code: def test_tfidf_transformer(
      data, norm, use_idf, smooth_idf, sublinear_tf, client
  ):
      # Testing across multiple-n_parts
      for n_parts in range(1, data.shape[0]):
          dask_sp_array = create_cp_sparse_dask_array(data, n_parts)
          tfidf = TfidfTransformer(
              norm=norm,
              use_idf=use_idf,
              smooth_idf=smooth_idf,
              sublinear_tf=sublinear_tf,
          )
          sk_tfidf = SkTfidfTransformer(
              norm=norm,
              use_idf=use_idf,
              smooth_idf=smooth_idf,
              sublinear_tf=sublinear_tf,
          )
  
          res = tfidf.fit_transform(dask_sp_array)
          res = create_scipy_sparse_array_from_dask_cp_sparse_array(
              res
          ).todense()
          ref = sk_tfidf.fit_transform(data).todense()
  
          cp.testing.assert_array_almost_equal(res, ref)
  
file_path: cuml/python/cuml/tests/dask/test_dask_tfidf.py
imports: from sklearn.feature_extraction.text import TfidfTransformer
  from cuml.dask.feature_extraction.text import TfidfTransformer
  import dask
  import dask.array
  from cuml.internals.safe_imports import gpu_only_import_from
  from cuml.internals.safe_imports import cpu_only_import_from
  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 5 for calling a global function with example usage: test_tfidf_transformer(data, norm, use_idf, smooth_idf, sublinear_tf, client) without return types