code: def generate_random_labels(
      random_generation_lambda, seed=1234, as_cupy=False
  ):
      """
      Generates random labels to act as ground_truth and predictions for tests.
  
      Parameters
      ----------
      random_generation_lambda : lambda function [numpy.random] -> ndarray
          A lambda function used to generate labels for either y_true or y_pred
          using a seeded numpy.random object.
      seed : int
          Seed for the numpy.random object.
      as_cupy : bool
          Choose return type of y_true and y_pred.
          True: returns Cupy ndarray
          False: returns Numba cuda DeviceNDArray
  
      Returns
      -------
      y_true, y_pred, np_y_true, np_y_pred : tuple
          y_true : Numba cuda DeviceNDArray or Cupy ndarray
              Random target values.
          y_pred : Numba cuda DeviceNDArray or Cupy ndarray
              Random predictions.
          np_y_true : Numpy ndarray
              Same as y_true but as a numpy ndarray.
          np_y_pred : Numpy ndarray
              Same as y_pred but as a numpy ndarray.
      """
      rng = np.random.RandomState(seed)  # makes it reproducible
      a = random_generation_lambda(rng)
      b = random_generation_lambda(rng)
  
      if as_cupy:
          return cp.array(a), cp.array(b), a, b
      else:
          return cuda.to_device(a), cuda.to_device(b), a, b
  
imports: import pytest
  from cuml.internals.mem_type import MemoryType
  from cuml.internals.input_utils import input_to_cuml_array, is_array_like
  from cuml.internals.base import Base
  import cuml
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import mean_squared_error, brier_score_loss
  from sklearn.datasets import make_classification, make_regression
  from sklearn import datasets
  from pylibraft.common.cuda import Stream
  from sklearn.datasets import make_regression
  from numba.cuda.cudadrv.devicearray import DeviceNDArray
  from numbers import Number
  from cuml.internals.safe_imports import gpu_only_import_from
  from itertools import dropwhile
  from copy import deepcopy
  from cuml.internals.safe_imports import cpu_only_import
  import inspect
  from textwrap import dedent, indent
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 16 for calling a global function with example usage: generate_random_labels(random_generation_lambda, seed, as_cupy) and returns: cp,  cp,  a,  b, cuda,  cuda,  a,  b