class_docstr: A SaliencyMask class that implements the integrated gradients method.
class_docstr_tok: ["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:
      node_idx: the index of the node to calculate gradients for.
      class_of_interest: the index for the class probability that the gradients will be calculated for.
      steps (int): The number of values we need to interpolate. Generally steps = 20 should give good enough results.
  
  return (float): Importance score for the node.
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code: def get_node_importance(
      self,
      node_idx,
      class_of_interest,
      steps=20,
  ):
      """
      The importance of the node is defined as the sum of all the feature importance of the node.
  
      Args:
          node_idx: the index of the node to calculate gradients for.
          class_of_interest: the index for the class probability that the gradients will be calculated for.
          steps (int): The number of values we need to interpolate. Generally steps = 20 should give good enough results.
  
      return (float): Importance score for the node.
      """
  
      gradients = self.get_integrated_node_masks(
          node_idx,
          class_of_interest,
          steps=steps,
      )
  
      return np.sum(gradients, axis=-1)
  
file_path: stellargraph/stellargraph/interpretability/saliency_maps/integrated_gradients.py
imports: import numpy
  from tensorflow.keras import backend
  from scipy.sparse import csr_matrix
  import tensorflow
  from stellargraph.mapper import SparseFullBatchSequence, FullBatchSequence
label_desc: Use image node_id 4 for calling the IntegratedGradients obj's underlying member method code with example usage: obj.get_node_importance(node_idx, class_of_interest, steps) and returns: np