class_docstr: A SaliencyMask class that implements the integrated gradients method.
docstr: The importance of the node is defined as the sum of all the feature importance of the node.
  
  Args:
      Refer to the parameters in get_integrated_node_masks.
  
  return (float): Importance score for the node.
code: def get_node_importance(self, node_id, class_of_interest, steps=20):
      """
      The importance of the node is defined as the sum of all the feature importance of the node.
  
      Args:
          Refer to the parameters in get_integrated_node_masks.
  
      return (float): Importance score for the node.
      """
      gradients = self.get_integrated_node_masks(
          node_id, class_of_interest, steps=steps
      )
      return np.sum(gradients, axis=1)
  
file_path: stellargraph/stellargraph/interpretability/saliency_maps/integrated_gradients_gat.py
imports: import numpy
  from .saliency_gat import GradientSaliencyGAT
  import scipy.sparse
  from tensorflow.keras import backend
label_desc: Use image node_id 4 for calling the IntegratedGradientsGAT obj's underlying member method code with example usage: obj.get_node_importance(node_id, class_of_interest, steps) and returns: np