code: def test_squeeze(backend, io_type):
      model = SqueezeModel()
      model.eval()
  
      X_input = np.random.rand(1, 5)
  
      pytorch_prediction = (
          model(torch.Tensor(X_input)).detach().numpy().flatten()
      )
  
      config = config_from_pytorch_model(model)
      del config["Model"][
          "InputsChannelLast"
      ]  # We don't want anything touched for this test
      output_dir = str(
          test_root_path
          / f"hls4mlprj_pytorch_api_squeeze_{backend}_{io_type}"
      )
  
      hls_model = convert_from_pytorch_model(
          model,
          (None, 5),
          hls_config=config,
          output_dir=output_dir,
          backend=backend,
          io_type=io_type,
      )
  
      hls_model.compile()
  
      hls_prediction = hls_model.predict(X_input).flatten()
  
      np.testing.assert_allclose(
          hls_prediction, pytorch_prediction, rtol=1e-2, atol=0.01
      )
  
      if io_type == "io_parallel":
          assert (
              list(hls_model.get_layers())[1].attributes["class_name"]
              == "Reshape"
          )
          assert list(hls_model.get_layers())[1].attributes[
              "target_shape"
          ] == [1, 5]
          assert (
              list(hls_model.get_layers())[3].attributes["class_name"]
              == "Reshape"
          )
          assert list(hls_model.get_layers())[3].attributes[
              "target_shape"
          ] == [3]
      elif io_type == "io_stream":
          assert list(hls_model.get_layers())[1].class_name == "Repack"
          assert list(hls_model.get_layers())[1].attributes[
              "target_shape"
          ] == [1, 5]
          assert (
              list(hls_model.get_layers())[3].attributes["class_name"]
              == "Reshape"
          )  # Exists as in-place variable
          assert list(hls_model.get_layers())[3].attributes[
              "target_shape"
          ] == [3]
  
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 8 for calling a global function with example usage: test_squeeze(backend, io_type) without return types