code: def test_training_data_to_numpy(input_type):
      X, y, *_ = datagen.gen_data(
          "blobs", input_type, n_samples=100, n_features=10
      )
      X_np, y_np = _training_data_to_numpy(X, y)
      assert isinstance(X_np, np.ndarray)
      assert isinstance(y_np, np.ndarray)
  
imports: from cuml.benchmark.bench_helper_funcs import fit, fit_predict
  import time
  from sklearn import metrics
  from cuml.internals.safe_imports import gpu_only_import_from
  import pytest
  from cuml.internals.safe_imports import gpu_only_import
  from cuml.benchmark import datagen, algorithms
  from cuml.benchmark.bench_helper_funcs import _training_data_to_numpy
  from cuml.benchmark.runners import AccuracyComparisonRunner, SpeedupComparisonRunner, run_variations
  from cuml.internals.import_utils import has_umap
  from cuml.internals.import_utils import has_xgboost
  from cuml.internals.safe_imports import cpu_only_import
label_desc: Use image node_id 10 for calling a global function with example usage: test_training_data_to_numpy(input_type) without return types