input: Write a python function to train and evaluate a k-nearest neighbors (KNN) classifier on the Iris dataset.
  assert isinstance(train_and_evaluate_knn(), float)
  assert 0 <= train_and_evaluate_knn() <= 1
output: def train_and_evaluate_knn():
      from sklearn.datasets import load_iris
      from sklearn.model_selection import train_test_split
      from sklearn.neighbors import KNeighborsClassifier
      from sklearn.metrics import accuracy_score
      
      # Load the Iris dataset
      iris = load_iris()
      X, y = iris.data, iris.target
      
      # Split the dataset into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
      
      # Initialize the KNN classifier
      knn = KNeighborsClassifier(n_neighbors=3)
      
      # Train the classifier on the training data
      knn.fit(X_train, y_train)
      
      # Make predictions on the testing data
      y_pred = knn.predict(X_test)
      
      # Evaluate the classifier
      accuracy = accuracy_score(y_test, y_pred)
      
      return accuracy