code: def test_suggestion_parameters_work(tmpdir):
      """Test that default skipping does not alter results in basic case."""
      seed_everything(1)
  
      class CustomBoringModel(BoringModel):
          def __init__(self, lr):
              super().__init__()
              self.lr = lr
  
          def configure_optimizers(self):
              return torch.optim.SGD(self.parameters(), lr=self.lr)
  
      # logger file to get meta
      model = CustomBoringModel(lr=1e-2)
      trainer = Trainer(default_root_dir=tmpdir, max_epochs=3)
      tuner = Tuner(trainer)
      lr_finder = tuner.lr_find(model)
      lr1 = lr_finder.suggestion(skip_begin=10)  # default
      lr2 = lr_finder.suggestion(
          skip_begin=70
      )  # way too high, should have an impact
  
      assert lr1 is not None
      assert lr2 is not None
      assert (
          lr1 != lr2
      ), "Skipping parameter did not influence learning rate"
  
imports: import logging
  import math
  import os
  from copy import deepcopy
  from typing import Any
  from unittest import mock
  import pytest
  import torch
  from lightning.pytorch import Trainer, seed_everything
  from lightning.pytorch.callbacks.lr_finder import LearningRateFinder
  from lightning.pytorch.demos.boring_classes import BoringModel
  from lightning.pytorch.tuner.lr_finder import _LRFinder
  from lightning.pytorch.tuner.tuning import Tuner
  from lightning.pytorch.utilities.exceptions import MisconfigurationException
  from lightning.pytorch.utilities.types import STEP_OUTPUT
  from lightning_utilities.test.warning import no_warning_call
  from tests_pytorch.helpers.datamodules import ClassifDataModule
  from tests_pytorch.helpers.runif import RunIf
  from tests_pytorch.helpers.simple_models import ClassificationModel
  from tests_pytorch.helpers.utils import getattr_recursive
label_desc: Use image node_id 9 for calling a global function with example usage: test_suggestion_parameters_work(tmpdir) and returns: torch