code: def regression_dataset(request):
      regression_type = request.param
  
      out = {}
      for test_status in ["regular", "stress_test"]:
          if test_status == "regular":
              n_samples, n_features = 100000, 5
          elif test_status == "stress_test":
              n_samples, n_features = 1000000, 20
  
          data = (np.random.rand(n_samples, n_features) * 2) - 1
  
          if regression_type == "binary":
              coef = (np.random.rand(n_features) * 2) - 1
              coef /= np.linalg.norm(coef)
              output = (data @ coef) > 0
          elif regression_type.startswith("multiclass"):
              n_classes = 3 if regression_type == "multiclass-3" else 7
              coef = (np.random.rand(n_features, n_classes) * 2) - 1
              coef /= np.linalg.norm(coef, axis=0)
              output = (data @ coef).argmax(axis=1)
          output = output.astype(np.int32)
  
          out[test_status] = (regression_type, data, coef, output)
      return out
  
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 26 for calling a global function with example usage: regression_dataset(request) and returns: out