code: def test_asadpour_real_world_path():
      """
      This test uses airline prices between the six largest cities in the US. This
      time using a path, not a cycle.
  
          * New York City -> JFK
          * Los Angeles -> LAX
          * Chicago -> ORD
          * Houston -> IAH
          * Phoenix -> PHX
          * Philadelphia -> PHL
  
      Flight prices from August 2021 using Delta or American airlines to get
      nonstop flight. The brute force solution found the optimal tour to cost $872
      """
      np = pytest.importorskip("numpy")
      pytest.importorskip("scipy")
  
      G_array = np.array(
          [
              # JFK  LAX  ORD  IAH  PHX  PHL
              [0, 243, 199, 208, 169, 183],  # JFK
              [277, 0, 217, 123, 127, 252],  # LAX
              [297, 197, 0, 197, 123, 177],  # ORD
              [303, 169, 197, 0, 117, 117],  # IAH
              [257, 127, 160, 117, 0, 319],  # PHX
              [183, 332, 217, 117, 319, 0],  # PHL
          ]
      )
  
      node_map = {
          0: "JFK",
          1: "LAX",
          2: "ORD",
          3: "IAH",
          4: "PHX",
          5: "PHL",
      }
  
      expected_paths = [
          ["ORD", "PHX", "LAX", "IAH", "PHL", "JFK"],
          ["JFK", "PHL", "IAH", "ORD", "PHX", "LAX"],
      ]
  
      G = nx.from_numpy_array(G_array, create_using=nx.DiGraph)
      nx.relabel_nodes(G, node_map, copy=False)
  
      def fixed_asadpour(G, weight):
          return nx_app.asadpour_atsp(G, weight, 56)
  
      path = nx_app.traveling_salesman_problem(
          G, weight="weight", cycle=False, method=fixed_asadpour
      )
  
      assert path in expected_paths
  
imports: import random
  import pytest
  import networkx
  import networkx.algorithms.approximation
label_desc: Use image node_id 19 for calling a global function with example usage: test_asadpour_real_world_path() and returns: nx_app