code: def test_metric_params():
      X = np.array([[0.0, 1.0], [2.0, 0.5]])
      kde = KernelDensity(
          metric="minkowski", metric_params={"p": 1.0}
      ).fit(X)
      kde2 = KernelDensity(
          metric="minkowski", metric_params={"p": 2.0}
      ).fit(X)
      assert not np.allclose(
          kde.score_samples(X), kde2.score_samples(X)
      )
  
file_path: cuml/python/cuml/tests/test_kernel_density.py
imports: from cuml.testing.utils import as_type
  from sklearn.model_selection import GridSearchCV
  import pytest
  from hypothesis.extra.numpy import arrays
  from hypothesis import given, settings, assume, strategies
  from cuml.neighbors import KernelDensity, VALID_KERNELS, logsumexp_kernel
  from cuml.common.exceptions import NotFittedError
  from sklearn.metrics import pairwise_distances
  from sklearn.neighbors._ball_tree import kernel_norm
  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_metric_params() without return types