docstr: Returns transformed X to the original number of column.
  This operation is lossy and all columns not in the transformed data
  will be returned as columns of 0s.
docstr_tok: ["Returns","transformed","X","to","the","original","number","of","column",".","This","operation","is","lossy","and","all","columns","not","in","the","transformed","data","will","be","returned","as","columns","of","0s","."]
code: def inverse_transform(self, X):
      """
      Returns transformed X to the original number of column.
      This operation is lossy and all columns not in the transformed data
      will be returned as columns of 0s.
      """
      self._get_support_mask()
      X_new = np.zeros((X.shape[0], self.scores_.shape[0]))
      X_new[self.selected_features_] = X
      return X_new
  
imports: import time
  import numpy
  import pandas
  from sklearn.base import BaseEstimator
  from sklearn.feature_selection import SelectorMixin
  from sklearn.utils.validation import check_is_fitted
  import torch
  from nni.feature_engineering.feature_selector import FeatureSelector
  from .None import constants
  from .fginitialize import PrepareData
  from .fgtrain import _train
label_desc: Use image node_id 13 for calling the FeatureGradientSelector obj's underlying member method code with example usage: obj.inverse_transform(X) and returns: X_new