input: def classifyDigits(features, labels):
      """
      Given a set of features and corresponding labels from the MNIST dataset,
      train a Logistic Regression classifier and fit the model to the data.
      Print the shapes of the features and labels before training the model.
  
      >>> classifyDigits(mnist.data, mnist.target)
      (70000, 784) (70000,)
      """
      # Import the necessary libraries 
      from sklearn.linear_model import LogisticRegression
  
      # Reshape data to be valid
      features = features.reshape((features.shape[0], -1))
      print(features.shape, labels.shape)
  
      # Create a Logistic Regression classifier 
      clf = LogisticRegression()
  
      # Fit the model to the data 
      clf.fit(features, labels)
output:     # Print the shapes of the features and labels before training the model
      print(features.shape, labels.shape)