code: def sparsify(sess, eval_model, pruning_strategy, pruning_params):
    
    weights = tf.trainable_variables()
  
    def should_prune(name):
      """Whether to prune a weight or not."""
      in_whitelist = not pruning_params.white_list or any(
          e in name for e in pruning_params.white_list)
      in_blacklist = any(e in name for e in pruning_params.black_list)
  
      if pruning_params.white_list and not in_whitelist:
        return False
      elif in_blacklist:
        return False
  
      return True
  
    weights = [w for w in weights if should_prune(w.name)]
    tf.logging.info("Pruning weights: %s" % weights)
    unpruned_weights = sess.run(weights)
  
    reset_op = tf.no_op()
    for w, ow in zip(weights, unpruned_weights):
      op = tf.assign(w, ow)
      reset_op = tf.group(reset_op, op)
  
    for sparsity in pruning_params.sparsities:
      set_weights_op = tf.no_op()
      for w in weights:
        op = tf.assign(w, pruning_strategy(w, sparsity))
        set_weights_op = tf.group(set_weights_op, op)
      sess.run(set_weights_op)
  
      acc = eval_model()
      tf.logging.info("\tPruning to sparsity = %f: acc = %f" % (sparsity, acc))
      sess.run(reset_op)
docs: Prune the weights of a model and evaluate.