input: 
  def train_iris_classifier(dataset_path: str) -> str:
      """ Trains a K-Nearest Neighbors classifier on the Iris dataset and prints the confusion matrix.
      >>> train_iris_classifier('iris.csv')
      [[11  0  0]
       [ 0 13  0]
       [ 0  0  6]]
      >>> train_iris_classifier('path_to_nonexistent_file.csv')
      FileNotFoundError: The file path does not exist.
      """
      try:
          # 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 str(cm)
      except FileNotFoundError:
          return 'FileNotFoundError: The file path does not exist.'
  
output:     
      except Exception as e:
          return f'Error: {str(e)}'
  