code: def mask_dataset(request, random_seed):
      cp.random.seed(random_seed)
      randint = cp.random.randint(30, size=(500, 20))
      randint = randint.astype(cp.float64)
      if request.param == "zero":
          mask_value = 0
      elif request.param == "one":
          mask_value = 1
      else:
          mask_value = cp.nan
      random_loc = cp.random.choice(
          randint.size, int(randint.size * 0.3), replace=False
      )
      randint.ravel()[random_loc] = mask_value
      return mask_value, randint.get(), randint
  
imports: import platform
  from sklearn.preprocessing import normalize
  from cuml.testing.test_preproc_utils import assert_allclose
  from cuml.thirdparty_adapters.sparsefuncs_fast import csr_mean_variance_axis0, csc_mean_variance_axis0, _csc_mean_variance_axis0, inplace_csr_row_normalize_l1, inplace_csr_row_normalize_l2
  from sklearn.utils._mask import _get_mask
  from cuml.thirdparty_adapters.adapters import check_array, _get_mask, _masked_column_median, _masked_column_mean, _masked_column_mode
  from cuml.internals.safe_imports import cpu_only_import_from
  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: mask_dataset(request, random_seed) and returns: mask_value,  randint,  randint