code: def test_activation_functionals(
      activation_function, backend, io_type
  ):
      model = activation_function
      model.eval()
  
      X_input = np.random.rand(1)
  
      pytorch_prediction = model(torch.Tensor(X_input)).detach().numpy()
  
      config = config_from_pytorch_model(model)
      output_dir = str(
          test_root_path
          / f"hls4mlprj_pytorch_api_activations_functional_relu_{backend}_{io_type}"
      )
      hls_model = convert_from_pytorch_model(
          model,
          (None, 1),
          hls_config=config,
          output_dir=output_dir,
          backend=backend,
          io_type=io_type,
      )
      hls_model.compile()
  
      hls_prediction = hls_model.predict(X_input)
  
      np.testing.assert_allclose(
          hls_prediction, pytorch_prediction, rtol=0, atol=0.05
      )
  
      from torch.fx import symbolic_trace
  
      traced_model = symbolic_trace(model)
  
      nNodes = 0
      for _node in traced_model.graph.nodes:
          nNodes += 1
  
      assert nNodes - 1 == len(hls_model.get_layers())
  
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 3 for calling a global function with example usage: test_activation_functionals(activation_function, backend, io_type) without return types