code: def test_sample_weight():
      dataset = {
          "url": "https://autogluon.s3.amazonaws.com/datasets/toyRegression.zip",
          "name": "toyRegression",
          "problem_type": REGRESSION,
          "label": "y",
          "performance_val": 0.183,
      }
      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.
      sample_weight = "sample_weights"
      weights = np.abs(
          np.random.rand(
              len(train_data),
          )
      )
      test_weights = np.abs(
          np.random.rand(
              len(test_data),
          )
      )
      train_data[sample_weight] = weights
      test_data_weighted = test_data.copy()
      test_data_weighted[sample_weight] = test_weights
      fit_args = {"time_limit": 20}
      predictor = TabularPredictor(
          label=dataset["label"],
          path=savedir,
          problem_type=dataset["problem_type"],
          sample_weight=sample_weight,
      ).fit(train_data, **fit_args)
      ldr = predictor.leaderboard(test_data)
      perf = predictor.evaluate(test_data)
      # Run again with weight_evaluation:
      # FIXME: RMSE doesn't support sample_weight, this entire call doesn't make sense
      predictor = TabularPredictor(
          label=dataset["label"],
          path=savedir,
          problem_type=dataset["problem_type"],
          sample_weight=sample_weight,
          weight_evaluation=True,
      ).fit(train_data, **fit_args)
      # perf = predictor.evaluate(test_data_weighted)  # TODO: Doesn't work without implementing sample_weight in evaluate
      predictor.distill(time_limit=10)
      ldr = predictor.leaderboard(test_data_weighted)
  
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 18 for calling a global function with example usage: test_sample_weight() without return types