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 import necessary libraries, load the dataset, split it into features and labels, create a Logistic Regression classifier, and fit the model to the data. If the dataset is loaded and processed successfully, the function should print the shape of the features and labels.
  assert generate_mnist_classifier()==None
  assert generate_mnist_classifier()==None
output: def generate_mnist_classifier():
      from sklearn.datasets import load_digits
      from sklearn.model_selection import train_test_split
      from sklearn.linear_model import LogisticRegression
      
      digits = load_digits()
      X = digits.data
      y = digits.target
      
      print(X.shape)
      print(y.shape)
      
      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)