code: def test_compute_loss_multimodal(multimodal_model, dataset):
      """Test if the loss computation for multiple loss functions matches the expectation."""
      y_pred = dataset["output"]["multimodal"]["y_pred"]
      y_true = dataset["output"]["multimodal"]["y_true"]
  
      loss_val, _ = multimodal_model.compute_loss(
          y_pred=y_pred, y_true=y_true
      )
      expected_loss0_val = loss_fns[0](
          input=y_pred[0], target=y_true[0]
      ).item()
      expected_loss1_val = loss_fns[1](
          input=y_pred[1], target=y_true[1]
      ).item()
      expected_loss_val = (
          loss_weights[0] * expected_loss0_val
          + loss_weights[1] * expected_loss1_val
      )
      assert np.allclose(loss_val, expected_loss_val)
  
imports: from typing import List
  import numpy
  import pytest
  import torch
  import torch.nn
  import torch.optim
  from torch.utils.data import DataLoader, TensorDataset
  from alibi.models.pytorch.metrics import AccuracyMetric, LossContainer
  from alibi.models.pytorch.model import Model
label_desc: Use image node_id 12 for calling a global function with example usage: test_compute_loss_multimodal(multimodal_model, dataset) without return types