code: def _test_dbscan_big_data_numpy_gen(
      eps: float,
      min_samples: int,
      metric: str,
      use_weights: bool,
      low=-100.0,
      high=100.0,
      samples_number=1000,
      sample_dimension=4,
  ):
      data, weights = generate_data(
          low=low,
          high=high,
          samples_number=samples_number,
          sample_dimension=sample_dimension,
      )
      if use_weights is False:
          weights = None
      initialized_daal_dbscan = DBSCAN_DAAL(
          eps=eps, min_samples=min_samples, metric=metric
      ).fit(X=data, sample_weight=weights)
      initialized_sklearn_dbscan = DBSCAN_SKLEARN(
          metric=metric, eps=eps, min_samples=min_samples
      ).fit(X=data, sample_weight=weights)
      check_labels_equals(
          initialized_daal_dbscan.labels_,
          initialized_sklearn_dbscan.labels_,
      )
  
file_path: scikit-learn-intelex/daal4py/sklearn/cluster/tests/test_dbscan.py
imports: import numpy
  import pytest
  from sklearn.cluster import DBSCAN
  from daal4py.sklearn.cluster import DBSCAN
label_desc: Use image node_id 3 for calling a global function with example usage: _test_dbscan_big_data_numpy_gen(eps, min_samples, metric, use_weights, low, high, samples_number, sample_dimension) without return types