code: def test_digits_cosine_naive():
      model = SumRedundancySelection(100, "cosine", optimizer="naive")
      model.fit(X_digits)
      assert_array_equal(model.ranking, digits_cosine_ranking)
      assert_array_almost_equal(model.gains, digits_cosine_gains, 4)
      assert_array_almost_equal(model.subset, X_digits[model.ranking])
  
imports: import scipy
  import numpy
  from apricot import SumRedundancySelection
  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 7 for calling a global function with example usage: test_digits_cosine_naive() without return types