code: def transform_weights(self, weights):
      max_val = np.max(weights)
      for i in range(len(weights)):
          if weights[i] < 0:
              weights[i] = 0
          else:
              if max_val == 0:
                  weights[i] = 0
              else:
                  weights[i] = weights[i] / max_val
      return weights
  
file_path: scikit-rebate/skrebate/iter.py
imports: from sklearn.base import BaseEstimator
  import copy
  import numpy
label_desc: Use image node_id 5 for calling the Iter obj's underlying member method code with example usage: obj.transform_weights(weights) and returns: weights