code: def test_PlateauFinder(value):
      """Test PlateauFinder on test data against expected output plateau features."""
      value = np.nan
      X = pd.DataFrame(
          pd.Series(
              [
                  pd.Series(
                      [value, 1, 2, 3, value, 2, 2, 3]
                  ),  # nan at start
                  pd.Series([value, value, 3, 3, value, 2, 2, 3]),
                  pd.Series(
                      [0, value, value, value, value, value, 2, value]
                  ),
                  # nan at end
                  pd.Series([0, value, value, 3, 4, 5, value, value]),
                  pd.Series([2, value, value, value, 2, value, 3, 1]),
                  pd.Series([0, value, value, 3, value, value, 2, 0]),
              ]
          )
      )
      n_samples = X.shape[0]
  
      t = PlateauFinder(value=value, min_length=2)
      Xt = t.fit_transform(X)
  
      actual_starts = Xt.iloc[:, 0]
      actual_lengths = Xt.iloc[:, 1]
  
      expected_starts = [
          np.array([]),
          np.array([0]),
          np.array([1]),
          np.array([1, 6]),
          np.array([1]),
          np.array([1, 4]),
      ]
      expected_lengths = [
          np.array([]),
          np.array([2]),
          np.array([5]),
          np.array([2, 2]),
          np.array([3]),
          np.array([2, 2]),
      ]
  
      # compare results
      for i in range(n_samples):
          np.testing.assert_array_equal(
              actual_starts[i], expected_starts[i]
          )
          np.testing.assert_array_equal(
              actual_lengths[i], expected_lengths[i]
          )
  
imports: import numpy
  import pandas
  import pytest
  from sktime.transformations.panel.summarize import PlateauFinder
label_desc: Use image node_id 1 for calling a global function with example usage: test_PlateauFinder(value) without return types