code: def test_confusion_matrix_report_display(
      tweet_emotion_train_test_textdata,
      tweet_emotion_train_test_predictions,
  ):
      # Arrange and Act
      check = ConfusionMatrixReport()
      result = check.run(
          tweet_emotion_train_test_textdata[0],
          predictions=tweet_emotion_train_test_predictions[0],
      )
  
      # Assert
      assert_that(
          result.display[0],
          equal_to(
              "The overall accuracy of your model is: 92.04%.<br>Best accuracy achieved on samples with "
              "<b>anger</b> label (96.59%).<br>Worst accuracy achieved on samples with <b>sadness</b> "
              "label (88.86%)."
          ),
      )
      # First is the text description and second is the heatmap
      assert_that(len(result.display), equal_to(2))
      assert_that(len(result.display[1].data), equal_to(1))
      assert_that(result.display[1].data[0].type, equal_to("heatmap"))
  
file_path: deepchecks/tests/nlp/checks/model_evaluation/confusion_matrix_test.py
imports: import numpy
  from hamcrest import assert_that, close_to, equal_to
  from deepchecks.core.condition import ConditionCategory
  from deepchecks.nlp.checks.model_evaluation import ConfusionMatrixReport
  from deepchecks.utils.strings import format_number, format_percent
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 8 for calling a global function with example usage: test_confusion_matrix_report_display(tweet_emotion_train_test_textdata, tweet_emotion_train_test_predictions) without return types