code: def model(
      num_classes: int,
      backbone: Optional[TorchvisionBackboneConfig] = None,
      remove_internal_transforms: bool = True,
      pretrained: bool = True,
      **retinanet_kwargs,
  ) -> nn.Module:
      if backbone is None:
          model = retinanet_resnet50_fpn(
              pretrained=pretrained,
              pretrained_backbone=pretrained,
              **retinanet_kwargs,
          )
          model.head = RetinaNetHead(
              in_channels=model.backbone.out_channels,
              num_anchors=model.head.classification_head.num_anchors,
              num_classes=num_classes,
          )
          resnet_fpn.patch_param_groups(model.backbone)
      else:
          model = RetinaNet(
              backbone=backbone.backbone,
              num_classes=num_classes,
              **retinanet_kwargs,
          )
  
      patch_retinanet_param_groups(model)
  
      if remove_internal_transforms:
          remove_internal_model_transforms(model)
  
      return model
  
imports: from icevision.imports import *
  from icevision.models.torchvision.utils import *
  from icevision.models.torchvision.retinanet.backbones import resnet_fpn_configs
  from icevision.models.torchvision.backbone_config import TorchvisionBackboneConfig
  from torchvision.models.detection.retinanet import retinanet_resnet50_fpn, RetinaNet, RetinaNetHead
label_desc: Use image node_id 1 for calling a global function with example usage: model(num_classes, backbone, remove_internal_transforms, pretrained) and returns: model