input: 
  def classify_mnist_digits(model_name: str = "LogisticRegression"):
      """ Generate a machine learning model using scikit-learn for classifying digits in the MNIST dataset.
      
      >>> classify_mnist_digits("LogisticRegression")
      (70000, 784) (70000,)
      """
      
      # Import the necessary libraries 
      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:     # Predict the labels for the training data
      y_pred = clf.predict(X)
  
      # Return the shapes of the features and labels for verification
      return X.shape, y.shape