code: def test_naive_predictor(self, input_param, input_shape):
      net = NaiveNetwork(**input_param)
      net2 = NaiveNetwork2(**input_param)
      inferer = SlidingWindowInferer(
          roi_size=16, overlap=0.25, cache_roi_weight_map=True
      )
      network_output_keys = ["cls", "box_reg"]
  
      input_data = torch.randn(input_shape)
  
      result = predict_with_inferer(
          input_data, net, network_output_keys, inferer=inferer
      )
      self.assertTrue(len(result["cls"]) == 1)
  
      result = net(input_data)
      self.assertTrue(len(result["cls"]) == input_data.shape[0])
      ensure_dict_value_to_list_(result)
      self.assertTrue(len(result["cls"]) == 1)
  
      result = predict_with_inferer(
          input_data, net2, network_output_keys, inferer=inferer
      )
      self.assertTrue(len(result["cls"]) == 2)
  
      result = net2(input_data)
      self.assertTrue(len(result["cls"]) == 2)
      ensure_dict_value_to_list_(result)
      self.assertTrue(len(result["cls"]) == 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 1 for calling the TestPredictor obj's underlying member method code with example usage: obj.test_naive_predictor(input_param, input_shape) without return types