LEAF",
              "BRANCH_LEQ",
              "LEAF",
              "LEAF",
          ],
          nodes_nodeids=[0, 1, 2, 3, 4, 0, 1, 2, 3, 4],
          nodes_treeids=[0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
          nodes_truenodeids=[1, 2, 0, 0, 0, 1, 0, 3, 0, 0],
          nodes_values=[
              1.2495747804641724,
              -0.3050493597984314,
              0.0,
              0.0,
              0.0,
              -1.6830512285232544,
              0.0,
              -0.6751254796981812,
              0.0,
              0.0,
          ],
          post_transform=post_transform,
      )
      graph = make_graph([node1], "ml", [X], [In, Y])
      onx = make_model_gen_version(graph, opset_imports=OPSETS)
      check_model(onx)
      return onx
  
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 31 for calling the TestReferenceEvaluatorAiOnnxMl obj's underlying member method code with example usage: obj._get_test_tree_ensemble_classifier_multi(post_transform) and returns: onx