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",")"]
docstr: Save the output to a specific dir.
  
  Args:
      grid (numpy.ndarray): Gtid of all the smoothed gradients
      output_dir (str): Output directory path
      output_name (str): Output name
docstr_tok: ["Save","the","output","to","a","specific","dir",".","Args",":","grid","(","numpy.ndarray",")",":","Gtid","of","all","the","smoothed","gradients","output_dir","(","str",")",":","Output","directory","path","output_name","(","str",")",":","Output","name"]
code: def save(self, grid, output_dir, output_name):
      """
      Save the output to a specific dir.
  
      Args:
          grid (numpy.ndarray): Gtid of all the smoothed gradients
          output_dir (str): Output directory path
          output_name (str): Output name
      """
      save_grayscale(grid, output_dir, output_name)
  
file_path: tf-explain/tf_explain/core/vanilla_gradients.py
imports: from warnings import warn
  import tensorflow
  from tf_explain.utils.display import grid_display
  from tf_explain.utils.image import transform_to_normalized_grayscale
  from tf_explain.utils.saver import save_grayscale
label_desc: Use image node_id 6 for calling the VanillaGradients obj's underlying member method code with example usage: obj.save(grid, output_dir, output_name) without return types