code: def test_classifier_partial_fit(csr_container, average):
      classes = np.unique(y)
      data = csr_container(X) if csr_container is not None else X
      clf = PassiveAggressiveClassifier(
          random_state=0, average=average, max_iter=5
      )
      for t in range(30):
          clf.partial_fit(data, y, classes)
      score = clf.score(data, y)
      assert score > 0.79
      if average:
          assert hasattr(clf, "_average_coef")
          assert hasattr(clf, "_average_intercept")
          assert hasattr(clf, "_standard_intercept")
          assert hasattr(clf, "_standard_coef")
  
imports: import numpy
  import pytest
  from sklearn.base import ClassifierMixin
  from sklearn.datasets import load_iris
  from sklearn.linear_model import PassiveAggressiveClassifier, PassiveAggressiveRegressor
  from sklearn.utils import check_random_state
  from sklearn.utils._testing import assert_almost_equal, assert_array_almost_equal, assert_array_equal
  from sklearn.utils.fixes import CSR_CONTAINERS
label_desc: Use image node_id 2 for calling a global function with example usage: test_classifier_partial_fit(csr_container, average) without return types