code: def two_node_network_relu():
      """
              1           1
      x0 -------- (1) --------- (3)
       |                   /
       |                  /
       |                 / 5
       |                /
       |               |
       |    -1         |     1
       ---------- (2) --------- (4)
      """
      net = NetworkDefinition(scaled_input_bounds={0: (-10.0, 10.0)})
  
      input_layer = InputLayer([1])
      net.add_layer(input_layer)
  
      dense_layer_0 = DenseLayer(
          input_layer.output_size,
          [1, 2],
          activation="relu",
          weights=np.array([[1.0, -1.0]]),
          biases=np.array([0.0, 0.0]),
      )
      net.add_layer(dense_layer_0)
      net.add_edge(input_layer, dense_layer_0)
  
      dense_layer_1 = DenseLayer(
          dense_layer_0.output_size,
          [1, 2],
          activation="linear",
          weights=np.array([[1.0, 0.0], [5.0, 1.0]]),
          biases=np.array([0.0, 0.0]),
      )
      net.add_layer(dense_layer_1)
      net.add_edge(dense_layer_0, dense_layer_1)
  
      return net
  
file_path: OMLT/tests/conftest.py
imports: from pathlib import Path
  import numpy
  import pytest
  from pyomo.common.fileutils import this_file_dir
  from omlt.neuralnet.layer import DenseLayer, InputLayer
  from omlt.neuralnet.network_definition import NetworkDefinition
label_desc: Use image node_id 3 for calling a global function with example usage: two_node_network_relu() and returns: net