code: def test_display_params(self):
      # Arrange
      train = TextData(
          raw_text=["I think therefore I am" for _ in range(100)],
          label=[
              *([0, 0, 1] for _ in range(50)),
              *([0, 1, 1] for _ in range(50)),
          ],
          task_type="text_classification",
      )
      test = train.copy()
      check = TrainTestPerformance(min_samples=101)
      # Act
      result = check.run(
          train_dataset=train,
          test_dataset=test,
          train_predictions=list(train.label),
          test_predictions=list(test.label),
      )
      # Assert
      assert isinstance(result.value, pd.DataFrame), type(result.value)
      assert set(result.value["Metric"]) == {
          "F1",
          "Precision",
          "Recall",
      }
      assert set(result.value["Dataset"]) == {"Train", "Test"}
      assert (
          result.value["Value"].notna().sum() == 0
      )  # all values are NaNs
  
      check = TrainTestPerformance(
          n_top_classes=1, show_classes_by="test_largest"
      )
      # Act
      result = check.run(
          train_dataset=train,
          test_dataset=test,
          train_predictions=list(train.label),
          test_predictions=list(test.label),
      )
      # Assert
      assert isinstance(result.value, pd.DataFrame), type(result.value)
      assert set(result.value["Metric"]) == {
          "F1",
          "Precision",
          "Recall",
      }
      assert set(result.value["Dataset"]) == {"Train", "Test"}
      assert isinstance(result.display[1], pd.DataFrame)
      assert (
          result.display[1]["Classes"].loc[
              "Not shown classes (showing only top 1)"
          ]
          == "[1]"
      )
      assert (
          result.display[1]["Classes"].loc[
              "Classes without enough samples in either Train or Test"
          ]
          == "[0]"
      )
      assert result.display[0].data[0]["x"].shape == (
          1,
      )  # Make sure x-axis has only 1 class
  
      assert_that(
          calling(TrainTestPerformance).with_args(
              show_classes_by="blabla"
          ),
          raises(DeepchecksValueError),
      )
  
file_path: deepchecks/tests/nlp/checks/model_evaluation/train_test_performance_test.py
imports: import typing
  import pandas
  from hamcrest import *
  from deepchecks.core.errors import DeepchecksValueError
  from deepchecks.nlp.checks import TrainTestPerformance
  from deepchecks.nlp.text_data import TextData
label_desc: Use image node_id 4 for calling the TestMultilableClassification obj's underlying member method code with example usage: obj.test_display_params() without return types