input: Write a python function to create, fit, and predict using an SVM model from scikit-learn.
  assert svm_predictions([[0, 0], [1, 1]], [0, 1], [[0.5, 0.5]])==[1]
  assert svm_predictions([[2, 2], [4, 4]], [0, 1], [[3, 3]])==[1]
  assert svm_predictions([[1, 2], [3, 4], [5, 6]], [0, 1, 0], [[4, 5]])==[0]
output: def svm_predictions(X_train, y_train, X_test):
      from sklearn import svm
      model = svm.SVC()
      model.fit(X_train, y_train)
      return model.predict(X_test).tolist()