code: def make_dataset(
      dataset, n_rows, n_cols, n_classes=2, n_informative=2
  ):
      np.random.seed(137)
      if n_rows * 0.25 < 4000:
          # Use at least 4000 test samples
          n_test = 4000
          if n_rows > 1000:
              # To avoid a large increase in test time (which is between
              # O(n_rows^2) and O(n_rows^3)).
              n_rows = int(n_rows * 0.75)
          n_rows += n_test
      else:
          n_test = n_rows * 0.25
      if dataset == "classification1":
          X, y = make_classification(
              n_rows,
              n_cols,
              n_informative=n_informative,
              n_redundant=0,
              n_classes=n_classes,
              n_clusters_per_class=1,
          )
      elif dataset == "classification2":
          X, y = make_classification(
              n_rows,
              n_cols,
              n_informative=n_informative,
              n_redundant=0,
              n_classes=n_classes,
              n_clusters_per_class=2,
          )
      elif dataset == "gaussian":
          X, y = make_gaussian_quantiles(
              n_samples=n_rows, n_features=n_cols, n_classes=n_classes
          )
      elif dataset == "blobs":
          X, y = make_blobs(
              n_samples=n_rows, n_features=n_cols, centers=n_classes
          )
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, test_size=n_test
      )
      # correct case when not all classes made it into the training set
      if np.unique(y_train).size < n_classes:
          for i in range(n_classes):
              y_train[i] = i
      return X_train, X_test, y_train, y_test
  
imports: import platform
  from sklearn.preprocessing import StandardScaler
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import mean_squared_error
  from sklearn.datasets import make_classification, make_gaussian_quantiles
  from sklearn.datasets import make_regression, make_friedman1
  from sklearn.datasets import load_iris, make_blobs
  from sklearn import svm
  from cuml.testing.utils import unit_param, quality_param, stress_param, compare_svm, compare_probabilistic_svm, svm_array_equal
  from cuml.common import input_to_cuml_array
  import cuml.svm
  import cuml
  from cuml.internals.safe_imports import gpu_only_import_from
  from cuml.internals.safe_imports import cpu_only_import
  import pytest
  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(dataset, n_rows, n_cols, n_classes, n_informative) and returns: X_train,  X_test,  y_train,  y_test