code: def test_configure_optimizers_with_sgd_cosine_annealing(
      self, trainer
  ):
      # Test SGD optimizer configuration with CosineAnnealingLR scheduler. Return a configured SGD optimizer.
      trainer.optimizers = ClassNet()
      trainer._adapt_lr = True
      trainer._optimizer_params = {
          "type": "SGD",
          "optim_params": {"momentum": 0.2, "weight_decay": 0.3},
      }
      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.CosineAnnealingLR
      )
  
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 7 for calling the TestBaseTrainer obj's underlying member method code with example usage: obj.test_configure_optimizers_with_sgd_cosine_annealing(trainer) without return types