code: def test_fit_bandwidth():
      kde = KernelDensity(bandwidth=100, leaf_size=10)
      s = MultivariateKernelDensitySampler(kde=kde, fit_bandwidth=True)
      s.fit([[0, 1], [1, 1], [0, 2]])
      assert s.kde_.bandwidth != kde.bandwidth
      assert s.kde_.leaf_size == kde.leaf_size
  
      s = MultivariateKernelDensitySampler(kde=kde, fit_bandwidth=False)
      s.fit([[0, 1], [1, 1], [0, 2]])
      assert s.kde_.bandwidth == kde.bandwidth
      assert s.kde_.leaf_size == kde.leaf_size
  
file_path: eli5/tests/test_samplers.py
imports: from functools import partial
  import numpy
  import pytest
  from hypothesis import given
  from hypothesis.strategies import integers, text
  from eli5.lime.samplers import MaskingTextSampler, MaskingTextSamplers, UnivariateKernelDensitySampler, MultivariateKernelDensitySampler
  from sklearn.neighbors import KernelDensity
label_desc: Use image node_id 12 for calling a global function with example usage: test_fit_bandwidth() without return types