code: def test_configure_optimizers(test_model, num_classes, url):
      optimizers = test_model.configure_optimizers()
      assert len(optimizers) == 4
      assert all(
          isinstance(optimizer, torch.optim.Adam)
          for optimizer in optimizers
      )
      for modality in range(test_model.num_modalities):
          assert optimizers[modality].param_groups[0]["lr"] == 5e-4
      assert (
          optimizers[test_model.num_modalities].param_groups[0]["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 3 for calling a global function with example usage: test_configure_optimizers(test_model, num_classes, url) without return types