code: def test_differencer_prediction(y, lags, index_type):
      """Test transform against inverse_transform."""
      if index_type == "int":
          y = y.reset_index(drop=True)
  
      y_train = y.iloc[:-12].copy()
      y_true = y.iloc[-12:].copy()
  
      transformer = Differencer(lags=lags, na_handling="drop_na")
      y_transform = transformer.fit_transform(y)
  
      # Use the actual transformed values as predictions since we know we should
      # be able to convert them to the units of the original series and exactly
      # match the y_true values for this period
      y_pred = y_transform.iloc[-12:].copy()
  
      # Redo the transformer's fit and transformation
      # Now the transformer doesn't know anything about the values in y_true
      # This simulates use-case with a forecasting pipeline
      y_transform = transformer.fit_transform(y_train)
  
      y_pred_inv = transformer.inverse_transform(y_pred)
  
      _assert_array_almost_equal(y_true, y_pred_inv)
  
file_path: sktime/sktime/transformations/series/tests/test_differencer.py
imports: import numpy
  import pandas
  import pytest
  from sktime.datasets import load_airline
  from sktime.transformations.series.difference import Differencer
  from sktime.utils._testing.estimator_checks import _assert_array_almost_equal
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 4 for calling a global function with example usage: test_differencer_prediction(y, lags, index_type) without return types