input: Write a python function to find the most important features for a given dataset.
  assert find_important_features(np.array([[1, 2], [3, 4], [5, 6]]), np.array([1, 2, 3])) == [1, 0]
  assert find_important_features(np.array([[1, 0], [0, 1], [1, 1]]), np.array([0, 1, 1])) == [1, 0]
  assert find_important_features(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), np.array([1, 0, 1])) == [2, 1, 0]
output: def find_important_features(X, y):
      from sklearn.ensemble import RandomForestClassifier
      import numpy as np
      
      model = RandomForestClassifier(n_estimators=100, random_state=42)
      model.fit(X, y)
      
      importances = model.feature_importances_
      indices = np.argsort(importances)[::-1]
      
      return list(indices)