code: def load_tf_batchNorm(weights, layer):
      """Load tensorflow weights into nn.BatchNorm object.
  
      Arguments:
          weights {list} -- Tensorflow parameters.
          layer {torch.nn.Module} -- nn.BatchNorm.
      """
      layer.bias.data = torch.tensor(weights[0]).view(
          layer.bias.data.shape
      )
      layer.weight.data = torch.ones_like(layer.weight.data)
      layer.running_mean = torch.tensor(weights[1]).view(
          layer.running_mean.shape
      )
      layer.running_var = torch.tensor(weights[2]).view(
          layer.running_var.shape
      )
  
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 3 for calling a global function with example usage: load_tf_batchNorm(weights, layer) without return types