input: Write a function to evaluate the accuracy of a machine-learning model. The function should take an SVC classifier, training data (X_train, y_train), and testing data (X_test, y_test) as inputs, fit the model, predict on the test data, and return the accuracy score. If any input is missing, the function should return False.
  assert evaluate_model_accuracy(SVC(kernel='poly', degree=2, gamma=0.05), X_train, y_train, X_test, y_test) is not False
  assert evaluate_model_accuracy(None, X_train, y_train, X_test, y_test) == False
  assert evaluate_model_accuracy(SVC(kernel='poly', degree=2, gamma=0.05), X_train, y_train, X_test) == False
output: def evaluate_model_accuracy(model, X_train, y_train, X_test, y_test):
      if model is None or X_train is None or y_train is None or X_test is None or y_test is None:
          return False
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      return accuracy_score(y_test, y_pred)