code: def test_dask_methods_and_sklearn_equivalents_have_similar_signatures(
      methods,
  ):
      dask_spec = inspect.getfullargspec(methods[0])
      sklearn_spec = inspect.getfullargspec(methods[1])
      dask_params = inspect.signature(methods[0]).parameters
      sklearn_params = inspect.signature(methods[1]).parameters
      assert dask_spec.args == sklearn_spec.args[: len(dask_spec.args)]
      assert dask_spec.varargs == sklearn_spec.varargs
      if sklearn_spec.varkw:
          assert (
              dask_spec.varkw
              == sklearn_spec.varkw[: len(dask_spec.varkw)]
          )
      assert dask_spec.kwonlyargs == sklearn_spec.kwonlyargs
      assert dask_spec.kwonlydefaults == sklearn_spec.kwonlydefaults
      for param in dask_spec.args:
          error_msg = f"param '{param}' has different default values in the methods"
          assert (
              dask_params[param].default
              == sklearn_params[param].default
          ), error_msg
  
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 36 for calling a global function with example usage: test_dask_methods_and_sklearn_equivalents_have_similar_signatures(methods) without return types