code: def multimodal_model():
      encoders = [_TestEncoder(5, 10) for _ in range(2)]
      fusion = _TestFusion(20, 10)
      head = _TestClassifier(10, 2)
      model = MultimodalNNTrainer(encoders, fusion, head)
      return model
  
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 a global function with example usage: multimodal_model() and returns: model