code: def test_bandwidth(bandwidth):
      n_samples, n_features = (100, 3)
      rng = np.random.RandomState(0)
      X = rng.randn(n_samples, n_features)
      kde = KernelDensity(bandwidth=bandwidth).fit(X)
      samp = kde.sample(100)
      kde_sc = kde.score_samples(X)
      assert X.shape == samp.shape
      assert kde_sc.shape == (n_samples,)
  
      # Test that the attribute self.bandwidth_ has the expected value
      if bandwidth == "scott":
          h = X.shape[0] ** (-1 / (X.shape[1] + 4))
      elif bandwidth == "silverman":
          h = (X.shape[0] * (X.shape[1] + 2) / 4) ** (
              -1 / (X.shape[1] + 4)
          )
      else:
          h = bandwidth
      assert kde.bandwidth_ == pytest.approx(h)
  
imports: import joblib
  import numpy
  import pytest
  from sklearn.datasets import make_blobs
  from sklearn.exceptions import NotFittedError
  from sklearn.model_selection import GridSearchCV
  from sklearn.neighbors import KDTree, KernelDensity, NearestNeighbors
  from sklearn.neighbors._ball_tree import kernel_norm
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import StandardScaler
  from sklearn.utils._testing import assert_allclose
label_desc: Use image node_id 12 for calling a global function with example usage: test_bandwidth(bandwidth) without return types