input: Write a function to generate a machine learning model 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, and fit a Logistic Regression model to the data.
  assert mnist_classifier()==(70000, 784)
  assert mnist_classifier()[:5000].shape==(5000, 784)
output: def mnist_classifier():
      from sklearn.datasets import fetch_openml
      from sklearn.model_selection import train_test_split
      from sklearn.linear_model import LogisticRegression
      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)
      model = LogisticRegression(max_iter=1000)
      model.fit(X_train, y_train)
      return X.shape