code: def test_model_and_local_version_are_picklable_whether_or_not_client_set_explicitly(
      serializer, task, set_client, tmp_path, cluster, cluster2
  ):
      with Client(cluster) as client1:
          # data on cluster1
          X_1, _, _, _, dX_1, dy_1, _, dg_1 = _create_data(
              objective=task, output="array", group=None
          )
  
          with Client(cluster2) as client2:
              # create identical data on cluster2
              X_2, _, _, _, dX_2, dy_2, _, dg_2 = _create_data(
                  objective=task, output="array", group=None
              )
  
              model_factory = task_to_dask_factory[task]
  
              params = {
                  "time_out": 5,
                  "n_estimators": 1,
                  "num_leaves": 2,
              }
  
              # at this point, the result of default_client() is client2 since it was the most recently
              # created. So setting client to client1 here to test that you can select a non-default client
              assert default_client() == client2
              if set_client:
                  params.update({"client": client1})
  
              # unfitted model should survive pickling round trip, and pickling
              # shouldn't have side effects on the model object
              dask_model = model_factory(**params)
              local_model = dask_model.to_local()
              if set_client:
                  assert dask_model.client == client1
              else:
                  assert dask_model.client is None
  
              with pytest.raises(
                  lgb.compat.LGBMNotFittedError,
                  match="Cannot access property client_ before calling fit",
              ):
                  dask_model.client_
  
              assert "client" not in local_model.get_params()
              assert getattr(local_model, "client", None) is None
  
              tmp_file = tmp_path / "model-1.pkl"
              pickle_obj(
                  obj=dask_model,
                  filepath=tmp_file,
                  serializer=serializer,
              )
              model_from_disk = unpickle_obj(
                  filepath=tmp_file, serializer=serializer
              )
  
              local_tmp_file = tmp_path / "local-model-1.pkl"
              pickle_obj(
                  obj=local_model,
                  filepath=local_tmp_file,
                  serializer=serializer,
              )
              local_model_from_disk = unpickle_obj(
                  filepath=local_tmp_file, serializer=serializer
              )
  
              assert model_from_disk.client is None
  
              if set_client:
                  assert dask_model.client == client1
              else:
                  assert dask_model.client is None
  
              with pytest.raises(
                  lgb.compat.LGBMNotFittedError,
                  match="Cannot access property client_ before calling fit",
              ):
                  dask_model.client_
  
              # client will always be None after unpickling
              if set_client:
                  from_disk_params = model_from_disk.get_params()
                  from_disk_params.pop("client", None)
                  dask_params = dask_model.get_params()
                  dask_params.pop("client", None)
                  assert from_disk_params == dask_params
              else:
                  assert (
                      model_from_disk.get_params()
                      == dask_model.get_params()
                  )
              assert (
                  local_model_from_disk.get_params()
                  == local_model.get_params()
              )
  
              # fitted model should survive pickling round trip, and pickling
              # shouldn't have side effects on the model object
              if set_client:
                  dask_model.fit(dX_1, dy_1, group=dg_1)
              else:
                  dask_model.fit(dX_2, dy_2, group=dg_2)
              local_model = dask_model.to_local()
  
              assert "client" not in local_model.get_params()
              with pytest.raises(AttributeError):
                  local_model.client
                  local_model.client_
  
              tmp_file2 = tmp_path / "model-2.pkl"
              pickle_obj(
                  obj=dask_model,
                  filepath=tmp_file2,
                  serializer=serializer,
              )
              fitted_model_from_disk = unpickle_obj(
                  filepath=tmp_file2, serializer=serializer
              )
  
              local_tmp_file2 = tmp_path / "local-model-2.pkl"
              pickle_obj(
                  obj=local_model,
                  filepath=local_tmp_file2,
                  serializer=serializer,
              )
              local_fitted_model_from_disk = unpickle_obj(
                  filepath=local_tmp_file2, serializer=serializer
              )
  
              if set_client:
                  assert dask_model.client == client1
                  assert dask_model.client_ == client1
              else:
                  assert dask_model.client is None
                  assert dask_model.client_ == default_client()
                  assert dask_model.client_ == client2
  
              assert isinstance(fitted_model_from_disk, model_factory)
              assert fitted_model_from_disk.client is None
              assert fitted_model_from_disk.client_ == default_client()
              assert fitted_model_from_disk.client_ == client2
  
              # client will always be None after unpickling
              if set_client:
                  from_disk_params = fitted_model_from_disk.get_params()
                  from_disk_params.pop("client", None)
                  dask_params = dask_model.get_params()
                  dask_params.pop("client", None)
                  assert from_disk_params == dask_params
              else:
                  assert (
                      fitted_model_from_disk.get_params()
                      == dask_model.get_params()
                  )
              assert (
                  local_fitted_model_from_disk.get_params()
                  == local_model.get_params()
              )
  
              if set_client:
                  preds_orig = dask_model.predict(dX_1).compute()
                  preds_loaded_model = fitted_model_from_disk.predict(
                      dX_1
                  ).compute()
                  preds_orig_local = local_model.predict(X_1)
                  preds_loaded_model_local = (
                      local_fitted_model_from_disk.predict(X_1)
                  )
              else:
                  preds_orig = dask_model.predict(dX_2).compute()
                  preds_loaded_model = fitted_model_from_disk.predict(
                      dX_2
                  ).compute()
                  preds_orig_local = local_model.predict(X_2)
                  preds_loaded_model_local = (
                      local_fitted_model_from_disk.predict(X_2)
                  )
  
              assert_eq(preds_orig, preds_loaded_model)
              assert_eq(preds_orig_local, preds_loaded_model_local)
  
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 27 for calling a global function with example usage: test_model_and_local_version_are_picklable_whether_or_not_client_set_explicitly(serializer, task, set_client, tmp_path, cluster, cluster2) without return types