code: def test_metalearning_cs_size(self):
      self.cwd = os.getcwd()
      data_dir = os.path.dirname(__file__)
      data_dir = os.path.join(data_dir, "test_meta_base_data")
      os.chdir(data_dir)
  
      # Total: 176, categorical: 3, numerical: 7, string: 7
      total = 179
      num_numerical = 6
      num_string = 11
      num_categorical = 3
      for feat_type, cs_size in [
          ({"A": "numerical"}, total - num_string - num_categorical),
          ({"A": "categorical"}, total - num_string - num_numerical),
          ({"A": "string"}, total - num_categorical - num_numerical),
          ({"A": "numerical", "B": "categorical"}, total - num_string),
          ({"A": "numerical", "B": "string"}, total - num_categorical),
          ({"A": "categorical", "B": "string"}, total - num_numerical),
          (
              {"A": "categorical", "B": "string", "C": "numerical"},
              total,
          ),
      ]:
          pipeline = autosklearn.pipeline.classification.SimpleClassificationPipeline(
              feat_type=feat_type
          )
          self.cs = pipeline.get_hyperparameter_search_space(
              feat_type=feat_type
          )
  
          self.logger = logging.getLogger()
          meta_base = MetaBase(self.cs, data_dir, logger=self.logger)
          self.meta_optimizer = metalearner.MetaLearningOptimizer(
              "233", self.cs, meta_base, logger=self.logger
          )
          self.assertEqual(
              len(self.meta_optimizer.configuration_space),
              cs_size,
              feat_type,
          )
  
file_path: auto-sklearn/test/test_metalearning/pyMetaLearn/test_metalearning_configuration.py
imports: import logging
  import os
  import autosklearn.metalearning.optimizers.metalearn_optimizer.metalearner
  import autosklearn.pipeline.classification
  from autosklearn.metalearning.metalearning.meta_base import MetaBase
  import unittest
label_desc: Use image node_id 1 for calling the MetalearningConfiguration obj's underlying member method code with example usage: obj.test_metalearning_cs_size() without return types