code: def test_bandit_classifier_with_each_policy(policy):
      models = [
          linear_model.LogisticRegression(optimizer=optim.SGD(lr=lr))
          for lr in [0.0001, 0.001, 1e-05, 0.01]
      ]
  
      dataset = datasets.Phishing()
      model = (
          preprocessing.StandardScaler()
          | model_selection.BanditClassifier(
              models, metric=metrics.Accuracy(), policy=policy
          )
      )
      metric = metrics.Accuracy()
  
      score = evaluate.progressive_val_score(dataset, model, metric)
      assert score.get() > 0.5
  
file_path: river/river/model_selection/test_bandit.py
imports: from __future__ import annotations
  import importlib
  import inspect
  import pytest
  from river import bandit, datasets, evaluate, linear_model, metrics, model_selection, optim, preprocessing
label_desc: Use image node_id 2 for calling a global function with example usage: test_bandit_classifier_with_each_policy(policy) without return types