code: def test_nowarn_if_metric_no_bool():
      # make sure no conversion warning is raised if
      # metric isn't boolean, no matter what the data type is
      pairwise_metric = "minkowski"
      X_bool = np.random.randint(2, size=(5, 2), dtype=bool)
      X_num = np.random.randint(2, size=(5, 2), dtype=np.int32)
  
      with warnings.catch_warnings():
          warnings.simplefilter("error", DataConversionWarning)
  
          # fit boolean data
          OPTICS(metric=pairwise_metric).fit(X_bool)
          # fit numeric data
          OPTICS(metric=pairwise_metric).fit(X_num)
  
imports: import warnings
  import numpy
  import pytest
  from sklearn.cluster import DBSCAN, OPTICS
  from sklearn.cluster._optics import _extend_region, _extract_xi_labels
  from sklearn.cluster.tests.common import generate_clustered_data
  from sklearn.datasets import make_blobs
  from sklearn.exceptions import DataConversionWarning, EfficiencyWarning
  from sklearn.metrics.cluster import contingency_matrix
  from sklearn.metrics.pairwise import pairwise_distances
  from sklearn.utils import shuffle
  from sklearn.utils._testing import assert_allclose, assert_array_equal
  from sklearn.utils.fixes import CSR_CONTAINERS
label_desc: Use image node_id 12 for calling a global function with example usage: test_nowarn_if_metric_no_bool() without return types