code: def trainer(self):
      # Create a CNNTransformerTrainer for testing.
      cnn = SimpleCNNBuilder([[4, 3], [16, 1]])
      classifier = ClassNet()
      return CNNTransformerTrainer(
          feature_extractor=cnn,
          task_classifier=classifier,
          lr_milestones=[1, 2],
          lr_gamma=0.1,
          optimizer={
              "type": "SGD",
              "optim_params": {"momentum": 0.2, "weight_decay": 0.3},
          },
          max_epochs=5,
          adapt_lr=True,
      )
  
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 1 for calling the TestCNNTransformerTrainer obj's underlying member method code with example usage: obj.trainer() and returns: CNNTransformerTrainer