code: def retinanet_resnet50_fpn(
      *,
      weights: Optional[RetinaNet_ResNet50_FPN_Weights] = None,
      progress: bool = True,
      num_classes: Optional[int] = None,
      weights_backbone: Optional[
          ResNet50_Weights
      ] = ResNet50_Weights.IMAGENET1K_V1,
      trainable_backbone_layers: Optional[int] = None,
      **kwargs: Any,
  ) -> RetinaNet:
      """
      Constructs a RetinaNet model with a ResNet-50-FPN backbone.
  
      .. betastatus:: detection module
  
      Reference: `Focal Loss for Dense Object Detection <https://arxiv.org/abs/1708.02002>`_.
  
      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.
  
      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 of each detection
  
      For more details on the output, you may refer to :ref:`instance_seg_output`.
  
      Example::
  
          >>> model = torchvision.models.detection.retinanet_resnet50_fpn(weights=RetinaNet_ResNet50_FPN_Weights.DEFAULT)
          >>> model.eval()
          >>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
          >>> predictions = model(x)
  
      Args:
          weights (:class:`~torchvision.models.detection.RetinaNet_ResNet50_FPN_Weights`, optional): The
              pretrained weights to use. See
              :class:`~torchvision.models.detection.RetinaNet_ResNet50_FPN_Weights`
              below for more details, and possible values. By default, no
              pre-trained weights are used.
          progress (bool): 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.ResNet50_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 3.
          **kwargs: parameters passed to the ``torchvision.models.detection.RetinaNet``
              base class. Please refer to the `source code
              <https://github.com/pytorch/vision/blob/main/torchvision/models/detection/retinanet.py>`_
              for more details about this class.
  
      .. autoclass:: torchvision.models.detection.RetinaNet_ResNet50_FPN_Weights
          :members:
      """
      weights = RetinaNet_ResNet50_FPN_Weights.verify(weights)
      weights_backbone = ResNet50_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 = 91
  
      is_trained = weights is not None or weights_backbone is not None
      trainable_backbone_layers = _validate_trainable_layers(
          is_trained, trainable_backbone_layers, 5, 3
      )
      norm_layer = (
          misc_nn_ops.FrozenBatchNorm2d
          if is_trained
          else nn.BatchNorm2d
      )
  
      backbone = resnet50(
          weights=weights_backbone,
          progress=progress,
          norm_layer=norm_layer,
      )
      # skip P2 because it generates too many anchors (according to their paper)
      backbone = _resnet_fpn_extractor(
          backbone,
          trainable_backbone_layers,
          returned_layers=[2, 3, 4],
          extra_blocks=LastLevelP6P7(256, 256),
      )
      model = RetinaNet(backbone, num_classes, **kwargs)
  
      if weights is not None:
          model.load_state_dict(
              weights.get_state_dict(progress=progress, check_hash=True)
          )
          if weights == RetinaNet_ResNet50_FPN_Weights.COCO_V1:
              overwrite_eps(model, 0.0)
  
      return model
  
imports: import math
  import warnings
  from collections import OrderedDict
  from functools import partial
  from typing import Any, Callable, Dict, List, Optional, Tuple
  import torch
  from torch import nn, Tensor
  from ...ops import boxes, misc, sigmoid_focal_loss
  from ...ops.feature_pyramid_network import LastLevelP6P7
  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 ..resnet import resnet50, ResNet50_Weights
  from .None import _utils
  from ._utils import _box_loss, overwrite_eps
  from .anchor_utils import AnchorGenerator
  from .backbone_utils import _resnet_fpn_extractor, _validate_trainable_layers
  from .transform import GeneralizedRCNNTransform
label_desc: Use image node_id 4 for calling a global function with example usage: retinanet_resnet50_fpn() and returns: model