code: def test_two_node_bigm(two_node_network_relu):
      m = pyo.ConcreteModel()
      m.neural_net_block = OmltBlock()
      formulation = FullSpaceNNFormulation(two_node_network_relu)
      m.neural_net_block.build_formulation(formulation)
      m.obj1 = pyo.Objective(expr=m.neural_net_block.outputs[0, 0])
  
      m.neural_net_block.inputs[0].fix(-2)
      m.obj1 = pyo.Objective(expr=0)
      status = pyo.SolverFactory("cbc").solve(m, tee=False)
      assert (
          abs(pyo.value(m.neural_net_block.outputs[0, 0]) - 10) < 1e-3
      )
      assert abs(pyo.value(m.neural_net_block.outputs[0, 1]) - 2) < 1e-3
  
      m.neural_net_block.inputs[0].fix(1)
      status = pyo.SolverFactory("cbc").solve(m, tee=False)
      assert abs(pyo.value(m.neural_net_block.outputs[0, 0]) - 1) < 1e-3
      assert abs(pyo.value(m.neural_net_block.outputs[0, 1]) - 0) < 1e-3
  
file_path: OMLT/tests/neuralnet/test_relu.py
imports: import numpy
  import pyomo.environ
  import pytest
  from omlt.block import OmltBlock
  from omlt.dependencies import onnx_available
  from omlt.neuralnet import FullSpaceNNFormulation, ReluBigMFormulation, ReluComplementarityFormulation, ReluPartitionFormulation
  from omlt.neuralnet.activations import ComplementarityReLUActivation
label_desc: Use image node_id 1 for calling a global function with example usage: test_two_node_bigm(two_node_network_relu) without return types