code: def ssd300_vgg16(
      *,
      weights: Optional[SSD300_VGG16_Weights] = None,
      progress: bool = True,
      num_classes: Optional[int] = None,
      weights_backbone: Optional[
          VGG16_Weights
      ] = VGG16_Weights.IMAGENET1K_FEATURES,
      trainable_backbone_layers: Optional[int] = None,
      **kwargs: Any,
  ) -> SSD:
      """The SSD300 model is based on the `SSD: Single Shot MultiBox Detector
      <https://arxiv.org/abs/1512.02325>`_ paper.
  
      .. betastatus:: detection module
  
      The input to the model is expected to be a list of tensors, each of shape [C, H, W], one for each
      image, and should be in 0-1 range. Different images can have different sizes, but they will be resized
      to a fixed size before passing it to the backbone.
  
      The behavior of the model changes depending on if it is in training or evaluation mode.
  
      During training, the model expects both the input tensors and targets (list of dictionary),
      containing:
  
          - boxes (``FloatTensor[N, 4]``): the ground-truth boxes in ``[x1, y1, x2, y2]`` format, with
            ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``.
          - labels (Int64Tensor[N]): the class label for each ground-truth box
  
      The model returns a Dict[Tensor] during training, containing the classification and regression
      losses.
  
      During inference, the model requires only the input tensors, and returns the post-processed
      predictions as a List[Dict[Tensor]], one for each input image. The fields of the Dict are as
      follows, where ``N`` is the number of detections:
  
          - boxes (``FloatTensor[N, 4]``): the predicted boxes in ``[x1, y1, x2, y2]`` format, with
            ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``.
          - labels (Int64Tensor[N]): the predicted labels for each detection
          - scores (Tensor[N]): the scores for each detection
  
      Example:
  
          >>> model = torchvision.models.detection.ssd300_vgg16(weights=SSD300_VGG16_Weights.DEFAULT)
          >>> model.eval()
          >>> x = [torch.rand(3, 300, 300), torch.rand(3, 500, 400)]
          >>> predictions = model(x)
  
      Args:
          weights (:class:`~torchvision.models.detection.SSD300_VGG16_Weights`, optional): The pretrained
                  weights to use. See
                  :class:`~torchvision.models.detection.SSD300_VGG16_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.VGG16_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 5, with 5 meaning all backbone layers are trainable. If ``None`` is
              passed (the default) this value is set to 4.
          **kwargs: parameters passed to the ``torchvision.models.detection.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.SSD300_VGG16_Weights
          :members:
      """
      weights = SSD300_VGG16_Weights.verify(weights)
      weights_backbone = VGG16_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,
          5,
          4,
      )
  
      # Use custom backbones more appropriate for SSD
      backbone = vgg16(weights=weights_backbone, progress=progress)
      backbone = _vgg_extractor(
          backbone, False, trainable_backbone_layers
      )
      anchor_generator = DefaultBoxGenerator(
          [[2], [2, 3], [2, 3], [2, 3], [2], [2]],
          scales=[0.07, 0.15, 0.33, 0.51, 0.69, 0.87, 1.05],
          steps=[8, 16, 32, 64, 100, 300],
      )
  
      defaults = {
          # Rescale the input in a way compatible to the backbone
          "image_mean": [0.48235, 0.45882, 0.40784],
          "image_std": [
              1.0 / 255.0,
              1.0 / 255.0,
              1.0 / 255.0,
          ],  # undo the 0-1 scaling of toTensor
      }
      kwargs: Any = {**defaults, **kwargs}
      model = SSD(
          backbone, anchor_generator, (300, 300), num_classes, **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 typing import Any, Dict, List, Optional, Tuple
  import torch
  import torch.nn.functional
  from torch import nn, Tensor
  from ...ops import boxes
  from ...transforms._presets import ObjectDetection
  from ...utils import _log_api_usage_once
  from .._api import register_model, Weights, WeightsEnum
  from .._meta import _COCO_CATEGORIES
  from .._utils import _ovewrite_value_param, handle_legacy_interface
  from ..vgg import VGG, vgg16, VGG16_Weights
  from .None import _utils
  from .anchor_utils import DefaultBoxGenerator
  from .backbone_utils import _validate_trainable_layers
  from .transform import GeneralizedRCNNTransform
label_desc: Use image node_id 3 for calling a global function with example usage: ssd300_vgg16() and returns: model