code: def test_logistic_regression_decision_function(
      dataset, fit_intercept, sparse_input
  ):
      X_train, X_test, y_train, y_test = dataset
  
      # Assumption needed to avoid qn.h: logistic loss invalid C error.
      assume(set(np.unique(y_train)) == set(np.unique(y_test)))
      num_classes = len(np.unique(np.concatenate((y_train, y_test))))
  
      if sparse_input:
          X_train = csr_matrix(X_train)
          X_test = csr_matrix(X_test)
  
      culog = cuLog(fit_intercept=fit_intercept, output_type="numpy")
      culog.fit(X_train, y_train)
  
      sklog = skLog(fit_intercept=fit_intercept)
      sklog.coef_ = culog.coef_
      sklog.intercept_ = culog.intercept_ if fit_intercept else 0
      sklog.classes_ = np.arange(num_classes)
  
      cu_dec_func = culog.decision_function(X_test)
      if cu_dec_func.shape[0] > 2:  # num_classes
          cu_dec_func = cu_dec_func.T
      sk_dec_func = sklog.decision_function(X_test)
  
      assert array_equal(cu_dec_func, sk_dec_func)
  
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 20 for calling a global function with example usage: test_logistic_regression_decision_function(dataset, fit_intercept, sparse_input) without return types