code: def test_logistic_regression_unscaled(dtype, penalty, l1_ratio):
      if penalty == "elasticnet":
          assume(l1_ratio is not None)
  
      # Test logistic regression on the breast cancer dataset. We do not scale
      # the dataset which could lead to numerical problems (fixed in PR #2543).
      X, y = load_breast_cancer(return_X_y=True)
      X = X.astype(dtype)
      y = y.astype(dtype)
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, random_state=42
      )
      params = {
          "penalty": penalty,
          "C": 1,
          "tol": 1e-4,
          "fit_intercept": True,
          "max_iter": 5000,
          "l1_ratio": l1_ratio,
      }
      culog = cuLog(**params)
      culog.fit(X_train, y_train)
  
      score_train = culog.score(X_train, y_train)
      score_test = culog.score(X_test, y_test)
  
      target(1 / score_train, label="inverse train score")
      target(1 / score_test, label="inverse test score")
  
      # TODO: Use a more rigorous approach to determine expected minimal scores
      # here. The values here are selected empirically and passed during test
      # development.
      assert score_train >= 0.94
      assert score_test >= 0.94
  
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 16 for calling a global function with example usage: test_logistic_regression_unscaled(dtype, penalty, l1_ratio) without return types