code: def indices2one_hot(indices, nb_classes):
      
      if nb_classes < 1:
          raise ValueError('nb_classes={}, but positive number expected'
                           .format(nb_classes))
  
      one_hot = []
      for index in indices:
          one_hot.append([0] * nb_classes)
          one_hot[-1][index] = 1
      return one_hot
docs: Convert an iterable of indices to one-hot encoded list.
  
      You might also be interested in sklearn.preprocessing.OneHotEncoder
  
      Parameters
      ----------
      indices : iterable
          iterable of indices
      nb_classes : int
          Number of classes
      dtype : type
  
      Returns
      -------
      one_hot : list
  
      Examples
      --------
      >>> indices2one_hot([0, 1, 1], 3)
      [[1, 0, 0], [0, 1, 0], [0, 1, 0]]
      >>> indices2one_hot([0, 1, 1], 2)
      [[1, 0], [0, 1], [0, 1]]