class_docstr: Base class for SparsePCA and MiniBatchSparsePCA
class_docstr_tok: ["Base","class","for","SparsePCA","and","MiniBatchSparsePCA"]
docstr: Transform data from the latent space to the original space.
  
  This inversion is an approximation due to the loss of information
  induced by the forward decomposition.
  
  .. versionadded:: 1.2
  
  Parameters
  ----------
  X : ndarray of shape (n_samples, n_components)
      Data in the latent space.
  
  Returns
  -------
  X_original : ndarray of shape (n_samples, n_features)
      Reconstructed data in the original space.
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code: def inverse_transform(self, X):
      """Transform data from the latent space to the original space.
  
      This inversion is an approximation due to the loss of information
      induced by the forward decomposition.
  
      .. versionadded:: 1.2
  
      Parameters
      ----------
      X : ndarray of shape (n_samples, n_components)
          Data in the latent space.
  
      Returns
      -------
      X_original : ndarray of shape (n_samples, n_features)
          Reconstructed data in the original space.
      """
      check_is_fitted(self)
      X = check_array(X)
  
      return (X @ self.components_) + self.mean_
  
imports: from numbers import Integral, Real
  import numpy
  from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, TransformerMixin, _fit_context
  from ..linear_model import ridge_regression
  from ..utils import check_random_state
  from ..utils._param_validation import Hidden, Interval, StrOptions
  from ..utils.extmath import svd_flip
  from ..utils.validation import check_array, check_is_fitted
  from ._dict_learning import MiniBatchDictionaryLearning, dict_learning
label_desc: Use image node_id 4 for calling the _BaseSparsePCA obj's underlying member method code with example usage: obj.inverse_transform(X) and returns: unknown