class_docstr: The e2e test for hparam tuning using Tuner API.
class_docstr_tok: ["The","e2e","test","for","hparam","tuning","using","Tuner","API","."]
code: def test_tuner_run_config_override(self):
      trainer = DummyTrainer(run_config=RunConfig(stop={"metric": 4}))
      tuner = Tuner(trainer)
  
      assert tuner._local_tuner._run_config.stop == {"metric": 4}
  
imports: import os
  from pathlib import Path
  from unittest.mock import patch
  import pytest
  import unittest
  from typing import Optional
  from sklearn.datasets import load_breast_cancer
  from sklearn.utils import shuffle
  import ray
  from ray import train, tune
  from ray.train import CheckpointConfig, RunConfig, ScalingConfig
  from ray.train.constants import RAY_CHDIR_TO_TRIAL_DIR
  from ray.train.examples.pytorch.torch_linear_example import train_func
  from ray.data import Dataset, Datasource, ReadTask, from_pandas, read_datasource
  from ray.data.block import BlockMetadata
  from ray.train.data_parallel_trainer import DataParallelTrainer
  from ray.train.torch import TorchTrainer
  from ray.train.trainer import BaseTrainer
  from ray.train.xgboost import XGBoostTrainer
  from ray.tune import Callback, CLIReporter
  from ray.tune.tune_config import TuneConfig
  from ray.tune.tuner import Tuner
label_desc: Use image node_id 7 for calling the TunerTest obj's underlying member method code with example usage: obj.test_tuner_run_config_override() without return types