input: Write a python function to train an SVM classifier using a linear kernel and fit it to the given data.
  assert train_svm_classifier(np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16], [17, 18, 19, 20]]), np.array([1, 2, 1, 2, 1])).kernel == 'linear'
  assert train_svm_classifier(np.array([[1, 0, 0, 1], [0, 1, 1, 0], [1, 1, 0, 0], [0, 0, 1, 1]]), np.array([0, 1, 0, 1])).random_state == 0
output: def train_svm_classifier(X, y):
      from sklearn.svm import SVC
      clf = SVC(kernel='linear', random_state=0)
      clf.fit(X, y)
      return clf