input: Write a function to implement a supervised learning algorithm in Python for a classification task using the iris dataset. The function should load the dataset, split it into training and test sets, preprocess the data, define a KNeighborsClassifier model, fit the model to the training set, make predictions on the test set, and evaluate the model's performance by printing the confusion matrix.
  assert type(implement_iris_classification())==np.ndarray
  assert implement_iris_classification().shape==(3, 3)
  assert np.sum(implement_iris_classification())==30
output: def implement_iris_classification():
      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=3)
      knn.fit(X_train, y_train)
  
      y_pred = knn.predict(X_test)
      conf_matrix = confusion_matrix(y_test, y_pred)
  
      return conf_matrix