class_docstr: Base class for PCA methods.
  
  Warning: This class should not be used directly.
  Use derived classes instead.
docstr: Transform data back to its original space.
  
  In other words, return an input `X_original` whose transform would be X.
  
  Parameters
  ----------
  X : array-like of shape (n_samples, n_components)
      New data, where `n_samples` is the number of samples
      and `n_components` is the number of components.
  
  Returns
  -------
  X_original array-like of shape (n_samples, n_features)
      Original data, where `n_samples` is the number of samples
      and `n_features` is the number of features.
  
  Notes
  -----
  If whitening is enabled, inverse_transform will compute the
  exact inverse operation, which includes reversing whitening.
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code: def inverse_transform(self, X):
      """Transform data back to its original space.
  
      In other words, return an input `X_original` whose transform would be X.
  
      Parameters
      ----------
      X : array-like of shape (n_samples, n_components)
          New data, where `n_samples` is the number of samples
          and `n_components` is the number of components.
  
      Returns
      -------
      X_original array-like of shape (n_samples, n_features)
          Original data, where `n_samples` is the number of samples
          and `n_features` is the number of features.
  
      Notes
      -----
      If whitening is enabled, inverse_transform will compute the
      exact inverse operation, which includes reversing whitening.
      """
      if self.whiten:
          return (
              np.dot(
                  X,
                  np.sqrt(self.explained_variance_[:, np.newaxis])
                  * self.components_,
              )
              + self.mean_
          )
      else:
          return np.dot(X, self.components_) + self.mean_
  
file_path: catboost/contrib/python/scikit-learn/py3/sklearn/decomposition/_base.py
imports: from abc import ABCMeta, abstractmethod
  import numpy
  from scipy import linalg
  from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, TransformerMixin
  from ..utils.validation import check_is_fitted
label_desc: Use image node_id 5 for calling the _BasePCA obj's underlying member method code with example usage: obj.inverse_transform(X) and returns: unknown, unknown