code: def test_lr_scheduler_set_params(
      self, classifier_module, classifier_data
  ):
      scheduler = LRScheduler(
          CyclicLR, base_lr=123, max_lr=999, step_every="batch"
      )
      net = NeuralNetClassifier(
          classifier_module,
          max_epochs=0,
          callbacks=[("scheduler", scheduler)],
      )
      net.set_params(callbacks__scheduler__base_lr=456)
      net.fit(*classifier_data)  # we need to trigger on_train_begin
      assert net.callbacks[0][1].lr_scheduler_.base_lrs[0] == 456
  
imports: from unittest.mock import Mock
  import numpy
  import pytest
  from sklearn.base import clone
  from torch.optim import SGD
  from torch.optim.lr_scheduler import CosineAnnealingLR
  from torch.optim.lr_scheduler import ExponentialLR
  from torch.optim.lr_scheduler import LambdaLR
  from torch.optim.lr_scheduler import MultiStepLR
  from torch.optim.lr_scheduler import ReduceLROnPlateau
  from torch.optim.lr_scheduler import StepLR
  from torch.optim.lr_scheduler import CyclicLR
  from skorch import NeuralNetClassifier
  from skorch.callbacks.lr_scheduler import WarmRestartLR, LRScheduler
label_desc: Use image node_id 8 for calling the TestLRCallbacks obj's underlying member method code with example usage: obj.test_lr_scheduler_set_params(classifier_module, classifier_data) without return types