code: def get_benchmark_sets():
      # Information about each dataset in benchmark is stored in dict.
      # performance_val = expected performance on this dataset (lower = better),should update based on previously run benchmarks
      binary_dataset = {
          "url": "https://autogluon.s3.amazonaws.com/datasets/AdultIncomeBinaryClassification.zip",
          "name": "AdultIncomeBinaryClassification",
          "problem_type": BINARY,
          "label": "class",
          "performance_val": 0.129,
      }  # Mixed types of features.
  
      multi_dataset = {
          "url": "https://autogluon.s3.amazonaws.com/datasets/CoverTypeMulticlassClassification.zip",
          "name": "CoverTypeMulticlassClassification",
          "problem_type": MULTICLASS,
          "label": "Cover_Type",
          "performance_val": 0.032,
      }  # big dataset with 7 classes, all features are numeric. Runs SLOW.
  
      regression_dataset = {
          "url": "https://autogluon.s3.amazonaws.com/datasets/AmesHousingPriceRegression.zip",
          "name": "AmesHousingPriceRegression",
          "problem_type": REGRESSION,
          "label": "SalePrice",
          "performance_val": 0.076,
      }  # Regression with mixed feature-types, skewed Y-values.
  
      toyregres_dataset = {
          "url": "https://autogluon.s3.amazonaws.com/datasets/toyRegression.zip",
          "name": "toyRegression",
          "problem_type": REGRESSION,
          "label": "y",
          "performance_val": 0.183,
      }
      # 1-D toy deterministic regression task with: heavy label+feature missingness, extra distraction column in test data
  
      # List containing dicts for each dataset to include in benchmark (try to order based on runtimes)
      return [
          toyregres_dataset,
          binary_dataset,
          regression_dataset,
          multi_dataset,
      ]
  
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 9 for calling a global function with example usage: get_benchmark_sets() and returns: list