class_docstr: A pipeline of transformers with an optional final estimator stage
  
  The pipeline object chains together an arbitrary number of named, ordered
  transformations, passing the output from one as the input to the next. As
  the last stage, an ``ARIMA`` or ``AutoARIMA`` object will be fit. This
  pipeline takes after the scikit-learn ``sklearn.Pipeline`` object, which
  behaves similarly but does not share the same time-series interface
  that ``pmdarima`` follows.
  
  The purpose of the pipeline is to assemble several steps that can be
  cross-validated together while setting different parameters.
  For this, it enables setting parameters of the various steps using their
  names and the parameter name separated by a `'__'`, as in the example
  below.
  
  Parameters
  ----------
  steps : list
      List of (name, transform) tuples (implementing fit/transform) that are
      chained, in the order in which they are chained, with the last object
      an ARIMA or AutoARIMA estimator.
  
  Examples
  --------
  >>> from pmdarima.datasets import load_wineind
  >>> from pmdarima.arima import AutoARIMA
  >>> from pmdarima.pipeline import Pipeline
  >>> from pmdarima.preprocessing import FourierFeaturizer
  >>>
  >>> wineind = load_wineind()
  >>> pipeline = Pipeline([
  ...     ("fourier", FourierFeaturizer(m=12, k=3)),
  ...     ("arima", AutoARIMA(seasonal=False, stepwise=True,
  ...                         suppress_warnings=True,
  ...                         error_action='ignore'))
  ... ])
  >>> pipeline.fit(wineind)
  Pipeline(steps=[('fourier', FourierFeaturizer(k=3, m=12)),
                  ('arima', AutoARIMA(D=None, alpha=0.05, callback=None,
                                      d=None, disp=0, error_action='ignore',
                                      information_criterion='aic', m=1,
                                      max_D=1, max_P=2, max_Q=2, max_d=2,
                                      max_order=10, max_p=5, max_q=5,
                                      maxiter=None, method=None,
                                      n_fits=10, n...s_warnings=True,
                                      test='kpss', trace=False,
                                      transparams=True, trend=None,
                                      with_intercept=True))])
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docstr: Generate in-sample predictions from the fit pipeline.
  
  Predicts the original training (in-sample) time series values. This can
  be useful when wanting to visualize the fit, and qualitatively inspect
  the efficacy of the model, or when wanting to compute the residuals
  of the model.
  
  Parameters
  ----------
  X : array-like, shape=[n_obs, n_vars], optional (default=None)
      An optional 2-d array of exogenous variables. If provided, these
      variables are used as additional features in the regression
      operation. This should not include a constant or trend. Note that
      if an ``ARIMA`` is fit on exogenous features, it must be provided
      exogenous features for making predictions.
  
  start : int, optional (default=None)
      Zero-indexed observation number at which to start forecasting, ie.,
      the first forecast is start.
  
  end : int, optional (default=None)
      Zero-indexed observation number at which to end forecasting, ie.,
      the first forecast is start.
  
  dynamic : bool, optional (default=False)
      The `dynamic` keyword affects in-sample prediction. If dynamic
      is False, then the in-sample lagged values are used for
      prediction. If `dynamic` is True, then in-sample forecasts are
      used in place of lagged dependent variables. The first forecasted
      value is `start`.
  
  return_conf_int : bool, optional (default=False)
      Whether to get the confidence intervals of the forecasts.
  
  alpha : float, optional (default=0.05)
      The confidence intervals for the forecasts are (1 - alpha) %
  
  inverse_transform : bool, optional (default=True)
      Whether to inverse transform predictions, if they are in log or
      BoxCox scale. Any endog transformer will be inverse-transformed.
  
  **kwargs : keyword args
      Extra keyword arguments used for each stage's ``transform`` stage.
      Similar to scikit-learn pipeline keyword args, the keys are
      compound, comprised of the stage name and the argument name
      separated by a "__". For instance, if you have a FourierFeaturizer
      whose stage is named "fourier", your transform kwargs could
      resemble::
  
          {"fourier__n_periods": 50}
  
  Returns
  -------
  preds : array
      The predicted values.
  
  conf_int : array-like, shape=(n_periods, 2), optional
      The confidence intervals for the predictions. Only returned if
      ``return_conf_int`` is True.
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code: def predict_in_sample(
      self,
      X=None,
      start=None,
      end=None,
      dynamic=False,
      return_conf_int=False,
      alpha=0.05,
      inverse_transform=True,
      **kwargs,
  ):
      """Generate in-sample predictions from the fit pipeline.
  
      Predicts the original training (in-sample) time series values. This can
      be useful when wanting to visualize the fit, and qualitatively inspect
      the efficacy of the model, or when wanting to compute the residuals
      of the model.
  
      Parameters
      ----------
      X : array-like, shape=[n_obs, n_vars], optional (default=None)
          An optional 2-d array of exogenous variables. If provided, these
          variables are used as additional features in the regression
          operation. This should not include a constant or trend. Note that
          if an ``ARIMA`` is fit on exogenous features, it must be provided
          exogenous features for making predictions.
  
      start : int, optional (default=None)
          Zero-indexed observation number at which to start forecasting, ie.,
          the first forecast is start.
  
      end : int, optional (default=None)
          Zero-indexed observation number at which to end forecasting, ie.,
          the first forecast is start.
  
      dynamic : bool, optional (default=False)
          The `dynamic` keyword affects in-sample prediction. If dynamic
          is False, then the in-sample lagged values are used for
          prediction. If `dynamic` is True, then in-sample forecasts are
          used in place of lagged dependent variables. The first forecasted
          value is `start`.
  
      return_conf_int : bool, optional (default=False)
          Whether to get the confidence intervals of the forecasts.
  
      alpha : float, optional (default=0.05)
          The confidence intervals for the forecasts are (1 - alpha) %
  
      inverse_transform : bool, optional (default=True)
          Whether to inverse transform predictions, if they are in log or
          BoxCox scale. Any endog transformer will be inverse-transformed.
  
      **kwargs : keyword args
          Extra keyword arguments used for each stage's ``transform`` stage.
          Similar to scikit-learn pipeline keyword args, the keys are
          compound, comprised of the stage name and the argument name
          separated by a "__". For instance, if you have a FourierFeaturizer
          whose stage is named "fourier", your transform kwargs could
          resemble::
  
              {"fourier__n_periods": 50}
  
      Returns
      -------
      preds : array
          The predicted values.
  
      conf_int : array-like, shape=(n_periods, 2), optional
          The confidence intervals for the predictions. Only returned if
          ``return_conf_int`` is True.
      """
      kwargs = _warn_for_deprecated(**kwargs)
      Xt, est, predict_kwargs = self._pre_predict(0, X, **kwargs)
  
      return_vals = est.predict_in_sample(
          X=Xt,
          start=start,
          end=end,
          return_conf_int=return_conf_int,
          alpha=alpha,
          dynamic=dynamic,
          **predict_kwargs,
      )
  
      return self._post_predict(
          Xt, return_vals, return_conf_int, inverse_transform
      )
  
imports: from itertools import islice
  import pandas
  import warnings
  from sklearn.base import BaseEstimator, clone
  from .base import BaseARIMA
  from .preprocessing.base import BaseTransformer
  from .preprocessing.endog.base import BaseEndogTransformer
  from .preprocessing.exog.base import BaseExogTransformer, BaseExogFeaturizer
  from .utils import check_endog
  from .compat import DTYPE, check_is_fitted
  from .compat.sklearn import if_delegate_has_method
label_desc: Use image node_id 13 for calling the Pipeline obj's underlying member method code with example usage: obj.predict_in_sample(X, start, end, dynamic, return_conf_int, alpha, inverse_transform) and returns: self