code: def test_head(device, dtype):
      N = 2
      in_channels = [32, 64, 128]
      sizes = [(32, 24), (16, 12), (8, 6)]
      num_classes = 5
      num_queries = 10
      decoder = (
          RTDETRHead(num_classes, 32, num_queries, in_channels, 2)
          .to(device, dtype)
          .eval()
      )
      fmaps = [
          torch.randn(N, ch_in, h, w, device=device, dtype=dtype)
          for ch_in, (h, w) in zip(in_channels, sizes)
      ]
  
      logits, boxes = decoder(fmaps)
      assert logits.shape == (N, num_queries, num_classes)
      assert boxes.shape == (N, num_queries, 4)
  
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 3 for calling a global function with example usage: test_head(device, dtype) without return types