code: def load_tf_model_weights(
      mdl, layer_lookup, tf_mdl_dir, is_resnet=True, arg_num=None
  ):
      """Load tensorflow parameters into a pytorch model.
  
      Arguments:
          mdl {torch.nn.Module} -- Pytorch model.
          layer_lookup {[type]} -- Dictionary mapping pytorch attribute names to (partial)
              tensorflow variable names, and a function suitable for loading weights.
              Expects dict of the form {'attr': ['tf_name', function]}.
          tf_mdl_dir {str} -- Location of protobuf, checkpoint, meta files.
      """
      tf.reset_default_graph()
      with tf.Session() as sess:
          tf_layers, tf_params, tf_shapes = import_tf_params(
              tf_mdl_dir, sess
          )
          layer_info = get_layer_indices(layer_lookup, tf_layers)
  
          for layer_name, info in layer_info.items():
              print(f"Loading {info[0]}/* into {layer_name}")
              weights = [tf_params[i] for i in info[2]]
              layer = getattr(mdl, layer_name)
              info[1](weights, layer)
  
          test_loaded_params(mdl, tf_params, tf_layers)
  
          if is_resnet:
              compare_model_outputs(
                  mdl, sess, torch.randn(5, 160, 160, 3).detach()
              )
  
imports: import tensorflow
  import torch
  import json
  import os, sys
  from dependencies.facenet.src import facenet
  from dependencies.facenet.src.models import inception_resnet_v1
  from dependencies.facenet.src.align import detect_face
  from models.inception_resnet_v1 import InceptionResnetV1
  from models.mtcnn import PNet, RNet, ONet
label_desc: Use image node_id 19 for calling a global function with example usage: load_tf_model_weights(mdl, layer_lookup, tf_mdl_dir, is_resnet, arg_num) without return types