code: def test_anchor_generator(self):
      images = torch.randn(2, 3, 15, 15)
      features = self.get_features(images)
      image_shapes = [i.shape[-2:] for i in images]
      images = ImageList(images, image_shapes)
  
      model = self._init_test_anchor_generator()
      model.eval()
      anchors = model(images, features)
  
      # Estimate the number of target anchors
      grid_sizes = [f.shape[-2:] for f in features]
      num_anchors_estimated = 0
      for sizes, num_anchors_per_loc in zip(
          grid_sizes, model.num_anchors_per_location()
      ):
          num_anchors_estimated += (
              sizes[0] * sizes[1] * num_anchors_per_loc
          )
  
      anchors_output = torch.tensor(
          [
              [-5.0, -5.0, 5.0, 5.0],
              [0.0, -5.0, 10.0, 5.0],
              [5.0, -5.0, 15.0, 5.0],
              [-5.0, 0.0, 5.0, 10.0],
              [0.0, 0.0, 10.0, 10.0],
              [5.0, 0.0, 15.0, 10.0],
              [-5.0, 5.0, 5.0, 15.0],
              [0.0, 5.0, 10.0, 15.0],
              [5.0, 5.0, 15.0, 15.0],
          ]
      )
  
      assert num_anchors_estimated == 9
      assert len(anchors) == 2
      assert tuple(anchors[0].shape) == (9, 4)
      assert tuple(anchors[1].shape) == (9, 4)
      assert_equal(anchors[0], anchors_output)
      assert_equal(anchors[1], anchors_output)
  
file_path: vision/test/test_models_detection_anchor_utils.py
imports: import pytest
  import torch
  from common_utils import assert_equal
  from torchvision.models.detection.anchor_utils import AnchorGenerator, DefaultBoxGenerator
  from torchvision.models.detection.image_list import ImageList
label_desc: Use image node_id 5 for calling the Tester obj's underlying member method code with example usage: obj.test_anchor_generator() without return types