input: Write a python function to train a LinearSVC model using the given training data and predict the class of new points. 
  assert svm_predict([[1, 2], [-2, 3], [-1, -4], [0, 0]], [1, -1, -1, 0], [[3, -1], [-2, 0]]) == [1, -1]
  assert svm_predict([[1, 1], [2, 2], [3, 3]], [1, -1, 1], [[4, 4], [0, 0]]) == [1, 1]
  assert svm_predict([[0, 0], [1, 1], [2, 2]], [1, -1, 1], [[3, 3], [1, 0]]) == [1, 1]
output: def svm_predict(X_train, y_train, X_test):
      from sklearn.svm import LinearSVC
      model = LinearSVC()
      model.fit(X_train, y_train)
      return model.predict(X_test).tolist()