input: Write a function to generate a machine learning model in Python using scikit-learn for classifying digits in the MNIST dataset. If the model is successfully generated and trained, return True; otherwise, return False. If the dataset is empty, return False.
  
  assert classify_mnist()==True
  assert classify_mnist()==False
  assert classify_mnist()==False
  assert classify_mnist()==False
  assert classify_mnist()==False
output: def classify_mnist():
      from sklearn.datasets import load_digits
      from sklearn.model_selection import train_test_split
      from sklearn.svm import SVC
      from sklearn.metrics import accuracy_score
  
      digits = load_digits()
      if len(digits.data) == 0:
          return False
  
      X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.2, random_state=42)
      model = SVC(gamma='scale')
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
  
      if accuracy_score(y_test, y_pred) > 0.9:
          return True
      else:
          return False