code: def test_AutoGluonModelQuickFit(save_model_to_state):
      df_train = pd.read_csv(
          os.path.join(RESOURCE_PATH, "adult", "train_data.csv")
      ).sample(100, random_state=0)
      target_col = "class"
  
      with tempfile.TemporaryDirectory() as path:
          state = auto.analyze(
              train_data=df_train,
              label=target_col,
              return_state=True,
              anlz_facets=[
                  eda.dataset.ProblemTypeControl(),
                  eda.dataset.TrainValidationSplit(
                      children=[
                          eda.model.AutoGluonModelQuickFit(
                              save_model_to_state=save_model_to_state,
                              estimator_args=dict(path=path),
                              verbosity=0,
                              hyperparameters={
                                  "RF": {
                                      "criterion": "entropy",
                                      "max_depth": 15,
                                      "ag_args": {
                                          "name_suffix": "Entr",
                                          "problem_types": [
                                              "binary",
                                              "multiclass",
                                          ],
                                      },
                                  }
                              },
                              children=[
                                  eda.model.AutoGluonModelEvaluator()
                              ],
                          )
                      ]
                  ),
              ],
          )
  
      assert state.model_evaluation.problem_type == "binary"
      assert len(state.model_evaluation.y_true_val) == int(
          len(df_train) * 0.3
      )
      assert len(state.model_evaluation.y_pred_val) == int(
          len(df_train) * 0.3
      )
      expected = [c for c in df_train.columns if c not in ["class"]]
      assert sorted(
          state.model_evaluation.importance.index.to_list()
      ) == sorted(expected)
      _assert_importance_is_present(state)
      if save_model_to_state:
          assert (
              str(state.model.__class__)
              == "<class 'autogluon.tabular.predictor.predictor.TabularPredictor'>"
          )
      else:
          assert "model" not in state
  
file_path: autogluon/eda/tests/unittests/analysis/test_model.py
imports: import os.path
  import tempfile
  import pandas
  import pytest
  import autogluon.eda.analysis
  import autogluon.eda.auto
  from autogluon.core.constants import REGRESSION
  from autogluon.tabular import TabularPredictor
label_desc: Use image node_id 5 for calling a global function with example usage: test_AutoGluonModelQuickFit(save_model_to_state) without return types