code: def test_make_n_channel_input():
      conv = nn.Conv2d(3, 16, kernel_size=3, padding=1)
  
      conv6 = make_n_channel_input(conv, in_channels=6)
      assert conv6.weight.size(0) == conv.weight.size(0)
      assert conv6.weight.size(1) == 6
      assert conv6.weight.size(2) == conv.weight.size(2)
      assert conv6.weight.size(3) == conv.weight.size(3)
  
      conv5 = make_n_channel_input(conv, in_channels=5)
      assert conv5.weight.size(0) == conv.weight.size(0)
      assert conv5.weight.size(1) == 5
      assert conv5.weight.size(2) == conv.weight.size(2)
      assert conv5.weight.size(3) == conv.weight.size(3)
  
imports: import numpy
  import pytest
  import torch
  from pytorch_toolbelt.inference.functional import unpad_xyxy_bboxes, pad_image_tensor, unpad_image_tensor, pad_tensor_to_size
  from pytorch_toolbelt.modules.encoders import make_n_channel_input
  from pytorch_toolbelt.utils import match_bboxes, match_bboxes_hungarian, get_collate_for_dataset
  from torch import nn
  from torch.utils.data import ConcatDataset
  from functools import partial
label_desc: Use image node_id 5 for calling a global function with example usage: test_make_n_channel_input() without return types