code: def test_digits_sieve_minibatch_sparse():
      model = MaxCoverageSelection(100, random_state=0)
      model.partial_fit(X_digits_sparse[:50])
      model.partial_fit(X_digits_sparse[50:150])
      model.partial_fit(X_digits_sparse[150:])
      assert_array_equal(model.ranking, digits_sieve_ranking)
      assert_array_almost_equal(model.gains, digits_sieve_gains, 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 18 for calling a global function with example usage: test_digits_sieve_minibatch_sparse() without return types