code: def test_configure_optimizers_with_default(self, trainer):
      # Test default SGD optimizer configuration. Return a configured SGD optimizer.
      trainer._adapt_lr = False
      optimizers = trainer.configure_optimizers()
      assert len(optimizers) == 1
      assert isinstance(optimizers, list)
      assert isinstance(optimizers[0], torch.optim.SGD)
      assert optimizers[0].defaults["lr"] == 0.001
      assert optimizers[0].defaults["momentum"] == 0.2
      assert optimizers[0].defaults["weight_decay"] == 0.3
  
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 4 for calling the TestCNNTransformerTrainer obj's underlying member method code with example usage: obj.test_configure_optimizers_with_default(trainer) without return types