input: Write a function to evaluate the accuracy of a machine-learning model using the sklearn library in Python. The function should take four parameters: X_train (training data), y_train (target values for training data), X_test (testing data), and y_test (target values for testing data). The function should return the accuracy score. If any of the inputs are None or empty, the function should return None.
  
  assert evaluate_model_accuracy([[1, 2], [2, 3]], [0, 1], [[3, 4]], [1])==1.0
  assert evaluate_model_accuracy([[1, 2], [2, 3]], [0, 1], [], [])==None
  assert evaluate_model_accuracy(None, [0, 1], [[3, 4]], [1])==None
  assert evaluate_model_accuracy([[1, 2], [2, 3]], None, [[3, 4]], [1])==None
  assert evaluate_model_accuracy([[1, 2], [2, 3]], [0, 1], [[3, 4]], None)==None
  assert evaluate_model_accuracy([], [], [], [])==None
output: def evaluate_model_accuracy(X_train, y_train, X_test, y_test):
      if X_train is None or y_train is None or X_test is None or y_test is None:
          return None
      if len(X_train) == 0 or len(y_train) == 0 or len(X_test) == 0 or len(y_test) == 0:
          return None
      from sklearn.linear_model import LogisticRegression
      from sklearn.metrics import accuracy_score
      model = LogisticRegression()
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      return accuracy_score(y_test, y_pred)