code: def retinanet_resnet50_fpn_detector(
      num_classes: int,
      anchor_generator: AnchorGenerator,
      returned_layers: Sequence[int] = (1, 2, 3),
      pretrained: bool = False,
      progress: bool = True,
      **kwargs: Any,
  ) -> RetinaNetDetector:
      """
      Returns a RetinaNet detector using a ResNet-50 as backbone, which can be pretrained
      from `Med3D: Transfer Learning for 3D Medical Image Analysis <https://arxiv.org/pdf/1904.00625.pdf>`
      _.
  
      Args:
          num_classes: number of output classes of the model (excluding the background).
          anchor_generator: AnchorGenerator,
          returned_layers: returned layers to extract feature maps. Each returned layer should be in the range [1,4].
              len(returned_layers)+1 will be the number of extracted feature maps.
              There is an extra maxpooling layer LastLevelMaxPool() appended.
          pretrained: If True, returns a backbone pre-trained on 23 medical datasets
          progress: If True, displays a progress bar of the download to stderr
  
      Return:
          A RetinaNetDetector object with resnet50 as backbone
  
      Example:
  
          .. code-block:: python
  
              # define a naive network
              resnet_param = {
                  "pretrained": False,
                  "spatial_dims": 3,
                  "n_input_channels": 2,
                  "num_classes": 3,
                  "conv1_t_size": 7,
                  "conv1_t_stride": (2, 2, 2)
              }
              returned_layers = [1]
              anchor_generator = monai.apps.detection.utils.anchor_utils.AnchorGeneratorWithAnchorShape(
                  feature_map_scales=(1, 2), base_anchor_shapes=((8,) * resnet_param["spatial_dims"])
              )
              detector = retinanet_resnet50_fpn_detector(
                  **resnet_param, anchor_generator=anchor_generator, returned_layers=returned_layers
              )
      """
  
      backbone = resnet.resnet50(pretrained, progress, **kwargs)
      spatial_dims = len(backbone.conv1.stride)
      # number of output feature maps is len(returned_layers)+1
      feature_extractor = resnet_fpn_feature_extractor(
          backbone=backbone,
          spatial_dims=spatial_dims,
          pretrained_backbone=pretrained,
          trainable_backbone_layers=None,
          returned_layers=returned_layers,
      )
      num_anchors = anchor_generator.num_anchors_per_location()[0]
      size_divisible = [
          s * 2 * 2 ** max(returned_layers)
          for s in feature_extractor.body.conv1.stride
      ]
      network = RetinaNet(
          spatial_dims=spatial_dims,
          num_classes=num_classes,
          num_anchors=num_anchors,
          feature_extractor=feature_extractor,
          size_divisible=size_divisible,
      )
      return RetinaNetDetector(network, anchor_generator)
  
imports: from __future__ import annotations
  import warnings
  from collections.abc import Callable, Sequence
  from typing import Any
  import torch
  from torch import Tensor, nn
  from monai.apps.detection.networks.retinanet_network import RetinaNet, resnet_fpn_feature_extractor
  from monai.apps.detection.utils.anchor_utils import AnchorGenerator
  from monai.apps.detection.utils.ATSS_matcher import ATSSMatcher
  from monai.apps.detection.utils.box_coder import BoxCoder
  from monai.apps.detection.utils.box_selector import BoxSelector
  from monai.apps.detection.utils.detector_utils import check_training_targets, preprocess_images
  from monai.apps.detection.utils.hard_negative_sampler import HardNegativeSampler
  from monai.apps.detection.utils.predict_utils import ensure_dict_value_to_list_, predict_with_inferer
  from monai.data.box_utils import box_iou
  from monai.inferers import SlidingWindowInferer
  from monai.networks.nets import resnet
  from monai.utils import BlendMode, PytorchPadMode, ensure_tuple_rep, optional_import
label_desc: Use image node_id 1 for calling a global function with example usage: retinanet_resnet50_fpn_detector(num_classes, anchor_generator, returned_layers, pretrained, progress) and returns: RetinaNetDetector