code: def test_loaded_params(mdl, tf_params, tf_layers):
      """Check each parameter in a pytorch model for an equivalent parameter
      in a list of tensorflow variables.
  
      Arguments:
          mdl {torch.nn.Module} -- Pytorch model.
          tf_params {list} -- List of ndarrays representing tensorflow variables.
          tf_layers {list} -- Corresponding list of tensorflow variable names.
      """
      tf_means = torch.stack(
          [torch.tensor(p).mean() for p in tf_params]
      )
      for name, param in mdl.named_parameters():
          pt_mean = param.data.mean()
          matching_inds = ((tf_means - pt_mean).abs() < 1e-8).nonzero()
          print(
              f"{name} equivalent to {[tf_layers[i] for i in matching_inds]}"
          )
  
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 16 for calling a global function with example usage: test_loaded_params(mdl, tf_params, tf_layers) without return types