class_docstr: Column selection transformer.
  
  In transform, subsets `X` to `columns` provided as hyper-parameters.
  
  Sequence of columns in `Xt=transform(X)` is as in `columns` hyper-parameter.
  Caveat: this means that `transform` may change sequence of columns,
      even if no columns are removed from `X` in `transform(X)`.
  
  Parameters
  ----------
  columns : pandas compatible index or index coercible, optional, default = None
      columns to which X in transform is to be subset
  integer_treatment : str, optional, one of "col" (default) and "coerce"
      determines how integer index columns are treated
      "col" = subsets by column iloc index, even if columns is not in X.columns
      "coerce" = coerces to integer pandas.Index and attempts to subset
  index_treatment : str, optional, one of "remove" (default) or "keep"
      determines which column are kept in `Xt = transform(X, y)`
      "remove" = only indices that appear in both X and columns are present in Xt.
      "keep" = all indices in columns appear in Xt. If not present in X, NA is filled.
  
  Examples
  --------
  >>> from sktime.transformations.series.subset import ColumnSelect
  >>> from sktime.datasets import load_longley
  >>> X = load_longley()[1]
  >>> transformer =  ColumnSelect(columns=["GNPDEFL", "POP", "FOO"])
  >>> X_subset = transformer.fit_transform(X=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.
      There are currently no reserved values for transformers.
  
  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.
          There are currently no reserved values for transformers.
  
      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`
      """
      params1 = {"columns": None}
      params2 = {"columns": [0, 2, 3]}
      params3 = {
          "columns": ["a", "foo", "bar"],
          "index_treatment": "keep",
      }
      params4 = {"columns": "a", "index_treatment": "keep"}
  
      return [params1, params2, params3, params4]
  
file_path: sktime/sktime/transformations/series/subset.py
imports: import pandas
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 3 for calling the ColumnSelect obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: list