code: def test_data_generator_types(input_type):
      X, *_ = datagen.gen_data(
          "blobs", input_type, n_samples=100, n_features=10
      )
      if input_type == "numpy":
          assert isinstance(X, np.ndarray)
      elif input_type == "cudf":
          assert isinstance(X, cudf.DataFrame)
      elif input_type == "pandas":
          assert isinstance(X, pd.DataFrame)
      elif input_type == "gpuarray":
          assert cuda.is_cuda_array(X)
      elif input_type == "gpuarray-c":
          assert cuda.is_cuda_array(X)
      else:
          assert False
  
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 2 for calling a global function with example usage: test_data_generator_types(input_type) without return types