code: def test_logistic_regression_sparse_only(dtype, nlp_20news):
      # sklearn score with max_iter = 10000
      sklearn_score = 0.878
      acceptable_score = sklearn_score - 0.01
  
      X, y = nlp_20news
  
      X = csr_matrix(X.astype(dtype))
      y = y.get().astype(dtype)
  
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, random_state=42
      )
  
      culog = cuLog()
      culog.fit(X_train, y_train)
      score = culog.score(X_test, y_test)
  
      assert score >= acceptable_score
  
imports: from contextlib import nullcontext
  from distutils.version import LooseVersion
  from functools import lru_cache
  import pytest
  import sklearn
  from cuml.internals.array import elements_in_representable_range
  from cuml.internals.safe_imports import cpu_only_import, cpu_only_import_from, gpu_only_import
  from cuml.testing.strategies import regression_datasets, split_datasets, standard_classification_datasets, standard_regression_datasets
  from cuml.testing.utils import array_difference, array_equal, quality_param, small_classification_dataset, small_regression_dataset, stress_param, unit_param
  from hypothesis import assume, example, given, note
  from hypothesis import strategies
  from hypothesis import target
  from hypothesis.extra.numpy import floating_dtypes
  from sklearn.datasets import load_breast_cancer, load_digits, make_classification, make_regression
  from sklearn.linear_model import LinearRegression
  from sklearn.linear_model import LogisticRegression
  from sklearn.linear_model import Ridge
  from sklearn.model_selection import train_test_split
  from cuml import ElasticNet
  from cuml import LinearRegression
  from cuml import LogisticRegression
  from cuml import Ridge
label_desc: Use image node_id 19 for calling a global function with example usage: test_logistic_regression_sparse_only(dtype, nlp_20news) without return types