input: 
  def iris_classification(dataset_path: str) -> None:
      """ Implements a supervised learning algorithm for a classification task using the iris dataset.
      The function loads the dataset, preprocesses it, splits it into training and test sets, 
      trains a K-Nearest Neighbors classifier, makes predictions on the test set, 
      and prints the confusion matrix.
      >>> iris_classification('iris.csv')
      [[11  0  0]
       [ 0 13  0]
       [ 0  0  6]]
      """
      # Import necessary libraries
      import numpy as np
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
      from sklearn.neighbors import KNeighborsClassifier
      from sklearn.metrics import confusion_matrix
  
      # Load and explore the data
      dataset = pd.read_csv(dataset_path)
      X = dataset.iloc[:, :-1].values
      y = dataset.iloc[:, -1].va
output:     lues
  
      # Split the dataset into training and test sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Standardize the features
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      # Train the K-Nearest Neighbors classifier
      classifier = KNeighborsClassifier(n_neighbors=3)
      classifier.fit(X_train, y_train)
  
      # Make predictions on the test set
      y_pred = classifier.predict(X_test)
  
      # Print the confusion matrix
      print(confusion_matrix(y_test, y_pred))