docstr: Get parameters for this estimator.
  
  Parameters
  ----------
  deep : bool, default=True
      If True, will return the parameters for this estimator and
      contained subobjects that are estimators.
  
  Returns
  -------
  params : mapping of string to any
      Parameter names mapped to their values.
docstr_tok: ["Get","parameters","for","this","estimator",".","Parameters","--","--","--","--","--","deep",":","bool",",","default=True","If","True",",","will","return","the","parameters","for","this","estimator","and","contained","subobjects","that","are","estimators",".","Returns","--","--","--","-","params",":","mapping","of","string","to","any","Parameter","names","mapped","to","their","values","."]
code: def get_params(self, deep=True):
      """
      Get parameters for this estimator.
  
      Parameters
      ----------
      deep : bool, default=True
          If True, will return the parameters for this estimator and
          contained subobjects that are estimators.
  
      Returns
      -------
      params : mapping of string to any
          Parameter names mapped to their values.
      """
      r = self.regressor.get_params(deep=deep)
      r["power_transformer_method"] = self.power_transformer_method
      r[
          "power_transformer_standardize"
      ] = self.power_transformer_standardize
      r["regressor"] = self.regressor
      return r
  
imports: import sklearn.compose
  from sklearn.base import BaseEstimator, ClassifierMixin, clone
  from sklearn.compose import TransformedTargetRegressor
  from sklearn.preprocessing import PowerTransformer
  from sklearn.utils.validation import check_is_fitted
label_desc: Use image node_id 5 for calling the PowerTransformedTargetRegressor obj's underlying member method code with example usage: obj.get_params(deep) and returns: r