input: Write a function to evaluate the accuracy of a machine-learning model using the accuracy_score from scikit-learn. The function should fit the model on training data, predict on test data, and then calculate and return the accuracy.
  
  
  assert evaluate_model_accuracy(clf, X_train, y_train, X_test, y_test) == 0.95
  assert evaluate_model_accuracy(clf, X_train, y_train, X_test, y_test) == 0.88
  assert evaluate_model_accuracy(clf, X_train, y_train, X_test, y_test) == 0.76
  assert evaluate_model_accuracy(clf, X_train, y_train, X_test, y_test) == 0.67
  assert evaluate_model_accuracy(clf, X_train, y_train, X_test, y_test) == 0.54
  
output: def evaluate_model_accuracy(model, X_train, y_train, X_test, y_test):
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      return accuracy_score(y_test, y_pred)