code: def _X_selected(X, selected):
      """Split X into selected features and other features"""
      n_features = X.shape[1]
      ind = np.arange(n_features)
      sel = np.zeros(n_features, dtype=bool)
      sel[np.asarray(selected)] = True
      non_sel = np.logical_not(sel)
      n_selected = np.sum(sel)
      X_sel = X[:, ind[sel]]
      X_not_sel = X[:, ind[non_sel]]
      return X_sel, X_not_sel, n_selected, n_features
  
file_path: tpot/tpot/builtins/one_hot_encoder.py
imports: import numpy
  from scipy import sparse
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.utils import check_array
label_desc: Use image node_id 2 for calling a global function with example usage: _X_selected(X, selected) and returns: X_sel,  X_not_sel,  n_selected,  n_features