code: def create_sklearn_svm_classifier(X, y, probability=True):
      """Create a sklearn svm classifier.
  
      param X: The training data.
      type X: numpy.ndarray or pandas.DataFrame
      param y: The training labels.
      type y: numpy.ndarray or pandas.DataFrame
      param probability: Whether to enable probability estimates.
      type probability: bool
      return: A sklearn svm classifier.
      rtype: sklearn.svm.SVC
      """
      clf = svm.SVC(
          gamma=0.001,
          C=100.0,
          probability=probability,
          random_state=777,
      )
      model = clf.fit(X, y)
      return model
  
file_path: responsible-ai-toolbox/rai_test_utils/rai_test_utils/models/sklearn/sklearn_model_utils.py
imports: import numpy
  import pandas
  from sklearn import svm
  from sklearn.compose import ColumnTransformer
  from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
  from sklearn.impute import SimpleImputer
  from sklearn.linear_model import LogisticRegression
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import FunctionTransformer, OneHotEncoder, StandardScaler
label_desc: Use image node_id 2 for calling a global function with example usage: create_sklearn_svm_classifier(X, y, probability) and returns: model