code: def blur_input_each_step():
    
    def inner(T):
      t_input = T("input")
      t_input_blurred = tf.stop_gradient(_tf_blur(t_input))
      return 0.5*tf.reduce_sum((t_input - t_input_blurred)**2)
    return inner
docs: Minimizing this objective is equivelant to blurring input each step.
  
    Optimizing (-k)*blur_input_each_step() is equivelant to:
  
      input <- (1-k)*input + k*blur(input)
  
    An operation that was used in early feature visualization work.
    See Nguyen, et al., 2015.