code: def test_svm_regressor(self):
      x = np.arange(9).reshape((-1, 3)).astype(np.float32) / 10 - 0.5
      expected_kernel = {
          "LINEAR": (
              [0.42438405752182007, 0.0, 3.0],
              np.array(
                  [[-0.468206], [0.227487], [0.92318]], dtype=np.float32
              ),
          ),
          "POLY": (
              [0.3426632285118103, 0.0, 3.0],
              np.array(
                  [[0.527084], [0.543578], [0.546506]], dtype=np.float32
              ),
          ),
          "RBF": (
              [0.30286383628845215, 0.0, 3.0],
              np.array(
                  [[0.295655], [0.477876], [0.695292]], dtype=np.float32
              ),
          ),
          "SIGMOID": (
              [0.30682486295700073, 0.0, 3.0],
              np.array(
                  [[0.239304], [0.448929], [0.661689]], dtype=np.float32
              ),
          ),
      }
      for kernel, (params, expected) in expected_kernel.items():
          with self.subTest(kernel=kernel):
              onx = self._get_test_svm_regressor(kernel, params)
              self._check_ort(onx, {"X": x}, atol=1e-6)
              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 28 for calling the TestReferenceEvaluatorAiOnnxMl obj's underlying member method code with example usage: obj.test_svm_regressor() without return types