code: def make_classification_dataset(
      datatype, nrows, ncols, n_info, n_classes=2
  ):
      X, y = make_classification(
          n_samples=nrows,
          n_features=ncols,
          n_informative=n_info,
          n_classes=n_classes,
          random_state=0,
      )
      X = X.astype(datatype)
      y = y.astype(datatype)
  
      return X, y
  
imports: from cuml.internals.safe_imports import gpu_only_import
  import pytest
  from cuml.dask.common import utils
  from functools import partial
  from sklearn.metrics import accuracy_score, mean_squared_error
  from sklearn.datasets import make_classification
  from sklearn.linear_model import LogisticRegression
  from cuml.internals.safe_imports import cpu_only_import
  from cuml.testing.utils import array_equal
  from scipy.sparse import csr_matrix
label_desc: Use image node_id 3 for calling a global function with example usage: make_classification_dataset(datatype, nrows, ncols, n_info, n_classes) and returns: X,  y