code: def test_pipeline_predict_inverse_transform(
      pipeline, X, inverse_transform, return_conf_ints
  ):
      X_train, X_test = X
  
      pipeline.fit(train, X=X_train)
  
      # show we can get a summary
      pipeline.summary()
  
      # first predict
      predictions = pipeline.predict(
          n_periods=test.shape[0],
          X=X_test,
          inverse_transform=inverse_transform,
          return_conf_int=return_conf_ints,
      )
  
      if return_conf_ints:
          assert (
              isinstance(predictions, tuple) and len(predictions) == 2
          )
          y_pred, conf_ints = predictions
          assert conf_ints.shape[1] == 2
          assert np.all(
              (conf_ints[:, 0] <= y_pred) & (y_pred <= conf_ints[:, 1])
          )
  
      # now in sample
      in_sample = pipeline.predict_in_sample(
          X=X_train,
          inverse_transform=inverse_transform,
          return_conf_int=return_conf_ints,
      )
  
      if return_conf_ints:
          assert isinstance(in_sample, tuple) and len(in_sample) == 2
          y_pred, conf_ints = predictions
          assert conf_ints.shape[1] == 2
          assert np.all(
              (conf_ints[:, 0] <= y_pred) & (y_pred <= conf_ints[:, 1])
          )
  
imports: from pmdarima.compat.pytest import pytest_error_str
  from pmdarima.model_selection import train_test_split
  from pmdarima.pipeline import Pipeline, _warn_for_deprecated
  from pmdarima.preprocessing import BoxCoxEndogTransformer, FourierFeaturizer, DateFeaturizer, LogEndogTransformer
  from pmdarima.arima import ARIMA, AutoARIMA
  from pmdarima.datasets import load_wineind
  from pmdarima.datasets._base import load_date_example
  import numpy
  from numpy.testing import assert_array_almost_equal
  import pytest
label_desc: Use image node_id 3 for calling a global function with example usage: test_pipeline_predict_inverse_transform(pipeline, X, inverse_transform, return_conf_ints) without return types