input: Write a function to generate a machine learning model using scikit-learn for classifying digits in the MNIST dataset. The function should load the MNIST dataset, preprocess it, and fit a Logistic Regression model to the data. After fitting the model, it should return the trained model.
  
  def build_mnist_classifier():
      # 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))
  
      # Create a Logistic Regression classifier 
      clf = LogisticRegression()
  
      # Fit the model to the data 
      clf.fit(X, y)
  
      return clf
  
  assert build_mnist_classifier() is not None
  assert isinstance(build_mnist_classifier(), LogisticRegression)
output: def build_mnist_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