input: Write a function to implement a supervised learning algorithm for classification using the iris dataset. The function should load the dataset, preprocess the data, split it into training and test sets, train a KNeighborsClassifier, make predictions on the test set, and finally print the confusion matrix.
  assert implement_iris_classification()
  # Confusion matrix output should be printed as result of the function call.
output: def implement_iris_classification():
      from sklearn.datasets import load_iris
      from sklearn.model_selection import train_test_split
      from sklearn.neighbors import KNeighborsClassifier
      from sklearn.metrics import confusion_matrix
  
      iris = load_iris()
      X = iris.data
      y = iris.target
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
  
      knn = KNeighborsClassifier(n_neighbors=3)
      knn.fit(X_train, y_train)
  
      y_pred = knn.predict(X_test)
  
      print(confusion_matrix(y_test, y_pred))