class_docstr: Apply a transformer columnwise to multivariate series.
  
  Overview: input multivariate time series and the transformer passed
  in `transformer` parameter is applied to specified `columns`, each
  column is handled as a univariate series. The resulting transformed
  data has the same shape as input data.
  
  Parameters
  ----------
  transformer : Estimator
      scikit-learn-like or sktime-like transformer to fit and apply to series.
  columns : list of str or None
          Names of columns that are supposed to be transformed.
          If None, all columns are transformed.
  
  Attributes
  ----------
  transformers_ : dict of {str : transformer}
      Maps columns to transformers.
  columns_ : list of str
      Names of columns that are supposed to be transformed.
  
  See Also
  --------
  OptionalPassthrough
  
  Examples
  --------
  >>> from sktime.datasets import load_longley
  >>> from sktime.transformations.series.detrend import Detrender
  >>> from sktime.transformations.compose import ColumnwiseTransformer
  >>> _, X = load_longley()
  >>> transformer = ColumnwiseTransformer(Detrender())
  >>> Xt = transformer.fit_transform(X)
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`
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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 sktime.transformations.series.detrend import Detrender
  
      return {"transformer": Detrender()}
  
imports: import pandas
  from sktime.base._meta import _ColumnEstimator, _HeterogenousMetaEstimator
  from sktime.transformations.base import BaseTransformer
  from sktime.utils.multiindex import rename_multiindex
  from sktime.utils.validation.series import check_series
label_desc: Use image node_id 6 for calling the ColumnwiseTransformer obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: dict