code: def test_default_names(self):
      df = pd.DataFrame({"input": self.x})
      df["input"] = df["input"].apply(np.array)
  
      # Test with probabilities
      spec = libsvm.convert(self.libsvm_model).get_spec()
      if _is_macos() and _macos_version() >= (10, 13):
          (_, _, probability_lists) = svm_predict(
              self.y, self.x, self.libsvm_model, "-b 1 -q"
          )
          probability_dicts = [
              dict(zip([1, 2], cur_vals))
              for cur_vals in probability_lists
          ]
          df["classProbability"] = probability_dicts
          metrics = evaluate_classifier_with_probabilities(
              spec, df, verbose=False, probabilities="classProbability"
          )
          self.assertLess(metrics["max_probability_error"], 0.00001)
  
      # Test model without probabilities
      no_probability_model = svmutil.svm_train(
          self.prob, svmutil.svm_parameter()
      )
      spec = libsvm.convert(no_probability_model).get_spec()
      self.assertEqual(len(spec.description.output), 1)
      self.assertEqual(spec.description.output[0].name, "target")
      if _is_macos() and _macos_version() >= (10, 13):
          (df["prediction"], _, _) = svm_predict(
              self.y, self.x, no_probability_model, " -q"
          )
          metrics = evaluate_classifier(spec, df, verbose=False)
          self.assertEquals(metrics["num_errors"], 0)
  
imports: import unittest
  import tempfile
  import os
  import pandas
  import random
  import pytest
  import numpy
  from coremltools.models.utils import evaluate_classifier, evaluate_classifier_with_probabilities, _macos_version, _is_macos
  from coremltools._deps import _HAS_LIBSVM, _HAS_SKLEARN
label_desc: Use image node_id 2 for calling the CSVCLibSVMTest obj's underlying member method code with example usage: obj.test_default_names() without return types