code: def inverse_transform(self, X, copy=None):
          
          check_is_fitted(self, 'scale_')
  
          copy = copy if copy is not None else self.copy
          if sparse.issparse(X):
              if self.with_mean:
                  raise ValueError(
                      "Cannot uncenter sparse matrices: pass `with_mean=False` "
                      "instead See docstring for motivation and alternatives.")
              if not sparse.isspmatrix_csr(X):
                  X = X.tocsr()
                  copy = False
              if copy:
                  X = X.copy()
              if self.scale_ is not None:
                  inplace_column_scale(X, self.scale_)
          else:
              X = numpy.asarray(X)
              if copy:
                  X = X.copy()
              if self.with_std:
                  X *= self.scale_
              if self.with_mean:
                  X += self.mean_
  
          return X
docs: Scale back the data to the original representation.
  
          :param X: Scaled data matrix.
          :type X: numpy.ndarray, shape [n_samples, n_features]
          :param bool copy: Copy the X data matrix.
          :return: X data matrix with the scaling operation reverted.
          :rtype: numpy.ndarray, shape [n_samples, n_features]