input: Write a python function to classify iris flowers.
  assert classify_iris(np.array([[5.1, 3.5, 1.4, 0.2]])) == np.array(['setosa'])
  assert classify_iris(np.array([[6.7, 3.0, 5.2, 2.3]])) == np.array(['virginica'])
  assert classify_iris(np.array([[5.9, 3.0, 4.2, 1.5]])) == np.array(['versicolor'])
output: def classify_iris(features):
      from sklearn.datasets import load_iris
      from sklearn.ensemble import RandomForestClassifier
      import numpy as np
  
      # Load Iris dataset
      iris = load_iris()
      X, y = iris.data, iris.target
      target_names = iris.target_names
  
      # Train a RandomForestClassifier
      clf = RandomForestClassifier(n_estimators=100, random_state=42)
      clf.fit(X, y)
  
      # Predict the class of the given features
      predictions = clf.predict(features)
  
      # Map numeric predictions to iris target names
      return target_names[predictions]