input: def implementation_supervised_learning(dataset_path: str) -> np.ndarray:
      """ Implement a supervised learning algorithm in Python for a classification task using the iris dataset.
      Given the path to the iris dataset, the function loads the data, preprocesses it, trains a K-Nearest Neighbors 
      classifier, and then evaluates and returns the confusion matrix.
  
      >>> implementation_supervised_learning('iris.csv')
      [[11  0  0]
       [ 0 13  0]
       [ 0  1 13]]
      """
output:     
      import numpy as np
      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
  
      # Load the dataset
      data = load_iris()
      X = data.data
      y = data.target
  
      # Split the dataset into training and testing 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 a K-Nearest Neighbors classifier
      knn = KNeighborsClassifier(n_neighbors=3)
      knn.fit(X_train, y_train)
  
      # Predict the labels for the test set
      y_pred = knn.predict(X_test)
  
      # Compute the confusion matrix
      cm = confusion_matrix(y_test, y_pred)
  
      return cm
  