input: Write a function to evaluate the accuracy of a machine-learning model by fitting the model on training data and predicting on test data, then calculating the accuracy using sklearn's accuracy_score function. 
  assert evaluate_model(clf, X_train, y_train, X_test, y_test)==0.92
  assert evaluate_model(clf, X_train, y_train, X_test, y_test)==0.85
  assert evaluate_model(clf, X_train, y_train, X_test, y_test)==0.78
  assert evaluate_model(clf, X_train, y_train, X_test, y_test)==0.89
  assert evaluate_model(clf, X_train, y_train, X_test, y_test)==0.95
  assert evaluate_model(clf, [], [], [], [])==0.0
  assert evaluate_model(clf, X_train, y_train, X_test, y_test)==0.91
output: 
  def evaluate_model(clf, X_train, y_train, X_test, y_test):
      if not X_train or not X_test:
          return 0.0
      clf.fit(X_train, y_train)
      y_pred = clf.predict(X_test)
      return accuracy_score(y_test, y_pred)
  