code: def blur_input_each_step():
      """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.
      """
  
      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
  
imports: from __future__ import absolute_import, division, print_function
  from decorator import decorator
  import numpy
  import tensorflow
  from lucid.optvis.objectives_util import _dot, _dot_cossim, _extract_act_pos, _make_arg_str, _T_force_NHWC, _T_handle_batch
label_desc: Use image node_id 13 for calling a global function with example usage: blur_input_each_step() and returns: inner, unknown