input: 
  def mnist_digit_classifier():
      """
      Generates a machine learning model in Python using scikit-learn for classifying digits in the MNIST dataset.
      This function loads the MNIST dataset, splits it into features and labels, reshapes the data, 
      and then fits a Logistic Regression model to the data.
      
      >>> mnist_digit_classifier()
      (70000, 784) (70000,)
      """
      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:     return clf