class_docstr: NN definition for deeplabv3_resnet101 i.e. DeepLabV3 with resnet 101 as backend
code: def __init__(self, num_classes=21, **kwargs):
      backbone = resnet.resnet101(
          pretrained=True,
          replace_stride_with_dilation=[False, True, True],
      )
      return_layers = {"layer4": "out"}
      return_layers["layer3"] = "aux"
      backbone = IntermediateLayerGetter(
          backbone, return_layers=return_layers
      )
  
      inplanes = 1024
      aux_classifier = FCNHead(inplanes, num_classes)
      inplanes = 2048
      classifier = DeepLabHead(inplanes, num_classes)
  
      super(DeepLabV3ImageSegmenter, self).__init__(
          backbone, classifier, aux_classifier
      )
  
file_path: serve/examples/image_segmenter/deeplabv3/model.py
imports: from torchvision.models.segmentation.deeplabv3 import DeepLabV3
  from torchvision.models import resnet
  from intermediate_layer_getter import IntermediateLayerGetter
  from deeplabv3 import DeepLabHead
  from fcn import FCNHead
label_desc: Use image node_id 1 to create a new DeepLabV3ImageSegmenter object from inherited base classes: DeepLabV3 with example: obj = DeepLabV3ImageSegmenter(num_classes)