code: def make_dataset(request):
      nrows, ncols, n_info, datatype = request.param
      X, y = make_classification(
          n_samples=nrows,
          n_informative=n_info,
          n_features=ncols,
          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=10
      )
  
      y_train = y_train.astype(datatype)
      y_test = y_test.astype(datatype)
  
      return nrows, X_train, X_test, y_train, y_test
  
file_path: cuml/python/cuml/tests/test_mbsgd_classifier.py
imports: from sklearn.model_selection import train_test_split
  from sklearn.metrics import accuracy_score
  from cuml.datasets import make_classification
  from sklearn.linear_model import SGDClassifier
  from cuml.testing.utils import unit_param, quality_param, stress_param
  from cuml.linear_model import MBSGDClassifier
  from cuml.internals.safe_imports import gpu_only_import
  import pytest
  from cuml.internals.safe_imports import cpu_only_import
label_desc: Use image node_id 1 for calling a global function with example usage: make_dataset(request) and returns: nrows,  X_train,  X_test,  y_train,  y_test