code: def run_tabular_benchmark_toy(fit_args):
      dataset = {
          "url": "https://autogluon.s3.amazonaws.com/datasets/toyClassification.zip",
          "name": "toyClassification",
          "problem_type": MULTICLASS,
          "label": "y",
          "performance_val": 0.436,
      }
      # 2-D toy noisy, imbalanced 4-class classification task with: feature missingness, out-of-vocabulary feature categories in test data, out-of-vocabulary labels in test data, training column missing from test data, extra distraction columns in test data
      # toyclassif_dataset should produce 1 warning and 1 error during inference:
      # Warning: Ignoring 181 (out of 1000) training examples for which the label value in column 'y' is missing
      # ValueError: Required columns are missing from the provided dataset. Missing columns: ['lostcolumn']
  
      # Additional warning that would have occurred if ValueError was not triggered:
      # UserWarning: These columns from this dataset were not present in the training dataset (AutoGluon will ignore them):  ['distractioncolumn1', 'distractioncolumn2']
  
      directory_prefix = "./datasets/"
      train_file = "train_data.csv"
      test_file = "test_data.csv"
      train_data, test_data = load_data(
          directory_prefix=directory_prefix,
          train_file=train_file,
          test_file=test_file,
          name=dataset["name"],
          url=dataset["url"],
      )
      print(f"Evaluating Benchmark Dataset {dataset['name']}")
      directory = directory_prefix + dataset["name"] + "/"
      savedir = directory + "AutogluonOutput/"
      shutil.rmtree(
          savedir, ignore_errors=True
      )  # Delete AutoGluon output directory to ensure previous runs' information has been removed.
      predictor = TabularPredictor(
          label=dataset["label"], path=savedir
      ).fit(train_data, **fit_args)
      assert (
          len(predictor._trainer._models_failed_to_train_errors.keys())
          == 0
      )
      print(predictor.feature_metadata)
      print(predictor.feature_metadata.type_map_raw)
      print(predictor.feature_metadata.type_group_map_special)
      try:
          predictor.predict(test_data)
      except (
          KeyError
      ):  # KeyError should be raised because test_data has missing column 'lostcolumn'
          pass
      else:
          raise AssertionError(
              f'{dataset["name"]} should raise an exception.'
          )
  
imports: import os
  import shutil
  import sys
  import tempfile
  import warnings
  from random import seed
  import numpy
  import pandas
  import pytest
  from networkx.exception import NetworkXError
  from autogluon.common import space
  from autogluon.common.utils.simulation_utils import convert_simulation_artifacts_to_tabular_predictions_dict
  from autogluon.core.constants import BINARY, MULTICLASS, PROBLEM_TYPES_CLASSIFICATION, QUANTILE, REGRESSION
  from autogluon.core.utils import download, unzip
  from autogluon.tabular import TabularDataset, TabularPredictor
  from autogluon.tabular.configs.hyperparameter_configs import get_hyperparameter_config
label_desc: Use image node_id 8 for calling a global function with example usage: run_tabular_benchmark_toy(fit_args) without return types