code: def _get_test_svm_classifier_linear_sv(
      post_transform, probability=True
  ):
      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])
      kwargs = {
          "classlabels_ints": [0, 1],
          "coefficients": [
              0.766398549079895,
              0.0871576070785522,
              0.110420741140842,
              -0.963976919651031,
          ],
          "support_vectors": [
              4.80000019073486,
              3.40000009536743,
              1.89999997615814,
              5.0,
              3.0,
              1.60000002384186,
              4.5,
              2.29999995231628,
              1.29999995231628,
              5.09999990463257,
              2.5,
              3.0,
          ],
          "kernel_params": [0.122462183237076, 0.0, 3.0],
          "kernel_type": "LINEAR",
          "prob_a": [-5.139118194580078],
          "prob_b": [0.06399919837713242],
          "rho": [2.23510527610779],
          "post_transform": post_transform,
          "vectors_per_class": [3, 1],
      }
  
      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 38 for calling the TestReferenceEvaluatorAiOnnxMl obj's underlying member method code with example usage: obj._get_test_svm_classifier_linear_sv(post_transform, probability) and returns: onx