code: def test_feature_importance(
      regression_data,
      model_config_class,
      continuous_cols,
      categorical_cols,
  ):
      (train, test, target) = regression_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"] = "regression"
      model_config = model_config_class(**model_config_params)
      trainer_config = TrainerConfig(
          max_epochs=3,
          checkpoints=None,
          early_stopping=None,
          accelerator="cpu",
          fast_dev_run=True,
      )
      optimizer_config = OptimizerConfig()
  
      tabular_model = TabularModel(
          data_config=data_config,
          model_config=model_config,
          optimizer_config=optimizer_config,
          trainer_config=trainer_config,
      )
      tabular_model.fit(
          train=train,
          validation=test,
      )
      feat_imp = tabular_model.feature_importance()
      assert len(feat_imp) == len(continuous_cols + categorical_cols)
  
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 4 for calling a global function with example usage: test_feature_importance(regression_data, model_config_class, continuous_cols, categorical_cols) without return types