code: def make_regression_dataset(datatype, nrows, ncols, n_info, **kwargs):
      if nrows * ncols < 1e8:  # Keep cache under 4 GB
          dataset = _make_regression_dataset_from_cache(
              nrows, ncols, n_info, **kwargs
          )
      else:
          dataset = _make_regression_dataset_uncached(
              nrows, ncols, n_info, **kwargs
          )
  
      return map(lambda arr: arr.astype(datatype), dataset)
  
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 3 for calling a global function with example usage: make_regression_dataset(datatype, nrows, ncols, n_info) and returns: map