code: def build_backbone(args):
      position_embedding = build_position_encoding(args)
      train_backbone = args.lr_backbone > 0
      return_interm_layers = args.masks
      backbone = Backbone(
          args.backbone,
          train_backbone,
          return_interm_layers,
          args.dilation,
      )
      model = Joiner(backbone, position_embedding)
      model.num_channels = backbone.num_channels
      return model
  
imports: from collections import OrderedDict
  import torch
  import torch.nn.functional
  import torchvision
  from torch import nn
  from torchvision.models._utils import IntermediateLayerGetter
  from typing import Dict, List
  from util.misc import NestedTensor, is_main_process
  from .position_encoding import build_position_encoding
label_desc: Use image node_id 1 for calling a global function with example usage: build_backbone(args) and returns: model