code: def restore_params(network, model_path="model.npz"):
      logging.info("Restore pre-trained weights")
  
      try:
          npz = np.load(model_path, allow_pickle=True)
      except:
          print("Download the model file, placed in the /model ")
          print(
              "Weights download: ",
              weights_url["link"],
              "password:",
              weights_url["password"],
          )
  
      txt_path = "model/pose_weights_config.txt"
      f = open(txt_path, "r")
      line = f.readlines()
      for i in range(len(line)):
          # mask weights
          if len(npz[line[i].strip()].shape) == 2:
              _weight = mask_weight(npz[line[i].strip()])
          else:
              _weight = npz[line[i].strip()]
          network.all_weights[i].assign(_weight)
          logging.info(
              "  Loading weights %s in %s"
              % (
                  network.all_weights[i].shape,
                  network.all_weights[i].name,
              )
          )
  
imports: import numpy
  import tensorflow
  from tensorlayer.layers import Layer, Dropout, Dense, Input, BatchNorm, Reshape, Elementwise
  from tensorlayer.models import Model
  from tensorlayer import logging
  from .common import mask_weight, neighbour_matrix
label_desc: Use image node_id 6 for calling a global function with example usage: restore_params(network, model_path) without return types