input: Write a function named `train_iris_classifier` that implements a supervised learning algorithm in Python for a classification task using the iris dataset. The function should load the iris dataset, split it into training and test sets, preprocess the data, train a KNeighborsClassifier model, and evaluate its performance using a confusion matrix. If all steps are executed correctly, the function should print the confusion matrix for the test set predictions.
  assert train_iris_classifier()
output: def train_iris_classifier():
      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)
      cm = confusion_matrix(y_test, y_pred)
      print(cm)