code: def test_init(test_model, num_classes, url):
      assert isinstance(test_model.dataset, SparseMultiomicsDataset)
      assert test_model.num_modalities == 3
      assert test_model.num_classes == num_classes
      assert all(
          isinstance(encoder, MogonetGCN)
          for encoder in test_model.unimodal_encoder
      )
      assert all(
          isinstance(decoder, LinearClassifier)
          for decoder in test_model.unimodal_decoder
      )
      assert isinstance(test_model.loss_fn, CrossEntropyLoss)
      assert isinstance(test_model.multimodal_decoder, VCDN)
      assert test_model.train_multimodal_decoder
      assert test_model.gcn_lr == 5e-4
      assert test_model.vcdn_lr == 1e-3
  
file_path: pykale/tests/pipeline/test_multiomics_trainer.py
imports: import pytest
  import pytorch_lightning
  import torch
  from torch.nn import CrossEntropyLoss
  from kale.embed.mogonet import MogonetGCN
  from kale.loaddata.multiomics_datasets import SparseMultiomicsDataset
  from kale.pipeline.multiomics_trainer import MultiomicsTrainer
  from kale.predict.decode import LinearClassifier, VCDN
  from kale.prepdata.tabular_transform import ToOneHotEncoding, ToTensor
  from kale.utils.seed import set_seed
label_desc: Use image node_id 2 for calling a global function with example usage: test_init(test_model, num_classes, url) without return types