class_docstr: Base class for all sklearn wrappers of the StructureModel.
  Implements the sklearn .fit and .predict interface.
class_docstr_tok: ["Base","class","for","all","sklearn","wrappers","of","the","StructureModel",".","Implements","the","sklearn",".fit","and",".predict","interface","."]
docstr: Unsigned importances of the features wrt to the target.
  NOTE: these are used as the graph adjacency matrix.
  Returns:
      the L2 relationship between nodes.
docstr_tok: ["Unsigned","importances","of","the","features","wrt","to","the","target",".","NOTE",":","these","are","used","as","the","graph","adjacency","matrix",".","Returns",":","the","L2","relationship","between","nodes","."]
code: def feature_importances_(self) -> np.ndarray:
      """
      Unsigned importances of the features wrt to the target.
      NOTE: these are used as the graph adjacency matrix.
      Returns:
          the L2 relationship between nodes.
      """
      return np.asarray(self.get_edges_to_node(self._target))
  
imports: import copy
  from abc import ABCMeta
  from typing import Dict, Iterable, List, Union
  import numpy
  import pandas
  from IPython.lib.display import IFrame
  from sklearn.base import BaseEstimator
  from sklearn.preprocessing import StandardScaler
  from sklearn.utils.validation import check_is_fitted, check_X_y
  from causalnex.plots import EDGE_STYLE, NODE_STYLE, display_plot_ipython, plot_structure
  from causalnex.structure.pytorch import notears
label_desc: Use image node_id 5 for calling the DAGBase obj's underlying member method code with example usage: obj.feature_importances_() and returns: np