code: def resnet_weight():
      hp = hparam.HParams()
      hp.add_hparam("strategy", "weight")
      hp.add_hparam("black_list", ["logits", "bias"])
      hp.add_hparam("white_list", ["td_conv"])
      hp.add_hparam("sparsities", [0.1 * i for i in range(10)])
      return hp
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_layers
  from tensor2tensor.utils import hparam
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 26 for calling a global function with example usage: resnet_weight() and returns: hp