input: Write a function to generate a machine learning model for classifying digits in the MNIST dataset using scikit-learn. Return the fitted Logistic Regression classifier.
  
  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))
      
      # Create a Logistic Regression classifier
      clf = LogisticRegression()
      
      # Fit the model to the data
      clf.fit(X, y)
      
      return clf
  
  assert isinstance(mnist_digit_classifier(), LogisticRegression) == True
output: def mnist_digit_classifier():
      from sklearn.datasets import fetch_openml
      import numpy as np
      from sklearn.linear_model import LogisticRegression
      
      mnist = fetch_openml('mnist_784')
      X = mnist.data
      y = mnist.target
      X = X.reshape((X.shape[0], -1))
      clf = LogisticRegression()
      clf.fit(X, y)
      return clf