class_docstr: Transformer wrapper to delay fit to the transform phase.
  
  In panel settings, e.g., time series classification, it can be preferable
  (or, necessary) to fit and transform on the test set, e.g., interpolate within the
  same series that interpolation parameters are being fitted on. `FitInTransform` can
  be used to wrap any transformer to ensure that `fit` and `transform` happen always
  on the same series, by delaying the `fit` to the `transform` batch.
  
  Warning: The use of `FitInTransform` will typically not be useful, or can constitute
  a mistake (data leakage) when naively used in a forecasting setting.
  
  Parameters
  ----------
  transformer : Estimator
      scikit-learn-like or sktime-like transformer to fit and apply to series.
  skip_inverse_transform : bool
      The FitInTransform will skip inverse_transform by default, of the param
      skip_inverse_transform=False, then the inverse_transform is calculated
      by means of transformer.fit(X=X, y=y).inverse_transform(X=X, y=y) where
      transformer is the inner transformer. So the inner transformer is
      fitted on the inverse_transform data. This is required to have a non-
      state changing transform() method of FitInTransform.
  
  Examples
  --------
  >>> from sktime.datasets import load_longley
  >>> from sktime.forecasting.naive import NaiveForecaster
  >>> from sktime.forecasting.base import ForecastingHorizon
  >>> from sktime.forecasting.compose import ForecastingPipeline
  >>> from sktime.split import temporal_train_test_split
  >>> from sktime.transformations.compose import FitInTransform
  >>> from sktime.transformations.series.impute import Imputer
  >>> y, X = load_longley()
  >>> y_train, y_test, X_train, X_test = temporal_train_test_split(y, X)
  >>> fh = ForecastingHorizon(y_test.index, is_relative=False)
  >>> # we want to fit the Imputer only on the predict (=transform) data.
  >>> # note that NaiveForecaster can't use X data, this is just a show case.
  >>> pipe = ForecastingPipeline(
  ...     steps=[
  ...         ("imputer", FitInTransform(Imputer(method="mean"))),
  ...         ("forecaster", NaiveForecaster()),
  ...     ]
  ... )
  >>> pipe.fit(y_train, X_train)
  ForecastingPipeline(...)
  >>> y_pred = pipe.predict(fh=fh, X=X_test)
docstr: Get fitted parameters.
  
  Returns
  -------
  fitted_params : dict
docstr_tok: ["Get","fitted","parameters",".","Returns","--","--","--","-","fitted_params",":","dict"]
code: def _get_fitted_params(self):
      """Get fitted parameters.
  
      Returns
      -------
      fitted_params : dict
      """
      return {}
  
file_path: sktime/sktime/transformations/compose/_fitintransform.py
imports: from sklearn import clone
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 4 for calling the FitInTransform obj's underlying member method code with example usage: obj._get_fitted_params() and returns: dict