code: def deeplabv3_resnet101(
      *,
      weights: Optional[DeepLabV3_ResNet101_Weights] = None,
      progress: bool = True,
      num_classes: Optional[int] = None,
      aux_loss: Optional[bool] = None,
      weights_backbone: Optional[
          ResNet101_Weights
      ] = ResNet101_Weights.IMAGENET1K_V1,
      **kwargs: Any,
  ) -> DeepLabV3:
      """Constructs a DeepLabV3 model with a ResNet-101 backbone.
  
      .. betastatus:: segmentation module
  
      Reference: `Rethinking Atrous Convolution for Semantic Image Segmentation <https://arxiv.org/abs/1706.05587>`__.
  
      Args:
          weights (:class:`~torchvision.models.segmentation.DeepLabV3_ResNet101_Weights`, optional): The
              pretrained weights to use. See
              :class:`~torchvision.models.segmentation.DeepLabV3_ResNet101_Weights` below for
              more details, and possible values. By default, no pre-trained
              weights are used.
          progress (bool, optional): If True, displays a progress bar of the
              download to stderr. Default is True.
          num_classes (int, optional): number of output classes of the model (including the background)
          aux_loss (bool, optional): If True, it uses an auxiliary loss
          weights_backbone (:class:`~torchvision.models.ResNet101_Weights`, optional): The pretrained weights for the
              backbone
          **kwargs: unused
  
      .. autoclass:: torchvision.models.segmentation.DeepLabV3_ResNet101_Weights
          :members:
      """
      weights = DeepLabV3_ResNet101_Weights.verify(weights)
      weights_backbone = ResNet101_Weights.verify(weights_backbone)
  
      if weights is not None:
          weights_backbone = None
          num_classes = _ovewrite_value_param(
              "num_classes",
              num_classes,
              len(weights.meta["categories"]),
          )
          aux_loss = _ovewrite_value_param("aux_loss", aux_loss, True)
      elif num_classes is None:
          num_classes = 21
  
      backbone = resnet101(
          weights=weights_backbone,
          replace_stride_with_dilation=[False, True, True],
      )
      model = _deeplabv3_resnet(backbone, num_classes, aux_loss)
  
      if weights is not None:
          model.load_state_dict(
              weights.get_state_dict(progress=progress, check_hash=True)
          )
  
      return model
  
imports: from functools import partial
  from typing import Any, Optional, Sequence
  import torch
  from torch import nn
  from torch.nn import functional
  from ...transforms._presets import SemanticSegmentation
  from .._api import register_model, Weights, WeightsEnum
  from .._meta import _VOC_CATEGORIES
  from .._utils import _ovewrite_value_param, handle_legacy_interface, IntermediateLayerGetter
  from ..mobilenetv3 import mobilenet_v3_large, MobileNet_V3_Large_Weights, MobileNetV3
  from ..resnet import ResNet, resnet101, ResNet101_Weights, resnet50, ResNet50_Weights
  from ._utils import _SimpleSegmentationModel
  from .fcn import FCNHead
label_desc: Use image node_id 4 for calling a global function with example usage: deeplabv3_resnet101() and returns: model