input: Write a python function to create and train a K-Nearest Neighbors classifier using the MNIST dataset.
  assert model_score() > 0.95
output: def create_and_train_knn():
      from sklearn.datasets import fetch_openml
      from sklearn.model_selection import train_test_split
      from sklearn.neighbors import KNeighborsClassifier
      from sklearn.preprocessing import StandardScaler
      
      # Load MNIST dataset
      mnist = fetch_openml('mnist_784', version=1)
      X, y = mnist["data"], mnist["target"]
      
      # Split data into training and test sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      # Standardize the features
      scaler = StandardScaler()
      X_train_scaled = scaler.fit_transform(X_train)
      X_test_scaled = scaler.transform(X_test)
      
      # Create and train K-Nearest Neighbors classifier
      knn = KNeighborsClassifier(n_neighbors=3)
      knn.fit(X_train_scaled, y_train)
      
      return knn
  
  def model_score():
      knn = create_and_train_knn()
      from sklearn.datasets import fetch_openml
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
      
      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)
      
      scaler = StandardScaler()
      X_test_scaled = scaler.fit_transform(X_test)
      
      return knn.score(X_test_scaled, y_test)