input: 
  from sklearn.datasets import fetch_openml
  import numpy as np
  from sklearn.linear_model import LogisticRegression
  
  def classify_mnist_digits() -> LogisticRegression:
      """ The function generates a machine learning model using scikit-learn's Logistic Regression
      for classifying digits in the MNIST dataset. 
      For example, if you run the function, it will return a fitted Logistic Regression classifier 
      ready for predictions.
      >>> classify_mnist_digits()
      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)
      
      return clf
  
output:     # Normalize the data
      X = X / 255.0
  
      # Split the data into training and testing sets
      X_train, X_test = X[:60000], X[60000:]
      y_train, y_test = y[:60000], y[60000:]
  
      # Train the classifier on the training data
      clf.fit(X_train, y_train)
  
      # Return the trained classifier
      return clf