code: def test_label_for_display():
      # Arrange
      text = ["a", "b b b", "c c c c"]
      single_label = ["PER", "ORG", "GEO"]
      multi_label = np.array([[1, 0, 1], [0, 1, 0], [1, 1, 1]])
  
      # Act
      dataset = TextData(
          raw_text=text,
          task_type="text_classification",
          label=single_label,
      )
      result = dataset.label_for_display()
  
      # Assert
      assert_that(len(result), equal_to(3))
      assert_that(result, contains_exactly("PER", "ORG", "GEO"))
  
      # Act
      dataset = TextData(
          raw_text=text,
          task_type="text_classification",
          label=multi_label,
      )
      result = dataset.label_for_display()
  
      # Assert
      assert_that(len(result), equal_to(3))
      assert_that(result[0], contains_exactly(0, 2))
  
      # Act
      result = dataset.label_for_display(
          model_classes=["PER", "ORG", "GEO"]
      )
  
      # Assert
      assert_that(len(result), equal_to(3))
      assert_that(result[0], contains_exactly("PER", "GEO"))
  
imports: import numpy
  import pandas
  from hamcrest import assert_that, calling, contains_exactly, equal_to, raises
  from deepchecks.core.errors import DeepchecksValueError
  from deepchecks.nlp.text_data import TextData
label_desc: Use image node_id 8 for calling a global function with example usage: test_label_for_display() without return types