code: def deeplabv3_mobilenetv3_large(
      aux, num_classes=21, pretrain_backbone=False
  ):
      # 'mobilenetv3_large_imagenet': 'https://download.pytorch.org/models/mobilenet_v3_large-8738ca79.pth'
      # 'depv3_mobilenetv3_large_coco': "https://download.pytorch.org/models/deeplabv3_mobilenet_v3_large-fc3c493d.pth"
      backbone = mobilenet_v3_large(dilated=True)
  
      if pretrain_backbone:
          # 载入mobilenetv3 large backbone预训练权重
          backbone.load_state_dict(
              torch.load("mobilenet_v3_large.pth", map_location="cpu")
          )
  
      backbone = backbone.features
  
      # Gather the indices of blocks which are strided. These are the locations of C1, ..., Cn-1 blocks.
      # The first and last blocks are always included because they are the C0 (conv1) and Cn.
      stage_indices = (
          [0]
          + [
              i
              for i, b in enumerate(backbone)
              if getattr(b, "is_strided", False)
          ]
          + [len(backbone) - 1]
      )
      out_pos = stage_indices[-1]  # use C5 which has output_stride = 16
      out_inplanes = backbone[out_pos].out_channels
      aux_pos = stage_indices[
          -4
      ]  # use C2 here which has output_stride = 8
      aux_inplanes = backbone[aux_pos].out_channels
      return_layers = {str(out_pos): "out"}
      if aux:
          return_layers[str(aux_pos)] = "aux"
  
      backbone = IntermediateLayerGetter(
          backbone, return_layers=return_layers
      )
  
      aux_classifier = None
      # why using aux: https://github.com/pytorch/vision/issues/4292
      if aux:
          aux_classifier = FCNHead(aux_inplanes, num_classes)
  
      classifier = DeepLabHead(out_inplanes, num_classes)
  
      model = DeepLabV3(backbone, classifier, aux_classifier)
  
      return model
  
imports: from collections import OrderedDict
  from typing import Dict, List
  import torch
  from torch import nn, Tensor
  from torch.nn import functional
  from .resnet_backbone import resnet50, resnet101
  from .mobilenet_backbone import mobilenet_v3_large
label_desc: Use image node_id 3 for calling a global function with example usage: deeplabv3_mobilenetv3_large(aux, num_classes, pretrain_backbone) and returns: model