code: def test_lr_schedulers_reduce_lr_on_plateau(
      tmpdir, scheduler_as_dict
  ):
      class TestModel(BoringModel):
          def __init__(self, scheduler_as_dict):
              super().__init__()
              self.scheduler_as_dict = scheduler_as_dict
              self.automatic_optimization = False
  
          def on_train_epoch_end(self):
              scheduler = self.lr_schedulers()
              scheduler.step(torch.tensor(0.0))
  
          def configure_optimizers(self):
              optimizer = torch.optim.SGD(self.parameters(), lr=0.1)
  
              if self.scheduler_as_dict:
                  scheduler = {
                      "scheduler": torch.optim.lr_scheduler.ReduceLROnPlateau(
                          optimizer
                      ),
                      "monitor": "train_loss",
                      "interval": "step",  # not warned
                  }
              else:
                  scheduler = (
                      torch.optim.lr_scheduler.ReduceLROnPlateau(
                          optimizer
                      )
                  )
  
              return [optimizer], [scheduler]
  
      model = TestModel(scheduler_as_dict=scheduler_as_dict)
  
      trainer = Trainer(
          default_root_dir=tmpdir,
          max_epochs=1,
          limit_train_batches=1,
          limit_val_batches=1,
          limit_test_batches=1,
      )
  
      if scheduler_as_dict:
          with pytest.warns(
              RuntimeWarning,
              match=r"\['monitor'\], but the keys will be ignored",
          ):
              trainer.fit(model)
          assert trainer.lr_scheduler_configs[0].interval == "step"
      else:
          trainer.fit(model)
  
imports: import collections
  import contextlib
  from copy import deepcopy
  from unittest import mock
  from unittest.mock import ANY, call, patch
  import pytest
  import torch
  import torch.distributed
  import torch.nn.functional
  from lightning.fabric.utilities.exceptions import MisconfigurationException
  from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_0
  from lightning.pytorch import Trainer, seed_everything
  from lightning.pytorch.demos.boring_classes import BoringModel, ManualOptimBoringModel
  from lightning.pytorch.strategies import Strategy
  from tests_pytorch.helpers.runif import RunIf
label_desc: Use image node_id 16 for calling a global function with example usage: test_lr_schedulers_reduce_lr_on_plateau(tmpdir, scheduler_as_dict) and returns: list,  list