code: def lraspp_mobilenet_v3_large(
      *,
      weights: Optional[LRASPP_MobileNet_V3_Large_Weights] = None,
      progress: bool = True,
      num_classes: Optional[int] = None,
      weights_backbone: Optional[
          MobileNet_V3_Large_Weights
      ] = MobileNet_V3_Large_Weights.IMAGENET1K_V1,
      **kwargs: Any,
  ) -> LRASPP:
      """Constructs a Lite R-ASPP Network model with a MobileNetV3-Large backbone from
      `Searching for MobileNetV3 <https://arxiv.org/abs/1905.02244>`_ paper.
  
      .. betastatus:: segmentation module
  
      Args:
          weights (:class:`~torchvision.models.segmentation.LRASPP_MobileNet_V3_Large_Weights`, optional): The
              pretrained weights to use. See
              :class:`~torchvision.models.segmentation.LRASPP_MobileNet_V3_Large_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.MobileNet_V3_Large_Weights`, optional): The pretrained
              weights for the backbone.
          **kwargs: parameters passed to the ``torchvision.models.segmentation.LRASPP``
              base class. Please refer to the `source code
              <https://github.com/pytorch/vision/blob/main/torchvision/models/segmentation/lraspp.py>`_
              for more details about this class.
  
      .. autoclass:: torchvision.models.segmentation.LRASPP_MobileNet_V3_Large_Weights
          :members:
      """
      if kwargs.pop("aux_loss", False):
          raise NotImplementedError(
              "This model does not use auxiliary loss"
          )
  
      weights = LRASPP_MobileNet_V3_Large_Weights.verify(weights)
      weights_backbone = MobileNet_V3_Large_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"]),
          )
      elif num_classes is None:
          num_classes = 21
  
      backbone = mobilenet_v3_large(
          weights=weights_backbone, dilated=True
      )
      model = _lraspp_mobilenetv3(backbone, num_classes)
  
      if weights is not None:
          model.load_state_dict(
              weights.get_state_dict(progress=progress, check_hash=True)
          )
  
      return model
  
imports: from collections import OrderedDict
  from functools import partial
  from typing import Any, Dict, Optional
  from torch import nn, Tensor
  from torch.nn import functional
  from ...transforms._presets import SemanticSegmentation
  from ...utils import _log_api_usage_once
  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
label_desc: Use image node_id 2 for calling a global function with example usage: lraspp_mobilenet_v3_large() and returns: model