input: 
  def classification_model_iris() -> object:
      """ Implement a supervised learning algorithm for a classification task using the iris dataset.
      The model will use k-nearest neighbors (k=5) and return the fitted model.
      >>> clf = classification_model_iris()
      >>> clf.predict([[5.1, 3.5, 1.4, 0.2]])
      array(['Iris-setosa'], dtype=object)
      """
  
output:     from sklearn.datasets import load_iris
      from sklearn.model_selection import train_test_split
      from sklearn.neighbors import KNeighborsClassifier
      from sklearn.preprocessing import StandardScaler
  
      iris = load_iris()
      X = iris.data
      y = iris.target
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      clf = KNeighborsClassifier(n_neighbors=5)
      clf.fit(X_train, y_train)
  
      return clf