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)] * 2,
          self.box_reg_key: [torch.randn(out_box_reg_shape)] * 2,
      }
  
file_path: MONAI/tests/test_retinanet_predict_utils.py
imports: from __future__ import annotations
  import unittest
  import torch
  from parameterized import parameterized
  from monai.apps.detection.utils.predict_utils import ensure_dict_value_to_list_, predict_with_inferer
  from monai.inferers import SlidingWindowInferer
label_desc: Use image node_id 2 for calling the NaiveNetwork2 obj's underlying member method code with example usage: obj.forward(images) and returns: dict