input: Write a function to generate a machine learning model in Python using scikit-learn for classifying digits in the MNIST dataset. The function should load the MNIST dataset, split it into features and labels, reshape the data, create a Logistic Regression classifier, and fit the model to the data. Return the fitted model.
  
  assert generate_mnist_model().classes_.shape[0] == 10
  assert generate_mnist_model().coef_.shape == (10, 784)
output: def generate_mnist_model():
      from sklearn.datasets import fetch_openml
      from sklearn.model_selection import train_test_split
      from sklearn.linear_model import LogisticRegression
      import numpy as np
      
      mnist = fetch_openml('mnist_784', version=1)
      X, y = mnist["data"], mnist["target"]
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      X_train = X_train.astype('float32') / 255
      X_test = X_test.astype('float32') / 255
      
      model = LogisticRegression(max_iter=1000)
      model.fit(X_train, y_train)
      
      return model