input: Write a function to generate a machine learning model using scikit-learn to classify digits in the MNIST dataset. The function should load the MNIST dataset, split it into features and labels, fit a Logistic Regression classifier to the data, and print the shape of the loaded data.
  from sklearn.datasets import fetch_openml
  import numpy as np
  from sklearn.linear_model import LogisticRegression
  
  def generate_mnist_classifier():
      # 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)
  
  assert generate_mnist_classifier() is None
  assert generate_mnist_classifier() is None
output: 
  def generate_mnist_classifier():
      mnist = fetch_openml('mnist_784')
      X = mnist.data
      y = mnist.target
      X = X.reshape((X.shape[0], -1))
      print(X.shape, y.shape)
      clf = LogisticRegression()
      clf.fit(X, y)
  