code: def test_train_classifier(selenium_standalone_micropip):
      from pytest_pyodide import run_in_pyodide
  
      @run_in_pyodide(
          packages=[
              "xgboost",
              "pandas",
              "numpy",
              "scipy",
              "scikit-learn",
              "lightgbm",
          ]
      )
      async def run(selenium, test, train_data, test_data):
          expected_score_range = test["expected_score_range"]
          expected_score_range = {
              k: (v[0], 0.02)
              for k, v in expected_score_range.items()
              if k
              not in [
                  "CatBoost",
                  "NeuralNetFastAI",
                  "NeuralNetTorch",
              ]
          }
  
          from autogluon.tabular import TabularPredictor
  
          print(train_data.head())
          label = "label"
          print(
              "Summary of class variable: \n",
              train_data[label].describe(),
          )
          save_path = "agModels-predictClass"  # specifies folder to store trained models
          predictor = TabularPredictor(label=label, path=save_path).fit(
              train_data
          )
  
          y_test = test_data[label]  # values to predict
          test_data_nolab = test_data.drop(
              columns=[label]
          )  # delete label column to prove we're not cheating
          test_data_nolab.head()
          predictor = TabularPredictor.load(save_path)
  
          y_pred = predictor.predict(test_data_nolab)
          print("Predictions:  \n", y_pred)
          perf = predictor.evaluate_predictions(
              y_true=y_test, y_pred=y_pred, auxiliary_metrics=True
          )
          leaderboard = predictor.leaderboard(test_data)
          print(leaderboard[["model", "score_test", "score_val"]])
          trained_models = leaderboard[leaderboard.columns[0]]
          print(trained_models)
          assert set(trained_models) == set(
              expected_score_range.keys()
          ), "Not all models were trained"
          for model in leaderboard["model"]:
              score = leaderboard[leaderboard["model"] == model][
                  "score_test"
              ].values[0]
              score_range = expected_score_range[model]
              assert (
                  score_range[0] - score_range[1]
                  <= score
                  <= score_range[0] + score_range[1]
              ), f"Score for {model} ({score}) is out of expected range {score_range}"
  
      from .utils import make_dataset, tests
  
      test_case = tests[-1]
      data_train, data_test = make_dataset(request=test_case, seed=0)
      run(
          selenium_standalone_micropip, test_case, data_train, data_test
      )
  
imports: from pathlib import Path
  import pytest
label_desc: Use image node_id 2 for calling a global function with example usage: test_train_classifier(selenium_standalone_micropip) without return types