class_docstr: Base class for random projections.
  
  Warning: This class should not be used directly.
  Use derived classes instead.
docstr: Project data back to its original space.
  
  Returns an array X_original whose transform would be X. Note that even
  if X is sparse, X_original is dense: this may use a lot of RAM.
  
  If `compute_inverse_components` is False, the inverse of the components is
  computed during each call to `inverse_transform` which can be costly.
  
  Parameters
  ----------
  X : {array-like, sparse matrix} of shape (n_samples, n_components)
      Data to be transformed back.
  
  Returns
  -------
  X_original : ndarray of shape (n_samples, n_features)
      Reconstructed data.
code: def inverse_transform(self, X):
      """Project data back to its original space.
  
      Returns an array X_original whose transform would be X. Note that even
      if X is sparse, X_original is dense: this may use a lot of RAM.
  
      If `compute_inverse_components` is False, the inverse of the components is
      computed during each call to `inverse_transform` which can be costly.
  
      Parameters
      ----------
      X : {array-like, sparse matrix} of shape (n_samples, n_components)
          Data to be transformed back.
  
      Returns
      -------
      X_original : ndarray of shape (n_samples, n_features)
          Reconstructed data.
      """
      check_is_fitted(self)
  
      X = check_array(
          X,
          dtype=[np.float64, np.float32],
          accept_sparse=("csr", "csc"),
      )
  
      if self.compute_inverse_components:
          return X @ self.inverse_components_.T
  
      inverse_components = self._compute_inverse_components()
      return X @ inverse_components.T
  
imports: import warnings
  from abc import ABCMeta, abstractmethod
  from numbers import Integral, Real
  import numpy
  import scipy.sparse
  from scipy import linalg
  from .base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, TransformerMixin, _fit_context
  from .exceptions import DataDimensionalityWarning
  from .utils import check_random_state
  from .utils._param_validation import Interval, StrOptions, validate_params
  from .utils.extmath import safe_sparse_dot
  from .utils.random import sample_without_replacement
  from .utils.validation import check_array, check_is_fitted
label_desc: Use image node_id 5 for calling the BaseRandomProjection obj's underlying member method code with example usage: obj.inverse_transform(X) and returns: unknown, unknown