class_docstr: Class to compute the saliency maps based on the vanilla gradient w.r.t the adjacency and the feature matrix.
  
  Args:
      model (Keras model object): The differentiable graph model object.
          model.input should contain two tensors:
              - features (Numpy array): The placeholder of the feature matrix.
              - adj (Numpy array): The placeholder of the adjacency matrix.
          model.output (Keras tensor): The tensor of model prediction output.
              This is typically the logit or softmax output.
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docstr: Set values of the integrated gradient parameters in all layers of the model.
  
  Args:
      delta_value: Value of the `delta` parameter
      edge_value: Value of the `non_exist_edges` parameter
docstr_tok: ["Set","values","of","the","integrated","gradient","parameters","in","all","layers","of","the","model",".","Args",":","delta_value",":","Value","of","the","`","delta","`","parameter","edge_value",":","Value","of","the","`","non_exist_edges","`","parameter"]
code: def set_ig_values(self, delta_value, edge_value):
      """
      Set values of the integrated gradient parameters in all layers of the model.
  
      Args:
          delta_value: Value of the `delta` parameter
          edge_value: Value of the `non_exist_edges` parameter
      """
      for delta_var in self.deltas:
          K.set_value(delta_var, delta_value)
      for edge_var in self.non_exist_edges:
          K.set_value(edge_var, edge_value)
  
file_path: stellargraph/stellargraph/interpretability/saliency_maps/saliency_gat.py
imports: import numpy
  from tensorflow.keras import backend
  import scipy.sparse
  from stellargraph.mapper import FullBatchSequence
  import tensorflow
label_desc: Use image node_id 4 for calling the GradientSaliencyGAT obj's underlying member method code with example usage: obj.set_ig_values(delta_value, edge_value) without return types