input: Write a function to evaluate the accuracy of a machine-learning model using scikit-learn. The function should take the training data (X_train and y_train), the test data (X_test and y_test), and the model as input, fit the model on the training data, predict on the test data, and return the accuracy.
  assert evaluate_accuracy(X_train, y_train, X_test, y_test, clf)==0.95
  assert evaluate_accuracy(X_train_2, y_train_2, X_test_2, y_test_2, clf)==0.88
  assert evaluate_accuracy(X_train_3, y_train_3, X_test_3, y_test_3, clf)==0.75
  assert evaluate_accuracy(X_train_4, y_train_4, X_test_4, y_test_4, clf)==0.92
  assert evaluate_accuracy(X_train_5, y_train_5, X_test_5, y_test_5, clf)==0.80
output: def evaluate_accuracy(X_train, y_train, X_test, y_test, model):
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      return accuracy_score(y_test, y_pred)