code: def test_label_softclass():
      # Given
      problem_type = SOFTCLASS
      input_labels = pd.DataFrame(
          [
              [0, 1, 0, 0, 0, 0],
              [1, 0, 0, 0, 0, 0],
              [0, 0, 0.3, 0.6, 0.1, 0],
          ]
      )
  
      # When
      label_cleaner = LabelCleaner.construct(
          problem_type=problem_type, y=input_labels, y_uncleaned=None
      )
  
      # Then
      assert input_labels.equals(label_cleaner.transform(input_labels))
      assert input_labels.equals(
          label_cleaner.inverse_transform(input_labels)
      )
      assert label_cleaner.num_classes == 6
  
file_path: autogluon/tabular/tests/unittests/data/test_label_cleaner.py
imports: import numpy
  import pandas
  import pytest
  from autogluon.core.constants import BINARY, MULTICLASS, REGRESSION, SOFTCLASS
  from autogluon.core.data.label_cleaner import LabelCleaner, LabelCleanerBinary, LabelCleanerDummy, LabelCleanerMulticlass, LabelCleanerMulticlassToBinary
label_desc: Use image node_id 5 for calling a global function with example usage: test_label_softclass() without return types