code: def test_compute_metrics_multimodal(multimodal_model, dataset):
      """Test if the metrics computation for a multimodal model matches the expectation."""
      y_pred = dataset["output"]["multimodal"]["y_pred"]
      y_true = dataset["output"]["multimodal"]["y_true"]
  
      results = multimodal_model.compute_metrics(
          y_pred=y_pred, y_true=y_true
      )
      expected_acc1 = torch.mean(
          (y_true[0] == torch.argmax(y_pred[0], dim=-1)).float()
      ).item()
      expected_acc2 = torch.mean(
          (y_true[1] == torch.argmax(y_pred[1], dim=-1)).float()
      ).item()
      assert np.isclose(expected_acc1, results["output_1_accuracy"])
      assert np.isclose(expected_acc2, results["output_2_accuracy"])
  
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 14 for calling a global function with example usage: test_compute_metrics_multimodal(multimodal_model, dataset) without return types