code: def build_unit_convnet_backbone(args):
      position_embedding = PositionEmbeddingSine(
          args.encoder_hidden_dim // 2, normalize=True
      )
      train_backbone = args.lr_backbone > 0
      return_interm_layers = False
      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: import math
  from collections import OrderedDict
  import torch
  import torch.nn.functional
  import torchvision
  from mmf.models.unit.misc import NestedTensor
  from torch import nn
  from torchvision.models._utils import IntermediateLayerGetter
  from torchvision.ops.misc import FrozenBatchNorm2d
label_desc: Use image node_id 1 for calling a global function with example usage: build_unit_convnet_backbone(args) and returns: model