code: def test_lhs_pdist():
      n_dim = 2
      n_samples = 20
      lhs = Lhs()
  
      h = lhs._lhs_normalized(n_dim, n_samples, 0)
      d_classic = spatial.distance.pdist(np.array(h), "euclidean")
      lhs = Lhs(criterion="maximin", iterations=100)
      h = lhs.generate(
          [
              (0.0, 1.0),
          ]
          * n_dim,
          n_samples,
          random_state=0,
      )
      d = spatial.distance.pdist(np.array(h), "euclidean")
      assert np.min(d) > np.min(d_classic)
  
imports: import pytest
  import numbers
  import numpy
  import os
  import yaml
  from tempfile import NamedTemporaryFile
  from numpy.testing import assert_array_almost_equal
  from numpy.testing import assert_almost_equal
  from numpy.testing import assert_array_equal
  from numpy.testing import assert_equal
  from numpy.testing import assert_raises
  from scipy import spatial
  from skopt import Optimizer
  from skopt.space import Space
  from skopt.space import Real
  from skopt.space import Integer
  from skopt.space import Categorical
  from skopt.space import check_dimension
  from skopt.sampler.sobol import _bit_lo0, _bit_hi1
  from skopt.sampler.halton import _van_der_corput_samples, _create_primes
  from skopt.sampler import Hammersly, Halton, Lhs, Sobol, Grid
  from skopt.sampler import InitialPointGenerator
  from skopt.sampler.grid import _create_uniform_grid_include_border
  from skopt.sampler.grid import _create_uniform_grid_exclude_border
  from skopt.sampler.grid import _quadrature_combine
  from skopt.sampler.grid import _create_uniform_grid_only_border
  from skopt.utils import cook_initial_point_generator
label_desc: Use image node_id 4 for calling a global function with example usage: test_lhs_pdist() without return types