code: def test_bagging_classification(
      classification_data,
      model_config_class,
      continuous_cols,
      categorical_cols,
      cv,
      aggregate,
  ):
      (train, test, target) = classification_data
      model_config_class, model_config_params = model_config_class
      data_config = DataConfig(
          target=target,
          continuous_cols=continuous_cols,
          categorical_cols=categorical_cols,
          handle_missing_values=True,
          handle_unknown_categories=True,
      )
  
      model_config_params["task"] = "classification"
      pred_df = _run_bagging(
          model_config_class,
          model_config_params,
          data_config,
          train,
          test,
          cv,
          aggregate,
      )
      assert len(pred_df) == len(test)
      assert (
          len(
              set(pred_df["prediction"].values.tolist())
              - set(test[target[0]].values.tolist())
          )
          == 0
      )
  
imports: import copy
  import os
  import numpy
  import pytest
  import torch
  from pytorch_tabular import TabularModel, TabularModelTuner, model_sweep
  from pytorch_tabular.config import DataConfig, OptimizerConfig, TrainerConfig
  from pytorch_tabular.config.config import SSLModelConfig
  from pytorch_tabular.feature_extractor import DeepFeatureExtractor
  from pytorch_tabular.models import AutoIntConfig, CategoryEmbeddingModelConfig, FTTransformerConfig, GANDALFConfig, GatedAdditiveTreeEnsembleConfig, NodeConfig, TabNetModelConfig
  from pytorch_tabular.ssl_models import DenoisingAutoEncoderConfig
  from scipy.stats import randint, uniform
  from sklearn.metrics import accuracy_score, r2_score
  from sklearn.model_selection import KFold
label_desc: Use image node_id 16 for calling a global function with example usage: test_bagging_classification(classification_data, model_config_class, continuous_cols, categorical_cols, cv, aggregate) without return types