code: def make_dataset(datatype, nrows, ncols, n_info):
      X, y = make_regression(
          n_samples=nrows,
          n_features=ncols,
          n_informative=n_info,
          random_state=0,
      )
      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
      )
      return X_train, y_train, X_test
  
file_path: cuml/python/cuml/tests/dask/test_dask_base.py
imports: from sklearn.model_selection import train_test_split
  from cuml.dask.datasets import make_regression
  from cuml.dask.linear_model import LinearRegression
  from cuml.internals.safe_imports import cpu_only_import_from
  import pytest
  from cuml.internals.safe_imports import cpu_only_import
  import cuml
  from cuml.dask.datasets import make_blobs
  from cuml.testing.dask.utils import load_text_corpus
  from cuml.dask.naive_bayes.naive_bayes import MultinomialNB
  from cuml.dask.cluster import KMeans
  from dask_ml.wrappers import ParallelPostFit
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 1 for calling a global function with example usage: make_dataset(datatype, nrows, ncols, n_info) and returns: X_train,  y_train,  X_test