class_docstr: Column-wise application of transformers.
  
  Applies transformations to columns of an array or pandas DataFrame. Simply
  takes the column transformer from sklearn
  and adds capability to handle pandas dataframe.
  
  This estimator allows different columns or column subsets of the input
  to be transformed separately and the features generated by each transformer
  will be concatenated to form a single feature space.
  This is useful for heterogeneous or columnar data, to combine several
  feature extraction mechanisms or transformations into a single transformer.
  
  Parameters
  ----------
  transformers : list of tuples
      List of (name, transformer, column(s)) tuples specifying the
      transformer objects to be applied to subsets of the data.
      name : string
          Like in Pipeline and FeatureUnion, this allows the transformer and
          its parameters to be set using ``set_params`` and searched in grid
          search.
      transformer : estimator or {"passthrough", "drop"}
          Estimator must support `fit` and `transform`. Special-cased
          strings "drop" and "passthrough" are accepted as well, to
          indicate to drop the columns or to pass them through untransformed,
          respectively.
      column(s) : str or int, array-like of string or int, slice, boolean
      mask array or callable
          Indexes the data on its second axis. Integers are interpreted as
          positional columns, while strings can reference DataFrame columns
          by name.  A scalar string or int should be used where
          ``transformer`` expects X to be a 1d array-like (vector),
          otherwise a 2d array will be passed to the transformer.
          A callable is passed the input data `X` and can return any of the
          above.
  remainder : {"drop", "passthrough"} or estimator, default "drop"
      By default, only the specified columns in `transformations` are
      transformed and combined in the output, and the non-specified
      columns are dropped. (default of ``"drop"``).
      By specifying ``remainder="passthrough"``, all remaining columns that
      were not specified in `transformations` will be automatically passed
      through. This subset of columns is concatenated with the output of
      the transformations.
      By setting ``remainder`` to be an estimator, the remaining
      non-specified columns will use the ``remainder`` estimator. The
      estimator must support `fit` and `transform`.
  sparse_threshold : float, default = 0.3
      If the output of the different transformations contains sparse matrices,
      these will be stacked as a sparse matrix if the overall density is
      lower than this value. Use ``sparse_threshold=0`` to always return
      dense.  When the transformed output consists of all dense data, the
      stacked result will be dense, and this keyword will be ignored.
  n_jobs : int or None, optional (default=None)
      Number of jobs to run in parallel.
      ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
      ``-1`` means using all processors.
  transformer_weights : dict, optional
      Multiplicative weights for features per transformer. The output of the
      transformer is multiplied by these weights. Keys are transformer names,
      values the weights.
  preserve_dataframe : boolean
      If True, pandas dataframe is returned.
      If False, numpy array is returned.
  
  Attributes
  ----------
  transformers_ : list
      The collection of fitted transformations as tuples of
      (name, fitted_transformer, column). `fitted_transformer` can be an
      estimator, "drop", or "passthrough". In case there were no columns
      selected, this will be the unfitted transformer.
      If there are remaining columns, the final element is a tuple of the
      form:
      ("remainder", transformer, remaining_columns) corresponding to the
      ``remainder`` parameter. If there are remaining columns, then
      ``len(transformers_)==len(transformations)+1``, otherwise
      ``len(transformers_)==len(transformations)``.
  named_transformers_ : Bunch object, a dictionary with attribute access
      Read-only attribute to access any transformer by given name.
      Keys are transformer names and values are the fitted transformer
      objects.
  sparse_output_ : bool
      Boolean flag indicating whether the output of ``transform`` is a
      sparse matrix or a dense numpy array, which depends on the output
      of the individual transformations and the `sparse_threshold` keyword.
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docstr: Return testing parameter settings for the estimator.
  
  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",".","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):
      """Return testing parameter settings for the estimator.
  
      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.exponent import (
          ExponentTransformer,
      )
  
      TRANSFORMERS = [
          ("transformer1", ExponentTransformer()),
          ("transformer2", ExponentTransformer()),
      ]
  
      return {
          "transformers": [
              (name, estimator, [0]) for name, estimator in TRANSFORMERS
          ]
      }
  
imports: from warnings import warn
  import numpy
  import pandas
  from sklearn.compose import ColumnTransformer
  from sktime.transformations.base import BaseTransformer, _PanelToPanelTransformer
  from sktime.utils.multiindex import flatten_multiindex
  from sktime.utils.validation.panel import check_X
label_desc: Use image node_id 4 for calling the ColumnTransformer obj's underlying member method code with example usage: obj.get_test_params(cls) and returns: dict