input: def implement_supervised_learning(dataset_path: str) -> np.ndarray:
      """ Implement a supervised learning algorithm using the K-Nearest Neighbors classifier
      for a classification task using the iris dataset. The function will load the dataset
      from a given file path, preprocess the data, split it into training and test sets, 
      train the model, make predictions, and return the confusion matrix that evaluates 
      the model's performance.
      >>> implement_supervised_learning('iris.csv')
      array([[11,  0,  0],
             [ 0, 13,  0],
             [ 0,  0,  6]])
      """
output:     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 the dataset
      data = pd.read_csv(dataset_path)
  
      # Preprocess the data
      X = data.iloc[:, :-1].values
      y = data.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=0.3, 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
      knn = KNeighborsClassifier(n_neighbors=3)
      knn.fit(X_train, y_train)
  
      # Make predictions
      y_pred = knn.predict(X_test)
  
      # Return the confusion matrix
      return confusion_matrix(y_test, y_pred)