class_docstr: The PowerTransformer() applies power or exponential transformations to
  numerical variables.
  
  The PowerTransformer() works only with numerical variables.
  
  A list of variables can be passed as an argument. Alternatively, the
  transformer will automatically select and transform all numerical
  variables.
  
  More details in the :ref:`User Guide <power>`.
  
  Parameters
  ----------
  {variables}
  
  exp: float or int, default=0.5
      The power (or exponent).
  
  Attributes
  ----------
  {variables_}
  
  {feature_names_in_}
  
  {n_features_in_}
  
  Methods
  -------
  {fit}
  
  {fit_transform}
  
  {inverse_transform}
  
  transform:
      Apply the power transformation to the variables.
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docstr: Convert the data back to the original representation.
  
  Parameters
  ----------
  X: Pandas DataFrame of shape = [n_samples, n_features]
      The data to be transformed.
  
  Returns
  -------
  X_tr: pandas Dataframe
      The dataframe with the power transformed variables.
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code: def inverse_transform(self, X: pd.DataFrame) -> pd.DataFrame:
      """
      Convert the data back to the original representation.
  
      Parameters
      ----------
      X: Pandas DataFrame of shape = [n_samples, n_features]
          The data to be transformed.
  
      Returns
      -------
      X_tr: pandas Dataframe
          The dataframe with the power transformed variables.
      """
  
      # check input dataframe and if class was fitted
      X = super().transform(X)
  
      # inverse_transform
      X.loc[:, self.variables_] = np.power(
          X.loc[:, self.variables_], 1 / self.exp
      )
  
      return X
  
file_path: feature_engine/feature_engine/transformation/power.py
imports: from typing import List, Optional, Union
  import numpy
  import pandas
  from feature_engine._base_transformers.base_numerical import BaseNumericalTransformer
  from feature_engine._docstrings.fit_attributes import _feature_names_in_docstring, _n_features_in_docstring, _variables_attribute_docstring
  from feature_engine._docstrings.init_parameters import _variables_numerical_docstring
  from feature_engine._docstrings.methods import _fit_not_learn_docstring, _fit_transform_docstring, _inverse_transform_docstring
  from feature_engine._docstrings.substitute import Substitution
  from feature_engine._variable_handling.init_parameter_checks import _check_init_parameter_variables
label_desc: Use image node_id 4 for calling the PowerTransformer obj's underlying member method code with example usage: obj.inverse_transform(X) and returns: X