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 dataset, split it into features and labels, reshape the features, create and fit a Logistic Regression classifier to the data.
  
  assert generate_mnist_model() is None
output: def generate_mnist_model():
      from sklearn.datasets import load_digits
      from sklearn.model_selection import train_test_split
      from sklearn.linear_model import LogisticRegression
      from sklearn.preprocessing import StandardScaler
  
      digits = load_digits()
      X = digits.data
      y = digits.target
  
      scaler = StandardScaler()
      X = scaler.fit_transform(X)
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      model = LogisticRegression(max_iter=1000)
      model.fit(X_train, y_train)
  
      return None