code: def test_configure_optimizers_with_adapt_lr(self, trainer):
      # Test SGD optimizer configuration with MultiStepLR scheduler. Return a configured SGD optimizer.
      trainer._adapt_lr = True
      optimizers = trainer.configure_optimizers()
      assert len(optimizers) == 2
      assert isinstance(optimizers, tuple)
      assert isinstance(optimizers[0], list)
      assert isinstance(optimizers[1], list)
      assert isinstance(optimizers[0][0], torch.optim.SGD)
      assert isinstance(
          optimizers[1][0], torch.optim.lr_scheduler.MultiStepLR
      )
  
imports: import pytest
  import torch
  import torch.nn
  from torch.utils.data import DataLoader, TensorDataset
  from kale.embed.image_cnn import SimpleCNNBuilder
  from kale.pipeline.base_nn_trainer import BaseNNTrainer, CNNTransformerTrainer, MultimodalNNTrainer
  from kale.predict.class_domain_nets import ClassNet
label_desc: Use image node_id 5 for calling the TestCNNTransformerTrainer obj's underlying member method code with example usage: obj.test_configure_optimizers_with_adapt_lr(trainer) without return types