class_docstr: This class implements the preprocessing of adjacency matrices in GCN. We implement it in tensorflow so that
  while computing the saliency maps, we are able to calculate the gradients in an end-to-end way.
  We currently only support this for tensorflow backend.
  
  Args:
  num_of_nodes (int pair): The number of nodes in the graph.
class_docstr_tok: ["This","class","implements","the","preprocessing","of","adjacency","matrices","in","GCN",".","We","implement","it","in","tensorflow","so","that","while","computing","the","saliency","maps",",","we","are","able","to","calculate","the","gradients","in","an","end-to-end","way",".","We","currently","only","support","this","for","tensorflow","backend",".","Args",":","num_of_nodes","(","int","pair",")",":","The","number","of","nodes","in","the","graph","."]
code: def build(self, input_shape):
      super().build(input_shape)
  
file_path: stellargraph/stellargraph/layer/preprocessing_layer.py
imports: from tensorflow.keras import backend
  import tensorflow
  from tensorflow.keras.layers import Layer
  from tensorflow import keras
  import numpy
label_desc: Use image node_id 2 for calling the GraphPreProcessingLayer obj's underlying member method code with example usage: obj.build(input_shape) without return types