code: def sparse_random_dataset(request, random_seed):
      cp.random.seed(random_seed)
      X = cp.random.rand(100, 10)
      random_loc = cp.random.choice(
          X.size, int(X.size * 0.3), replace=False
      )
      X.ravel()[random_loc] = 0
      if request.param == "cupy-csr":
          X_sparse = cpx.scipy.sparse.csr_matrix(X)
      elif request.param == "cupy-csc":
          X_sparse = cpx.scipy.sparse.csc_matrix(X)
      return X.get(), X, X_sparse.get(), X_sparse
  
imports: from cuml.testing.test_preproc_utils import assert_allclose
  from sklearn.utils.sparsefuncs import inplace_csr_column_scale, inplace_csr_row_scale, inplace_column_scale, mean_variance_axis, min_max_axis
  from cuml._thirdparty.sklearn.utils.sparsefuncs import inplace_csr_column_scale, inplace_csr_row_scale, inplace_column_scale, mean_variance_axis, min_max_axis
  from sklearn.utils.extmath import row_norms, _incremental_mean_and_var
  from cuml._thirdparty.sklearn.utils.extmath import row_norms, _incremental_mean_and_var
  from cuml._thirdparty.sklearn.utils.validation import check_X_y
  from cuml.internals.safe_imports import gpu_only_import
  import pytest
  from cuml.internals.safe_imports import cpu_only_import
label_desc: Use image node_id 2 for calling a global function with example usage: sparse_random_dataset(request, random_seed) and returns: X,  X,  X_sparse,  X_sparse