code: def test_digits_greedi_nn():
      model = MaxCoverageSelection(
          100,
          optimizer="greedi",
          optimizer_kwds={"optimizer1": "naive", "optimizer2": "naive"},
          random_state=0,
      )
      model.fit(X_digits)
      assert_array_equal(model.ranking[:2], digits_greedi_ranking[:2])
      assert_array_almost_equal(
          model.gains[:2], digits_greedi_gains[:2], 4
      )
      assert_array_almost_equal(model.subset, X_digits[model.ranking])
  
imports: import scipy
  import numpy
  from apricot import MaxCoverageSelection
  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_greedi_nn() without return types