code: def test_configure_optimizers_with_adam(self, trainer):
      # Test Adam optimizer configuration. Return a configured Adam optimizer.
      trainer.optimizers = ClassNet()
      trainer._optimizer_params = {
          "type": "Adam",
          "optim_params": {"eps": 0.2, "weight_decay": 0.3},
      }
      optimizers = trainer.configure_optimizers()
      assert len(optimizers) == 1
      assert isinstance(optimizers, list)
      assert isinstance(optimizers[0], torch.optim.Adam)
      assert optimizers[0].defaults["eps"] == 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 5 for calling the TestBaseTrainer obj's underlying member method code with example usage: obj.test_configure_optimizers_with_adam(trainer) without return types