code: def test_onnx_bounds_loader_writer(datadir):
      onnx_model = onnx.load(datadir.file("keras_conv_7x7_relu.onnx"))
      scaled_input_bounds = dict()
      for i in range(7):
          for j in range(7):
              scaled_input_bounds[0, i, j] = (0.0, 1.0)
      with tempfile.NamedTemporaryFile(suffix=".onnx") as f:
          write_onnx_model_with_bounds(
              f.name, onnx_model, scaled_input_bounds
          )
          net = load_onnx_neural_network_with_bounds(f.name)
      for key, value in net.scaled_input_bounds.items():
          assert scaled_input_bounds[key] == value
  
file_path: OMLT/tests/neuralnet/test_onnx.py
imports: import tempfile
  import numpy
  import pytest
  from pyomo.common.dependencies import DeferredImportError
  from omlt.dependencies import onnx, onnx_available
  from pyomo.environ import *
  from omlt import OffsetScaling, OmltBlock
  from omlt.neuralnet import FullSpaceNNFormulation
label_desc: Use image node_id 5 for calling a global function with example usage: test_onnx_bounds_loader_writer(datadir) without return types