code: def test_network_params_not_required_but_respected_if_given(
      task, listen_port, cluster
  ):
      with Client(cluster) as client:
          _, _, _, _, dX, dy, _, dg = _create_data(
              objective=task, output="array", chunk_size=10, group=None
          )
  
          dask_model_factory = task_to_dask_factory[task]
  
          # rebalance data to be sure that each worker has a piece of the data
          client.rebalance()
  
          # model 1 - no network parameters given
          dask_model1 = dask_model_factory(
              n_estimators=5,
              num_leaves=5,
          )
          dask_model1.fit(dX, dy, group=dg)
          assert dask_model1.fitted_
          params = dask_model1.get_params()
          assert "local_listen_port" not in params
          assert "machines" not in params
  
          # model 2 - machines given
          workers = list(client.scheduler_info()["workers"])
          workers_hostname = _get_workers_hostname(cluster)
          (
              remote_sockets,
              open_ports,
          ) = lgb.dask._assign_open_ports_to_workers(client, workers)
          for s in remote_sockets.values():
              s.release()
          dask_model2 = dask_model_factory(
              n_estimators=5,
              num_leaves=5,
              machines=",".join(
                  [
                      f"{workers_hostname}:{port}"
                      for port in open_ports.values()
                  ]
              ),
          )
  
          dask_model2.fit(dX, dy, group=dg)
          assert dask_model2.fitted_
          params = dask_model2.get_params()
          assert "local_listen_port" not in params
          assert "machines" in params
  
          # model 3 - local_listen_port given
          # training should fail because LightGBM will try to use the same
          # port for multiple worker processes on the same machine
          dask_model3 = dask_model_factory(
              n_estimators=5,
              num_leaves=5,
              local_listen_port=listen_port,
          )
          error_msg = "has multiple Dask worker processes running on it"
          with pytest.raises(lgb.basic.LightGBMError, match=error_msg):
              dask_model3.fit(dX, dy, group=dg)
  
imports: import inspect
  import random
  import socket
  from itertools import groupby
  from os import getenv
  from platform import machine
  from sys import platform
  from urllib.parse import urlparse
  import pytest
  from sklearn.metrics import accuracy_score, r2_score
  import lightgbm
  from .utils import sklearn_multiclass_custom_objective
  import dask.array
  import dask.dataframe
  import numpy
  import pandas
  import sklearn.utils.estimator_checks
  from dask.array.utils import assert_eq
  from dask.distributed import Client, LocalCluster, default_client, wait
  from scipy.sparse import csc_matrix, csr_matrix
  from scipy.stats import spearmanr
  from sklearn.datasets import make_blobs, make_regression
  from .utils import make_ranking, pickle_obj, unpickle_obj
label_desc: Use image node_id 33 for calling a global function with example usage: test_network_params_not_required_but_respected_if_given(task, listen_port, cluster) without return types