code: def test_value(self):
      box_coder = BoxCoder(weights=[1, 1, 1, 1, 1, 1])
      test_dtype = [torch.float32, torch.float16]
      for dtype in test_dtype:
          gt_boxes_0 = torch.rand((10, 3)).abs()
          gt_boxes_1 = gt_boxes_0 + torch.rand((10, 3)).abs() + 10
          gt_boxes = torch.cat((gt_boxes_0, gt_boxes_1), dim=1)
          gt_boxes = CastToType(dtype=dtype)(gt_boxes)
  
          proposals_0 = (
              gt_boxes_0 + torch.rand(gt_boxes_0.shape)
          ).abs()
          proposals_1 = (
              proposals_0 + torch.rand(gt_boxes_0.shape).abs() + 10
          )
          proposals = torch.cat((proposals_0, proposals_1), dim=1)
  
          rel_gt_boxes = box_coder.encode_single(gt_boxes, proposals)
          gt_back = box_coder.decode_single(rel_gt_boxes, proposals)
          assert_allclose(
              gt_back,
              gt_boxes,
              type_test=True,
              device_test=True,
              atol=0.1,
          )
  
file_path: MONAI/tests/test_box_coder.py
imports: from __future__ import annotations
  import unittest
  import torch
  from monai.apps.detection.utils.box_coder import BoxCoder
  from monai.transforms import CastToType
  from tests.utils import assert_allclose
label_desc: Use image node_id 1 for calling the TestBoxTransform obj's underlying member method code with example usage: obj.test_value() without return types