class_docstr: Setup the MOGONET model via the config file.
  
  Args:
      cfg (CfgNode): A YACS config object.
      dataset (SparseMultiomicsDataset): The input dataset created in form of :class:`~torch_geometric.data.Dataset`.
docstr: Return the prepared MOGONET model based on user preference.
  
  Args:
      pretrain (bool, optional): Whether to return the pretrain model. (default: ``False``)
  
  Returns:
      MultiomicsTrainer: The prepared MOGONET model.
code: def get_model(self, pretrain: bool = False) -> MultiomicsTrainer:
      """Return the prepared MOGONET model based on user preference.
  
      Args:
          pretrain (bool, optional): Whether to return the pretrain model. (default: ``False``)
  
      Returns:
          MultiomicsTrainer: The prepared MOGONET model.
      """
      num_modalities = self.cfg.DATASET.NUM_MODALITIES
      num_classes = self.cfg.DATASET.NUM_CLASSES
      gcn_lr_pretrain = self.cfg.MODEL.GCN_LR_PRETRAIN
      gcn_lr = self.cfg.MODEL.GCN_LR
      vcdn_lr = self.cfg.MODEL.VCDN_LR
  
      if pretrain:
          multimodal_model = None
          train_multimodal_decoder = False
          gcn_lr = gcn_lr_pretrain
      else:
          multimodal_model = self.multimodal_decoder
          train_multimodal_decoder = True
          gcn_lr = gcn_lr
  
      model = MultiomicsTrainer(
          dataset=self.dataset,
          num_modalities=num_modalities,
          num_classes=num_classes,
          unimodal_encoder=self.unimodal_encoder,
          unimodal_decoder=self.unimodal_decoder,
          loss_fn=self.loss_function,
          multimodal_decoder=multimodal_model,
          train_multimodal_decoder=train_multimodal_decoder,
          gcn_lr=gcn_lr,
          vcdn_lr=vcdn_lr,
      )
  
      return model
  
file_path: pykale/examples/multiomics_mogonet/model.py
imports: from typing import List, Optional
  from torch.nn import CrossEntropyLoss
  from yacs.config import CfgNode
  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
label_desc: Use image node_id 3 for calling the MogonetModel obj's underlying member method code with example usage: obj.get_model(pretrain) and returns: model