code: def random_dataset(request, random_seed):
      cp.random.seed(random_seed)
      X = cp.random.rand(100, 10)
      return X.get(), X
  
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 1 for calling a global function with example usage: random_dataset(request, random_seed) and returns: X,  X