code: def test_confusion_matrix_with_multioutput_samples():
      # Given
      input_solution = [["cat", "ant", "cat"]]
      input_prediction = [["ant", "ant", "cat"]]
  
      # When-Then
      with pytest.raises(ValueError):
          observed_output = confusion_matrix(
              input_solution, input_prediction
          )
  
imports: import numpy
  import pytest
  import sklearn
  from autogluon.core.metrics import confusion_matrix, log_loss, quadratic_kappa, roc_auc
  from autogluon.core.metrics.softclass_metrics import soft_log_loss
label_desc: Use image node_id 6 for calling a global function with example usage: test_confusion_matrix_with_multioutput_samples() without return types