code: def test_pipeline(test_model, num_classes, url):
      set_seed(2023)
      trainer_pretrain = pl.Trainer(
          default_root_dir="./tests/outputs",
          max_epochs=2,
          accelerator="cpu",
          enable_model_summary=False,
      )
      trainer_pretrain.fit(test_model)
      result = trainer_pretrain.test(test_model)
      if num_classes > 2:
          assert 0 <= result[0]["Accuracy"] <= 1
          assert 0 <= result[0]["F1 weighted"] <= 1
          assert 0 <= result[0]["F1 macro"] <= 1
      else:
          assert 0 <= result[0]["Accuracy"] <= 1
          assert 0 <= result[0]["F1"] <= 1
          assert 0 <= result[0]["AUC"] <= 1
  
      assert str(test_model) is not None
      assert len(str(test_model)) > 0
  
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 5 for calling a global function with example usage: test_pipeline(test_model, num_classes, url) without return types