class_docstr: Apply elementwise square root transformation to a time series.
  
  Transformation performs the following operations element-wise:
      * adds the constant `offset` (shift)
      * applies the square root
  Offset="auto" computes offset as the smallest offset that ensure all elements
  are non-negative before taking the square root.
  
  Parameters
  ----------
  offset : "auto", int or float, default="auto"
      Offset to be added to the input timeseries prior to raising
      the timeseries to the given `power`. If "auto" the series is checked to
      determine if it contains negative values. If negative values are found
      then the offset will be equal to the absolute value of the most negative
      value. If not negative values are present the offset is set to zero.
      If an integer or float value is supplied it will be used as the offset.
  
  Attributes
  ----------
  offset : int or float
      User supplied offset value.
  
  See Also
  --------
  BoxCoxTransformer :
      Applies Box-Cox power transformation. Can help normalize data and
      compress variance of the series.
  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.
  
  Notes
  -----
  For an input series `Z` the square root transformation is defined as
  :math:`(Z + offset)^{0.5}`.
  
  Examples
  --------
  >>> from sktime.transformations.series.exponent import SqrtTransformer
  >>> from sktime.datasets import load_airline
  >>> y = load_airline()
  >>> transformer = SqrtTransformer()
  >>> y_transform = transformer.fit_transform(y)
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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.
      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`
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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.
          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`
      """
      return [{}, {"offset": 4.2}]
  
file_path: sktime/sktime/transformations/series/exponent.py
imports: import numpy
  import pandas
  from sktime.transformations.base import BaseTransformer
  from sktime.utils.warnings import warn
label_desc: Use image node_id 2 for calling the SqrtTransformer obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: list