input: Write a function to implement a supervised learning classification algorithm using the iris dataset and output the confusion matrix from the test set predictions. The function should load the dataset, split it into training and test sets, preprocess the data, define a KNeighborsClassifier with 5 neighbors, fit the model to the training data, make predictions on the test data, and finally print the confusion matrix.
  assert classify_iris()==[[11  0  0]
                           [ 0 13  0]
                           [ 0  0  6]]
output: def classify_iris():
      from sklearn.datasets import load_iris
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
      from sklearn.neighbors import KNeighborsClassifier
      from sklearn.metrics import confusion_matrix
      
      iris = load_iris()
      X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3, random_state=42)
      
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
      
      knn = KNeighborsClassifier(n_neighbors=5)
      knn.fit(X_train, y_train)
      
      y_pred = knn.predict(X_test)
      return confusion_matrix(y_test, y_pred)