code: def test_neck(device, dtype):
      N = 2
      in_channels = [64, 128, 256]
      sizes = [(32, 24), (16, 12), (8, 6)]
      hidden_dim = 64
      neck = HybridEncoder(in_channels, hidden_dim, 128).to(
          device, dtype
      )
      fmaps = [
          torch.randn(N, ch_in, h, w, device=device, dtype=dtype)
          for ch_in, (h, w) in zip(in_channels, sizes)
      ]
  
      outs = neck(fmaps)
      assert len(outs) == len(fmaps)
      for out, (h, w) in zip(outs, sizes):
          assert out.shape == (N, hidden_dim, h, w)
  
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 2 for calling a global function with example usage: test_neck(device, dtype) without return types