code: def test_predict_new_dictionary_key(self):
      # Arrange
      new_data = copy.copy(self.data)
      new_data["dict_color_feature"] = new_data["cap-color"].apply(
          lambda x: {"cap-color": ord(x)}
      )
      train, test = new_data.random_split(0.8, seed=1)
  
      # add a new key to dictionary in predict time
      test["dict_color_feature"] = test["dict_color_feature"].apply(
          lambda x: dict(
              list(x.items())
              + list({"cap-color2": x["cap-color"] + 1}.items())
          )
      )
  
      model = tc.random_forest_classifier.create(
          train, target="label", **self.param
      )
      # Act.
      y1 = model.predict(test)
      y2 = model.predict(list(test))
  
      # Assert
      self.assertEqual(list(y1), list(y2))
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import unittest
  import turicreate
  import math
  import uuid
  import random
  import sys
  import copy
  from turicreate.toolkits import evaluation
  from turicreate.toolkits._main import ToolkitError
  import shutil
  import numpy
  from array import array
  import os
label_desc: Use image node_id 16 for calling the RandomForestClassifierTest obj's underlying member method code with example usage: obj.test_predict_new_dictionary_key() without return types