code: def sparsify(sess, eval_model, pruning_strategy, pruning_params):
      """Prune the weights of a model and evaluate."""
      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)
  
imports: import numpy
  from tensor2tensor.layers import common_layers
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
label_desc: Use image node_id 3 for calling a global function with example usage: sparsify(sess, eval_model, pruning_strategy, pruning_params) and returns: True, False, False