input: Write a python function to create and train a SVC classifier with given input and label data, and make a prediction using the same input data.
  assert svm_classifier(np.array([[3, 5, 7, 1, 0]]), np.array([0])) == [0]
  assert svm_classifier(np.array([[2, 4, 6, 8, 10]]), np.array([1])) == [1]
  assert svm_classifier(np.array([[1, 1, 1, 1, 1]]), np.array([0])) == [0]
output: def svm_classifier(input_data, labels):
      from sklearn.svm import SVC
      clf = SVC()
      clf.fit(input_data, labels)
      predictions = clf.predict(input_data)
      return predictions.tolist()