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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code: def _get_kwargs(self, **params):
      params_steps = {
          name: {} for name, step in self.steps if step is not None
      }
      for pname, pval in params.items():
          step, param = pname.split("__", 1)
          params_steps[step][param] = pval
      return params_steps
  
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 5 for calling the Pipeline obj's underlying member method code with example usage: obj._get_kwargs() and returns: params_steps