code: def test_confusion_matrix_with_valid_inputs_without_labels_and_weights():
      # Given
      input_solution = [2, 0, 2, 2, 0, 1]
      input_prediction = [0, 0, 2, 2, 0, 2]
      expected_output = np.array([[2, 0, 0], [0, 0, 1], [1, 0, 2]])
  
      # When
      observed_output = confusion_matrix(
          input_solution, input_prediction
      )
  
      # Then
      assert np.array_equal(expected_output, observed_output)
  
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 1 for calling a global function with example usage: test_confusion_matrix_with_valid_inputs_without_labels_and_weights() without return types