class_docstr: Missing value imputation.
  
  The Imputer transforms input series by replacing missing values according
  to an imputation strategy specified by `method`.
  
  Parameters
  ----------
  method : str, default="drift"
      Method to fill the missing values.
  
      * "drift" : drift/trend values by sktime.PolynomialTrendForecaster(degree=1)
          first, X in transform() is filled with ffill then bfill
          then PolynomialTrendForecaster(degree=1) is fitted to filled X, and
          predict values are queried at indices which had missing values
      * "linear" : linear interpolation, uses pd.Series.interpolate()
          WARNING: This method can not extrapolate, so it is fitted always on the
          data given to transform().
      * "nearest" : use nearest value, uses pd.Series.interpolate()
      * "constant" : same constant value (given in arg value) for all NaN
      * "mean" : pd.Series.mean() of *fit* data
      * "median" : pd.Series.median() of *fit* data
      * "backfill" to "bfill" : adapted from pd.Series.fillna()
      * "pad" or "ffill" : adapted from pd.Series.fillna()
      * "random" : random values between pd.Series.min() and .max() of *fit* data
          if pd.Series dtype is int, sample is uniform discrete
          if pd.Series dtype is float, sample is uniform continuous
      * "forecaster" : use an sktime Forecaster, given in param forecaster.
          First, X in *fit* is filled with ffill then bfill
          then forecaster is fitted to filled X, and *predict* values are queried
          at indices of X data in *transform* which had missing values
      For the following methods, the train data is used to fit them:
      "drift", "mean", "median", "random". For all other methods, the
      transform data is sufficient to compute the impute values.
  
  missing_values : int/float/str, default=None
      The placeholder for the missing values. All occurrences of
      missing_values will be imputed, in addition to np.nan.
      If None, then only np.nan values are imputed.
  value : int/float, default=None
      Value to use to fill missing values when method="constant".
  forecaster : Any Forecaster based on sktime.BaseForecaster, default=None
      Use a given Forecaster to impute by insample predictions when
      method="forecaster". Before fitting, missing data is imputed with
      method="ffill" or "bfill" as heuristic. in case of multivariate X,
      the forecaster is applied separate to each column like a
      ColumnEnsembleForecaster.
  random_state : int/float/str, optional
      Value to set random.seed() if method="random", default None
  
  Examples
  --------
  >>> from sktime.transformations.series.impute import Imputer
  >>> from sktime.datasets import load_airline
  >>> from sktime.split import temporal_train_test_split
  >>> y = load_airline()
  >>> y_train, y_test = temporal_train_test_split(y)
  >>> transformer = Imputer(method="drift")
  >>> transformer.fit(y_train)
  Imputer(...)
  >>> y_test.iloc[3] = np.nan
  >>> y_hat = transformer.transform(y_test)
docstr: Return testing parameter settings for the estimator.
  
  Parameters
  ----------
  parameter_set : str, default="default"
      Name of the set of test parameters to return, for use in tests. If no
      special parameters are defined for a value, will return `"default"` set.
  
  
  Returns
  -------
  params : dict or list of dict, default = {}
      Parameters to create testing instances of the class
      Each dict are parameters to construct an "interesting" test instance, i.e.,
      `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
      `create_test_instance` uses the first (or only) dictionary in `params`
docstr_tok: ["Return","testing","parameter","settings","for","the","estimator",".","Parameters","--","--","--","--","--","parameter_set",":","str",",","default=","''","default","''","Name","of","the","set","of","test","parameters","to","return",",","for","use","in","tests",".","If","no","special","parameters","are","defined","for","a","value",",","will","return","`","``","default","''","`","set",".","Returns","--","--","--","-","params",":","dict","or","list","of","dict",",","default","=","{","}","Parameters","to","create","testing","instances","of","the","class","Each","dict","are","parameters","to","construct","an","``","interesting","''","test","instance",",","i.e.",",","`","MyClass","(","*","*","params",")","`","or","`","MyClass","(","*","*","params","[","i","]",")","`","creates","a","valid","test","instance",".","`","create_test_instance","`","uses","the","first","(","or","only",")","dictionary","in","`","params","`"]
code: def get_test_params(cls, parameter_set="default"):
      """Return testing parameter settings for the estimator.
  
      Parameters
      ----------
      parameter_set : str, default="default"
          Name of the set of test parameters to return, for use in tests. If no
          special parameters are defined for a value, will return `"default"` set.
  
  
      Returns
      -------
      params : dict or list of dict, default = {}
          Parameters to create testing instances of the class
          Each dict are parameters to construct an "interesting" test instance, i.e.,
          `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
          `create_test_instance` uses the first (or only) dictionary in `params`
      """
      from sklearn.linear_model import LinearRegression
  
      from sktime.forecasting.compose import make_reduction
      from sktime.forecasting.trend import TrendForecaster
  
      linear_forecaster = make_reduction(
          LinearRegression(), strategy="multioutput"
      )
  
      return [
          {"method": "drift"},
          {"method": "linear"},
          {"method": "nearest"},
          {"method": "constant", "value": 1},
          {"method": "median"},
          {"method": "backfill"},
          {"method": "bfill"},
          {"method": "pad"},
          {"method": "random"},
          {"method": "forecaster", "forecaster": TrendForecaster()},
          {"method": "forecaster", "forecaster": linear_forecaster},
      ]
  
file_path: sktime/sktime/transformations/series/impute.py
imports: import numpy
  import pandas
  from sklearn.utils import check_random_state
  from sktime.forecasting.base import ForecastingHorizon
  from sktime.forecasting.trend import PolynomialTrendForecaster
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 7 for calling the Imputer obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: list