code: def test_svm_classifier_binary(self):
      x = (np.arange(9).reshape((-1, 3)) - 5).astype(np.float32) / 5
      expected_post = {
          "NONE": (
              np.array([0, 1, 1], dtype=np.int64),
              np.array(
                  [
                      [0.993287, 0.006713],
                      [0.469401, 0.530599],
                      [0.014997, 0.985003],
                  ],
                  dtype=np.float32,
              ),
          ),
          "LOGISTIC": (
              np.array([0, 1, 1], dtype=np.int64),
              np.array(
                  [
                      [0.729737, 0.501678],
                      [0.615242, 0.629623],
                      [0.503749, 0.7281],
                  ],
                  dtype=np.float32,
              ),
          ),
          "SOFTMAX": (
              np.array([0, 1, 1], dtype=np.int64),
              np.array(
                  [
                      [0.728411, 0.271589],
                      [0.484705, 0.515295],
                      [0.274879, 0.725121],
                  ],
                  dtype=np.float32,
              ),
          ),
          "SOFTMAX_ZERO": (
              np.array([0, 1, 1], dtype=np.int64),
              np.array(
                  [
                      [0.728411, 0.271589],
                      [0.484705, 0.515295],
                      [0.274879, 0.725121],
                  ],
                  dtype=np.float32,
              ),
          ),
          "PROBIT": (
              np.array([0, 1, 1], dtype=np.int64),
              np.array(
                  [
                      [2.469393, -2.469391],
                      [-0.076776, 0.076776],
                      [-2.16853, 2.16853],
                  ],
                  dtype=np.float32,
              ),
          ),
      }
      for post, expected in expected_post.items():
          with self.subTest(post_transform=post):
              onx = self._get_test_svm_classifier_binary(post)
              self._check_ort(onx, {"X": x}, rev=True, atol=1e-5)
              sess = ReferenceEvaluator(onx)
              got = sess.run(None, {"X": x})
              assert_allclose(expected[1], got[1], atol=1e-5)
              assert_allclose(expected[0], got[0])
  
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 34 for calling the TestReferenceEvaluatorAiOnnxMl obj's underlying member method code with example usage: obj.test_svm_classifier_binary() without return types