code: def test_lr_fit_predict_score(
      nrows,
      ncols,
      n_parts,
      fit_intercept,
      datatype,
      gpu_array_input,
      solver,
      client,
  ):
      sk_solver = select_sk_solver(cuml_solver=solver)
  
      def imp():
          import cuml.comm.serialize  # NOQA
  
      client.run(imp)
  
      from cuml.dask.extended.linear_model import (
          LogisticRegression as cumlLR_dask,
      )
  
      n_info = 5
      nrows = int(nrows)
      ncols = int(ncols)
      X, y = make_classification_dataset(datatype, nrows, ncols, n_info)
  
      gX, gy = _prep_training_data(client, X, y, n_parts)
  
      if gpu_array_input:
          gX = gX.values
          gX._meta = cp.asarray(gX._meta)
          gy = gy.values
          gy._meta = cp.asarray(gy._meta)
  
      cuml_model = cumlLR_dask(
          fit_intercept=fit_intercept, solver=solver, max_iter=10
      )
  
      # test fit and predict
      cuml_model.fit(gX, gy)
      cu_preds = cuml_model.predict(gX)
      accuracy_cuml = accuracy_score(y, cu_preds.compute().get())
  
      sk_model = skLR(
          fit_intercept=fit_intercept, solver=sk_solver, max_iter=10
      )
      sk_model.fit(X, y)
      sk_preds = sk_model.predict(X)
      accuracy_sk = accuracy_score(y, sk_preds)
  
      assert (accuracy_cuml >= accuracy_sk) | (
          np.abs(accuracy_cuml - accuracy_sk) < 1e-3
      )
  
      # score
      accuracy_cuml = cuml_model.score(gX, gy).compute().item()
      accuracy_sk = sk_model.score(X, y)
  
      assert (accuracy_cuml >= accuracy_sk) | (
          np.abs(accuracy_cuml - accuracy_sk) < 1e-3
      )
  
      # predicted probabilities should differ by <= 5%
      # even with different solvers (arbitrary)
      probs_cuml = cuml_model.predict_proba(gX).compute()
      probs_sk = sk_model.predict_proba(X)[:, 1]
      assert np.abs(probs_sk - probs_cuml.get()).max() <= 0.05
  
imports: from cuml.internals.safe_imports import gpu_only_import
  import pytest
  from cuml.dask.common import utils
  from functools import partial
  from sklearn.metrics import accuracy_score, mean_squared_error
  from sklearn.datasets import make_classification
  from sklearn.linear_model import LogisticRegression
  from cuml.internals.safe_imports import cpu_only_import
  from cuml.testing.utils import array_equal
  from scipy.sparse import csr_matrix
label_desc: Use image node_id 5 for calling a global function with example usage: test_lr_fit_predict_score(nrows, ncols, n_parts, fit_intercept, datatype, gpu_array_input, solver, client) without return types