code: def load_tf_conv2d(weights, layer, transpose=False):
      """Load tensorflow weights into nn.Conv2d object.
  
      Arguments:
          weights {list} -- Tensorflow parameters.
          layer {torch.nn.Module} -- nn.Conv2d.
      """
      if isinstance(weights, list):
          if len(weights) == 2:
              layer.bias.data = torch.tensor(weights[1]).view(
                  layer.bias.data.shape
              )
          weights = weights[0]
  
      if transpose:
          dim_order = (3, 2, 1, 0)
      else:
          dim_order = (3, 2, 0, 1)
  
      layer.weight.data = (
          torch.tensor(weights)
          .permute(dim_order)
          .view(layer.weight.data.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 4 for calling a global function with example usage: load_tf_conv2d(weights, layer, transpose) without return types