code: def fit(self, X, y):
      self.X_fit = X
      self.y_fit = y
      return self
  
imports: import numpy
  import pandas
  import pytest
  from sklearn.base import BaseEstimator, RegressorMixin
  from sklearn.dummy import DummyRegressor
  from sklearn.linear_model import LinearRegression
  from sklearn.pipeline import make_pipeline
  from sktime.datasets import load_airline
  from sktime.forecasting.base import ForecastingHorizon
  from sktime.forecasting.compose import DirectTabularRegressionForecaster, DirectTimeSeriesRegressionForecaster, DirRecTabularRegressionForecaster, DirRecTimeSeriesRegressionForecaster, MultioutputTabularRegressionForecaster, MultioutputTimeSeriesRegressionForecaster, RecursiveTabularRegressionForecaster, RecursiveTimeSeriesRegressionForecaster, make_reduction
  from sktime.forecasting.compose._reduce import _sliding_window_transform
  from sktime.forecasting.tests._config import TEST_OOS_FHS, TEST_WINDOW_LENGTHS_INT
  from sktime.performance_metrics.forecasting import mean_absolute_percentage_error
  from sktime.regression.base import BaseRegressor
  from sktime.regression.interval_based import TimeSeriesForestRegressor
  from sktime.split import SlidingWindowSplitter, temporal_train_test_split
  from sktime.split.tests.test_split import _get_windows
  from sktime.transformations.panel.reduce import Tabularizer
  from sktime.utils._testing.forecasting import make_forecasting_problem
  from sktime.utils.validation.forecasting import check_fh
label_desc: Use image node_id 1 for calling the _Recorder obj's underlying member method code with example usage: obj.fit(X, y) and returns: self