code: def test_inplace_csr_column_scale(
      failure_logger, random_seed, sparse_random_dataset
  ):
      _, _, X_sparse_np, X_sparse = sparse_random_dataset
      if X_sparse.format != "csr":
          pytest.skip()
      cp.random.seed(random_seed)
      scale = cp.random.rand(10)
      cu_inplace_csr_column_scale(X_sparse, scale)
      sk_inplace_csr_column_scale(X_sparse_np, scale.get())
      assert_allclose(X_sparse, X_sparse_np)
  
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 6 for calling a global function with example usage: test_inplace_csr_column_scale(failure_logger, random_seed, sparse_random_dataset) without return types