code: def test_skipped_layers(backend, io_type):
      model = ModelSkippedLayers()
      model.eval()
  
      input_shape = (3, 8)
      batch_input_shape = (None,) + input_shape
      config = config_from_pytorch_model(
          model,
          default_precision="ap_fixed<32,16>",
          inputs_channel_last=True,
          transpose_outputs=False,
      )
      output_dir = str(
          test_root_path
          / f"hls4mlprj_pytorch_api_skipped_{backend}_{io_type}"
      )
      hls_model = convert_from_pytorch_model(
          model,
          batch_input_shape,
          hls_config=config,
          output_dir=output_dir,
          io_type=io_type,
          backend=backend,
      )
  
      hls_model.compile()
  
      input = torch.randn(10, 3, 8)
      hls_input = np.ascontiguousarray(
          torch.permute(input, (0, 2, 1)).detach().numpy()
      )  # Transpose to channels_last
  
      pytorch_prediction = model(input).detach().numpy().flatten()
      hls_prediction = hls_model.predict(hls_input).flatten()
  
      np.testing.assert_allclose(
          hls_prediction, pytorch_prediction, rtol=0, atol=5e-2
      )
  
imports: import math
  from pathlib import Path
  import numpy
  import pytest
  import torch
  import torch.nn
  from torch.nn import AvgPool1d, AvgPool2d, MaxPool1d, MaxPool2d
  from hls4ml.converters import convert_from_pytorch_model
  from hls4ml.utils.config import config_from_pytorch_model
label_desc: Use image node_id 10 for calling a global function with example usage: test_skipped_layers(backend, io_type) without return types