class_docstr: Constructs a transformer from an arbitrary callable.
  
  A FunctionTransformer forwards its y (and optionally X) arguments to a
  user-defined function (or callable object) and returns the result of this
  function. This is useful for stateless transformations such as taking the
  log of frequencies, doing custom scaling, etc.
  
  Note: If a lambda function is used as the ``func``, then the
  resulting transformer will not be pickleable.
  
  Parameters
  ----------
  func : callable (X: X_type, **kwargs) -> X_type, default=identity (return X)
      The callable to use for the transformation. This will be passed
      the same arguments as transform, with args and kwargs forwarded.
      If func is None, then func will be the identity function.
  inverse_func : callable (X: X_type, **kwargs) -> X_type, default=identity
      The callable to use for the inverse transformation. This will be
      passed the same arguments as inverse transform, with args and
      kwargs forwarded. If inverse_func is None, then inverse_func
      will be the identity function.
  check_inverse : bool, default=True
     Whether to check that or ``func`` followed by ``inverse_func`` leads to
     the original inputs. It can be used for a sanity check, raising a
     warning when the condition is not fulfilled.
  kw_args : dict, default=None
      Dictionary of additional keyword arguments to pass to func.
  inv_kw_args : dict, default=None
      Dictionary of additional keyword arguments to pass to inverse_func.
  X_type : str, one of "pd.DataFrame, pd.Series, np.ndarray", or list thereof
      default = ["pd.DataFrame", "pd.Series", "np.ndarray"]
      list of types that func is assumed to allow for X (see signature above)
      if X passed to transform/inverse_transform is not on the list,
          it will be converted to the first list element before passed to funcs
  
  See Also
  --------
  sktime.transformations.series.boxcox.LogTransformer :
      Transformer input data using natural log. Can help normalize data and
      compress variance of the series.
  sktime.transformations.series.exponent.ExponentTransformer :
      Transform input data by raising it to an exponent. Can help compress
      variance of series if a fractional exponent is supplied.
  sktime.transformations.series.exponent.SqrtTransformer :
      Transform input data by taking its square root. Can help compress
      variance of input series.
  
  Examples
  --------
  >>> import numpy as np
  >>> from sktime.transformations.series.func_transform import FunctionTransformer
  >>> transformer = FunctionTransformer(np.log1p, np.expm1)
  >>> X = np.array([[0, 1], [2, 3]])
  >>> transformer.fit_transform(X)
  array([[0.        , 0.69314718],
         [1.09861229, 1.38629436]])
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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`
      """
      # default params, identity transform
      params1 = {}
  
      # log-transformer, with exp inverse
      params2 = {"func": np.expm1, "inverse_func": np.log1p}
  
      return [params1, params2]
  
file_path: sktime/sktime/transformations/series/func_transform.py
imports: import numpy
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 6 for calling the FunctionTransformer obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: list