code: def transform(self, X, y=None):
      if self.selector == "KeepAll":
          return X
  
      if scipy.sparse.issparse(X):
          if X.getformat() == "csr":
              # convert to a csc (column) matrix, rather than a csr (row) matrix
              X = X.tocsc()
  
          # Slice that column matrix to only get the relevant columns that we already calculated in fit:
          X = X[:, self.index_mask]
  
          # convert back to a csr matrix
          return X.tocsr()
  
      # If this is a dense matrix:
      else:
          X = X[:, self.index_mask]
          return X
  
file_path: auto_ml/auto_ml/utils_feature_selection.py
imports: from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
  import scipy
  import itertools
  from sklearn.feature_selection import GenericUnivariateSelect, RFECV, SelectFromModel
label_desc: Use image node_id 4 for calling the FeatureSelectionTransformer obj's underlying member method code with example usage: obj.transform(X, y) and returns: X, X, X