code: def test_pipeline():
      """Test results of TransformedTargetForecaster."""
      y = load_airline()
      y_train, y_test = temporal_train_test_split(y)
  
      forecaster = TransformedTargetForecaster(
          [
              ("t1", ExponentTransformer()),
              ("t2", TabularToSeriesAdaptor(MinMaxScaler())),
              ("forecaster", NaiveForecaster()),
          ]
      )
      fh = np.arange(len(y_test)) + 1
      forecaster.fit(y_train, fh=fh)
      actual = forecaster.predict()
  
      def compute_expected_y_pred(y_train, fh):
          # fitting
          yt = y_train.copy()
          t1 = ExponentTransformer()
          yt = t1.fit_transform(yt)
          t2 = TabularToSeriesAdaptor(MinMaxScaler())
          yt = t2.fit_transform(yt)
          forecaster = NaiveForecaster()
          forecaster.fit(yt, fh=fh)
  
          # predicting
          y_pred = forecaster.predict()
          y_pred = t2.inverse_transform(y_pred)
          y_pred = t1.inverse_transform(y_pred)
          return y_pred
  
      expected = compute_expected_y_pred(y_train, fh)
      np.testing.assert_array_equal(actual, expected)
  
imports: import numpy
  import pandas
  import pytest
  from sklearn.preprocessing import MinMaxScaler, StandardScaler
  from sklearn.svm import SVR
  from sktime.datasets import load_airline, load_longley
  from sktime.datatypes import get_examples
  from sktime.datatypes._utilities import get_window
  from sktime.forecasting.arima import ARIMA
  from sktime.forecasting.compose import ForecastingPipeline, TransformedTargetForecaster, make_reduction
  from sktime.forecasting.model_selection import ForecastingGridSearchCV
  from sktime.forecasting.naive import NaiveForecaster
  from sktime.forecasting.sarimax import SARIMAX
  from sktime.forecasting.trend import PolynomialTrendForecaster
  from sktime.split import ExpandingWindowSplitter, temporal_train_test_split
  from sktime.transformations.compose import OptionalPassthrough
  from sktime.transformations.hierarchical.aggregate import Aggregator
  from sktime.transformations.series.adapt import TabularToSeriesAdaptor
  from sktime.transformations.series.boxcox import LogTransformer
  from sktime.transformations.series.detrend import Detrender
  from sktime.transformations.series.difference import Differencer
  from sktime.transformations.series.exponent import ExponentTransformer
  from sktime.transformations.series.impute import Imputer
  from sktime.transformations.series.outlier_detection import HampelFilter
  from sktime.utils._testing.estimator_checks import _assert_array_almost_equal
  from sktime.utils._testing.series import _make_series
  from sktime.utils.estimators import MockForecaster
  from sktime.utils.validation._dependencies import _check_estimator_deps, _check_soft_dependencies
label_desc: Use image node_id 1 for calling a global function with example usage: test_pipeline() and returns: y_pred