code: def test_digits_cosine_sample():
      model = FacilityLocationSelection(
          100, "cosine", optimizer="sample", random_state=0
      )
      model.fit(X_digits)
      assert_array_equal(model.ranking, digits_cosine_sample_ranking)
      assert_array_almost_equal(
          model.gains, digits_cosine_sample_gains, 4
      )
      assert_array_almost_equal(model.subset, X_digits[model.ranking])
  
imports: import scipy
  import numpy
  from apricot import FacilityLocationSelection
  from apricot.optimizers import *
  from sklearn.datasets import load_digits
  from sklearn.metrics import pairwise_distances
  from numpy.testing import assert_almost_equal
  from numpy.testing import assert_array_equal
  from numpy.testing import assert_array_almost_equal
label_desc: Use image node_id 31 for calling a global function with example usage: test_digits_cosine_sample() without return types