input: 
  def train_mnist_classifier() -> None:
      """
      Loads the MNIST dataset, preprocesses it, and trains a Logistic Regression classifier 
      using scikit-learn. 
      Example:
      >>> train_mnist_classifier()
      """
      from sklearn.datasets import fetch_openml
      import numpy as np
      from sklearn.linear_model import LogisticRegression
  
      # Load the dataset
      mnist = fetch_openml('mnist_784')
  
      # Split into features and labels
      X = mnist.data
      y = mnist.target
  
      # Reshape data to be valid
      X = X.reshape((X.shape[0], -1))
      print(X.shape, y.shape)
  
      # Create a Logistic Regression classifier 
      clf = LogisticRegression()
  
      # Fit the model to the data 
      clf.fit(X, y)
  
output:     # Print the accuracy of the model on the training data
      accuracy = clf.score(X, y)
      print(f"Training accuracy: {accuracy * 100:.2f}%")