input: from typing import List
  
  def classify_mnist_digits(data: List[float], labels: List[float]) -> LogisticRegression:
      """ Given the MNIST dataset features and labels, create and train a Logistic Regression model.
      >>> classify_mnist_digits([1.0, 2.0, 3.0], [0, 1, 2])
      LogisticRegression(max_iter=100)
      >>> classify_mnist_digits([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], [5, 6, 7, 8, 9, 0])
      LogisticRegression(max_iter=100)
      """
output:     from sklearn.linear_model import LogisticRegression
      from sklearn.model_selection import train_test_split
  
      X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.2, random_state=42)
      model = LogisticRegression(max_iter=100)
      model.fit(X_train, y_train)
      return model