code: def import_tf_params(tf_mdl_dir, sess):
      """Import tensorflow model from save directory.
  
      Arguments:
          tf_mdl_dir {str} -- Location of protobuf, checkpoint, meta files.
          sess {tensorflow.Session} -- Tensorflow session object.
  
      Returns:
          (list, list, list) -- Tuple of lists containing the layer names,
              parameter arrays as numpy ndarrays, parameter shapes.
      """
      print("\nLoading tensorflow model\n")
      if callable(tf_mdl_dir):
          tf_mdl_dir(sess)
      else:
          facenet.load_model(tf_mdl_dir)
  
      print("\nGetting model weights\n")
      tf_layers = tf.trainable_variables()
      tf_params = sess.run(tf_layers)
  
      tf_shapes = [p.shape for p in tf_params]
      tf_layers = [l.name for l in tf_layers]
  
      if not callable(tf_mdl_dir):
          path = os.path.join(tf_mdl_dir, "layer_description.json")
      else:
          path = "data/layer_description.json"
      with open(path, "w") as f:
          json.dump({l: s for l, s in zip(tf_layers, tf_shapes)}, f)
  
      return tf_layers, tf_params, tf_shapes
  
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 1 for calling a global function with example usage: import_tf_params(tf_mdl_dir, sess) and returns: tf_layers,  tf_params,  tf_shapes