input: Write a function to generate a machine learning model in Python using scikit-learn for classifying digits in the MNIST dataset. The function should load the MNIST dataset, split it into features and labels, reshape the features, and train a Logistic Regression classifier. If the model training is successful, the function should return True, otherwise return False.
  assert mnist_classification() == True
output: def mnist_classification():
      from sklearn.datasets import fetch_openml
      from sklearn.model_selection import train_test_split
      from sklearn.linear_model import LogisticRegression
      from sklearn.preprocessing import StandardScaler
      from sklearn.metrics import accuracy_score
  
      mnist = fetch_openml('mnist_784', version=1)
      X, y = mnist["data"], mnist["target"]
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      model = LogisticRegression(max_iter=200)
      model.fit(X_train, y_train)
  
      y_pred = model.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
  
      return accuracy > 0.9