input: from typing import List, Tuple
  
  
  def classify_iris(dataset_path: str) -> Tuple[np.ndarray, np.ndarray]:
      """ Classify the iris dataset using a K-Nearest Neighbors algorithm and return
      the confusion matrix and predicted labels for the test set. The dataset is split
      into 80% for training and 20% for testing. The data is standardized before
      training.
      >>> class_, cm = classify_iris('iris.csv')
      """
      
      # 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].values
  
      # 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 = 0)
  
      # Preprocess the data
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      # Define the model
      clf = KNeighborsClassifier(n_neighbors=5)
  
      # Fit the model to the training set
      clf.fit(X_train, y_train)
  
      # Make predictions on the test set
      y_pred = clf.predict(X_test)
  
      # Evaluate the model's performance
      cm = confusion_matrix(y_test, y_pred)
      
      return y_pred, cm
output:     return y_pred, cm