code: def sparse_imputer_dataset(request, random_seed):
      datatype, val = request.param
      randint = create_rand_integers(random_seed)
      random_loc = cp.random.choice(
          randint.size, int(randint.size * 0.3), replace=False
      )
  
      randint.ravel()[random_loc] = val
      X_sp, X = sparsify_and_convert(
          randint, datatype, sparsify_ratio=0.15
      )
      X_sp = X_sp.tocsc()
      return val, X_sp, X
  
imports: from cuml.common import input_to_cuml_array
  from scipy.sparse import coo_matrix
  from scipy.sparse import csc_matrix
  from cupyx.scipy.sparse import coo_matrix
  from cuml.internals.safe_imports import gpu_only_import_from
  from cuml.internals.safe_imports import gpu_only_import
  from cuml.internals.safe_imports import cpu_only_import
  import pytest
  from cuml.datasets import make_classification, make_blobs
  from cuml.internals.safe_imports import cpu_only_import_from
label_desc: Use image node_id 15 for calling a global function with example usage: sparse_imputer_dataset(request, random_seed) and returns: val,  X_sp,  X