code: def test_label_cleaner_regression():
      # Given
      problem_type = REGRESSION
      input_labels_numpy = np.array([2, 4, 2, 2, 4, 1])
      input_labels = pd.Series(input_labels_numpy)
      input_labels_new = pd.Series([3, 5, 2])
      expected_output_labels = input_labels.copy()
      expected_output_labels_new = input_labels_new.copy()
      expected_output_labels_new_inverse = input_labels_new.copy()
  
      # When
      label_cleaner = LabelCleaner.construct(
          problem_type=problem_type, y=input_labels, y_uncleaned=None
      )
  
      # Then
      assert isinstance(label_cleaner, LabelCleanerDummy)
      assert label_cleaner.problem_type_transform == REGRESSION
  
      output_labels = label_cleaner.transform(input_labels)
      output_labels_with_numpy = label_cleaner.transform(
          input_labels_numpy
      )
      output_labels_new = label_cleaner.transform(input_labels_new)
  
      output_labels_inverse = label_cleaner.inverse_transform(
          output_labels
      )
      output_labels_new_inverse = label_cleaner.inverse_transform(
          output_labels_new
      )
  
      assert expected_output_labels.equals(output_labels)
      assert expected_output_labels.equals(output_labels_with_numpy)
      assert expected_output_labels_new.equals(output_labels_new)
  
      assert input_labels.equals(output_labels_inverse)
      assert expected_output_labels_new_inverse.equals(
          output_labels_new_inverse
      )
  
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 4 for calling a global function with example usage: test_label_cleaner_regression() without return types