code: def _test_across_grid_parameter_numpy_gen(
      queue, metric, use_weights: bool
  ):
      eps_begin = 0.05
      eps_end = 0.5
      eps_step = 0.05
      min_samples_begin = 5
      min_samples_end = 15
      min_samples_step = 1
      for eps in np.arange(eps_begin, eps_end, eps_step):
          for min_samples in range(
              min_samples_begin, min_samples_end, min_samples_step
          ):
              _test_dbscan_big_data_numpy_gen(
                  queue,
                  eps=eps,
                  min_samples=min_samples,
                  metric=metric,
                  use_weights=use_weights,
              )
  
file_path: scikit-learn-intelex/onedal/cluster/tests/test_dbscan.py
imports: import numpy
  import pytest
  from sklearn.cluster import DBSCAN
  from sklearn.cluster.tests.common import generate_clustered_data
  from onedal.cluster import DBSCAN
  from onedal.tests.utils._device_selection import get_queues
label_desc: Use image node_id 5 for calling a global function with example usage: _test_across_grid_parameter_numpy_gen(queue, metric, use_weights) without return types