code: def forward(self, images):
      out_cls_shape = (
          images.shape[0],
          self.num_classes * self.num_anchors,
      ) + images.shape[-self.spatial_dims :]
      out_box_reg_shape = (
          images.shape[0],
          2 * self.spatial_dims * self.num_anchors,
      ) + images.shape[-self.spatial_dims :]
      return {
          self.cls_key: [torch.randn(out_cls_shape)],
          self.box_reg_key: [torch.randn(out_box_reg_shape)],
      }
  
file_path: MONAI/tests/test_retinanet_detector.py
imports: from __future__ import annotations
  import random
  import unittest
  import torch
  from parameterized import parameterized
  from monai.apps.detection.networks.retinanet_detector import RetinaNetDetector, retinanet_resnet50_fpn_detector
  from monai.apps.detection.utils.anchor_utils import AnchorGeneratorWithAnchorShape
  from monai.networks import eval_mode, train_mode
  from monai.utils import optional_import
  from tests.utils import SkipIfBeforePyTorchVersion, skip_if_quick, test_script_save
label_desc: Use image node_id 2 for calling the NaiveNetwork obj's underlying member method code with example usage: obj.forward(images) and returns: dict