code: def ssdlite320_mobilenet_v3_large(
      *,
      weights: Optional[SSDLite320_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,
      trainable_backbone_layers: Optional[int] = None,
      norm_layer: Optional[Callable[..., nn.Module]] = None,
      **kwargs: Any,
  ) -> SSD:
      """SSDlite model architecture with input size 320x320 and a MobileNetV3 Large backbone, as
      described at `Searching for MobileNetV3 <https://arxiv.org/abs/1905.02244>`__ and
      `MobileNetV2: Inverted Residuals and Linear Bottlenecks <https://arxiv.org/abs/1801.04381>`__.
  
      .. betastatus:: detection module
  
      See :func:`~torchvision.models.detection.ssd300_vgg16` for more details.
  
      Example:
  
          >>> model = torchvision.models.detection.ssdlite320_mobilenet_v3_large(weights=SSDLite320_MobileNet_V3_Large_Weights.DEFAULT)
          >>> model.eval()
          >>> x = [torch.rand(3, 320, 320), torch.rand(3, 500, 400)]
          >>> predictions = model(x)
  
      Args:
          weights (:class:`~torchvision.models.detection.SSDLite320_MobileNet_V3_Large_Weights`, optional): The
              pretrained weights to use. See
              :class:`~torchvision.models.detection.SSDLite320_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).
          weights_backbone (:class:`~torchvision.models.MobileNet_V3_Large_Weights`, optional): The pretrained
              weights for the backbone.
          trainable_backbone_layers (int, optional): number of trainable (not frozen) layers
              starting from final block. Valid values are between 0 and 6, with 6 meaning all
              backbone layers are trainable. If ``None`` is passed (the default) this value is
              set to 6.
          norm_layer (callable, optional): Module specifying the normalization layer to use.
          **kwargs: parameters passed to the ``torchvision.models.detection.ssd.SSD``
              base class. Please refer to the `source code
              <https://github.com/pytorch/vision/blob/main/torchvision/models/detection/ssd.py>`_
              for more details about this class.
  
      .. autoclass:: torchvision.models.detection.SSDLite320_MobileNet_V3_Large_Weights
          :members:
      """
  
      weights = SSDLite320_MobileNet_V3_Large_Weights.verify(weights)
      weights_backbone = MobileNet_V3_Large_Weights.verify(
          weights_backbone
      )
  
      if "size" in kwargs:
          warnings.warn(
              "The size of the model is already fixed; ignoring the parameter."
          )
  
      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 = 91
  
      trainable_backbone_layers = _validate_trainable_layers(
          weights is not None or weights_backbone is not None,
          trainable_backbone_layers,
          6,
          6,
      )
  
      # Enable reduced tail if no pretrained backbone is selected. See Table 6 of MobileNetV3 paper.
      reduce_tail = weights_backbone is None
  
      if norm_layer is None:
          norm_layer = partial(nn.BatchNorm2d, eps=0.001, momentum=0.03)
  
      backbone = mobilenet_v3_large(
          weights=weights_backbone,
          progress=progress,
          norm_layer=norm_layer,
          reduced_tail=reduce_tail,
          **kwargs,
      )
      if weights_backbone is None:
          # Change the default initialization scheme if not pretrained
          _normal_init(backbone)
      backbone = _mobilenet_extractor(
          backbone,
          trainable_backbone_layers,
          norm_layer,
      )
  
      size = (320, 320)
      anchor_generator = DefaultBoxGenerator(
          [[2, 3] for _ in range(6)], min_ratio=0.2, max_ratio=0.95
      )
      out_channels = det_utils.retrieve_out_channels(backbone, size)
      num_anchors = anchor_generator.num_anchors_per_location()
      if len(out_channels) != len(anchor_generator.aspect_ratios):
          raise ValueError(
              f"The length of the output channels from the backbone {len(out_channels)} do not match the length of the anchor generator aspect ratios {len(anchor_generator.aspect_ratios)}"
          )
  
      defaults = {
          "score_thresh": 0.001,
          "nms_thresh": 0.55,
          "detections_per_img": 300,
          "topk_candidates": 300,
          # Rescale the input in a way compatible to the backbone:
          # The following mean/std rescale the data from [0, 1] to [-1, 1]
          "image_mean": [0.5, 0.5, 0.5],
          "image_std": [0.5, 0.5, 0.5],
      }
      kwargs: Any = {**defaults, **kwargs}
      model = SSD(
          backbone,
          anchor_generator,
          size,
          num_classes,
          head=SSDLiteHead(
              out_channels, num_anchors, num_classes, norm_layer
          ),
          **kwargs,
      )
  
      if weights is not None:
          model.load_state_dict(
              weights.get_state_dict(progress=progress, check_hash=True)
          )
  
      return model
  
imports: import warnings
  from collections import OrderedDict
  from functools import partial
  from typing import Any, Callable, Dict, List, Optional, Union
  import torch
  from torch import nn, Tensor
  from ...ops.misc import Conv2dNormActivation
  from ...transforms._presets import ObjectDetection
  from ...utils import _log_api_usage_once
  from ..None import mobilenet
  from .._api import register_model, Weights, WeightsEnum
  from .._meta import _COCO_CATEGORIES
  from .._utils import _ovewrite_value_param, handle_legacy_interface
  from ..mobilenetv3 import mobilenet_v3_large, MobileNet_V3_Large_Weights
  from .None import _utils
  from .anchor_utils import DefaultBoxGenerator
  from .backbone_utils import _validate_trainable_layers
  from .ssd import SSD, SSDScoringHead
label_desc: Use image node_id 5 for calling a global function with example usage: ssdlite320_mobilenet_v3_large() and returns: model