code: def test_validate_prediction_labels3(multimodal_model, dataset):
      """Test if an error is raised when multiple loss functions were compiled but the length of the target and
      the length of the predictions do not match."""
      y_pred = dataset["output"]["multimodal"]["y_pred"]
      y_true = dataset["output"]["multimodal"]["y_true"][:1]
  
      with pytest.raises(ValueError) as err:
          multimodal_model.validate_prediction_labels(
              y_pred=y_pred, y_true=y_true
          )
  
      assert (
          "Number of predictions differs from the number of labels."
          in str(err.value)
      )
  
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 9 for calling a global function with example usage: test_validate_prediction_labels3(multimodal_model, dataset) without return types