code: def _conversion_and_evaluation_helper_for_linear_svc(
      self, class_labels
  ):
      ARGS = [
          {},
          {"C": 0.75, "loss": "hinge"},
          {"penalty": "l1", "dual": False},
          {"tol": 0.001, "fit_intercept": False},
          {"intercept_scaling": 1.5},
      ]
  
      x, y = GlmCassifierTest._generate_random_data(class_labels)
      column_names = ["x1", "x2"]
      df = pd.DataFrame(x, columns=column_names)
  
      for cur_args in ARGS:
          cur_model = LinearSVC(**cur_args)
          cur_model.fit(x, y)
  
          spec = convert(
              cur_model,
              input_features=column_names,
              output_feature_names="target",
          )
  
          if _is_macos() and _macos_version() >= (10, 13):
              df["target"] = cur_model.predict(x)
  
              cur_eval_metics = evaluate_classifier(
                  spec, df, verbose=False
              )
              self.assertEqual(cur_eval_metics["num_errors"], 0)
  
file_path: coremltools/coremltools/test/sklearn_tests/test_glm_classifier.py
imports: import itertools
  import random
  import unittest
  import pandas
  from coremltools._deps import _HAS_SKLEARN
  from coremltools.converters.sklearn import convert
  from coremltools.models.utils import _is_macos, _macos_version, evaluate_classifier, evaluate_classifier_with_probabilities
label_desc: Use image node_id 7 for calling the GlmCassifierTest obj's underlying member method code with example usage: obj._conversion_and_evaluation_helper_for_linear_svc(class_labels) without return types