code: def fasterrcnn_resnet50_fpn(
      *,
      weights: Optional[FasterRCNN_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,
  ) -> FasterRCNN:
      """
      Faster R-CNN model with a ResNet-50-FPN backbone from the `Faster R-CNN: Towards Real-Time Object
      Detection with Region Proposal Networks <https://arxiv.org/abs/1506.01497>`__
      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.
  
      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 a 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 for both the RPN and the R-CNN.
  
      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`.
  
      Faster R-CNN is exportable to ONNX for a fixed batch size with inputs images of fixed size.
  
      Example::
  
          >>> model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights=FasterRCNN_ResNet50_FPN_Weights.DEFAULT)
          >>> # For training
          >>> images, boxes = torch.rand(4, 3, 600, 1200), torch.rand(4, 11, 4)
          >>> boxes[:, :, 2:4] = boxes[:, :, 0:2] + boxes[:, :, 2:4]
          >>> labels = torch.randint(1, 91, (4, 11))
          >>> images = list(image for image in images)
          >>> targets = []
          >>> for i in range(len(images)):
          >>>     d = {}
          >>>     d['boxes'] = boxes[i]
          >>>     d['labels'] = labels[i]
          >>>     targets.append(d)
          >>> output = model(images, targets)
          >>> # For inference
          >>> model.eval()
          >>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
          >>> predictions = model(x)
          >>>
          >>> # optionally, if you want to export the model to ONNX:
          >>> torch.onnx.export(model, x, "faster_rcnn.onnx", opset_version = 11)
  
      Args:
          weights (:class:`~torchvision.models.detection.FasterRCNN_ResNet50_FPN_Weights`, optional): The
              pretrained weights to use. See
              :class:`~torchvision.models.detection.FasterRCNN_ResNet50_FPN_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.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.faster_rcnn.FasterRCNN``
              base class. Please refer to the `source code
              <https://github.com/pytorch/vision/blob/main/torchvision/models/detection/faster_rcnn.py>`_
              for more details about this class.
  
      .. autoclass:: torchvision.models.detection.FasterRCNN_ResNet50_FPN_Weights
          :members:
      """
      weights = FasterRCNN_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,
      )
      backbone = _resnet_fpn_extractor(
          backbone, trainable_backbone_layers
      )
      model = FasterRCNN(backbone, num_classes=num_classes, **kwargs)
  
      if weights is not None:
          model.load_state_dict(
              weights.get_state_dict(progress=progress, check_hash=True)
          )
          if weights == FasterRCNN_ResNet50_FPN_Weights.COCO_V1:
              overwrite_eps(model, 0.0)
  
      return model
  
imports: from typing import Any, Callable, List, Optional, Tuple, Union
  import torch
  import torch.nn.functional
  from torch import nn
  from torchvision.ops import MultiScaleRoIAlign
  from ...ops import misc
  from ...transforms._presets import ObjectDetection
  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 ..resnet import resnet50, ResNet50_Weights
  from ._utils import overwrite_eps
  from .anchor_utils import AnchorGenerator
  from .backbone_utils import _mobilenet_extractor, _resnet_fpn_extractor, _validate_trainable_layers
  from .generalized_rcnn import GeneralizedRCNN
  from .roi_heads import RoIHeads
  from .rpn import RegionProposalNetwork, RPNHead
  from .transform import GeneralizedRCNNTransform
label_desc: Use image node_id 2 for calling a global function with example usage: fasterrcnn_resnet50_fpn() and returns: model