class_docstr: Test class for third set of voting learner experiment tests.
code: def test_predict_task(self):
      # test various combinations of experiment configuration options
      option_names = [
          "with_soft_voting",
          "with_model_kwargs_list",
          "with_grid_search",
          "with_existing_model",
          "with_prediction_prefix",
          "with_individual_predictions",
      ]
  
      for option_values in product(
          ["classifier", "regressor"],
          [False, True],
          [False, True],
          [False, True],
          [False, True],
          [False, True],
          [False, True],
      ):
          # assign the learner type separately
          learner_type = option_values[0]
  
          # create a dictionary for all the other options; we are using
          # a dictionary class that returns `False` for non-existent keys
          options_dict = BoolDict(zip(option_names, option_values[1:]))
  
          # voting regressors do not support soft voting
          if (
              learner_type == "regressor"
              and options_dict["with_soft_voting"]
          ):
              continue
  
          # if we are using an existing model, there will be no grid search
          if (
              options_dict["with_existing_model"]
              and options_dict["with_grid_search"]
          ):
              continue
  
          yield self.check_predict_task, learner_type, options_dict
  
file_path: skll/tests/test_voting_learners_expts_3.py
imports: import unittest
  from itertools import product
  from pathlib import Path
  from unittest.mock import DEFAULT, patch
  from skll.data import FeatureSet
  from skll.experiments import run_configuration
  from skll.learner.voting import VotingLearner
  from skll.utils.testing import BoolDict, config_dir, create_jsonlines_feature_files, fill_in_config_options_for_voting_learners, other_dir, output_dir, remove_jsonlines_feature_files, test_dir, train_dir
label_desc: Use image node_id 4 for calling the TestVotingLearnersExptsThree obj's underlying member method code with example usage: obj.test_predict_task() without return types