input: Write a function to generate a machine learning model in Python using the scikit-learn library for classifying digits in the MNIST dataset. The function should include loading the dataset, splitting it into features and labels, reshaping the data, creating and fitting a Logistic Regression model, and print the shapes of the features and labels.
  
  def mnist_digit_classifier():
      from sklearn.datasets import fetch_openml
      import numpy as np
      from sklearn.linear_model import LogisticRegression
      import joblib
      
      # 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 'Model training complete'
  
  assert mnist_digit_classifier()=='Model training complete'
output: def mnist_digit_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)
      
      return 'Model training complete'