code: def _get_test_svm_classifier_binary(
      post_transform, probability=True, linear=False
  ):
      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])
      if linear:
          kwargs = {
              "classlabels_ints": [0, 1, 2, 3],
              "coefficients": [
                  -1.55181212e-01,
                  2.42698956e-01,
                  7.01893432e-03,
                  4.07614474e-01,
                  -3.24927823e-02,
                  2.79897536e-04,
                  -1.95771302e-01,
                  -3.52437368e-01,
                  -2.15973096e-02,
                  -4.38190277e-01,
                  4.56869105e-02,
                  -1.29375499e-02,
              ],
              "kernel_params": [0.001, 0.0, 3.0],
              "kernel_type": "LINEAR",
              "prob_a": [-5.139118194580078],
              "prob_b": [0.06399919837713242],
              "rho": [
                  -0.07489691,
                  -0.1764396,
                  -0.21167431,
                  -0.51619097,
              ],
              "post_transform": post_transform,
          }
      else:
          kwargs = {
              "classlabels_ints": [0, 1],
              "coefficients": [
                  1.0,
                  1.0,
                  1.0,
                  1.0,
                  -1.0,
                  -1.0,
                  -1.0,
                  -1.0,
              ],
              "kernel_params": [0.3824487328529358, 0.0, 3.0],
              "kernel_type": "RBF",
              "prob_a": [-5.139118194580078],
              "prob_b": [0.06399919837713242],
              "rho": [0.16708599030971527],
              "support_vectors": [
                  0.19125767052173615,
                  -1.062204122543335,
                  0.5006636381149292,
                  -0.5892484784126282,
                  -0.3196830451488495,
                  0.0984845906496048,
                  0.24746321141719818,
                  -1.1535362005233765,
                  0.4109955430030823,
                  -0.5937694907188416,
                  -1.3183348178863525,
                  -1.6423596143722534,
                  0.558641254901886,
                  -0.9218668341636658,
                  0.6264089345932007,
                  -0.16060839593410492,
                  -0.6365169882774353,
                  0.8335472345352173,
                  0.7539799213409424,
                  -0.3970031440258026,
                  -0.1780400276184082,
                  -0.616622805595398,
                  0.49261474609375,
                  0.4470972716808319,
              ],
              "vectors_per_class": [4, 4],
              "post_transform": post_transform,
          }
  
      if not probability:
          del kwargs["prob_a"]
          del kwargs["prob_b"]
      node1 = make_node(
          "SVMClassifier",
          ["X"],
          ["I", "Y"],
          domain="ai.onnx.ml",
          **kwargs,
      )
      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 33 for calling the TestReferenceEvaluatorAiOnnxMl obj's underlying member method code with example usage: obj._get_test_svm_classifier_binary(post_transform, probability, linear) and returns: onx