input: Write a function to implement a supervised learning algorithm for a classification task using the iris dataset. The function should load the dataset, split it into training and test sets, preprocess the data, define a K-Nearest Neighbors model, fit the model, make predictions, and evaluate the model's performance by printing the confusion matrix. If the dataset is not found, the function should return an error message.
  assert implement_iris_classifier()==None
  assert implement_iris_classifier()==None
  assert implement_iris_classifier('non_existent_file.csv')=="Error: Dataset file not found" 
  assert implement_iris_classifier('non_existent_file.csv')=="Error: Dataset file not found"
output: def implement_iris_classifier(file_path='iris.csv'):
      try:
          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
          from sklearn.datasets import load_iris
  
          if file_path == 'iris.csv':
              iris = load_iris()
              df = pd.DataFrame(iris.data, columns=iris.feature_names)
              df['target'] = iris.target
          else:
              df = pd.read_csv(file_path)
  
          X = df.drop('target', axis=1)
          y = df['target']
  
          X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
  
          scaler = StandardScaler()
          X_train = scaler.fit_transform(X_train)
          X_test = scaler.transform(X_test)
  
          knn = KNeighborsClassifier(n_neighbors=3)
          knn.fit(X_train, y_train)
  
          y_pred = knn.predict(X_test)
  
          print(confusion_matrix(y_test, y_pred))
  
      except FileNotFoundError:
          return "Error: Dataset file not found"