code: def test_cross_validate_classification(
      classification_data,
      model_config_class,
      continuous_cols,
      categorical_cols,
      cv,
      metric,
      return_oof,
      reset_datamodule,
  ):
      (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"
      if cv is None:
          cv_splits = 5
      elif isinstance(cv, int):
          cv_splits = cv
      else:
          cv_splits = cv.n_splits
      cv_scores, oof_predictions = _run_cv(
          model_config_class,
          model_config_params,
          data_config,
          train,
          metric,
          cv,
          return_oof,
          reset_datamodule,
      )
      assert len(cv_scores) == cv_splits
      if return_oof:
          assert len(oof_predictions) == cv_splits
  
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 13 for calling a global function with example usage: test_cross_validate_classification(classification_data, model_config_class, continuous_cols, categorical_cols, cv, metric, return_oof, reset_datamodule) without return types