input: Write a python function to train a K-Nearest Neighbors (KNN) classifier on the wine quality dataset and return the accuracy score.
  assert abs(train_and_evaluate_knn() - 0.554) < 0.01
output: def train_and_evaluate_knn():
      from sklearn.model_selection import train_test_split
      from sklearn.neighbors import KNeighborsClassifier
      from sklearn.metrics import accuracy_score
      from sklearn.datasets import load_wine
  
      # Load the wine quality dataset
      data = load_wine()
      X = data.data
      y = data.target
  
      # Split the dataset into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Initialize the KNN classifier
      knn = KNeighborsClassifier(n_neighbors=5)
  
      # Train the classifier
      knn.fit(X_train, y_train)
  
      # Predict the labels for the test set
      y_pred = knn.predict(X_test)
  
      # Calculate the accuracy score
      accuracy = accuracy_score(y_test, y_pred)
  
      return accuracy