code: def test_inverse_transform(
      orig_label,
      ord_label,
      expected_reverted,
      bad_ord_label,
      use_fit_transform,
      client,
  ):
      n_workers = len(client.has_what())
      orig_label = dask_cudf.from_cudf(
          orig_label, npartitions=n_workers
      )
      ord_label = dask_cudf.from_cudf(ord_label, npartitions=n_workers)
      expected_reverted = dask_cudf.from_cudf(
          expected_reverted, npartitions=n_workers
      )
      bad_ord_label = dask_cudf.from_cudf(
          bad_ord_label, npartitions=n_workers
      )
  
      # prepare LabelEncoder
      le = LabelEncoder()
      if use_fit_transform:
          le.fit_transform(orig_label)
      else:
          le.fit(orig_label)
      assert le._fitted is True
  
      # test if inverse_transform is correct
      reverted = le.inverse_transform(ord_label)
      reverted = reverted.compute().reset_index(drop=True)
      expected_reverted = expected_reverted.compute()
  
      assert len(reverted) == len(expected_reverted)
      assert len(reverted) == len(
          reverted[reverted == expected_reverted]
      )
      # test if correctly raies ValueError
      with pytest.raises(
          ValueError, match="y contains previously unseen label"
      ):
          le.inverse_transform(bad_ord_label).compute()
  
file_path: cuml/python/cuml/tests/dask/test_dask_label_encoder.py
imports: from cuml.common.exceptions import NotFittedError
  import pytest
  from cuml.internals.safe_imports import cpu_only_import
  import cuml
  from cuml.dask.preprocessing.LabelEncoder import LabelEncoder
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 6 for calling a global function with example usage: test_inverse_transform(orig_label, ord_label, expected_reverted, bad_ord_label, use_fit_transform, client) without return types