code: def test_svm_classifier_binary_noprob(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(
                  [
                      [-0.986073, 0.986073],
                      [0.011387, -0.011387],
                      [0.801808, -0.801808],
                  ],
                  dtype=np.float32,
              ),
          ),
          "LOGISTIC": (
              np.array([0, 1, 1], dtype=np.int64),
              np.array(
                  [
                      [0.271688, 0.728312],
                      [0.502847, 0.497153],
                      [0.690361, 0.309639],
                  ],
                  dtype=np.float32,
              ),
          ),
          "SOFTMAX": (
              np.array([0, 1, 1], dtype=np.int64),
              np.array(
                  [
                      [0.122158, 0.877842],
                      [0.505693, 0.494307],
                      [0.832523, 0.167477],
                  ],
                  dtype=np.float32,
              ),
          ),
          "SOFTMAX_ZERO": (
              np.array([0, 1, 1], dtype=np.int64),
              np.array(
                  [
                      [0.122158, 0.877842],
                      [0.505693, 0.494307],
                      [0.832523, 0.167477],
                  ],
                  dtype=np.float32,
              ),
          ),
      }
      for post, expected in expected_post.items():
          with self.subTest(post_transform=post):
              onx = self._get_test_svm_classifier_binary(
                  post, probability=False
              )
              if post not in {
                  "LOGISTIC",
                  "SOFTMAX",
                  "SOFTMAX_ZERO",
                  "PROBIT",
              }:
                  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-6)
              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 35 for calling the TestReferenceEvaluatorAiOnnxMl obj's underlying member method code with example usage: obj.test_svm_classifier_binary_noprob() without return types