code: def test_digits_cosine_sieve_minibatch():
      model = FacilityLocationSelection(
          100, "cosine", random_state=0, reservoir=X_digits
      )
      model.partial_fit(X_digits[:300])
      model.partial_fit(X_digits[300:500])
      model.partial_fit(X_digits[500:])
      assert_array_equal(model.ranking, digits_cosine_sieve_ranking)
      assert_array_almost_equal(
          model.gains, digits_cosine_sieve_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 34 for calling a global function with example usage: test_digits_cosine_sieve_minibatch() without return types