class_docstr: Perform gradients backpropagation for a given input
  
  Paper: [Deep Inside Convolutional Networks: Visualising Image Classification
      Models and Saliency Maps](https://arxiv.org/abs/1312.6034)
class_docstr_tok: ["Perform","gradients","backpropagation","for","a","given","input","Paper",":","[","Deep","Inside","Convolutional","Networks",":","Visualising","Image","Classification","Models","and","Saliency","Maps","]","(","https",":","\/\/arxiv.org\/abs\/1312.6034",")"]
code: def set_model(self, model):
      super().set_model(model)
      self.score_model = self.explainer.get_score_model(model)
  
file_path: tf-explain/tf_explain/callbacks/vanilla_gradients.py
imports: from datetime import datetime
  from pathlib import Path
  import numpy
  import tensorflow
  from tensorflow.keras.callbacks import Callback
  from tf_explain.core.vanilla_gradients import VanillaGradients
label_desc: Use image node_id 2 for calling the VanillaGradientsCallback obj's underlying member method code with example usage: obj.set_model(model) without return types