code: def get_model(cfg, device):
      """
      Builds and returns an MMNN model according to the config object passed.
  
      Args:
          cfg: A YACS config object.
      """
      encoders = [
          LeNet(
              cfg.MODEL.LENET_IN_CHANNELS,
              cfg.MODEL.CHANNELS,
              cfg.MODEL.LENET_ADD_LAYERS_IMG,
          ),
          LeNet(
              cfg.MODEL.LENET_IN_CHANNELS,
              cfg.MODEL.CHANNELS,
              cfg.MODEL.LENET_ADD_LAYERS_AUD,
          ),
      ]
  
      if cfg.MODEL.FUSION == "late":
          fusion = Concat()
          head = MLPDecoder(
              cfg.MODEL.MLP_IN_DIM,
              cfg.MODEL.MLP_HIDDEN_DIM,
              cfg.MODEL.OUT_DIM,
              include_decoder_layers=False,
          )
      elif cfg.MODEL.FUSION == "bimodal_interaction_fusion":
          fusion = BimodalInteractionFusion(
              cfg.MODEL.MULTIPLICATIVE_FUSION_IN_DIM,
              cfg.MODEL.MULTIPLICATIVE_FUSION_OUT_DIM,
              cfg.MODEL.MULTIPLICATIVE_OUTPUT,
          )
          head = MLPDecoder(
              cfg.MODEL.MLP_IN_DIM,
              cfg.MODEL.MLP_HIDDEN_DIM,
              cfg.MODEL.OUT_DIM,
              include_decoder_layers=False,
          )
      elif cfg.MODEL.FUSION == "low_rank_tensor":
          fusion = LowRankTensorFusion(
              cfg.MODEL.LOW_RANK_TENSOR_IN_DIM,
              cfg.MODEL.LOW_RANK_TENSOR_OUT_DIM,
              cfg.MODEL.LOW_RANK_TENSOR_RANK,
          )
          head = MLPDecoder(
              cfg.MODEL.MLP_LOW_RANK_IN_DIM,
              cfg.MODEL.MLP_HIDDEN_DIM,
              cfg.MODEL.OUT_DIM,
              include_decoder_layers=False,
          )
  
      model = MultimodalNNTrainer(
          encoders,
          fusion,
          head,
          lr=cfg.SOLVER.BASE_LR,
          weight_decay=cfg.SOLVER.WEIGHT_DECAY,
      )
      return model
  
imports: from kale.embed.feature_fusion import BimodalInteractionFusion, Concat, LowRankTensorFusion
  from kale.embed.image_cnn import LeNet
  from kale.pipeline.base_nn_trainer import MultimodalNNTrainer
  from kale.predict.decode import MLPDecoder
label_desc: Use image node_id 1 for calling a global function with example usage: get_model(cfg, device) and returns: model