code: def test_clustering() -> None:
      data = np.random.normal(size=(20, 2))
      num_clusters = 5
      with patch(
          "nevergrad.functions.mlda.datasets.get_data"
      ) as data_getter:
          data_getter.return_value = data
          func = problems.Clustering.from_mlda(
              name="Ruspini", num_clusters=num_clusters, rescale=True
          ).copy()
          np.testing.assert_equal(func.dimension, 10)
      func(np.arange(10).reshape((num_clusters, -1)))
      instru_str = "Array{(5,2)}"
      testing.printed_assert_equal(
          func.descriptors,
          {
              "parametrization": instru_str,
              "function_class": "Clustering",
              "dimension": 10,
              "name": "Ruspini",
              "num_clusters": 5,
              "rescale": True,
          },
      )
  
file_path: nevergrad/nevergrad/functions/mlda/test_problems.py
imports: from unittest.mock import patch
  import numpy
  import pandas
  from ...common import testing
  from .None import problems
label_desc: Use image node_id 2 for calling a global function with example usage: test_clustering() without return types