code: def small_regression_dataset(datatype):
      X, y = make_regression(
          n_samples=1000,
          n_features=20,
          n_informative=10,
          random_state=10,
      )
      X = X.astype(datatype)
      y = y.astype(datatype)
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, train_size=0.8, random_state=0
      )
  
      return X_train, X_test, y_train, y_test
  
imports: import pytest
  from cuml.internals.mem_type import MemoryType
  from cuml.internals.input_utils import input_to_cuml_array, is_array_like
  from cuml.internals.base import Base
  import cuml
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import mean_squared_error, brier_score_loss
  from sklearn.datasets import make_classification, make_regression
  from sklearn import datasets
  from pylibraft.common.cuda import Stream
  from sklearn.datasets import make_regression
  from numba.cuda.cudadrv.devicearray import DeviceNDArray
  from numbers import Number
  from cuml.internals.safe_imports import gpu_only_import_from
  from itertools import dropwhile
  from copy import deepcopy
  from cuml.internals.safe_imports import cpu_only_import
  import inspect
  from textwrap import dedent, indent
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 10 for calling a global function with example usage: small_regression_dataset(datatype) and returns: X_train,  X_test,  y_train,  y_test