input: Write a function to evaluate the accuracy of a machine-learning model using the scikit-learn library. The function should take the model, the training data (X_train, y_train), and the testing data (X_test, y_test) as inputs. It should return the accuracy of the model on the test data.
  
  def evaluate_model_accuracy(model, X_train, y_train, X_test, y_test):
      from sklearn.metrics import accuracy_score
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      return accuracy_score(y_test, y_pred)
  
  assert evaluate_model_accuracy(clf, X_train, y_train, X_test, y_test) == 0.85
  assert evaluate_model_accuracy(clf, X_train, y_train, X_test, y_test) == 0.92
  assert evaluate_model_accuracy(clf, X_train, y_train, X_test, y_test) == 0.78
  assert evaluate_model_accuracy(clf, X_train, y_train, X_test, y_test) == 0.89
  assert evaluate_model_accuracy(clf, X_train, y_train, X_test, y_test) == 0.95
output: 
  def evaluate_model_accuracy(model, X_train, y_train, X_test, y_test):
      from sklearn.metrics import accuracy_score
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      return accuracy_score(y_test, y_pred)
  