input: Write a python function to classify a new iris flower using K-Nearest Neighbors and return the predicted class name.
  assert classify_iris_flower([5.2, 3.5, 1.4, 0.2]) == 'Setosa'
  assert classify_iris_flower([6.1, 2.8, 4.7, 1.2]) == 'Versicolor'
  assert classify_iris_flower([7.7, 2.6, 6.9, 2.3]) == 'Virginica'
output: def classify_iris_flower(features):
      from sklearn.datasets import load_iris
      from sklearn.neighbors import KNeighborsClassifier
  
      # Load iris dataset
      iris = load_iris()
      X, y = iris.data, iris.target
      target_names = iris.target_names
  
      # Initialize KNN classifier
      knn = KNeighborsClassifier(n_neighbors=3)
  
      # Fit the model
      knn.fit(X, y)
  
      # Predict the class for the new flower
      prediction = knn.predict([features])
  
      # Return the predicted class name
      return target_names[prediction[0]]