code: def test_dynamo(self, variant, device, dtype, torch_optimizer):
      # NOTE: This test passes on Mac M1 CPU, PyTorch 2.0.0,
      # but fails on GitHub Actions Ubuntu-latest CPU, PyTorch 2.0.0.
      # Perhaps random weights cause outputs to be much more different?
      # Using pre-trained weights might see a smaller difference.
      model = (
          RTDETR.from_config(
              RTDETRConfig(variant, 10, head_num_queries=10)
          )
          .to(device, dtype)
          .eval()
      )
      model_optimized = torch_optimizer(model)
  
      img = torch.rand(1, 3, 224, 256, device=device, dtype=dtype)
      expected = model(img)
      actual = model_optimized(img)
  
      self.assert_close(actual, expected)
  
imports: from functools import partial
  import pytest
  import torch
  from kornia.contrib.models.rt_detr.architecture.hgnetv2 import PPHGNetV2
  from kornia.contrib.models.rt_detr.architecture.hybrid_encoder import HybridEncoder, RepVggBlock
  from kornia.contrib.models.rt_detr.architecture.resnet_d import ResNetD
  from kornia.contrib.models.rt_detr.architecture.rtdetr_head import RTDETRHead
  from kornia.contrib.models.rt_detr.model import RTDETR, RTDETRConfig
  from kornia.testing import BaseTester, assert_close
label_desc: Use image node_id 6 for calling the TestRTDETR obj's underlying member method code with example usage: obj.test_dynamo(variant, device, dtype, torch_optimizer) without return types